School Choice, Racial Segregation, and Poverty Concentration: Evidence From Pennsylvania Charter School Transfers

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School Choice, Racial Segregation, and Poverty Concentration: Evidence From Pennsylvania Charter School Transfers

Stephen Kotok1, Erica Frankenberg2, Kai A. Schafft2, Bryan A. Mann2, and Edward J. Fuller2

AbstractThis article examines how student movements between traditional public schools (TPSs) and charters—both brick and mortar and cyber—may be associated with both racial isolation and poverty concentration. Using student-level data from the universe of Pennsylvania public schools, this study builds upon previous research by specifically examining student transfers into charter schools, disaggregating findings by geography. We find that, on average, the transfers of African American and Latino students from TPSs to charter schools were segregative. White students transferring within urban areas transferred to more racially segregated schools. Students from all three racial groups attended urban charters with lower poverty concentration.

Keywordscharter schools, segregation, race, poverty, geographic context

1The University of Texas at El Paso, El Paso, USA2The Pennsylvania State University, University Park, USA

Corresponding Author:Stephen Kotok, The University of Texas at El Paso, Education Building 503, El Paso, 79968 USA. Email: [email protected]

604112 EPXXXX10.1177/0895904815604112Educational PolicyKotok et al.research-article2015

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2 Educational Policy

Sixty years after the Brown decision declared segregation in public education to be unconstitutional, school segregation is on the rise in the United States (Orfield & Frankenberg, 2014). Decades of research since Brown finds that segregated minority schools are academically harmful to the students who attend them, and that most racially segregated schools are also economically concentrated (Linn & Welner, 2007; Mickelson, 2008). On the contrary, an additional body of research concludes that diverse schools benefit all students—White students and students of color—in ways that help to better prepare them to live in a diverse society as an adult (Mickelson & Nkomo, 2012; Wells & Crain, 1994). These findings about the benefits of diverse schools have been reflected in federal law and policy in recent years (Parents Involved in Community Schools v. Seattle School District, 2007; U.S. Department of Justice, Civil Rights Division, & U.S. Department of Education, Office of Civil Rights, 2011).

The rise in school segregation has been influenced by several factors including reduced judicial oversight of school district student assignment, resulting in a tighter coupling of school composition with housing segrega-tion patterns (Frankenberg, 2013; Reardon, Grewal, Kalogrides, & Greenberg, 2012; Reardon & Yun, 2005). Another emerging trend in public education potentially affecting segregation is the growth of school choice, of various types, including charter schools. Despite mixed results in regard to student achievement (Bifulco & Ladd, 2007; Carnoy, Jacobsen, Mishel, & Rothstein, 2005; Finnegan et al., 2004; Henig, 2008; Hoxby, 2004; Zimmer, Gill et al., 2009), charter schools have expanded rapidly since they began just over two decades ago, spurred in part by governmental incentives (Kirst, 2007) and philanthropic support (Scott, 2009).

The expansion of school choice, especially charter schools, raises a vari-ety of civil rights concerns (Orfield & Frankenberg, 2013; Scott, 2012), although these concerns have received less attention as charters have expanded.1 Some estimates suggest that school choice can disproportionately attract certain student groups and exacerbate segregation (Frankenberg, Siegel-Hawley, & Wang, 2011; Finnegan et al., 2004; Saporito & Sohoni, 2006). Yet, some have argued that due to charter schools’ disproportionate location in high minority urban areas, aggregate comparisons of traditional public schools (TPSs) and charter enrollments are misleading (Henig & MacDonald, 2002; Jacobs, 2013; Ritter, Jensen, Kisida, & McGee, 2014). Thus, debates persist regarding whether charter schools contribute to school segregation (Fiel, 2013; Richards & Stroub, 2014) or whether the composi-tion of charters simply reflects local demographics.

Given the rapid expansion of charter schools and the continuing impor-tance of school composition for students’ experiences and outcomes in schools,


Kotok et al. 3

we focus here on changes in the student body composition experienced by students transferring between public schools and charter schools. Specifically, we address these research questions:

Research Question 1: To what extent are students and schools affected by movement between charter schools and TPSs?Research Question 2: Are student transfers from TPSs to brick and mor-tar (B&M) charter schools associated with increasing racial isolation? How does this vary by geography?Research Question 3: Are student transfers from TPSs to charter schools associated with increasing exposure to low-income students? How does this vary by geography?Research Question 4: What are the demographic characteristics of the TPSs from which cyber students transfer?

Longitudinal student-level data allow us to compare the student racial and economic composition of the TPS each transferring student leaves with that of their new charter school, eliminating methodological debate about the appropriate comparison group when analyzing compositional differ-ences (Frankenberg, Siegel-Hawley, & Wang, 2011; Ritter, Jensen, Kisida, & McGee, 2010). In contrast to past studies using individual data (Bifulco & Ladd, 2007; Booker, Zimmer, & Buddin, 2005; Garcia, 2007; Weiher & Tedin, 2002; Zimmer, Blanc, Gill, & Christman, 2008), our study disaggre-gates student movement by geographic location, accounts for both cyber and B&M charter school choices, and examines the segregation of students entering charter schools for three racial/ethnic groups. Because cyber char-ter schools in the analysis enroll an increasing share of students and ques-tions regarding demographic composition of students and their effect on racial segregation remain understudied, we believed it was important to include them in our analysis. Yet, cyber schools differ structurally from B&M schools in that students do not sit in a physical classroom with their classmates and therefore the literature about the benefits of interracial expo-sure are not applicable in the same way. As such, the research questions for the two types of charter schools differ with the inquiry on cybers focusing on the previous schools attended rather than the current as in the B&M analysis. Before proceeding with our analyses, we first discuss the theoretical under-pinnings of school choice and its relationship to segregation. We then review empirical studies of charter student movement and segregation, examining the limited literature on cyber schools as it relates to racial composition of students.



Literature Review

Market Metaphor and EquitySome advocates of charter schools argued that school choice would inadver-tently advance goals of equity and diversity as families living in high poverty and racially segregated neighborhoods would no longer be constrained by traditional attendance zones and could thus send their children to more racially and economically integrated charter schools (Chubb & Moe, 1990; Frankenberg, Siegel-Hawley, & Wang, 2011; Lubienski, Gulosino, & Weitzel, 2009). In this scenario, even though some students would remain at these segregated TPSs, the overall outcome could be to create more diverse schools without having to address deeply entrenched housing patterns and politically precarious school catchment boundaries (Finn, Manno, & Vanourek, 2006; Viteritti, 1999). Moreover, this would provide choice within the educational marketplace for students from more disadvantaged families who would not have the opportunity to move to more affluent districts with better quality schools serving more advantaged students.

These market-based assumptions about school choice have spurred the rapid expansion of charter schools, but also may not fully take into account the complex nature of schools and dynamics that create and reproduce social stratification (Orfield, 2013). First, for any market to operate fairly and as intended, information must be equally available to all and potential partici-pants must be aware of all possible school options. It also assumes that every-one is able to process complex information about school quality generally and a school’s fit for a child, in particular. However, information about schooling options is generally transmitted and evaluated through social net-works, which are often racially and socioeconomically segregated (e.g., Bell, 2009; Holme, 2002). Second, it assumes that families are equally able to choose all possible schools. Yet, families have different resources to be able to investigate choice options as well as constraints such as transportation needs or educational or social program requirements (Fuller, Elmore, & Orfield, 1996). Moreover, for choice to be integrative, families across racial/ethnic lines must view choices as equally attractive and make decisions using similar criteria. Finally, schools should be open and welcoming to all students, but there are a variety of ways in which schools actively shape stu-dent enrollments (e.g., selective marketing, requirements for enrollment; Lubienski, 2007).

According to the market model, school choice should elevate student achievement for all public schools—TPS and charter—through increased competition and innovation (Chubb & Moe, 1990), but the effect of choice on


Kotok et al. 5

achievement levels have been mixed (Bifulco & Bulkley, 2008; Center for Research on Education Outcomes [CREDO], 2013; Zimmer & Buddin, 2006). Moreover, this faith in market-based solutions to promote higher achievement is in part undermined by unintended consequences such as seg-regation by race and poverty and increased student attrition (CREDO, 2009; Research for Action [RFA], 2013), and may be widening the racial achieve-ment gap rather than narrowing it. The mechanisms for why that happens are likely to be the result of segregation of those transferring to charter schools as well as the impact of transfers on nonchoosers (e.g., students remaining in public schools). In one of the few existing studies of the transfers from char-ter schools to TPSs, Ni (2012) found that low-income students are signifi-cantly more likely than their advantaged peers to transfer from charter schools back to a TPS and that advantaged students were especially likely to leave public schools that had lower achievement, effectively concentrating low achievers who remained in the TPS.

Although mobility occurs for a variety of economic and social reasons, school choice has also been linked directly to an increase in student tran-siency (Gagne & Lyons, 2009; Ni, 2012), which may disrupt both individual and school-wide learning (Rumberger & Thomas, 2000). Attrition from char-ter schools is difficult to measure, although some estimates suggest it is sub-stantial, perhaps as high as 20% (Gagne & Lyons, 2009; Ni, 2012). In Chicago, Gagne and Lyons (2009) found that TPS students were 3% more likely to attend the same school 2 years in a row than charter students. However, regardless of what school type has higher attrition, school choice by its very nature encourages parents to act as consumers moving to the best school. Yet, this revolving door of students moving between schools presents an additional challenge for teachers and students. Therefore, even when a market model functions as intended with parents “voting with their feet,” school choice presents unique challenges for school diversity and student success.

Racial Segregation of Students in Charter SchoolsAs charter schools continue to rapidly expand, they may exacerbate growing segregation. Research has consistently found that charter schools are segre-gated compared with TPSs (Finnegan et al., 2004; Frankenberg, 2003, Frankenberg, Siegel-Hawley, & Wang, 2011; Garcia, 2007; Miron, Urschel, Mathis, & Tornquist, 2010; Nelson et al., 2000; Renzulli & Evans, 2005; Weiher & Tedin, 2002). This general finding has been consistent over time and holds across contexts, albeit with variation in the extent of segregation depend-ing on various contextual factors. There is a vast literature that consistently



finds racially isolated minority schools tend to have fewer resources that are important for students’ learning such as advanced classes, middle-class peers, and a stable, experienced, and qualified teaching force (see Linn & Welner, 2007; Mickelson, 2008, for summaries). Perhaps not surprisingly, the consen-sus of research studies is that students have lower outcomes such as academic achievement and educational attainment in racially isolated minority schools (Balfanz & Legters, 2004). Fewer studies have focused specifically on the out-comes of segregated charter schools, but Bifulco and Ladd (2007) found that black students in North Carolina moved to more segregated charter schools with lower achievement than the TPSs they left. The fact that some high- performing charter schools “beat the odds” of achieving in spite of such high segregation seems to be the result of a selection effect (Rothstein, 2004) and a massive influx of philanthropic support for some charters (Baker & Ferris, 2011).

Descriptive analyses of charter school segregation have focused on two ways of assessing segregation: comparisons between TPSs and charter schools and the school-level composition of charter school student bodies. At the national level, and in many, but not all states, charter schools dispropor-tionately enroll students of color in comparison with TPSs (Finnegan et al., 2004; Frankenberg, Siegel-Hawley, & Wang, 2011; Miron et al., 2010; Nelson et al., 2000).2 In some states, this is because charter schools are only authorized in urban or distressed districts. In other states, there are financial incentives to locate and/or serve urban students (Frankenberg, & Siegel-Hawley, 2011). When analyzing the distribution of these disproportionately non-White students within the charter school sector, research has found high levels of minority concentration (Frankenberg, Siegel-Hawley, & Wang, 2011; Miron et al., 2010; Ni, 2012). A few studies have also found evidence of charter schools in some areas attracting disproportionately White students (Institute on Race & Poverty, 2008; Renzulli & Evans, 2005).

For the most part, studies of charter school enrollment and segregation at more local levels of geography have reported similar trends (Bifulco & Ladd, 2007; Zimmer & Buddin, 2006; Zimmer et al., 2009). Ritter and colleagues (2010) argued that comparisons of charter schools at the regional and state level overestimate the extent of charter school segregation, but their analyses utilizing a more localized comparison still indicate charters are more racially segregated than nearby TPSs. Other studies have used student-level data, which enable a more accurate picture of how student moves to charter schools from TPSs affect segregation. The consensus across various contexts (Arizona, Denver, San Diego, North Carolina, Philadelphia, and Texas) is that African American students who transfer to charter schools from a TPS attend more racially isolated schools (e.g., with proportionally more African American


Kotok et al. 7

students; Bifulco & Ladd, 2007; Booker et al., 2005; Garcia, 2007; Weiher & Tedin, 2002; Zimmer, Blanc, Gill, & Christman, 2008). In their study of eight jurisdictions, Zimmer et al. (2009) found only two—Milwaukee and Chicago—where African American students enrolled in less racially segregated schools, and even these differences were small. Some studies find that Latino students transferring to charter schools from a TPS go to less isolated schools (Booker et al., 2005; Garcia, 2007) but Latinos are not as widely studied.

In many studies, White students—who are already the most isolated of any racial group (Orfield & Frankenberg, 2014)—transferred from a TPS to charter schools with higher percentages of other White students than the schools they left (Bifulco & Ladd, 2007; Cowen & Winters, 2013; Garcia, 2007; Ni, 2012; Weiher & Tedin, 2002). For instance, Bifulco and Ladd (2007) examined White exposure to other racial groups, and found that those white students moving to charter schools in five North Carolina metros attended schools with more White students than they did in TPSs. Moreover, when controlling for other characteristics, White students were more likely to move to charter schools than non-White students leading to statistically sig-nificant reduction in the percentage of White students in TPSs (Cowen & Winters, 2013; Dee & Fu, 2004; Ertas, 2013; Ni, 2012). Although a few stud-ies find mixed results for Whites enrolling in charter schools (see Booker et al., 2005; Zimmer et al., 2008), the majority of studies suggest a net segre-gative effect for Whites.

Taken together, analyses of school-level patterns in charter schools along with the handful of studies examining student-level transfers from TPSs to charter schools are suggestive of higher segregation. This trend is strongest for African American students while segregation patterns are more mixed for Whites and Latinos (where the latter group is included in analysis). Importantly, these findings hold across contexts, years, and variations in methodological approaches (such as geographic scope, segregation mea-sures) suggesting that these conclusions are not artifacts of particular analytic decisions. Most of these individual-level studies analyze data, however, that are more than a decade old, raising questions of whether these patterns shifted at all as the public school enrollment grows more diverse, public schools in general become more segregated, and as the number of charter schools expand. In addition, little is known about whether there is geographic varia-tion in these trends, particularly outside of urban districts.

Growth of Cyber Charter SchoolsIn many states, full-time cyber charter schools—schools of choice where the entirety of instruction is delivered online—have emerged as a popular option



for students and parents (Ahn, 2011; DeJarnatt, 2013), but few studies have considered the effect of cyber charters on the racial composition of public schools. National figures examining the demographic composition of all vir-tual school students (including cyber charter schools as well as the rapidly growing number of district-run virtual schools) found that these students were disproportionately White in comparison with the public school enroll-ment overall (Molnar et al., 2014). In fact, Whites constituted around three quarters of all virtual students even though they represented only 54% of students nationally. A recent report about Pennsylvania’s cyber charter schools found that the racial composition of cyber schools was very similar to all public school students in the state, 70% to 75% White, although cyber charters enroll disproportionately lower percentages of English Language Learner (ELL) students and higher shares of economically disadvantaged students (RFA, 2013).

The expansion of cyber charter schools potentially affects both racial seg-regation and poverty concentration of the TPSs students leave. Questions about what type of public schools cyber students transfer from are less stud-ied, in part because many cyber students are assumed to have been home schooled prior to enrolling (Huerta, Gonzalez, & d’Entremont, 2006). Although assessing the number of home schooled students now choosing cyber charters is difficult to quantify, a report from the largest cyber charter company indicates that about one third of its students were not previous pub-lic school students and two thirds transferred from public schools (K12 Inc., 2013). The relationship between cyber charter enrollment and the composi-tion of exiting school districts remains an understudied issue.

Method

Pennsylvania ContextPennsylvania is an informative state to study these issues given its history of residential segregation, changing demographics, rapid proliferation of char-ter schools over the last decade, and large cyber charter school enrollment—the fourth highest in the United States (Watson, Paper, Murin, Gemin, & Vashaw, 2014). Charters were authorized in 1997 through Pennsylvania’s Act 22 and, after a series of court challenges, Act 88 explicitly authorized cyber charter schools, which do not have a physical building, but provide online instruction. Practically speaking, B&M charter schools have a more defined and geographically limited area from which to attract students because of the limits of transportation time to the school. However, Pennsylvania does require districts to provide transportation to students attending charter schools


Kotok et al. 9

within their district or within 10 miles of the district boundary. Alternatively, cyber charter schools are not limited by geographic location, as students who are residents of Pennsylvania may choose to attend any cyber charter approved for operation within the state.

Charter schools have grown rapidly: In the 2011-2012 school year, there were 150 B&M charter schools and 12 cyber charter schools enrolling stu-dents in Pennsylvania combining to enroll approximately 105,000 students, or approximately 5% of all public school students in the state (Pennsylvania Department of Education [PDE], 2014). Although charter school enrollees have traditionally been concentrated in the Philadelphia metropolitan area, B&M charter schools exist throughout the state and, as of the 2011-2012 school year, every TPS district lost enrollments to either a cyber or B&M charter school (PDE, 2014).

DataWe use two data sources. First, we use individual student and school/district data for 2008-2009 to 2011-2012 (the last year of data at the time we began our analysis) from the PDE indicating what school a student attended in each year, his or her race, and his or her grade. We omitted pre-k students and ungraded students for this analysis, but 12th graders were left in if they repeated 12th grade. Second, we linked the individual data with school-level data from the National Center of Educational Statistics (NCES) Common Core of Data (CCD), Public School Universe. The CCD variables included demographics of the student enrollment and other school or district charac-teristics. We recoded the census-defined 12 category urbanicity variable into three groupings: (a) urban area, (b) suburban area, and (c) town or rural area. Throughout the analysis, we refer to B&M charter interchangeably as “char-ters” or “B&M charters,” and we refer to full-time cyber charter schools as “cybers.”

Our analysis is restricted to students who attended one school type in year X and one school type in year X + 1, doing so in three cycles of between-year moves (e.g., 2008-2009 to 2009-2010, 2009-2010 to 2010-2011, and 2010-2011 to 2011-2012). We did not include within-year school movers, defined as students who left a school within year X rather than between year X and year X + 1, as the data did not indicate the temporal order of the moves. However, this was the case for less than 5% of all students in the analysis. Further analysis indicates that in 2010-2011, for example, 90% of these cases consisted of students moving between two TPS districts or in and out of cyber charters rather than B&M charter schools (see the appendix). In terms of racial characteristics, we did not observe major differences in within and



between year charter movers. Therefore, while we limit our analysis to only students who move between years, our findings likely apply to within-year movers as well. As such, our estimates of the segregative effect of charter school transfers may underestimate the impact of movements as additional students making moves during the middle of the year are likely to parallel those in between-year moves considering the similarities in demographics.

To compare the types of students who chose to move school types, we calculated summary statistics for all three cohorts of movers by race and urbanicity. We generally report the summary statistics for the year prior to school type change. To understand the isolation that groups of students have (e.g., exposure to one’s own racial group), we rely on the exposure/isolation index as our measure of segregation (Massey & Denton, 1988) as it provides a measure of the student body encountered by the typical student from each racial group. While there are many dimensions of segregation that could be used (Massey & Denton, 1988), we believe this is aptly suited for our analy-sis because of the interpretation of the interracial exposure (or lack thereof) students is experiencing. Research varies considerably on the ways in which school segregation is conceptualized and is dependent in part on regional context (Frankenberg, Siegel-Hawley, & Wang, 2011), but schools with higher percentages of own-race students (e.g., isolation) in a student’s school and few other race students (some studies suggest 10%-20%) are associated with lower outcomes for minority students. Furthermore, because research also suggests a dynamic process of schools becoming racially identifiable, we should be concerned with any segregative changes (Brief of the American Psychological Association, 2006).

We calculate the isolation in the student’s sending and receiving school separately for African American, Latino, and White students who transferred from a TPS to a B&M charter school. We then compare the isolation at the two schools to ascertain the net effect of the school transfers on student isola-tion. The analysis was further disaggregated by geographic location and grade level, but we dropped the grade-level3 analysis after observing minimal differences. To examine socioeconomic patterns, we also compare the expo-sure to free and reduced lunch (FRL) students between sending TPS and receiving charter. However, since some TPS and charter schools failed to report FRL data in earlier years, we only analyze the most recent cohort of movers as it contained the most complete coverage of schools.

For cyber charter students, we altered the analysis described above as stu-dents do not encounter other students in the cyber setting. Therefore, we cal-culate the racial isolation of the TPS they attended prior to cyber charter school. We then compare the isolation to the state average racial isolation for all students attending TPSs and those transferring to B&M charters.


Kotok et al. 11

FindingsAs detailed throughout this section, our findings reveal that the trend of stu-dents moving into and out of B&M and cyber charter schools from TPSs may be associated with segregation, isolation patterns, and poverty concentration. The vast majority of TPSs in our study had students leaving for charter schools. The moves are associated with increased isolation for students of color— a finding consistent for African American students regardless of geo-graphic location. In addition, the typical student leaving a TPS for a charter in an urban area attends a higher socioeconomic status (SES) school while the opposite is true in nonurban areas. Finally, cyber enrollees on average come from schools that closely reflect the state’s school demographics, and the majority of White students transferring to cyber charter schools come from rural and town TPSs while the majority of Black and Latino transfers come from urban TPSs. The details of these findings are described below.

Extent to Which Students Are Affected by Transfers to Charter SchoolsAs the charter schools expand within and beyond metropolitan areas, more schools and students are experiencing changing school composition due to choice-based student movement. Between 2008-2009 and 2011-2012, about 80% of TPSs in Pennsylvania had at least one student transfer to attend a charter—cyber or B&M—the following year.4 More than one third of TPSs had a student attend a B&M charter the subsequent year when cumulatively examining the 4 years during our study. Around a quarter of schools lost students to both cyber and B&M schools, while almost half of schools lost students only to cyber schools over this time period. Notably, at a time when the number of TPSs in Pennsylvania decreased, the number and proportion of schools affected by leavers in any given year has increased. We find that for each individual year, more than 25% of all schools had students leave for a B&M charter the next year with the proportion of schools being affected increasing in the last years for which we have data. When we include cyber students in the analysis, we observe that almost 60% of TPSs in 2011 had at least one student attend a charter in 2012.

During the time period examined in this study, the total number of stu-dents transferring between types of schools consistently increased despite the fact that the Pennsylvania student population—TPS and charter—decreased by more than 20,000 students (PDE, 2014). Most of these transfers are from TPSs to charter schools. In all three cohorts of students transferring between academic years from 2008-2009 to 2011-2012, the majority of moves



involved students leaving TPSs to attend charter schools (see Figure 1). The number of students moving from TPSs to B&M charters between 2009 and 2012 nearly doubled. Movement to cyber charter schools from TPSs also increased over this time, but was much less pronounced, approximately 30%. The number of students leaving cybers to go back to TPSs also increased, indicating high turnover to and from cyber schools. Of the four types of movements, only charter to TPS movements experienced a decline, albeit a slight decrease of 161 students. In sum, students increasingly moved between school types, but most movement occurred toward charter schools—both cyber and B&M. As such, our isolation analysis below concentrates on move-ments to charter schools rather than from them.

Before analyzing the difference in racial isolation, it is helpful to compare the demographic traits of students making movements between sectors (see Table 1). More than 62% of the TPS to B&M movers were African American and around 20% were Latino. As a point of comparison, the African American share of all public school students in the state was only about 15% and the Latino share was approximately 8% (Kotok & Reed, 2015). Most TPS to B&M movers had left a school in an urban district. Interestingly, those stu-dents who transferred from a B&M charter to a TPS differed slightly from students transferring back to a TPS from a B&M charter. Whites made up almost 25% of students returning to TPSs compared with about 15% of students leaving TPSs for charters.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

2009-2010 2010-2011 2011-2012

Cyber to TPS

B&M to TPS

TPS to Cyber

TPS to B&M

Figure 1. Charter movements by year and transition type, 2009-2010 to 2011-2012.Source. Pennsylvania Department of Education.


Kotok et al. 13

In general, the racial distribution of students entering and exiting cyber charters closely resembled the student demographics of the state, but is sub-stantially different from students who transfer to and from B&M charter schools. For instance, African Americans, who constitute around 15% of the state student population, make up about 14% of all students transferring to cybers, but more than 62% of all students transferring to B&M charter schools. In terms of geography, a higher proportion of rural than urban and suburban students transferred to and from cyber schools. Notably, a greater share of urban students moved from a TPS to a cyber (23%) than moved back from a cyber to TPS (16%).

To summarize, more students are choosing to enter and exit both types of charters, which affects a large and increasing number of schools. Although the majority of students entering B&M charters live in urban areas, B&M charters are opening increasingly in suburban and rural areas. Moreover, cyber charters increasingly attract students from across the state. Notably, White students, on average, accounted for around 15% of students entering B&M charters, but almost a quarter of the students exiting charter schools. It is unclear from our analysis as to why minorities represent a greater

Table 1. Descriptive Statistics for Students Transferring Between Traditional Public Schools and Charter Schools, 2008-2012, by Ethnicity and School Locale.

Transferring from traditional public school to

Transferring to traditional public school from

Brick and mortar charter

(n = 22,258)

Cyber charter

(n = 10,196)

Brick and mortar charter

(n = 11,378)

Cyber charter

(n = 5,561)

Ethnicity % African American 62.11 17.31 55.94 14.42 Latino 19.40 6.95 16.44 5.43 White 15.63 73.99 24.15 78.13 Asian 2.37 1.11 1.92 1.33 Othera 0.49 0.64 1.55 0.69School locale %b

Urban 84.52 23.45 71.84 16.27 Suburban 11.04 37.37 23.04 41.07 Town/rural 4.43 39.18 5.12 42.62

Source. Pennsylvania Department of Education; NCES Common Core of Data, 2011-2012.aIncludes American Indian and multiracial.bRefers to the locale of the school that the student transferred from except for last column, which uses the school that the student transferred to.



proportion of students entering B&M charters than exiting them, but it may be a function of several TPSs being shut down in high minority and heavily populated neighborhoods, thus limiting noncharter choices in these areas, differences in school quality attended by students of different backgrounds, or differing levels of parental information.

Isolation AnalysisIncreased isolation for students of color. African Americans and Latino stu-dents, on average, transferred from more racially diverse TPSs to more racially isolated charter schools for all three between-year transition periods (see Figure 2). This is not to suggest that students in Pennsylvania’s TPSs are racially integrated, as racial segregation is actually extremely high throughout the state’s schools (Kotok & Reed, 2015). Rather, this compari-son indicates that isolation for African American and Latino students is even more pronounced in charter schools. This is especially noteworthy because, as we mentioned earlier, almost 82% of all B&M charter movers during this time period were either African American or Latino. The typical African American student transferring to a charter school enrolled in a school that was even more racially isolated and remained consistently so during this time period, at more than 80% African American students, on average. Con-versely, the TPS left by the typical African American student B&M charter transfer are actually relatively less racially segregated—from almost 66% African American in 2009 to approximately 62% African American in 2012, although this still represents a very high level of isolation. Latinos are con-siderably less isolated than either African Americans or Whites, but on aver-age, their movement to a charter still resulted in a more isolated school setting for all 3 years. For instance, in 2011-2012, Latinos, on average, left a TPS with 42.8% other Latinos and enrolled in charters that were more than 55% Latino.

On average, White students choosing charter schools transferred to slightly less racially homogeneous charter schools over this period. However, White student transferees attended charter schools with around two-thirds other White students, on average, indicating that they still have low exposure to students of other races. Moreover, the statewide integrative “advantage” of charter schools for White students—that is, the difference in the average White percentage between sending TPS and receiving charters for Whites—decreased from around 10.5% in 2008-2009 to less than 8% in 2011-2012 largely because White isolation in TPS schools declined slightly while White isolation at charter schools remained stable.


Kotok et al. 15

0

10

20

30

40

50

60

70

80

90

2009 to 2010

2010 to 2011

2011 to 2012

2009 to 2010

2010 to 2011

2011 to 2012

2009 to 2010

2010 to 2011

2011 to 2012

Black White Latino

Raci

al Is

olat

ion

Sending TPS Receiving Charter

Figure 2. Racial isolation of schools charter transferees leave and enroll in, by student race and year 2008-2009 to 2011-2012.Source. Pennsylvania Department of Education; NCES Common Core of Data.

Variations in segregation by urbanicity. Our analysis also probed whether these trends were similar across the state or varied by geographic location of schools. We sought to understand whether these findings were due, in part, to the high numbers of urban students attending charter schools given a history of racial segregation in Pennsylvania’s urban areas (Hodge, 2014; Milby, 1996; Morrison, 2004). When we disaggregated the changes in racial isola-tion by geography, the general patterns of charters being more racially iso-lated for African Americans remains regardless of geographic location (see Table 2). However, we find varying levels of increased segregation for Afri-can Americans and Latinos depending on geography, and we find differing patterns for Whites depending on urban or suburban locations. Although we combined all years of movement for this analysis, the trends were similar in each year.

While the overall difference in isolation for Whites decreased when they enrolled in charter schools across the state, White students in urban areas moved to charters that were, on average, more racially isolated. For instance, White students, on average, transferred from a TPS with a far lower propor-tion of Whites—44.8% compared with 59.1% at the charter school they enrolled in. Thus, albeit in a limited sample of around 1,800 White students, this analysis provides some evidence charter schools may be aiding White flight from majority minority TPSs within urban areas. The fact that the over-all difference in isolation diminished for Whites transferring to charters can be



explained by larger numbers of White students enrolling in charter schools in nonurban settings. White students, on average, moved from overwhelmingly white suburban TPSs (79.3%) to charters with a lower proportion of White students (68.9%). In town/rural areas, White students attended charter schools with similar percentages of White students, on average, as their former TPS.

Conversely, on average, charter schools resulted in more segregated school settings for African Americans compared with their former TPS, regardless of geography. Even within already highly segregated urban areas, the typical African American student mover departs a TPS with an average of 71.3% African American students and enters a charter with even more African American students: 83.5% African American students, on average, which represents very few other race students. Suburban African Americans also transferred to charter schools with higher levels of racial isolation. For instance, on average, African American students left suburban TPSs with around 52% African Americans for a charter with more than 68% African American students. Thus, while one of the arguments to explain high segre-gation for minority students is the urban location of charters, we find that

Table 2. Racial Isolation for Students Moving From a TPS to B&M Charters, 2008-2009 to 2011-2012 (n = 21,783).

Number of students

Sending TPS

Receiving charter Difference

African American Urban 12,527 71.31 83.48 12.17 Suburb 1,268 51.84 68.16 16.32 Rural/town 179 21.12 47.02 25.90 Total 13,974 64.51 81.10 16.59White Urban 1,803 44.77 59.09 14.32 Suburb 950 79.33 68.94 −10.39 Rural/town 732 88.49 87.28 −1.21 Total 3,485 76.33 67.05 −9.28Latino Urban 4,050 49.92 62.01 12.09 Suburb 207 18.88 19.01 0.13 Rural/town 67 17.98 25.66 7.68 Total 4,324 41.77 56.51 14.74

Note. TPS = traditional public schools; B&M = brick and mortar.Source. Pennsylvania Department of Education; NCES Common Core of Data.


Kotok et al. 17

more than two thirds of students in the charter schools suburban African American transfer to are of the student’s own race. Surprisingly, in rural areas, the disparity in the racial composition of receiving charter schools as compared with the TPSs students leave was wider than that found for African American transfer students in urban and suburban areas. Rural African American leavers transferred from a TPS to a charter whose percentage of African American students was more than 25 percentage points higher on average. While the sample of rural African Americans moving to charters is fairly small, at less than 200 students, it remains notable as the TPSs in these communities already typically are significantly lacking in racial diversity. Thus, the loss of even a few African American students from a rural TPS can even further isolate the remaining white students.

Latino students moving from a TPS to a charter also experienced an increase in racial isolation regardless of geography when they enrolled in a charter, though the difference was not as large in nonurban areas as that observed for the typical African American student. However, in terms of urban settings where most Latino movers lived, Latinos moved to a charter school whose percentage of Latino students was, on average, 12.1 percentage points higher than their previous TPS. In suburban areas, there was only a trivial increase in racial isolation on average for Latinos leaving a TPS for a charter. Yet, similar to African Americans, Latino leavers transferring from a rural TPS enrolled in charters with more racial isolation. Thus, like African American students, Latino students moving from a TPS to a charter typically enroll in a more segregative environment regardless of geography.

These results suggest that B&M charter schools in general represent a more racially isolated setting when compared with a TPS. However, within this larger finding, we find evidence of variation by geography and student race and ethnicity. The typical African American student moving to a charter school attended a more racially isolated school regardless of geography, and the results are not explained by disproportionate residence of African American students in urban areas suggesting that student segregation in char-ter schools in not simply an urban phenomenon. In fact, the disparities are actually larger for African Americans living in nonurban areas. On average, Latinos are also moving to more racially isolated schools, especially in urban and rural areas. Although there are few Latino charter choosers outside urban areas, this is likely to change given an ongoing demographic shift involving more Latinos moving across Pennsylvania (Kotok & Reed, 2015). Notably, White students in urban areas are choosing charter schools that have a higher percentage of White students. At the same time, there is some limited evi-dence that charters represent a less segregated option for Whites in the



suburban marketplace than TPSs, but are doing no better in rural settings where White students experience high isolation.

Exposure to Low-Income StudentsGiven the association between concentrated race and poverty in American schools, we also assessed the extent that charter movers were exposed to low-income students over this time period. Due to high levels of missing free/reduced lunch data for 2008-2009 and 2009-2010, we focused only on 2011-2012 for this part of the analysis. On average, African Americans, Latinos, and Whites all moved to charter schools in 2011-2012 with a higher percent-age of low-income students than in the TPSs they attended in 2010-2011, but the pattern varied by geography. In urban areas, the average student from all three racial groups moved to charter schools with lower poverty than their prior TPS with the largest discrepancy being for White students who attended charters with more than 11% fewer FRL students (Table 3). The tendency for Whites to choose more mixed-income charter schools in urban areas is espe-cially interesting considering our prior finding that Whites in urban areas may be utilizing charter schools as White flight schools (see also Henig & MacDonald, 2002). In addition, urban White students transferring to charter schools have much lower exposure to low-income students than do their African American or Latino urban peers, on average. In the case of urban areas, on average, White students enrolled in charters where about half the students were in low-income range compared with the African Americans and Latinos who enrolled in charters that were still well over three-fourths low income.

Conversely, White, African American, and Latino student movers from suburban and rural areas enrolled in a charter with a greater proportion of FRL students. The difference in concentrated poverty in nonurban settings was more substantial, on average, for African American students. For instance, African American students moving to charters in suburban areas attended a school with a percentage of FRL students that was more than 15 percentage points higher than their former TPS and the difference was almost 20 percentage points in rural areas. Recall that Pennsylvania law requires districts to provide transportation for charter school students including beyond district boundaries, which may help to explain why students are transferring to charters with higher percentages of low-income students if the charters themselves locate in higher poverty areas relative to the suburban and rural marketplace.

In sum, the typical student leaving a TPS for a charter in an urban area attends a higher SES school while the opposite is true in nonurban areas. This


Kotok et al. 19

is significant considering that the majority of B&M charters are located in urban areas. These differences beg the question whether urban charters do not attract as many poor students, whether they have more difficulty retaining poor students, or even whether they push out more poor students. Because we do not have individual-level data about FRL status, survey data, or qualitative data, this question is out of the scope of this study (see Ni, 2012).

Cyber Charter School MovementThe racial isolation of schools exited by cyber enrollees differs little from the state percentage of each racial group, but tends to differ from the schools of students who moved from a TPS to B&M charter (see Table 4). For example, African American leavers for B&M charter schools left a TPS with an aver-age of 64.5% other African Americans, which was around 13 percentage points higher than the 2010-2011 TPS state average. However, the typical African American student who exited a TPS for a cyber left a school with

Table 3. Exposure to Free/Reduced Lunch Students for Students Transferring From TPS to B&M Charters, 2010-2011 to 2011-2012.

NumberSending

TPSReceiving charter Difference

African American Urban 5,039 82.69 77.84 −4.85 Suburb 397 55.33 70.71 15.38 Rural/town 80 44.02 63.77 19.75 Total 5,516 74.07 76.73 2.66White Urban 654 63.71 52.14 −11.57 Suburb 301 25.78 32.27 6.49 Rural/town 307 34.16 47.61 13.45 Total 1,262 34.98 43.35 8.37Latino Urban 1,715 83.98 80.14 −3.84 Suburb 66 42.22 53.12 10.9 Rural/town 34 40.53 66.21 25.68 Total 1,815 72.39 76.92 4.53

Note. Nine charter schools did not report FRL data for 2011-2012. Therefore, 6% of the charter mover sample was not used in this analysis. TPS = traditional public schools; B&M = brick and mortar; FRL = free and reduced lunch.Source. Pennsylvania Department of Education; NCES Common Core of Data.



53.2% other African Americans, which is only about two percentage points higher in terms of African American students than the average TPS in 2010-2011. On average, Latino leavers for B&M charters left a TPS with almost 42% Latino students while Latinos leaving for a cyber left a TPS with less than 36% other Latino students. White students, who constitute the majority of students leaving TPSs for cyber schools, had a different trend. Notably, on average, White students choosing cyber schools came from schools with greater racial isolation than those White students choosing a B&M charter. However, this disparity may be a function of cyber enrollments being less concentrated in high minority urban districts. In sum, the populations of stu-dents moving to a cyber and a B&M charter differ greatly, with cyber enroll-ees on average coming from schools that closely reflect the state’s school demographics and B&M charter schools attracting African American and Latino students from more racially isolated schools.

Geographically, students choosing cyber charters differed from those choosing B&M charter schools. While around 60% of African American and Latino cyber choosers came from urban areas.around 90% of Latinos and African Americans choosing B&M charters came from urban areas.

Table 4. TPS Racial Isolation Percent Before Transferring to Cyber and B&M Charter, 2008-2009 to 2010-2011 (n = 10,018).

Number of students

Sending TPS to cyber

Sending TPS to B&M

All TPS 2010-2011

African American Urban 1,075 67.28 71.31 66.56 Suburban 542 39.70 51.84 37.39 Rural/town 148 14.04 21.12 12.71 Total 1,765 53.22 64.51 51.61White Urban 835 52.09 44.77 51.19 Suburban 3,032 84.15 79.33 83.50 Rural/town 3,677 91.94 88.49 92.17 Total 7,544 85.29 76.33 85.42Latino Urban 430 50.95 49.92 52.82 Suburban 156 17.00 18.88 17.75 Rural/town 123 13.11 17.98 12.60 Total 709 35.92 41.77 36.19

Note. TPS = traditional public schools; B&M = brick and mortar.Source. Pennsylvania Department of Education; NCES Common Core of Data.


Kotok et al. 21

The majority of White students choosing cyber schools came from rural and town areas—a very different distribution than white transferees going to B&M charter schools. Almost 40% of African American students trans-ferring to cyber charters came from a nonurban TPS, and in these schools their isolation was slightly higher than isolation of African American stu-dents in similar locales across the state. Yet, their isolation was consider-ably lower than their peers leaving TPSs for B&M charters. Most Latino cyber enrollees came from urban areas—like African Americans—and those that left an urban TPS left a school with slightly less racial isolation than that attended by the average urban Latino student. Almost 9 out of 10 White cyber enrollees came from a nonurban district with relatively even shares coming from suburbs and town/rural areas. For each geography, the typical White cyber enrollee left a TPS that had higher isolation than a White transferee moving to a B&M charter. Thus, the considerations for enrolling in a cyber and B&M charter school may differ when it comes to racial diversity. The findings reveal at least some differences worthy of continued examination as cyber schools expand (Frankenberg et al., 2014; Knight, 2005). Moreover, the fact that characteristics of students and the TPSs they leave to enroll in B&M charters and cyber charters differ so much suggest future analysis needs to consider these two distinct popula-tions separately. At the same time, further work should also examine whether there is a segregating effect for those who remain in TPS of both types of movement to charter schools.

DiscussionEarly charter school advocates often viewed school choice as advancing goals of equity (Chubb & Moe, 1990; Frankenberg, Siegel-Hawley, & Wang, 2011; Lubienski et al., 2009), but our findings add to a growing literature about how charter schools actually limit equity by segregating students by race and poverty as well as increasing student mobility. Specifically, our study illuminates the importance of utilizing individual data, conducting a more geographically specific analysis, examining the segregation of both Whites and Latinos in addition to African American students, and finally by considering all charter options including cybers.

Charter schools may be undermining the academic goals of school choice given what we know about the negative effects of racial segregation, poverty concentration and the effect of mobility on various academic outcomes (Linn & Welner, 2007; Mickelson, 2008; Rumberger & Thomas, 2000). Several decades of research indicates that segregating students by race and poverty



harms students academically and socially. In the case of Pennsylvania, both African American and Latino students (along with urban White students), on average, move to charter schools with far more racial isolation than their previous TPSs. These wide disparities are even more concerning given that Pennsylvania is already characterized by highly racially isolated public schools (Kotok & Reed, 2015).

In terms of poverty concentration, we find mixed results based on geogra-phy with African Americans, Whites, and Latinos in urban areas moving to less economically disadvantaged schools in which TPSs are left with propor-tionally more disadvantaged students. Conversely, on average, students of all three racial groups living in suburban and rural areas moved to charters with much more concentrated poverty. The important distinction here is that urban schools had high levels of poverty regardless of school type, whereas, on average, African American and Latino students in suburban and rural areas attended a TPS with a percentage of low-income students that approximated their overall statewide share of students. However, when African American and Latino students in nonurban areas moved to charters, they tended to attend schools with much higher poverty concentration than the TPS they left and thus may lose out on the peer effects and other benefits associated with attending a higher SES school.

Our study also draws attention to the high degree of student mobility in terms of transfers to and from both types of charters, but especially cyber schools. Although our study focused on the difference in isolation for stu-dents moving to charter schools, our descriptive analysis indicated that many of these students move back to the TPSs suggesting a revolving door dynamic. While this type of educational consumerism may stem from a genuine des-peration among families to find the right fit for their child, this high degree of attrition has been found to be disruptive for both the individual and the schools losing and taking in such students (Heinlein & Shinn, 2000; Rumberger & Thomas, 2000). The negative effects of the high student mobil-ity are likely to be the strongest at schools with other types of concentrated disadvantage.

This mobility is exacerbated by the fact that many of charter schools in Pennsylvania perform worse academically than their sending TPS; some, of course, outperform TPS schools (CREDO, 2011). For school choice to fulfill its promise of equity, the trade-off for this increased student transience should at least benefit students through more equitable, racially diverse schools. While there are some examples of successful, segregated minority charter schools, policymakers must also consider the impact on schools that students leave, the less successful charter schools, and the increased segregation and


Kotok et al. 23

mobility in this new marketplace of schooling to judge whether school choice has improved education. As mentioned above, because of the benefits of diverse schools for students of all races (Mickelson & Nkomo, 2012)—that extend beyond the benefits of reducing racially isolated schools—it is impor-tant to understand how the growth of charter schools relates to students hav-ing diverse schooling experiences.

While our overall findings suggest that charter schools in Pennsylvania are associated with increased mobility and racial segregation, this association varies by geography, racial group, and charter school type. These differences highlight the importance of studying within state variation as well as examin-ing school segregation beyond central cities to understand the complexities of charter school sorting in different geographies. In particular, our examina-tion of White students reveals the significance of a more localized analysis. On the surface, it appears that the typical White student choosing a B&M charter in Pennsylvania tends to end up in a more racially and economically diverse school. Yet, when we examined the movement of White students only in urban areas, we find that Whites ended up attending B&M charters with more White students and fewer economically disadvantaged students. Although our study does not capture the reasons that White parents select these charter schools, the trend suggests some parents may be using charters as a mechanism for White flight as a means to avoid more extensive racial and economic diversity in urban TPSs. The fact that Whites in nonurban areas are choosing charter schools with slightly more racial and economic diversity may represent a concerted effort among parents to offer their chil-dren a diverse learning experience, but it may be simply a reflection of Pennsylvania’s extreme district fragmentation which has resulted in TPSs being highly segregated along racial and socioeconomic lines (Bischoff, 2008; Lewis & Hamilton, 2011).

Whereas, charter segregation varied for Whites by geography, African American and Latino students, on average, transferred to more racially iso-lated charter schools regardless of where they lived. Even in rural areas, the typical African American chooser moved to a charter school where racial isolation increased twofold. The findings are also noteworthy for Latinos, who have not been included in as many studies of charter school segregation to date and for whom prior studies were mixed. Overall, we find that on aver-age, Latinos attended more segregated charters compared with their former TPS. Although there was little difference in racial isolation for Latinos in a suburban setting, urban and even rural charters were clearly more segregative for Latinos. As Latinos continue to constitute a growing share of the school population both within and beyond urban areas, it will be important to see



whether suburban charters begin to represent a more isolating choice as they do in urban and rural areas. Therefore, while there is a tendency for charter schools to open in high minority neighborhoods in large urban areas, location of charters alone does not seem to explain disparities in racial isolation as some have suggested. At least in Pennsylvania, this phenomenon of charter schools being more racially isolated for African Americans extends beyond central cities and even metropolitan areas.

Finally, our study makes a critical first step in the conversation on how cyber schools affect racial diversity. We find that students leaving TPSs for cyber charter schools as compared with those leaving TPS for B&M charter schools differ in terms of students’ personal characteristics as well as the racial and economic composition of TPS from which they are transferring. Yet, the characteristics of students enrolling in cyber schools reflect the diversity of the state in terms of race, SES, and geography. Moreover, they come from schools with similar racial isolation compared with their racial and geographic subgroups. We think it is important to consider cyber students in the context of segregation and other equity issues especially considering their role in receiving students across geographic locations and thus poten-tially affecting racial distributions and poverty concentration across a variety of geographic settings. Indeed, as shown in the findings section, these pat-terns are different than those seen in B&M charter schools as cyber charter schools are receiving students from TPSs that demographically tend to match the state average.

Given the minimal empirical work about the extent to which cyber charter schools are enrolling TPS students, whether they are disproportionately more likely to enroll (as well as retain) TPS transfers of certain racial or economic groups, it is difficult to know how cyber schools affect overall segregation. In addition, we do not know how patterns of gentrification in some urban neigh-borhoods as well as the higher mobility of cyber charter school students (RFA, 2013) affect the flow of students in and out of TPS and cyber charters. Thus, future analyses should understand how both types of charter schools, in potentially different ways, affect the diversity or segregation of K-12 students who move to charters as well as who remain in TPSs.

This study has some limitations because we are not able to trace within-year movements and because we lack individual student data on FRL status. Future analyses should look more closely at within-year movers, as the absence of these students likely underestimates how many students are seg-regated by race and poverty at charter schools. Moreover, while our study primarily illuminated the effects for students moving to charter schools, future research should also closely consider students moving back to TPSs.


Kotok et al. 25

These findings have implications not only for choosers transferring to charter schools, but also for nonchoosers who remain in public schools who experi-ence the churn of students leaving and returning to these schools. To resolve some of these limitations, future research could concentrate on a smaller geo-graphic area, delving deeper into the multiple dimensions of movement and factors compelling student movements.

Policy ImplicationsCharter schools and their enrollments are rapidly increasing in many Northeastern states such as Pennsylvania at a time when overall enrollment is generally decreasing. Policymakers in Pennsylvania have embraced school choice as a remedy for failing schools—many of which are located in high minority and high poverty areas (Kahlenberg & Potter, 2012; Scott, 2012). Although Philadelphia has the highest number of B&M charter schools in the state, charters are increasingly opening their doors in other cities, towns, and even rural areas across the state. Moreover, the strong presence of cyber char-ter schools in Pennsylvania makes it such that almost every student in the state theoretically has an exit option if they are dissatisfied with their local TPS. As such, the case of Pennsylvania provides an illuminating context to study as well as a cautionary tale for policymakers as charter schools con-tinue to play an increasingly larger role in public education. While its state policy and legal context may differ from other states, it is useful to under-stand how transfers to and from different kinds of charter schools affect stu-dents’ racial and economic exposure in all regions of the state. Given the long history of neighborhood segregation and hyper-fragmentation of school districts within metro areas, charter schools were once suggested as a model to create more diverse schools rather than more racially isolated ones. This study and other evidence suggest that this is rarely the case, but there are actions that policymakers and educational leaders could take to increase charter school diversity. Indeed, there are some examples of choice that has promoted—rather than stifled—diversity (see Kahlenberg & Potter, 2012; Neumann, 2008, for examples). However, these models of choice promoting diversity are typically the result of carefully designed policies with integration as an explicit goal.

Often, when schools of choice are part of school districts, there is a more centralized approach that oversees outreach and admissions procedures, structures schools in a way that they are welcoming to all, ensures provi-sion of transportation, considers location and academic theme in the con-text of larger demographic patterns, and institutes other district policies that



represent a comprehensive approach to trying to achieve integration. Examples include operating a handful of magnet schools,5 controlled choice policies that affect assignment to all district schools (Orfield & Frankenberg, 2013), and interdistrict magnet schools along with urban-suburban transfer programs (Wells, Warner, & Grzesikowski, 2013). More recently, there is an emerging focus on diverse charter schools, including a network of “diverse charters” that includes several dozen schools across the country.6 Kahlenberg and Potter (2012) illustrated the promise of charter schools to transcend segregation due to boundary lines, but find that they require a high amount of coordination including political inducements for diversity, a strategically located school, safeguards such as weighted admissions for low-income students, and a school culture dedicated to diversity (see also Petrilli, 2009).

If states want to take a more active approach, they could require that charter schools develop a diversity plan before authorizing charters and hold charters accountable for diversity before reauthorizing a charter and/or granting charters for additional schools to educational management organi-zations. Where schools are disproportionately White, for example, charters could expand outreach to communities of color, hire teachers of color, and admit applicants of color from a waiting list, if it exists. Likewise, heavily non-White charter schools could consider outreach, educational programs, and faculty composition to attract White students. However, if charters are unwilling or unsuccessful at such outreach and states prove inept at crafting policies to encourage racial diversity at charter schools, the expansion of the charter school sector may impede other efforts to eliminate racially isolated schools.

Appendix

Students of 2010-2011 Attending Two Schools in Same Year.

All (N = 53,717)

Attended a TPSand B&M Charter

(n = 5,277)

Attended a Cyber anda TPS/B&M Charter

(n = 10,689)

African American 24.2 54.8 16.87Latino 10.4 14.8 6.47White 63.3 28.4 75.15Asian 1.5 1.5 0.77


Kotok et al. 27

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

Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded, in part, through a grant from the Center for Rural Pennsylvania. All conclusions are those soley of the authors.

Notes1. Instead, choice and charter schools have been framed as a civil rights issue to

allow Black and Latino students the opportunity to escape underperforming, seg-regated traditional public schools (TPSs).

2. Though not an explicit focus here, other studies question the extent to which charter schools equitably enroll students from other subgroups including free and reduced lunch (FRL) students (Carnoy, Jacobsen, Mishel, & Rothstein, 2005; Finnegan et al., 2004); ELL students, and students with disabilities (Welner & Howe, 2005).

3. Distribution of movers was fairly evenly distributed across grades. There was a slight increase in sector moves after the 8th grade. Therefore, we tested to see whether the results differed when we ran the analysis separately for k-7th grade and 8th grade students. Because there were minimal differences in magnitude and no differences in the direction, we elected not to provide these analyses for the sake of parsimony.

4. Between 2008-2009 and 2011-2012, 446 school districts—about 90% of all dis-tricts in Pennsylvania—had at least one student transfer from a TPS to either a cyber or B&M charter in the following year. Thus, while school choice options affect some districts more than others, nearly all districts in Pennsylvania contain students who transferred to charter schools.

5. Whole-school magnet schools are much more likely to provide students the opportunity to be in diverse classrooms rather than magnet programs that oper-ate within a school and may result in racially segregated classrooms.

6. The network provides best practices for how to create and maintain diverse char-ter schools and includes research about how to attain academically successful, diverse charters.

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Author BiographiesStephen Kotok is an Assistant Professor in the Department of Educational Leadership and Foundations at the University of Texas at El Paso. His research focuses on the extent that schools expand opportunity or act as a stratifying mechanism. Specific areas of research include school climate, charter schools, and segregation.

Erica Frankenberg is an Associate Professor in the Department of Education Policy Studies at the Pennsylvania State University. Her research interests focus on racial desegregation and inequality in K–12 schools, school choice and racial stratification, and the connections between school segregation and other metropolitan policies. Recent book publications include Educational Delusions? Why Choice Can Deepen Inequality and How to Make Schools Fair (with Gary Orfield), The Resegregation of Suburban Schools: A Hidden Crisis in American Education (with Gary Orfield), and Integrating Schools in a Changing Society: New Policies and Legal Options for a Multiracial Generation (with Elizabeth DeBray).

Kai A. Schafft is an Associate Professor of Education at Pen State University in the Department of Education Policy Studies where he directs the Center on Rural Education and Communities and edits the Journal of Research in Rural Education. He has published widely on rural education and rural development.

Bryan A. Mann is a PhD Candidate in the Educational Theory and Policy program at Pennsylvania State University. His research interests include education policy and politics, online and blended learning, charter schools, and institutional analysis.

Ed Fuller is an Associate Professor in the Department of Education Policy Studies in the College of Education at the Pennsylvania State University. His research interests include: educator quality, distribution, mobility, turnover, and career pathways; edu-cator preparation; school improvement; evaluation; and, charter schools.


School Choice, Racial Segregation, and Poverty Concentration: Evidence From Pennsylvania Charter School Transfers

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