Student Mobility,Segregation,and Achievement Gaps: Evidence From Clark County,Nevada

Abstract

Student mobility and school segregation are two important issues with significant equity implications for urban school districts that are often addressed separately. This article examines the relationship between student mobility and school segregation. The findings indicate that more segregated schools typically have smaller within-school achievement gaps, a lower proportion of proficient students, a higher proportion of low-income and minority students, and higher nonstructural mobility rates (especially within-year mobility) than less segregated schools. The results also suggest that, regardless of the timing of school changes, high levels of achievement segregation are a significant predictor of student mobility. Policy implications are discussed.

Keywords

student mobility school segregation achievement gaps urban school districts educational equity

In the past decade, increasing focus has been placed on education in urban contexts (Milner & Lomotey, 2014).¹ Racial, ethnic, income, and achievement segregation is a critical concern in urban school districts nationwide. Even though the 1954 landmark Brown v. Board decision resulted in the desegregation of schools in the 1970s, there has been persistent resegregation (Frankenberg, Lee, & Orfield, 2003; Orfield, 1983; Orfield & Yun, 1999). Moreover, court decisions in recent decades have made it more challenging for districts to maintain integrated schools (Condron, Tope, Steidl, Freeman, & Colleges, 2013; Orfield & Lee, 2007). Student mobility, or the sorting of students across schools, is also an important issue facing urban school districts. Although student mobility is pervasive across the United States, it is especially prevalent in urban school districts (Institute of Medicine & National Research Council, 2010; U.S. Government Accountability Office, 2010). Frequent student mobility is most common and has adverse educational outcomes for low-income and minority students (Hanushek, Kain, & Rivkin, 2004; Reynolds, Chen, & Herbers, 2009; Schwartz, Stiefel, & Chalico, 2009; Xu, Hannaway, & D’Souza, 2009). Student mobility has acquired greater importance in recent decades as districts have expanded open enrollment options. School choice policies provide an alternative way of assigning students to schools by giving parents the freedom to choose which schools their child attend and is viewed as a potential mechanism for promoting integration in school districts (Finn, 1990).

Both student mobility and school segregation concern the equality of educational opportunity. Segregation in urban districts is a prominent educational equity issue (Orfield, 1983). School segregation and student mobility may be a cause and consequence of each other. Students may switch schools because of school segregation, and student mobility may maintain or expand segregation. This has significant policy implications as prior research has highlighted the benefits of desegregation for all students. Moreover, the organizational perspectives of student mobility, or how changing schools shape how learning occurs in schools and districts may help policymakers utilize student mobility to promote desegregation. Student mobility and segregation are particularly concerning in light of ongoing demographic shifts. The influx of minority students in urban school districts has had adverse effects on desegregation (Bifulco & Ladd, 2007; Frankenberg et al., 2003; Orfield & Lee, 2007). Although there is much to learn about how student mobility and segregation phenomena interact and how both affect education equity in urban school districts, the relationship between school segregation and student mobility in urban school districts has been largely overlooked by researchers and policymakers. The resegregation of American schools coupled with the growth of school choice policies nationwide make it important to learn more about the relationship between educational inequality, student mobility, and school segregation.

Clark County School District (CCSD) in Nevada provides an exemplary case study. Clark County is one of 16 counties in Nevada and consists of five major cities (Las Vegas, North Las Vegas, Boulder City, Henderson, and Mesquite) and a number of surrounding smaller jurisdictions. Currently, Clark County has the largest population in Nevada with more than 2 million people, and CCSD has 70% of Nevada’s public school students. CCSD is similar to most urban districts with a traditional governance structure (a locally elected school board operating most public schools), low-performing schools, and a high concentration of low-income and minority students. In recent decades, there has also been a marked demographic shift characterized by the growth of English language learner (ELL) and Hispanic students. Whether defined by size or the presence of economic and educational inequality, CCSD meets the criteria of an “urban” school district. The geographically diverse nature of the district—the interesting mixture of central-city, suburban, and rural schools, coupled with the presence of attendance zones—makes CCSD a rich setting to explore the relationship between student mobility and school segregation.

This article examines the relationship between student mobility and school segregation across racial, achievement, and income groups within CCSD. This study employs the dissimilarity index and school-level indicators to provide a descriptive analysis of racial, income, and achievement school segregation. The analysis moves beyond the Black–White comparisons and includes several racial and income groups to reflect the multiethnic nature of an urban school district. The association between school-level mobility rates across the timing of school changes and school segregation is also analyzed. Following this, I use linear probability models to predict the likelihood of making a school change based on prior schools’ segregation. This is one of the first studies to examine the relationship between intradistrict student mobility and school segregation. Specifically, I ask the following research questions:

Research Question 1: How does school segregation and schools’ characteristics differ by schools’ student mobility rates?

Research Question 2: To what extent does school segregation affect the likelihood of making a school change?

The focus of this study fits nicely with the sociological perspectives and the policy and reform areas of urban education (Milner & Lomotey, 2014). This article contributes to an expanding literature examining the relationship between student assignment and segregation. The findings provide a critical and empirical assessment of the challenges faced by urban school districts by examining the intersection of two prevalent and important phenomena. A better understanding of the relationship between student mobility and school segregation offers valuable insights about the educational equity. The results may also help shape effective strategies to improve urban schools. The rest of the article proceeds as follows. I first provide a brief overview of the literature on student mobility and school segregation. Following this, I describe the data and methodological approach employed in this study. Next, I present results and conclude with a discussion of policy implications and directions for future research.

The Causes and Consequences of Student Mobility and School Segregation

Student Mobility

Intradistrict student mobility is important for three main reasons.² First, the majority of student mobility occurs within the same school district as opposed to switching to schools in a different school district (Hanushek et al., 2004; Kerbow, 1996; Pribesh & Downey, 1999; Xu et al., 2009). Second, intradistrict mobility is generally limited to poor and minority students who tend to switch schools frequently within an urban school district (Alexander, Entwisle, & Dauber, 1996; Hanushek et al., 2004; Mao, Whitsett, & Mellor, 1997; Xu et al., 2009). Alexander et al. (1996) found that lower income students transferred within the school district more often while rich, White students were more likely to move across districts (Alexander et al., 1996). Hanushek et al. (2004) highlighted that African American and Hispanic students were at least twice as likely to switch schools within a district than White students and attributed some of the difference to the concentration of minority students in large urban districts (Hanushek et al., 2004). Third, intradistrict student mobility, especially for frequent movers, is typically not linked to improvements in school quality (Hanushek et al., 2004; Xu et al., 2009).

Although student mobility can be initiated by families or schools, the majority of school changes is initiated by families (Rumberger, 2015). Student mobility is driven by a confluence of social and economic factors, including residential mobility, family circumstances and income, economic opportunity, or the preferences for higher quality or better matched schools (Kerbow, 1996; Kerbow, Azcoitia, & Buell, 2003; Pribesh & Downey, 1999; Rumberger, 2003; Rumberger & Larson, 1998; Rumberger, Larson, Ream, & Palardy, 1999; Swanson & Schneider, 1999). Although students may change schools for many different reasons, the majority of student mobility overlaps with residential mobility (Institute of Medicine & National Research Council, 2010; Reynolds et al., 2009; Rumberger, 2003). Historically, this is largely due to the presence of attendance zones that link school assignment to a student’s residence. In urban areas and densely populated cities, residential mobility is even more likely to result in student mobility (Temple & Reynolds, 1999). However, not all school changes are caused by residential mobility, and about 40% of student mobility is due to school-related factors (Kerbow, 1996; Rumberger et al., 1999). Typically, administrative data provide little information about the exact reasons why students change schools (Grigg, 2012; Hanushek et al., 2004; Institute of Medicine & National Research Council, 2010; Xu et al., 2009). A substantial proportion of intradistrict student mobility is generally associated with negative reasons such as job loss or family disruption (“reactive”) rather than transferring to a higher quality or a better fit school (“strategic”; Alexander et al., 1996; Hanushek et al., 2004; Rumberger et al., 1999; Xu et al., 2009).

Nonstructural mobility may occur at different points throughout the course of a given school year. For instance, students may switch schools between school years (in the summer) or during the academic year. Student mobility during the school year may be more disruptive than moves between academic years (Alexander et al., 1996; Burkam, Lee, & Dwyer, 2009; Grigg, 2012; Hanushek et al., 2004; Schwartz et al., 2009). The timing of school changes may reflect the reasons for student mobility. It is presumed that strategic school changes are more likely to occur in the summer whereas reactive school changes are more likely to occur during the school year. In addition, some school policies such as student discipline policies may also induce school changes.

Student mobility has consequences at the student (for mobile and nonmobile students), school, and district level. Although changing schools is typically associated with lower test scores, increased grade retention, and higher rates of school dropout (Institute of Medicine & National Research Council, 2010; Mehana & Reynolds, 2004; Reynolds et al., 2009; U.S. Government Accountability Office, 2010), changes to higher quality schools may result in positive effects (de la Torre & Gwynne, 2009; Engberg, Gill, Zamarro, & Zimmer, 2012; Hanushek et al., 2004; Rumberger et al., 1999; Temple & Reynolds, 1999). Student mobility affects schools by influencing the school climate and creating burdens in the classrooms of both sending and receiving schools. For instance, teachers may be overwhelmed by the demands of providing attention to both movers and nonmovers, resulting in “reteaching,” “backtracking,” and reduction in the pace of instruction to accommodate mobile students (Kerbow, 1996; Lash & Kirkpatrick, 1990; Rumberger et al., 1999). Student mobility may maintain or expand stratification within a school district as students of different achievement levels and racial and income groups are increasingly unevenly distributed within a district and have less interactions with each other. Although the lack of a formal definition of segmentation makes it difficult for one to determine how differentiated an educational system has to be to label it as “segmented,” evidence of differential mobility patterns imply changing schools may lead to unintended consequences over time, such as maintaining or expanding segmentation of student populations by students’ backgrounds, achievement, or school quality (Kerbow, 1996; Welsh, Duque, & McEachin, 2016).

School Segregation

Although there are various conceptualizations and operationalizations, segregation refers to the physical separation of different racial, ethnic, income, and achievement groups (Massey & Denton, 1988; Reardon, Yun, & Kurlaender, 2006). Racial segregation across schools within an urban school district is significantly higher than racial segregation within schools (Conger, 2005). School segregation separates children and stratifies the type of school they attend, leaving minority children in inferior schools (Orfield & Yun, 1999). Orfield and Lee (2005) also found that Black or Hispanic students are more likely to attend urban and high-poverty schools compared with White and Asian students (Orfield & Lee, 2005). Although White students are the most racially isolated racial/ethnic group, segregation is rising for African American and Latino students (Frankenberg et al., 2003). Prior studies have highlighted the isolation of Black students in segregated schools (Berends & Penaloza, 2008; Vigdor & Ludwig, 2008).

The causes of school segregation can be broadly classified into two categories: structural and systemic inequities, and preferences. Structural reasons include economic conditions, residential segregation, and student assignment policies. Segregation is caused by institutional mechanisms such as lending discrimination, restrictive zoning, and mortgage redlining (Meyer, 2001). Differences in location preferences based on race or class lead to segregation in housing, schools, and churches (Saporito, 2003). Prior research has shown that school choice increases in racial school segregation in urban districts (Bifulco & Ladd, 2007; Sohoni & Saporito, 2009). Sohoni and Saporito (2009) found that public schools are more segregated than the neighborhoods in their attendance zones as White students attend private schools outside the area and exit integrated neighborhood public schools at a greater rate than non-White children (Sohoni & Saporito, 2009).

There is a growing body of research evaluating the effect of racial segregation on student and school performance (Bifulco & Ladd, 2007; Logan, Minca, & Adar, 2012). The Coleman report, published in 1966, highlighted the prevalence of school segregation in the United States and its adverse effects on the equality of educational opportunity and students’ educational outcomes (Coleman, Campbell, & Hobson, 1966). Coleman and colleagues (1966) found a negative association between the concentration of poverty within a school and student performance, which has been confirmed by several studies in recent decades (Coleman et al., 1966). Numerous studies indicate that racial integration has direct and independent effects on student performance (Kahlenberg, 2001; Logan et al., 2012). Racial isolation of minorities in majority-minority school concentrations are associated with lower academic achievement and inferior educational opportunities (Coleman et al., 1966; Logan et al., 2012). There is evidence of the positive influence of desegregation on educational and labor market outcomes of minority as well as nonminority students (Johnson, 2011; Kurlaender & Yun, 2001; Wells & Crain, 1994). Johnson (2011) found desegregation’s impact on racial equality to be deep, wide, and long-lasting (Johnson, 2011). Black Americans who attended schools integrated by court order were more likely to graduate, go on to college, and earn a degree than Black Americans who attended segregated schools (Johnson, 2011). Desegregation also had a positive impact on labor market and other lifestyle outcomes (Johnson, 2011). Overall, the majority of studies have found that desegregation is helpful for students of all races, especially disadvantaged subgroups.

Educational Inequality, Sorting, and the Distribution of Students Within a School District

Figure 1 provides a conceptual framework of the relationship between educational inequality, student mobility, and school segregation. Both student mobility and segregation are largely influenced by out-of-school factors and represent the intersection of society and schooling. Economic opportunity and the intersection of race and poverty may play a pivotal role in explaining student mobility and segregation. Similarly, residential segregation plays an important role in both phenomena. The majority of school changes are accompanied by changes in residences (Reynolds et al., 2009). Income residential segregation has increased in the past decades (Reardon & Bischoff, 2011); thus, it is plausible that schools have become more segregated by income over time. Segregation and student mobility are widely regarded as critical issues in education policy as both phenomena partly explain the racial achievement gap (Bifulco & Ladd, 2007; Card & Rothstein, 2007; Condron et al., 2013; Hanushek et al., 2004; Hanushek & Rivkin, 2009; Rumberger & Palardy, 2005). The achievement gap between Black and White students is an important component of Black/White economic inequality (Condron et al., 2013; Jencks & Phillips, 2011). There are several possible ways that desegregation and student mobility impact students, schools, and districts. Presumably, the central impact of desegregation comes from the peers of students’ or the peer effect. Simply put, it is advantageous to attend a school where students are more successful (Hanushek, Kain, Markman, & Rivkin, 2003). However, Owens (2010) found that the educational attainment of students from poorer neighborhoods is adversely affected when they attend schools with more White and high socioeconomic status (SES) counterparts (Owens, 2010). Peer effects are not the only consideration as school context and characteristics may also be crucial factors. Segregated schools typically are unequally resourced; thus, attending such schools may adversely affect achievement, especially for low-income and minority students (Condron et al., 2013). Johnson (2011) posited improvement in access to school resources as one of the mechanisms through which desegregation benefits students (Johnson, 2011). Similarly, the impact of changing schools on student achievement is dependent on school quality.

Figure 1.

Educational inequality, student mobility, and school segregation.

Differential mobility patterns imply that the sorting of students between schools may maintain or expand the uneven distribution of students in an urban school district. Notwithstanding, school segregation may be a motivating factor for student mobility. For instance, as Figure 1 demonstrates, changing from School C to D may maintain, decrease, or increase segregation in a school district; however, a student may change from School A to School B because of school segregation. Prior research suggests that the demographic composition of schools and intragroup solidarity play an important role in families’ decision to switch (Hastings, Kane, & Staiger, 2006). Low-achieving, low-income, and minority students may be more likely to exit segregated schools, experience disruptive effects on achievement, and attend similarly segregated and/or lower quality schools. Segregated schools with high turnover may also face a range of school organization issues such as teacher expectations, safety, and offering rigorous courses that adversely affect student achievement (Rumberger & Palardy, 2005).

Little attention has been paid to the relationship between student mobility and school segregation. The majority of the extant literature on student mobility has examined changing schools from the students’ perspective and focused primarily on how student mobility affects student achievement (Institute of Medicine & National Research Council, 2010; Reynolds et al., 2009; Welsh, 2017). Few studies consider student mobility from the perspective of schools and districts (Nelson, Simoni, & Adelman, 1996; Rumberger et al., 1999) even though schools and districts grapple with student turnover. The vast majority of studies on segregation has focused on the Black–White dichotomy even though Asian and Hispanic students account for an increasing part in the racial composition of the U.S. student population (Frankenberg et al., 2003; Logan et al., 2012; Orfield & Lee, 2007). The majority of the extant literature also tends to focus on segregation in school choice contexts or states with districts with court-ordered desegregation plans (Bifulco & Ladd, 2007; Condron et al., 2013; Johnson, 2011). Although researchers also conceptualize and measure segregation in a myriad of ways, the most oft-used indicator is a measure of the proportion of minority students in a school, which may not accurately capture segregation between groups within a district (Condron et al., 2013). The relationship between segregation and achievement gaps is also understudied (Condron et al., 2013).

This study provides a descriptive analysis of the complex relationship between segregation and student mobility and its relation to educational disparities. This article builds on the extant literature in a few ways. First, the context of this study is a “traditional” school district with attendance zones and limited open enrollment options rather than a choice-based district or a district undergoing mandated desegregation efforts; thus, the findings offer insights on how student mobility as opposed to purposeful desegregation efforts interact with school segregation within urban school districts. Second, this article analyzes separate but interrelated dimensions of school segregation. The conceptualization and operationalization of school segregation have been broadened from Black/White racial comparison to include other racial/ethnic combinations, income, and achievement student subgroups as well as the intersection of race, income, and achievement that characterizes the contemporary urban school district. Third, no prior study has examined whether racial, income, and achievement school segregation predicts student mobility across the timing of school changes. Local, state, and federal policies aiming to reduce achievement gaps can benefit from a better understanding of the nuanced relationship between school segregation, student mobility, and educational inequality in urban school districts. In the next section, I describe the data and methodological approach employed in this study.

Data and Method

Data

I use a 6-year panel of student-level data for all students in the CCSD from 2007 to 2008 through to 2012 to 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, White), free and reduced priced lunch (FRPL), ELL, and special education statuses. Students are tested in reading and math in Grades 3 to 8 and take the High School Proficiency Exam (HSPE) in Grade 10. I standardize test scores for students in Grades 3 through 10 by grade and year, relative to the school mean, as well as relative to the district mean. 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 assume that all school changes between school years in Grades 6 and 9 are transitions from elementary to middle and middle to high schools, respectively, with the exception of students enrolled in combination schools, of which there are relatively few. I complement the student-level data with publicly available school-level accountability data. I use a sample of students that have been continuously enrolled in a CCSD school for at least 2 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). This sample includes 1,826,170 student-years with 428,247 unique students.³

Method

Categorizing student mobility

I categorize nonstructural movers 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. To examine student mobility at the school level and better understand the variation in nonstructural mobility across the timing of school changes in CCSD, I focus on the percent of students leaving each school or the average school turnover across the timing of nonstructural school changes. Entry mobility rates (students entering schools) are almost identical to exit rates across the timing of school changes; thus, exit rates can be interpreted as the overall churn in schools. Discipline-related mobility is classified as all school changes to and from behavior or continuation schools or juvenile detention centers based on data reported by the schools. I also categorize schools’ characteristics into quintiles.

Measuring segregation

I use the dissimilarity index to evaluate segregation between schools in CCSD over time. The dissimilarity index captures unevenness or the distribution of racial groups (Massey & Denton, 1988). The dissimilarity index measures what percentage of the racial group’s population would need to change schools for the racial groups to be evenly distributed within the school district. Generally, a dissimilarity index below .3 is low segregation, between .3 and .6 is moderate segregation, and above .6 is high segregation (Massey & Denton, 1988). I calculate the dissimilarity index for multiple combinations of four racial categories (Black, White, Asian, and Hispanic), one income category (FRPL students), and two achievement categories (whether the student was below math in the district or proficient in math) using the following formula:

D I_{d t} = ½ \sum | (a_{s t} / A_{d t}) - (b_{s t} / B_{d t}) | .

where DI_dt is the dissimilarity index of district d at time t, a_st is the number of “a” students in school s at time t, and A_dt is the number of “a” students in all schools in district d at time t. Then b_st is the number of “b” students in school s at time t, and B_dt is the number of “b” students in all schools in district d at time t. First, I calculate indices for the entire district that include mixing schools of different levels into one analysis. Next, similar to prior research (Sohoni & Saporito, 2009), I disaggregate schools by level and calculate the index separately for elementary, middle, and high schools, which allows for comparison of racial, income, and achievement segregation across multiple school levels.

I also create several school-level racial, income, and achievement segregation indicators. I focus on intensely segregated, extreme-poverty and intensely low-achieving schools to illustrate how the relationship between student mobility and school segregation offers useful insights about educational inequality in urban districts. The indicators include the following: (a) predominantly minority (Black and Hispanic students)—greater than 50% of students in a school are non-White, (b) intensely segregated minority schools—more than 80% of student body are minority, (c) multiracial schools—schools with at least 10% of students from four racial groups (Black, White, Hispanic, and Asian), (d) high-poverty schools—greater than 50% of students in a school are FRPL recipients, (e) extreme-poverty schools—more than 80% of student body are FRPL recipients, (f) predominantly low achieving—greater than 50% of students in school are achieving below district average, (g) intensely low achieving—more than 80% of student body are achieving below the district average, and (h) intensely segregated, high poverty—greater than 80% minority and FRPL recipients.

Achievement gaps

Consistent with prior research (Condron et al., 2013), at the school-year level, I compute achievement gaps in both math and reading across various racial and income combinations. For example, to compute the White–Black achievement gap, I subtract the standardized mean math achievement of Black students from that of White students (mean of White students − mean of Black students / standard deviation of subject test scores in a school). School-level achievement gaps are then aggregated to the district level. The achievement gaps measure the extent to which Black students’ test scores lag behind White students relative to the standard deviation of the distribution.

Predicting student mobility using school segregation

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

Y_{i s t} = β_{0} + β_{1} T_{i s t} + β_{2} Z_{s t} + β_{3} N_{s t} + π_{t} + γ_{g} + e_{i t} .

where Y_ist is a dichotomous outcome variable that is equal to 1 if student i in school s at time t made a nonstructural school change. I estimate the probability of changing schools separately for the aforementioned three categories of mobile students. T_ist is a vector of student-level characteristics including lagged student achievement (relative to the district), gender, racial/ethnic categories (White is the reference group), FRPL, ELL, and special education statuses. Z_st is a vector of school-level characteristics including school quality (measured by the percentage of students in a school scoring proficient or above on state accountability tests) and the percentage of Black, Hispanic, Asian, FRPL, ELL, Special Education, and male students in the school. N_st is a vector of the aforementioned school-level segregation indicators. β₃ is the coefficient of interest that illustrates whether students in more segregated schools are more likely to switch schools relative to students in less segregated schools across the timing of school changes. In all models, I utilize grade (γ_g) and year (π_t) fixed effects to control for unobservable differences across time and between grades and use robust standard errors clustered at the school level.

Results

CCSD is a large, diverse school district with average annual enrollment of more than 300,000 students. On average, roughly 42% of students are Hispanic, 32% are White, 13% are African American, 8% are Asian, 11% are special education students, 17% are ELL, and 50% are FRPL students. Over the period of study, CCSD experienced an increase in low-income (47% to 56%), Hispanic (41% to 44%), and special education status (10% to 12%) students. Conversely, the proportion of African American (14% to 12%), White (35% to 29%), Asian (9% to 7%), and ELL (20% to 16%) declined.

About 16% of students changed schools annually: 7% switched schools between school years, 6% changed schools during the school year, and 3% changed schools both in the summer and midyear in the same academic year. Black, Hispanic, low-income, special education status, and ELL students had higher mobility rates, especially for midyear school changes, whereas White and Asian students had lower mobility rates. For instance, 26% of Black students changed schools, with 11% being midyear movers and 5% being ultra-movers, compared with 12% for White students, with only 4% being midyear movers and 2% being ultra-movers. Mobile students also had math achievement about a quarter of a standard deviation below their schools’ average and a third of a standard deviation below the district average.

Student Mobility and Segregation From the Schools’ Perspective

Figure 2 illustrates that there is a strong relationship between schools’ demographic and achievement characteristics and student mobility rates across the timing of school changes. This association is particularly apparent when one considers within-year student mobility (midyear and ultra-movers). As the proportion of low-income and minority students in schools increases, within-year mobility rates also increase. For instance, schools in the bottom quintile of proportion of low-income students (0%-27% of FRPL students) had an average midyear exit rate of 4% compared with 10% for schools in the top quintile (greater than 79% of FRPL students). Schools in the bottom quintile of proportion of minority students (between 4% and 31% of Black and Hispanic students) had an average midyear exit rate of 3% relative to 10% for schools in the top quintile (greater than 82% of Black and Hispanic students). Conversely, there is a negative relationship between nonstructural exit rates and school quality: Schools with higher mobility rates typically have a lower proportion of math proficient students. Schools in the bottom quintile of proportion of math proficient students (less than 46% of proficient students) had a midyear exit rate of 11% and an ultra-mover exit rate of 10% compared with 4% and 1%, respectively, for schools in the top quintile (greater than 75% of proficient students).

Figure 2.

School characteristics by mobility rates across the timing of school changes.

Figure 3 shows that there is also an apparent relationship between nonstructural mobility rates and school segregation. The results suggest that more segregated schools typically have a higher nonstructural mobility rate (midyear and ultra-moves are especially prevalent in highly segregated schools). For instance, intensely segregated minority schools had a midyear exit rate of 10% and an ultra-mover rate of 5% compared with 6% and 3% for schools that were not intensely segregated minority. Extreme-poverty schools had a midyear exit rate of 10% relative to 6% for schools that were not classified as extreme poverty. Intensely segregated, low-achievement schools had a midyear rate of 13% and a ultra-mover rate of 15% compared with 7% and 3%, respectively, for schools that were not categorized as intensely segregated, low-achieving schools. In addition, more segregated schools typically have a lower proportion of proficient students than less segregated schools.

Figure 3.

School segregation and student mobility rates.

School discipline partly explains the relatively high within-year mobility rates of low-achieving, high-minority, and poverty schools. School discipline is an important yet overlooked example of school policies and practices that may induce student mobility. Although the average discipline-related exit rate in CCSD was roughly 2%, the lowest achieving schools and schools with a high proportion of Black and male students had high discipline-related exit rates. For instance, the discipline-related exit rate for schools in the top quintile for proportion of Black students (19%-92% of Black students) was 6% or 3 times the district average. Lower quality schools typically have higher discipline-related mobility rates. Schools in the bottom quintile of proportion of proficient students had a discipline exit rate of 8% or 4 times the district average. In addition, alternative schools including behavior and continuation schools as well as schools in the Clark County Juvenile Justice System had some of the highest nonstructural mobility rates that were largely driven by within-year mobility (midyear and ultra-movers). There is also a strong correlation between ultra-mover exit rate and discipline-related exit rate (0.9) that suggests that the majority of ultra-moves are school-initiated midyear mobility. The striking relationship between the lowest achieving schools in the district and school discipline may be attributed to various reasons. It is plausible that the lowest achieving schools also serve the student population that provides the greatest behavioral management challenges in urban school districts. Another reason may be that these schools are responding to accountability pressure by placing certain students in alternative schools. Schools classified as “in need of improvement” had the highest within-year exit rate (midyear and ultra-movers), and schools classified as “high-achieving and above” had the lowest within-year exit rate.

The relationship between student mobility, schools’ characteristics, and school segregation may also be explained by the level of schooling. In elementary and high schools, the between-year rate was higher than the within-year exit rate (especially for high schools where the between-year rate was more than twice that of the within-year rate). The within-year exit rate in middle schools was slightly higher than midyear exit rates in high schools. Interestingly, in middle schools, the within-year (midyear and ultra-movers combined) exit rates were higher than the between-year exit rates. This suggests that midyear moves are especially relevant in middle schools. Furthermore, the discipline-related mobility rate in middle schools is slightly higher than that of high schools. The results also draw attention to school discipline in middle schools.⁴

Segregation, Student Mobility, and Achievement Gaps

Figure 4 shows segregation among schools in CCSD from 2007 to 2008 through to 2012 to 2013 using the dissimilarity index. The results indicate that although overall racial segregation in CCSD was moderate, unevenness in the distribution of students by race/ethnicity in the district increased over the period of study.⁵ The results indicate that Hispanic students were the most highly unevenly distributed racial group. Unlike racial segregation, income segregation decreased over the period of study. The distribution of proficient students between schools grew slightly more uneven over time, whereas the distribution of below average students did not increase over the period of study. The results suggest that the segregation of high-achieving students is increasing in CCSD. Overall, the results imply that there is increasing stratification within the district as racial and achievement segregation rose over time.⁶

Figure 4.

School segregation in CCSD, K-12, dissimilarity index.

Figure 5 presents district-level achievement gaps over the period of study. The results indicate sizable achievement gaps between racial groups that increased over time. For instance, the achievement gap between White and Black students increased from 0.53 SD in 2007 and 2008 to 0.57 SD in 2012 and 2013. The achievement gap between White and Hispanic students decreased over the period of study and was smaller than the White–Black achievement gap. The Asian–Black achievement gap increased over time and was the largest in CCSD, with Asian students performing about four fifths of a standard deviation above Black students. The Asian–White (on average 0.19 SD) and the Hispanic–Black (on average 0.27 SD) were the smallest gaps in test scores in CCSD. Non-FRPL recipients outperformed FRPL students by about a third of a standard deviation. However, the income achievement gap remained fairly constant over time. In addition, the results also indicate that racial and income achievement gaps are lower in more segregated schools. Overall, the White–Black, White–Hispanic, Asian–Hispanic, and the non-FRPL–FRPL within-school achievement gaps were lower, whereas the Asian–White, Asian–Black, Hispanic–Black gap was higher in intensely segregated minority and high-poverty schools. The results imply that the achievement gap is smaller in more segregated schools because of the presence of similar low-achieving students regardless of race/ethnicity, whereas larger achievement gaps in less segregated schools suggest minority students in these schools tend to be low-achieving, and nonminority students are higher achieving, resulting in considerable achievement gaps. From the district’s perspective, this is not a beneficial trend given that prior research demonstrates that high-achieving peers improve the student achievement of all students in a school.⁷

Figure 5.

Racial and income achievement gaps in CCSD.

I also examine the segregation levels of origin and destination schools across the timing of school changes. The results indicate that regardless of the timing of school changes, high levels of racial, income, and achievement school segregation may spur students to change schools. For example, 45% of between-year movers and 43% of midyear movers in intensely segregated minority schools switch to schools that were not classified as intensely segregated schools. The trends are similar for mobile students in extreme-poverty and intensely segregated achievement schools. There are interesting differences in exit and destination patterns by the degree of segregation in schools. Regardless of the timing of school changes, the majority of students in predominantly minority or low-achieving and high-poverty schools tended to transfer to similar segregated schools. For instance, 75% of between-year movers and 80% of midyear movers in predominantly minority schools transferred to another predominantly minority schools. However, a nontrivial proportion of students in schools that are not categorized as predominantly minority or low achieving or high poverty switched to more segregated schools at a greater extent than students in predominantly minority, high poverty, or low achieving switched to lesser segregated schools. For example, 25% of between-year movers and 37% of midyear movers in schools that were not predominantly minority school switched to predominantly minority schools, whereas 21% of between-year movers and 19% of midyear movers left predominantly minority schools for schools that were not classified as predominantly minority. Similarly, roughly half to two thirds of movers in multiracial schools transferred to schools that were not classified as multiracial across the timing of school changes. The findings imply that student mobility patterns in relatively less segregated schools may increase overall segregation in the district, whereas exiting from the most segregated schools may decrease overall segregation. In the next section, I present the empirical results on whether school segregation predicts the probability of student mobility.

Does School Segregation Predict the Likelihood of Student Mobility?

Table 1 presents the likelihood of switching schools across the timing of school changes based on student, schools’ demographic, and achievement characteristics and school segregation. The results indicate that high levels of achievement segregation are a strong predictor of student mobility across the timing of school changes. Students in intensely segregated achieving schools were roughly 10 percentage points more likely to switch schools between school years than students in schools that were not intensely segregated achieving schools. Students in predominantly low-achieving schools were less than 1 percentage point more likely to change schools in the summers than students in schools that were not predominantly low achieving. The results for racial and income segregation, irrespective of the degree of segregation, were insignificant for between-year school changes.

Table 1.

Estimating the Likelihood of Student Mobility (N = 774,211).

	Between	Midyear	Ultra-movers
Prior Achievement	−0.009*** (0.000)	−0.014*** (0.000)	−0.014*** (0.000)
Black	0.021*** (0.001)	0.025*** (0.001)	0.027*** (0.001)
Hispanic	−0.004*** (0.001)	−0.001 (0.001)	−0.006*** (0.001)
Asian	−0.001 (0.002)	−0.003** (0.001)	−0.005*** (0.001)
Male	−0.001 (0.001)	0.008*** (0.001)	0.007*** (0.000)
Special Education	−0.007*** (0.001)	−0.006*** (0.001)	−0.003** (0.001)
ELL	−0.010*** (0.001)	−0.001 (0.001)	−0.007*** (0.001)
FRPL	0.013*** (0.001)	0.016*** (0.001)	0.017*** (0.001)
School Quality	−0.028 (0.024)	−0.025** (0.008)	−0.008 (0.011)
Black_sch	0.084* (0.041)	−0.007 (0.021)	−0.022 (0.021)
Hispanic_sch	0.038 (0.037)	−0.030 (0.019)	−0.065** (0.020)
White_sch	−0.010 (0.036)	−0.026 (0.018)	−0.050* (0.019)
Asian_sch	0.086 (0.055)	0.012 (0.021)	−0.042 (0.025)
Male_sch	0.145 (0.080)	0.104*** (0.022)	0.203** (0.069)
SpecEd_sch	−0.110* (0.047)	0.008 (0.014)	−0.067 (0.043)
ELL_sch	−0.045** (0.016)	0.016 (0.009)	0.019 (0.011)
FRPL_sch	−0.000 (0.018)	0.012 (0.008)	0.024* (0.009)
Predominantly Minority	−0.006 (0.005)	−0.001 (0.002)	0.003 (0.003)
Intensely Segregated Minority	0.005 (0.005)	0.000 (0.002)	0.004 (0.002)
High Poverty	−0.002 (0.005)	0.002 (0.002)	−0.002 (0.002)
Extreme Poverty	0.004 (0.004)	0.003 (0.002)	0.004 (0.003)
Predominantly Low Achieving	0.008* (0.004)	−0.002 (0.002)	−0.001 (0.002)
Intensely Segregated Low Achieving	0.096* (0.037)	0.026 (0.014)	0.135*** (0.033)
Multiracial	−0.001 (0.003)	0.001 (0.001)	−0.000 (0.001)
Intensely Segregated, Extreme Poverty	−0.007 (0.006)	0.000 (0.002)	−0.003 (0.003)
Constant	0.922*** (0.063)	−0.040 (0.023)	−0.058 (0.044)

Note. ELL = English language learner; FRPL = free and reduced priced lunch.

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

Achievement segregation is not as strong a predictor of midyear school changes. Students in intensely segregated, low-achieving schools were 3 percentage points more likely to switch schools during the year than students in schools that were not intensely segregated achieving schools (p value of .06). However, the results for ultra-movers were similar to those of between-year movers. Students in intensely segregated achieving schools were about 14 percentage points more likely to make ultra-moves. The results of racial and income segregation were also insignificant for both midyear and ultra-movers.

I conduct a few specification checks to examine the sensitivity of the results. First, I estimate Equation 2 separately for all segregation indicators. The results are qualitatively similar except in two instances. There is weak suggestive evidence that income segregation predicts midyear mobility and racial segregation predicts ultra-moves. Students in extreme-poverty schools were less than 1% more likely to switch schools during the year (p value of .08), and students in intensely segregated minority schools were less than 1% more likely to make ultra-moves (p value of .08). Next, I rerun the models excluding open enrollment options (charter and magnet schools). The results remain qualitatively similar when charter schools are excluded. Following this, I rerun the models excluding discipline-related mobility. For between-year school changes, high levels of achievement segregation were no longer a significant predictor; however, students in predominantly low achieving were more likely to exit. For midyear school changes, achievement segregation was a significant predictor but the directions of the coefficient reversed. Students in predominantly low-achieving and intensely segregated schools were less likely to exit schools during the school year when discipline-related mobility was excluded. This suggests that the role of achievement segregation as a predictor of midyear school changes is largely driven by discipline-related mobility. For ultra-movers, the results remain qualitatively similar when discipline-related mobility was excluded. These findings imply that students who switch schools based on achievement segregation, who are not subjected to school-initiated discipline mobility, are between-year or ultra-movers. In separate models, interactions of student characteristics and segregation indicators suggest that higher achieving students are more likely to exit achievement segregated schools and White students are more likely to exit racially and income segregated schools across the timing of school changes.

Finally, I also estimated Equation 2 separately by the levels of schooling. The results vary the levels of schooling and the timing of school changes. In elementary schools, for between-year movers, achievement segregation is no longer a significant predictor, and there is suggestive evidence that students in extreme-poverty schools are more likely to switch schools in the summer. For midyear movers, achievement segregation is not a significant predictor, and there is evidence to suggest that students in intensely segregated minority schools are more likely to switch schools. For ultra-movers, the results indicate that students in intensely segregated, minority and extreme-poverty schools were more likely to be ultra-movers, but students in intensely segregated, extreme-poverty schools were less likely to be ultra-movers. The findings imply that for elementary school students, racial and income segregation predict changing schools at different times. These students appear to change schools between school years due to income segregation and switch schools midyear due to racial segregation. Ultra-movers change schools for both racial and income segregation but not due to “double segregation” as they are less likely to exit schools with both high levels of racial and income segregation. For middle school students, high levels of achievement segregation remained a significant predictor but only for between-year and ultra-movers. Between-year movers in middle schools were also less likely to exit multiracial schools. For midyear movers in middle schools, there is evidence that students in high- and extreme-poverty schools are more likely to exit, whereas students in predominantly low-achieving schools were less likely to exit. The findings also suggest that ultra-movers in middle schools are less likely to exit high-poverty schools. These results suggest that between-year and ultra-movers in middle schools are exiting schools with high levels of achievement segregation, whereas midyear movers appear to be driven by income segregation. The findings also imply that for ultra-movers in middle schools, the role of achievement segregation in exit patterns is partly related to school discipline. For high schools, the results are qualitatively similar across the timing of school changes.

Concluding Discussion

This study offers new insights into the relationship between school segregation and student mobility in urban school districts. The results indicate that racial, ethnic, and achievement segregation persists in CCSD, whereas income segregation is declining. This article adds to a growing number of studies that have found that segregation is a pervasive and concerning phenomenon (Rumberger & Palardy, 2005). The results highlight an important mechanism linking student mobility to school segregation and achievement gaps, namely, the demographic and achievement characteristics of schools. More segregated schools typically have smaller within-school achievement gaps, a lower proportion of proficient students, a higher proportion of minority students, and higher nonstructural mobility rates (especially within-year mobility) than less segregated schools. The findings are similar to prior research that found that as Black–White dissimilarity increased, racial achievement gaps also increased (Condron et al., 2013).

Rising racial and achievement school segregation raises serious concerns about educational equity and the equality of educational opportunity in urban school districts. Historically, the segregation of African American children has been the main focus for educators and policymakers. The results of this article highlight that in 21st-century urban school districts, uneven distribution is multiracial, and desegregation is no longer only a Black–White issue. The findings imply that the segregation of Hispanic students, the fastest growing demographic group, is a pertinent concern. The importance of achievement segregation is particularly noteworthy, and this form of segregation is just as or even more important than racial and income segregation. The patterns in student mobility and segregation suggest the evolution of a tiered system of schooling, as low-achieving students are concentrated in the same schools and vice versa for high-achieving students.

The results indicate that high levels of achievement segregation are a significant predictor of student mobility. The findings imply that some parents are actively seeking less achievement segregated schools, especially those switching schools in the summer. School discipline is a significant reason why high levels of achievement segregation predict within-year mobility (midyear and ultra-movers). Overall, the results raise equity concerns as there seem to be centers of educational inequality in urban districts, or highly segregated, low-quality schools with a high proportion of minority and low-income students and considerable rates of discipline-related student mobility.

This study has a few limitations. First, the data do not capture student mobility from public to private schools and vice versa. This may affect the relationship between student mobility and school segregation. Nevertheless, a relatively small proportion of students in CCSD attend private schools—about 11% (Sohoni & Saporito, 2009). Second, although CCSD is a large, countywide, and highly diverse district, it is important to note that CCSD does not resemble a stereotypical “inner-city” school district; thus, there are some limitations of generalizing the findings.

Policy Implications

A few policy implications emerge from this study. First, the findings support the call for renewed investment in desegregation. However, in the wake of the 2007 Supreme Court decisions on desegregation that deemed the majority of voluntary desegregation programs by school districts unconstitutional, there is a need to consider feasible options within the law to attain integrated schools (Orfield & Lee, 2007). Given the unconstitutionality of assignment policies based on race, student mobility is a possible policy lever to affect desegregation that warrants further consideration. Districts may explore the use of students’ income and prior achievement as opposed to race to attain balanced schools in addition to providing information and incentives for low-income, low-achieving, and minority students to switch to more integrated schools. Considering SES and academic background as the key factors in student assignment policies may promote school integration and reduce school segregation (Potter, Quick, & Davies, 2016). Districts may also explore incorporating stratification limits into transfer policies that prohibit school changes that will add to achievement segregation.

Second, policymakers should pay greater attention to reforming schools with an eye to segregated schools with high-mobility rates and provide additional support to these schools. As states revise funding formulas, increased funding to highly segregated schools with substantial student churn should be a priority and key component of focusing education policy to address educational inequality in urban districts. It would be prudent to focus on factors such as class size and teacher quality in these high-mobility schools that may contribute to achievement gaps in urban school districts. Greater curricular and pedagogical focus for schools with high rates of during-the-year student mobility may help improve student achievement in urban school districts. Policymakers should find ways to ensure greater instructional continuity and mitigate the adverse effects of turnover on students and schools. This may entail resources for personalized instruction for mobile students especially those in middle schools where midyear school changes are relatively prevalent. Districts may also create a student mobility office that bridges the communication gap between sending and receiving schools to better coordinate curriculum and pedagogy. Receiving schools would then have detailed information on mobile students to tailor curriculum and teaching techniques.

Third, policymakers may also consider targeting different types of segregation at different levels of schooling using varying initiatives. Student mobility and racial and income segregation is typically higher in elementary schools, whereas achievement segregation is higher in high schools. Programs fostering and incentivizing racial and income desegregation may pay the biggest dividends at the elementary level where younger students are affected. Desegregation initiatives at the high school level such as adjusting attendance zones may target the clustering of low-achieving students of all races. Combination schools present a special challenge as they are afflicted with racial, income, and achievement segregation and tend to serve an at-risk student subgroup. The findings suggest policymakers should closely rethink the operation of alternative schools and how learning and student remediation takes place in these highly segregated and mobile environments.

Directions for Future Research

The findings also provide some directions for future research. First, a better understanding of families’ preferences that may influence the relationship between student mobility and school segregation and how this may vary with the timing of school changes is needed. A complementary qualitative study may provide better insights on how segregation levels of origin schools affect mobility decisions and a sense of how the segregation level of destination schools may affect the impact of student mobility on student achievement. Similar to switching to schools of higher quality, transfers to less segregated schools may result in net positive effects of changing schools, and thus segregation may be a key determinant of the overall impact of student mobility.

Second, studies with classroom-level data that allow for estimation of within-school segregation may provide a stronger link between segregation, student mobility, and achievement gaps at the school level. These investigations will further illuminate how segregation and student mobility affect educational inequality at a granular level. Finally, differences in neighborhoods in urban school districts may play an important role in explaining the relationship between student mobility and school segregation. Future studies should incorporate the location of schools and neighborhood characteristics to gain a better understanding of patterns in student mobility and school segregation. A better understanding of the interaction of school and neighborhood contexts has important policy implications such as whether student mobility is more appropriately addressed by the coordination of education, housing, and economic policy.

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.

Notes

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).

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