The Segregation of Students By Income in Public Schools

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Educational Researcher, Vol. 51 No. 4, pp. 245 –254
DOI: 10.3102/0013189X221081853

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Over the past three decades, children from low-income
families have increasingly been attending different
public schools than children from affluent families

(Owens et al., 2016). Importantly, segregation of students by
income appears to be highly localized, growing fastest between
schools within the same district.1 As we will review, economic,
social, and demographic changes have shaped these patterns, but
education policy has played a role, too. The processes through
which students are mapped to traditional public schools have
changed in ways that have contributed to growing income segre-
gation of students at the school level. For example, segregation
of students by income between schools has grown as school
choice options grew due to the introduction of charter schools
nearby (Marcotte & Dalane, 2019; Monarrez et al., 2019).2

Although recent work has documented trends in the segrega-
tion of students by income between districts and schools, we
know very little about such segregation of students as they expe-
rience school—in the classroom. This is a potential oversight
since the factors shaping income segregation between schools
may not be barred by the school door. As neighborhoods change
and school choice options grow, administrators at the district
and school levels may seek to attract or retain students by chang-
ing what is offered inside the school building. For example,
administrators might expand ability tracking or offer targeted

school-within-a-school curricula to placate parents most likely to
choose among multiple schools.

In this paper, we attempt to advance knowledge of trends in
income segregation of students by examining changes within
schools—at the classroom level. In our administrative data of all
students attending public schools in North Carolina, the average
public school has more than 110 students per grade, grouped
into five or more classes. Our data include a measure of a stu-
dent’s free/reduced-price lunch eligibility as well as information
on classroom assignments. We refer to students who are eligible
for free or reduced-price lunch as economically disadvantaged
(ED) in accordance with our data-sharing agreement with the
North Carolina Education Research Data Center (NCERDC).3
Using the ED status of each student in each classroom, we assess
whether ED students are assigned to classes in the same pattern
as other students or are clustered into different classrooms. That
is, we measure within-school segregation at the classroom level
of ED students from other students.4

Our main contribution is to model trends in income segrega-
tion of students within schools as a function of school and dis-
trict characteristics. We include school fixed effects in all of our
models, so that we estimate changes in within school ED

1081853 EDRXXX10.3102/0013189X221081853EDUCATIONAL RESEARCHERMONTH XXXX
research-article2022

1American University, Washington, DC

The Segregation of Students by Income
in Public Schools
Kari Dalane1 and Dave E. Marcotte1

Over the past three decades, children from low-income families have increasingly been attending different public schools
than those from more affluent families. Though recent work has helped us understand patterns of income segregation
between districts and schools within districts, we know little about segregation of students as they experience school: in
the classroom. We attempt to advance knowledge of segregation of students by income at the classroom level. We use
data from North Carolina that includes information on classroom assignments and students’ economically disadvantaged
(ED) status. We assess whether ED students are clustered/segregated into different classrooms than other students. We
find that within-school segregation rose by about 10% between 2007 and 2014 in elementary and middle schools we study.

Keywords: at-risk students; descriptive analysis; disparities; educational policy; regression analyses; secondary data

analysis

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246 EDuCATIONAl RESEARCHER

segregation that are due to patterns at the school level—rather
than due to compositional changes in where students are attend-
ing school. So, our estimates provide insight into how income
segregation by classroom is changing at the typical school. Our
paper is among the first to shed light on the magnitude of segre-
gation within schools by economic disadvantage and whether
such segregation has increased. We further consider whether ED
segregation within a school is associated with the broader con-
text of ED segregation in its district. This second question is vital
for understanding whether any segregation between schools is
offsetting or exacerbating broader trends in segregation of stu-
dents by income within schools.

Background

Between-School

Segregation

The topic of segregation in American public schools has long
been a concern for researchers and policymakers. Much of that
attention has focused on the between-school segregation of stu-
dents by race. Though race and socioeconomic status (SES) are
highly correlated, only recently have analysts employed data from
various sources to study socioeconomic segregation specifically.5
For example, Owens et al. (2016) reported increasing levels of
income segregation between districts in large metropolitan areas
and between schools within the 100 largest districts. Marcotte
and Dalane (2019) use more recent Common Core of Data data
and report similar patterns of rising segregation of students by
income in large districts but no substantial increase in small
districts.

Rising socioeconomic segregation between schools and dis-
tricts raises concerns for education policy by shaping the distribu-
tion of economic opportunity. Schools with higher proportions
of low-income students have fewer educational resources, so the
concentration of poor students in poor schools could further
exacerbate disparities of opportunity (Betts et al., 2000). Indeed,
states with the highest levels of between-district segregation also
have the highest level of variation in achievement between dis-
tricts (Fahle & Reardon, 2018), and the income achievement gap
is largest in U.S. metropolitan areas with the most residential seg-
regation by income (Owens, 2018).

Within-

School Segregation

Although researchers continue to sort out the possible drivers
and consequences of socioeconomic segregation between school
districts and between schools within districts, a question that
has received almost no attention is whether socioeconomic seg-
regation is changing within schools. This is surprising, since the
changing neighborhoods and schooling options that may be
driving system wide patterns may also be affecting the organiza-
tion of schools themselves.

Previous work provides evidence that the assignment of stu-
dents to different classes based on SES is driven by parent and
teacher preferences (Kalogrides & Loeb, 2013). For example,
Lareau (1987, 2000) has documented that parents (and espe-
cially mothers) from families with higher incomes and educa-
tion are more involved in their children’s schools, are more

likely to know the names and reputations of teachers within
those schools, and more often intervene with principals in class-
room assignment decisions on behalf of their children. Because
wealthier, more educated families can vote with their feet,
administrators can feel pressure to accede to their classroom
assignment requests in order to retain them in their schools/
districts (Clotfelter et al., 2005). Principals may receive similar
pressure from their teachers, who also have substantial interest
in classroom assignment decisions as well as information about
student performance. Students from low-income families have
been found to have lower levels of classroom engagement
because of poorer health or nutrition (Basch, 2011; Jansen,
2013) or expectations (Odéen et al., 2012) and are more likely
to exhibit problems with attention and impulsive behavior
(Liston et al., 2009). In an effort to retain their best teachers,
principals may acquiesce to preferences for assigned students
who present with fewer behavioral or academic challenges
(Kalogrides & Loeb, 2013).6 Related, there is evidence that
socioeconomic attributes of students affect assignment to hon-
ors/gifted academic tracks (Gamoran, 1992; Grissom &
Redding, 2016; Rosenbaum, 1976).7

Although this literature provides good evidence that parent
SES shapes classroom assignment, we have little empirical evi-
dence on levels and trends in within-school socioeconomic segre-
gation of students. We are aware of five existing studies that
examine within-school segregation, four of which focus on race.
These studies find that most segregation takes place between
schools, but within-school sorting adds to racial isolation, espe-
cially at the high school level, where tracking is more prevalent
(Clotfelter et al., 2002, 2021; Conger, 2005; Morgan &
McPartland, 1981). Kalogrides and Loeb (2013) examine within-
school segregation by both race and SES in elementary, middle,
and high schools in three large urban school districts and find
more segregation by race and SES than would be expected if stu-
dents were placed into classes randomly at all levels. As in previous
studies, segregation was highest at the high school level. Most, but
not all, of the segregation could be explained by prior achievement
levels, suggesting tracking plays an important role in how students
are sorted into classes and contributes substantially to segregation.
The authors further find that classes with higher proportions of
minority and low-income students were more likely to be taught
by novice teachers, which has clear equity implications.

Although no researchers have documented patterns of within-
school segregation by income over time, Clotfelter et al. (2021)
have done so for racial segregation using North Carolina adminis-
trative data from 1995, 2006, and 2017. Specifically, the authors
calculated within-school segregation as the difference in total
district and between school segregation. Using this measure, they
found that segregation within schools is “substantial” and is com-
plementary to segregation between schools, meaning when one is
higher, the other tends to be lower. Although their work is similar
to ours, it is not directly comparable due to their focus on race (vs.
economic disadvantage).8

Our objectives are to document levels and trends of within-
school segregation of students by economic disadvantage. In pre-
vious work on racial within-school segregation, researchers have
found higher levels of segregation in middle grades than in

MAy 2022 247

elementary grades. Similarly, we anticipate higher levels of
within-school segregation in middle school grades. One reason
for this is that middle schools are generally larger than elemen-
tary schools, so they are more likely to draw from larger, more
diverse geographic zones. A more diverse student body could
lead to higher levels of segregation than a highly homogenous
student body. Second, achievement is correlated with SES, and
tracking is more likely to take place in higher grades than in
lower grades (Loveless, 2012).

Determinants of Segregation

With this context in mind, it is useful to recognize that trends
in segregation within schools are affected by one (or both) of
two potential mechanisms. First, segregation of ED students
could increase or decrease as the composition of a school’s stu-
dents changes over time. Any change in the level of educational
disadvantage in a school would affect the mean against which all
classrooms in the school are compared—compositionally
changing within-school segregation even if there are no inten-
tional changes in classroom assignment policies. Second, even if
there is no change in the ED composition of students in a
school, within-school segregation can change if the way stu-
dents are assigned to classrooms changes. Evidence from previ-
ous research along with changes in the broader educational
context of public education suggests potential changes in
within-school segregation via both the compositional and
assignment mechanisms.

The rise in between-school segregation has meant that the
student body within each school is becoming more homogenous
over time. Clotfelter et al. (2021) find more homogenous stu-
dent bodies are associated with lower levels of within-school seg-
regation by race, and the same could be true for within-school
socioeconomic segregation. However, the literature on assign-
ment of students to classes described earlier suggests a different
pattern should be expected for SES. Districts experiencing more
growth in between-school segregation are necessarily undergoing
substantial change that could include migration or economic
and housing growth.9 High-income parents and experienced
teachers have better capacity to change schools in response to
these changes. Because of these preferences, school principals in
districts experiencing changes in segregation between schools
could face more pressure to employ within-school student group-
ing to placate their most important constituencies: active parents
and experienced teachers. Broader changes in the context of
American education suggest that even without compositional
changes, there may be upward pressure on within-school segre-
gation by ED status through the assignment mechanism. Most
relevant, the high-stakes-testing pressures associated with the
accountability movement could lead to increased academic
tracking, which would likely increase levels of within-school
socioeconomic segregation.

In our empirical models we examine the net effect of district-
level changes in ED segregation on within-school ED segrega-
tion. As we describe later, we estimate a series of models of
within-school segregation, adding in lagged measures of
between-school ED segregation in a school’s district.

Analytic Plan and Methods

Data

To study segregation of students by classroom within schools, we
use student-level administrative data from the NCERDC begin-
ning in the 2006-2007 school year and ending in the 2013-2014
school year.10 These data provide information for every public
school student in the state and provide measures of student
demographic characteristics, school and district attributes, and
whether a student was ED, as measured by eligibility for free/
reduced-price meals. Although this measure of a student’s SES is
limited, Domina et al. (2018) find that ED status is a better
predictor of educational disadvantage than family income
obtained from IRS tax records. This suggests ED status measures
SES and family background beyond income that are associated
with student educational outcomes.

The NCERDC data enable us to identify the classrooms to
which each student is assigned during the school day and
thereby assess the socioeconomic and demographic attributes of
students in each classroom and those of the schools overall.
Because classes are grouped by grade, our unit of analysis is the
grade-school level, by academic year. We limit our analysis to
students in Grades 3 to 8 for both substantive and practical
reasons. This is partly because these are the grades tested annu-
ally for school and district accountability.11 Further, we cannot
include lower grades because course enrollment data are
reported only beginning in Grade 3. We exclude high school
students from our analysis since high school classes are more
likely to include students from multiple grades, which makes
determining the appropriate reference grade for each course
more difficult.

Measurement

For each year in our panel, we generate a district dissimilarity
index measuring income segregation between schools for each
grade within that district. This measure is generated by compar-
ing the number of ED and non-ED students in each grade/
school with the total number of ED and non-ED students in the
appropriate grade/district. If all schools serving the same grade
in a district had an equal portion of ED students, the between-
school dissimilarity index would be 0, regardless of the mean ED
rate. If ED students and non-ED students in a particular grade
and district were perfectly segregated into different schools, the
between-school dissimilarity index would be 1.

We next generate measures of within-school segregation for
each grade/school in our panel. Income segregation within a
grade/school occurs when the characteristics of students in class-
rooms deviate from the characteristics of students in the grade/
school overall, with some classrooms having more ED students
than the grade/school mean and some fewer. The within-school
dissimilarity index for school s, grade g, in year t is

Ds g t
c

g s t

g s t

c g s t

g s t
c g s t

, ,

, , ,

, ,

, , ,

, ,
, , ,

= −∑12

ED

ED

non-ED

non-ED
,

248 EDuCATIONAl RESEARCHER

where c indexes classrooms within the grade/school, EDc,g,s,t and
non-EDc,g,s,t measure the number of ED and not-ED students
within the classroom in each grade in a school in year t, and
EDg.s,t and non-EDg,s,t are, respectively, the grade/school total
number of ED and not-ED students enrolled in all classes.

The magnitude of the within-school dissimilarity index can
be interpreted as the proportion of students that would need to
be reallocated to equalize the proportion of low-income to
higher-income students in each classroom. Zero means that no
reallocation is needed, and 1 means all students would be
affected by reallocation to equalize. An advantage of the dissimi-
larity index is that it makes clear that the level of segregation in
a school is a function of both the composition of students within
a grade/school (i.e., the total number of ED and non-ED stu-
dents enrolled) and how students are assigned to classrooms
within schools (i.e., EDc,g,t and non-EDc,g,t for all c).

To calculate the within-school dissimilarity index, we aggre-
gate our student-level data to the course level. We use a large set
of variables to map students to courses, including school code,
district code, course code, meeting code, section, cycle, period,
teacher identification code, course title, and reported enroll-
ment. We first generate the dissimilarity indexes for all courses in
all periods within a grade. This includes all types of courses,
including courses such as physical education and even home-
room.12 In some elementary grades/schools, students can spend
most/all of their day in one classroom. In middle school grades,
students have more courses during a day. We next use course
codes to isolate those courses identified as math courses and cal-
culate the dissimilarity index within each grade/school/year
using just these courses.13 We do the same for all English lan-
guage arts (ELA) courses.14 We focus on math and ELA since
they are the subjects to which students are nearly universally
enrolled each grade. Just as importantly, math and ELA are most
frequently tested on high-stakes tests. School administrators may
feel more pressure to improve achievement in these subjects and
may view tracking as a way to accomplish this goal. In all cases,
an individual student can appear in more than one class in the
same grade.15 This is easiest to see for middle school grades,
when one student is assigned to different classrooms during vari-
ous periods during the day. Courses may also appear more than
once since some schools report the same course over different
semesters. Rather than attempt to isolate one iteration of each
course, we use the full set of unique course observations reported
by each school. So, the dissimilarity index for a grade/school/
year measures unevenness in classroom ED characteristics across
all classrooms during the academic year.

Once we have mapped students to classes, we then generate
counts of the ED and non-ED students in each course. We cal-
culate the total number of ED students in a grade as the sum of
all ED students in relevant courses in that grade. We then do the
same for non-ED students. In calculating each of the all-course,
math, and reading dissimilarity indexes, we exclude any courses
with just one student enrolled and those with more than 50 stu-
dents enrolled since these are not typical courses. There are some
instances in which courses consist of students from more than
one grade.16 In those cases, we use the modal grade of enrolled
students to assign courses to grades.

We generate grade-specific dissimilarity indexes for two
main reasons. First, the composition of each grade within a
school or district may differ. For example, if a school is becom-
ing increasingly poor over time, lower grades in the school may
have higher concentrations of ED students than higher grades.
Since students typically take courses with students from their
own grades, ED and non-ED enrollments in their own grades
are the appropriate comparison group to generate dissimilarity
indexes. Second, there may be different segregation patterns by
grade. This may be especially true when comparing elementary
grades with middle grades. Since we know from prior research
that within-class ability grouping is more common in elemen-
tary grades and academic tracking in separate classrooms is
more likely to start in middle grades (Loveless, 2012), we expect
there to be higher levels of within-school segregation in middle
grades.

Empirical Models

To assess trends in segregation, we first generate time-series
graphs of the enrollment weighted average district dissimilarity
index, by grade. We create similar graphs to illustrate how
within-school segregation is changing over time. Then, we esti-
mate models of segregation at the grade-school level, over time,
that take the following form:

D X tsgt sgt
g

g g g s sgt= + + + + + +∑α β δ τ δ θ θ( ( * )) LEA 

where Dsgt is the segregation index within school s for grade g in
year t. X measures the total enrollment and racial and ethnic
composition of each school, grade, and year. Enrollment in the
grade is an important predictor, since larger grades make student
grouping more feasible. We control for race and ethnic composi-
tion because they are related to segregation more generally and
to limit the possibility that observed patterns of segregation by
economic disadvantage are driven by racial segregation of stu-
dents.17 We also include grade fixed effects and grade-specific
linear trends.

We estimate this model separately for within-school segrega-
tion in math, reading, and all classes. The coefficients of interests
are the grade fixed effects (δg) and grade-specific time trends (τg).
The grade fixed-effects measure differences in levels of segrega-
tion by grade, and the time trends measure changes in within-
school segregation by grade, net of what might have been
expected due to changes in enrollments or racial composition. In
all models, we control for district and school fixed effects (θLEA
and θs). These models provide direct tests on whether there have
been changes in the way schools allocate students into class-
rooms over the panel.

To provide insight into how segregation between classrooms
within schools is shaped by segregation between schools at the
district level, we augment our empirical model of within-school
segregation by adding in lagged measures of between-school ED
segregation in a school’s district. Specifically, we add in grade-
specific measures of the segregation of students by income
between schools to our school, grade, and year panel models.

MAy 2022 249

Results

In Table 1 we provide descriptive statistics for our sample. Our
unit of analysis is the grade/school/year.18 The average grade
within a school in our panel enrolls about 114 students in a typical
year and is 53% White, 27% Black, and 12% Hispanic.
Approximately 55% of students in a typical grade are ED. The
mean district dissimilarity index is 0.31, and the mean school dis-
similarity indexes are 0.23 for all courses, 0.24 for math courses,
and .025 for ELA courses. So, in the average grade/school, about
23% of students would be affected by reallocation to equalize the
proportion of ED students in all classrooms. About 7% of our
grade/school/year observations are charter schools.

Descriptive Analyses

To begin understanding how segregation is changing over time, in
Figure 1 we show trends in the average level of ED segregation
between and within schools in North Carolina, separately by
grade, and weighted by district enrollment. In Panel A, for context
we illustrate that between-school segregation rose substantially
between 2007 and 2014 in North Carolina. This is consistent
with prior research in other settings, discussed earlier. The mean
district dissimilarity index increased by about 20% for all grades.
Figure 1 also makes clear that elementary school grades have
higher levels of between-school segregation than middle school
grades, with an average 2007 district dissimilarity index of about
0.3 in elementary grades compared with 0.24 in middle grades.
This is likely because elementary schools are typically smaller than
middle schools and draw students from smaller geographic areas.

In Panel B of Figure 1 we focus on the outcome that has been
less documented: trends in segregation between classrooms
within schools. Middle school grades have higher levels of
within-school segregation when compared with elementary
grades. This is the opposite of what we observe in

between-school segregation. Elementary grades also have lower
levels of within-school than between-school segregation, with a
mean of 0.23 for the former and 0.34 for the latter. For middle
school grades, within-school and between-school segregation
both average about 0.27 over the period. Note that in absolute
terms, within-school segregation grew more slowly than
between-school segregation. Also note that these patterns are
consistent with the patterns of racial segregation identified by
Clotfelter et al. (2021): Between-school segregation is lower in
middle school than in elementary school but offset by higher
within-school segregation.

The trends in within-school ED segregation in Figure 1
include all courses offered in a school, including nonacademic
subjects, such as physical education and band. Because within-
school segregation might be higher in academic subjects that are
more likely to be tracked, we plot the mean within-school dis-
similarity index for only math courses by grade in Figure 2.
Figure 2 makes clear that for middle school grades, within-school
segregation is higher for math classes than overall levels for all
courses in general (Figure 1, Panel B). This is to be expected,
since tracking often intensifies in these grades. It is also notable
that there is a more pronounced positive trend in segregation of
students by ED status in math courses than in all courses overall.
For example, in elementary grades, the dissimilarity index for
math classes increased from about 0.22 to just over 0.25, whereas
overall the increase was from approximately 0.22 to 0.24.

Multivariate Analyses

To further examine these trends, we next turn to our regression
models of patterns of within-school segregation over our panel.
Because the descriptive trends illustrate differences in segrega-
tion by grade, we estimate separate trends for each grade. Our
unit of analysis in these models is the grade/school/year, and the
results are presented in Table 2. We include controls for

Table 1
Descriptive Statistics of Administrative Data of North Carolina Classrooms Grades 3 to 8 (N = 47,385)

Variable M SD Min. Max.

Grade 4.97 1.63 3 8
Total enrollment 114.07 85.65 10 674
Schools offering grades per district 27.76 31.93 1 119
% Black 27.44 25.20 0 100
% Hispanic 11.87 11.60 0 82.76
% Asian 2.17 4.21 0 78.57
% White 53.07 29.13 0 100
% ED 55.32 24.03 0 100
Percentage of grade with ED status 0.97 1.96 0 10
District dissimilarity index 0.31 0.13 0 0.85
Grade dissimilarity index (all courses) 0.23 0.13 0 1
Grade dissimilarity index (math courses)1 0.24 0.15 0 1
Grade dissimilarity index (ELA courses)1 0.25 0.15 0 1
Charter school 0.07 0.25 0 1

Note. Number of time periods: 8 (2007–2014); number of school districts: 115; number of schools: 2,097. ED = economically disadvantaged; ELA = English language arts.
1Because not all elementary school grades identify a unique math or ELA class, can measure math dissimilarity for 40,748 and ELA dissimilarity for 41,277 grade/school/
year observations. See text for details.

250 EDuCATIONAl RESEARCHER

enrollment, racial composition, and grade, school, and district
fixed effects. We estimate trends in segregation by economic dis-
advantage across all classes within schools, math classes, and
then reading/ELA classes. We include both school and district
fixed effects in all models, so the coefficients on the grade-spe-
cific trends are net of school and district averages over the panel.

Enrollment is positively associated with segregation within
schools. Schools with more students may have more flexibility in
how they assign students to classes. The grade fixed effects con-
firm what we saw in our graphs, with higher levels of within-
school segregation in middle grades than in elementary grades.
The proportion of students who are Hispanic is associated with
meaningful differences in ED segregation. For example, a school/
grade that is 50% Hispanic has a math dissimilarity index that is
0.05 higher than a school/grade with no Hispanic enrollment.
The coefficients on the linear time trends are positive and signifi-
cant in all models for all grades, and there are no large differences
in the trends between grades. The coefficients for the trends in
math and ELA courses are larger than those for all courses.

To put the magnitudes of the coefficients on these trends in
context, over the course of our panel, they imply that within-
school ED segregation increased between 0.003 to 0.006 each
year, depending on grade and type of course. This range is
between 10% and 20% of the mean and about 0.18 to 0.32
standard deviations of the overall within school dissimilarity
index values for the typical grade/school/year over the 8 years of
the panel (Table 1).

In our final model, we estimate the impact of between-school
segregation on within-school segregation. Here our unit of anal-
ysis is the grade/school/year, and we use the math dissimilarity
index as the outcome. Since we are interested in the relationship
between segregation in the district and patterns within schools,
we cluster standard errors at the district level. We include enroll-
ment, racial composition controls, and grade, school, and dis-
trict fixed effects. We also include a separate lag of the district
dissimilarity index for each grade. The lag of the district dissimi-
larity index captures the level of between-school income segrega-
tion for a grade/district in the prior year. A positive coefficient
on this lag would indicate that a growing dissimilarity between
schools in a district is associated with higher dissimilarity indexes
within school in math the following year. A negative coefficient
would indicate the opposite relationship. We also include a lin-
ear time trend that captures average growth in within-school seg-
regation over time.

We present these results in Table 3. The coefficients on the
grade indicators make clear the patterns described: Within-
school segregation is higher in middle school grades. The coef-
ficients of interest are those on the grade-specific lags of
between-school segregation in the district. We find no real evi-
dence that changes in segregation in the district are associated
with changes in within-school segregation. The only exception is
a significant coefficient on the lag of district-level segregation in
Grade 3. That coefficient implies a unit increase in the between-
school segregation index is associated with an increase in the
within school segregation index of 0.064. To scale the coefficient,
a one-standard-deviation increase in district level segregation
(0.13) is associated with an increase in within-school segregation
of 0.008. This is a bit less than a 10th of a standard deviation.
On the whole, we find no evidence that changes in between-
school segregation are associated with changes in within-school
segregation in other grades. For ELA and all course measures,
there is no significant relationship between district- and school-
level segregation.

Panel A

Panel B

.1
5

.2
.2
5

.3
.3
5

M
ea
n
G
ra
de
D
is
si
m
ia
rti
y
In
de
x*

2007 2008 2009 2010 2011 2012 2013 2014
Year

Grade 3 Grade 4
Grade 5 Grade 6
Grade 7 Grade 8

*weighted by enrollment

North Carolina Schools Serving Grades 3-8
Within School Segregation Over Time by Grade

FIGURE 1. Trends in between- and within-school segregation by
economic disadvantage.

.1
5

.2
.2
5

.3
.3
5

M
ea
n
G
ra
de
M
at
h
D
is
si
m
ia
rti
y
In
de
x*

2007 2008 2009 2010 2011 2012 2013 2014
Year

Grade 3 Grade 4
Grade 5 Grade 6
Grade 7 Grade 8

*weighted by enrollment

North Carolina Schools Serving Grades 3-8
Within School Math Segregation Over Time by Grade

FIGURE 2. Within-school math segregation over time by grade.

MAy 2022 251

Discussion

In this paper, we illustrate that in North Carolina, segregation
of students by income at the classroom level has increased in
elementary and middle school grades. Our empirical models
suggest that over the course of our panel, within-school segrega-
tion increased between 10% and 20% (or approximately 0.18
to 0.32 standard deviations). The increases in within-school
segregation tended to be larger in math and ELA than in overall
courses. We also find that across grades, patterns of between-
school and within-school segregation are very different. We find
that between-school ED segregation is higher in elementary
versus middle school grades, whereas within-school segregation
is higher than between-school segregation for middle school
grades.

This is the same pattern that Clotfelter et al. (2021) identify
for racial segregation of students in North Carolina using the
NCERDC data. They examine patterns of racial segregation
between and within schools, rather than trends over time, and
find that when one is low, the other tends to be high. Since race
and SES are correlated, the parallels between our findings and
those of Clotfelter et al. (2021) are unsurprising. In a series of
auxiliary analyses not reported here to conserve space, we find
smaller increases in segregation by race (between Black and
White students) than by income, and these increases are limited
to elementary school grades. It appears that even as racial segre-
gation of students remains endemic, the segregation of students
by economic disadvantage has been rising.

To interpret the magnitude of these increases in within-school
segregation, consider that the mean math segregation index at the
grade level in our panel was about 0.25.19 So, about 25% of stu-
dents would have needed to change classroom assignments to
equalize the proportion of disadvantaged students in each class-
room within a school. In our models in Table 2, we estimate that
the segregation index in Grades 3 through 8 rose by about 0.05
annually in math classes and by 0.04 in ELA classes. Over the full
8 years of our panel, this implies an increase of 0.04 and 0.032 in
within-school segregation in math and ELA classrooms. So, by the
end of our panel, about 28% to 29% of students (rather than
25%) would need to change classrooms to equalize the proportion
of disadvantaged students across classrooms. This would mean
that in a school with four classrooms of 25 students in a grade, by
the end of the panel, two math classrooms would have one addi-
tional ED student, and two would have one fewer.20 This is not a
large change on average, but we view this as non-negligible.

Our findings help us to better understand the landscape of
income segregation. The experience a student has in school is
shaped by not just the district and school they attend but also
the classrooms within a school where they receive instruction
and interact with peers. These patterns are concerning in part
because they challenge principles of egalitarianism in public edu-
cation. Of course, if students are grouped based on ability, there
may be pedagogical or other advantages that benefit all groups of
students. If so, our concerns about segregation may be assuaged
if SES is a valid proxy for ability. However, there are many rea-
sons to doubt this supposition. Regardless of whether SES is a
proxy for demonstrated achievement, we can assess whether and
how ED students and their better-off peers are affected by segre-
gation. To do this, we carry out supplementary analyses of
changes in math achievement in state end-of-grade assessments
between 2007 and 2014 by changes in ED segregation of math
classrooms over the same period, summarized and described in
the online appendix. We find suggestive evidence that within-
school segregation had no beneficial effect for ED students but
improved math achievement for higher-income students. As we
illustrate in Appendix Figure A1, math achievement for ED stu-
dents grew no faster or slower in schools experiencing the most
growth in income segregation. However, math achievement
grew more for higher-income students in schools where ED seg-
regation grew. The different relationships between a school’s
changing level of segregation and achievement for ED and other
students illustrated in Figure A1 is merely descriptive and sug-
gestive, not conclusive. It is clear that ED segregation may affect

Table 2
Regression Estimates of Trends in Within-School

Segregation

All Math ELA

Variable B (SE) B (SE) B (SE)

Enrollment (100s) 0.01097**
(0.00270)

0.02070**
(0.00324)

0.01683**
(0.00321)

Grade 41 0.00103
(0.00276)

–0.00377
(0.00397)

0.00053
(0.00375)

Grade 5 –0.00964**
(0.00278)

–0.00637
(0.00397)

–0.01135**
(0.00377)

Grade 6 0.01169**
(0.00395)

0.02040**
(0.00484)

0.00061
(0.00468)

Grade 7 0.01894**
(0.00407)

0.03056**
(0.00496)

0.00893†

(0.00481)
Grade 8 0.01299**

(0.00407)
0.03123**

(0.00496)
0.00297

(0.00481)
Grade 3 trend 0.00336**

(0.00047)
0.00482**

(0.00063)
0.00394**

(0.00060)
Grade 4 trend 0.00402**

(0.00047)
0.00614**

(0.00063)
0.00478**

(0.00060)
Grade 5 trend 0.00346**

(0.00048)
0.00483**

(0.00063)
0.00445**

(0.00060)
Grade 6 trend 0.00267**

(0.00067)
0.00306**

(0.00075)
0.00305**

(0.00073)
Grade 7 trend 0.00361**

(0.00068)
0.00447**

(0.00076)
0.00315**

(0.00074)
Grade 8 trend 0.00410**

(0.00068)
0.00539**

(0.00076)
0.00403**

(0.00074)
% Black –0.00012

(0.00017)
0.00007

(0.00021)
–0.00033
(0.00021)

% White 0.00013
(0.00017)

0.00025
(0.00021)

–0.00007
(0.00020)

% Hispanic 0.00071**
(0.00019)

0.00100**
(0.00024)

0.00059*
(0.00023)

R 2 0.36966 0.39411 0.40648
Observations 47,385 40,748 41,277

Note. All models include district and school fixed effects. Standard errors clustered
at the school level, in parentheses.
1Grade 3 is omitted/reference.
†p < .10. *p < .05. **p < .01.

252 EDuCATIONAl RESEARCHER

achievement and achievement gaps. Assessing the implications
of rising segregation at the classroom level is a topic that merits
further attention.

Although we find evidence of upward trends in within-school
segregation across Grades 3 through 8, we have not yet explored
the factors shaping this trend. One possible mechanism is the
introduction and growth of school-choice options within a stu-
dent’s school district. Like many states, North Carolina saw
growth in the charter school sector over the past two decades.
The threat of losing students or staff to charters may lead tradi-
tional public school administrators to make strategic decisions to
retain students and teachers in public schools. Increased within-
school segregation could be a by-product of efforts to make pub-
lic school more appealing, such as specialized tracking or

school-within-a-school curricula. Although the goal of tracking
is not to separate students by income, this could be an unin-
tended consequence. Of course, academic tracking may be on
the rise for reasons unrelated to school choice growth. Our panel
falls during the No Child Left Behind (NCLB) era, which may
also have shaped how students were sorted into classrooms
within schools. It is possible that school leaders responded to the
high-stakes-testing pressures of NCLB by reintroducing or
ramping up academic tracking. Clearly, rising segregation of stu-
dents by economic disadvantage raises many questions about
origins and implications. What is clear from the current paper is
that such segregation has occurred within school hallways as well
as between school buildings.

NoTES

We have benefited from comments and suggestions by discussants
and participants at the 2020 research conferences of the Association
for Education Finance and Policy and the Association for Public Policy
Analysis and Management. We acknowledge funding from the Smith
Richardson Foundation. All interpretations and any errors are our own.

1Owens et al. (2016) estimate that segregation of students by
income increased by 40% within school districts from 1990 to 2010,
compared with an increase of about 15% between different districts
within metropolitan areas over the same period.

2For example, Marcotte and Dalane (2019) find that a one-stan-
dard-deviation increase in enrollments in charter schools in a district is
associated with an increase in the segregation of students by economic
disadvantage of 6% of a standard deviation.

3We discuss the limitations of economically disadvantaged (ED)
status as a measure of socioeconomic status later.

4Unless otherwise specified, when we refer to segregation, we
mean by income or economic disadvantage rather than race or ethnicity.

5Reardon and Owens (2014) discuss the evidence on segregation
between schools and document patterns of segregation by race, ethnic-
ity, and income.

6There is a long-standing literature on factors shaping how prin-
cipals assign students to classrooms (e.g. Monk, 1987). In a study of 22
elementary schools and how principals in these schools assigned stu-
dents to more than 200 classrooms, Burns and Mason (1998) describe a
multistep process that includes principals asking teachers for assignment
preferences; principals’ assessment of student achievement, motivation,
needs, and behavior; and principals’ desires to create balanced class-
rooms. Burns and Mason report a final step after rosters were drawn up
in which principals adjusted class assignments based on possible student
conflict, parent requests, and attrition. Osborne-Lampkin and Cohen-
Vogel (2014) further report that principals heavily weight classroom
heterogeneity and a sense of “fairness” to teachers.

7Mickelson (2001) provides a relevant example in North
Carolina, finding that decades after a court-ordered racial desegregation
order, academic tracks within schools in the Charlotte-Mecklenburg
school district remained racially segregated.

8Further, unlike Clotfelter et al. (2021), we calculate changes in
within-school segregation directly rather than as the difference in total
district and between school segregation. More importantly, we estimate
segregation at the classroom relative to the school mean rather than
the district mean. That is, we focus on changes in each grade/school
over time as the relevant unit of analysis. This means we directly assess
whether the typical school is becoming more segregated over time rather
than whether all classrooms in a district are diverging from overall dis-
trict characteristics. Finally, Clotfelter et al. (2021) do not examine asso-
ciations in between- and within-school segregation over time.

Table 3
Impact of Between-School Segregation on Within-

School Segregation

Variable B (SE)

Total enrollment (100s) 0.01694**
(0.00423)

Grade 41 0.01090
(0.00768)

Grade 5 0.01251
(0.00801)

Grade 6 0.03131**
(0.01140)

Grade 7 0.05299**
(0.01196)

Grade 8 0.06789**
(0.01326)

% Black –0.00030
(0.00030)

% White –0.00006
(0.00028)

% Hispanic 0.00072*
(0.00032)

Lag of district dissimilarity index
Grade 3 0.06383*

(0.02999)
Grade 4 0.03627

(0.02585)
Grade 5 0.00701

(0.02665)
Grade 6 0.02145

(0.02865)
Grade 7 0.00198

(0.02916)
Grade 8 –0.03248

(0.03775)
Time trend 0.00524**

(0.00052)
R2 0.40140
Observations 34,971

Note. Models include school and district fixed effects (not shown). Standard errors
clustered at the school level, in parentheses.
1Grade 3 is omitted/reference Unit of analysis is the grade-school-year.
†p < .10. *p < .05. **p < .01.

MAy 2022 253

9For example, in the Wake County Public School System (North
Carolina), total enrollment increased from 99,000 to 143,000 during
first decade of the century, driven by large increases in enrollment from
Hispanic and Asian families (Domina et al., 2021). In an attempt to
prevent and address socioeconomic concentration of students at the
school level, the district implemented a school reassignment policy as it
built new schools, with the aim of ensuring the socioeconomic charac-
teristics of all schools matched the overall district characteristics.

10We do not include data after the 2013-2014 school year because a
new school meals program, the Community Eligibility Provision, became
available in North Carolina in 2014-2015. This program changed the
way participating schools in the state reported students as ED.

11The schools in our sample include 1,275 elementary schools,
558 middle schools, and 264 schools that enroll students in elementary
and middle school grades. In North Carolina, most elementary schools
enroll students in Grades K through 5, and most middle schools enroll
students in Grades 6 through 8. Of the 1,275 elementary schools, 1,152
have the highest grade of 5; 43 enroll students until fourth grade and 80
until sixth grade. Of the 558 middle schools, 470 enroll students begin-
ning in sixth grade; 38 enroll students beginning in fifth grade and 50
beginning in seventh grade.

12We focus on all classes rather than in a given period since schools
vary in how they arrange schedules. The resultant measure assesses
within school segregation over the course of a day—which can happen
between classes in a given period or between periods. In cases where
students are in more than one math/English language arts (ELA) class,
it is not obvious how to assign the student to only one course. A com-
plete list of all courses for recent years can be found on North Carolina
Department of Public Instruction’s website (https://www.dpi.nc.gov/
educators/home-base/powerschool-sis/nc-sis-resources#courses). In the
most recent year available, there are over 3,600 unique course codes.
Many of these courses are available only to high school students, so they
are not relevant for our analysis. A typical elementary student in our
data set might have either an elementary course code reported along
with separate course codes for all specials or course codes for all aca-
demic courses along with all specials. Middle school students usually
have course codes for all academic subjects and specials or electives.
Some schools also report “homeroom”-type courses, which we also
include for all students. If special education students take courses with
non–special education students, special education and general edu-
cation students have similar sets of course codes within each school.
Special education students who are in self-contained classrooms typi-
cally have fewer course codes reported than other students if they spend
their entire days in those rooms.

13We also check course names for words like “technology” and
“computer” to eliminate courses classified as math courses that are
focused on computer skills.

14We calculate math and ELA dissimilarity indexes only if 90% or
more of the students in a grade/school/year have at least one math or
ELA course, respectively. Since some schools report general “elemen-
tary” courses rather than subject-specific courses, especially in Grades
3 to 5 early on in the panel, we calculate a math dissimilarity index
for approximately 86% of our grade/school/year observations and an
ELA dissimilarity index for approximately 87% of our grade/school/
year observations.

15In our sample, 94.8% of students are enrolled in only one math
course and 74% of students are enrolled in only one ELA course.
Elementary grade students are more likely to have two ELA courses
since these students sometimes have separate courses for reading and
language arts, both of which are classified as ELA.

16Students from more than one grade are included in 5.9% of
course observations.

17In separate analyses not reported here, we find that increases in

within-school segregation by race were smaller and more limited than
increases in within-school segregation by ED status.

18Our sample consists of 2,097 unique schools. Most elementary
schools end in Grade 5 (1,152), and most middle schools begin in
Grade 6 (470). There are 191 schools that serve all Grades 3 through 8.

19The mean was between 0.23 and 0.25 for Grades 3 through 5
and between 0.26 and 0.28 for Grades 6 through 8.

20In a 100 student grade, the reallocation of one ED student affects
the dissimilarity index by 0.01.

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AuThoRS

KARI DALANE is a PhD candidate in the School of Public Affairs,
American University, 4400 Massachusetts Ave. NW, Washington, DC
20016; kd5010a@student.american.edu. Her research focuses on equity
issues in K–12 education.

DAVE E. MARCOTTE, PhD, is a professor in the School of
Public Affairs, American University, 4400 Massachusetts Ave. NW,
Washington, DC 20016; marcotte@american.edu. His research interests
include education policy, equity, and achievement.

Manuscript received December 16, 2020
Revisions received July 20, 2021;

October 7, 2021; January 13, 2022
Accepted February 1, 2022

mailto:kd5010a@student.american.edu

mailto:marcotte@american.edu

ED 500: Criteria for Article Summary Assignments

In ED 500, you will be assigned various articles to read and summarize. These assignments serve two main purposes. First, the articles will build upon what you read in your text, thereby, expanding your understanding of the material. Secondly, the article summary assignments will allow you to practice your skills of reading and synthesizing academic research and policy briefs. You will use these skills in your future classes.

Your summaries must include a description of the article’s purpose, a discussion of two main points from the article, and a discussion of what the article means to you. Be sure to follow the directions below and read the assigned articles carefully.

Article Summary Content Requirements

· In the first paragraph, introduce the article. Your introduction should include a description of the article’s purpose, audience, and significance.
Why was the article written? Who was it written for? Why is it important / why did it need to be written?

· In the second and third paragraphs, describe two key points from the article. The points can be something you think is particularly important or interesting; however, they should also be central to the article. You should summarize the key points in your own words. Don’t forget to use in-text citations when paraphrasing. For each point, provide sufficient detail to show that you truly understand the article.

· In the final paragraph, describe what this article means to you. What experiences have you had with the topic? What do you take away from it?

Article Summary Formatting Requirements

· Your critique must be typed in Microsoft Word, 12-point font, 1” margins on all sides, and double spaced.

· The summary should be between approximately 500-650 words.

· No abstract is required; do not include a title page. Do not include a header or any other information on the summary page.

· The summary must include references in APA format.
The only source you should reference for the summary is the assigned article. Include in-text citations when needed and a reference list.

· If you need help with APA formatting, you can consult the APA Resources Page in Blackboard, the APA Manual, the APA Website, or the Purdue OWL Website.

· Use direct quotes sparingly. You are expected to summarize the article in your own words. By paraphrasing, you show that you truly understand the article. You will lose points if your summary primarily consists of quoted material.

For a full description of the grading scale for article summaries in this course, see the Article Summary Grading Rubric that is posted in Blackboard.

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