Intra-District Student Mobility,School Discipline and Gender: Evidence From Clark County,Nevada

Abstract

Student mobility and school discipline are two prominent challenges in urban school districts. The interaction of gender with school discipline in shaping patterns of student mobility has received little attention. This article examines student mobility patterns across gender and the timing of school changes in Clark County, Nevada. The findings draw attention to discipline-related mobility or the placement of students in alternative schools, especially during the school year. Male students are more likely to switch schools mid-year than female students, and the disproportionate rates of student mobility between male and female students can be explained by disciplinary incidents. Gender is a significant predictor of the destination school quality of discipline-related movers. Policy implications and areas for future research are discussed.

Keywords

student mobility school discipline timing of school changes alternative schools gender

Student mobility, or the movement of students across schools, is a widespread phenomenon in the United States (Welsh, 2017; U.S. Government Accountability Office, 2010). Students may change schools at different times (e.g., students may switch schools in the summer or during the school year) for a multitude of reasons. Nonstructural student mobility, or students changing schools on their own accord such as switching elementary schools, is driven by a confluence of social and economic factors, including residential mobility, family circumstances, or the preference to attend a higher quality school or a school that better suits a student’s needs (Welsh, 2017; Kerbow, 1996). Notwithstanding, the reasons for changing schools can be broadly classified into two categories: family-initiated versus school-initiated. Families initiate the majority of school changes; however, schools can also initiate student mobility (Rumberger, 2015).

School discipline provides an important example of school policies and practices that may induce student mobility. Disciplinary or safety reasons consistently rank as one of the most frequent school-related reasons for changing schools behind school quality (Kerbow, 1996; U.S. Government Accountability Office, 2010). Previous studies have found that student mobility is related to suspension and expulsion policies in schools and school districts (Engec, 2006; Grigg, 2012). Engec (2006) found that students who changed schools during the school year had higher suspension rates. Grigg (2012) found that mobility during the school year due to disciplinary reasons had a substantial negative effect on student achievement. Frequent student mobility among the most disadvantaged families is a salient concern for policymakers and may be partly driven by school discipline policies and practices. Zero-tolerance school discipline policies and alternative schools intended as remedial schools have been the most prominent policy interventions across the United States in response to school violence (American Psychological Association Zero Tolerance Task Force, 2008; Lehr, Tan, & Ysseldyke, 2009; Skiba, Arredondo, & Williams, 2014).

The unequal use of exclusionary discipline practices across racial groups, low socioeconomic, and other underrepresented groups is well documented, and the long-term consequences of these policies are significant, including increased justice system interactions and higher dropout rates (Noltemeyer, Ward, & Mcloughlin, 2015; Skiba, Arredondo, & Williams, 2014; Skiba, Michael, Nardo, & Peterson, 2002). African American, Hispanic, and Native American students have higher suspension rates than their White counterparts, and students with disabilities are suspended or expelled more often than able-bodied students (Engec, 2006; Kim, Losen, & Hewitt, 2010; Losen, 2011; Losen & Skiba, 2010). However, there is no evidence that these students are instigators or misbehave more frequently to warrant higher rates of suspension and expulsion (Bradshaw, Mitchell, O’Brennan, & Leaf, 2010; Skiba et al., 2002). Minority students are punished more severely for similar infractions than White students (Losen, 2011). Discipline-related student mobility, specifically the placement of students in alternative schools or juvenile detention centers, raises important concerns regarding educational equity and school discipline policies.

School discipline is a central mechanism for school-initiated student mobility and may play an important role in explaining differences in student mobility patterns among different student subgroups. Student mobility and school discipline play pivotal roles in the Black male “challenge” facing urban schools (Noguera, 2014). The role of gender and its interaction with school discipline in shaping student mobility patterns have received little attention in the extant literature on student mobility. As such, the relationship between student mobility (especially mid-year moves), school discipline, and gender is a topic in need of informed thought and debate that warrants the attention of researchers, policymakers, and practitioners.

This article examines the relationship between student mobility and school discipline by analyzing patterns of student mobility across the timing of school changes within the Clark County School District (CCSD) in Nevada, with a specific focus on gender and school-initiated mobility. Using student-level data from 2007 - 2008 through 2012 - 2013, this study compares and contrasts the exit patterns and destination schools of male and female mobile students. By closely examining how the characteristics of mobile students vary across gender, the timing of school changes, and school discipline, this article contributes to a better understanding of the who (mobile students), why (reasons for changing schools), when (timing of school changes), and where (destination school quality or the quality of the schools that students switch to) of student mobility. Specifically, the study is guided by the following research questions: (a) Are male students more likely to switch schools during the academic year than female students? (b) Are male students more likely to make a school change due to school discipline than female students? and (c) Are mobile male students more likely to transfer to lower quality schools than mobile female students?

The rest of the article proceeds as follows. First, an overview of the relationship between the timing of school changes, school discipline and gender is provided. Next, data and methods are described before results are presented. The article concludes with a discussion of policy implications and areas for future research.

Educational Equity: The Relationship Between Student Mobility and School Discipline

The majority of attention on the reasons for mobility is typically placed on family-initiated mobility (Welsh, 2017, 2018; Dauter & Fuller, 2016; Rumberger, 2015). Whether or not a student moves to a higher quality school can be predicted by student characteristics (Welsh, Duque, & McEachin, 2016; Cullen, Jacob, & Levitt, 2005; Hanushek, Kain, & Rivkin, 2004; Schwartz, Stiefel, & Chalico, 2009; Xu, Hannaway, & D’Souza, 2009). Students who choose to move to a higher quality school are often higher achieving, less likely to live in poverty, and more likely to be White (Welsh et al., 2016; Cullen et al., 2005; Hanushek et al., 2004; Schwartz et al., 2009; Xu et al., 2009). Although school-initiated mobility may account for a considerable proportion of school changes, none of these previous studies focus on school-initiated mobility. More specifically, none of the aforementioned studies examine how school discipline policies may shape the likelihood of students exiting schools.

Even though previous research has posited that school practices should reduce rather than increase student mobility (Kerbow, 1996; Rumberger, 2003; Temple & Reynolds, 1999), school discipline policies, in particular, the placement of students in alternative schools, encourage changing schools. School discipline policies may facilitate the “gaming” of accountability incentives and result in schools initiating student mobility during the school year as students are expelled or placed in alternative schools. School discipline policies and practices may also affect mid-year student mobility in ways that are not school initiated. Disciplinary reasons may prompt nonstructural school changes as interactions with school personnel or disagreements with school discipline policies and practices may spur parents to change schools during the school year. Hence, school discipline policies may affect student mobility patterns in a variety of ways. To be clear, students also play a role in disciplinary incidents. Thus, it may not be appropriate to characterize school discipline as exclusively school-driven mobility, even though discipline-related mobility is different from family-initiated mobility.

In recent decades, a punitive approach to school discipline has emerged in many schools and districts in the form of zero-tolerance policies. Alternative schools are increasingly used as the main policy mechanism for addressing students with suspensions and expulsions or students who display “disruptive behavior” in regular schools in many states and school districts across the United States (Lehr et al., 2009; Skiba & Rausch, 2006). There is a variation among states’ alternative schools policy. Some require placement in alternative schools; others have made alternative schools a choice of the student after expulsion or suspension; others require placement in alternative schools if the disciplinary infraction was related to assault, felony, or possession of a firearm or weapon in school; and some states use alternative schools as interim placement to assist suspended or expelled students to re-enter the regular system after infraction (Lehr et al., 2009). The placement of students in alternative schools may have negative consequences on student achievement (Skiba & Rausch, 2006). Because there is a positive relationship between time spent in school and student’s academic achievement, exclusionary discipline policies, which decrease the number of days spent in the classroom, have a negative impact on achievement (Arcia, 2006; Noltemeyer et al., 2015).

There is a growing fear that school discipline policies have become discriminatory toward certain student subgroups, namely minority students and students with disabilities (Gregory, Skiba, & Noguera, 2010; Losen, Hodson, Keith, Morrison, & Belway, 2015; Skiba et al., 2002). Discipline-related mobility may be especially prevalent in urban schools that typically have a higher concentration of low-income, minority, and “hard to educate” students. With the exception of a handful of studies (Engec, 2006; Grigg, 2012), there has been little empirical work on the relationship between student mobility and school discipline. Gender may interact with school discipline to shape student mobility patterns in ways that have considerable equity implications. Black male students are significantly more likely to be suspended or expelled or subjected to exclusionary school discipline than any other student subgroup (Losen et al., 2015; Mendel, 2011; Skiba et al., 2002). Even though previous research has highlighted that student mobility affects different students in different ways and identified the need for more in-depth exploration of the conditional effects of changing schools (Institute of Medicine and National Research Council, 2010), little attention has been paid to the role of gender in student mobility studies. Indeed, a review of some of the major studies in student mobility research indicates that although race and poverty are commonplace, gender is typically not reported or explicitly addressed. For example, neither the 2010 GAO report nor Grigg (2012) discuss gender (Grigg, 2012; U.S. Government Accountability Office, 2010).

It is important to investigate whether mobility patterns vary systematically by gender, the timing of school changes, and assignment to alternative schools. If within a school district, female students are disciplined less and tend to enroll in or are more likely to switch to higher quality schools, while male students are disciplined more and tend to enroll in or are more likely to switch to lower quality schools, then differential mobility patterns exist that raise important equity concerns as they may lead to increasing student segmentation within a school district by gender, achievement, or school quality, with school discipline as a central contributing factor. Due to data limitations, few studies have differentiated among the reasons of mobility (e.g., family- vs. school-initiated school changes) or definitively disentangled school-driven sorting from student-driven sorting.

Using a rich data set that allows for the differentiation of student mobility due to placement in behavior and continuation schools or juvenile detention centers, this study examines the relationship among gender, school discipline, and the timing of school changes. This article adds to the literature examining contributing factors to the propensity to exit schools. Unlike the majority of previous studies that have focused on factors related to family-initiated student mobility, this study focuses on school-initiated mobility, in particular changing schools due to assignment to alternative schools. As such, this article contributes to unraveling the complexity of the reasons prompting school changes with a particular focus on how schools may shape mobility patterns through school discipline policies. Examining the relationship between school discipline policies and student mobility is salient and timely as policymakers at the local, state, and federal level rethink the use of exclusionary discipline practices. A better understanding of the relationships among discipline-related mobility, the timing of school changes, and the gender of mobile students may offer insights on why mobility affects different students in different ways and can help policymakers shape appropriate solutions. In the next section, the data and the empirical approach employed to investigate mobility patterns in CCSD are described.

Data and Methods

Data

This study uses a 6-year panel of student-level data for all students in the CCSD from 2007–2008 through 2012–2013. The data contain students’ demographic characteristics and annual test scores from the Nevada Proficiency Examination Program. Demographic data include indicators for students’ gender, race/ethnicity (Black, Hispanic, Asian, and White), free and reduced priced lunch (FRPL), English language learner (ELL), and special education statuses. Students are tested in reading and math in Grades 3 to 8 and take the High School Proficiency Examination in Grade 10. Test scores for students in Grades 3 through 10 are standardized by grade and year, relative to the district. Detailed longitudinal data that track the dates and sequence of school changes allow for in-depth classification of the timing of student mobility across a range of grades (K-12). Unique student and school identifiers in the data link students to schools in each year and across multiple school years. I use the grade span of schools and information on the sequences of school moves to categorize the type (structural mobility-school changes in the summers in Grade 6 [elementary to middle schools] and Grade 9 [middle to high schools] vs. nonstructural mobility) and timing of nonstructural school changes.

I use a sample of students who have been continuously enrolled in a CCSD school for at least two consecutive academic years (in other words, students need at least two observations to be included, and students with only one observation were dropped from the sample). Although the majority of the students have been enrolled for the duration of the panel, the sample also includes entrants, leavers, and students who left and returned to the district. This sample includes 1,826,058 student-years with 428,247 unique students. Approximately 51% of the sample is male students.

CCSD is a large, diverse urban school district with an average annual enrollment of more than 300,000 students. On average, roughly 42% of students are Hispanic, 33% are White, 13% are African American, and 8% are Asian. Over the period of study, CCSD experienced ongoing demographic shifts, including an increasing proportion of Hispanic, special education status, and low-income students and a decreasing proportion of Asian, White, African American, and ELL students.

Methods

The empirical analysis consists of two parts: an exit analysis and a destination analysis. First, this article examines the probability of a student making non-structural and discipline-related school changes based on the (a) gender of the student, (b) demographic and achievement characteristics of the student, and (c) the characteristics of the school they exit. Second, this study investigates the role of the quality of origin schools and students’ gender and other characteristics in predicting the quality of the destination schools of nonstructural movers across the different timing of school changes as well as discipline-related moves. Throughout this study, school quality refers to the average academic achievement of schools. In particular, I use the percentage of students in a school classified as proficient or higher as the indicator of school quality. For brevity’s sake, I present results for math achievement.

This study focuses on nonstructural mobility that occurs when students change schools of their own volition (e.g., switching elementary schools) rather than structural moves that occur after the completion of a terminal grade (e.g., elementary to middle school transitions) and school-initiated student mobility due to disciplinary reasons. Nonstructural movers are the student subgroup that mobility policies in school districts may target and influence. Nonstructural movers are categorized by the timing of school changes: between-year switcher or a student who made a nonstructural move between school years, within-year switcher or a student who switched schools at least once during the school year, and “ultra-mover” or a student who changed schools both between and during the school year in the same academic year.

Discipline-related mobility is classified as all school changes to and from behavior or continuation schools or juvenile detention centers. The data on school discipline are as reported by the schools. CCSD offers two main alternative education programs for students with disciplinary problems: five behavior schools (Grades 6-12) and three continuation schools (two serve Grades 9-12, one serves Grades 6-8). Students in behavior schools are either recommended for expulsion or placed directly in the behavior school by their principals without a recommendation for expulsion. Behavior schools are intended to be brief intervention programs lasting about 4 to 9 weeks for students who committed disciplinary infractions at regular schools (CCSD, 2016). Continuation schools are limited to students expelled from their home school via a Board of Trustee ratified expulsion. After successfully completing continuation schools, students with “limited” expulsions may return to a regular school under trial enrollment conditions. In addition, students who commit infractions that not only violate school rules but also federal, state, and local laws are typically referred to Juvenile Detention Centers in CCSD.

Exit analysis

To examine the relationship between exiting patterns and student and school characteristics, I use the following linear probability model:

Y_{i s t} = β_{0} + β_{1} G e n d e r_{i s t} + β_{2} X_{i s t} + β_{3} Z_{s t} + β_{4} G e n d e r_{i s t} \times Z_{s t} + π_{t} + γ_{g} + e_{i t}

where Y_ist is a dichotomous outcome variable that is equal to one if student i in school s at time t made a nonstructural or discipline-related school change. I estimate the probability separately for nonstructural moves and discipline-related mobility. Gender_ist is equal to one if the student is male. X_ist is a vector of student-level characteristics, including the aforementioned racial categories (White is the reference group), FRPL, ELL, and special education statuses, as well as lagged student achievement. Z_st is a vector of school-level characteristics, including the percentage of Black, Hispanic, Asian, FRPL, ELL, special education, and male students in the school, as well as school quality measured by the percentage of students in a school scoring proficient or above on state accountability tests. I also include interactions between gender and school-level characteristics in additional models. In all models grade (γ_g) and year (π_t) fixed effects are used to control for unobservable differences across time and between grades, and I use robust standard errors clustered at the school level.

Destination analysis

To evaluate whether students are switching to higher quality schools, a multinomial framework as follows is utilized to predict the quality of the destination school for nonstructural and discipline-related movers:

\begin{array}{l} P r o b (D e s t i n a t i o n_{i t} = j) \\ = \frac{\exp (\begin{array}{l} β 0 + β 1 A c h i e v e_{i (t - 1) j} + β 2 O r i g i n Q u a l i l t y_{i (t - 1) j} \\ + β 3 A c h i e v e \times O r i g i n Q u a l i t y_{i (t - 1) j} + β 4 X_{i t j} \end{array})}{\sum_{j = 1}^{J} (\begin{array}{l} β 0 + β 1 A c h i e v e_{i (t - 1) j} + β 2 O r i g i n Q u a l i t y_{i (t - 1) j} \\ + β 3 A c h i e v e \times O r i g i n Q u a l i t y_{i (t - 1) j} + β 4 X_{i t j} \end{array})} \end{array}

where Destination_it represents school quality and is the categorical outcome variable for a student i at time t, with j = 1 for schools in the bottom third of the district achievement distribution in the prior year, j = 2 for schools in the middle third of the distribution (base category), and j = 3 for schools in the top third of achievement distribution. Achieve indicates whether the student was in the bottom, middle, or top third of the district’s achievement distribution for their grade in the previous year, OriginQuality represents the prior year school quality of the movers’ previous school, calculated in the same manner as the Destination variable. Achieve*OriginQuality is an interaction between the student’s prior year achievement and the origin school’s prior school quality to examine how destination school quality varies by the relationship between student achievement and the quality of the school they exited. The full set of student-level controls, X, and an indicator for year are also included.

Results

Table 1 presents the nonstructural and discipline-related mobility rates, as well as the demographic and achievement characteristics of mobile students by gender and the timing of student mobility. When all non-structural school changes are combined, there is a slight gender gap in mobility rates, with males (17%) having a higher rate of mobility than females (16%). The characteristics of male and female mobile students are almost identical when all nonstructural moves are lumped together, except in two instances. The proportion of male mobile students with a special education status were twice that of female mobile students, and male mobile students performed more than a 10th of standard deviation below female mobile students in math (the achievement gap was even wider in reading). The gender gap in student mobility becomes readily apparent when the timing of school changes is differentiated. The results illustrate that between school years mobility rates were roughly the same for male and female students; however, male students switched schools at higher rates within the academic year (mid-year and ultra-moves) than female students. The demographic and achievement characteristics of male and female mobile students that switched schools in the summer were also very similar with the exception of special education status. Male and female students that switched schools in the summer had nearly identical math achievement levels (−0.15SD vs. −0.13SD). The special education status gap remained and the achievement gap between male and female mobile students that switched schools within the school year widened.

Table 1.

Demographic and Achievement Characteristics of Mobile Students by Gender and the Timing of Student Mobility.

Student Characteristics	Male	Female	BY male	BY female	MY male	MY female	UM male	UM female
Nonstructural mobility rate	17.2	15.7	7.5	7.4	6.6	5.7	3.0	2.5
Discipline-related mobility rate	6.3	2.6	0.002	0.0008	16.9	7.5	20.2	9.9
Black	0.22	0.21	0.18	0.18	0.24	0.23	0.28	0.26
Black	(0.41)	(0.41)	(0.38)	(0.38)	(0.42)	(0.42)	(0.45)	(0.44)
Hispanic	0.44	0.44	0.42	0.43	0.46	0.46	0.43	0.44
Hispanic	(0.50)	(0.50)	(0.49)	(0.50)	(0.50)	(0.50)	(0.50)	(0.50)
Asian	0.06	0.06	0.07	0.07	0.05	0.06	0.04	0.05
Asian	(0.23)	(0.24)	(0.25)	(0.25)	(0.23)	(0.23)	(0.20)	(0.21)
White	0.24	0.24	0.28	0.27	0.22	0.21	0.21	0.20
White	(0.43)	(0.42)	(0.45)	(0.44)	(0.41)	(0.41)	(0.40)	(0.40)
Special education	0.18	0.09	0.18	0.09	0.17	0.09	0.20	0.10
Special education	(0.38)	(0.29)	(0.38)	(0.29)	(0.38)	(0.28)	(0.40)	(0.30)
English language learner	0.19	0.18	0.19	0.17	0.22	0.21	0.16	0.15
English language learner	(0.39)	(0.39)	(0.39)	(0.37)	(0.41)	(0.41)	(0.36)	(0.36)
FRPL	0.62	0.63	0.57	0.57	0.65	0.66	0.69	0.70
FRPL	(0.50)	(0.48)	(0.49)	(0.49)	(0.46)	(0.47)	(0.46)	(0.46)
Math achievement (z score relative to district) Math proficiency	−0.37	−0.29	−0.15	−0.13	−0.52	−0.43	−0.61	−0.50
	(1.00)	(1.00)	(1.00)	(0.94)	(1.00)	(0.95)	(1.00)	(0.94)
	0.47	0.50	0.57	0.58	0.39	0.43	0.38	0.42
	(0.50)	(0.50)	(0.49)	(0.49)	(0.49)	(0.49)	(0.49)	(0.49)
N	161,560	139,001	70,750	66,036	62,291	50,932	28,519	22,033

Note. Ultra-mover refers to student who changed schools both between and within the school year in the same academic year. Standard deviations in parentheses. BY = between school years; MY = mid-year; FRPL = free and reduced priced lunch; UM = ultra-mover.

The results suggest that discipline-related mobility accounts for a nontrivial amount of student mobility that occurs within an urban school district. Five percent of all student mobility (structural and nonstructural) and about 8% of all nonstructural school changes were discipline related. The majority of discipline-related mobility (about three-fourths) was due to placement in behavior schools as opposed to placement in continuation schools or referral to juvenile services. The overwhelming majority of discipline-related mobility occurred in Grades 8 through 10 and was highest in Grades 8 and 9, which are key transitional years in middle and high schools. The results suggest that school-initiated student mobility largely occurs during the academic year. Less than 1% of school changes that occurred between school years were discipline related. Conversely, 13% of mid-year moves and 16% of ultra-moves were discipline related. Multiple school changes during the school year were largely discipline related. For instance, of the students who changed schools three times during the school year, 34% made discipline-related school changes compared with less than 5% of students who made one mid-year move. In other words, frequent student mobility, especially during the school year, may be driven in large part by school discipline.

Table 1 illustrates that males have a higher discipline-related mobility rate than females. When all nonstructural moves are combined, the discipline-related mobility rate of male mobile students is more than double that of female mobile students. Roughly 6% of student mobility by male students was discipline related compared with about 3% for female mobile students. The gap in discipline-related mobility between male and female students is particularly stark for within-year mobility. About 17% of mid-year student mobility by male students was discipline related compared with roughly 8% for female mobile students. In addition, approximately one-fifth of ultra-moves by male students were due to school discipline compared with 10% for female mobile students.

Table 2 compares the achievement and demographic characteristics of discipline-related movers across gender. When all discipline-related moves are combined, irrespective of gender, the results indicate that mobile students affected by school discipline are disproportionately Black, male, low income, low-achieving, and receiving special education services. For instance, 29% of discipline-related movers were Black, and more than double the average district enrollment of 13% and 73% of discipline-related movers were male students. About 60% of discipline-related mobile students were FRPL recipients and 16% were special education students. Discipline-related mobile students were also some of the lowest achieving students in the CCSD—discipline-related mobile students’ average achievement was nearly a standard deviation below the district average (−0.83SD). In addition, discipline-related mobile students had statistically significant differences with nonstructural movers across all demographic and achievement characteristics, and these differences remained statistically significant when the timing of school changes was differentiated.

Table 2.

Characteristics of Discipline-Related Student Mobility by Gender and the Timing of School Changes.

	District average	DR mover	DR mover male	DR mover female	DR MY male	DR MY female	DR UM male	DR UM female
Black	0.13	0.29	0.27	0.32	0.25	0.30	0.30	0.36
Black	(0.34)	(0.45)	(0.44)	(0.47)	(0.44)	(0.46)	(0.46)	(0.48)
Hispanic	0.42	0.41	0.42	0.39	0.43	0.40	0.40	0.38
Hispanic	(0.49)	(0.49)	(0.49)	(0.49)	(0.50)	(0.49)	(0.49)	(0.48)
Asian	0.08	0.03	0.03	0.03	0.03	0.03	0.03	0.03
Asian	(0.27)	(0.17)	(0.17)	(0.17)	(0.18)	(0.17)	(0.17)	(0.17)
White	0.32	0.23	0.24	0.21	0.25	0.23	0.22	0.19
White	(0.47)	(0.42)	(0.43)	(0.41)	(0.43)	(0.42)	(0.42)	(0.39)
Special education	0.11	0.16	0.19	0.09	0.18	0.09	0.20	0.10
Special education	(0.31)	(0.37)	(0.39)	(0.29)	(0.39)	(0.28)	(0.40)	(0.30)
English language learner	0.17	0.12	0.13	0.09	0.14	0.10	0.13	0.09
English language learner	(0.37)	(0.33)	(0.34)	(0.29)	(0.34)	(0.30)	(0.34)	(0.28)
FRPL	0.50	0.60	0.60	0.62	0.58	0.60	0.63	0.64
FRPL	(0.50)	(0.49)	(0.49)	(0.49)	(0.49)	(0.49)	(0.48)	(0.48)
Math achievement (z score relative to district)	0.00	−0.83	−0.85	−0.78	−0.81	−0.74	−0.95	−0.87
Math achievement (z score relative to district)	(1.00)	(0.94)	(0.95)	(0.90)	(0.95)	(0.91)	(0.92)	(0.87)
Math proficiency	0.61	0.22	0.22	0.22	0.23	0.23	0.20	0.21
Math proficiency	(0.49)	(0.42)	(0.42)	(0.42)	(0.42)	(0.42)	(0.40)	(0.41)
N	1,826,058	23,010	16,783	6,227	10,531	3,833	5,750	2,172

Note. DR Ultra-mover refers to students who changed schools both between and within the school year in the same academic year due in part to school discipline. Standard deviations in parentheses. DR = discipline-related mover; DR MY = mid-year school changes that are discipline-related; FRPL = free and reduced priced lunch; UM = ultra-mover.

Table 2 also highlights differences in discipline-related movers by gender. The results indicate that there is a special education status gap between male and female discipline-related movers—19% of male students who switched schools due to school discipline were special education students relative to 9% for female students. Male discipline-related movers were also slightly lower achieving than female discipline-related movers (−0.85SD vs. −0.78SD). The results are qualitatively similar across mid-year and ultra-moves—discipline-related movers had the highest proportion of Black, low-income, and special education students and the lowest achievement levels.

Exit Analysis

Predicting nonstructural mobility

Table 3 illustrates the linear probability of making a nonstructural move based on student and school characteristics across the timing of school changes. As Column (1) illustrates, when all non-structural moves are combined, similar to prior research, lower-achieving, Black, and low-income students were more likely to switch schools. Hispanic, Asian, special education, and ELL students were less likely to change schools. Students in higher quality schools and schools with a higher proportion of special education students were less likely to change schools. Students in schools with a higher proportion of male students were more likely to switch schools. Column (1) shows that male students were one percentage point more likely to change schools than female students.

Table 3.

Estimation of Linear Probability of NonStructural Move by Timing of School Changes (N = 774,211).

	All moves	Between-year	Mid-year	Ultra-mover
Prior achievement	−0.038***	−0.009***	−0.014***	−0.014***
Prior achievement	(0.001)	(0.000)	(0.000)	(0.000)
Black	0.073***	0.021***	0.025***	0.027***
Black	(0.003)	(0.001)	(0.001)	(0.001)
Hispanic	−0.011***	−0.004***	−0.001	−0.006***
Hispanic	(0.002)	(0.001)	(0.001)	(0.001)
Asian	−0.009***	−0.001	−0.003**	−0.005***
Asian	(0.002)	(0.002)	(0.001)	(0.001)
Male	0.014***	−0.001^†	0.008***	0.007***
Male	(0.001)	(0.001)	(0.001)	(0.000)
Special education	−0.016***	−0.007***	−0.006***	−0.003**
Special education	(0.002)	(0.001)	(0.001)	(0.001)
ELL	−0.017***	−0.010***	−0.001	−0.007***
ELL	(0.003)	(0.001)	(0.001)	(0.001)
FRPL	0.046***	0.013***	0.016***	0.017***
FRPL	(0.002)	(0.001)	(0.001)	(0.001)
School quality	−0.103**	−0.059**	−0.023**	−0.021
School quality	(0.032)	(0.021)	(0.007)	(0.012)
Black_sch	0.094	0.086*	0.004	0.005
Black_sch	(0.065)	(0.042)	(0.019)	(0.026)
Hispanic_sch	−0.041	0.034	−0.025	−0.051*
Hispanic_sch	(0.056)	(0.036)	(0.017)	(0.023)
White_sch	−0.055	−0.003	−0.014	−0.038
White_sch	(0.054)	(0.036)	(0.017)	(0.021)
Asian_sch	0.067	0.082	0.026	−0.041
Asian_sch	(0.071)	(0.052)	(0.020)	(0.025)
Male_sch	0.687***	0.229*	0.128***	0.329***
Male_sch	(0.176)	(0.091)	(0.030)	(0.090)
SpecEd_sch	−0.145*	−0.102*	0.009	−0.052
SpecEd_sch	(0.067)	(0.042)	(0.015)	(0.034)
Ell_sch	0.035	−0.027	0.024**	0.039**
Ell_sch	(0.028)	(0.016)	(0.009)	(0.012)
FRPL_sch	0.014	−0.012	0.016*	0.010
FRPL_sch	(0.025)	(0.014)	(0.007)	(0.010)
Constant	0.705***	0.899***	−0.066**	−0.128**
Constant	(0.108)	(0.066)	(0.023)	(0.048)

Note. Cluster-robust standard errors at the school level are used to account for within-school correlation of the student-level error terms, as well as school-level serial correlation. ELL = English language learner; FRPL = free and reduced priced lunch.

†

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

Interesting differences in exit patterns of male and female students emerge when student mobility is differentiated by the timing of school changes. Male students were less likely to switch schools between school years and more likely to change schools within the school year (mid-year and ultra-movers). The significance of the proportion of male students in a school also remained consistent across the timing of school changes. The results indicate that gender, both at the student-and school-level, is a significant predictor of student mobility, especially school changes that occur within the school year.

In separate models, I include interactions between gender and school characteristics. The results illustrate that interactions of gender and schools’ achievement and demographic characteristics vary across the timing of school changes. For between school years moves, male students were 14 percentage points less likely to switch schools upon the inclusion of interactions. The interactions between male students and school quality, and the proportion of male and low-income students were positive and significant. For mid-year moves, the coefficient for gender remained positive but lost statistical significance upon the inclusion of interactions. Unlike between school year moves, the interaction between male students and school quality was negative and significant. For ultra-moves, male students were less likely to switch schools than female students even when interactions were included. Unlike mid-year and between year moves, the interaction of gender and school quality was insignificant for ultra-moves. Male students in schools with a higher proportion of male students were more likely to be ultra-movers whereas male students in schools with a higher proportion of special education status were less likely to be ultra-movers. Overall, the results imply that (a) male students are more likely to switch schools during the year relative to in the summer; (b) male students who switch schools in the summer tend to exit from higher quality schools, whereas mid-year mobility for male students is largely due to exiting from lower quality schools; and (c) the interactions between school characteristics and gender play an important role in explaining why male students are more likely to switch schools within the school year.

To examine whether exit patterns are driven in part by school discipline, I re-run the analysis using a sample that excludes discipline-related mobility disaggregated by the level of schooling. When discipline-related school changes are excluded, the results confirm that male students are less likely to switch schools in the summer, except in middle schools, where male students were more likely to change schools in the summer than female students. There were further variations across the levels of schooling for mid-year school changes. For elementary and high schools, the results remained similar to the results in Table 3—male students were more likely to switch schools during the school year. However, male students in middle schools were no longer more likely to change schools during the school year. This suggests that school discipline plays a significant role in mid-year mobility for male students in middle schools and partly contributes to differential exit patterns. Male students in elementary and high schools were no longer more likely to be ultra-movers whereas male students in middle schools were more likely to be ultra-movers. This implies that ultra-moves by male students in middle schools are driven by other factors outside of school discipline whereas ultra-moves by male students in high schools are partly attributed to school discipline.

Predicting discipline-related mobility

Table 4 predicts the likelihood of making a discipline-related school change based on student and school characteristics across the timing of school changes. The results illustrate the probability of making a discipline-related move conditional on changing schools. For instance, in Column (1), when all moves are combined, discipline-related movers are compared to all other mobile students in the district. The findings indicate that higher achieving mobile students were less likely to be discipline-related mobile students. A one standard deviation increase in prior achievement was associated with a one percentage point decrease in the likelihood of changing schools due to school discipline. Black and male mobile students were more likely to make discipline-related school changes whereas Hispanic, Asian and special education status students were less likely to be discipline-related movers.

Table 4.

Estimation of Linear Probability of Discipline-Related Move Conditional on Changing Schools.

	All moves	Mid-year	Ultra-moves
	(N = 126,083)	(N = 49,438)	(N = 19,749)
Prior achievement	−0.025***	−0.029***	−0.030***
Prior achievement	(0.002)	(0.004)	(0.003)
Black	0.009**	−0.002	0.011+
Black	(0.003)	(0.007)	(0.006)
Hispanic	−0.008**	−0.014*	−0.028***
Hispanic	(0.003)	(0.006)	(0.006)
Asian	−0.037***	−0.070***	−0.065***
Asian	(0.004)	(0.009)	(0.009)
Male	0.069***	0.109***	0.114***
Male	(0.003)	(0.007)	(0.005)
Special education	−0.014***	−0.026***	−0.022**
Special education	(0.003)	(0.007)	(0.007)
ELL	0.005	0.003	0.011
ELL	(0.003)	(0.006)	(0.007)
FRPL	−0.002	−0.017**	−0.012+
FRPL	(0.002)	(0.005)	(0.006)
School quality	0.008	0.044	0.062
School quality	(0.024)	(0.043)	(0.051)
Black_sch	0.056	0.072	0.139
Black_sch	(0.072)	(0.155)	(0.143)
Hispanic_sch	0.022	0.032	−0.049
Hispanic_sch	(0.066)	(0.149)	(0.128)
White_sch	0.040	0.040	0.005
White_sch	(0.070)	(0.156)	(0.144)
Asian_sch	−0.003	−0.048	−0.098
Asian_sch	(0.080)	(0.168)	(0.158)
Male_sch	0.201**	0.409**	0.224
Male_sch	(0.062)	(0.155)	(0.154)
SpecEd_sch	−0.122	−0.300*	−0.229
SpecEd_sch	(0.074)	(0.119)	(0.140)
ELL_sch	−0.002	−0.007	0.096
ELL_sch	(0.028)	(0.054)	(0.051)
FRPL_sch	0.002	0.003	−0.008
FRPL_sch	(0.022)	(0.042)	(0.043)
Constant	−0.164*	−0.345*	−0.230
Constant	(0.081)	(0.174)	(0.154)

Note. Cluster-robust standard errors at the school level are used to account for within school correlation of the student-level error terms, as well as school-level serial correlation. ELL = English language learner; FRPL = free and reduced priced lunch.

†

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

In Column (2), the probability of discipline-related mobility is conditional on changing schools during the school year. The results illustrate that lower achieving mid-year movers were more likely to make discipline-related mobility. A one standard deviation increase in prior achievement was associated with roughly a three percentage point decrease in the likelihood of making a discipline-related move. Hispanic, Asian, special education, and low-income mid-year switchers were less likely to be discipline-related movers. Among mid-year movers, gender was the strongest predictor of discipline-related mobility. Male mid-year movers were roughly 11 percentage points more likely than female mid-year movers to change schools due to school discipline. Column (2) also shows that gender at the school-level is a significant predictor of mid-year moves due to school discipline as mobile students in schools with a higher proportion of male students were more likely to change schools due to school discipline. The results in Column (3) for ultra-movers are similar to those of mid-year movers. Lower achieving and male ultra-movers are more likely to be discipline-related mobility. There is also evidence that Black ultra-movers are more likely to make discipline-related school changes, whereas low-income ultra-movers are less likely to make discipline-related moves. The results imply that male mobile students are more likely to switch schools due to school discipline than female mobile students.

I also re-run the models predicting discipline-related mobility and include interactions between gender and school characteristics. When all moves are combined, the interaction between gender and school quality was significant and negative. The interactions of gender with the proportion of special education and ELL students were also significant and negative whereas the interaction of gender and proportion of Hispanic and low-income students was positive and significant. When interactions were included in models for mid-year school changes, male mobile students were 40 percentage points more likely to switch schools during the school year due to school discipline than female mid-year movers. The interaction between school quality and gender was also significant and negative suggesting that male mid-year movers in lower quality schools are more likely to make discipline-related mobility. The interactions of gender and proportion of ELL and special education students were also negative and significant. Overall, the results suggest that within the mobile student population, male mobile students in higher quality schools are less likely to make discipline-related school changes, whereas male mobile students in low-quality schools and, to a lesser degree, schools with a higher proportion of low-income students are more likely to exit due to school discipline. The findings also imply that female mobile students who experience school discipline exit higher quality schools, whereas male students in lower quality appear to be more subjected to discipline-related moves.

Destination Analysis

This analysis examines the quality of schools that mobile students transfer to. Table 5 presents the change in log odds of moving to a low- and high-achieving school relative to moving to a school in the middle third of the district’s achievement distribution across the timing of school changes. The results are somewhat inconclusive based on student achievement and school quality separately across the timing of school changes. The findings are clearer for the interactions of student achievement and school quality. The results show that for both between-year and mid-year movers, low-achieving students in low-achieving schools were more likely to switch to another low-achieving school, whereas low-achieving students in high-achieving schools were more likely to transfer to another high-achieving school. For ultra-movers, high-achieving students in high-achieving schools were more likely to switch to high-achieving schools. These findings imply that origin school quality is a significant predictor of destination school quality irrespective of prior student achievement and the timing of nonstructural school changes.

Table 5.

Multinomial Logit Regression Predicting Destination School Achievement Across the Timing of School Changes.

	Between year		Mid-year		Ultra-mover		Discipline related
Student and School Characteristics	Low achieving	High achieving	Low achieving	High achieving	Low achieving	High achieving	Low achieving	High achieving
Low-achieving student	−0.15**	−0.14**	−0.27***	−0.03	−0.45***	−0.06	−0.47***	−0.12
Low-achieving student	(0.05)	(0.05)	(0.05)	(0.06)	(0.07)	(0.11)	(0.11)	(0.20)
High-achieving student	−0.40***	−0.07	−0.41***	−0.12	−0.65***	−0.00	−0.80***	0.01
High-achieving student	(0.08)	(0.06)	(0.06)	(0.07)	(0.12)	(0.11)	(0.19)	(0.23)
Low-achieving origin school	3.03***	0.97***	2.39***	0.87***	2.34***	0.97***	1.73***	1.18***
Low-achieving origin school	(0.11)	(0.12)	(0.09)	(0.11)	(0.09)	(0.13)	(0.16)	(0.26)
High-achieving origin school	1.08***	3.09***	0.80***	2.73***	1.07***	3.04***	0.76***	2.75***
High-achieving origin school	(0.13)	(0.12)	(0.10)	(0.10)	(0.11)	(0.12)	(0.14)	(0.21)
Low student × low school	0.18*	0.15	0.23**	0.10	0.45***	0.13	0.22	0.35
Low student × low school	(0.07)	(0.10)	(0.07)	(0.09)	(0.11)	(0.15)	(0.13)	(0.23)
Low student × high school	−0.02	0.39***	0.05	0.20*	0.30	0.27	0.35	0.31
Low student × high school	(0.09)	(0.07)	(0.09)	(0.08)	(0.16)	(0.15)	(0.19)	(0.25)
High student × low school	0.51***	0.38**	0.32***	0.35**	0.62***	0.27	0.34	0.66*
High student × low school	(0.10)	(0.12)	(0.09)	(0.12)	(0.15)	(0.18)	(0.25)	(0.26)
High student × high school	0.06	0.52***	−0.04	0.46***	0.16	0.40*	−0.09	0.16
High student × high school	(0.12)	(0.08)	(0.11)	(0.10)	(0.20)	(0.16)	(0.35)	(0.30)
Black	0.48***	−0.26***	0.38***	−0.27***	0.48***	−0.18*	0.49***	−0.10
Black	(0.06)	(0.05)	(0.05)	(0.05)	(0.06)	(0.08)	(0.09)	(0.13)
Hispanic	0.50***	−0.31***	0.33***	−0.34***	0.40***	−0.21**	0.34**	−0.19
Hispanic	(0.06)	(0.04)	(0.05)	(0.05)	(0.06)	(0.07)	(0.11)	(0.11)
Asian	−0.07	0.04	−0.19*	−0.05	−0.02	0.12	−0.02	0.02
Asian	(0.08)	(0.07)	(0.08)	(0.08)	(0.11)	(0.11)	(0.18)	(0.19)
Male	0.03	−0.05	0.03	−0.03	0.05	−0.03	0.18**	0.10
Male	(0.03)	(0.03)	(0.03)	(0.03)	(0.05)	(0.05)	(0.06)	(0.08)
Special education	0.05	0.06	0.07	0.02	0.04	−0.01	−0.07	−0.06
Special education	(0.04)	(0.04)	(0.04)	(0.05)	(0.06)	(0.07)	(0.06)	(0.09)
ELL	−0.03	−0.29***	−0.01	−0.31***	−0.09	−0.30***	0.06	−0.39**
ELL	(0.05)	(0.06)	(0.04)	(0.05)	(0.06)	(0.08)	(0.07)	(0.12)
FRPL	0.30***	−0.25***	0.22***	−0.23***	0.16**	−0.16**	0.19**	−0.28*
FRPL	(0.05)	(0.05)	(0.04)	(0.04)	(0.05)	(0.06)	(0.07)	(0.12)
Constant	−2.13***	−1.38***	−1.38***	−1.16***	−1.39***	−1.44***	−1.04***	−1.55***
Constant	(0.14)	(0.13)	(0.08)	(0.09)	(0.10)	(0.12)	(0.14)	(0.26)
Pseudo R²	.38		.27		.28		.18
Model chi-square	3,269.67		4,001.48		3,908.97		1928.93
N	56,896		49,438		19,749		10,667

Note. ELL = English Language Learner; FRPL = free and reduced priced lunch.

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

Student demographic characteristics also predict destination school quality of nonstructural moves. Regardless of the timing of school changes, Black, Hispanic and low-income students were more likely to switch to low-achieving schools and less likely to switch to high-achieving schools. ELL students were less likely to switch to high-achieving schools across the timing of school changes. Asian students who switched schools mid-year were less likely to switch to low-achieving schools. Although the direction of the coefficients is suggestive, gender was not a significant predictor of destination school quality across the timing of nonstructural school changes.

The results for discipline-related movers were similar to non-structural school changes, except in a few instances. High-achieving students in low-achieving schools who change schools due to school discipline were more likely to switch to high-achieving schools. Male students were more likely to switch to low-achieving schools. This implies that gender plays a major role in discipline-related exit patterns, as well as the quality of schools that discipline-related movers transfer to.

As it is possible to still have a multiplicative interaction with nonlinear regression models (Buis, 2010), I calculate the marginal effects of these interactions to investigate the potential for a multiplicative interaction between student and school achievement and student mobility. The marginal effects are the differences in predicted probabilities of attending a given quality school between students in the bottom and top third of the achievement distribution relative to students in the middle third by school of origin. Table 6 presents the marginal effects for all nonstructural moves combined. The destination patterns are similar across the different timing of nonstructural school changes and discipline related with a few exceptions. For brevity’s sake, only results for all nonstructural moves are presented in Table 6; results by the timing of school changes are available upon request. The results confirm that there are differential mobility patterns by origin school quality regardless of the timing of student mobility or students’ prior achievement. For instance, students in a low-achieving school, whether the student is low, average, or high achieving, were more likely to transfer to a low-achieving school and less likely to switch to a high-achieving school, relative to their counterparts in average-achieving schools. Conversely, students in high-achieving schools, regardless of their prior achievement, were more likely to transfer to another high-achieving school and less likely to transfer to low-achieving schools.

Table 6.

Marginal Effects of Origin School Quality on Destination School Quality by Student Achievement (All Moves, N = 126,083).

Student achievement/origin school quality	Destination school
Student achievement/origin school quality	Low-achieving school	Average-achieving school	High-achieving school
Low-achieving school vs. middle-achieving school
Low-achieving student	0.53***	−0.48***	−0.05***
Low-achieving student	(0.02)	(0.01)	(0.01)
Middle-achieving student	0.57***	−0.53***	−0.04***
Middle-achieving student	(0.02)	(0.01)	(0.01)
High-achieving student	0.59***	−0.56***	−0.03*
High-achieving student	(0.02)	(0.01)	(0.01)
High-achieving school vs. middle-achieving school
Low-achieving student	−0.07***	−0.47***	0.54***
Low-achieving student	(0.01)	(0.01)	(0.01)
Middle-achieving student	−0.07***	−0.54***	0.61***
Middle-achieving student	(0.01)	(0.01)	(0.01)
High-achieving student	−0.07***	−0.59***	0.66***
High-achieving student	(0.01)	(0.01)	(0.01)

Note. Standard errors in parentheses. The reference group for each student is a student in the same third of achievement in a school in the middle third of achievement. For example, in the first row, a low-achieving student in a low-achieving school has a significantly higher probability of moving to a low-achieving school than a low-achieving student in an average-achieving school.

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

The results for between-year school changes are qualitatively similar to those in Table 6 for when all moves are combined. For mid-year and ultra-movers, there was a significant difference. High-achieving students in low-achieving schools were not significantly less likely to move to high-achieving schools than a high-achieving student in an average school. This implies that high-achieving students in low-achieving schools that switch schools within the school year are seeking and moving to higher quality schools. There were also differences in the results for discipline-related movers. First, average students in high-achieving schools were not less likely to switch to low-achieving schools than average students in average schools. Second, high-achieving students in low-achieving schools were more likely to switch to high-achieving schools than high-achieving students in average schools. Low-achieving and average students in low-achieving schools were not significantly less likely to transfer to high-achieving schools than their counterparts in average schools. These findings suggest that student achievement plays a relatively more important role in the destination school quality of discipline-related mobility compared with nonstructural school changes.

I conducted a series of specification checks to examine the sensitivity of the findings to alternative measures of student achievement, school quality, and limited open enrollment options, as well as account for unobserved changes over time at the school-level. The results imply that time-invariant school characteristics may partly shape the likelihood of discipline-related mobility. There were interesting changes in exit patterns for within-year movers when magnet schools and career academies were excluded. The differences in results suggest that the role of school quality in mid-year patterns may be largely attributed to these schools in the CCSD, and that differential exit patterns during the year mobility are partly driven by open enrollment options in the CCSD.

Discussion

This article examines the relationship between student mobility patterns and school discipline in a large urban school district. The results highlight an important, yet overlooked relationship between mid-year school changes and school discipline. School-initiated student mobility due to disciplinary reasons (moves to and from alternative schools) is most likely to occur during the school year. There is also evidence of an interesting relationship between gender, the timing of school changes, and school discipline. During the year mobility is prevalent, disproportionately affects male students, and may be driven in part by school discipline policies. Male students are more likely to switch schools during the school year when discipline-related moves are included, especially in middle schools. The results imply that school discipline policies and practices play an important role in the within-year mobility patterns of male and low-achieving students and add to a growing body of research that dispels the notion that school discipline disparities are largely due to poor children behaving badly (Kinsler, 2013; Skiba, Chung, et al., 2014; Skiba, Shure, & Williams, 2011). In many ways, the findings of this study regarding discipline-related mobility are conservative as student mobility due to suspensions or other disciplinary outcomes are not identified. In other words, assignment to alternative schools is only one of several ways that students may switch schools because of school discipline.

The results highlight that school-initiated sorting is an important cause of student mobility affecting males in an urban school district. Even though a considerable portion of student mobility may be attributed to out of school factors, discipline-related mobility is an important, yet overlooked school-related cause of student mobility. There are two main alternative interpretations of the findings. Some schools may be “pushing out” male and low-achieving students, especially during the school year due to accountability pressure. Alternatively, these students may be the most behaviorally challenged, thus warranting higher rates of discipline-related school changes. It is also conceivable that differential exit patterns may be the product of a mixture of both undesirable school-initiated student mobility and behavioral management. Given that male students who change schools due to school discipline are more likely to switch to low-achieving schools, student mobility and its relationship with gender and school discipline may be a mechanism for maintaining and expanding inequality rather than producing it within school districts.

The findings of this article raise important equity questions about the growing disparity in opportunities and the educational experiences of students in low-quality schools in urban school districts. This study illustrates that origin school quality is a significant predictor of destination school quality regardless of the timing of school changes and students’ prior achievement. The results also draw attention to the plight of male mobile students, especially those moving due to school discipline. For discipline-related mobility, gender predicts the likelihood of exiting, as well as the destination school quality. Alternative schools, or separate schools of behavior management, are creating systems of exclusion that are disproportionately affecting low-achieving and male students who are placed in these schools. It is reasonable to posit that zero-tolerance policies, specifically the placement of students in alternative schools, produce a double burden—the disruptive effect associated with changing schools and the production of exclusion for a historically marginalized subgroup—African American males. The findings highlight that one of the overlooked elements of the increasing attention on school discipline is the resultant school-initiated mobility, in particular males being sent to alternative schools during the school year, especially in middle schools.

Policy Implications and Directions for Future Research

The examination of the relationship between student mobility and school discipline offers valuable insights and highlights a few policy implications. First, the findings provide strong justification for identifying and monitoring mid-year and ultra-movers. The considerable variation in mobility rates and patterns across the timing of school changes reinforces the importance of extensive data collection on student mobility in urban school districts. Detailed data on the timing of school changes make it possible to identify students who move during the school year, as well as schools that have a relatively high within-year exit rate. If within-year and school-initiated moves are not differentiated, possible undesirable school practices may be overlooked. Identification of discipline-related mobility is also an important step toward supporting male mobile students who are disproportionately affected by this phenomenon.

Second, the findings compel policymakers to place greater attention on student mobility and school discipline in middle schools, a key transitional period for young children transforming into adolescents. Mid-year mobility is relatively higher in middle schools compared to high schools, and discipline-related moves are also prevalent in middle schools. Addressing school-initiated mobility in middle schools is imperative for successful child development and may be an avenue to stymie high school dropouts and boost college attendance. The results also hint that school discipline affects the likelihood of student mobility differently in middle versus high schools.

Third, the prevalence of mid-year student mobility and the variation in mobility rates by the timing of school changes within an urban school district may also have implications for curriculum. As policymakers nationwide and in urban districts mull decisions for curriculum, student mobility should be a pertinent consideration, given the prevalence of mid-year moves. Highly standardized curriculum may prove beneficial in many senses but may also have other costs. It is worth considering whether variations in curriculum by the timing of school changes may better accommodate mobile students and ease the strain of student mobility on students, teachers, and schools.

The results also highlight a need for research on the impact of alternative schools, which are increasingly used for behavior remediation, on students’ educational outcomes. The results raise questions on school discipline policies and the discretion of school officials in placing students in alternative schools for disciplinary reasons. These schools play an important role in school discipline for middle and high school students and ideally should be improving students’ outcomes. Thus, it is pivotal to learn more about how alternative schools affect the cognitive and noncognitive outcomes of students. The effectiveness of alternative schools has significant implications for zero-tolerance and exclusionary school discipline policies and practices. The findings of this study suggest that it is an opportune time to rethink the theory of action of alternative schools and the reintegration of discipline-related mobile students into regular schools.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Author Biography

Richard O. Welsh is an Assistant Professor of Educational Administration and Policy at the University of Georgia. His research focuses on the economics of education, K-12 education policy such as school choice policies and key mechanisms such as student mobility.

References

American Psychological Association Zero Tolerance Task Force. (2008). Are zero tolerance policies effective in the schools? An evidentiary review and recommendations. The American Psychologist, 63, 852-862.

Arcia

(2006). Achievement and enrollment status of suspended students outcomes in a large multicultural school district. Education and Urban Society, 38, 359-369.

Buis

Maarten L.

(2010). Interpretations of interactions in non-linear models. The Stata Journal, 10(2), 305-308.

Bradshaw

Mitchell

O’Brennan

Leaf

(2010). Multilevel exploration of factors contributing to the overrepresentation of black students in office disciplinary referrals. Journal of Educational Psychology, 102, 508-520. doi:10.1037/a0018450

Cullen

J. B.

Jacob

B. A.

Levitt

S. D.

(2005). The impact of school choice on student outcomes: An analysis of the Chicago Public Schools. Journal of Public Economics, 89, 729-760. doi:10.1016/j.jpubeco.2004.05.001

Dauter

Fuller

(2016). Student movement in social context: The influence of time, peers, and place. American Educational Research Journal, 53, 33-70.

Education Services Division. (2016). Division Overview Guide: Clark County, NV: Clark County School District.

Engec

(2006). Relationship between mobility and student performance and behavior. The Journal of Educational Research, 99, 167-178. doi:10.3200/JOER.99.3.167-178

Gregory

Skiba

R. J.

Noguera

P. A.

(2010). The achievement gap and the discipline gap: Two sides of the same coin? Educational Researcher, 39, 59-68. doi:10.3102/0013189X09357621

10.

Grigg

(2012). School enrollment changes and student achievement growth: A case study in educational disruption and continuity. Sociology of Education, 85, 388-404. doi:10.1177/0038040712441374

11.

Hanushek

E. A.

Kain

J. F.

Rivkin

S. G.

(2004). Disruption versus Tiebout improvement: The costs and benefits of switching schools. Journal of Public Economics, 88, 1721-1746. doi:10.1016/S0047-2727(03)00063-X

12.

Institute of Medicine and National Research Council. (2010). Student mobility: Exploring the impact of frequent moves on achievement (Summary of a workshop) (Rapporteur) ( Beatty

Ed.). Washington, DC: The National Academies Press.

13.

Kerbow

(1996). Patterns of urban student mobility and local school reform. Journal of Education for Students Placed at Risk, 1, 147-169. doi:10.1207/s15327671espr0102_5

14.

Kim

Losen

Hewitt

(2010). The school-to-prison pipeline: Structuring legal reform. New York: New York University Press.

15.

Kinsler

(2013). School discipline: A source or salve for the racial achievement gap? International Economic Review, 54, 355-383. doi:10.1111/j.1468-2354.2012.00736.x

16.

Lehr

Tan

Ysseldyke

(2009). Alternative schools: A synthesis of state-level policy and research. Remedial and Special Education, 30, 19-32.

17.

Losen

(2011). Discipline policies, successful schools, and racial justice. Boulder, CO: National Education Policy Center.

18.

Losen

Hodson

Keith

Morrison

Belway

(2015). Are we closing the school discipline gap? Los Angeles, CA: The Civil Rights Project.

19.

Losen

Skiba

R. J.

(2010). Suspended education: Urban middle schools in crisis. Los Angeles, CA: The Civil Rights Project at UCLA and the Southern Poverty Law Center.

20.

Mendel

R. A.

(2011). No place for kids: The case for reducing juvenile incarceration. Baltimore, MD: Annie E. Casey Foundation.

21.

Noguera

(2014). Urban schools and the Black male “challenge.” In Milner IV

H. R.

Lomotey

(Eds.), Handbook of urban education (pp. 114-128). New York, NY: Routledge.

22.

Noltemeyer

A. L.

Ward

R. M.

Mcloughlin

(2015). Relationship between school suspension and student outcomes: A meta-analysis. School Psychology Review, 44, 224-240.

23.

Rumberger

R. W.

(2003). The causes and consequences of student mobility. Journal of Negro Education, 72, 6-21. doi:10.2307/3211287

24.

Rumberger

R. W.

(2015). Student mobility: Causes, consequences and solutions. Boulder, CO: National Education Policy Center.

25.

Schwartz

A. E.

Stiefel

Chalico

(2009). The multiple dimensions of student mobility and implications for academic performance: Evidence from New York City elementary and middle school students. New York, NY: New York Education Finance Research Consortium Institute for Education and Social Policy.

26.

Skiba

R. J.

Arredondo

M. I.

Williams

N. T.

(2014). More than a metaphor: The contribution of exclusionary discipline to a school-to-prison pipeline. Equity & Excellence in Education, 47, 546-564.

27.

Skiba

R. J.

Chung

C.-G.

Trachok

Baker

T. L.

Sheya

Hughes

R. L.

(2014). Parsing disciplinary disproportionality: Contributions of infraction, student, and school characteristics to out-of-school suspension and expulsion. American Educational Research Journal, 51, 640-670. doi:10.3102/0002831214541670

28.

Skiba

R. J.

Michael

R. S.

Nardo

A. C.

Peterson

R. L.

(2002). The color of discipline: Sources of racial and gender disproportionality in school punishment. The Urban Review, 34, 317-342. doi:10.1023/A:1021320817372

29.

Skiba

R. J.

Rausch

M. K.

(2006). Zero tolerance, suspension, and expulsion: Questions of equity and effectiveness. In Evertson

C. M.

Weinstein

C. S.

(Eds.), Handbook of classroom management: Research, practice, and contemporary issues (pp. 1063-1089). Mahwah, New Jersey: Lawrence Erlbaum Associates Publishers.

30.

Skiba

Shure

Williams

(2011, September 15). What do we know about racial and ethnic disproportionality in school suspension and expulsion? Briefing paper developed for the Atlantic Philanthropies’ Race and Gender Research-to- Practice Collaborative, Bloomington, IN: The Equity Project, Indiana University.

31.

Temple

Reynolds

A. J.

(1999). School mobility and achievement: Longitudinal findings from an urban cohort. Journal of Social Psychology, 37, 355-377. doi:10.1016/S0022-4405(99)00026-6

32.

U.S. Government Accountability Office. (2010). Many challenges arise in educating students who change schools frequently (GAO-11-40). Report to Congressional Requesters. United States Government Accountability Office.

33.

Welsh

(2017). School Hopscotch: A Comprehensive Review of K-12 Student Mobility in the U.S. Review of Educational Research, 87(3), 475-511.

34.

Welsh

(2018). Student Mobility, Segregation and Achievement Gaps: Evidence from Clark County, Nevada. Urban Education, 53(1), 55-85.

35.

Welsh

Duque

McEachin

(2016). School Choice, Student Mobility and School Quality: Evidence from post-Katrina New Orleans. Education Finance and Policy, 11(2), 150-176.

36.

Hannaway

D’Souza

(2009). Student transience in North Carolina: The effect of mobility on student outcomes using longitudinal data. Washington, DC: National Center for Analysis of Longitudinal Data in Education Research.