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School choice at the crossroads of race, class, and accountability :
b an analysis of the effects of voluntary school choice on elementary schools in a large district in the southeastern united states
h [electronic resource] /
by Teresa Evans.
[Tampa, Fla] :
University of South Florida,
Title from PDF of title page.
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Dissertation (EDD)--University of South Florida, 2010.
Includes bibliographical references.
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ABSTRACT: Abstract In recent years, the responsibility for the desegregation of American public schools has transitioned from federal court mandates to school board programs and policies. There is widespread belief that this has resulted in the resegregation of schools across the country. One popular policy that is purported to provide the opportunity for voluntary integration, along with accountability for academic quality, is school choice. The purpose of this study was to consider the implications of such a policy in one large school district. There is an extensive body of research exploring who participates in school choice, how they make their choices, and why they choose the schools their children attend. In contrast, this study was designed to investigate the actual choices made by parents and the impact of those choices on the elementary schools in the district. This quantitative descriptive study examined the racial and socioeconomic composition of students in one district's elementary schools during the 2009-2010 school year, and explored the extent to which the student populations in these schools would differ if all students had attended their attendance area schools, rather than participating in the district's voluntary choice plan. The actual 2009-2010 demographics were compared to "counterfactual" demographics for each school. The researcher generated the counterfactual data by removing the students who chose to attend the school and adding back the students who chose to exit the school. These actual and counterfactual demographics for each school were used to compare dissimilarity indices calculated for the district's elementary schools as they actually were, and as they theoretically would have been without the school choice program. Additionally, the quality of the schools parents chose was investigated. The results showed that, in this district, the school choice plan did not impact the level of integration in the elementary schools. The schools were moderately segregated with the school choice plan in place, but were also moderately segregated based on the counterfactual demographics that represented the district without school choice. Most parents (60%) chose high quality schools, as identified by the state's accountability plan. However, parents who chose low achieving schools were disproportionately black and poor. Further research is warranted to determine if the mechanics of the school choice plan could be manipulated to improve the level of integration in the district, and to better understand the decisions made by some parents to send their children to low performing schools.
Advisor: Bobbie Greenlee, Ed.D.
Desegregation dissimilarity index education poverty school quality
x Leadership Development
t USF Electronic Theses and Dissertations.
School Choice at the Crossroads of Race, Class, and Accountability : An Analysis of the Effects of Voluntary School Choice on Elementary Schools in a Large District in the Southeastern United States b y Teresa Craig Evans A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Education Department of Education Leadership and Policy Studies College of Education University of South Florida Major Professor: Bobbie Greenlee, Ed.D. Darlene Bruner, Ed.D. Darlene DeMarie, Ph.D. William Young, Ed.D. Date of Approval : November 9 2010 Key words: desegregation, dissimilarity index education, poverty, school quality Copyright 2010, Teresa C raig Evans
Dedication This wor k i s dedicated to my family: my daughters, Amanda and Elizabeth, because they have always been the foundation of my passion for education; my grandson, Grant, because he represents the future and the importance c hildren; my mom for her understanding, patience and financial support just when I needed it; and especially my dad, for his encouragement and unwavering belief in me. I wish with all my heart that he could still be here to share this accomplishment.
Acknowledgements This work would not have been possible without the guidance and support of my C hair, Dr. Bobbie Greenlee. Her patience, commitment, and support were resolute Her insightful comments and questions were invaluable. And her quick turnaround on the steady stream of very rough drafts that I sent her way was deeply appreciated I also extend much gratitude to my dissertation committee members: Dr. Darlene Bruner, Dr Darlene De Marie, and Dr. William Young. Additionally, there have been a number of special friends and colleagues along the way who have encouraged, sustained, and supported me in this endeavor For that I will always be grateful.
i Table of Contents List of Tables iii 1 Abstract iv Chapter 1: Introduction 1 3 Statement of the Problem 3 6 Purpose 4 6 Rational e 5 7 The Research Questions 7 9 Definitions 9 General terms 9 Socioeconomic status 9 Racial categories 10 10 Attendance area school 10 Charter scho o ls 10 Choice 10 Choice hardship 10 Home education or home schooling 10 Magnet schools 11 NCLB Choice 11 Sc hool c hoice 11 10 Delimitations and Limitations 11 12 Chapter 2: Review of the Literature 12 13 A Brief Legal History of Schoo l Choice 13 13 The Value of Integration 16 Short term a chievement 17 Intergroup r elations 18 Long term results and b eyond 19 16 Resegregation 21 21 Theoretical Framework 23 23 Defining School Choice 25 Themes 27 24 Parent Choices 27 26 Constraints to Choice 31 29 Making Choices 39 36 The Chosen Schools 45 42 Reflections on the Literature 56 51
ii Chapter 3: Metho dology 60 56 56 Design of the Study 61 The d istrict 62 Data s ources 63 Actual and counte rfactual d emographics 65 Measuring segregation 65 The desegregated school 66 The dissimilarity i ndex 67 Measuring school q uality 69 Data Analysis 71 Limitations and Delimit ations 74 Summary 76 Chapter 4: Findings 78 D ata Collection 79 Historical data 79 2009 2010 data 82 Counterfactual data 83 Quantifying integration 83 Quantifying school quality 84 The Res ults 86 Research question 1 86 Research question 2 91 Research question 3 97 Research q uestion 4 102 Limitations 107 Summary 108 Chapter 5 : Discussion of the Findings 110 Findings and Interpretations 110 The research questions 111 Implications 116 Recommendations 117 Reflections 124 Sugg estions for Further Research 125 Summary and Co nclusions 127 References 128 123 Appendix A: Extra Tables 144
iii List of Tables Table 1: Percentage Black Students in Selected Elementary Schools in 1972, 1995, and 2009 81 Table 2: 2009 2010 Count and Percentage of Elementary Schools Based on Per centage Black Enrollment by Decile 87 Table 3: 2009 2010 Count and Percentage of Elementary Schools Based on Percentage of Enrollment Eligible for Free or Re duced Price Lunches by Decile 89 Table 4: Counterfactual Count and Percentage of Elementary Schools Based on Percenta ge Black Enrollment by Decile 93 Table 5: Counterfactual Count and Percentage of Elementary Schools Based on Percentage of Enrollment Eligible for Free or Reduced Price Lunch by Decile 95 Table 6: Actual Enrollment by Race Compared to Counterfactual Enrollment by Race in the lementary Schools 99 Table 7: Actual Enrollment by Free and Reduced Price Lunch Eligibility Compared to Counterfactual Enrollment by Free and Reduced Price Lunch Eligibility in the 100 Table 8: Dissimilarity Indices Based on Race and Free or Reduced Price Lunch Statu s in Elementary Schools Only 101 Table A1: 2009 2010 Enrollment b y Race in Elementary Schools 145 Table A2: 2009 2010 Enrollment by FRL Eligi bility in Elementary Schools 149 Table A3: Counterfactual Enrollment b y Race in Elementary Schools 153 Table A4: Counterfactual Enrollment by FRL Eligi bility in Elementary Schools 157 Table A5: Comparison of Actual and C ounterfactual Demogr aphics 161
iv Abstract In recent years, the responsibility for the desegregation of American public schools has transitioned from federal court mandates to school board programs and policies. There is widespread belief that this has resulted in the resegregation of schools acro ss the country. One popular policy th at is purported to provide the opportunity for voluntary integration, along with accountability for academic quality, is school choice. The purpose of this study was to consider the implications of such a policy in one large school district. There is an extensive body of research e xploring who participates in school choice, how they make their choices, and why they choose the schools their children attend. In contrast, this study was designed to investigate the actual choices made by parents and the impact of those choices on the el ementary schools in the district. This q uantitative descriptive study examined the racial and socioeconomi c composition of students in one 2010 school year, and explored the extent to which the student populati ons in these schools would differ if all students had attended their attendance area schools, rather than participating T he actual 2009 2010 demographics were compared to for each schoo l T he researcher generated the counterfactual data by removing the students who chose to attend the school and adding back the studen ts who chose to exit the school. These actual and counterfactual demographics for each school were used to compare dissimi larity indices calculated for
v have been without the school choice program. Additionally, the quality of the schools parents chose was investigated. The results showed that, in this district, the school choice plan did not impact the level of integration in the elementary schools. The schools were moderately segregated with the school choice plan in place, but were also moderately segregated based on the counterfactual demographics that represented the district without school choice. Most However, parents who chose low achieving schools were disproportionately black and poor. Fur ther research is warranted to determine if the mechanics of the school choice plan could be manipulated to improve the level of integration in the district, and to better understand the decisions made by some parents to send their children to low performin g schools.
1 Chapter 1 Introduction T he notion of school choice, in its variety of forms, has inspired controversy and debate at all levels of American educational and political systems. In reality, certain parents have alw ays had school choice, which was exercised by purchasing homes in neighborhoods where public schools wer e reputed to be excellent and easily accessible, thereby providing their children the opportunity to walk or bike to school with neighborho od peers. Wealthier parents opted to send their children to exclusive private schools and academies. In the early 197 0s many northern school districts developed magnet schools with specialized programs designed specifically to attract suburban, usually middle class w hite students, to urban schools. This earliest form of p ublic school choice was an effort to voluntarily integrate schools and avoid court ordered busing for desegregation purposes. Magnet schools rapidly spread across the country, with more than three thousand themed magnet schools in the United States by 2008 (Magnet Schools of Amer ica, 2009). In recent years, school choice has expanded and taken on new meaning and purpo se. Once again associated with Federal interventions, school choice plans across the country have been developed to address integration and equity issues, but also t o advance education reform for improvement in the education of all students. The No Child Left Behind Act of 2001 (NCLB) includes a choice provision for children whose schools are deemed failing. School choice has become an umbrella under which many differ ent plans
2 operate. By and large, choice plans may include a wide variety of options from home schooling and vouchers fo r private and parochial schools; to magnet schools, virtu al schools, and charter schools; to transfers allowed to other public schools w ithin or outside or more parents for a n individual child. Ye t t hese familial choic es impact the populations of classroom s and school s across the country These decisions are critical because each of these individual choices contributes to the educatio nal landscape for all children. School choice truly represents th e intersection of personal choice and societal needs. During the past sixty years, judicial intervention has expanded the opportunities and choices available to many children, dramatically changing that educational terrain. Before Brown v. Board of Educati on (1954), by law, most children of color attended inferior, segregated schools. This de jure schools in most states. Slowly, and painfully in many cases, American public education began to change. Dozens of cou rt cases across the country and the passage of the Civil R ights Acts of 1957 and 1964, as well as the Elementary and Secondary Education Act of 1965, finally led to desegregated schools during the two decades that followed. Several Supreme Court cases ( Green v. County School Board of New Kent County, 1968; Swann v. Charlotte Mecklenburg Board of Education, 1971; Alexander v Holmes County, 1969; Keyes v. School District No 1, Denver, Colorado, 1973; Miliken v. Bradley II, 1977 ) enumerated specific instru ctions to school districts as to exactly how they were expected to eliminate de jure segregation throughout all facets of school operations. School
3 by the courts because there was no longer a sep arate second class system of schools for b lack children. This Federal intervention proved effective. Whi le in 1963 only 1% of b lack children in the s outh attended schools with any w hite children, by 1973, 91 % of southern b lack children atte nded school wi th at least some w hite children (Thernstrom & Thernstrom, 1997). According to Orfield and Yun (1999), desegregation conti nued across the south, with 44% of b lack students attendi ng majority w hite schools in 1988. More recently however, increasing racial se gregation has been widely reported, with only 27% of b la ck students attending majority w hite schools in 2004 (Orfield and Lee, in Looking to the Future 2005), leading some to suggest that public education in the United States has moved into the post deseg regation era (Brown, K., 2005). Statement of the Problem After many years of dozens of school desegregation cases on the Supreme Court docket, there were nearly ten years of inactivity before the Parents Involved in Community Schools v. Seattle School District No. 1 (2007), was decided. The Parents Involved case, while related to school integration, was not actually about mandatory desegregation, but related to voluntary plans for integrat ion. Dere k Black (2008) suggested that this case illust rates also been suggested that when school districts a chieve unitary status, resegregation is inevit able (United States Commission on Civil Rights, 2007). There is a large body of research and analysis of school d esegregation nationally, and within the different regions and states. However, for integration to be meaningful, it must exist at the school le vel. Consequently, the analysis
4 of integration must also be undertaken at that level. Large scale studies may illustrate trends, but do not illuminate the day to day educational environment for individual studen ts. In their study of 21 school districts, Saporito and Sohoni (2007) f ound that nearly half of socio economic segregation was due to students attending schools ou tside their own neighborhoods. This kind of a nalysis is needed at the school level within districts to truly understan d the racial and socio economic effects of voluntary integration plans such as school choice Purpose By 1972, while under Federal c ourt supervision, the school district in this study was purported to be a model for the desegregation of large school distric ts in the United States (Eitle, 2003; NAACP, 1972). Since being released from court supervision in 2001, the district has offered a variety of options to families which were designed to encourage urban students to attend suburban schools and suburban students to attend urban schools. Unlike some less popular choice plans described in the literature, this resulted in la rge numbers of students attending magnet and attractor schools. The district reported that by 2006 nearly 40,000 students were attending schools other than those assigned by home address. The purpose of this study was to examine the role school choice ha s played in the sch ools after eight years of vol untary integration through the choice plan How does this voluntary movement of 40,000 or more students (k 12) to schools outside their neighborhoods eac h day actually impact the socio economic and racial composition of individual district schools?
5 This study did not address the stated reasons parents cho se a particular school for their children. Many studies have considered how parents choose how they obtain and process information, what they say is important about a school, and how they ultimately make the choice. For this study, the researcher considered only the actual choices families made T he analysis focused on the ra ce and socioeconomic status of t he students who decided to leave their assigned schools, and the impact of tho se decisions on the schools that were exited and the schools that were chosen Rationale The challenge of integrating American society has fallen, to a great extent, on public schools and public schools have met that challenge with varying degrees of success. Public opinion is complex but most Americans support the goal of integrated schools and integration has been seen as the surest way to guarantee the constitutional right to an equal education (Brown, 1996; Carter, 1996; Orfield, 1996). Many studies have found a relationship between integrated schools and achievement (Borman, et al., 2004 ; Crain & Mahard, 1978; Fram, et al., 2007; Hanushek, et al., 2002; Schoefield, 1989). Specifically, Benson and Borman (2010) found that social contexts of race and Penaloz a (2010) found a relationship between racial isolation in schools and the achievement gap between white and minority students in mathematics. The 2007 amici curiae brief presented in support of Respondents Jefferson County Board of Education, et al. and Se attle School District No. 1, et al. (2007), cited extensive research conducted since the Brown decision in 1954, concluding that:
6 a. racially integrated schools provide significant benefits to students and communities, b. racially isolated schools have harmful e ducation implications for all students; and c. race consc i ous policies are necessary to maintain racial integration in schools (Brief, 2007). Additionally, perpetuation theorists have long held that racial contexts experienced while growing up can play an imp ortant role in the development of interracial skills, dispositions, and soc ial networks that carry over in to adulthood (Goldsmith, 2010). As part of an increasingly diverse society, Americans must continue to develop the ability and willingness to live and work together. In this district, the original desegregation lawsuit was filed in 1958. In 1962, the District C ourt found that the school district wa s operating a segregated (dual) system which violated the Fourteenth Amendment to the United States Const itution Nine years later in 1971, the Fifth Circuit Court of Appeals concluded that the requirements of three of the Green factors (transportation, extracurricular activities, and facilities) had been met by the district ( Green v. School Board of New Kent County 1968). However, three other factors to be used in determining whether a school district was unitary, (faculty desegregation, staff desegregation, and student assignments) had not been achieved. The case was remanded to the District C ourt with inst ructions to remedy the deficiencies. Shortly after the Swann decision, in 1971, the District C o urt directed the school board to submit a comprehensive desegr egation plan that conformed to the requirements of Swann, including busing to achieve desegregation in student assignment ( Swann v.
7 Charlotte Mecklenberg Bd. of Education, 1971). This was accomplished and the school district proceeded with a variety of desegregation strategies, including student busin g, for the next twenty years, result ing in all school s meeting the C or racial integration A key part of the plan was sixth and seventh grade centers, and junior high schools for eighth and ninth grade students (Hall, 1992). In 1991 all parties entered into a consent decree allowing the dist rict to reorganize, creating middle schools in place of the grade level centers and junior high schools Ten years later, after two District C our t rulings to the contrary, the a ppellate C that Federal court supervision would cease. A critical factual question at the ev identiary hearing leading to that determination was whether racial imbalances that had developed de jure segregation, or whether instead, the racial imbalances were caused by demographic shifts in the community In the end, the C ourt was convinced that all vestiges of intentional segregatio n by t he district had been eliminated. Given that integrated public schools are indeed a valued American societal goal, and that the Court discontinued supervision of is important to understand how the racial balance of the schools has changed since court voluntary integration plan was implemented. The Research Questions ration, one must investigate the characteristics of the students who have chosen to leave their neighborhood school s Then one must consider the racial and socio economic
8 characteristics of the attendance area school s that were bei ng exited and the schools that were chos en. Thi s was a descriptive study that illustrate d the impact of school choice on the racial and socio schools were chosen as the unit of study because they are traditionally neighborhood based. Middle and high schools have attendance z ones made up of clusters of elementary schools, resulting in a duplication of elementa ry school data. This analysis was essentially a snapshot of the school dist rict eight years after the implementation of the Since each individual choice contributes to the educational landscape of each school, every student in k indergarten through fifth grade who attended a school other than his or her assigned neighborhood elementary school in th e 2009 2010 school year, was considered in this stud y. D emographic d ata ass ociated with each choice was analyzed based on the school that was exited and the school that was chosen and attended A seco nd objective of this study was to consider the academ ic quality (as measured by the s system) of the schools chosen by families. Specifical ly, the following questions were addressed in this study: 1. 2010 racial and socioeconomic demographics, can the elementary schools be described as integrated? 2. Would the distric students attended their attendance area schools? 3. Overall, what impact plan have on the racial and socio economic character of elementary schools in the school district?
9 4. Given that nearly all parents identify academic quality as a reason for choosin g a school, do parents in this d istrict choose high quality schools (as determined by the s tate) and do ethnicity or socioeconomic status appear to influence choice ? Definitions Two terms, de segregation and integration, were foundational in this study. In 1962, Martin Luther King, Jr. contrasted desegregation and integration in the following way: Desegregation is eliminative and negative, for it simply removes these legal and social p rohibitions. Integration is creative, and is therefore more profound and far reaching than desegregation. Integration is the positive acceptance of desegregation and the welcomed participation of Negroes into the total range of human activities. Integratio n is genuine intergroup, interpersonal Stulberg 2008 ). In this study I have attempt ed to delineate between these two terms, which are oft en used interchangeably in the litera ture a nd in common usage. I use d the term desegregation in reference t o government policies, which were imposed upon families in an effort to redu ce segregation. Integration refer red to actions taken by famili es voluntarily, whether they ch ose the action for the express purposes of integration or not. Genera l terms Socio economic status For the purpose s of this analysis, this was a binary term. The two categories were a) students who were eligible for free or reduc ed price school
10 lunches (FRL) and b) students who were not eligible for free or reduced price school lunches. Racial categories For this anal ysis, student categories were black and w hite. These categories were originally used in desegregation cases. While other minorities, primarily Hispanic s, now make up a substantial segment of elementary school populations students in this study were cate gorized as either black or w hite (all other racial groups). The racial and ethnic data in this dist rict were based solely on self reported characteristic s taken from The d ist erms Attendance area school The school to which a student is assigned based on his or her home address. Charter schools Public elementary, middle and high schools that are self governed by boards of directors and offer programs not available in traditional public schools. Choice The umbrella term for school cho ice that includes magnet schools, NCLB Choice, and all other c hoice options in this district. Choice hardship Formerly known as Special assignment this option is an opportunity to submit a request for a school that is at or over capacity. To qualify for choice hardship, parents or guardians must be residents of the district and provide documentation of a hardship. Home education or home schooling Statutorily defined sequentially progressive student instruction directed by a parent.
11 Magnet schools Public elementary, middle and high schools with theme based, technology rich programs. NCLB Choice Federal ly required school choice for students in Tit le 1 schools that do not make adequate yearly progress for two consecutive years. School c hoice A district p rogram that allows students in K indergarten through 11 th grade to choose a non magnet public school with available space. Delimitations and Limitations This study is specific to one large, county based school district in the southeastern United States Student d ata used in the study were from the academic year 2009 2 010. Sch ool gra de data were from the 2007 2008 school year. Only elementary s chools were included in this analysis ; as such, the generalizability of this study is limited.
12 Chapter 2 Review of the Literature Until the 1970s, school choice in America was limited to families who were economically able to send their children to private schools or purchase homes in proximity to desirable public schools. Magnet schools designed to limit or supplement court ordered bu sing were the first real choice for middle and lower income families. The granting of unitary status for many school districts in the late 1990s gave rise to locally designed controlled and open choice plans and charter schools These plans opened new opportunities for many families but scholars and others have expressed concerns that these voluntary plans may attenuate progress in integrating schools. made by a family, for a child from within its ow n social context(s). These individual choices are personal and based on the varied life experiences of the parents and the family. The decision making process, the research, and the final school choice decisions are each as individual as the family that ma kes a choice, yet these choices have a social construct component in which race and class have significant influence individually and collectively for our society. The literature on individual parent choice and the public consequences of those choices revi ewed for this research was diverse and extensive.
13 A Brief Legal History of School Choice School choice in the United States has its foundation in judicial remedies for segregation. In Brown v. Board of Education in 1954, de jure segregation was declared United State Supreme Court offered no immediate remedy for children in these segregated schools and the decision one year later in Brown II ( vague language which had done little to actually move schools along the path to integration The Brown decision s may have been the catalyst s for desegregation, but it w as not until 1968, in Green v. County School Board of New Kent, Virginia that the Supreme Court specifically placed the burden of desegregating schools directly on local school boards. Chief Justice Brennen, in his majority opinion, declared that the time for integrate, including student assignment, faculty and staff assignment, transportation, extracurricular activities, and facilities. This case also up held the responsibi lity of the Federal District C ourts to supervise school board desegregation plans ( Green v Cty. Sch. Bd.,1968). In Swann v. Charlotte Mecklenberg (1973), the Supreme Court took stronger steps to ensure that districts complied with the intent of previous d ec isions, first by reiterating that the Federal district c ourts had full authority to supervise and to craft desegregation orders and second by specifically allowing forced busing for sc hool integration. I n the early 1970s, at the height of judicial inter vention in desegregation, more than 500 school districts across the United States were under Court supervision (Holley, 2004).
14 Morgan v. Kerrigan (1976) is of particular importance in the development of choic e policies. In this case the Supreme C ourt upheld magnet schools as one means of voluntary integration However, the Court limited magnet school enrollment to within generally required of regular schools. The magnet schools down in earlier cases as final acts of intentional segregation these plans had been attempts at avoiding judicial supervision and did not actually allow true choice, particularly for b lack students. expanded remedies to those that marked the end of judicial supervision of desegregation. In Oklahoma Ci ty Board of Education v. Dowell (1991), after only five y ears of desegregation, the C Bd. of Ed. V. Dowell, 1991 ). Th e C ourt went on to say that no further remedy would be required Freeman v. Pitts (1992), the Court allowed partial release from supervision, emphasizing race schools did not necessarily mean that the system required remediation (Ho lley, 2004). In both cases the C ourt focused on the link between lingering segregation and past C onstitutional violations. Where they found no link, continuing court supervision was found to be unnecessary. In Manning (1998), the desegregation case in Hillsborough County, Florida, the school board contended that the presence of some racially
15 identifi able schools in the district was caused by loc al demographic changes. The District C ourt agreed that "a shift in demographics was a substantial cause" in creating the racially identifiable schools, and a few years later the district was released from judic ial supervision. The Missouri v Jenkins (1995) case was somewhat unusual in that the school board of Kansas City filed suit had caused and perpetuated a system of racial segregation in the schools of the Kansas and the remedial order was entered in 1985. Th e Supreme Court ruled that the District C ourt went beyond its authority in requiring that the school district salaries and in ordering an interdistrict remedy for an intradistrict Constitutional violation. Busing across district lines from urban to suburban ar eas would not be required. The C ourt also allowed boards to attribute racial disparity in schools to outside factors such as demographics and socioeconomic characteristics (Holley, 2004). Between 1990 and 2002, 38 districts across the United States achieved unitary status. Of those, according to the Harvard Civil Rights Project, 34 had alre ady begun to resegregate before the unitary status was granted (Holley, 2004). According to Holley, a n examination of the district court resegregation cases reveals many commonalities. They include: (1) the initiation of unitary status proceedings by the defendant school board; (2) a short amount of time after the desegregation order was entered that the school board sought unitary status; (3) a lack of opposition by plaintiffs or the United States to the declaration of unitary status;
16 (4) increasing reseg regation even prior to the formal lifting of the desegregation decree; and (5) arguments by defendant school boards that resegregation is inevitable due to demographic shifts and other factors (Holley, 2004, p.43). One other factor that appears to have pla yed a role in lifting desegregation orders was the C up of the school boards. The presence of African American members on the boards was mentioned in rulings as being a positive sign that past discrimination would not retu rn (Holley, 2004). The Value of Integration School choice policies are often designed as a strategy to promote voluntary integration. Americans have faith that public schools promote social justice and that societal change begins in the schools (Lageman, 1996). During the desegregation era, schools just as education had been counted on in the seventeenth century to shield the civic virtues that would enable the new Republic to survive and thereafter also to teach the attitudes and skills necessary for productive work, so now [after the Brown decision, and beyond] was it being called o n to open equal opportunity to b lack Americans (Lageman, 1996, p. 7). Integration has been seen as the surest way to guarantee the Constitutional right to an and that failure to support forced busing does not equate to failure to support integrated
17 schools. While methods used to desegregate schools may be questioned, most Americans believe in the ultimate goal of integrated schools (Brown, 1996). Research on the effects of integration can be divided int o three main areas: short term achievement, intergroup relations, and long term results. Orfield (1996) found that studies conducted during the fifty years after the Brown decision tended to be simplistic an d look ed only at aggregate school test scores, or scores from only a few years leading to mixed conclusions Crain and Mahard (1978) reviewed literature on b lack achievement and also found that the studies were too limited and only looked at test scores. Part of the confusion surrounding desegregation research arises because academics have frequently not vie wed desegregation from a policy making viewpoint. They have focused on what is l the mixing o f races alone result in higher b lack achievement? That question cannot be answered because in the real wo rld, desegregation is never an Mahard p. 49 ). Short term a chievement M any scholars however, have studied the relationship between achievement and integration. Schoefield (1989) concluded that desegregation did not appear t o have any negative effects on black, Latino, or w hite students with regard to academic achievement. She also found i n her extensive review of the literature, that there were in fact positive resu lts in reading achievement for b lack students in integrated settings. Her research also found that it was the differences in desegregation studies have used sophisticated statistical techniques to better isolate the impact of segregation. A large ( n =1547) study in Florida found that there was an association
18 between the racial segregation of a school and the percentage of students passing the Florida Comprehensive Assessment Test (FCAT) (Borman, et al., 2004). The researchers examined mean differences in the percentage of middle and high school students passing the FCAT in three different types of schools: b lack seg regated, integrated, and w hite segregated. They used multivariate models to consider segregation within the context of other predictors of test performance. In Texas, Hanushek, et al. (2002) found that not only did having a higher pro portion of b lack schoolmates have a neg ative effect on achievement of b lacks, the impact was more adverse on the higher ability b lack students. Racial composition had little effect on lower ability blacks, Hispanics, or w hites. Hanushek et al. (2002) studied three matched panel cohorts in the UTD Texas School Project, with each cohort including over 200,000 students from over 3,000 public schools. Their results suggested that nearly 25% of the achievement gap in seventh gr ade could be attributed to the racial makeup of the school attended. A 2007 study of young children found that in schools with a high p ercentage of ethnic minorities, students had lower gains in first grade reading scores (Fram, et al., 2007). They used hi erarchical linear modeling to examine data from the first two years of the Early Childhood Longitudinal Study Kindergarten Cohort. They also found that these high minority schools had less experienced teachers and that home and family variables were highly predictive of achievement in this study of 3500 children in the southern United States, but that race was not. Intergroup r elations In addition to achievement, improved intergroup relations are an expected outcome of integrated schools. Intergroup relat ions have been noted as important reasons for integration since before the Brown decision. During the 1940s,
19 psycholog lack children showed that they often assign ed positive characteristics to w hite dolls a nd negative charact eristics to b lack dolls (Mintz, 2004). Though his research i ndicated that b lack children in segregated schools in the South and integrated schools in the North were similar in their behaviors, Clark was widely quoted in the popular media. The Washington Po st explaining his influential research, reported that of inferiority in the early stages of personality development in b in Mintz, 2004 p. 304) More recent research has pointed to the issue of within school segregation. Tracking, gifted and advanced placement courses, remedial classes, and even suspension rates reflect concerns referred to as second g eneration integration issues (Winston, 1996). F rom a civil rights perspective, these issues include overrepresentation of minorities in lower track classes and special education programs, underrepresentation of minorities and women and girls in math and science and high achievement programs, testing a nd assessment practices, and racial and sexual harassment (Winston, 1996, p.6). Long term r esults and b eyond A final area of research on integrated schools is that of long term effects, beyond school achievement. These studies focus on social networks, perpetuation theory, network analysis, occupational aspirations, graduation rates, college attendance and graduation, and occupational attainment (Wells, 1996). While research has found that in many cases cross racial ties are weak in integrated schools, e ven these weak links appear to help students on the low end of the social structure scale (Granovetter, 1986). Additionally, stu dents who attend predominantly
20 w hite, middle class schools have significantly greater access to information abou t colleges (even traditionally b lack colleges) and careers (Wells, 1996; Hardy, 2006). Some of these findings were related to structural systems within the school and others were related to social networks (Wells, 1996). The challenge of integrating our society has falle n, to a large extent, on public schools. American schools have met that challenge with varying degrees of success in the years since the Brown decision. In 2007, an extensive brief was presented to the Supreme Court as amici curiae in support of Respondent s Jefferson County Board of Education, et al. and Seattle School District No. 1, et al. The amici curiae were social scientists and scholars who had extensively studied issues related to school desegregation, diversity and race relations in K 12 schools. The amici included 553 researchers from 42 states and the District of Columbia, and 201 different educational institutions and research centers throu ghout the United States, extending across numerous disciplines, including education, psychology, sociology, economics, polit ical science, and history. The B rief presented as amici curiae sup ported three interrelated conclusions based on extensive research conducted since the Brown decisio n in 1954. These conclusions were : a. Racially integrated schools provide significant benefits to students and communities, b. Racially isolated schools have harmful educational implications for all students; and c. Race conscious policies are necessary to maintain racial integration in schools ( Brief, 2007 ).
21 Virtually all of the significant research on desegregation and integrated schools from the past 50 years was summarized and cited in this B rief. Resegregation In the past fort c schools has changed. In 1968 w hites made up 80% of publ ic school enrollment. By 2003, w hite enrollment was less than 60%, with 7 million fewer w hite students than in the late 1960s. Latino an d Asi an enrollment, however, had increased (Bhargava, Frankenberg, & Le, 2008). These statistics might lead one to believe that American schools are more integrated now than in the past, however, resegregation has ensued in recent years (Orfield & Yun, 1999). S eventy two percent of b lack students and 77% of Latino students now attend schools in which students of color constitute a majority. Nearl y 2.4 million students, mostly b lack and Latino, attend hyper segregated schools where 99 % to 100% of the population are students of color. In contrast, only about 1% of w hite students attend such intensely segregated schools. Whites are, nonethele ss, also isolated. The average w hite public school student attends a school where nearly 80% of the studen t population is white. Resegregation for b lack and Latino students in large school districts has been especially significant because many of these districts have very few w hite students and integration plans cannot be successful without a heterogeneous po pulation from which to draw students (Orfield, 1999). Segregation levels are highest in the Northeast, and nationwide, suburban schools are somewhat more likely to be integrated ( Civil Rights Project 2008). In most large s uburban districts, the typical b l a ck or Latino student attends a w hite majority school, though countywide districts such as those in Florida tend
22 to remain more integrated (Bhargava, Frankenberg, & Le, 2008). Orfield (2001) suggests that suburban housing segregation is a problem and that the 2000 census suggests that the suburbs now dom inate our society. Orfield contends that in the mid 1990s America elected its first suburban Congress, and that continuing segregation in the suburbs will present ever i ncreasing challenges to the integratio n of public schools (2001). Residential segregation continues to impede school integration across the United States. Many social scientists are calling on communities, not just school boards, to accept more responsibility for integration (Murphy, 1996). Pa tricia Albjerg Graham (2005), in Needs suggested that Americans are indebted to educational institutions, but that eive in their homes, their communities, and through the media. Those influences, while more important, are much more difficult for a society to regulate, and thus our attention remains upon the educational institutions (Graham, 2005, p.246). Graham went o n to discuss the importance of integration at all le vels of education, and described colleges for example, re persons of color study with w hites and where men and women learn to work together; they are the breeding ground for the new demogra being held accountable for true integration, for closing the achievement gap, and for providing a setting where Americans learn to live togeth er. In concluding, Graham posited
23 designed school choice policies as a volunt ary means of integrating schools may provide that accountability. Theoretical Framework Many scholars have expressed concerns that teacher education re search, and education research in general need s to be more solidly grounded in theory (Cochran Smith & Zeichner, 2005; Johnston Parsons, 2007; Ladson Billings, 1999; Milner, 2008) L adson Billin g s (1999) suggested that education research must focus on how we define and build knowledge, and how we theorize about it. In their analysis of school desegregation law, Tate, Ladson Bil lings, and Grant (1993) referred to critical race theory and its application as foundational in desegregation scholarship Milner (2008) suggested and in particular interest convergence may be a useful tool Critical race theory and interest convergence are a foundation for understanding school choice policies from within a framework of property rights. Stated goals for most school choice plans i nclude equity, equality of opportunity, and/or school improvement. Critique of school choice tends to focus on stratification effects caused by parent choices and/or arguments about whether the economic pressure of choice can improve schools, and thus indi vidual achievement. The focus of this study is the stratification critique as it relates to critical race theory as defined by Lad son Billings and Tate (1995). Ladson Billings and Tate illustrate d their theory through the use of three non hierarchical prop ositions that are equal and central to the overall theory, as in an equilateral triangle. Their three propositions are:
24 Race continues to be a significant factor in determining inequity in the United States. U.S. society is based on property rights. The intersection of race and property create an analytic tool through which we can understand social (and, consequently, school) inequity ( Ladson Billings & Tate, 1995). It is through thi s lens that school choice is explored herein stratification based on race and ongoing school segregation due to the geographical influences of hou sing segregation patterns, time, and distance When the d istrict being studied in this research was granted u nitary status in 2001, 17 elementary schools were found to be outside the racial pa rameters originally set by the c ourt s As in other cases, this was attributed to changes in housing patterns and not to d istrict policies or historic de jure segregation This intersection of race and proper ty was (1980) principle of interest c onvergence, in which he contended that equality and equity were only pursued when the interests of the oppressed converge d with the interests of the oppressors. Bell (1980) explained that the Brown decision only came about bec ause th ere were pragmatic reasons for w hites to gain advan tage from the decision. He listed d countries, the resentment of b lack World War II veterans who returned to discrimination, and the need for transition from a plantation society to industrialization in the South as the interests of w hites which opened the door to Brown (Bell, 1980). In addition to property rights as a function of housing and geography, public schooling and school choic e policies impact intellectual property rights through the
25 varying curriculum provided at d ifferent schools. Access to excellent and appropriate curriculum, along with computer labs, science labs, state of the art technology, and experienced, credentialed teachers has a substantial impa ct on the opportunity to learn This opportunity to learn can be defined as an intellectual property right (Ladson Billings & Tate, 1995). School choice discourse sits at these intersections the crossroads of race and propert y rights and the convergence of interests. School choice is a policy. Policy Cockrell, 2000). In this context, intentions ar e defined as goals aimed at shaping behavior. Transformation of intentions is more specific stated actions taken to solve a problem. Placier, et. a l also suggest ed an even more flexible and less linear pr ocess, whereby actors with divergent or conflicting intentions enter the process at different points and adjust to one another within 261). School choice policies exemplify these divergent and conflicting inten tions and the convergence of varied interests. Negotiating these interests and intentions is necessary for public institutions, especially schools, to thrive in a multicultural democratic society. Defining School Choice In 1955, Milton Friedman fi rst suggested school choice in The Role of Government in Education (Friedman, 1955). T hough a type of choice plan with vouchers was suggested even earlier by Thomas Paine (Wheeler, 1908). Brown (2005) consid ered cant racially neutral educational reform movements sweeping the co
26 policy that is designed to reduce the constraint that current school configurations place on he umbrella for many different school reform plans that promote parent involvement (Bauch & Goldring, 1995). Bifulco, Ladd, and Ross (2009) explain ed place of residence. Finn (1990) advocated choice and suggested six reasons choice is needed in the United States: 1. The alternative is incompatible with American democracy. 2. Choice fosters equality of opportunity. 3. Choice helps parents play their proper roles with respect to the education of their ch ildren. 4. Choice stimulates autonomy among schools, professionalism among teachers, and good leadership on the part of principals. 5. Schools of choice are more effective educational institutions; that is, students learn more in them. 6. Choice is a potent mechani sm for accountability. In this framework, school choice is seen as an economic model for school reform. The analysis of schools as markets is not new (Chubb & Moe, 1988). Weeres ( 1990) addressed the Tiebout Hypothesis as it relates to suburban growth and i ncome. Hoxby (2000) analyzed school choice from this same economic perspective. The Tiebout model, developed in 1956 by Charles Tiebout, allowed Hoxby to consider public school choice with regard to productivity and competition with pri vate schools. Her st udy suggested that areas with Tiebout choice have more productive public schools and less private schooling. She found no significant difference between low and high income families or
27 between minority and non minority families. In a response to her study in Economist, it is clarity, its empirical thoroughness, and its wonderful ingenuity in finding ways to answer Economist, 200 0, p.78). Archbald (2004) referr ed to the economics assumes that school choice would be accessible to all regardless of income, race, or ethnicity from information to transportation. There is public support of school choice, though in Gokcekus, Phillips, and Congressional voting patterns, that support was not reflected in Congressional campaign contributions, and thus not reflected in Congressional votes. In their study they found that higher proportions of Afr ican Americans in a district were related to a higher probability of Congressional support of school choice. Nationally, school choice plans appear ed to appeal to African American parents more than other s (Cooper, 2005). Themes Three general themes were addressed as background for this study: When families engage in the work of school choice, what choices do they have? Who engages in the work of choo sing schools for their children and why? What schools a re chosen and why? Parent Choices The alternatives that make up school choice plans across the United States vary. They range from very limited controlled choice intradistrict designs to homeschooling. Interdistrict or cross district plans were imposed by Federal courts in some districts,
28 including St. Louis and Kansas City Minnesota had a statewide cross district option, and several predominantly rural states, including Vermont New Hampshire, and Maine, had extensive cross district participation, though not always with desegregation as their purpose. These plans allow ed students to attend schools across district l ines and funding usually followed the students (Maddaus, 1990; Martin & Burke, 1990). For some i nner city minority students, interdistrict choice may be the only option for achieving an integrated public education but according to Liu and Taylor (2005), only about 1% of public school students participate in this form of choice Intradistrict, or withi n district plans allow parents to choose among district schools rather than assigning children based upon residence location. These plans are and enrollment in each school may be based on racial and ethnic ratios that must be maintained by allocating seats in proportion to community ethnicity, with socioeconomic balance sometimes required (Liu & Taylor, 2005; Martin & Burke, 1990). In some plans the choices are district wide, while other plans require students to choos e within particular catchment areas. These intradistrict open or controlled choice plans are relatively new in the United States, designed in many cases as part of the release from court supervision. Magnet schools however, are an exception; being an intr adistrict strategy that has been used since the 1970s for voluntary integration. According to Waldrip, of the Magnet Schools of America organization, a few school districts experimented with organizational designs and specialized curricula in the late 1960 s and early 1970s. One such school, the Performing and Visual Arts School in Houston, Texas was once described as working in
29 Federal legislation related to fiscal assistance to schools (MSA, 2009). Boston was the first city to extensively use magnet schools for voluntary desegregation (Gelber, 2008). Magnet schools can be whole school or within school program designs, and are usually developed around a specialized academic th eme or alternative curriculum delivery model. They are usually open to all students in a district, but may have entrance requirements such as auditions for performing arts schools or academic requirements for gifted academies. The Federal government define special curriculum capable of attracting substantial numbers of students of different racial Magnet schools were funded by the Federal Emergency School Assistance Act (1972) from 1972 to 1981. Then funding became a part of the Chapter 2 block grant pro gram. Explicit Federal support as a means for desegregation, resumed in 1985 in the Magnet Schools Assistance Program as part of the Education for Economic Security Act (Rossell, 2005). A s part of this p rogram, most magnet schools were required to meet specific racial quotas (Martin & Burke, 1990). By 1981 there were nearly 1000 magnet schools, and by 1991, over 2400 such schools in 229 different districts in the United States, making them the most common type of school choice based on number of districts and number of children involve d (Archbald, 2004; Rosell, 2005). Charter s chools legislation began in the late 1980s, allowing educators to create independent, but publicly funded, schools with specialized programs that are a rapidly growing choice option. Goldhaber (1999) refers to ch schools are often developed to meet a perceived need for specialized instruction for particular types of students such as those with learning disabilities or gifted students.
30 States differ in requirements, but in general, s uccessful charter schools are released from some regulations that traditional public schools must follow. This freedom distinguishes charter schools from magnet schools (Martin & Burke, 1990; Teske & Schneider, 2000). Private and parochial schools enroll a pproximately 10% of Ameri mostly those of K indergarten and elementary school age. These schools are privately funded and receive minimal public financial support except for some transportation and special education programs (Martin & Burke, 1 990). Chubb (1987) suggests that private than f rom actual excellence in educating students The No Child Left Behind Act of 2001 (NCLB) amended the Elementary and Secondary Education Act of 1965 (ESEA) in a number of areas to strengthen choice and parent involvement in education. According to the U.S. Department of Education, No Child Left Behind is based on stronger accountability for results, more freedom for states and communities, proven education methods, and more choices for parents ( Choices for Parents ). NCLB Choice is a significant part of the Federal accountability legislation for Title I schools. This legislation establishes the right of parents whose children are assigned to a school in need of improvement to choose a better school. The choice however, is limited to other schools in the same district. This provision became effective in 2002 and participation rates have been very low (Howell, 2006; Liu & Taylor, 2005). In voucher plans, students receive tuition certificates, which may be redeemed at value of the voucher is usually based on a
31 whether private schools are included in the choices and in who is eligible to receive the vouchers. In universal voucher plans w ith no income requirements, all students theoretically have access to all schools. In targeted plans designed with the explicit purpose of integration, usually only poor and minority students receive the vouchers which allow then to attend better public sc hools and private schools that they could not afford without the subsidy (Liu & Taylor, 2005). Home has grown rapidly in recent years. Different states regulate home schooling to g reater or lesser degrees and there are differences in levels of cooperation between public schools and home school families. Constraints to Choice Much of the literature suggests that the re are equity concerns with choice plans (Andre Bechely, 2005; Archbald, 2004; Bastian, 1990; Fuller & Elmore, 1996; Goldhaber, 1999; Hardy, 2006; Hoxby, 2000; Liu & Taylor, 2005; Metz, 1990; Orfield, 1996, 1999; Smreker & Goldring, 1999; Weeres, 1990). The factors that inhibit choi ce plans from fulfilling the lofty goals of desegregation, parent empowerment, and equity fall into three main categories: the availability of quality alternatives from which to choose, access to appropriate school information upon which to base school cho ice decisions, and the time and distance limitations related to housing patterns and school locations (Hastings & Weinstein, 2008). In contrast, Bast and Wolberg (2004) describe these same factors as reasons why parents should, f rom an economic perspective be expected to choose appropriate schools for their children. They describe d these three
32 and ben play a role in each area and one signi ficant constraint to choices that parents have is the availability of quality options (Dillon, 2008; Liu & Taylor, 2005). Liu and Taylor, in the Fordham Law Review (2005), suggest ed that while and Goldhaber (1999) specifically questioned whether any of the alternatives to attendance area school choice plan, including the NCLB Choice mandates, there must be qu ality alternatives for the choices to matter Neild (2005) suggested that in many urban areas, there are few schools that represent a real improvement over the failing school from which a st udent transferred. She concluded achieving stude nts, high school choice was Real choice depends not just on having quality schools in the plan, but having actual available seats in those schools. Constraints such as overcrowded schools in fast growing suburbs, racial quotas, and resistance to full participation by suburban schools limit the effectiveness of choice plans. Neild (2005) found that Philadelphia parents did not know that acceptance rates at different types of choice schools var ied from 43% at magnet school s and 38% at vocational schools; and averaged only 20% at the lottery based programs A ctual acceptance in these programs was as low as 0% in individual schools (Neild, 2005). So while there are theoretical choices, in reality, choosing and getting many highly sought after schools or programs are not realistic options. Real differences among schools and the limited available seats in highly sought after school reduce the impact of choice policies.
33 In the real world of school cho ice, the quality of information parents have about schools is important and can have significant impact on the choices made (Schneider & Buckley 2002). Howell (2006) concluded s presented are the options that parents want and then that parents learn about their existence and have the information needed to take (1990), there is an assumption that parents would gather extensive information and make important in attracting students, yet a significant body of research indicates that parents hey need and that available data is not user friendly (Lubienski, 2007; Schneider & Buckley, 2002). Howell (2006) surveyed Massachusetts public school parents and found that while ow that of eligible students in Massachusetts took advantage of NCLB Choice in the 2003 2004 school year. Nationally, only about 0.1% participated that year (Casserl y, 2004 in Howell, 2006). In her 1996 research, Neild conducted semistructured interviews with 19 parents of eighth grade students in Philadelphia. The intradistrict choice plan in Philadelphia allowed all eighth grade students to apply to the 150 differen t schools and programs in the district for high school. About 65% of eighth grade students participated in the choice plan, with variation by neighborhood ranging from 50% to nearly 100% out their choices was from a booklet provided by the district with short descriptions provided by the
34 schools themselves. No data were included regarding student population characteristics, graduation rates, achievement levels, or the proportion of applica nts admitted to the into the system and that their ability to manage the process dwind led (Neild, 2005). She concluded due to lack of information (Neild, 2005, p. 287). Van Dunk and Dickman (2002) found a similar lack of parent information and understanding in Milwaukee. Early choice plans in Milwaukee began in 1976 and have continued to evolve since then. Choices for Milwaukee parents are extensive and include interdistrict public school choice, intradistrict public school choice, and tuition based private school choice (Rouse, 1998). Van Dunk and Dickman (2002) surveyed 678 Milwaukee pa rents whose children participated in the choice program by phone in 1999, percent of students who qualify for free or reduced price lunch, who read at or above grade lev identified the correct range of percentages when compared to the actual school data (Van Dunk & Dickman, 2002). This study seems to contradict the research cited by Bausch an information catches up to that of higher income families and that socioeconomic status may play a less significant role in school choice than originally predicted. Van Dunk a nd Dickman (2002) also asked parents about what factors they consider important in choosing a school and what information they gathered. This study did find a high degree of consistency between what parents are looking for and the data they gather. There w as
35 variation (65 % to 87%) depending on the type of choice program, but not on individual characteristics of the parent (Van Dunk & Dickman, 2002). In his study of choice plans in Holland, Michigan, Lubienski (2007) found that it appears that most schools do not market their services in ways that would indicate that education in a competitive environment exhibits substantial search scores for some of the schools, there was essen tially no effort to inform achievement test scores may fulfill a role as proxy information for a hypothetical rational consumer seeking to find the most effective schools, they pr obably tell that consumer more about who attends the school rather than what value added effects the school has for current or future students (Lubienski, 2007, p.131). Lubienski (2007) also found that the charter schools in Holland, Michigan, which admit ted fewer students with disabilities and minorities, were growing. The se schools had test scores that were comparable to the nearby public schools that work ed with less advantaged students, suggesting that the public schools were actually more academically effective. Raw test score data is a poor indicator of school effectiveness for parents. Magnet schools, however, tend to show higher achievement than regular public schools, even when controlled for student ability and family background (Orfield, 2008; R ouse, 1998). There are concerns that these schools have an incentive to market only to the most preferred consumers middle and upper class students with high test scores thus racializing the process and increasing stratification based on disabilities (Howe & Welner, 2002; Lubienski, 2007). Based on research in Massachusetts, Howell (2006)
36 suggested that there was no incentive for school districts to fully inform parents about low performing schools and their NCLB Choice option s. He advocated the distributio n of information directly from state or even Federal sources rather than from districts themselves. information search. They refer red specifically to the content based digital divide in which there was a lack of not only access and hardware for low income consumers, but also appropriate content such as local information sources about services like employment opportun ities and low income housing, as well as barriers due to langua ge and literacy. One question proposed in their study was whether school based content was sufficient to draw an underserved population into using modern information technologies, thus crossing the digital divide. Schneider and Buckley reviewed two nationa l school related internet sites, Empowering Parents for Informed Choices in Education (EPIC) at www.uwm.edu/EPIC and GreatSchools.net as well as DCSchoolSearch.com and how th ey used the information on the site. Significantly, this study did not rely on schools. DCSchoolSearch.com went beyond just providing information on the internet. The y used extensive measures to reach out to underserved parents in the community. They hired a public relations firm, entered into partnership with the Washington, D.C. bus system to put posters on hundreds of buses, presented a slide show in movie theaters, and arranged for extensive press coverage in The Washington Post and on local television stations. Additionally, staff members made numerous presentations at community,
37 church, and school meetings and fairs. These efforts during the 1999 2000 school year resulted in only 8000 hits, with 1567 unique individual users identified, leading the staff to conclude that even their extensive efforts had achieved only limited access with income population. The Sch neider and Buckley (2002 ) study concluded that more than 80% of the users on the site were regular internet users, not new users from the u nderserved population. They found that nearly all users signed on from home or work, not libraries or other free public access points. Additi onally, most users reported having a college education or even graduate education, not at all congruent with the education levels of underserved D.C. residents. of educationa Andre Bechely (2007) considered choice from a geographic perspective. She re analyzed data from her own two year ethnographic study of school choice in California and concluded that Rossell (1985) was correct school iss ues identified by parents as important were much the same as for real estate location, location, location. Andre geographically, through the lens of district organization and processes. She found that h istoric racial inequalities were pervasive in developing and implementi ng district policies. She quoted Pu lido (2000), in describing how w hite privilege impacted the district environment, Landscapes are artifacts of past and present racisms, they embody generations of s ocio larly, w hite privilege, as a form of racism, is spatially expressed, indeed it is partially contingent upon a particular set of spatial arrangements (p. 16).
38 Andre Bechely argued that school choice has a geographical component ba sed on residential patterns, school attendance zones, and man made boundaries such as highways and even certain businesses. Andre Bechely (2007) found that parents took choice seriously and made school choice decisions within the context of transportation and time. The resources required for stubbornly embedded themselves into the socio (Andre Bechely, 2005, p. 301). Liu and Taylor (2005) suggest ed to desegregation has been racial isolation in housing. Lubienski (2007), in his analysis of school choice through a marketing lens, found largely in terms of geographical proxim ity, where different schools are situated within policy and physical infrastructures that allow them to compete for a common pool of f geography and demographics were also discussed by Hardy (2006). He sug gested that in so me urban areas, schools that were economically different may be hours away on a bus. Other areas, however, are better suited to real c hoice. Hardy specifically referred to Louisville, Kentucky and Wak e County, North Carolina as districts where economic and or racial integration through choice plans was the size and structure, along with the demographic history, of a district that had a significant impact on the geography of choice plan s. In reality, information, availability of seats in good schools, and geography school choice, she used Geographic Information Systems (GIS) mapping technology a nd
39 a set of well defined assumptions to determine that the potential of a choice plan is limited by geography, capacity, and the shape of district boundaries. She looked at Florida, California, and Texas schools, determined whether they were higher perform ing or lower performing based on state test scores, and then estimated the driving time from the lower performing schools to the higher performing schools. The higher performing schools had to have the capacity to increase enrollment by at least 10% to be considered as an option. Based on this analysis, the true availability of choice varied tremendously across states, districts, and the location of the school s rural, suburban, or city. Since most choice plans are promoted as strategies to integrate schools and low income and minority students concentrated in urban areas stand the most to gain from choice, Dillon (2008) compared access to choice by student characteristics and found that these disadvantaged students were more concentrated and faced more comp etition for spaces in higher performin g schools than did w hite or higher income students. Making Choices Research shows that parents, when given the opportunity, take the work of school choice seriously. The parents who engage in school choice vary by rac e and income and there are significant differences across states and districts (Laiereno Paquet & Brantley, 2008). Systems that encourage choice, however, must overcome decades of school assignment based on residence and mandated by school district policie s. Chubb and Moe (1990) suggest ed that In a system where virtually all the important choices are the responsibility of others, parents have little incentive to be informed or involved. In a market based system, much of the responsibility would be shifted t o parents (their choices
40 become informed and involved would be dramatically different (in Buckley & Schneider, 2002, p. 452). While there may be a lack of quality information a vailable to parents Bast and Walberg (2004) support ed choice and purport ed that parents were hospitals, homes, automobiles, food, and many other complicated and expensive goods less than adequate information. Andre Bechely (2005) suggested that just providing choices for parents is simplistic and ignores the race and class issues that impede integration. Parental decision making in school choice is a complex relationship that d evelops between parents and educational institutions that have historically not been equitable, especially for minority, poor, and non native English speakers (Andre Bechely, 2005; Kraus, 2008). In contrast, the literature indi cated that many parents were a ble to discern which schools were good and did attempt to send their children to those schools. Conversely, Howell (2006) found that parents of children in low performing schools often lack ed adequate information, even such ba sic information as whether the school made AYP, to make rational choices. One real concern about the impact of stratification and its self sustaining tendency is that not all parents have the same level of information on which to base decisions. For examp le, 57% of Massachusetts parents of children in high performing schools knew whether their school made AYP, while only 29% of parents of children in low performing schools had the same information (Howell, 2006). In that same survey of Massachusetts parent s, when asked to list schools they would prefer, those parents with
41 children in underperforming schools were three times more likely to choose other underperforming schools than parents of students in higher performing schools. He also found that parents b be better than they actually were and concluded (2008) found that wh en given direct information about the academic quality of schools, low income parents chose the higher performing schools. Schneider and Buckley (2002) found that parents who actively sought out information on schools from the DCSchools website were daily internet users and had access to the internet at home and a t school. This implied that low income or unemployed parents may not seek out official information to assist them in choo sing schools. Neild (2005) cited extensive research in proposing that crit e ria for evaluating schools varied by race and social class. Parents in her study often cited the negative reputation of a particular high school. Many of the parents based their judgments of school quality on observations of how students behaved coming and going from school and the presence of police cars in the area. Most knew little about the curriculum (Neil d, 2005). She did, however, find that the very process of choosing schools help e d parents to reflect on and to consider what makes a school good and what they really want ed for th eir children (2005). Neild discussed it applied to how parents manage d the external world fo r their children. She emphasized the particular importance of parental management of the school choice process as urban students enter ed high school. Mothers with better education were more able to steer their
42 children through school choice and the placement and tracking processes of high schools (Howell, 2006; Neild, 2006). Parents who were non native English speakers had additional barriers to participation in choice plans (Howell, 2006; Kraus, 2008; Neild, 2005). In addition to limited information availa ble in their home languages, immigrants often had fewer knowledgeable friends and family members to turn to for help. Immigrant families are also less likely to participate in neighborhood and s chool activities where they had the opportunity to d evelop soc ial contacts that had information about schools. In a study of New York City schools, it was found that many poor and immigrant families did not join parent organizations because they did not identify with the hierarchical nature and middle class norms tha t dominate these groups (Jackson & Cooper, 1989). These cultural differences, coupled with language difficulties in understa nding official information, made participation in ch oice programs virtually inaccessible for some immigrant families. In their stu Ross (2009) analyzed which students were opting out of their assigned schools. White students with highly educated parents were eleven percent more likely to leave neighborhood schools, as w ere students who lived the farthest away from their assigned schools. At all grade levels, students with college educated families were more likely to choose schools which were more stratified on the basis of class. I n considering whether choice wou l d lead to improved educational o utcomes, Goldhaber (1999) echoed est convergence when he suggested that it was useful to remember that
43 the free market, under certain conditions, guarantees efficiency but it does not guarantee equit y, and market efficiency refers to the maximization of utility, or happiness, not the efficiency of educational delivery. This implies that only if what maximizes parental happiness coincides with what we would consider educational quality will competition bring out efficiency of educational delivery (p.23). Goldhaber (1999) also found that parents were able to identify quality schools and sent their children to tho se schools. However, he cautioned that enrollment in choice schools was dependent on socioec onomic status and race/ethnicity. Educated families were much more likely to participate in choice programs (Bifulco, et. al., 2009; Goldhaber, 1999; Howell, 2006). Saporito and Lareau (1999) found that higher s ocioeconomic status families were likely to leave schools with a high percentage of poor stude nts, but that poorer students did not appear to be influenced into leaving by increased levels of poverty in a school. They also found that w hite students tend ed to leave sch ools with a high proportion of b lack student s, but that the proportions of black and white students did not appear to increase the likelihood of b lack students leaving. Being better informed may play a role in the decision to leave a school. Native born parents, those who own their homes married parents, and mothers with higher levels of education were associated with more information about NCLB Choice in Massachusetts. Parents who volunteer ed in the schools or attend ed religious services were also more informed. Parents of special needs children were somewhat better informed, but not at statistically significant levels (Howell, 2006; Teske & Schneider, 2000). These findings suggest ed that choice could
44 options from which to choose are strongly shaped by the wealth, ethnicity, and social ed four propositions drawn from empirical research: 1. Increasing educational choice is likely to increase separation of students by race, social class, and cultural background. 2. Greater choice in public education is unlikely, by itself, to increase either the variety of programs available to students or the overall performance of schools. Coupled with strong ed ucational improvement measures, however, choice may increase variety and performance. 3. Details matter in the design and implementation of choice policies. 4. Context matters in the design and implementation of school choice policies (p.39). Howell (2006) concl uded that paren more (1990) reviewed the literature surrounding school choice and identified several negative issues with regard to equi ty. She noted the possibility of students and teachers being o that good schools got better and poor schools go t poorer; competition may result in a stratifying effect; parent involvement may actually decrease due to distance; changing enrollme nt may lead to program changes that erode the desegregation impact; public relations may win out over substance; and standardized testing may gain more significance as schools compete d (Bastian, 1990).
45 While research does suggest that choice can lead to s tratification, that stratification can be difficult to measure due to the multidimensional nature of the social variables that are being considered (Andre Bechley, 2005; Bastian, 1990; Goldhaber, 1999). Metz (1986, 1990) looked specifically at magnet schoo ls. Because magnet schools are purposefully different, they sometimes attract attention and are sometimes subject to questions about equity. Metz (2003) argued that suburban and city schools, there is enormous irony in the anger that magnet schools She acknowledged that magnet schools sometimes attract ed more p rivileged students, but contended that the absence of magnet schools would not guarantee equity across a di strict. Arc hbald (2000) explained tha t schools in districts that had school choice may be stratified on some measure of student characteristics, but so are schools in districts without school choice. Smreker and Goldring (1999), in their study of St. Louis and Cincin nati schools found that there was a racial balance in magnet schools that reflected the district norms, but even then, magnet parents were much hig her in education and income. Thus, the creaming effect that Metz (1990) discussed was still a concern. The Ch osen Schools The most common type of school choice in America, historically and currently, is the unofficial choice of schools based on place of residence. Advocates of voucher plans often allude to this middle and upper class option in promoting their pl ans. Vouchers and school choice plans in general, are often developed in an attempt at leveling the playing field for families of all races and income levels, allowing everyone to choose schools as middle and upper class families already do ( Rouse, 1998). Holme (2002) found that the
46 argument for subsidizing school choice for poor a nd minority families was compelling in par t because wealthier families were already reaping advantages granted by the government through mortgage interest and real estate tax dedu cti ons. She studied parents who were h igh status parents, almost all w hite, who intentionally chose to move to particular neighborhoods so their children could attend the publi c schools in those areas. Her 002, p. 179). Her research shed light on how these middle and upper income parents made decisions within some of the same information constraints as poor and minority parents. As previously discussed, the lack of useful data about schools can impede y to m ake appropriate choices and f ew parents relied exclusively on official i nformation M ost consult ed friends and relatives. College educated parents were more likely to have wide social contacts, including those within the school system. Lower socioeconomic status parents were more likely to depend on relatives (Neild, 2005). While middle and upper class parents may have had more access to school personnel, the internet, and other official resources, Holme ( 2002), found that this information was seldom influential in their choices. The upper and middle class families she interviewed based their assumption s about school quality on the social status of other parents who chose those schools for their own children. They actively sought insight from their social networks, not about curriculum, instructional effectiveness, or achievement data, but about which sc
47 status parents, school reputations were not only transmitted through social networks, they were actually constructed by the networks. attributing the motivation, behavior, and the academic ability of students to their race and with high minority or low socioeconomic status populations and choose wealthy w hite schools (Holme, 20 02). These status ideologies were a missing link in much of the school choice literature. When surveyed parents indicate d the criteria they use d to choose schools, they indicate d academic qu ality, discipline, or location and there has been little analysis of exactly what those criteria mean. There is an assumption that the criteria have textually associate minority students with poor achievement, allowing them to justify avoiding integrated schools in an effort to meet the academic needs of their own chil dren, rather than seeing their choices as racially biased. Nearly a third of the parents Holmes for her to recognize Not all unofficial school choice is limited to upper income families, but w hites are significantly more likely to have moved to their neighborhood f or that particular school (42.7%) than are people of color (21.9%), and school choice based on place of residence is also related to age, income, and education (Goyette, 2008). Some of the lower income art of town to avoid schools they
48 mobility was due to the fact that they lived in rented apartments which allowed them to choose schools based on geography much as mid dle and upper class parents do. It was also common among these poorer families to send children to live with relatives in preferred schools (Neild, 2005). Studies of have found some mixed results, but in general support the stratification critique. Early studies generally depended on survey responses from parents with regard to what criteria were i mportant in guiding their choices of schools for their children (Kleitz, et.al., 2000). The flaw in these studies is that respondents tended to give socially and politically correct, expected answers on the surveys, but then made actual school choice decis ions privately and the choices often did not match the survey responses (Tedin & Weiher, 2004). Bausch and Goldring (1995) studied the characteristics of families who participate d in choice plans and their reasoning. In their analysis, they found that most parents (86.2%) in their sample of 575 parents of 12 th grade students stated that they chose a school for academic reasons. However, chi square analysis indicated that minorities were more influenced by transportation and proximity issues and lower socioe record or reputation (Bausch & Goldring, 1995). oice data. Saporito and Lareau (1999) studied how families chose schools within the framework of school choice in a large
49 urban district. They were able to utilize the total population of applications for the transfer program as eighth grade students moved into high school, using actual choices, not attitudes. They also individually interviewed twenty of the applicants. The district was representative of many urban districts in the United States, but did include a relat ively large population of poor w hite s tudents and a relativel y large population of affluent b lack students, allowing them to better separate the issues of race and class in their study. They specifically set out to find which factors truly influence d school choice decisions. Unlike some other studies, they found wealthier and poorer families participat ed in the open choice program in similar proportions, though wealthier families were more likely to apply to magnet programs, as has been reported in much of the literature (Hausman & Goldring, 2000; Smrekar & Goldring, 1999). They found that race played a significant role in the choice s parents made. Their results did not support the proposal that school choice can increase integration and improve eq uity. In fact, they found that whit study most of the variation in w hit explained by the proportion of b lack students in the school. Even though there was a wide range of schools in the distr ic t with a wide rang e of different characteristics race was the only school characteristic that explained w housing segregation, and the resulting distances to different schools could be masking the geo graphic constraint to choice for these w hite families, but found that race researchers found no relationship between rac e and school preferences among b lack families, though in the interviews, most expressed a belief that diversity is a strength in
50 schools. Blacks did have a modest tendency to avoid schools with a high proportion of poor students. Saporito and Lar eau (1999) also looked at the process by which families made sc hool choice decisions. They found that, contrary to some other research, schoo l choice was a multis tage decision particularly for w d ltiple factors. In this study, w hite families first eliminated sch ools with a high proportion of b lack students and then, in a second decision stage, considered various attributes of the remaining schools. Black families also used a two st ep process, but there was no single criteria that led to elimination of certain school s from consideration. Overall, black and w hite parents interviewed in this research seemed to focus on bus routes, school safety, and even sports progra ms, but very little on academics. Like Holme (2002), Saporito and Lareau (1999 ) strongly advocated additional s chool choice research that looked at social factors which influence d choices families mad e for their children and suggest ed that it should not b e assumed that school choice was merely an indi vidual decision. The choices were contexts, and these choices and their reasoning were as diverse as the soc ial contexts from which they were developed (Cooper, 2005; Hausman & Goldring, 2000). For example, Neild (2005) found that it was common among less educated parents to encourage attendance at a vocational school to develop skills to fall back on. She illustrated this strategy with a discussion of a student who wanted to b e a doctor, but chose a high school cosmetology program with the belief that she could make some
51 money doing hair to help with her graduate education. Schneider, et.al (1998) found that parents with dif ferent socioeconomic levels and rac es had different va lues and ch ose schools for different reasons. They quote d the difference in values between those of different social classes. They emphasize d some important differences, such as lower socioeconomic status paren ts wanting their children they believed help ed their children gain acces s to the middle class. There were also cultural differences in preferences in other areas of school reform such as open classrooms and other progressive education p ractices. Different parents had different preferences not only for racial balances but for other attributes of the school system as well They found that school attribut es were indeed socially constructed. For example, parents with less education value d test scores as representing academic quality. They also look ed for strong discipline. More educated parents tend ed to look for more abstract indicators of quality (Cullen, et.al., 2006; Schneider, et.al., 199 8). Much recent school choice literature has been developed around race, class, and other social factors. Goyette (2008) used multivariate models to separate the effects of race, social background, and area of residence. She found that people of color cons ider ed more school choice options. In contrast to Smekar and Gold ring (1999), Goyette found non w hites as like ly to choose magnet schools as w hites. Sikkink and Emerson (2008) school choice. They found that the racial population proportions of schools were important predictors of the choices of highly educated w hite parents. Sikkink and Emerson (2008) postulate d that t so imbedded in everyday lif e that
52 parents participate d in actions that sustain ed inequality without intending to do so. They consider ed the interactions of education, race, and school choice from within the context of education as a resource a property and an important form of socia l capital. Education as property an d its intersection with race, was at the heart of Billings and theory. Highly educated w hites may see high quality education erson (2008) also cl aim ed that these highly educated w hite parents assoc iate d increasing proportions of b lack students with low quality and low status schools. They argue d that school choice provide s a new avenue for white flight. Highly educated w hite par ents can use the system to move their children to schools that are more segregated by race (Bifulco, et.al, 2009; Sikkink & Emeron, 2008). In his 2003 research, Saporito studied magnet school applications in Philadelphia to better understand how race and c lass were related to school choice. In four different analytic models he used regression analysis to clarify choices families made in applying to various magnet schools. As in previous research, he found that w hite families were much more likely than non w hite families to attempt to leave schools with a high minority enrollment; non poor families were more likely to leave high poverty schools at a greater rate than poor families; race was still a str ong predictor of w hite family behaviors, even when economi c conditions were controlled; and school quality, as measured by test scores did correlate with the likelihood that students would leave a school, but school quality factors did not reduce the influence of race (Saporito, 2003). The tendency to choose schools with student popu lations that mirror their own was n ot entirely limited to wealthy w hite families (Bifulco, Ladd, & Ross, 2009 ). Neild (2005)
53 interviewed a group of East African immigrants who wanted their children to attend a school where there was a group of other East African students because they believed that these other students would help support their children. They knew little about the curriculum or low achievement at the school they chose. Saporito (2003) reflect e It was in this analysis that he considered the possible changes in segregation in a school district if all magnet applications were approved. He found that magnet schools would remain racially integrated, but that neighborhood schools would see an increase in racial segregation. Similar results were found in an analysis of changes based on socioeconomic status. Overall, Saporito (2003) found that segregation between different racial an d socioeconomic status groups was higher than it would be if the magne t program did not exist. This was (2003) research was base d, not on what people said in s urveys that they might do or would do, but on actual data from magnet applications that were submitted. The out group avoidance that he documented could result in significant segregation when district policies to control admi ssions to magnet schools were lacking (Bifulco, et.al., 2009; Saporito, 2003). In his 2006 research, Saporito, along with Sohoni, investigated the impact of private school enrollment, along with other choice options such as magnet schools, on segregation in 22 of the largest school districts in the United States. They used census data and district maps to compare the racial balance of students living in a particular district to the racial balance of students enrolled in area schools. They found that the ex istence of private schools in a on the proportion of w hite students who attend the public schools. They also found an
54 association between the existence of other public school choices (magnet, charter, e tc.) and a lower percentage of w hite students in the regular neighborhood schools. The s egregation patterns between w hite and Hispanic students were found to be greater than those between white and b lack student s When they expanded the analysis to compare district differences between residential racial balance and the racial balance in the overall district school enrollment, they were able to discern that districts with specialty schools designed to decrease segregati on, were indeed able to reduce black w h ite segregation, but were not successful in reducing the isolation of Hispanic students. Saporito and Sohoni, (2006) concluded that unless voucher programs or othe r controlled choice programs were truly targeted at low income minority stu dents, they would increase school segregation. The United State s what effect the increase in the number of schools obtaining unitary status has had on the racial balance of schools that were previously under court or Commission on Civil Rights, 2007). This analysis was important because release from court supervision is often associated with voluntary school choice plans. The Commission used the dissimilarity index, as well as the entropy index to m e asure desegregation. They used analysis of v ariance (ANOVA) to consider the effects of legal status (unitary, under court supervision, or never litigated) on integration. Regression analysis was used to consider other factors that may have influenced the level of integration. These factors included district enrollment, number of schools, and proportion of minority students (United States Commission on Civil Rights, 2007). In contrast to much of the data from The Civil Rights Project, the Commission found t hat unitary status did not lead to resegregation in their study of districts in Alabama, Florida, Georgia,
55 Louisiana, Mississippi, North Carolina, and South Carolina (United States Commission on Civil Rights, 2007). Tedin and Weiher (2008) developed an exp erimental design to better understand the roles of academic quality and the racial make up of a school in influencing parental choice. They conducted phone surveys of 1,291 parents of charter school students and 529 parents of traditional public school stu dents in Texas. The sample was stratified to match the population by grade level and race. In addition to traditional questions about the most important considerations in choosing a school, the researchers used random assignment of questions with differing stimuli parents were asked about the likelihood of choosing to send their child to a fictitious new charter school with different test scores and racial balanc es. This experimental method had several advantages including the nt to respondents, the capacity to make causal inferences, and the generalizability of the results (Tedin & Weiher, 2008). In this study, the researchers concluded that race was an important factor in parent decision making; howe ver, all of the racial grou ps (black, w hite, and Hispan ic) preferred schools with high test scores. In contrast to most of the literature, academic quality was more important than race as a predictor in the likelihood that they would send their child to the fictitious charter school and when actual data was studied for this population, race was also risk (Tedin & Weiher, 2004). Schneider and Buckley (2002), in their previously discussed research, used detailed monitoring o f the internet usage of Washington, D.C. parents to better
56 processes for school choice. They found that parents did look at academic criteria, but also considered rac ial proportions in schools. They conclude d that these behaviors match ed with research conducted on actual choices, but contrast ed with what parents said they were looking for in a school. They found that race and class play ed important roles in actual parent choice. Reflections on the Literature While various methods of research have evolved over the past thirty years, and there is variation in the conclusions reached by different scholars, stratification based on race and cla ss is a recurring theme in the study of school choice. Liu and Taylor (2005) conclude d that middle class, suburban parents, who were happy with their schools, had little incentive to s upport choice policies that would change the racial or socioeconomic mak eup of sc hools in the suburbs. Interest convergence t heory seems to suggest that real illuminate d the geographic relationship between suburban resistan ce to choice and Be (1980) interest convergence t heory. They explain ed that suburban support of lo cal control of public schools was based on three factors: a basic belief in self governance; efficiency and effectiveness that is less bureaucratic; and community difference s that allow more affluent districts to spend more on education thus protecting school quality. These families that were heavily invested in local schools did not have interests that readily intersect ed with the needs of struggling urban students in nearby districts. They believe d 990). Goyette (2008) emphasized the importance of including non choosers who may have migrated to the
57 suburbs to avoid integration in school choice re search because this type of unofficial choic e had the potential to impact stratification and inequality (Holme, 2004). The studies of choice plans show mixed results and stratification is a widespread c oncern. Archbald (2000) believed that the arguments about stratifica tion and the effectiveness of choice plans had not been studied appropriately. He argued that stratification had that i n reality, stratification occurred with or wi thout school choice. He su ggested that to establish a link between stratification and school choice, two needs must be met: 1. Greater precision in defining and measuring stratification, and 2. More explicit use of a comparative policy perspective (Archbald, 2000). He described several models for defining and measu ring stratification and explained that choice, as a policy, must be compared to neighborhood based plans and to forced busing plan s. Other variables that he found important in policy analysis were consequences of stratification, cost recommendations, for this analysis proporti ons of black and w hite students i n each elementary school and dissimilarity indices (D I ) to measure str atification in the district durin g the 2009 2010 school year, were compare d to those actual school proportions an d dissimilarity ind ices to the counterfactua l black/w hite proportions and D I in which all students remained at their assigned schools with no s chool choice movement. Lubienski (2007) stated 133). While the foundational goals of choice plans are equity, pare nt empowerment, and excellence, research indicates that most school choice plans across the United States are
58 failing to meet or even approach these high aspirations. Saporito and Sohoni (2007) studied the 21 largest school districts in the country and con cluded that nearly half of the segregation of l ow income children in schools was related to the enrollment of children in non neighborhood sch ools and that these patterns were the most significant for minority children. While historic patterns of residenti al segregation have led to school segregation, voluntary choice plans do not appear to provide a remedy that benefits poor or minority students (Kraus, 2008). Perry (2007) suggested a model for analyzing education policy that could serve to aid districts in seeing choice plans and their out comes more clearly. She proposed that democratic education policy has five key concepts: equality, diversity, participation, cohesion, and choice. Equality, in this model meant equality of opportunity and outcome, emph asizing social mobility not reproduction, rather than just equalized expenditure s and resources. Diversity meant equal access to quality education for all groups as well as plurality of viewpoints. Participation referred particularly to governance and cont rol, with an emphasis on local control an integration of concepts such as solid arity and inclusion that created t he whole. And finally, choice was described in terms of choosing a school and having a diverse range of choice s. These five key concepts can be a used to analyze and judge the value and merit of a school choice policy in a democratic society. It is difficult to truly show causal effects of school choice on school effectiveness (Cullen, et.al ., 2006). Archbald (1996) argued understand and compare sch ool choice policies. He suggested input indicators such as expenditures, and student and staff characteristics; process indicators such as curriculum
59 and instruction strategies a nd school climate; and output indicators such as test scores and graduation rates. These indicators, given widely accepted definitions, could provide po licy makers with data that better show the effectiveness of school choice policies as implemented. The study of school choice policies is multidimensional. It is important to evaluate the impact of such policies from within the framework of many different considerations and questions. Does the choice policy contribute to stratification? What degree of s tratification is acceptable? What are the financial costs of different student assignment policies? What policies are actually most effective at improving student achievement? Many of these questions are based on political values that must be considered by policy makers. The role of government in encouraging social and racial integration, individual liberties and choices, and achievement versus school and societal cultures are issues that must be addressed by policy makers in our democratic society. The lit erature illustrates the complex nature of private choices as they intersect with public goals for our schools and society. The study of school choice plans must be individually tailored to the context of the community in which it exists. Historical patter ns of residential segregation differ greatly across the United States. Comparisons across the political landscape are d ifficult. Policy analysis of a stratification, equity, and academic ac hievement must be situated firmly within its local context. The intersection of private decision making and public consequence is indeed a complex and local political issue.
60 Chapter 3 Methodology Supreme Court Justice Anthony Kennedy has written that confirms that while interracial exposure can have short and long term educational and societal benefits, integrated schools are fundamentally important because they provide minority students with key elements of a quality education: excellent teachers, adequate funding, and middle class peers (Black, 2008). As stated in the introduction, the funda mental question in this study was : What impact plan have on the racial and socioeconomic character of its elementary schools ? A secondary question was : Does it appear that parents are choosing high quality schools for their children school grades ? This quantitative descriptive research study examine d the racial and socioeconomi c composition of students in one during the 2009 2010 school year, and explore d the extent to which the student populations in these schools would differ if all students had attended their attendance area school s rather than In essence, the researcher created a school by school picture of voluntary integration in a district deemed unitary by the C ourt in 2001. Each of those snaps hots of actual school demographics was compared to how the school would look if the school choice plan did not exist, i.e. all students returned to their attendance area school s the counterfactual data The educational
61 landscape that was created by the choice plan was also quantified by calculating a dissimilarity i ndex for the district with and without the movement of students due to school choice (actual and counterfactual demographics) Additionally, the movement of students was analyzed in light of the academic quality of t he schools that were chosen The purpose of this study was to examine the impact of school choice on the demographics of the ls. The research questions addr essed in this study were : 1. 2010 racial and socioecono m ic demographics, can the elementary schools still be described as integrated? 2. if all students attended their attendance area schools (the counterfactual demographics)? 3. economic character of its elementary schools? 4. Given that nearly all parents identify academ ic quality as a reason for choosing a school, do parents in this d istrict choose high quality schools (as determined by the State) and do ethnicity or socioeconomic status appear to influence choice ? Design of the Study The design of this study was based on the notion of minimally sufficient analysis for conducting research. This concept, described by Peterson (2009) and based on Wilkinson and the A.P.A. Task Force on Statistical Inferences (1999) specifies that
62 research design and analysis should be as s imple as possible while still attending to the research questions thoroughly and appropriately. This was a quantitative descriptive study of one large county based school district in the southeastern United States. To clarify the impact of voluntary integration on the lives of individual school children, the primary unit of analysis was the school. The 5 schools were analyzed in this study. Additionally, to illuminate the effects of the c hoice plan on the distr dissimilarity i ndex was calculat ed using district level demographics with and without the choice plan For the purposes of this study, racial categories were based upon those used in the original desegr egation case black and w hi te, with the w hite category including all categories of students except b lack Socioeconomic status was based on (FRL) All students were categorized a s eligible or not eligible f or FRL f or this study The d istrict The school district that was studied is a very large county based district in the southeastern United States. The county covers more than 1000 square miles and has a population of over 1,180,700. It is one of the largest public school districts in the United States with pre K through grade 12 enrollment of more than 193,000 students of which 96,100 are K 5 students The district has a total of 250 schools, and employs more than 15,000 teachers. Since the school district and county share boundaries, it is important to note that in 2008, 16.6 % lack while the proportion of b lack students in the distr only 13.9 % % of students in the district were eligible for free or reduced price lunches (130% of the
63 poverty line or below). These numbers reflect the national demographic topography where pub lic school populations tend to have higher proportions of racial minorities and children from poor families than their surrounding communities The district is in a state which through F Districts are als the state began including di stricts in the grading system. A substantial proportion 76%, of schools in this district received an A or B for the 2008 2009 school year Elementary schools score d even higher in the state grading system, with 85% earning an A or B. In the 2008 2009 school year, none of the schools in this district received an F, and only 5% earned a D. The district is accredited, has more than half of its high schools listed among qualify as National Merit Scholarship semi finalists. Data s ources The 2009 2010 school year data were analyzed in this research. and A ccountability provided the ethnic, FRL, and school choice data for this study. The data were compi led by a supervisor, who provided spreadsheets with the categories of information requested by the researcher. For each elementary school, the district provided the following information: total enrollment count, enrollment by ethnicity count, and enrollment by FRL status count There were six different ethnicit y categories (Asian, Hispanic, black, w hite, Indian, and m ixed), which were combined by the res earcher to r esult in the two categories of black and w hite (all oth er categories) for this study. Ethnic data was based on sc hool enrollment cards, which are completed by parents each school year. The ethnicity is self determined and self reported. The Free and Reduced Price Lunch counts for each elementary school were also
64 provided. There were nine categories of FRL status (did not apply, applied not eligible free lunch, reduced price lunch, direct certificate refused, free lunch information not supplied, reduced lunch information not supplied, free meals direct, temporary free lunch), which were combined into the two categories of eligible (free lunch, reduc ed price lunch, free and reduced with information not supplied, free meals direct, and temporary free lunch) and not eligible (did not apply, applied not eligible, direct certificate refused) for this study In all cases, the counts provided by the distric t were converted by the researcher to proportions of b lack students (B/W) and of FRL students (FRL/non FRL) in percentages for each school. All student information was de identified by the department of assessment and accountability before release of the d ata. Additionally, after analysis, all schools were de identified by the researcher. The researcher was required to de identify the district and its schools as a condition of cooperation and timely provision of the data by the district. The resea rcher was also provided school choice data in spreadsheet software that included individual data for more than 18,000 K 5 students who attended a school other than the one to which they were assigned These 18,061 choosers included 4,846 black students (26.83%) and 9,846 students who were eligible for FRL (54.51%). D ata were organized by school Only traditional K 5 schools were included in this study, resulting in analysis of 15,511 individual students involved in school choice. For each school, the spreadsheet incl uded the number of students who were assigned to the school but chose to exit and attend a different school, by ethnicity and by FRL status The spreadsheet also included, for each elementary school, the number of students who chose to attend that
65 school i nstead of their attendance area school These data were also listed by ethnicity and by FRL status. School grade data were retrie E ducation website. School grades from the 2007 2008 school year were used in this study because those would have been the most recent grades available to parents as they navigated the school choice process during the 2008 2009 school year, and ultimately chose schools for their children for the 2009 2010 school year. Actua l and counterfactual demographics For the purpose of determining the impact of the school choice plan on the racial and socioeconomic demographics of each school, the actual 2009 2010 the effects of school choice by school. For each school, the students who chose to attend the school were removed; and the students who chose to exit the school were returned. These counterfactual student counts we re then us ed to calculate proportions of b lack students and proportions of FRL students. This resulted in counterfactual demographics for each elementary school that represent ed what the school population would be, theoretically, if there were no school ch oice option s Th ese counterfactual demographics were th en compared with the actu al demographics for each school providing a clear picture of the impact of the school Measuring s egregation For t he duration of legal proceedings from 1958 through proportions of black students and w hite students in each school. Ladson B illings and Tate (1995) were
66 d other racial groups in the non white category. However, the courts have historicall y defined the groups simply as black and white, with the emphasis being on integrating black, predominantly African American students with the white majority. T he school district being studied has met the guidelines prov ided by the courts by grouping b lack students separately and including all o ther racial and ethnic gro For the purposes of this analysi s of school choice policy, the b lack category only include d students who identified themselv es as black, non Hispanic. The w hite studen t category include d all other students. In this research, free or reduced lunch (FRL) eligibility was used as a proxy for socioeconomic status, FRL eligibility is a commonly used indicator of SES in education research. To qualify for FRL, a family must have a total income of less that 130% of the Federal ly defined poverty line, and complete a FRL application form Families participating in other government assistance programs can be automatically enrolled. There is little oversight or accountability in this program and income levels are self reported by parents each school year on the application s Literacy levels and the ability to speak English impact application rates. Additiona lly, the FRL program is based solely on income, and does not actually reflect other indicators of socioecono mic status such as family wealth, education, or occupation. The desegregated school The courts determined in 1971 that schoo ls in this district should have a proportion of black to w hite students that mirrored the community and the district as a whole, and set the standard for desegregation at approximately 80%
67 white students and 20% b lack students in each school Th e C ourt a lso stated that any sc hool that had a population of more than 50% b lack students would be considered Since the population of b approximately 20% in 2008, the 80/20 ratio set by the courts was determined to be a pp ropriate as the standard of racial integration for this study Additionally, the researcher determined that for the purposes of this study, the same standard would be applied to FRL proportions Since approximately 60% of the nts qualify for FRL, schools that re flected that proportion were considered socioeconomically integrated in this study. In considering racial and socioeconomic integration, schools that were within five percentage points of the ideal proportions were consi dered to be integrated. This range of 10 percentage points was also based on court standards. The dissimilarity index. I n addition to analyzing indi of black and white students and proportions of students eligible and not eligible for free or reduced price lunches, the d issimila rity i ndex (DI) was calculated for the district (elementary school population only) as it was in the 2 009 2010 school year using the actual demographics and for the district as it would have been if all elementary students had attended their attendance area school s that year using the counterfactual demographics The DI is widely used in social science literature as a measure of r esidential and school seg regation. The dissimilarity index is the proportion of any one group of children that would have to switch schools to create a racial balance throughout all elementary schools across the district. An advantage of the dissimilarity in dex is that it measures what is controllable by school officials -the assignment of pupils to schools not
68 the actual proportion of the different categories of students in the district. The index measures whether one particular racial/ethnic group is distributed across all schoo ls in a district in the same way as another group. The formula holds the racial composition of the district fixed, but measures the exten t to which students could be re sorted among district schools to match the district proportions Examples of this resort ing would include magnet schools, school choice, or redrawing school attendance boundaries. Importantly, the DI measures pupil assignment components of desegregation and not the effects of housing patterns that might result in racial clustering. In other w ords, the dissimilarity are spread across schools within the district. Like most other measures of desegregation, the dissimilarity index contrasts only two groups In this study, proportions of black and w hite students (and separately, the concentrations of students eligible and not eligible for free and reduced price lunches) were analyzed because court ordered desegregation primarily focused on integrating these student groups. Furthermore, this approach to analysis avoided the misleading impression that could otherwise arise where school systems appear to be more or less integrated because of growth in other minority populations. The DI was calculated with the actual elementary school demographics and then separately for the counterfactual demographics for race and for socioeconomic status. The dissimilarity i ndex ranges in value from 0 to 1 and meas ures whether the proportion of b lack students at each school is the same as the proportion of b lack students in the entire district. In this study population of all K 5 elementary schools. High values indicate that to create statistical racial balance am ong
69 a greater number of pupils. The index, which is expressed as a proportion, can be multiplied by 100 to indicate the percent of students that would need to change schools to achieve exact statisti cal racial balance across the district. One computes the index of dissimilarity (DI), by summing over all the schools in the district: j /N w j /W| where n j is the number of non w hite students in school j ; w j is the number of w hite students in school j ; j the sum of non w that is, the total non w hite enrollment in the district; and j the sum of the number of w schools, that is, the total w hite enrollment in the district (Clotfelter, Ladd, & Vigdor, 2005). To compute the diss imilarity index for blacks and w hites in this stu dy, the number of b lack students was substituted f w hite students and then all other rac ial groups were included in the w hite category; in essence including only those two groups in the computations. Following this for mula, for each school, one found s proportion of the dist lack students and it hite students. The researcher used the data provided by the district to calc ulate the DI using spreadsheet s oftware Measuring school q uality schools are
70 given a grade of A, B, C, D, or F each year. was implemented in 1999 a schools. Students are tested in reading and math in grades 3 through 5; writing in grades 4, 8, and 10; and in science in grades 5, 8, and 10. The plan was tied to financial rewards for high performing schools, sanctions and technical assistance for chronically low performing schools, and private school vouchers for students in failing schools (Figlio & Rouse, 2006). The plan also call assigned to schools based on their performance on the high stakes testing each year. The criteria for the grades was adjusted periodically w ith regard to which students were included, the actual performance thresholds for each grade and subject, the inclusion of improvement scores an d reduction of the achievement gap between subgroups. These grades were used in this study to identify the quality of each school. The school grading system is based solely on high stakes testing, and thus is far from a perfect indicator of overall school quality. Adequate Yearly Progress (AYP), based on Federal standards, is also a publicly available indicator of school quality do not necessarily meet the requirements for AYP. The me thods for calculating AYP are somewhat more complex than those for calculating school grades, and more importantly, AYP is reported in such a way that parents find difficult to understand. A fter more than ten years under the school grading s students, parents, educators, and the public have come to see these grades as key indicators of how students in a particular school are doing, and thus school grades were used in this study because they were more e.
71 Data Analysis This was a descriptive study in which the re were two dependent variables (the effects) which were the proportions of black and w hite students in each school and the proportions of students eligible and not eligible for free or reduced price lunch (FRL) in each schoo l. The independent variables were the ethnic and socioeconomic characteristics of the students who participate d in the choice program. For research question one 2010 racial and socioecono m ic demographics, c ould the elementary schools still be described as integrated the district data w ere used to compute the percent age of b lack students and the percent age of w hite students (which include d all oth er racial/ethnic categories) in each elementary school in the district Descriptive statistics w ere used to clarify this snapshot the 2009 2010 school year. These descriptive statistics w ere analyzed in light of the 80/20 white to b lack ratio that was originally required by the courts Schools with ratios within five percen tage points of the ideal 80/20 white to b lack ratio were con sidered to be integrated. The same computations, statistics, and analyses w ere completed for the socioeconomic categories for each school. The categories used for socioeconomic analysis w ere eligible for free or reduced price lunch and not eligible for fre e or reduced price lunch. Schools that were within five percentage points of the ideal 60/40 eligible to not eligible for FRL ratio were considered integrated. The dissimilarity index for the district w as calculated based only on elementary school data. For this index, the unit of study was the district.
72 For research question two integrated if all students had attended their attendance area schools, th e counterfactual data w ere used to compute the p ercentage b lack and the percent age w hite (which include d all other racial/ethnic categories) for each elementary school in the district as if there was no school choice plan. Descriptive statistics w ere used to clarify this counterfactual representation of each elementary school popula tion characteristics as they would be without the choice plan These descriptive statistics w ere also analyzed in light of the 80/20 white to b lack ratio that was originally required by the courts The same computations, statistics, and analyses w ere completed for the socioeconomic categories for each school. The categories used for socioeconomic analysis w ere eligible for free or reduced price lunch and not eligible for free or reduced price lunch The idea l proportions w ere determined to be 60/40 eligible to not eligible for FRL. Again, based on court standards, schools that were within five percentage points of the ideal ratios were considered integrated. The dissimilarity index for the district w as also calculated for the district (elementary only) using the counterfactual demographics. In computing this index, the district was the unit of study. For research question three what impact did the racial and socio economic character of el ementary schools in the school district each elementary hite students, as well as actual proportions of students who were eligible and not eligible for FRL were compared to the counterfactual proportions which wer e calculated by the researcher Descriptive statistics were calculated using spreadsheet software Additionally, the dissimilarity i ndices based
73 on race and FRL status, for the actual 2009 2010 data and for the counterfactual data, were compared. For research question f our g iven that nearly all parents identify academic quality as a reason for choosin g a school, did parents in this d istrict choose high quality schools (as determined by the s tate) and did ethnicity or socioeconomic status appear to influence choice the unit of study was the district The percent of students who chose a school at each of the five state assigned grade levels (A, B, C, D, or F) wa s determined. Additional analyse s focused on the proportions of black and w hite, and eligible and not eligible for FRL students that chose high performing schools and those that chose schools identified as D or F schools The chi s quare test of independence/h omogeneity was used to better understand whether those choosing A schools B and C schools, and those choosing D or F schools matched overall district ethnic and socioeconomic proportions. The effect size ( w ) was important in interpreting these results because the sample size was so large (Cohen, 1988) These tests reflected the degree to which the racial and socioe c onomic proportions of choosers fit the theoretical expectations which were the actual district proportions This investigation of the race and socioeconomic status of families who chose scho ols of different academic quality was done with the district as the unit of analysis. Additionally, a small pu rposeful sample of five schools was selected for more specific analysis to better understand the quality of the schools from which the choosers ca me. These five schools were selected because they attracted large numbers of choosers and were schools that had stable school grades no dramatic changes over a period of several years These five schools were also selected because they represented a wide
74 array of demographic characteristics and were located in different areas of the district This analysis was done with the school as the unit of analysis. Each school was investigated with regard to the grade s of the schools from which its choosers came to shed light on whether students were moving from lower performing to higher performing schools. Limitations and Delimitations This chapter addressed the quantitative descriptive design used in this study. Data collection and analysis have been described. An important assumpt ion inherent in this study was the belief that data pro vided by the school district were accurate. Some basic facts about th e elementary schools were cross check e ducation website and the Common Core Da ta website. However, these organizations are also dependent on accurate self reporting from the individual schools and distri cts. The validity of the data was also dependent on accurate self reporting of ethnic in formation for each student. Data were taken directly from student enrollment cards completed by students and families each year. Socioeconomic (SES) data were based on student free and reduced price lunch information from the district This is the most commonly used indicator of SES for educational research. Howeve r, it actually only reflected family income, not other indicators of economic well being or social st atus. Additionally, the data had inherent flaws in that not all eligible families apply for FRL due to misunderstanding, langua ge barriers, or embarrassment. And there is minimal accountability and verification, so some parents may fabricate income information to receive free lunches for their children even though they should not be eligible. The FRL data were also based on a part icular date
75 during the school year, and there is constant flu ctuation as students are added to and sometimes removed from eligibility. In this case, the data reflected the last day of school during the 2009 2010 school year. The entire analysis was based moment is not only a particular school year, 2009 2 010, but also a particular day. School di strict demographics change constantly as students move in and out of the district, and transfer from one school to another within the district All data provided by the district reflected the demographics at the end of the 2009 2010 school year. The researcher assumed that the movement of students into, out of, a nd between schools that took p lace during the school year did not radically affect the racial and socioeconomic during the school year studied, and the United States continued to feel the impact of an ongoing housing/mortgag e crisis. These critical e conomic factors were not accounted for in this study. This economic situation may or may not have impacted student movement during the school year. The matching of actual school data with the counterfactual data calculated by th e researcher, was a control technique used to improve the reliability and validity of the study. The analysis did not need to account for movement of students during the year because all comparisons were based on the single snapshot of data on a single day It is simply important to understand that on any given day, there is some variation in many There a re two weaknesses in using the dissimilarity i ndex integration First, it only compared the p roportions of two groups. But as previously
76 stated, this p erceived weakness actually kept this study true to the legal history of school des egregation by focusing only on black/w hite pro portions. The second weakness was g that it did not provide information about the spatial patterns of segregation, only the relative degree of segregatio n. In other words the index did not describe the integration within the individual classrooms within a school. For example, while a distr ict may have had an index of .5 overall, students of different ethnicities or socioeconomic groups may not have be en evenly distributed in each classroom with in each school. An example of this situation could be a school where students are assigned to teachers based on achievement. This kind of student assignment can lead to higher proportions of minority students in special education classes and lower proporti ons of minority students in gifted classes. This kind of in school segregation was n ndex. The chi square analysis was used to better understand whether the race and SES of parents choosing schools of differi ng academic quality approximate the race and SES of the district as a whole This statistic is very sensitive to large numbers in the comparative w ) were used to more effectively interpret the substantive, not just statistical s ignificance of the results. Su mmary The district being studied had a school choice plan which has attracted a large number of st udents. The district controled student choice through a lottery with weighted factors for each student. These include demograph ic characteristics grade level, and sibling attendanc e, among others. This study was significant because if the design of this choice plan can accomplish the movement of so many students each school year, and
77 maintain or even improve racial and socioeconomic integration, it should be a model for other large districts struggling with volun tary integration plans. If, however, the di choice plan contributed to more segregated schools, this study may provide needed data to impact policy and improve the weighted lottery system, the parent and community information process, or other elem ents of the plan to reduce this negative outcome.
78 Chapter 4 Findings The purpo se of this research was to investigate the role school choice has played in the racial and socioeconomic distribution of elementary student populations in a large, county based school district. Specifically, four q uestions were posed for this study : 1. 2010 racial and socioecono m ic demographics, c ould the elementary schools still be described as integrated? 2. if all students attended their attendance area schools (the counterfactual demographics)? 3. What impact did economic character of its elementary schools? 4. Given that nearly all parents identify academic quality as a reason for choosin g a school, did parents in this d istrict choose high quality sch ools (as determined by the s tate) and do ethnicity or socioeconomic status appear to be related to choice ? The purpose of this chapter is to present the results of the analyses performed to address these research questions Initially, h istorical desegregation data were reviewed in light of
79 the 2009 2010 data. Next each of the four questions was addressed. The chapter ends with a summa ry of key findings of the study. Data Collection Historical d ata An understanding that was fundamental to this analysis was that the district being studied has clearly seen some resegregation since the 1972 1973 school year when, under C ourt supervision and mandatory busing, all schools were desegregated. According to the C ourt, the district wa s to eliminate racially identifiable schools by ensuring that no schools had an enrollment of more than 50% black students, and that the white/b lack ratio would be most acceptable and desirable at the following levels: High Schools 86/14 white/b lack Juni or High Schools 80/20 white/b lack Elementary Schools 79/21 white/b lack (Manning, 2001) These ratios were based on the overall proportions of black and w hite students enrolled in the district. For the purposes of this study, the optimum ratio was generalized to be 80/20 white/b lack. Court transcripts frequently refer red to this more generalized optimum proportion (Manning, 2001). In 199 1 the district requested changes in the original decree to allow the implementation of a three tier system: K 5 elementary schools, middle schools with grades 6 8, and high schools with grades 9 12 developed around a cluster system This replace d the five tier structure that was developed to meet desegregation gu idelines in 1971 That 1971 desegregation plan included clusters of white schools assigned to each b lack school f or busing purposes, with most b lack schools converted into single grade
80 centers for sixth and seventh grade students, with eighth and ninth grade students attending junior high schools. The primary objective of the modified plan in 1991 was to maintain desegregation while progressing toward the middle school model The district project ed that the new plan would result in additional schools being more than ten percentage point s from the 80/20 optimum ratio, b ut also expected more schools to be within five percentage points of the ideal 80/20 ratio. Later, when the C ourts began to consider unitary status for the district, data f rom the 1995 1996 school year were presented It was the C 17 schools which were outside of the required 80/20 racial proportions by more than 20 percentage points that year had resegregated due to residential housing patterns and other factors outside the control of the school board (Manning, 2001)
81 Table 1 Percent age Black Students in Selected Elementary Schools in 1972, 1995, and 2009 School 1972 1995 2009 1 24 % 91 % 87 % 2 19 % 74 % 79 % 3 23 % 69 % 82 % 4 35 % 67 % 74 % 5 21 % 61 % 70 % 6 26 % 58 % 51 % 7 15 % 57 % 51 % 8 18 % 54 % 60 % 9 21 % 53 % 47 % 10 18 % 49 % 42 % 11 14 % 47 % 24 % 12 35 % 43 $ 29 % 13 N/A 43 % 36 % Of the seventeen schools the C ourt discussed in 1995, thirteen were elementary schools These thirteen schools showed a dramatic increase in the prop ortion of b lack students enrolled between 1972 and 1995 and were considered by the C ourt to be
82 racially identifiable at that time (Table 1) The district had 108 elementary schools in 1995, so 12% of those sch ools had become racially imbalanced. Of those thirteen schools, six had an even higher propo rtion of b lack students in 2009 than they did in 1995 ( Manning 2001) 2009 2010 d ata B y 2009, 31 (22%) of the distri ional K 5 elementary schools h ad enrollment s with more that 40% b lack students. Twenty two elementary schools (16%) had student populations th at were more than half black Eight y two additional elementary schools (58%) had fewer than 20% b lack students In fact, only 25 (17.7 %) of th five percentage points of the C 80/20 white/b lack ratio in 2009 (Table A1 ). Data for this study that were related to 2009 2010 ethnic and socioeconomic demographics wer obtained from the Office of Assessment and A ccountability The researcher was provided with actual ethnicity and free and reduced price lunch (FRL) counts from each of the 377 school s /units in the district in the 2009 2010 school year on a spreadsheet These data included middle and high schools, as well as virtual school counts and data from charter, and o ther non traditional schools Ethnicity and free and reduced lunch eligibility status data from traditional K 5 elementary schools were extracted and used to create Table s A1 and A 2 The analyses in this research used only data related to K 5 elementary schools in the district Except where specifically noted, special education centers and other non traditional schools were not included Data related to school grades were retrie ved by the researcher from the State Department of E ducation website. The school d istrict provide a link to the S tate data directly from their website
83 Counterfactual d ata The counterfactual demographics used to represent each population without school choice, were calculated by the researcher from the actual data provided by the district. For each school, the number of students (by race and by FRL eligibility status) who were assigned to other schools but chose to attend that school was subtracted ; and the number of students (also by race and by FRL eligibility status) w ho chose to exit that school (which was their assigned school) was added These counterfactual student counts were then used to calculate the percentages of b l ack students and FRL eligible students in each school This resulted in counterfactual demographics for each elementary school that represented what each school population would be, theoretically, if there were no school choice options. Quantifying i nte gration During the desegregation case related directly to this district, th e C ourt used the proportion of black and w hite students (a ll students not identified as bl ack) in the district as a standard for integration fo r each school in the district. While t here was some variation between elementary schools, junior hi gh schools and high schools, the ratio of 80/20 white to b lack was generally the ideal proportion that the C ourt set as meeting the standards for integrated schools. Schools that were within five percentage points of this ideal balance were considered to be integrated. Based on the Supreme Court opinion in Parents I nvolved in Community Schools v. Seattle School District and Cristal D. Meredith v Jefferson County Board of Education (2007) the socioeconomic status of students may be considered in student assignment plans and in analyzing the distribution of students in school districts in lieu of racial quotas Using th e same criteria as the C ourts mandated in desegregat ion cases related to race, one could develop an optimum distribution of students who were eligible for free or reduced price
84 lunches based on the percent of those eligible students in the district as a whole Since 61% of elementary students in the distric t were eligible for FRL, an optimum ratio in each school would be approximately 60/40 FRL to not FRL eligible students For this analysis, i t was this 60/40 ratio that was considered the ideal proportion ; and in this s tudy, schools that were within five percentage points of this ratio were considered to be socioeconomically integrated. In addition to compar ing proportions of black/w hite students and FRL eligibl e/not eligible in each school, dissimilarity i ndices (D I ) were calculated to illu strate the e ven ness of the distribution of b lack students and FRL eligible students across all elementary schools in the district The dissimilarity indices were computed for b lack students and for students who were eligible for free or reduce d price lunch, and the distr indices represented how evenly these students were spread across elementary schools in the district. Quantifying school q uality In this study, the 2007 2008 school grades th at were assigned by the state were used as an indicator o f school quality. The grades are determined each year based on high stakes test scores in grades three through twelve. The school grade is actually based primarily on the percent of students that are deemed to be proficient calculated by awarding the school one point for each percent age of students who were proficient another point for each percent age of students that show ed improvement and an other point for each percentage of struggling (lowest quartile) student s who showed improvement There are also grade penalties in place for schools that do not make AYP,
85 as defined in NCLB. Each year the state has a range of point totals for each assigned school grade For example, a school woul d earn a grade of A if it had earned a total of at least 525 points, m et adequate progress of the lowest 25 % in reading and math, and test ed at least 95% of its students A school would earn a B if it had 495 to 525 points, m et adequate progress for the lowest 25% in reading and mathe matics within two years, and tested at least 90% of its students. A school grade of C would mean that the school earned 435 to 494 points, met adequ a te pro g ress of the lowest quartile in reading and math within two years, and tested at least 90% of its students. Schools earn ed a D if they had 395 to 434 points and tested at least 90% of their students Any school with fewer than 395 points was deemed failing In this study of elementary schools, the test sco res that made up a significant po rtion of the grading formula were reading and math tests in third, fourth, and fifth grades; a writing assessment in fourth grade; and a science test in fifth grade. The scores were but one passing glan However, the grades are somewhat intuitive and understandable to the public, and have become part of rewards for high scoring schools and a variety of sanctions and assistance for failing schools The grades from the 2007 2008 school year were used in this research because those would have been the most recent grades available to parents as they applied for school choice for the 2009 2010 school year. (School grades from the 2008 2009 school year would not have been released until the summer of 2009, after parents had made final choice decisions.)
86 The Results Research q uestion 1 The first research question of this study was: b ased on the 2010 racial and socioecono m ic demographics, c ould the elementary schools still be described as integrated? Table A1 shows the total enrollment count, the count of black and w hit e s tudents, and the percentage of b lack students in each of the 141 traditional K 5 elementary schools analyzed in this study. The percentage of b lack students ranged from 90% in one school, to 1% in two of the schools. The mean percent age of b lack s tudents i n elementary schools was 24% To clarify the distribution of b lack students in elementary schools, Table 2 shows the number and percent age of schools within ten given ranges.
87 Table 2 2009 2010 Count and Percent age of Elementary S chools B ased on Percent age Black Enrollment by Decile Enrollment Schools % Black Count % 90 99% 1 1.0 80 89% 7 5.0 70 79% 3 2.1 60 69% 2 1.4 50 59% 9 6.4 40 49% 9 6.4 30 39% 9 6.4 20 29% 19 13.5 10 19% 32 22.7 0 9% 50 35.5 Total 141 100.4 More than 70% of the elementary schools had a b lack population of less than 30%. More than 58% of the school s had a b lack population that is less than 20%. And more than 35%
88 of the schools had en rollments that include fewer than 10% b lack stud ents. Twenty two (15.6%) schools had student populations with more than 50% b lack students These schools would have been defined as racially identif iable by the C ourts. Using the C ourt standard of being within five percentage points of the ideal white/b lack proportion of 80 /20, only 25 schools (17.7%) would be considered racially integrated (Table A1) Table A2 shows the 2009 elementary enrollment count as well as the number and pe rcentage of students who qualified for free or reduced price lunches for each school. The percentage of eligibl e students in each school ranged from 98% in three of the schools to 8% in one school. The mean percentage of students eligible for FRL in elementary schools was 64 %. Table 3 clarifies the distribution of these student s across the schools by grouping the percent age eligible for FRL into deciles.
89 Table 3 2009 2010 Count and Percen tage of Elementary S chools based on P ercent age of Enrollment Eligible for Free or Reduced Price Lunches by D ecile. Enrollment Schools % Eligible for FRL Count % 90 99% 30 21.3 80 89% 26 18.4 70 79% 17 12.0 60 69% 14 9.9 50 59% 8 5.7 40 49% 16 11.3 30 39% 11 7.8 20 29% 9 6.4 10 19% 8 5.7 0 9% 2 1.4 Total 141 99.9
90 A su bstantial number of schools had student populations that have a large proportion (70% or more) of students who were eligible for free or reduced price lunches More than half (73 schools) fell within this range. Thirty schools (21.3%) had populations with 30% or fewer students qualifying for FRL. Using the C (derived from racial desegregation cases) overall proportions, only seven schools (5.0 %) would be identified as socioeconomically i ntegrated (Table A2) All other school s (95 %) would fail to meet the standard of being within five percentage points of the ideal proportion of 60/40 FRL eligible students to not eligible students. The d issimilarity index (D I ) was then calculated to provi de a better picture of the distribution of b lack and FRL eligible students across all elementary schools i n the district. With regard to b lack students, the D I = 0.479, which could be in terpreted to mean that in order for all bl ack students to be distribut ed evenly acro ss all elementary schools, 47.9% of those student would have to be reassigned to different school s For FRL eligible students, the D I = 0.497, meaning that 49.7% of students who were eligible for FRL would have to be reassigned to different s chools to create a situation where FRL students were distributed evenly across all elementary schools. So while more schools socioeconomically integrated (5.7%), the dissimilarity i ndices suggest that the degree of segregation by race and by poverty were very similar elementary schools, the answer to research question one was that in the 2009 2010 s chool year, very few schools were racially or socioeconomicall y integrated as defined by the
91 C ourts in past desegregation cases. Schools appear ed to be slightly more integrated racially than soci oeconomically, with 25 schools (17.7%) meeting the C for r acial desegregation while only 8 schools (5.7%) would meet the standard for socioeconomic integration. It was also notable that 22 element ary schools (15.6%) exceeded the C sta ndpoint 30 schools (21.3%) had more than 90% of their students qualifying for FRL and would clearly be identifiable in this regard. The dissimilarity i ndices calculated by race and FRL status suggest ed that a large number, nearly half, of all students would have to be reassigned to different schools to res ult in an even distribution of b lack students, and those who are eligible for free or reduced price lunch. According to the Lewis Mumford Center at th e University at Albany, the d issimilarity index can be interpreted bas ed on the following: D = 0.6 or higher represents a high degree of segregation D = 0.4 to 0.5 represents a moderate level of segregation D = below 0.4 represents a low degree of segregat ion. I = 0. 479) and socioeconomically (D I = 0.497), during the 2009 2010 school year. Research q uestion 2 The second resea rch question was: elementary schools be considered integrated if all students attended their attendance area schools (the counterfactual demographics)? Table A3 shows the relevant counterfactual enrollment counts and percentages related to race for each of the 141 elementary schools analyzed for research question 2 Th e counterfactual percentage of b lack students r anged
92 from 91% in one school to 1% in three of the schools The coun terfactual mean percent of b lack stud ents in eleme ntary schools was 24% To make clear the counterfactual distribution of b lack students across all elementary schools, Table 4 shows the number and percent of schools within ten ranges.
93 Table 4 Counterfactual Count and Percentage of Elementary Schools B ased on Percent age Black Enrollment by Decile Enrollment Schools % Black Count % 90 99% 1 1.0 80 89% 5 3.5 70 79% 5 3.5 60 69% 3 2.1 50 59% 9 6.4 40 49% 7 5.0 30 39% 10 7.1 20 29% 17 12.0 10 19% 39 27.7 0 9% 45 31.9 Total 141 100.2 Based on the counterfactual data, m ore than 70% of the schools would have had a b lack population of less than 30%. Nearly 60% would have had a b lack population that was less
94 than 20% And nearly 32% of the schools had fewer than 10% b lack students in their counterfactual enrollment. Twenty three school s (16.3%) had counterfactual student populations with more than 50% black students. These elementary schools would have been defined a s racially ide ntifiable by the Courts. Based upon the C 80/20 as the ideal balance of white to b lack students, 25 schools (17.7 %) had counterfactual demographics that would be within the five percentage point range that would distinguish them as racial ly integrated (Table A3) Table A4 shows the counterfactual elementary enrollment count s along with the number and percentage of student s who qualified for free or reduced price lunches for each school The counterfactual percentage of eligibl e students in each school ranged from 98% to 7%, with a mean of 61.9% FRL eligible students for all elementary schools.
95 Table 5 Counterfactual Count and Percent age of Elementary School s B ased on Percent age of Enrollment Eligible f or Free or Reduced Price Lunch by Decile Enrollment Schools % Eligible for FRL Count % 90 99% 21 14.9 80 89% 27 19.1 70 79% 19 13.5 60 69% 18 12.8 50 59% 7 5.0 40 49% 16 11.3 30 39% 14 9.9 20 29% 9 6.4 10 19% 7 5.0 0 9% 3 2.1 Total 141 100.0
96 Nea rly half (47.5%) of schools had counterfactual student populations where 70% or more of the students would qualify for free or reduced price lunches. Thirty three schools (23.4%) had counterfactual populations with 30% or fewer students eligible for FRL. Deriving the standard of matching individual school proportions to that of the district as a whole (60/40, eli gible to not eligible for FRL ) devised from desegregation case s schools which had a FRL eligible percentage between 55 % an d 65% would be con sidered socioeconomic ally integrated counterfactual demographics met that threshold (Table A4) The d issimilarity index (D I ) was then calculated to illuminate the counterfactual distribution of b lack and FRL eligible students across all elementary schools i n the district. With regard to b lack students, the D I = 0.4802, which can be in terpreted to mean that for all b lack students to be distributed evenly across al l elementary schools, 48% of those student s would have to be reassigned to a different school s For FRL eligible students, D I = 0.4687, meaning that 46.9 percent of students who qualified for FRL would have to be reassigned to different schools to create a situation where FRL students are distributed evenly acro standard for being racially integrated (17.7%) than socioecono mically integrated (6.4%), the dissimilarity i ndices suggest that the degree of segregation by race and poverty in th e counterfactual demographics was nearly identical. A review of the counterfactual demographics which represent ed the school populations as they would be, theoretically, with out the school choice plan showed that ntegrated in such a way as to meet the st andards of the C ourts Nearly 18 % of the schools would have me t the standard
97 for racial integration, and 6.4% would be considered socioeconomically integrated. The answer to research question 2 was that counterfactu al demographics show ed that without school choice the elementary schools would be moderately segregated. They would be slightly more racially integrated than socioeconomically integrated based on the C standards, but nearly identic al with regard to how unevenly b lack students and students who qualified for free or reduced price lunch were distributed across all elementary schools. Research q uestion 3 The third research question of this study was: What impact did e racial and socio economic character of its elementary schools? Appendix A5 shows the actual and coun terfactual enrollment, percent age b lack, and percent age eligible for free and reduced price lunch in each of the 141 elementary schools in this study. Nine of the 141 schools are magnet schools with no designated attendance area For this study, ac tual and counterfactual data were considered to be the same for magnet schools. All other schools had at least one student exit or choose to attend, creating a difference between actual and counterfactual enrollment. Among non magnet schools, 42 schools had an increase in enrollment due to choice, and 90 schools had a decrease in enrollment when comparing the counterfactual to actual data. Eleven schools gained 100 or more students. Twenty six schools gained from 10 to 99 students. Five schools gained fewer than ten students. Among the ninety schools that decreased in enrollment, seven lost fewer than ten students; 47 lost from ten to 99 students; and 36 schools had 100 or more students exit to attend different school s The mean change in school e nrollment for all schools was 44 students; the median was 31
98 students; and the mode, due to magnet demographics being artificially held constant in the actual and counterfactual data was 0. Analyzing the differences in racial characteristics of the actual and c ounterfactual demographics showed that 60 scho ols (42.6%) had a higher proportion of b lack students due to the choice plan (counterfactual actual percentages). Fifty sch ools (35.5%) had a lower proportion of b lack students Thirty one schools (21.9%) stayed the same in this regard.
99 Table 6 Actual Enrollment by Race Compared to Counterfac t ual Enrollment by Race in the Actual 2009 Enrollment Counterfactual Enrollment Racially Identifiable Schools (50% or more b lack) 22 (15.6%) 23 (16.3%) Schools with 21% to 49 % Black Enrollment 34 (24.1%) 34 (24.1%) Schools with Less than 20% Black Enrollment 85 (60.3 %) 84 (59.6 %)
100 Table 7 Actual Enrollment by Free and Reduced Price Lunch Eligibility Compared to Counterfac t Elementary Schools Actual 2009 Enrollment Counterfactual Enrollment Schools with More than 80% Free and Reduced Price Lunch Eligible Students 54 ( 38.3 %) 45 ( 3 1.9%) Schools with 20% to 80% Free and Reduced Price Lunch Eligible Students 77 ( 54.6 %) 85 ( 60 .3%) Schools with Less than 20% Free and Reduced Price Lunch Eligible Students 10 ( 7 .1%) 11 (7.8%)
101 Table 8 Dissimilarity Indi ces B ased on Race and Free or Reduce d Price Lunch Status in Elementary Schools Only Dissimilarity Index 2009 2010 Actual Counterfactual By R ace (B/W) 0.479 0.480 B y SES ( eligible/not eligible for FRL ) 0.497 0.469 Table 6 represents the differences be tween actual and counterfactual ethnic demographics in light of the C desegregation standard s When schools were grouped by percent age of b lack students in this way, the actual and counterfactual demographics were nearly identical. Table 7 shows sli ghtly less congruence between the actual and counterfactual demographics with regard to free and reduced price lunch eligible student enrollment. The dissimilarity indices calculated for b lack students based on actual and counterfactual data are nearly ide ntical, as are the indices calculated for FRL eligible students based on actual and counterfactual data (Table 8). C ontinuing the statistical tests were conducted on these near ly identical demographic proportions and
102 indices. The weighting system used by the district in the granting of choice appeared to hold the racial and socioeconomic distribution of students across schools very much as it would be if all students attended th eir attendance area schools. Research q uestion 4 The fourth research question of this study was: Given that nearly all parents identify academic quality as a reason for choosin g a school, did parents in this d istrict choose high quality schools (as determined by the State) and did ethnicity or socioeconomic status appear to influence choice? The school grades from 2008 (from the 2007 2008 school year) were used in this analysis because those were the most recent grades available to parents as they n avigated the school choice process for the 2009 2010 school year. The 2008 2009 grades were released during the summer of 2009, but that was after most parents had made the decision to leave an assigned area school and signed a commitment letter to attend the school of choice. In 2008 136 traditional elementary schools received grades: 69 (51%) earned As, 29 (21%) earned Bs, 30 (22%) earned Cs, 6 (4%) earned Ds, and 2 (1%) earned Fs. For this research question the choices of 15,511 students were analyzed Sixty percent chose A schools, 20% chose B schools, 17% chose C schools, 2% chose D schools, and 1% chose F schools. The race and socioeconomic status of the parents who chose A schools, as well as the thirty seven percent ( 5548 students) of students who selected B or C school s and the three percent of students (464 students) who chose D or F schools were further investigated to answer Research Question 4. It was clear that most parents chose schools that were rated highly by the state. Sixty percent chose scho ols that earned As. Intuitively, these schools would be conside red outstanding schools. An additional 20% of families chose schools that earned
103 Bs, and 17% chose schools that earned Cs Because school grades were structured to match the typical academic grades with which most Americans are familiar, one would assume that these B and C schools are average to above average schools, and that D and F schools need significant improvement schools ea rned As, the B and C schools were relegated to the lower half of all schools, along with the D and F schools, and generally perceived as below average. From a racial persp ective, the proportion of b lack and w hite students who chose A schools appeared to match the proportions in the district as a whole. The chi square test of independence/homogeneity was used to test the null hypothesis The null hypothesis was that the prop ortio n of b lack students choosing an A school (.19) would be eq ual to the proportion of b lack students in the district as a whole (.21). At p < .001, with 1 df the chi square value was determined to be 22.44 The null hypothesis was rejected. The large sample size impact ed the chi square value, so the very small effect size ( w = .049) was important in illustrating that there was a very limited difference between the proportion of black students in the district and the proportion of black students who cho se A schools. From a soc ioeconomic perspective, there was a visible difference between the percent of students eligible for FRL that chose A schools and the proportion of FRL eligible students in the district as a whole Only 43% of the A school choosers w ere eligible for FRL, while 62% of students in the d istrict are eligible for FRL. The null hypothesis was that the proportion of students eligible for free or reduced price lunch would be equal to the proportion of eligible students in the district as a whole. At p < 001, with 1 df the chi square was determined to be 1426.07 The null hypothesis was rejected In this case, t here was a medium effect size ( w = .39). Fewer poor st udents
104 chose A schools than would be predicted based on the proportion of poor students in the district. There was a more visible differ ence between the proportion of b lack students who chose B or C schools and the proportion of b lack s tudents in the distr ict. The null hypothe sis was that the proportion of b lack students who chose B or C schools would be equal to the proportion of b lack students in the district. At p < .001, with 1 df, the chi square was determined to be 219.76. The null hypothesis was reje cted. The effect size ( w = .19) was noticeable. There was a significant difference between the actual and expected proportions of black choosers of B and C schools. From a socioeconomic perspective, t he null hypothesis was that the proportion of students eligible for free or reduced price lunch that chose a B or C school would match the proportion of students eligible for free or reduced price lunch in the district. At p < .001, with 1 df, the chi squa re was determined to be 252. 183. The null hypothesis was rejected. There was a small to medium effect size ( w = .21). More poor students chose B or C schools than would be predicted based on the actual proportion of poor students in the district. Finally, there was an obvious differ ence between the proportion of b lack students who chos e D or F schools and the proportion of b lack students in the district as a whole. More than 70% of t he D or F school choosers were black, while b lack students only make up 21% The null hypothesis was that the proportion of black students who chose D or F school s would b e equal to the proportion of b lack students in the district. At p < .001, with 1 df, the chi square was determined to be 756. 27. The null hypothesis was rejected. The effect size ( w = 1.27) was very large
105 There was a much larger proportion of black students who chose D or F schools than would be expected based on the district demographics. The difference in proportions was even more significant with regard to socioeconomic status. Students who were eligible for FRL made up 95 % of the 400 that chose enrollment. The null hypothesis was that the proporti on of students eligible for free or reduced price lunch would be equal to the proportion of those eligible students in the district. At p < .001, with 1 df, the chi square was determined to be 7675.62. The null hypothesis was rejected The effect size ( w = 4.0672) was very high. The proportion of poor students who chose D or F schools was significantly higher than what would have been predicted based on the proportion of poor students in the district. F ive schools were selected for additional analysis as to the academic quality of the attendance area schools that their choosers had exited This was a small, purposeful sample of schools that were chosen because a large number of families chose them and because their school grades were stable over several year s They also represented a variety of demographic characteristics between their populations The first B school that was selected attracted a total of 228 students from 43 different schools This school ha d an enrollment of 92 9 students, 9% of whom were b lack, and 46% of whom were eligible for free or reduced price lunch. Of those students that chose this school 169 (74%) came from C, D, or F schools These families chose a school with a higher grade than their assigned school Twenty six students (11%) c ame from academically equivalent B schools Thirty three students (14%) had been assigned to an A school, but chose to attend this B school. The other B school that was analyzed attracted a total of 117
106 students from 30 different schools. This school ha d a n enrollment of 651 students, 22% of whom were Black, and 84% of whom were eligible for free or reduced price lunch. Of those, 71 (61%) came from C, D, or F schools. Thirty two (27%) came from equivalent B schools. And 14 (12%) came from A schools. Two C schools were also scrutinized at this level The se two schools were chosen by 139 and 91 families respectively The first C school had an enrollment of 943 students, of whom 36% were b lack and 73% were eligible for free or reduced price lunch This school had 22 choosers ((16%) who came from D or F schools and 51 (37%) f rom equivalent C schools. This school was also chosen by 66 students (47%) who had been assigned to A or B schools. The other C school had a total enrollment of 542 students of whom 80% were b lack and 93% were eligible for free or reduced price lunch. This C school was chosen by 33 students (36%) who had been assigned to D or F schools, and 42 students (46%) who had been assigned to equivalent C schools. There were 16 students (18%) w ho chose to attend this school even though their assigned attendance area schools earned As or Bs. One F school, which attracted 75 students, was selected for further analysis All of these families chose a school for their child that had earned a lower grade than the school to which the child was assigned. This school had a total enrollment of 387 students, of whom 56% were b lack and 92% qualified for free or reduced price lunch Th 75 choosers came from 21 different schools. Six (8%) had been assigned to D schools. Fifty nine students (79%) had been assigned to C schools. Seven students (9%) had been assigned to B schools, and 3 students (4%) had been assigned to atte ndance area schools that had earned A s
107 With respect to Research Question 4 did parents choose schools of high academic quality, based on the school grade, a majority of parents (60%) cho se high quality schools for their children. The others, a s ubstantial proportion, chose schools that earned school grades that were equal to or lower than their assigned attendance area schools. C hoosers of all ra cial and socioeconomic groups did not appear to be choosing evenly across schools of differing academi c quality. Families of different ethnic and socioeconomic levels appear ed to choose schools of different academic quality at a statistically significant level. Limitations The purpose of this study was to examine the role school choice has played in the r acial and socioeconomic distribution of student populations in the one schools after eight years of voluntary integration with a school choice plan As such, the design of the study was limited in scope to that one district and generaliza tion s cannot be made across other districts or states The da ta used in this study represented a single during the school year. The study was designed to consider the realities of resegregation after court mandated desegregation measures ceased As such, the variables were intended to match those considered in the original desegregation cases related to this district These dichotomous variables, b lack a nd w hite, and el igible or not eligible for free or reduced price lunch, limit ed the complete understanding of integration racial and socioeconomic in the district and in the individual schools.
108 The chi square test of independence/homogeneity was used to evaluate the sig nificance of the proportions of parents wh o chose schools of different academic quality as compared to the expected proportions based on the district socioeconomic demographics This test is highly vulnerable to large sample sizes, and so Cohe w ) was used to better interpret the substantive, not just statistical, significance of the differences. ting system for assigning stude nts to schools of choice appeared to be very effective at maintaining the racial and socioeconomic proportions of students at the level they would be if students attended their attendance area schools. Thus, no additional statistical analysis was appropriate to compare the actual and coun terfactual demographic data Summary The researcher sought to determine whether the school choice plan in this district had an impact on the racial and socioeconomic distribution of students in elementary schools. The comparison of actual and counterfactual data revealed th choice plan was effective at maintaining the racial and socioeconomic proportions that would exist if all students attended their assigned nei ghborhood school. While there was a widespread perce ption that sc hool choice caused resegregation, in this district, school choice appeared to maintain the level of segregation that occurred due to student assignment to neighborhood schools, but not contribute to or accelerate it. Most parents did choose high quality s chools for their children However, when the ethnic and socioeconomic status of choosers were e xamined in light of the academic quality of the schools they chose, there appeared to be significant differences as
109 compared to the district demographics. Poor s tudents were less likely to choose A schools. Poor and black students were somewhat more likely to choose B and C schools. Importantly, poor and b l ack families were much more likely to choose D or F schools for their children
110 Chapter 5 Discussion of Findings This chapter presents a summary of the study, a discussion of the findings, and conclusions related to the analysis of district data for each of the four research questions. Recommendations are presented. Also included are suggest ions for further research Findings and Interpretations Across the United States, school districts have been released from Federal scrutiny and have resegregated (Orfield & Yun, 1999). Yet as a society, we still purport to care about integrated schools We continue to believe in the short and long term value backgrounds and lives. We mourn the tragic death s of young people who are bulli ed because they are different. And c losin g the achievement gap, especially between white and black students is a national priority reflected in state and Federal legislation. All the while, stratification by race and class continues to increase. This study was designed to illuminate the current l determine the impact that school choice played in that stratification, and discern whether families who participated in the choice plan chose high quality schools. The findings suggested that the school choice plan in this particular school district was not the cause of elementary school segregation, either by race or by socioeconomic level. The comparison of actual and counterfactual demographics made it clear that the educational landscape ex perienced by elementary students would have be en the same,
111 with or without the movement of more than 18,000 elementary students (nearly 20% of all elementary students) participating in the school choice plan. A majority of parents chose high quality school s for their children ; however, a statistically and substantively sign ificant proportion of poor and b lack parents chose lower performing schools. The research q uestions The first research question, 2010 racial and socioeconomic demographics, c ould the elementary schools be described as integrated set the stage for considering the impact of the choice plan These actual demographics served as a snapshot of the level of integration that existed in the elementary scho ols and illustrated the educational environment for young children across the dis trict. The data showed that terrain to be very diverse. Elementary school populations range d from 1% b lack to 90% b lack. More than 15% of the schools were racially identifiabl e, with b lack students making up more than 50% of the population s in integration. Many scholars have documented that resegregation, after school districts are granted unit ary status a nd released from court scrutiny is widespread and dramatic (Bhargava, Frankenbuer, & Le, 2008; Orfield 2001; Orfield & Yun, 1999). Enrollment demographics in this district illustrate d that effective desegregation of schools was accomplished in the early 1970s with school clustering and busing. However, by the mid As was common in many cases across the country, the courts determined that this segregation was ca used by residential patterns and other factors outside the control of the
112 school district ( Bd. of Ed v. Dowell, 1991; Manning, 2001; Oklahoma City Bd. of Ed. v. Dowell, 1991 ) The level of poverty within schools in the 2009 2010 school year was also wide ranging. The percent of students who qualified for free or reduced price lunch ranged from 8% to 98%. More than half of all schools had 70% or more of their populations eligible for free or reduced price lunch, while 19 schools had 30% or fewer stu dents wh o qualified. Using the C district wide proportion, only eight schools (5.7%) would have been identified as socioeconomically integrated The second research question tary schools be considered integrated if all students attended their attendance area schools required the calculation of counterfactual data. For demographics, the researcher removed students who chose to attend the school and re turned those who se counterfactual demographics represent ed have been if there was no school choice option. By looking at the se counterfactual demographics one could in a preferred neighborhood. Counterfac tu al sch ool populations ranged from 1% black to 91% b lack. More than 16% of the schools we re racially identifiable, with b lack students making up more than 50% of the popul ations in those schools. In addition, only 25 scho ols (17.7%) would have met the C Bhargava, Frankenberg, & Le (2008) found that countywide school d istricts tended to be more integrated. In t his countywid e
113 district, however, fewer than 20% of its elementary schools would be identified as not just school boards, ne ed to accept r esponsibility for integration was born out in this study. The counterfactual level of poverty within schools was also wide ranging. The percent of students who qualified for free or r educed price lunch ranged from 7% to 98%. Nearly half of a ll schools had 70% or mo re of their counterfactual populations qualify for free or reduced price lunch, while 33 schools had 30% or fewer stu dents who qualified. Using the C district wide proport ion, only nine schools (6.4 %) would have been identified as socioeconomically integrated based on their counterfactual data Research questions 1 and 2 provided background information for r esearch question 3 which addressed the dy: W hat impact did choice plan have on the racial and socioeconomic charact er of its elementary schools? By comparing the data compiled in the first two research questions, the researcher discovered that even though more than 18,000 element ary students attended schools other than their assigned attendance area schools, there was virtually no change in the The dissimilarity index, determined to be 0.479 for b lack students for the 2009 2010 school year, and 0.480 for the counterfactual data developed by the researcher. For students eligible for free or reduced price lunch, the actual 2009 2010 dissimilarity index was 0.497, compared to the counterfactual dissimilarity index of 0.469.
114 The weighting system used to grant choice requests in this district appeared to maintain the level of racial and socioeconomic integration at virtually the exact level s that the elementary schools would have if there was no school choice option and all students attended their assigned attendance area schools. Elmore and Fu ller (1996) suggested four propositions drawn from research on school choice three of which were directly related to this study Their first proposition, that choice would increase stratification by race and social c lass, was not upheld by this re search The choice plan had no impact on the level of stratificatio n in this district. Second, t hey contended that detail s matter in the design and implementation of choice plans, and that appeared to be true in this situation The weighting system used by the dis trict was able to maintain the level of integration that existed due to residential patterns and the attendance area zones. Families completed applications listing three school choices. The system for assigning the choice schools used by the district was a detail in the ch oice plan that did impact the level of integration It was maintained at its non choice level. Finally, they suggested that context matters in school choice plans. The district developed this choice plan as it was released from Federal scr utiny. The C ourt had determined that residential housing patterns were to blame for the resegregation of schools and that the district was not responsible Thus, the plan was designed from within the context of the C ourt ruling, only to maintain the status quo, and it did that, based on the attendance area boundaries. The answer to research question 3, the fundamental question of the study, was that the school choice plan had virtually no impact on elementary school demographics. T he choice plan appeared to allow large numbers of students to attend schools of choice without contributing to or accelerating the resegregation, racially or socioeconomically,
115 This conclusion was in direct contrast to a large body of research that implicated school choice plans in the stratification of schools ( Dillon, 2008; Hardy, 2006; Hausman & Goldring, 2000; Howell, 2006; Lubienski, 2007; Neild, 2005; Saporito & Lareau, 1999; Saporito & Sohoni, 2007; Smrekar & Goldring, 1999). Whether due to poor information (Neild, 2005; Schneider & Buckley, 2002 ; Van Dunk & Dickman, 2002), lack o f quality choices (Dillon, 2008; Neild, 2005 ), or deeply imbedded racism (Andre Bechely, 2007; Holme, 2002; Liu & Taylor, 2005), the research overwhelmingly sugge sted that choice plans increase stratification This was not the case in this study. Research question 4 addressed the quality of schools chosen by parents of the elementary students This district studied was in a state that published annually, a list of based on high stakes test scores. These school grades were widely available and widely known in the district. Though imperfect and incomplete measures of school quality, these grades were an important par t of the information parents could have use d to choose schools for their children. The data showed that 60% of families chose A schools for their children. The remaining 40% selected B, C, D, or F schools, including over 400 families (3%) who chose to send t heir children to D or F schools. Many scholars have questioned whether school choice plans actually offer real alternatives to the poor schools that families wish to exit (Dillon, 2008; Goldhaber, 1999; Liu & Taylor, 2005; Neild, 2005). With more than half of the schools in this district earning the grade of A from the state there were clearly some quality options for students in low performing schools.
116 A statistically significant n umber of parents who chose the very low performing D and F schools were b l ack or poor. Schneider and Buckley (2002) cite d and why low income families are forced to make school decisions with less than adequate information. Howell (2006) found that parents of children who attended low perfor ming schools were less likely than parents of children in high performing schools to know whether or not Howell also found that family characteristics were more important than the characteristics of the schools being exit ed when families chose schools. Andre Bechely (2007) found that the urban landscape was fraught with obstacles that impacted the choices of urban students. She emphasized that location was the key to the choices parents made. Time and transportation were t choices. All of this research was congruent with the findings in research question 4 that minority and l ow income parents too often cho se low performing schools. Implications It was clear from this study that the choice plan has not contributed to the acceleration of resegregation in this district By removing the impact of students w ho ch ose to exit their assigned school and attend other schools, the counterfactual data showed that the district would be nearly identica l to the way i t was with the choice plan. However, it was also clear that the district could not be described as integrated Interpretation of the dissimilar i ty indices for the actual demographics and the counterfactual demographics suggested that y schools were moderately segregated. Analyzing the population characteristics of the elem entary schools in light of the C ourt standards for integratio n also showed a district that was not
117 evenly integra ted. This was disheartening in a district that achieved such high levels of integration for nearly 20 years, when mandated by the courts to do so. The analysis of parents who cho se to send their child ren to low performing schools was alarmin g because it, in part, clarified how the choice plan maintained the status qu o wi th regard to segregation Families tended to choose schools that were like themselves, as had been found in previous research (Holme, 2002; Howell, 2006) Parents who chose the low perfo rming schools were significantly more likely to be b lack or poor. Rather than choosing based on the academic quality as represented by the school grade, these families likely had other priorities Bausch and Goldring (1995) found that poor families were mo reputation, while minorities were influenced by location and proximity. It is likely that the families that chose very poor schools were indeed influenced by proximity to their own homes and neighb orhoods The large effect sizes in this chi square analysis demonstrated that th e difference between expected and actual proportions with regard to race and SES, was substantive ly not just statistically significant. The choice plan was not moving those a t risk students to better schools, but to other, equally inferior schools. These findi ngs le d to a number of recommendations for improvement in the execution of the choice plan. Recommendations The current school choice plan was developed as part of the voluntary integration plan to achieve unitary status and be released from Federal court supervision. As such, it was designed to maintain the level of integration that had occurred by 2001. The di strict
118 went to great lengths to involve the community in working toward unitary status and was quite successful in developing a high level of community engagement. The conversion of school choice from a legal argument to a district policy occurred witho ut significant forethought into long term consequences. School choice needs to be grounded in a clearly defined set of priorities and community beliefs. The continuous im provement, effective communication, leadership, accountability, and civic fficial policy manual enumerates a set of values as part of the philosophy of the board. Equality was among the values named on the website, and respe values and priorities, while constant, must always be evaluated and re evaluated in light of economic and other constraints, and must be the basis for all policies and prog rams. recommended that the school district and community re evaluate the commitment to integrated schools. There are several questions to be considered by policy makers and th e community, including: What degree of integration is ideal ? Acceptable? Unacceptable? What are the policies that impact stratification and resegregation? What are the financial costs of different student assignment policies? What policies are actually mos t effective at improving student achievement? priorities can the issue of resegregation be addressed. From this foundation, the district should set goals and develop policy. Rather than just maintain the cur rent level of moderate segregation, the board should set two lofty and challenging goals focusing on excellence and equity. An historical look back at the plans
119 designed to promote voluntary integration and to facilitate school accountability is needed to develop appropriate and effective policies in the future. Conversations with policy makers, faculty, and parents who experienced the extensive changes from the school c hoice plan was created and how it could be improved. Such a study would situate the current level of segregation within its historical and cultural context in the community. A high level of public engagement must be established. Based on the demographics revealed in the analysis of research questions 1 and 2, the current school choice plan did not, in and of itself, contribute to increasing resegregation. The district was deemed unitary in 2001 because the C ourt was convinced that all vestiges of de jure s egregation were gone, and that residential housing patterns had caused the resegregation that occurred. That appears to still be the case. However, the school choice plan falls und er what Derek Black (2008) called oretically, families in schools that have become unbalanced racially or socioeconomically, can choose different or better schools for their children. Even more important ly families in failing schools can choose better schools for their children. This ill ustrates the intersection of individual choice and societal goals, as well as the intersection of public and individual responsibility, and is the very heart of the dual goals of excellence and equity. Next, the school district must translate those goals into objectives. For the represented by school grades. However, excellence is much more and must be clearly defined by the district and community before it can be ach ieved. Excellence in schools
120 must be more than test scores. Objectives related to excellence must have global implications, be futuristic, and must consider the whole child. Equity must also be rendered into clear objectives. Voluntary integration, parent al empowerment in the choice of schools, and an even playing field for all families are strong equity objectives. Finally, there must be clear criteria for evaluating the goals and objectives. These criteria should include methods for measuring effectiven ess, comparing economic costs, determining the capacity for implementation, and evaluating political impact. Interactions between goals and objectives must also be assessed. For example, school choice, in addition to being a popular approach for promoting voluntary integration, is also purported to empower parents and provide accountability for schools. The school choice weighting system that is so effective in maintaining the current level of integration may, in fact, be hindering the accountability proces s. If parents were to be given unfettered access to the schools they want for their children, would some other schools be empty? And if so, how could those empty buildings be reconstituted in ways that would make them successful and attractive as a choice ? The allocation of scarce resources is an important school board responsibility. Clearly, there are economic considerations in balancing the enrollment in each school, but beyond those economic considerations, what are the academic achievement costs now a nd the costs to society down the road, when low performing schools continue to be filled with children and are not held accountable? Housing patterns clearly impact the demographics of schools. The demographics that result from students attending their as signed attendance area schools show ed that the d moderately segregated when the district is the unit of analysis. When looking at individual schools, the actual educational life that children live
121 each day varied widely dep ending on the school they attend ed Many children attend schools that are nearly as segregated as they would have been in the 1950s and 1960s under the dual system of de jure segregation. While changing attendance boundaries is seldom popular, it can be a measured approach, implemented over time that has minimal financial impact to the district The district should evaluate the how changes in school attendance zones could impa ct the goals of excellence and equity. The administration of the choice plan was very effective in maintaining the level of integration that matched the student assignment plan. The district should consider, in light of the Louisville/Seattle cases ( Pare nts Involved in Community Schools v. Seattle School District No.1 2007), whether the weighting system used to grant choice requests can or should be adjusted to reduce the stratification that exists in the district. The Supreme Court ruled in those cases to sharply limit the use of race in determining which schools children could attend. Both the Louisville and Seattle case were specifically related to voluntary choice plans, and the Court ruled that the use of race violated the equal protection clause of the U.S. Constitution. Chief Justice Roberts, in the majority opinion, wrote that neither district showed "compelling interest in remedying the effects of past intentional discrimination system and Louisvill e had eliminated all vestiges of a dual system. Justice Kennedy, in the minority opinion, said that race could perhaps be considered as a tool for school districts to bring "together students of diverse backgrounds and races." He specifically mentioned mag net schools and attendance boundaries. The majority opinion makes it very difficult to use race as an integral part of the weighting system for choice assignment. However, the case left open the possibility of using socioeconomic status in
122 student placemen t. The district should consider very carefully how the weighting system may or may not be used appropriately and legally to reduce stratification. Further study is recommended with regard to how school choice can hold schools more accountable in an era of located has very strict guidelines for the number of students that can be in one class room with one teacher Grades K 3 can have no more than 18 students, and grades 4 5 can have no more than 22 s tudents. These stringent rules have harsh financial penalties for the district if the class size limits are exceeded. Under these conditions, allowing more freedom of choice to hold schools more accountable is all but impossible, and school choice assignme nts must be carefully monitored. The district and community should consider how to balance equity and excellence within the se constraints imposed by the state. Finally, the significant number of poor and black families that chose to send their children to low performing schools was an equity concern. The literature suggests that parents, especially minority and low (Howell, 2006; Kraus, 2008; Neild, 2005; Schneider & Buckley, 2002). In fact, in this district, n early 27% of the fami lies participating in choice were black, while on ly 21% of district enrollment was black. This suggests that minority families are, indeed, taking advantage of choices. Information and transportation could play a role in leveling the p laying field in this regard. Andre Bechely (2005), in particular, asserts that historic inequalities are pervasive in the development and administration of policies, leaving minorities, poor families, and speakers of other languages needing more guidance a nd support in the choice process. The district should consider the quality and value of the
123 choice information that is made available to the public and how that information is disseminated. NCLB Choice processes, along with options for getting additional i nformation about schools need to be specifically targeted and appropriately written for parents who struggle with navigating the complexities of schooling in a large district. Weinstein (2008) found that low income parents chose higher performing schools w hen given clear information about the academic quality of those schools. The school district should make information about school quality, the importance of integration, and choice options more available, more appropriate to target audiences, and generally clearer to all constituents. that impacts urban families. In addition to adequate and appropriate information with which to make schooling decisions, poor urban parents nee d transportation options in order to take advantage of school choice. The district should consider whether providing transportation for all school choice, not just magnet schools and NCLB choice, could reduce stratification. Lubienski (2007) and Hardy (200 6) both found in their research that schools that are economically different may be too far away to be a real choice for some families. School buses can, for some families, make that distance surmountable. There is a high cost associated with transporting students across the district to schools that are actually different from the attendance area schools to which students are assigned. Thus, the district must evaluate its priorities and determine if transportation can potentially reduce stratification enoug h to justify the cost in time and money. The recommendations presented here are all centered on developing a clear policy with regard to excellence and equity. It is only from within that context that a school
124 choice plan can be evaluated appropriately an d effectively. Virtually all work of the school board should be focused on these goals and as such this examination should be a priority. Reflections This research began with the assumption that the school choice plan in this district was causing, at least to some degree, resegregation. One needed only to walk through a ed an ecdotal evidence, shared by many in the district that led to this study It was immediately apparent when reviewing the 2009 2010 demographic data that the district was no longer evenly integrated. After calculating the counterfactual demographics it was clear that the school choice process had virtually no impact on these racial and socioeconomic demographics. Research question 4, which led to examining the racial and socioeconomic characteristics of families that chose schools with different levels of academic quality became more important as the study progressed A majority of all the families tha t participated in school choice chose A schools. That was to be expected based on the literature It was surprising, however, that more than 3% of the familie s who participated in the choice process chose lower performing, D or F schools for their children While 3% is, in fact, a low proportion of the total number of children who attend a scho ol of choice, that 3% represents more than 400 children. In the era of No Child Left Behind and the disaggregation of data to include subgroups of as few as 30 children for Adequate Yearly Progress (AYP) 400 children is a substantial number. I t was also disconcerting that such a large proportion of those children were poor (95%) or b lack (75%) Many of
125 these children exited schools that were, by state standards, much better than their school of choice This led to many more questions related to why they chose those schools. Suggestions for Furthe r Research The data analyzed for this study included only the actual choices made by families to attend schools other than their assigned attendance area schools. Further study should, perhaps, focus on the intended and unintended consequences of school c hoice As part of the proposal to gain release from court supervision, school choice was a tool used to promote voluntary integration in a district that had already begun to resegregate. Two specific areas were identified in this research that warrant furt her research. First, unlike many such plans across the United States, a large number of families participated in this in the district (21%), a higher proportion of b lack students (27%) attended schools of choice than would be expected. However, compared to the proportion of students in the district who qualify for free or reduced price lunch (60%), there were fewer FRL students (55%) participating in the school choice process. Research specifically aimed at understanding who participates in school choice, and equally important, who does not participate in choice, could provide important insight into what inspires families to exit the attendance area school The spatial dimension should be pursued when investigating these choosers. Are the residential housing patterns that have result ed in moderately segregated schools also influencing the choices parents make? Are these spatial contexts human interactions with geograph y actually the last vestiges of institutional racism in this community ?
126 A second area for further research is in the area of the weighting system used in the school choice process. As it is currently implemented, the end result effectively maintains the level of integration that would exist without school choice. It maintained ified three choices on the school choice application. It would be enlighteni ng to know how many families were awarded their first c hoice, and whether families that do not get their first choice of schools attend ed a second or third choice, or if they just remain ed at their attendance area school. If all families were granted their first choice, would that increase the accountability aspect of school choice? Finally, this study was designed to be different from much of the literature reviewed as background for this research. Only the choices families actually made were evaluated, rather than who chose, how they chose, and why they chose, as was frequently addressed in previous research. The researcher also focused primarily on individual school characteristics. This was an attempt at discovering not just whether there was resegregation at the district level, but whether there was resegregation within each elementary school where that segregation impacted individual children. However, the information gleaned in examin ing individual schools in research question 4 suggested that the questions of who chooses schools and why, must be answered. Does integration even matter to parents? To the community? Many questions remain: Who are the families that choose magnet schools? Who are the families that choose other schools? Is location choices based on crit eria other than school grade or location? If so, what are the factors that truly infl up study, qualitative in nature, could shed
127 light on why parents, especially poor and black parents, sometimes chose low performing schools for their children. Summary and Conclusions This research was undertaken to dete rmine the role school choice played in the determined that the movement of more than 18,000 elementary students from their attendance area schools to schools of their own choosing did not impact the demographics of the schools. Parents in this district tended to choose schools graded A by the state, but a substantial proportion choose schools that were equivalent or lower performing than the assigned school that their child ren exited. Black and poor parents were significantly more likely to choose very low performing schools for their children. The stratifi cation in this district reflected resi dential housing patterns, and was not directly influenced by the school choice pla n.
128 References Andre Bechely, L. (2005). Public school choice at the intersection of voluntary integration and not so experiences. Educational Administration Quarterly 41(2), 267 305. Andre Bechely, L. (2007). Finding space and managing distance: Public school choice in an urban California district. Urban Studies 44(7), 1355 1376. Archbald, D. (1996). Measuring school choice using indicators. Educational Policy 10(1), 88 108. Archbal d, D. (2000). School choice and school stratification: Shortcomings of the stratification critique and recommendations for theory and research. Educational Policy 14(2), 214 240. Archbald, D. (2004). School choice, magnet schools, and the liberation model : An empirical study. Sociology of Education 77(October), 283 310. Alexander v. Holmes County Bd. of Ed., 396 U.S. 1218 (1969). Assessment and accountability briefing book: FCAT, School accountability, Teacher certification tests. (2007). Florida Departme nt of Education. Accountability, Research, and Measurement. Bastian, A. (1990). School choice: Unwrapping the package. In W. Boyd & H. Walberg (Eds.), Choice in education (pp. 177 186). Berkeley CA: McCutchan.
129 Bauch, P. & Goldring, E. (1995). Parent invol vement and school responsiveness: Facilitating the home school connection in schools of choice. Educational Evaluation and Policy Analysis 17(1), 1 21. Bell, D.A. (1980). Brown v. Board of Education and the interest convergence dilemma. Harvard Law Review 93(3), 518 533. Benson, J. & Borman, G. (2010). Teachers College Record, 112(6), 1631 1653. Berends, M. & Penaloza, R.V. (2010). Increasing racial isolation and test score gaps in mathematics: A 30 year perspective. Teachers College Record 112(4), 978 1 007. Bhargava, A., Frankenberg, E., & Le, C. (2008). Still looking to the future: Voluntary k 12 school integration a manual for parents, educators, and advocates The Civil Rights Project at Harvard University. Retrieved April 2, 2008 from www.naacpldf.org Bifulco, R., Lad, H., & Ross, S. (2009). Public school choice and integration evidence from Durham, North Carolina. Social Science Research 38, 71 85. Black, D.W. (2008). The uncertain future of school desegregatio n and the importance of goodwill, good sense, and a misguided decision. The Catholic University Law Review 57 Cath. U.L. Rev. 947. Blank, R., Levine, R., & Steel, L. (1996). After 15 years: Magnet schools in urban education. In B. Fuller & R. Elmore (Eds .), Who chooses? Who loses? Culture, institutions, and the unequal effects of school choice (pp. 154 172). New York: Teachers College Press. Bd. of Educ. V. Dowell, 498 U.S. 237, 247, 111 S.Ct. 630, 637, 112 L.Ed.2d 715 (1991).
130 Borman, K., et.al. (2004). A ccountability in a postdesegreation era: The continuing American Educational Research Journal 41(3), 605 631. Boyd, W. (ed.). (1990). Choice in education: Potential and problems Berkeley, CA: McCut chan Publishing Corporation. Brief of 553 Social Scientists as Amici Curiae in support of respondents. (2007). Parents involved in Community Schools v. Seattle School District and Cristal D. Meredith v Jefferson County Board of Education. On Writs of Certiorari to the U.S. Courts of Appeal for the Ninth and Sixth Circuits. Brigho use, H. (1997). Two philosophical errors concerning school choice. Oxford Review of Education 23(4), 503 510. Brown v. Board of Educ., 347 U.S. 483 (1954). Brown v. Board of Educ., 349 U.S. 294, 301 75 S.Ct. 753, 757, 99 L.Ed. 1083 (1995). Brown, K. (2005 ). Race, law, and education in the post desegregation era Durham, NC: Carolina Academic Press. Carter, R. (1996). The unending struggle for equal educational opportunity. In Lagemann, E. & Miller, L. (Eds.). Brown v. board of education: The challenge for schools (pp. 19 26). New York: Teachers College Press. Choices for parents United States Department of Education Website. Retrieved October 2, 2010, from www2.ed.gov/nclb/choice/index.html. Chubb, J. & Moe, T. (1990). Politics, markets and Americ Washington, D.C.: Brookings Institution Press.
131 Civil Rights Project at UCLA. Data retrieved April 13, 2008 from www.civilrightsproject.ucla.edu Cochran Smith, Marilyn and Zeichner, Ken (editors) (2005). Studying Teacher Education: The Report of the AERA Panel on Research and Teacher Education Mahweh, N.J.: Lawrence Erlbaum Publishers. Cohen, J. (1988). Statistical power analysis for the behavioral sciences Hillsdale, N.J: L. Erlbaum Associates. Cookson, P. (1991). Degrees of imperfection: A note from a political Pollyanna. Teachers College Record 93(1). Cooper, C. (2005). School choice and the standpoint of African American mothers: Con sidering the power of positionality. The Journal of Negro Education 74(2), 174 189. Crain, R. & Mahard, R. (1978). Desegregation and Black achievement: A review of the research. Law and Contemporary Problems 42(2), 17 53. Cullen, J., Jacob, B., & Levitt, S. (2006). The effect of school choice on participants: Evidence from randomized lotteries. Econometrica 74(5), 1191 1230. DeBray ordered desegregation: The conflict between tow Federal mandates in Richm ond County, Georgia, and Pinellas County, Florida. Educational Policy 21(5), 717 746. Dillon, E. (2008). Plotting school choice: The challenges of crossing district lines. Education Sector Reports Washington, D.C. Drummond, K. & Stipek, D. (2004). Low in The Elementary School Journal 104(3), 197 213.
132 Eitle, T.M. (2003). Segregation, diversity,and accountability in Florida schools Paper presented at the meeting of the American Sociolog ical Association, Atlanta, GA. Figlio, D.N. & Rouse, C.E. (2006). Do accountability and voucher threats improve low performing schools? Journal of Public Economics 90(1 2), 239 255. Finn, C. (1990). Why we need choice. In W. Boyd & H. Walberg (Eds.), Choi ce in Education, (pp. 3 20). Berkeley, CA: McCutchan. Fowler, F. (1991). The shocking ideological integrity of Chubb and Moe: A review of Journal of Education 173(3). Fram, M., M iller Cribbs, J., Van Horn, L. (2007). Poverty, race, and the contexts of achievement: Examining educational experiences of children in the U.S. south. Social Work 52(4), 309 319. Freeman v. Pitts 503 U.S. 467, 494, 112 S.Ct. 1430, 1447, 118 L.Ed.2d 108 (1 992). Friedman, M. (1955). The role of government in education. Retrieved April 17, 2008, from http://www.schoolchoices.org/roo/fried1.htm Fuller, B., & Elmore, R. (Eds.). (1996). Who chooses? Who loses? Culture, institutions, and the unequal effects of school choice. New York: Teachers College Press. Gelber, S. (2008). The crux and the magic: The political history of Boston magnet schools, 1968 1989. Equity and Excellence in Education 41(4), 453 4 66. Gokcekus, E., Phillips, J., & Tower, E. (2004). School choice: Money, race, and Congressional voting on vouchers. Public Choice 119, 241 254.
133 Goldhaber, D. (1999). School choice: An examination of the empirical evidence on achievement, parental decisi on making, and equity. Educational Researcher 28(9), 16 25. Goldhaber, D. & Eide, E. (2003). Methodological thought on measuring the impact of private sector competition on the educational marketplace. Educational Evaluation and Policy Analysis 25(2), 21 7 232. Goldsmith, P.R. (2010). Learning apart, living apar t: How the racial and ethnic seg regation of schools and colleges perpetuates residential segregation. Teachers College Record 112(6), 1602 1630. Gorard, S., Fitz, J., & Taylor, C. (2001). School choice impacts: What do we know? Educational Researcher 30(7), 18 23. Goyette, K. (2008). Race, social background, and school choice options. Equity and Excellence in Education 41(1), 114 129. Graham, P. (2005). Schooling in Am changing needs. New York: Oxford University Press. Granovetter, M. (1986). The micro structure of school desegregation. In School desegregation research: New directions in situational analysis. Prager, J., Lo ngshore, D., & Seeman, M. (Eds.). New York: Plenum Press. Green v. County School Board of New Kent County, 391 U.S. 430 (1968). Hall, J. (1992). School Desegregation in Hillsborough County, Florida. Tampa, History department, University of South Florida. Hanushek, E., Kain, J., & Ri vkin, S. (2002). New evidence a bout Brown v. Board of Education: The complex effects of school racial composition on achievement.
134 National Bureaus of Economic Research, Working Paper 8741, retrieved from http://w ww.nber.org/papers/28741. Hardy, L. (2006). Separate our student by race and income to meet NCLB? [Supplement]. American School Board Journal 193(April 2006), 46 52. Hastings, J. & Weinstein, J. (2008). Information, school choice, and academic achievement : Evidence from two experiments. The Quarterly Journal of Economics. Hausman, C. & Goldring, E. (2000). Parent involvement, influence, and satisfaction in magnet schools: Do reasons for choice matter? The Urban Review 32(2), 105 121. Holley, D. (2004, Apr il). Brown is dead? Is Brown dying? Exploring the resegregation trend in our public schools. Paper presented at Justice Action Center Symposium, New York Law School, New York. Holme, J. (2002). Buying homes, buying schools: school choice and the social con struction of school quality. Harvard Educational Review 72(2). Howe, K. & Welner, K. (2002). School choice and the pressure to perform. Remedial and Special Education 23(4), 212 221. knowledge about the choice provisions of No Child Left Behind. Peabody Journal of Education 81(1), 140 179. Hoxby, C. (2000). Does competition among public schools ben efit students and taxpayers? The American Economic Review 90(5), 1209 1238. Retrieved April 19, 2008, from ABI/INFORM Global database. (Document ID: 65382585).
135 Jackson, B. & Cooper, B. (1989). Parent choice and empowerment: New roles for parents. Urban Ed ucation 24(3), 263 286. Johnston Parsons, M., Lee, Y. A., & Thomas, J. M. (2007). Students of color as cultural consultants: A self study of race and social justice issues in a teacher education program. Studying Teaching Education, 3 (1), 67 84. Keyes v. Sch. Dist. No. 1, Denver, Colo., 413 U.S. 189, 208, 93 S.Ct. 2686, 2697, 37 L.Ed.2d 548 (1973). Kleitz, B., Weiher, G., Tedin, K., & Matland, R. (2000). Choice, charter schools, and household preferences. Social Science Quarterly 81(3), 846 854. Koedel, C ., Betts, J., Rice, L., & Zau, A. (2009). The Integrating and Segregating Effects of School Choice. Peabody Journal of Education 84(2), 110 129. in Minneapolis. Equity and Excellence in Education 41(2), 262 274. Lacireno Paquet, N. & Brantley, C. (2008). Who chooses schools, and why? Education Policy Research Unit. Tempe, AZ: Division of Educational Leadership and Policy Studies, Arizona State University. Retrieved from http://epsl.asu.edu/epru/documents/EPSL 0801 247 EPRU.pdf on March 15, 2008. Ladson Billings, L. & Tate, W. (1995). Toward a critical race theory of education. Teachers College Record 97(1), 47 68. Lagaemann, E.(1996). An American dilemma still. In Lagemann, E. & Miller, L. (Eds.). (pp. 1 7). New York: Teachers College Press.
136 Laureau, A. (2003). Unequal childhoods: c lass, race, and family life Berkeley, CA: University of California Press, Ltd. Ladd, H. & Fiske, E. (2001). The uneven playing field of school choice: Evidence from New Zealand. Journal of Policy Analysis and Management, 20(1), 43 64. Liu, G. & Taylor, W. (2005). School choice to achieve desegregation. Fordham Law Review 791. Looking to the future: Voluntary k 12 school integration a manual for parents, educators, and advocates. (2005). Civil Rights Project at Harvard University. Retrieved April 2, 2008 from www.naacpldf.org. Lubienski, C. (2007). Marketing schools: Consumer goods and competitive incentives for consumer information. Education and Urban Society 40(1), 118 141. Ma ddaus, J. (1990). Parental choice of school: What parents think and do. Review of Research in Education 16, 267 295. Magnet Schools of America.Retrieved from http://www.magnet.edu/modules /content/index.php?id=1 on January 15, 2009. Mannings v. Bd. of Pub. Instruction of Hillsborough County, Fla., 427 F.2d 874 (5th Cir.1970). Manning v. Sch. Bd. of Hillsborough County, Fla., 24 F.Supp.2d 1277 (M.D.Fla.), mot. to alter or amend den., mot. f or clarification granted in part, 28 F.Supp.2d 1353 (M.D.Fla.1998). of choice movement? Educational Policy 4(2), 73 91.
137 Martinez, V., Godwin, K., & Kemerer, F. (1996). Public school ch oice in San Antonio: Who chooses and with what affects? In B. Fuller & R. Elmore (Eds.), Who chooses? Who loses? Culture, institutions, and the unequal effects of school choice (pp. 50 69). New York: Teachers College Press. Metz, M. (1986). Different by de sign New York: Routledge. Metz, M. (1990). Magnet schools in the reform of public schooling. In W. Boyd & H. Walberg (Eds.), Choice in education (pp. 123 147). Berkeley CA: McCutchan. Metz, M. (2003). Different by design: The context and character of thre e magnet schools New York: Teachers College Press. Miller, L. (1996). Tracking the progress of Brown In Lagemann, E. & Miller, L. (Eds.). (pp. 9 13). New York: Teachers College Press. Millik en v. Bradley, 418 U.S. 717, 746 47, 94 S.Ct. 3112, 3128, 41 L.Ed.2d 109 (1974). Milner, H. (2008). Critical race theory and interest convergence as analytic tools in teacher education policies and practices. Journal of Teacher Education 59(4), 332 400. M intz, S. (2004). Cambridge, MA: The Belknap Press of Harvard University Press. Missouri v. Jenkins, 515 U.W. 70, 87, 115 S.Ct. 2038, 2048 132 L.Ed.2d 63 (1995). Morgan v. Kerrigan, 530 F.2d.401 (1976). Murphy, J. (1996). After forty years: The other half of the puzzle. In Lagemann, E. & Miller, L. (Eds.). (pp. 143 150). New York: Teachers College Press.
138 National Association for the Advancement of Colored People Legal Defense and Educational Fund. (1972). New York: NAACP Legal Defense Fund, Inc. Neild, R. (2005). Parent management of school choice in a large urban district. Urban Education 40(3), 270 297. No Child Left Behind (NCLB) Act of 2001, 20 U.S.C.A. Â§ 6301 et seq. (West 2003). Ogawa, R. & Dutton, J. (1997). Parent involvement and school choice: Exit and voice in public schools. Urban Education 32(3), 333 353. Orfield, G. (1996). Public opinion and school desegregation. In Lagemann, E. & Miller, L. (Eds.). (pp. 54 70). New York: Teachers College Press. Orfield, G. & Yun, J. (1999). Resegregatio n in American schools. The Civil Rights Project Harvard University. Cambridge, MA: Harvard University. Orfield, G. (2001). Schools more separate: Consequence of a decade of resegregation. The Civil Rights Project. Harvard Univerity. Cambridge, MA: Harvard University. Orfield, G., Frankenberg, E., & Lee, C. (2002). The resurgence of school segregation. Educational Leadership 60(4), 16 22. Parents Involved in Community Schools v. Seattle School District No. 1 551 U.S. 701 (2007). Together with No. 05 915, Meredith, Custodial Parent and Next Friend of McDonald v. Jefferson County Bd. of Ed et al., on certiorari to the United States Court of Appeals for the S ixth Circuit.
139 Pedhauzer, E.J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). Fort Worth, TX: Harcourt Brace College. Perry, L. (2007). Conceptualizing education policy in democratic societies. Education Policy OnlineFirst December 18, 2007. Peterson, C. (2009). Minimally sufficient research. Perspectives on Psychological Research 4(1), 7 9. Placier, M., Hall, P., McKendall, S., & Cockrell, K. (2000). Policy as the transformation of intentions: Making multicult ural education policy. Educational Policy 14(2), 259 289. Pulido, L. (2000). Rethinking environmental racism: White privilege and urban development in southern California. Annals of the Association of American Geographers 90(1), 12 40. Robelen, E. (2008) Mapping analysis finds interdistrict choice options to be limited. Education Week, 28(1), 8 8. Rossell, C. (1985). What is attractive about magnet schools? Urban Education 20(1), 7 22. Rossell, C. (2005). Social science research in educational equity c ases: A critical review. In Social Science Research. Education Next, 5(2), 44 49. Rouse, C. (1998). Schools and student achievement: More evidence from the Milwaukee parental choice program. Economic Policy Review
140 Sanders, N. (2002). Would privatization of K 12 schooling lead to competition and thereby improve education? An industrial organization analysis. Education Policy 16(2), 264 287. Saporito, S. (2003). Private choices, public consequences: Magnet school choice and segregation by race and poverty. Social Problems 50(2), 181 203. Saporito, S. & Lareau, A. (1999). School selection as a process: The multiple dimensions of race in framing educational choice. Social Problems 46(3), 418 439. Sapo rito, S. & Sohoni, D. (2007). Mapping educational inequality: Concentrations of poverty among poor and minority students in public schools. Social Forces 85 (3), 1227 1253. Saporito, S. & Sohoni, D. (2006). Coloring outside the lines: Racial segregation i n public schools and their attendance boundaries. Sociology of Education 79(2), 81 105. Schneider, M. & Buckley, J. (2002). What do parents want from schools? Evidence from the internet. Education Evaluation and Policy Analysis 24(2), 133 144. Schneider, M. & Buckley, J. (2002). Creating choosers: Information, the digital divide, and the propensity to change schools. Social Science Computer Review 20(4), 451 470. Schneider, M., Marschall, M., Teske, P., & Roch, C. (1998). School choice and culture wars i n the classroom: What different parents seek from education. Social Science Quarterly 79(3), 489 501. Schofield, J. (1989). Black and White in school: Trust, tension or tolerance ? New York: Teachers College Press.
141 Sikkink, D. & Emerson, M. (2008). School choice and racial segregation in US schools: Ethnic and Racial Studies 31(2), 267 293. Perspectives on Politics 3(2), 285 299. Smrekar, C. & Cohen Vogel, L. (2001). The voices of parents: Rethinking the intersection of family and school. Peabody Journal of Education 76(2), 75 100. Smrekar, C. & Goldring, E. (1999). School choice in urban America: Magnet schools and the pursuit of equity. New York: Teacher College Press. Stambach, A. & David, M. (2005). Feminit theory and educational policy: How gender Journal of Women in Culture and Society 30(2), 1633 1658. Stevens, J. (1999). Intermediate statistics: a modern approach (2nd ed.). Mahwh, NJ: Lawrence Erlbaum Associates, Inc. Stulberg, L. M. (2008). Race, schools, & hope: African Americans and school choice after Brown. New York: Teachers College Press. Swann v. Charlotte Mecklenburg Bd.of Educ., 402 U.S. 1, 13 (1971). Tate, W. F., Ladson Billings, G., & Grant, C. A. (1993) The Brown decision revisited: Mathematizing social problems Educational Policy 3, 255 275. Tedin, K. & Weiher, G. (2004). Racial/ethnic diversity and academic quality as components of school choice. The Journal of Politics, 66(4), 1109 1133. Teske, P. & Schneider, M. (2000). What research can tell policymakers about school choice. Journal of Policy Analysis and Management 20(4), 609 631. The difference that ch oice makes. (2001). Economist 358(8206), 78.
142 Thernstrom, S., & Thernstrom, A. M. (1997). America in black and white: One nation, indivisible New York, NY: Simon & Schuster. Thurston, P. (1980). Is good law good education? Review of Research in Education 8, 296 335. United States Commission on Civil Rights. (2007). Becoming less separate? School desegregation, Justice Department Enforcement, and the pursuit of unitary status. Retrieved January 21, 2010 from www.usccr.gov/pubs/092707_becominglessseparatere port.pdf. United States Department of Education website. Retrieved April 9, 2008 from http://www.ed.gov/programs/magnet/index.html Van Dunk, E. & Dickman, A. (2002). School choice accountability: An examination of informed consumers in different choice programs. Urban Affairs Review 35(6), 844 856. Weeres, J. (1990). Is more or less choice needed? In W. Boyd & H. Walberg (Eds.), Choice in education (pp. 177 186). Berkeley CA: McCutchan. Wells, A. (1996). Reexamining social science research on school desegregation: Long versus short term effects. In Lagemann, E. & Miller, L. (Eds.). Brown v. board of (pp. 91 106). New York: Teachers College Press. Wheel er, D. (Ed.). (1908). Life and Writings of Thomas Paine, vol. 5. New York: Vincent Parke and Company.
143 Wilkinson L. & The Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist 54, 594 604. Winston, J. (1996). An American dilemma still. In Lagemann, E. & Miller, L. (Eds.). (pp. 1 7). New York: Teachers College Press.
144 Appendix A Extra Tables
145 Table A1. 2009 2010 Enrollment by Race in Elementary Schools School Total Enrollment % Black 104 644 90 .8% 136 758 87.9% 2 544 87.5% 108 645 86.5% 27 609 83.6% 99 561 82.2% 132 526 80.2% 56 542 79.5% 119 624 79.5% 64 451 73.6% 60 519 69.9% 21 161 66.5% 139 575 59.8% 105 461 56.8% 18 300 56.7% 31 387 55.8% 48 491 54.6% 124 720 53.1% 46 381 51.4% 19 530 51.3% 116 638 51.3% 80 365 50.7% 68 808 49.3% 55 258 49.2% 75 322 48.8% 36 394 47.2% 70 495 44.4% 117 370 43.2% 102 725 42.5% 43 652 41.7% 100 539 41.6% 35 370 38.1% 77 411 37.5% 98 306 35.9% 9 943 35.7%
146 Table A1. Continued School Total Enrollment % Black 94 858 35.7% 89 800 34.4% 59 607 32.9% 134 343 32.7% 1 1060 31.3% 52 226 28.8% 111 703 28.7% 114 445 28.3% 66 627 27.8% 25 662 27.5% 78 768 24.7% 90 930 24.6% 85 751 24.2% 135 515 23.9% 69 452 23.0% 67 898 22.7% 3 772 22.5% 12 989 22.4% 88 651 22.0% 126 679 21.4% 125 486 20.8% 44 887 20.4% 123 814 20.3% 74 443 20.1% 141 846 18.7% 39 443 18.1% 7 913 18.0% 115 964 17.4% 6 362 16.9% 76 522 16.7% 10 687 16.6% 120 1025 16.0% 20 436 15.4% 71 682 15.2% 47 780 15.0%
147 Table A1. Continued School Total Enrollment % Black 103 947 14.5% 121 644 14.4% 40 765 14.1% 129 685 14.0% 63 420 13.8% 106 714 13.3% 8 951 12.6% 57 565 12.6% 30 1030 12.1% 37 825 12.1% 13 596 12.1% 113 659 11.7% 93 609 11.7% 140 597 11.2% 81 682 11.1% 97 997 10.6% 122 715 10.5% 95 934 10.1% 87 532 9.8% 26 655 9.6% 4 872 9.5% 51 1111 9.5% 24 890 9.4% 130 971 9.4% 82 912 9.3% 42 981 9.3% 28 701 9.1% 45 767 8.9% 86 670 8.8% 65 854 8.8% 91 594 8.8% 73 929 8.7% 53 622 8.7% 127 518 8.5% 23 418 8.1%
148 Table A1. Continued School Total Enrollment % Black 128 611 8.0% 15 1000 8.0% 41 742 8.0% 118 881 7.9% 62 593 7.9% 50 1015 7.8% 110 980 7.8% 112 631 7.4% 58 875 7.4% 34 662 6.9% 131 903 6.9% 137 401 6.7% 14 682 6.6% 38 756 6.3% 61 681 5.7% 96 714 5.5% 138 532 4.9% 32 778 4.9% 107 729 4.4% 101 666 4.2% 16 673 3.6% 83 721 3.5% 92 965 3.4% 33 1029 3.2% 54 905 3.2% 79 628 3.2% 84 819 2.8% 5 1110 2.8% 29 891 2.7% 17 634 2.7% 133 1086 2.5% 109 673 2.4% 72 702 2.3% 11 706 2.1% 22 593 1.0% 49 750 0.5%
149 Table A2. 2009 2010 Enrollment by FRL Eligibility in Elementary Schools School Total Enrollment % FRL 119 624 98.40% 104 644 97.52% 99 561 97.50% 26 609 97.04% 108 645 95.97% 63 451 95.57% 135 515 95.34% 45 381 95.01% 132 526 94.68% 17 300 94.67% 138 532 94.17% 116 638 94.04% 136 758 93.93% 139 575 93.91% 42 652 93.71% 24 662 93.66% 94 858 93.01% 55 542 92.62% 51 226 92.48% 100 539 92.21% 34 370 91.89% 30 387 91.73% 16 634 91.64% 59 519 91.33% 69 495 90.91% 122 715 90.91% 97 997 90.67% 89 800 90.25% 2 544 90.07% 127 518 89.96% 47 491 89.00% 49 1015 88.18% 56 565 88.14% 18 530 88.11% 134 343 87.76%
150 Table A2. Continued School Total Enrollment % FRL 10 706 87.54% 60 681 87.22% 1 1060 87.08% 35 394 86.80% 53 905 86.41% 137 401 86.03% 31 778 84.70% 73 443 84.42% 102 725 84.28% 110 980 84.18% 68 452 84.07% 88 651 83.72% 125 486 83.13% 105 461 82.86% 85 751 82.82% 128 611 82.49% 129 685 82.48% 4 872 81.88% 58 607 81.05% 54 258 79.84% 50 1111 79.84% 87 532 78.76% 20 161 78.26% 93 609 77.67% 107 729 77.64% 64 854 77.05% 98 306 76.80% 140 597 76.72% 118 881 76.50% 52 622 76.37% 124 720 76.25% 71 702 75.36% 38 443 74.72% 67 808 73.02% 8 943 72.85% 74 322 72.36% 114 445 72.36%
1 51 Table A2. Continued School Total Enrollment % FRL 141 846 70.09% 36 825 69.45% 3 772 69.43% 80 682 67.74% 70 682 67.60% 76 411 66.42% 46 780 66.15% 101 666 66.07% 120 1025 65.85% 13 682 65.84% 79 365 64.38% 48 750 63.60% 23 890 63.03% 111 703 61.74% 117 370 61.08% 90 930 57.63% 19 436 55.73% 115 964 53.73% 106 714 51.82% 113 659 51.59% 130 971 51.29% 62 420 50.95% 75 522 50.19% 11 989 49.04% 65 627 48.80% 57 875 48.11% 86 670 48.06% 9 687 47.45% 40 742 47.44% 95 934 47.00% 72 929 46.29% 77 768 45.44% 22 418 42.82% 66 898 42.32% 6 913 41.62%
152 Table A2. Continued School Total Enrollment % FRL 121 644 41.46% 37 756 41.14% 131 903 40.86% 44 767 40.29% 27 701 38.94% 29 1030 38.54% 96 714 37.11% 123 814 36.49% 21 593 35.41% 12 596 35.23% 43 887 34.05% 112 631 33.12% 81 912 32.89% 82 721 30.51% 84 362 30.39% 91 594 29.29% 33 662 28.85% 39 765 28.10% 25 655 26.11% 41 981 23.14% 14 1000 22.90% 7 951 22.61% 103 947 21.54% 126 679 21.50% 83 819 14.29% 78 628 13.85% 15 673 13.67% 61 593 13.66% 133 1086 11.69% 109 673 11.14% 5 1110 9.64% 92 965 9.53% 28 891 9.20% 32 1029 8.45%
153 Table A3. Cou nterfactual Enrollment by Race in Elementary S chools School CF Enrollment % Black 77 61 91.30% 103 1038 85.06% 135 613 82.32% 107 574 81.11% 27 774 80.80% 98 133 80.61% 97 1122 80.00% 131 872 77.74% 2 611 77.50% 118 904 77.04% 56 588 73.30% 81 599 70.84% 21 161 66.46% 64 689 64.59% 60 623 63.24% 138 635 57.26% 18 634 54.89% 48 491 54.58% 104 984 51.24% 115 1002 51.22% 31 517 51.16% 80 365 50.68% 19 576 50.52% 123 683 49.37% 55 258 49.22% 75 322 48.76% 36 394 47.21% 70 610 45.57% 68 1153 44.75% 117 370 43.24% 46 503 39.64% 101 757 39.44% 99 761 39.05% 43 727 37.83% 35 498 37.75% 59 759 35.57%
154 Table A3. Continued School CF Enrollment % Black 9 978 35.38% 93 688 34.56% 88 686 33.12% 1 1431 30.67% 110 1123 28.57% 133 1132 27.94% 66 665 27.82% 89 1081 27.54% 78 607 27.51% 69 744 27.42% 25 670 26.87% 12 1081 25.25% 67 860 25.23% 52 231 25.11% 3 814 24.94% 44 882 23.58% 134 562 21.86% 113 698 20.90% 124 632 20.70% 7 902 20.56% 116 788 20.00% 141 870 19.32% 122 703 19.03% 120 1181 18.79% 140 680 18.74% 87 615 17.93% 125 539 17.87% 39 432 17.87% 10 587 17.75% 114 335 17.56% 119 758 17.34% 47 781 17.29% 76 519 16.70% 74 524 16.22% 71 794 14.99% 102 535 14.93% Bold text signifies schools within
155 Table A3. Continued School CF Enrollment % Black 13 571 14.39% 53 764 14.27% 105 482 14.27% 20 494 14.06% 128 687 13.93% 112 660 13.61% 37 908 12.32% 8 984 11.99% 139 723 11.91% 40 743 11.84% 30 1104 11.60% 86 711 11.54% 92 949 10.90% 94 952 10.72% 51 1200 10.67% 57 519 10.02% 90 955 10.00% 4 898 9.91% 42 946 9.83% 26 620 9.69% 82 953 9.65% 96 742 9.63% 15 800 9.63% 121 635 9.54% 129 683 9.49% 62 603 9.45% 28 830 9.29% 45 769 9.23% 85 849 9.14% 111 721 8.92% 65 945 8.89% 24 1019 8.73% 127 406 8.44% 50 1155 8.13% 109 636 8.13%
156 Table A3. Continued School CF Enrollment % Black 73 856 7.83% 126 347 7.64% 58 902 7.46% 23 392 7.40% 41 716 7.22% 130 969 7.11% 34 669 7.03% 32 935 6.64% 14 851 6.58% 100 571 6.21% 95 1110 5.93% 38 678 5.31% 136 973 4.91% 63 328 4.88% 61 648 4.63% 137 387 4.57% 83 770 3.82% 91 569 3.48% 79 601 3.33% 33 1054 3.23% 16 683 3.07% 106 721 2.93% 108 900 2.83% 132 630 2.83% 5 1169 2.83% 29 763 2.49% 11 689 2.32% 17 737 1.90% 54 936 1.82% 72 811 1.73% 84 769 1.43% 22 553 0.90% 49 751 0.53% 6 362 0.00%
157 Table A4. Counterfactual Enrollment by FRL Eligibility in Elementary Schools School CF Enrollment % FRL 119 758 97.89% 99 761 95.80% 27 774 95.22% 104 984 94.21% 132 630 93.33% 18 634 92.74% 43 727 92.16% 116 788 92.13% 46 503 92.05% 25 670 91.79% 135 613 91.68% 94 952 91.28% 108 900 91.11% 2 611 91.00% 17 737 90.91% 56 588 90.48% 52 231 90.48% 100 571 90.37% 136 973 90.24% 138 635 90.24% 70 610 90.00% 50 1155 89.44% 107 574 89.02% 48 491 89.00% 139 723 88.66% 11 689 87.23% 77 61 86.89% 36 394 86.80% 122 703 86.49% 19 576 86.28% 89 1081 86.12% 61 648 85.80% 64 689 85.63% 60 623 85.39% 57 519 84.97%
158 Table A4. Continued School CF Enrollment % FRL 127 406 84.73% 97 1122 84.49% 35 498 83.94% 54 936 83.44% 1 1431 82.46% 4 898 81.63% 88 686 81.49% 31 517 80.85% 129 683 80.53% 85 849 80.33% 55 258 79.84% 32 935 79.79% 110 1123 79.61% 102 535 79.44% 128 687 79.33% 137 387 79.07% 93 688 78.63% 21 161 78.26% 65 945 77.67% 98 133 76.80% 124 632 76.58% 51 1200 76.17% 53 764 76.05% 87 615 75.28% 59 759 75.23% 125 539 75.14% 118 904 74.89% 105 482 74.48% 134 562 74.38% 69 744 74.19% 74 524 73.66% 75 322 72.36% 140 680 72.21% 9 978 69.02% 72 811 68.43% 39 432 67.59%
159 Table A4. Continued School CF Enrollment % FRL 141 870 67.36% 47 781 67.09% 81 599 66.94% 49 751 66.44% 3 814 66.22% 114 335 65.67% 37 908 65.53% 80 365 64.38% 101 757 64.20% 68 1153 63.75% 111 721 63.11% 120 1181 62.49% 14 851 61.46% 71 794 60.45% 24 1019 59.86% 90 955 55.08% 115 1002 53.19% 76 519 51.25% 10 587 50.94% 78 607 50.91% 20 494 50.40% 113 698 49.71% 130 969 48.61% 12 1081 48.29% 67 860 47.09% 58 902 47.01% 66 665 44.81% 106 721 44.66% 95 1110 44.23% 131 872 43.69% 41 716 42.74% 73 856 42.29% 7 902 41.69% 121 635 41.57% 86 711 41.35% 45 769 40.70% Bold text signifies schools within guidelines for
160 Table A4. Continued School CF Enrollment % FRL 63 328 40.55% 117 370 38.92% 13 571 38.00% 44 882 37.87% 22 553 37.07% 30 1104 37.05% 38 678 36.43% 96 742 36.12% 28 830 35.90% 123 683 34.85% 23 392 33.93% 91 569 32.16% 112 660 31.52% 6 362 30.39% 82 953 30.22% 34 669 29.00% 83 770 28.44% 40 743 28.26% 126 347 27.67% 26 620 27.10% 15 800 26.25% 42 946 24.52% 8 984 22.56% 103 1038 21.29% 62 603 15.75% 79 601 14.48% 16 683 12.88% 133 1132 12.46% 84 769 11.96% 109 636 11.79% 5 1169 10.09% 92 949 9.48% 29 763 7.99% 33 1054 7.02%
161 Table A5 Comparison of Actual and Counterfactual Demographics School Actual Enrollment CF E nrollment Actual Black CF Black Actual FRL CR FRL Count Count Percent Percent P ercent Percent 1 1060 1431 31 31 87 82 2 544 611 88 78 90 91 3 772 814 23 25 69 66 4 872 898 10 10 82 82 5 1110 1169 3 3 10 10 6 362 362 17 17 30 30 7 913 902 18 21 42 42 8 951 984 13 12 23 23 9 943 978 36 35 73 69 10 687 587 17 18 47 51 11 706 689 2 2 88 87 12 989 1081 22 25 49 48 13 596 571 12 14 35 38 14 682 851 7 7 66 61 15 1000 800 8 10 23 26 16 673 683 4 3 14 13 17 634 737 3 2 92 91 18 300 634 57 55 95 93 19 530 576 51 51 88 86 20 436 494 15 14 56 50 21 161 161 66 6 6 78 78 22 593 553 1 1 35 37 23 418 392 8 7 43 34 24 890 1019 9 9 63 60 25 662 670 27 27 94 92 26 655 620 10 10 26 27 27 609 774 84 81 97 95 28 701 830 9 9 39 36 29 891 763 3 2 9 8 30 1030 1104 12 12 39 37 31 387 517 56 51 92 81 32 778 935 5 7 85 80 33 1029 1054 3 3 8 7 34 662 669 7 7 29 29 35 370 498 38 38 92 84 36 394 394 47 47 87 87 37 825 908 12 12 69 66
162 Table A5 Continued School Actual Enrollment CF Enrollment Actual Black % CF Black % Actual FRL % CF FRL % 38 756 678 6 5 41 36 39 443 432 18 18 75 68 40 765 743 14 12 28 28 41 742 716 8 7 47 43 42 981 946 9 10 23 25 43 652 727 42 38 94 92 44 887 882 20 24 34 38 45 767 769 9 9 40 41 46 381 503 51 40 95 92 47 780 781 15 17 66 67 48 491 491 55 55 89 8 9 49 750 751 1 1 64 66 50 1015 1155 8 8 88 89 51 1111 1200 9 11 80 76 52 226 231 29 25 92 90 53 622 764 9 14 76 76 54 905 936 3 2 86 83 55 258 258 49 49 80 80 56 542 588 80 73 93 90 57 565 519 13 10 88 85 58 875 902 7 7 48 47 59 607 759 33 36 81 75 60 519 623 70 63 91 85 61 681 648 6 5 87 86 62 593 603 8 9 14 16 63 420 328 14 5 51 41 64 451 689 74 65 96 86 65 854 945 9 9 77 78 66 627 665 28 28 49 45 67 898 860 23 25 42 47 68 808 1153 49 45 73 64 69 452 744 23 27 84 74 70 495 610 44 46 91 90 71 682 794 15 15 68 60 72 702 811 2 2 75 68 73 929 856 9 8 46 42 74 443 524 20 16 84 74 75 322 322 49 49 72 72 76 522 519 17 17 50 51 77 411 411 37 37 66 66 78 768 607 25 28 45 51 79 628 601 3 3 14 14
163 Table A5. Continued School Actual Enrollment CF Enrollment Actual Black % CF Black % Actual FRL % CF FRL % 80 365 365 51 58 64 38 81 682 599 11 9 68 67 82 912 953 9 10 33 30 83 721 770 3 4 31 28 84 819 769 3 1 14 12 85 751 849 24 23 83 80 86 670 711 9 9 48 41 87 532 615 10 9 79 75 88 651 686 22 18 84 81 89 800 1081 34 33 90 86 90 930 955 25 28 58 55 91 594 569 9 10 29 32 92 965 949 3 3 10 9 93 609 688 12 11 78 79 94 858 952 36 35 93 91 95 934 1110 10 11 47 44 96 714 742 5 6 37 36 97 997 1122 11 10 91 84 98 306 306 36 36 77 77 99 561 761 82 81 98 96 100 539 571 42 39 92 90 101 666 757 4 6 66 64 102 725 535 42 39 84 79 103 947 1038 14 15 22 21 104 644 984 90 85 98 94 105 461 482 57 51 83 74 106 714 721 13 14 52 45 107 729 574 4 3 78 89 108 645 900 87 81 96 91 109 673 636 2 3 11 12 110 980 1123 8 8 84 80 111 703 721 29 29 62 63 112 631 660 7 9 33 32 113 659 698 12 14 52 50 114 445 335 28 18 72 66 115 964 1002 17 21 54 53 116 638 788 51 51 94 92 117 370 370 43 43 61 61 118 881 904 8 8 77 75 119 624 758 79 77 98 98 120 1025 1181 16 1 7 66 62 121 644 635 14 1 9 41 42
164 Table A5. Continued School Actual Enrollment CF Enrollment Actual Black % CF Black % Actual FRL % CF FRL % 122 715 703 10 1 0 91 86 123 814 683 20 19 36 35 124 720 632 53 49 76 77 125 486 539 21 21 83 75 126 679 347 21 18 22 28 127 518 406 8 8 90 85 128 611 687 8 8 82 79 129 685 683 14 14 82 81 130 971 969 9 9 51 49 131 903 872 7 7 41 44 132 526 630 80 78 95 93 133 1086 1132 2 3 12 12 134 343 562 33 2 8 88 74 135 515 613 24 22 95 92 136 758 973 88 82 94 90 137 401 387 7 2 86 79 138 532 635 5 5 94 90 139 575 723 60 57 94 89 140 597 680 11 1 2 77 72 141 846 870 19 19 70 67