Tamica Hasina Daniel, B.S. Washington, D.C. April 14, 2008

Transcription

1 SCHOOL ASSIGNMENT AFTER PARENTS INVOLVED IN COMMUNITY SCHOOLS V. SEATTLE SCHOOL DISTRICT, NO. 1: HOW RACE AND INCOME DESEGREGATION RELATE TO MINORITY STUDENT EDUCATIONAL ATTAINMENT IN SEATTLE A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in the Georgetown Public Policy Institute By Tamica Hasina Daniel, B.S. Washington, D.C. April 14, 2008

2 SCHOOL ASSIGNMENT AFTER PARENTS INVOLVED IN COMMUNITY SCHOOLS V. SEATTLE SCHOOL DISTRICT, NO. 1: HOW RACE AND INCOME DESEGREGATION RELATE TO MINORITY STUDENT EDUCATIONAL ATTAINMENT IN SEATTLE Tamica Hasina Daniel, B.S. Thesis Advisor: Laura LoGerfo, PhD ABSTRACT After the United States Supreme Court s 1954 decision in Brown v. Board of Education, several public school districts began to adopt plans to desegregate their schools. Without a court order, some school districts such as Seattle Public Schools chose to implement desegregation programs. Several studies evaluating these voluntary and mandatory desegregation programs suggest that school desegregation is positively associated with improved educational attainment of black and Hispanic students. In 1998, Seattle Public Schools adopted a program that allowed students to attend any school in the district, but allowed the district some control in student placement if the racial demographics of each school did not reflect the demographics of the district as a whole. In the 2007 case, Parents Involved in Community Schools v. Seattle School District, No. 1, the United States Supreme Court declared the Seattle Public Schools use of race in school assignment unconstitutional. However, because of potential differences in resources, teacher quality, and the social connections of peers and staff between predominantly minority schools and schools with larger white student populations, using race in school assignment could act as an important tool to narrow the gap between the educational attainment of white and minority students. This study examines the relationship between Seattle s plan and two outcomes for minority students: dropping out of high school and graduating on time. Additionally, it examines how much racial diversity an income desegregation program can create, and whether there is a relationship between a potential income desegregation program and ii

3 the attainment outcomes. The analysis reveals that Hispanic students in Seattle public high schools who attended desegregated schools during the program had lower dropout rates and higher on-time graduation rates than Hispanic students who attended segregated schools. However, black and Hispanic students who attended income desegregated schools do not experience different dropout or on-time graduation rates than their counterparts in income segregated schools. Additionally, using an income-based desegregation can still produce a racially segregated school system. iii

4 ACKNOWLEDGEMENTS There are several people who contributed to the successful completion of this thesis. I would like to thank my advisor, Laura LoGerfo who challenged me throughout this process. Thank you to the staff of the Seattle Public Schools Research, Evaluation, and Assessment Office for providing me with data, which made it possible for me to write a thesis on a topic that is readily aligned with my interests. I would like to thank Eric Gardner for providing me with tips that simplified and shortened my computer programming process. Thank you to my family and friends who provided me with an unending network of support. And to Wole, thank you for being patient and giving me words of encouragement when I needed them most. iv

5 TABLE OF CONTENTS Introduction...1 Definition of Terms...4 Seattle s Controlled Choice Program...5 Literature Review...9 Theory of Desegregation and Improved Educational Outcomes...9 Race-Based Assignment Programs & Educational Attainment...11 Controlled Choice Desegregation Programs & Outcomes...14 Income-Based School Assignment Programs & Outcomes...17 Literature Summary...18 Hypotheses...20 Description of Data...21 Samples...23 Methods...26 Hypothesis #1 Variables...26 Hypothesis #2 Variables...30 Analytic Methods...32 School Choice & Bias...39 Racially Desegregated Schools vs. Racially Segregated Schools...40 Probit Models...44 Results...52 Descriptive Analysis...52 Hypothesis #1 Model 1a...55 Hypothesis #1 Model 1b...58 Hypothesis #2 Model 2a...61 Hypothesis #2 Model 2b...63 Hypothesis # Conclusions & Policy Recommendations...69 Appendix A...74 References...80 v

6 Introduction Over fifty years have passed since the United States Supreme Court s ruling in Brown v. Board of Education, the landmark case that declared racial segregation in public schools unconstitutional. Before Brown, many school systems in the South segregated their schools by law, and numerous school districts in the North were segregated due to residential patterns. The court in Brown attempted to reverse these practices and declared that separate facilities are inherently unequal (Brown v. Board of Education, 347 U.S. 483, 495 (1954)). Although many school districts did not move to desegregate immediately and many actively opposed enforcement of the ruling, eventually this decision ushered in a wave of court-ordered school desegregation in the decades after Brown. Federal courts began to enforce desegregation increasingly in the 1970s. Many of these plans involved mandatory assignment of students of different racial and ethnic backgrounds to the same schools in order to desegregate the school system. In order to avoid mandatory assignment programs, a movement began in the early 1980s that shifted towards using school choice as a means to desegregate school districts (NAACP LDF, et. al., 2005; Wells & Crain, 2005). Additionally, during this time and into the 1990s, many districts that were not under court-ordered desegregation began to desegregate their schools voluntarily (Welner, 2006). Some schools chose to adopt programs to avoid potential court-enforced programs and/or to eliminate the de facto segregation in their school system derived from segregated housing patterns. 1

7 In the early 1990s, courts began to confer unitary status on many school systems that were under court-ordered desegregation. This status translated into the elimination of the legal obligation to actively desegregate schools if districts complied with the desegregation order for a reasonable period of time, eliminated all vestiges of past discrimination to the extent practicable, and demonstrated a good faith effort to comply with the desegregation decree (Board of Educ. of Oklahoma City Pub. Schools v. Dowell, 498 U.S. 237 (1991)). The court explained that reasonable period of time means that the school district can discontinue the desegregation decree once it has remed[ied] the effects of past intentional discrimination (Dowell, 498 U.S. at 248 (quoting, Spangler v. Pasadena City Bd. of Educ., 611 F.2d. 1239, 1246 (9th Cir. 1979))). Many researchers and desegregation advocates attribute the resegregation of many school districts today to the conferral of unitary status and the segregated housing patterns. When unitary status is conferred on a school district, students are allowed to attend their neighborhood schools. Due to residential segregation, the students attendance at neighborhood schools results in resegregation of the school district. (NAACP LDF, et. al., 2005; Scott, 2005; Orfield & Lee, 2007). But some school districts continued their court-ordered desegregation programs, and several of those which adopted plans voluntarily maintained these programs. One method that school districts adopted is the Controlled Choice Student Assignment Plan. This method allows parents to rank schools in the district that they want their students to attend in order of their preference. Some controlled choice programs allow parents and students to state a preference for any school in the district, 2

8 regardless of where they live. Others are slightly more limited, but allow parents and students to rank several schools within an attendance area. However, the controlled portion of the program gives districts the power to not assign a student to his/her first choice if it would lead to a racial and ethnic composition at the school that does not reflect the demographics of the district or attendance area (Willie & Alves, 1996). Thus, this method balances the choice of parents and the school district s goal of improving racial diversity in its schools (Willie & Alves, 1996). Despite efforts such as implementation of a controlled choice plan by some school districts, the gap in educational outcomes between white and minority students persists (Vigdor & Ludwig, 2007). The average black student scores below 75% of white students (Vigdor & Ludwig, 2007; Jencks & Phillips, 1998). There is a similar gap between the academic performance of Hispanic and white students (Jencks & Phillips, 1998). Some researchers believe these gaps can be reduced by re-emphasizing school desegregation or integration as a method for improving the outcomes of minority students (Vigdor & Ludwig, 2007; Hanushek, Rivkin, & Kain, 2004). This belief is premised on purported differences in resources, teacher quality, and social connections of peers and staff between predominantly minority schools and schools with more white students (Wells & Crain, 1994; Braddock, 1980). By desegregating the school system, students who were in predominantly minority schools could attend schools with more resources. These new resources could help minority students improve their academic performance. Therefore, studying a contemporary school desegregation program could shed light on 3

9 whether school desegregation efforts are related to minority student educational outcomes. Definition of Terms At the outset of studying school desegregation plans, education policy researchers define the terms desegregation, and integration differently (Johnson, 1976; Schofield, 1991). While some use integration and desegregation interchangeably to mean creating a school with two or more racial or ethnic groups, others argue these terms have separate and distinct meanings. For instance, Thomas Pettigrew, a social scientist who has examined racial prejudice in the United States for over fifty years, maintains that desegregation is the act of placing students of different racial or ethnic groups in one school, but integration is the presence of multiple groups with positive interracial contact, cross-racial acceptance, equal dignity, and equal access to resources (Pettigrew, et. al., 1973). This distinction mirrors the contact hypothesis of Gordon Allport, which states that intergroup contact of majority and minority groups play a role in reducing racial prejudice (Johnson, 1976). Generally, segregation is the difference in the distribution of social groups, such as blacks and whites, among units of social organization such as schools (James & Taeuber, 1985). Therefore, desegregated schools can be defined as those that have actively sought to obtain substantial percentages of students from two or more racial or ethnic groups. And integrated schools are those that have students from different racial or ethnic backgrounds and ensure all students, regardless of background, have equal status and experience a positive, respectful environment. Thus, integration is 4

10 distinguished from desegregation in that it involves further action than placing students of different racial or ethnic groups in the same school. In this study, I will focus on desegregation. Examining integration could be important in examining outcomes, but it is beyond the scope of the data the Seattle Public School district currently collects, which will be analyzed in this paper. Seattle s Controlled Choice Program Seattle School District No. 1 operates all of the city s public high schools. In 1998, the district implemented a controlled choice plan for elementary schools that was expanded to the high schools in Prior to 1998, the district adopted other desegregation measures, including a more limited version of controlled choice. Therefore, Seattle Public Schools terms the plan it enacted in 1998 and 1999 as open choice (Seattle Public Schools, n.d.a). This program was adopted to voluntarily integrate the schools, with the additional intent to improve the quality of education (Judge, 2007). This method of desegregation, designed by Willie and Alves in 1988, divorces school opportunities from distance from a school and gives students and parents the freedom to choose from several schools or any school in the district. However, this method also aims to create student body diversity by ensuring that different racial groups are well-represented in each school. By allowing parents and students to state their most preferred schools, the choice mechanism allows the school district to determine the most and least popular schools. This information creates competition between the schools, which is supposed to encourage all of the schools to increase their desirability by 5

11 improving their academic quality. But this competition is thought to particularly encourage the least popular schools to improve, which are often the schools that historically have predominantly minority student populations. The theory of change behind school desegregation is that it allows minority students to attend schools with higher levels of funding, more resources, and higher-quality teachers than schools in a system that reflects the residential segregation of the area (Harris, 2006). For minority students who previously attended more disadvantaged schools, attending schools that have these additional resources should improve the students academic achievement, likelihood of graduating from high school, and increase their chances of enrolling in postsecondary education (Wells & Crain, 1994; Braddock, 1980). Seattle s plan allowed incoming ninth grade students to rank high schools in order of their preference of attendance, with no geographical limitations. The district used these preferences to assign students to schools, but also enlisted the use of tiebreakers to determine how to fill spots in oversubscribed schools. The first tiebreaker was the presence of a sibling at that school, thus allowing siblings to attend the same school. The second tiebreaker used race to determine the placement of students (Seattle Public Schools, n.d.a; Judge, 1997). The district could choose to not assign a student to a school if the school s composition would not be within fifteen percentage points of the district s demographics of 41% white students and 59% of all other racial groups (Judge, 2007). In 1998, the Washington state voters passed initiative I-200, which outlawed the use of race in education, public employment, and contract decisions (Judge, 2007). Two years later, a group called Parents Involved in Community Schools, which formed as a 6

12 response to the controlled choice racial tiebreaker, filed a lawsuit to contest the program s legality under this initiative and the United States Constitution (Parents Involved in Community Schools v. Seattle School District, No. 1, 285 F.3d 1236 (9th Cir. 2002)). Seattle suspended its program in 2002 when the 9 th Circuit Court of Appeals declared it violated the initiative (Parents Involved, 285 F.3d at 1253 (9th Cir. 2002)). Subsequently, in its opinion issued on June 28, 2007, the Supreme Court of the United States declared Seattle s plan unconstitutional (Parents Involved in Community Schools v. Seattle School District, No. 1, 127 S.Ct (2007)). The Court stated that the use of race violated the Fourteenth Amendment right of students who were not assigned their first choice schools, because these students were discriminated against on the basis of race. The Court reasoned that the school district could not use this race-conscious method to desegregate its high schools. However, it left open the option for the district to adopt race-neutral methods to racially desegregate its schools (Parents Involved, 127 S.Ct. 2738, 2792 (2007) (Kennedy, J., concurring)). Even before this case was decided, some school districts shifted away from racial desegregation and moved towards incomebased measures to promote integration. This method appears to avoid the constitutional difficulties that now exist for race-based programs. Thus, due to the current judicial and political focus that Seattle s program has received, I will use data from Seattle Public Schools to examine on-time high school graduation rates and high school dropout rates to determine whether the program has a relationship with educational attainment of the district s minority students. Also, because this program has been declared unconstitutional, if Seattle wants to ensure desegregation, 7

13 it must find an alternative to using race as a tiebreaker in school assignment. Thus, using demographic data from the Seattle Public School District, I will examine whether using an income-based measure can promote racially integrated high schools in Seattle and whether an income-desegregation program has a relationship with education attainment of minority students. 8

14 Literature Review Theory of Desegregation and Improved Educational Outcomes Many school districts continue to racially desegregate their school systems today, because they believe desegregated schools afford benefits to students and segregated schools put minority students at a disadvantage for future success. Some studies suggest that there are resource and teacher quality differences for minority students as compared to white students. Phillips and Chin (2004) found differences between the opinions of teachers in predominantly minority schools and mostly white schools about the adequacy of teaching materials. Fifteen percent of teachers in predominantly minority schools believed that their textbooks, number of books in the library, science labs, computers, and arts education were grossly inadequate as compared to 5% of teachers in predominantly white schools. They also noted that African American, Hispanic, and Asian students were less likely than white students to use computers and the Internet at school. Phillips and Chin also evaluated teaching quality, finding that other studies suggest that schools with more novice teachers and/or higher turnover rates tend to have lower academic achievement than schools with more experienced and stable teachers. The results of their study revealed that teachers in predominantly minority schools had 2.5 fewer years of experience than teachers in schools with the highest percentages of white students, and predominantly minority schools experienced higher rates of teacher turnover. Clotfelter, Ladd, and Vigdor s (2004) study of North Carolina schools showed that black students were more likely to have novice teachers in math and English than their white counterparts. Thus, this suggests stark differences in the educational 9

15 resources available to students by race and ethnicity, which might explain some of the academic differences between white and minority student performance. Studies of school desegregation have yielded positive results on outcomes such as reducing racial prejudice (Pettigrew & Tropp, 2006), improving critical thinking skills (Antonio, 2004), and positive longer-term outcomes such as educational attainment and job opportunities (Crain & Strauss, 1985). Additionally, there might be an association between school desegregation and positive outcomes for minority students because of the greater access to educational resources and stronger connections to social networks that promote social mobility in desegregated schools (Wells & Crain, 1994; Braddock, 1980). In a review of studies examining the relationship between school desegregation and longterm outcomes, Wells & Crain (1994) postulate that when black students attend integrated schools, they gain access to information about topics such as college and benefit from the ties they create with students, faculty, and staff who are better-connected to job and higher education opportunities than students and staff in predominantly minority schools. Consequently, these connections enhance minority students opportunities and generate a greater willingness to enter into more desegregated postsecondary institutions or occupational settings, which tend to be more economically beneficial (Wells & Crain 1994). 10

16 Race-Based Assignment Programs & Educational Attainment The relationship between school desegregation and educational attainment has been studied less frequently than the relationship between school desegregation and short-term outcomes such as academic achievement and intergroup relations (Wells & Crain, 1994). For instance, one meta-analysis of experimental and observational studies on court-ordered school desegregation programs and academic achievement examined 93 studies (Crain & Mahard 1983). It found that desegregation programs improved achievement for black students and narrowed the black-white achievement gap in early primary grades. In a literature review of long-term results, Wells & Crain examined several long-term outcomes from 21 studies. The studies examined educational and occupational aspirations, educational attainment, and occupational attainment. Overall, the authors suggest that there is a positive relationship between desegregated schools and long-term outcomes. They found that black students who attended desegregated schools showed higher occupational aspirations, a greater willingness to attend a predominantly white college, higher rates of attendance at postsecondary schools, and entered higher status and higher paying jobs than black students who attended segregated schools. However, the studies they reviewed on educational attainment reveal the most mixed results. Wells and Crain reviewed a few studies that reveal a positive relationship between desegregated schools and black student educational attainment. Crain (1971) examined the relationship between school desegregation and educational attainment using survey data from 1,600 black adults living in metropolitan areas in the North. The results show 11

17 that black adults who attended schools that were at least half white were more likely to have graduated from high school and attended college than those from segregated schools. This positive relationship is stronger among blacks who migrated from the South to the North by age ten. Braddock and McPartland (1982) also found positive outcomes on educational attainment from analyses using a large subsample of the National Longitudinal Study of the High School Class of They discovered that students who attended desegregated schools exhibited higher rates of educational attainment after leaving high school than those who attended segregated schools. Additionally, Wilson (1979) studied a nationally representative sample of male public high school students and found that desegregated schools were positively associated with educational attainment, but the magnitude of this relationship was not particularly large. In contrast to these positive results, Wells and Crain examined other studies that showed no relationship between attending a desegregated school and black student educational attainment, and even a negative relationship for some subgroups. A study by Crain and Mahard (1978) found that black high school graduates in Northern desegregated schools had college attendance rates that were 8% higher and college persistence rates that were 10% higher than blacks from predominantly black schools. This seems to affirm Crain s previous study (1971) and suggests a positive relationship between desegregated schools and educational attainment. However, Southern blacks who attended predominantly black schools had college attendance rates that were 5% higher and college completion rates that were 6% higher than blacks from desegregated schools. Wells & Crain (1994) attribute this difference to the fact that many of the 12

18 desegregated Southern schools had only been desegregated for a few years and to the existence of a stronger network of black colleges in the South. But Green (1982) conducted a study that found blacks who attended predominantly black high schools had lower college grades and felt less prepared for college than their counterparts from desegregated schools. But Green s results also suggest that there is no difference in persistence in higher education and entry into graduate school between minority students who attended segregated and desegregated schools. More recent evaluations indicate a positive relationship between school desegregation and educational attainment outcomes. Guryan (2004) examined desegregation plans from across the country to study whether the plans were related to a decrease in dropout rates for black high school students. The study showed that a two to three-percentage point decline in black dropout rates could be attributed to the implementation of desegregation plans and there was no association between these plans and the white dropout rate. This showed that the plans benefited black students and were not detrimental to white students dropout rates. These results were stronger in school districts that experienced larger declines in racial segregation. Additionally, Echenique, Fryer, and Kaufman (2006) studied the relationship between educational attainment of minority students and segregation within schools. They found that Hispanic students who attended desegregated schools but did not associate with peers of a different ethnic background exhibited lower likelihoods of attending college than Hispanic students in desegregated schools who did associate with peers of a different ethnic background. There was no relationship between within-school integration and educational attainment 13

19 for black and Asian students. This suggests that there could be differences in educational attainment for Hispanic students attending a school that has simply been desegregated and Hispanic students attending a school where they are integrated amongst students of different ethnic backgrounds. Controlled Choice Desegregation Programs & Outcomes There are very few studies that discuss the controlled choice method and student outcomes (Wells & Crain, 2005). Of these few studies, most tend to focus on how effectively the method allowed parents and students to exercise choice, whether the programs achieve racial and ethnic compositions at each school that reflect the composition of the school district as a whole, if the program reduced white flight, or whether controlled choice is correlated with improvements in academic achievement. In a review of studies of controlled choice, Wells and Crain (2005) found positive results on some of these outcomes and mixed results on others. The evaluations of whether parents were given their first choice schools reveal positive results, but there are critiques about the choice system. Willie and Alves (1996) claim that 80 to 85% of parents typically secure spots for their children in their first choice school and 85 to 90% of parents receive their first or second choice. The authors examination of the Boston program revealed that 81% of all students entering kindergarten, sixth, or ninth grade were assigned their first choice school. Additionally, a study of the Cambridge choice program showed that 91% of parents received one of their top three choices, with 86% receiving their top choice (Fiske, 2002). 14

20 However, Rossell (1995) notes that reporting practices can inflate first choice statistics. Rossell discussed studies of controlled choice programs in Boston and Cambridge, emphasizing that these studies only present choice statistics from the first assignment period. Some controlled choice programs have multiple assignment phases and those in the first assignment phase tend to get their first choice schools at higher rates than those who participate in subsequent phases. Therefore, reporting only the first assignment phase can lead to deceptively high statistics. Additionally, Rossell highlights a concern of critics of controlled choice programs that parents are steered away from listing popular schools as their first choice. If parents are successfully discouraged from choosing a popular school, the statistics will reflect that parents received their first choice school even if they would have listed the popular school absent the school district employees intervention. Thus, overall, controlled choice might allow many parents to exercise their school choice for their child or children in theory, but the evidence from practice is somewhat inconclusive. Two studies on the ability of the controlled choice method to create a racial distribution of students that reflects the district s overall distribution show positive results. Willie, Alves, and Hagerty (1996) revealed that Boston s controlled choice program created a racial balance in the 33 popularly chosen schools. And regarding a controlled choice program in Rockford, Illinois, Taylor and Alves (1999) found that the program reduced the number of segregated elementary schools from fifteen to four in the first two years of implementation. These studies indicate that controlled choice can accomplish its goal of racial desegregation. 15

21 Controlled choice plans have been found to reduce white flight, or the number of white, middle class families that leave the public school system. Willie and Alves (1996), the developers of the controlled choice method, found reduced white flight in an urban public school district that adopted the method. Specifically, their study of the Cambridge, Massachusetts program that began in the early 1980s revealed a significant increase in the proportion of the school-age population that attended the public schools, with a 32% increase in white student attendance and a 13% increase in minority student attendance over a four-year period. This confirmed the results of a study by Rossell and Glenn (1988) that attributed the decline in white flight from the public school district to the controlled choice plan. The evidence on academic achievement is somewhat mixed. In the study on the Cambridge program, Fiske (2002) found that the plan did not have a positive impact on student achievement and gaps between minority student achievement and white students persist. But Fiske states that this gap might be due to the fact that the district did not intervene to improve the quality of under-chosen schools. Harris (2006) evaluated the potential impact of the elimination of the school choice program in Seattle on minority student achievement. The results suggest that students in Seattle would lose ground academically if the program is eliminated. Consequently, Harris states that Seattle and school districts with similar programs should maintain their desegregation plans. Thus, the literature ties controlled choice to improved academic achievement, but it might require an active role of the school district to obtain these positive results. 16

22 Income-Based School Assignment Programs & Outcomes Some school districts have used family income instead of race-based assignment policies such as controlled choice to attempt to improve the educational outcomes of disadvantaged students (Kahlenberg, 2007). The Cambridge, Massachusetts public school district initially had a race-based controlled choice program similar to Seattle s. But, as previously discussed, achievement gaps persisted after the introduction of the controlled choice program, potentially because the district failed to actively improve the under-chosen schools. Therefore, in 2002, Cambridge changed its program to use socioeconomic status as the primary criterion to achieve diversity (Fiske, 2002). Fiske suggests that due to the stronger relationship between socioeconomic status and academic performance than race and academic performance, this switch might lead to increases in academic performance. As with the racial desegregation programs, these studies tend to focus more on educational achievement of students than on educational attainment. The results of these studies reveal mixed results about the relationship between the policies and student outcomes. Kahlenberg (2007) examined twelve income-based assignment programs and discovered that while the results were mixed, several programs yielded academic gains for low-income and minority students. Additionally, Harris evaluation of desegregated schools and Seattle s controlled choice program (2006) reveals that minority students made academic gains in schools with more white students because of the economic advantages of students in the schools and not because of the race alone of the peers. This 17

23 suggests that an income-based assignment has the potential to improve the academic gains of students. Although Kahlenberg (2007) suggests that socioeconomic desegregation can both improve student performance and promote racial desegregation, a study by Reardon, Yun, and Kurlaender (2006) challenges this statement. Their 2006 study revealed that income-based assignment programs did not lead to even a modest amount of racial desegregation. The authors explain that income-based methods lack the ability to achieve desegregation because white and black income distributions are not sufficiently different. Thus, this lack of difference makes income an imprecise tool to create racially desegregated schools. But the authors maintain that the income-based methods could still have a positive relationship with academic performance. Literature Summary The literature suggests that lack of resources and less-connected social networks in high-percentage minority schools might explain differences in educational attainment between students who attend high and low percentage minority schools. Overall, the studies find a positive relationship between race-based school desegregation and minority student educational attainment. Additionally, Hispanic students exhibit higher educational attainment levels when they are integrated amongst other ethnic groups within a school. But black students in the South do not experience this same positive relationship. In fact, black Southern students in segregated schools experience higher rates of college enrollment than their counterparts in desegregated schools. Dropout rates 18

24 for black students in desegregated high schools are lower than those from segregated schools. The studies on controlled choice programs reveal that the method can successfully promote racial diversity within schools and reduce white flight in some communities. However, the evidence on their effectiveness in assigning students to the school of their choice is less certain; and the relationship between these plans and academic achievement might depend on the district s active involvement in improving the schools that are less frequently chosen by parents and students. The research on whether income-based desegregation programs can promote racial diversity is mixed; but in general there is a positive relationship between income-based desegregation programs and academic achievement. Although research on educational attainment and controlled choice plans is lacking, the results of existing research on desegregation plans and educational attainment and the improvements in student outcomes in districts with controlled choice plans indicate that adopting a controlled choice plan could be associated with improvements in educational attainment for minority students. Additionally, students might experience similar improvements in an income-based desegregation plan, but potentially at the cost of losing some racial diversity. 19

25 Hypotheses Based on the positive relationship between desegregation programs and educational attainment and the improved outcomes of students in controlled choice programs, I hypothesize that Seattle s race-based controlled choice desegregation program is associated with higher educational attainment for the district s minority students. Specifically, Seattle s program is likely related to lower black and Hispanic student high school dropout rates and with higher on-time graduation rates for these students. Additionally, I hypothesize that adopting an income-based method is probably related to lower black and Hispanic student high school dropout rates and higher on-time graduation rates. But income-based desegregation might reduce racial diversity in Seattle public high schools. 20

26 Description of Data Seattle Public Schools collects social, economic, and academic data annually on its students. The social and economic data include information on race, ethnicity, sex, and free and reduced-price lunch enrollment. Because students who participate in the Free or Reduced-Price Lunch Program have family incomes that are at or below 185% of the federal poverty line, it serves as a proxy for family income in this study. 1 In terms of academic information, Seattle Public Schools administers the reading, language, and math sections of the Iowa Test of Educational Development (ITED) to its 9 th grade students. The school district records these standardized test scores and also collects data on attainment measures such as on-time high school graduation and dropping out of high school. Additionally, the data include information on attendance, expulsion, students school choices, and which schools students attended each year. The data used to test the hypotheses in this study are from Seattle Public Schools (SPS) records from the to the school years. All 59,008 2 students in these data were enrolled in a traditional or alternative Seattle public high school for one or more of these school years. Table 1 shows the characteristics of the student population. About 40% of the population is white, 24% is black, 10% is Hispanic, 24% is Asian, and 3% is Native American. Approximately half of the population is male and about half is female. A little less than half of the students (48.4%) qualified for the Free 1 The data in this study capture students who are enrolled in the Free or Reduced-Price Lunch Program. Because some students who are eligible for the program do not enroll in it, some students may not be placed in the correct income category. Thus, this variable is an approximation of family income level. 2 One student was dropped from the population due to a potential coding error for the student s grade. 21

27 or Reduced-Price Lunch program during at least one school year from to Table 1. Characteristics of Seattle Public High School Students ( to ) Variables Frequency/Mean Race White 39.6% Black 24.0% Hispanic 10.0% Asian 23.7% Native American 2.7% Gender Male 51.0% Female 49.0% FRPL Enrollment Enrolled 48.4% Not Enrolled 51.6% Expulsion Expelled 2.3% Not Expelled 97.7% Average Attendance Rate 88.2% ITED Average Percentile Math ITED 55.8% Reading ITED 52.9% Language ITED 54.4% Dropout 12.7% Graduate On Time 93.1% Number of Students 59,008 Source: Seattle Public Schools Data ( to ) 22

28 Samples Analytic Sample #1 There are two main analytic samples used in this paper. The first sample contains data of students who were in a traditional or alternative high school during at least one year of the choice program with the racial tiebreaker ( to ). This sample includes 8,298 students. Students who were homeschooled during every year of the choice program are excluded from this sample. Also, students with missing attendance information, test scores, or attainment data are not included. Students in Sample #1 have up to three years of data for variables such as attendance that can change each year. The values of variables that have multiple years of data are averaged together. There are statistically significant differences between the students who are in this sample and in the overall SPS dataset. Table 2 shows that the racial composition of Analytic Sample #1 differs significantly from the Seattle school population. The sample has higher percentages of white and Asian students, and it has lower percentages of black, Hispanic, and Native American students than the population. And there are significantly fewer students enrolled in the Free or Reduced-Price lunch program in the sample than the population (40.4% vs. 48.4%, p<.01). Because these differences make the sample not representative of the population, the results from this study cannot be accurately generalized to the entire population, but can represent an initial look at patterns of attainment in Seattle Public Schools. 23

29 Table 2. Significance Testing for Analytic Sample #1 Variables Population Sample #1 Race White*** 39.6% 43.5% Black*** 24.0% 21.0% Hispanic*** 10.0% 8.5% Asian** 23.7% 24.7% Native American** 2.7% 2.2% Gender* Male 51.0% 50.0% Female 49.0% 50.0% FRPL Enrollment*** Enrolled 48.4% 40.4% Not Enrolled 51.6% 59.6% Expulsion Expelled 2.3% 2.5% Not Expelled 97.7% 97.5% Average Attendance Rate*** 88.2% 91.4% ITED Average Percentile Math ITED 55.8% 55.7% Reading ITED*** 52.9% 54.5% Language ITED 54.4% 54.5% Dropout*** 12.7% 8.0% Graduate On Time*** 92.0% 95.0% Number of Students 59,008 8,298 Source: Seattle Public Schools Data ( to ) *Statistical Significance at 10% level; **Statistical Significance at 5% level; ***Statistical Significance at 1% level Analytic Sample #2 To test the income desegregation hypothesis, a second sample is used, with data from to This sample consists of 17,114 high school students enrolled in a traditional or alternative school. Students who were not enrolled in Seattle public high schools during any of these years are excluded from the sample. And students with missing attendance data, test scores, or attainment data are also excluded from the sample. The values of variables that have multiple years of data are averaged together. 24

30 Table 3. Significance Testing for Analytic Sample #2 Variables Population Sample #2 Race White*** 39.6% 40.0% Black*** 24.0% 21.0% Hispanic*** 10.0% 9.0% Asian** 23.7% 24.5% Native American*** 2.7% 2.0% Gender** Male 51.0% 50.0% Female 49.0% 50.0% FRPL Enrollment*** Enrolled 48.4% 44.8% Not Enrolled 51.6% 55.2% Expulsion*** Expelled 2.3% 1.7% Not Expelled 97.7% 98.3% Average Attendance Rate*** 88.2% 91.7% ITED Average Percentile Math ITED* 55.8% 56.3% Reading ITED*** 52.9% 54.9% Language ITED** 54.4% 54.% Dropout*** 12.7% 7.1% Graduate On Time*** 92.0% 96.1% Number of Students 59,008 17,114 Source: Seattle Public Schools Data ( to ) *Statistical Significance at 10% level; ***Statistical Significance at 1% level Sample #2 is statistically different from the population on several background characteristics. The sample has significantly higher percentages of white and Asian students than the population and lower percentages of black, Hispanic, and Native American students. Additionally, there are lower percentages of students in the sample enrolled in the Free or Reduced-Price Lunch program in the sample than in the population. As previously stated, these differences might limit generalization of the results to the entire population. 25

31 Methods Hypothesis #1 Variables Model #1a Variables The first hypothesis posits that racial school desegregation is associated with improved educational attainment for minority students. The variables in the models to test this hypothesis are described below. Outcome Variables This study examines the outcomes of whether a student dropped out of high school and whether a student graduated on time. Dropouts are students who are not high school graduates, are no longer enrolled in a Seattle public school, and who did not transfer to another school or school district (Seattle Public Schools, 2007). On-time graduates are students who graduate from high school during their assigned year of graduation, four years after they enter the ninth grade (Seattle Public Schools, 2007). Gender Nationally, there are differences in high school graduation rates between male and female students (Green & Winters, 2006). Even when the data on high school graduation is separated by race, female students of every race graduate at higher rates than their male counterparts (Green & Winters, 2006). Thus, to ensure that the race and ethnicity variables are not absorbing some of the effects of gender, the female indicator variable is included in the model. 26

32 Race & Ethnicity In order to test the hypothesis that the program is positively associated with the likelihood of black and Hispanic students graduating from high school and graduating on time, indicators of race and ethnicity are included in the model. A measure of race and ethnicity from the Seattle Public Schools that separates students into five categories is used to create the indicator variables in this model: Black, Hispanic, White, Asian, and Native American. The coefficients β 1 through β 4 are indicator variables for the race or ethnicity of a student. The excluded category comprises white students, whose estimates are represented by the intercept, β 0. Free or Reduced-Price Lunch Enrollment The indicator variable, FRPL, measures whether a student is enrolled in the Free or Reduced-Price Lunch Program, which is part of the National School Lunch Program. This federally-funded meal program provides low-cost or free lunches to low-income students. Students whose families are at or below 130% of the federal poverty line receive free meals, and students whose families are between 130% and 185% of the poverty line receive reduced-price meals (United States Department of Agriculture, 2007). The federal poverty line in 2007 was $20,650 for a family of four. Thus, Seattle Public School students in 2007 from families of four were eligible to receive free lunches if their family income was below $26,845 and reduced-price lunches if their family income was between $26,845 and $38,203 (Seattle Public Schools, 2007). Although FRPL is a dichotomous indicator variable, and thus does not measure more than two income levels, it can be used as a proxy measure of family income 27

33 (Reardon, Yun, & Kurlaender, 2006). Studies have shown that children from poor or low-income families have lower levels of educational attainment than those from more affluent families (Haveman & Wolfe, 1995). Thus, in order to ensure the race variables are only measuring the relationship between race and the attainment variables, FRPL is included in this model. Desegregated School An indicator variable, desegschool, is in the model to compare students who attend desegregated schools with those who attend segregated schools. The variable takes on a value of 1 if a student attended a desegregated school in at least one year during the choice program 0 if a student never attended a desegregated school. Also, desegschool is interacted with a student s race to determine whether there is a difference in outcomes between minority students in desegregated schools and their counterparts in segregated schools. Schools will be labeled as desegregated or segregated using the Mutual Information Index, which will be discussed in the Analytic Methods section of this study. Attendance The model controls for the percentage of days a student attends school each year. Students who spend fewer days in the classroom might suffer academically, thus reducing the likelihood of a student graduating on time. Additionally, absenteeism is a very strong predictor of a student dropping out of high school (Bryk & Thum, 1989). Therefore, the student attendance rate variable, which is a continuous measure of each 28

34 student s attendance rate over the high school years, is included in the model to control for this relationship. Expelled Students who are expelled from a Seattle public school are not allowed to reenroll in that school, but can be assigned to another school in the district (Seattle Public Schools, n.d.b). But because these students have been removed from a school, it might reduce their likelihood of re-enrolling in any school. Thus, because expulsion could be correlated with dropping out of school altogether and not graduating on time, a variable indicating whether a student has been expelled from a school is in the model. Standardized Test Scores Because academic achievement could be negatively correlated with dropping out of high school and positively associated with graduating on time, the model needs to account for variation in achievement. The most available achievement measure is standardized test scores. Students in Seattle Public Schools take the math, reading, and language sections of the Iowa Test of Educational Development in the 9 th grade, each of which is reported in percentiles. The math section tests quantitative problem solving. The reading section seeks to measure a student s reading comprehension level. And the language section tests a student s skills at recognizing correct and effective use of standard American English in writing (The University of Iowa, 2007). The importance of academic achievement to attainment warrants their inclusion in the model. 29

35 Model #1b Variables All of the variables that are in Model #1a are in Model #1b, but Model #1b does not include the desegschool variable and instead includes a variable to control for potential student bias. Because choice plays a large role in student placement in Seattle public schools, there could be characteristics of students and parents that are associated with their participating in the choice process and with their choosing a certain school. These characteristics could be correlated with independent variables such as test scores and the dependent variables, which can bias the model s estimates. In order to control for this potential student selection bias, Model #2 includes, DesegPlacement, a variable to examine potential differences in outcomes between students who ranked schools with similar levels of racial segregation, but some were placed in desegregated schools and others were placed in segregated schools. The DesegPlacement variable takes on a value of 1 for students who participate in the school ranking process and attended desegregated schools and 0 for students who participate in the school ranking process and attended segregated schools. Hypothesis #2 Variables The second hypothesis posits that using enrollment in the Free or Reduced-Price Lunch program to desegregate the Seattle Public Schools is associated with positive outcomes on educational attainment measures for minority students. The variables to test this hypothesis are similar to those used to test the hypothesis of the racial desegregation program, with a few notable differences. 30

36 Model #2a Variables The dependent variables in this model are the same as those in the model to test the hypothesis for racially desegregated schools. However, this model uses income desegregated schools, which are schools that have student populations that reflect the district-wide enrollment in the Free or Reduced-Price Lunch program. Income segregated and desegregated schools are determined using the Mutual Information Index, which will be discussed further. The variable takes on a value of 1 if a student attends an income desegregated school and a value of 0 if a student attends an income segregated school. The race variables are interacted with attendance at an income-desegregated school to examine the relationship between minority student educational attainment and income desegregation. As in the models for racial desegregation, the remaining variables are individual background and academic performance variables gender, ITED scores, attendance, and expulsion. Model #2b Variables All of the variables used in this model are the same as the variables used in Model #1b of the racial desegregation hypothesis, but the variable that controls for potential student bias is based on income desegregation and not racial segregation. The income desegregated school placement variable, IncDesegPlacement, is an indicator for a student who was placed in an income desegregated school. The income desegregated school placement variable takes on a value of 1 if a student participates in the school ranking process and attends an income desegregated school and 0 if a student participates in the ranking but attends an income segregated school. 31

37 Analytic Methods Mutual Information Index/Segregation Index Seattle Public Schools has white, Asian, black, Hispanic, and Native American students. Because there are significant populations of students in each of these racial or ethnic groups, a multigroup measure of segregation is needed to capture whether a school is desegregated. One index, termed the Mutual Information Index, can be used to measure multigroup segregation in schools or housing (Mora & Ruiz-Castillo, 2007; Frankel & Volij, 2007). This index is based on a more widely-used segregation index called Theil s Entropy Index. However, the Mutual Information Index, M, measures the representativeness of an individual school s demographics of the district demographics. Below is an equation for measuring segregation at the school level (Mora and Ruiz- Castillo, 2007): M n = p G g n p g n ln g= 1 pg* g : racial/ethnic group n : school pgn p g n = : proportion of students in school n who belong to group g p n p gn = T n g T : proportion of students of group g and school n in the city T : total number of students in the city n T g : total number of students in group g and school n 32

38 p n p gn : proportion of students attending school n in the population = G g= 1 N p g * = p gn : proportion of students of group g in the population n= 1 This index can be used to measure the level of segregation for each school during each school year. When the proportions of racial groups in the district match the proportions of racial groups in a school, M n takes on its minimum value of 0.00 (Mora & Ruiz-Castillo, 2007). An M n score of ( ) means a school has low levels of segregation; ( ) is moderate segregation; ( ) is high segregation; and, (0.40 or greater) is extreme segregation (Tienda & Niu, 2006). To create two categories, segregated and desegregated, schools that have low levels of segregation are considered desegregated and schools with moderate, high, and extreme segregation are considered segregated. The relationship between desegregation and the outcome variables could change depending on the level of segregation. Additionally, the hypothesis focuses on the relationship between desegregated schools and these outcomes. Thus, in order to determine if a relationship exists, schools with low levels of segregation are compared against all other schools. This multigroup segregation index is also used to determine whether schools are income segregated or income desegregated in order to test the second hypothesis. In this context, the index measures whether the percentage of students enrolled in the Free or Reduced-Price Lunch program in a school is representative of the overall percentage of students in the district enrolled in the program. Therefore, schools with very high or low percentages of students enrolled in the program are income segregated, and schools with 33

39 percentages of students enrolled in the program closer to the district percentage are income desegregated. Normalized Exposure Index/V-Index In order to test the hypothesis that an income desegregation program can still lead to high levels of racial segregation, I will use a Normalized Exposure Index, or V-Index. Reardon, Yun, and Kurlaender (2006) conducted a simulation and an analysis to determine the minimum and maximum amount of racial segregation that can be created using income segregation. They used the V-index, to calculate the maximum amount of segregation possible when using a dichotomous measure of income desegregation such as Free or Reduced-Price Lunch enrollment. This index measures the difference in the actual exposure of one racial or ethnic group to another with the anticipated exposure level if the schools were perfectly racially desegregated. The maximum level of segregation can be computed using the following equation: V max = 1 π mp π wp V max : maximum possible segregation π mp π wp : proportion of black or Hispanic students in poverty (enrolled in Free or Reduced- Price Lunch Program) : proportion of white students in poverty (enrolled in Free or Reduced-Price Lunch Program) When V = 1.00, a school is perfectly racially segregated. A V-value of 0.50 is considered extremely segregated. This value means that a minority student attends school with only half the percentage of white students expected, given the school 34

40 district s racial composition. And a V-value of 0.00 represents a school that is completely racially desegregated (Reardon, Yun, & Kurlaender, 2006). This measure only examines the maximum amount of segregation in a population with two groups. Therefore, it is used here to determine the maximum amount of two-group segregation for black and white students, and the maximum for Hispanic and white students. Additionally, the data for black and Hispanic students are combined to determine the maximum amount of segregation possible between minority students and white students. Initial Data Analysis Racial and Income Disparities An initial examination of the data appears to show differences in dropout rates and on-time graduation rates across racial and ethnic groups. Figure 1 shows that during the years of the choice program ( to ) and for three years after the school district eliminated the racial tiebreaker in school assignment, black and Hispanic students consistently had higher dropout rates than white students. Additionally, in all school years from to , the dropout rates for black and Hispanic students are higher than the district-wide dropout rate. 35

41 Figure 1. Seattle Public Schools High School Dropout Rates by Race from to % 10.0% 8.0% % Dropout 6.0% Black Hispanic All Students White 4.0% 2.0% 0.0% School Year Similar disparities appear to emerge between white and minority students for ontime graduation. Figure 2 shows that black and Hispanic students on average have lower on-time high school graduation rates than white students during all school years from to Black students graduated on time at lower rates than the average for all students in the school district in every year from to Hispanic students had on-time graduation rates below the district average in every year, except for , when 92.9% of Hispanic students graduated on time and 92.6% of all high school students in the district graduated on time. 36

42 Figure 2. Seattle Public Schools On-Time High School Graduation Rates by Race & Ethnicity from to % 98.0% 96.0% % Graduated On Time 94.0% 92.0% 90.0% 88.0% 86.0% White All Students Hispanic Black 84.0% 82.0% 80.0% School Year In addition to the disparities between white and minority students, there appear to be differences in dropout and on-time graduation rates for students who are enrolled in the Free or Reduced-Price Lunch program and students who are not enrolled in the program. Figure 3, which shows the dropout rates of students who are and are not enrolled in the program, suggests that enrolled students have on average higher dropout rates in every year than students who are not enrolled and the overall district average. Additionally, Figure 4 demonstrates that on average, students who are enrolled in the program have lower on-time graduation rates than those who are not in the program. Overall, this indicates that, on average, students who are enrolled in the Free or Reduced- Price Lunch program might experience worse educational attainment outcomes than students who are not enrolled in the program. 37

43 Figure 3. Seattle Public Schools High School Dropout Rates by Free or Reduced-Price Lunch Enrollment from to % 7.0% 6.0% 5.0% % Dropout 4.0% 3.0% FRPL Students All Students Non-FRPL Students 2.0% 1.0% 0.0% School Year Figure 4. Seattle Public High Schools On-Time High School Graduation Rates by Enrollment in Free or Reduced-Price Lunch Program from to % 98.0% 96.0% % Graduated On Time 94.0% 92.0% 90.0% 88.0% Non-FRPL Students All Students FRPL Students 86.0% 84.0% 82.0% School Year 38

44 School Choice & Bias 3 The models in this study compare students who attend desegregated schools with students who attend segregated schools. All of the students included in the natural experiment models chose the type of school they wanted to attend. Because students who do not choose a school are not included in the natural experiment models, it is important to note that students who actively chose a school differ systematically from those who did not choose a school. Students who participated in the choice process are more likely to be white or Asian and less likely to be black or Hispanic than students who did not participate in the choice process (p<.01). Choosers are also somewhat less likely to be enrolled in the Free or Reduced-Price Lunch program than non-choosers (p <.05). And those who participate in the choice process are more likely to have higher test scores on the math, reading, and language sections of the ITED than non-choosers (p<.01). Choosers and non-choosers might also differ on other characteristics such as motivation and social characteristics that are not measured by any variables in this data set. Because students from poorer financial circumstances or with lower achievement might have lower attainment, excluding students who do not choose schools might bias the results somewhat. But the differences are not large enough to preclude analysis. Also, students who choose desegregated schools might be systematically different from students who choose segregated schools. When compared to students who chose segregated schools, students who chose desegregated schools are more likely to be white (p<.01), less likely to be enrolled in the Free or Reduced-Price Lunch program (p <.01), 3 Analytic results are available upon request 39

45 and more likely to have higher scores on all three sections of the ITED (p <.01). Additionally, those who chose segregated schools might place less value in attending a school with students of different backgrounds or they might be less willing to travel outside of their community to attend school than students who chose desegregated schools. However, these characteristics cannot be accurately captured with the variables in this dataset. These potential and actual differences between these students might bias the results somewhat; but not enough to completely undercut the value of the analysis. Racially Desegregated Schools vs. Racially Segregated Schools There are higher percentages of minority and low-income students in segregated schools than in desegregated schools. T-test results in Table 4 reveal higher percentages of black students in segregated schools in (p <.01) and (p <.01). In and , there are higher percentages of Hispanic students in desegregated schools (p <.01). Additionally, there are higher percentages of students enrolled in the Free or Reduced-Price Lunch program in segregated schools than desegregated schools in and

46 Table 4. Frequencies of Variables for Students in Racially Segregated & Desegregated Seattle Public High Schools during Choice Plan ( to ) Desegregated Schools Segregated Schools Variables Race White % 43.7% 40.0% 36.5% 36.6% 41.2% Black % 19.6% 23.4% 24.5% 27.0% 22.9% Hispanic 2 8.6% 9.5% 10.6% 7.7% 8.8% 8.7% Asian % 24.8% 23.7% 28.6% 25.1% 24.6% Native American 0 2.3% 2.4% 2.3% 2.7% 2.5% 2.5% Gender 1 Male 51.2% 50.8% 51.0% 49.1% 50.9% 51.0% Female 48.8% 49.2% 49.0% 50.9% 49.1% 49.0% FRPL Enrollment 3 Enrolled 34.3% 31.1% 37.5% 35.3% 36.6% 34.3% Not Enrolled 65.7% 68.9% 62.5% 64.7% 63.4% 65.7% Expulsion 0 Expelled 2.1% 1.9% 2.6% 1.8% 1.8% 2.1% Not Expelled 97.9% 98.1% 97.4% 98.2% 98.2% 97.9% Average Attendance Rate % 90.1% 89.7% 87.8% 87.7% 89.3% ITED Average Percentile Math ITED % 58.8% 56.1% 53.9% 53.4% 56.0% Reading ITED % 55.4% 52.1% 50.9% 50.5% 53.1% Language ITED % 57.7% 54.4% 52.9% 52.3% 54.9% Dropout 3 5.1% 3.9% 3.9% 6.1% 7.1% 4.9% Graduate On Time % 95.4% 91.7% 96.4% 90.1% 93.7% Number of Students 6,411 6,496 4,775 7,385 7,200 9,182 Source: Seattle Public Schools Data ( to ) 0 No Years of Statistically Significant Difference ; 1 One Year of Statistically Significant Difference; 2 Two Years of Statistically Significant Difference; 3 Three Years of Statistically Significant Difference 41

47 Income Desegregated Schools vs. Income Segregated Schools Income desegregated and income segregated schools have significantly different percentages of minority students and students enrolled in the Free or Reduced-Price Lunch Program. Table 5 shows that there are significantly higher percentages of black students in income segregated schools than in income desegregated schools in all six school years from to (p <.01). There are higher percentages of Hispanic students in income segregated schools than in income desegregated schools (p <.05 in and ; p <.01 in all other years). Additionally, the FRPL enrollment rate is much higher each year in income segregated schools than in income desegregated schools (p <.01). This difference is as high as 20.8 percentage points in

48 Table 5. Frequencies of Variables for Students in Income Desegregated & Segregated Seattle Public Schools from to (All Frequencies Reported in Percentages) Income Desegregated Schools Income Segregated Schools Variables Race White Black Hispanic Asian Native American Gender Male Female FRPL Enrollment 6 Enrolled Not Enrolled Expulsion 5 Expelled Not Expelled Average Attendance Rate ITED Average Percentile Math ITED Reading ITED Language ITED Dropout Graduate On Time Number of Students 11,418 12,190 11,512 11,628 12,611 11,582 2,395 1,523 2,462 2,728 1,805 2,848 Source: Seattle Public Schools Data ( to ) 0 No Years of Statistically Significant Difference; 3 Three Years of Statistically Significant Difference; 4 Four Years of Statistically Significant Difference; 5 Five Years of Statistically Significant Difference; 6 Six Years of Statistically Significant Difference 43

49 Probit Models The t-tests reported above show that there are relationships between demographic characteristics and school desegregation by income or race. To isolate the relationship between attainment and attending a desegregated school, a probit regression is used. Defining Probit Models To prevent the probability of the binary dependent variables from taking on a value that falls outside the range of probabilities, probit models are used to test the first two hypotheses. Figure 5, below is an example of a relationship between a binary dependent variable and a continuous independent variable. The line, which represents the linear relationship between the variables, does not pass through many data points and extends beyond the values of the dependent variable. However, drawing a curve more accurately captures the relationship between the two variables. Figure 5. Hypothetical Linear and S-Curve Relationships with a Binary Dependent Variable Binary Dependent Variable Continuous Independent Variable 44

50 A probit model takes on an s-shaped curve similar to the one above and constrains the values of the dependent variable between 0 and 1. Probit models assume that the errors are normally distributed. With that assumption in probit models, the function that constrains values is a standard normal cumulative distribution function. This function is an integral that measures the cumulative area under a standard normal probability density function. This assumption also allows the beta coefficients in a probit model to be interpreted as changes in Z-scores in the normal distribution. The coefficient measures how much a one-unit change in an independent variable changes the cumulative probability of the dependent variable, which is the effect of the independent variable on the Z-score of the dependent variable. Because these coefficients are difficult to interpret, a different method of reporting the effects of dependent variables, which is discussed below, is used in this study. Because the s-shape allows the effect of an independent variable on the dependent variable to change across values of the independent variable and the coefficients are difficult to interpret, this study reports probit regression results as changes in predicted probabilities. The predicted probability is the probability the dependent variable takes on a value of 1 when all independent variables are set at their means. Therefore, the effects of independent variables in probit models are measured as changes in the predicted probability of the dependent variable taking on a value of 1when the independent variable is increased by one unit and all other variables are set at their means. And for binary independent variables, the effect on dependent variables is the change in the predicted probability when the independent binary variable changes from 0 to 1 and all other 45

51 variables are set at their means. Changes in predicted probabilities are presented in the text of this study and probit coefficients are reported in the Appendix. 4 Hypothesis #1 Probit Models In order to test the first hypothesis that Seattle Public Schools desegregation program is related to lower minority student dropout rates and higher on-time graduation rates, this study examines whether there is a difference between students in desegregated schools and segregated schools during the years of the choice program with the racial tiebreaker. Because students were allowed to rank schools of their choice and the school district did not use the racial tiebreaker in every possible situation, some of the public high schools in Seattle remained segregated during the program. This creates an opportunity to study whether there are differences in the outcomes between students in racially segregated schools and those in racially desegregated schools, while simultaneously controlling for district-wide reforms in a year that could affect all students. This is measured by Model #1a. Additionally, I conducted a natural experiment (Model #1b) using student choice data to control for potential student selection bias due to the student choice element of the desegregation program. 4 The software program, CLARIFY, was used to generate changes in predicted probabilities for probit models throughout this study. Tomaz, M., Wittenberg, J. & King, G. (2001). CLARIFY: Software for Interpreting and Presenting Statistical Results. Version 2.0. Cambridge, MA: Harvard University. 46

52 Model #1a The equation that follows represents the model used to test the first hypothesis that school desegregation is related to improved educational attainment for minority students. Y i = β 0 + β 1 Black + β 2 Hispanic + β 3 Asian + β 4 NativeAmer + β 5 DesegSchool + β 6 DesegSchool*Black + β 7 DesegSchool*Hispanic + β 8 DesegSchool*Asian + β 9 DesegSchool*NativeAmer + β 10 FRPL+ β 11 Female + β 12 Attendance+ β 13 Expelled + β 14 MathITED + β 15 ReadITED+ β 16 LangITED In this model, Y i is an indicator variable for whether student i graduated on time or dropped out of high school. The race and interaction variables will test the relationships for the hypothesis. Additionally, this model controls for gender, Free or Reduced-Price Lunch program enrollment, ITED scores, attendance, and expulsion. Model #1b Model #1b is very similar to Model #1a, but it attempts to also control for possible student bias that is derived from the choice element of Seattle s program. This model is represented by the equation written below: Y i = β 0 + β 1 Black + β 2 Hispanic + β 3 Asian + β 4 NativeAmer + β 5 DesegPlacement + β 6 DesegPlacement*Black+ β 7 DesegPlacement *Hispanic + β 8 DesegPlacement *Asian + β 9 DesegPlacement*NativeAmer + β 10 FRPL + β 11 Female + β 12 Attendance + β 13 Expelled + β 14 MathITED + β 15 ReadITED+ β 16 LangITED This model will be used to run separate regressions for students who ranked desegregated schools and those who ranked segregated schools. The first set of regressions one regression for dropping out and another for graduating on time will only have students who listed desegregated schools in their choices, but some students were placed in 47

53 desegregated schools and some students were placed in segregated schools. The DesegPlacement variable takes on a value of 1 if a student attends a desegregated school and a value of 0 when a student attends a segregated school. Therefore, using the DesegPlacement variable, this model will compare students who chose and were placed in desegregated schools with students who chose a desegregated school but were placed in segregated schools. The second set of regressions will only contain students who listed a segregated school in their choices, but some students were placed in desegregated schools and some were placed in segregated schools. The DesegPlacement variable takes on the same values in these regressions as in the first set, but it compares students who chose a segregated school but were placed in a desegregated school with students who chose and were placed in a segregated school. Hypothesis #2 Probit Models The models used to test the hypothesis that attending an income desegregated schools is associated with improved educational attainment of minority students are very similar to the models used to test the first hypothesis. These models only differ in that the models here use income desegregated schools and income desegregated school placement instead of racially desegregated schools and racially desegregated school placement. 48

54 Model #2a Due to student choice, some of the high schools in Seattle are income segregated and others are income desegregated. The equation below compares students who attend these two types of schools: Y i = β 0 + β 1 Black + β 2 Hispanic + β 3 Asian + β 4 NativeAmer + β 5 incdeseg + β 6 FRPL + β 7 incdeseg*black + β 8 incdeseg*hispanic + β 9 incdeseg*asian + β 10 incdeseg*nativeamer + β 11 ITEDScores + β 12 Attendance + β 13 Female + β 14 Expelled This model also controls for the background and achievement variables of gender, ITED test scores, attendance, and expulsion. Model #2b In order to control for potential student selection bias, the equation below examines the difference between students who ranked schools with similar student income compositions, but some students were placed in income desegregated schools and others were placed in income segregated schools. Y i = β 0 + β 1 Black + β 2 Hispanic + β 3 Asian + β 4 NativeAmer + β 5 IncDesegPlacement + β 6 FRPL + β 7 IncDesegPlacement*Black + β 8 IncDesegPlacement *Hispanic + β 9 IncDesegPlacement*Asian + β 10 IncDesegPlacement *NativeAmer + β 11 ITEDScores + β 12 FRPL + β 13 Attendance + β 14 Female + β 15 Expelled The first set of regressions will only have students who listed income desegregated schools in their choices, but some students were placed in income desegregated schools and some students were placed in income segregated schools. The DesegPlacement variable takes on a value of 1 if the student attends an income desegregated school and a value of 0 if the student attends an income segregated school. Therefore, with this 49

55 variable, the model will compare students who chose and were placed in an income desegregated school with students who chose an income desegregated school but were placed in an income segregated school. The second set of regressions will only contain students who listed an income segregated school in their choices, but some students were placed in income desegregated schools and some were placed in income segregated schools. The DesegPlacement variable takes on the same values in these equations as in the first set of equations (1=attend an income desegregated school and 0 = attend an income segregated school), but these regressions compare students who chose an income segregated school but were placed in an income desegregated school with students who chose and were placed in an income segregated school. Goodness-of-fit Measures The main goodness-of-fit measure reported in this study for probit models is Pseudo R-Squared. As with R-Squared in Ordinary Least Squares models, values for Pseudo R-Squared lie within the range of 0 to 1, and the closer to 1, the greater the predictive power of the model. But for Pseudo R-Squared, a model is run with the constant as the only explanatory variable, and the log-likelihood or predictive value of this restricted model is derived. The log-likelihood of this restricted model is measured against the log-likelihood of a full, unrestricted model using the following formula: 1 L L ur r 50

56 This formula subtracts the log-likelihood ratio of the unrestricted model to the restricted model from 1. The ratio essentially shows how much more predictive power is in the unrestricted model than the restricted model. Additionally, this study includes Wald Chi- Square Tests for each probit regression. This simply tests whether at least one of the predictors in the probit regression has a coefficient that is not equal to zero. 51

57 Results Descriptive Analysis Mutual Information Index Calculating Mutual Information Index values for each school reveals that in , 6,411 students were in one of eight schools with low racial segregation (desegregated schools) and 7,316 students were in one of fifteen schools with moderate, high, or extreme racial segregation (segregated schools). In the following year, the district had 6,496 students in six desegregated schools and 7, 119 students in fifteen schools that were in segregated schools. And in , the district had six desegregated schools with 4,775 students and eighteen segregated schools with 9,090 students. Table 6. Levels of Racial Segregation in Seattle Public Schools ( to ) Level of Segregation Number of Schools Number of Students Number of Schools Number of Students Number of Schools Number of Students Low Segregation 8 6, , ,775 Moderate Segregation 8 5, , ,835 High Segregation 3 1, ,025 Extreme Segregation Totals 23 13, , ,865 Source: Seattle Public Schools Data ( to ) Tables 7a and 7b show that most of the schools in the district each year are income desegregated and most of the high school students attend income desegregated schools. For instance, in , 11,418 students attended the twelve income desegregated schools and the remaining 1,093 attended one of ten income segregated 52

58 schools. And in (Table 7b), 11,616 high school students attended thirteen desegregated schools and 2,618 students attended thirteen segregated schools. Table 7a. Income Desegregated & Segregated Schools ( to ) Level of Segregation Number of Schools Number of Students Number of Schools Number of Students Number of Schools Number of Students Low Segregation 12 11, , ,512 Moderate Segregation , ,864 High Segregation Extreme Segregation Totals 22 13, , ,865 Source: Seattle Public Schools Data ( to ) Table 7b. Income Desegregated & Segregated Schools ( to ) Level of Segregation Number of Schools Number of Students Number of Schools Number of Students Number of Schools Number of Students Low Segregation 13 11, , ,582 Moderate Segregation 8 2, , ,547 High Segregation Extreme Segregation Totals 26 14, , ,313 Source: Seattle Public Schools Data ( to ) 53

59 Correlations Examining correlations reveals relatively weak linear relationships between the background variables and the outcomes of dropping out of high school and graduating on time. Table 8, below, shows that attendance has a strong and negative correlation with dropping out of high school (r = -0.48, p <.01) and a strong and positive correlation with graduating from high school on time (r = 0.25, p <.01). Test scores have the next strongest relationships with the two outcome variables. Table 8. Correlations a between Variables of Interest from to Attendance Math ITED Lang. ITED Read ITED Dropout Graduate on Time Attendance ---- Math ITED Language ITED Reading ITED Drop Out Graduate on Time White Black Hispanic Female FRPL Expelled Source: Seattle Public Schools Data ( to ) a All correlations are statistically significant at the 1% level. The results in Table 8 also show that minority and low-income students have negative correlations with levels of educational attainment and test scores and white students show positive correlations with these outcomes. There is a very weak, negative correlation between white students and dropping out of high school (r = -0.09, p <.01), while this relationship is positive for black students (r = 0.08, p <.01) and Hispanic students (r = 0.05, p <.01). And the relationship for white students and graduating on 54

60 time is very weakly positive (r = 0.08, p <.01); but this relationship is weakly negative for both black (r = -0.09, p <.01) and Hispanic (r = -0.03, p <.01) students. White students also have positive correlations with test scores, but black and Hispanic students have negative correlations with these scores. Additionally, Free or Reduced-Price Lunch program enrollment has a weak, positive relationship with dropping out of high school (r = 0.13, p <.01) and a weak negative relationship with graduating from high school on time (r = -0.12, p <.01). Thus, these results suggest that minority students and lowincome students are not graduating on time or dropping out at the same rates as other students. Hypothesis #1 Model #1a The first hypothesis of this study is that attendance at desegregated schools is related to lower dropout rates and higher on-time graduation rates for black and Hispanic students. Initial t-tests reported in Table 4 reveal that dropout rates are higher for students in segregated schools than those in desegregated schools. The differences increase each year, with a 1.0 percentage point difference in (p <.01), a 3.2 percentage point difference in (p <.01), and a 4.5 percentage point difference in (p <.01). Also, students in segregated schools graduated on time at higher levels than students in desegregated schools in (2.4 percentage points, p <.01) and in (2.0 percentage points, p <.05). But students in desegregated schools in had an on-time graduation rate that was 5.2 percentage points higher than students in segregated schools. 55

61 Overall, the results show that Model #1a, which compares students in desegregated and segregated schools (Table 9), has stronger predictive power than the restricted model (Pseudo R 2 = 0.31), but none of the race or interaction variables is significant. However, many of the background variables are significant. Students who are enrolled in the FRPL program for at least one year during the choice program are 1.3 percentage points more likely to drop out of high school than those who are not enrolled in the program (p <.05). Female students are less likely to drop out of high school than male students (2.1 percentage points, p <.01), and students who are expelled from a school are 9.0 percentage points more likely to drop out than those who are not expelled (p <.01). Thus, although attending a desegregated school does not have a significant association with lower dropout rates for black or Hispanic students in this model, several of the background variables are significantly related with lower or higher dropout rates. 56

62 Table 9. Changes in Predicted Probabilities for Probit Models Comparing Students in Racially Desegregated & Segregated Schools (1) Dropout (2) Graduate on Time Variable Desegregated School * (White) (0.01) Black x Desegregated School Hispanic x Desegregated * School (0.01) FRPL Enrollment 0.013** * (0.01) (0.01) Female *** 0.029*** (0.00) (0.00) Expelled 0.090*** (0.02) Math ITED *** 0.084*** (0.02) (0.02) Number of Observations 8,298 6,088 Wald Chi 2 (16) Pseudo R Source: Seattle Public Schools Data to * = Statistically Significant at.10 level; ** = Statistically Significant at.05 level; *** = Statistically Significant at.01 level Standard Errors in Parenthesis Only predicted probabilities for statistically significant coefficients were determined using CLARIFY The results of the on-time graduation probit regression comparing students in racially desegregated schools to those in racially segregated schools (Table 9) reveal some marginally significant interaction variables. Hispanic students in desegregated schools are 1.6 percentage points more likely to graduate on time than Hispanic students in segregated schools (p <.10). And as in the dropout regression, female, attendance, and math ITED scores are significant at the 1% level. 57

63 Hypothesis #1 Model #1b The results of the natural experiment comparing students who were placed in racially desegregated schools to those who were placed in racially segregated schools show a significant difference in dropout rates for Hispanic students, but not for black students. The first column in Table 10 shows that Hispanic students who rank and attend desegregated schools are 1.4 percentage points less likely to drop out of high school than are Hispanic students who rank desegregated schools but attend segregated schools (p <.10). Also, the results in the next column show that Hispanic students who attend desegregated schools, but ranked segregated schools are 1.9 percentage points less likely to drop out of high school than Hispanic students who rank and attend segregated schools (p <.10). This 1.9 percentage point difference in probability of dropping out is greater than the 1.4 percentage point difference between Hispanic students who rank and attend desegregated schools and Hispanic students who rank desegregated schools but attend segregated schools. But there is no significant difference in dropout rates between black students in racially desegregated schools and those who were placed in racially segregated schools, regardless of whether they ranked desegregated or segregated schools. 58

64 Table 10. Changes in Predicted Probabilities for Racial Desegregation Natural Experiment Probit Models Variable Dropout Graduate on Time Desegregated School Choice (1) Segregated School Choice (2) Desegregated School Choice (3) Segregated School Choice (4) Desegregated School *** Placement (White) (0.01) Black x Desegregated School Placement Hispanic x Desegregated School * * 0.013* 0.016** Placement (0.01) (0.01) (0.01) (0.00) FRPL Enrollment * ** ** (0.01) (0.01) (0.01) Female *** *** 0.029*** 0.027*** (0.00) (0.00) (0.00) (0.00) Expelled 0.058*** (0.03) Math ITED *** *** 0.062*** 0.058*** (0.02) (0.02) (0.03) (0.02) Number of Observations 5,040 6,038 3,984 4,723 Wald Chi 2 (16) Pseudo R Source: Seattle Public Schools Data to * = Statistically Significant at.10 level; ** = Statistically Significant at.05 level; *** = Statistically Significant at.01 level Only predicted probabilities for statistically significant coefficients were determined using CLARIFY Standard Errors in Parenthesis On average, Hispanic students who attend desegregated schools are more likely to graduate on time than Hispanic students who attend segregated schools, but there is no significant difference for black students. Hispanic students who rank and attend desegregated schools are 1.3 percentage points more likely to graduate on time than Hispanic students who rank desegregated schools and attend segregated schools. However, black students who rank and attend desegregated schools do not show different 59

65 on-time graduation rates than black students who rank desegregated schools but attend segregated schools. The last column of Table 10 shows that the probability of graduating on time for white students in desegregated schools who ranked segregated schools is 1.7 percentage points lower than white students who ranked and attend segregated schools (p <.01). In contrast, Hispanic students who attend desegregated schools but ranked segregated schools are 1.6 percentage points more likely to graduate on time than Hispanic students who rank and attend segregated schools (p <.05). This difference in probability of graduating on time of 1.6 percentage points is greater than the 1.3 percentage point difference between Hispanic students who rank and attend desegregated schools and Hispanic students who rank desegregated schools but attend segregated ones. Thus, the relationship between attending a desegregated school and on-time graduation move in opposite directions for white and Hispanic students who rank segregated schools, and there is no relationship for black students. Overall, Hispanic students who attend desegregated schools are less likely to drop out of high school and more likely to graduate on time than Hispanic students who choose schools with similar demographics but attend segregated schools. The relationship between attending a desegregated school and the educational attainment variables seems to be stronger for Hispanic students who ranked a segregated school than Hispanic students who ranked desegregated schools. Attending a desegregated school does not lead to significantly higher or lower likelihoods of dropping out of high school or graduating on time for black students. Only white students who rank segregated schools and attend desegregated schools have lower probabilities of on-time graduation 60

66 than their counterparts who attend segregated schools. However, white students in desegregated schools do not experience a significant difference in probability of dropping out of high school than white students in segregated schools. Thus, the results for Hispanic students support the hypothesis that attending a racially desegregated school is related to lower dropout rates and higher on-time graduation rates for minority students. But the lack of a relationship for black students does not support the hypothesis. Hypothesis #2 Model #2a The second hypothesis posits that attendance at an income desegregated school has a negative relationship with dropping out of high school and is positively associated with graduating from high school on time. Initial t-tests reported in Table 5 show that students in income desegregated and income segregated schools have different dropout and on-time graduation rates. In all six years, the dropout rate is higher in income segregated schools (p <.01 for all years). Also, in every year, the on-time graduation rate is higher in income desegregated schools than in segregated schools (p <.05 for ; p <.01 for all other years). The probit results comparing students who attended income desegregated schools and those who attended income segregated schools show that white and black students experience significant changes in the probability of dropping out, but Hispanic students do not. White students in income desegregated schools are 2.7 percentage points less likely to drop out than white students in segregated schools (p <.01). But black students who attend income desegregated schools are 4.04 percentage points more likely to drop out than black students who attend income segregated schools (p <.01). There is no 61

67 significant difference in dropout rates between Hispanic students in desegregated schools and Hispanic students in segregated schools. The on-time graduation regression results in column 2 of Table 11 reveal no statistically significant relationships between students who attend income desegregated schools and on-time graduation. In contrast, female, attendance rate, and math ITED scores are positively associated with on-time graduation at the 1% level; and Free or Reduced-Price Lunch enrollment has a negative relationship with on-time graduation at the 5% level. Therefore, in this model, several background variables are correlated with the probability that a student will graduate on time, but attendance at a desegregated school is not significantly correlated with graduating from high school on time. 62

68 Table 11. Changes in Predicted Probabilities for Probit Models Comparing Students in Income Desegregated & Segregated Schools (1) Variable Dropout Black *** (0.01) (2) Graduate on Time Income Desegregated School ** (White) (0.01) Black x Income Desegregated 0.040*** School (0.02) FRPL Enrollment 0.014*** ** (0.00) (0.00) Female *** 0.020*** (0.00) (0.00) Expelled 0.094*** (0.02) Math ITED *** 0.044*** (0.01) (0.01) Language ITED ** (0.01) Number of Observations 17,114 10,322 Wald Chi 2 (16) Pseudo R Source: Seattle Public Schools Data to * = Statistically Significant at.10 level; ** = Statistically Significant at.05 level; *** = Statistically Significant at.01 level Only predicted probabilities for statistically significant coefficients were determined using CLARIFY Standard Errors in Parenthesis Hypothesis #2 Model #2b The results from the natural experiment comparing students who were placed in income desegregated and segregated schools do not reveal favorable outcomes for black or Hispanic students. The results in Table 12, column 1 suggest that white students who rank and attend income desegregated schools are 3.7 percentage points less likely to drop out than white students who rank income desegregated schools, but attend in income segregated schools (p <.01). However, black students who rank and attend income 63

69 desegregated schools are 3.1 percentage points more likely to drop out of high school than black students who rank income desegregated schools, but attend income segregated schools (p < 0.05); and Hispanic students who rank and attend income desegregated schools are 3.9 percentage points more likely to drop out of high school than Hispanic students who rank income desegregated schools but attend in income segregated schools (p <.10). Only white students who rank income segregated schools but attend income desegregated schools have significantly different dropout rates than their counterparts who rank and attend income segregated schools. Column 2 of Table 12 shows that white students who ranked income segregated schools but attend income desegregated schools are 3.8 percentage points less likely to drop out of high school than white students who rank and attend income segregated schools. There is no statistically significant difference in high school dropout rates for black or Hispanic students who attend income desegregated schools and their counterparts in income segregated schools. 64

70 Table 12. Changes in Predicted Probabilities for Income Desegregation Natural Experiment Probit Models Variable Dropout Graduate on Time Desegregated School Choice (1) Segregated School Choice (2) Desegregated School Choice (3) Segregated School Choice (4) Black *** ** (0.01) (0.01) Hispanic * (0.01) *** ** Income Desegregated School Placement (White) (0.01) (0.02) Black x Income Desegregated School 0.031** Placement (0.03) Hispanic x Income Desegregated School 0.039* Placement (0.03) FRPL Enrollment 0.010*** *** (0.00) (0.01) Female *** * 0.018*** 0.027*** (0.00) (0.01) (0.00) (0.01) Expelled 0.066*** 0.145*** (0.02) (0.06) Math ITED ** *** (0.01) (0.01) Language ITED * 0.084** (0.02) (0.05) Number of Observations 13,966 2,779 8,879 1,522 Wald Chi 2 (16) Pseudo R Source: Seattle Public Schools Data to * = Statistically Significant at.10 level; ** = Statistically Significant at.05 level; *** = Statistically Significant at.01 level Only predicted probabilities for statistically significant coefficients were determined using CLARIFY Standard Errors in Parenthesis There are no significant differences in on-time graduation between black, Hispanic, or white students who are placed in income desegregated schools and their counterparts in income segregated schools. Having ranked an income desegregated 65

71 school, a student s placement in an income desegregated school does not have a statistically significant relationship with on-time graduation from high school for white, black, or Hispanic students. The predictive power of this model in Column 3 of Table 12 is lower than other models for dropping out of high school (Pseudo R 2 = 0.19, as compared to 0.30). And with so few significant variables, there might be some currently omitted variables that, if added to the model, could increase the model s predictive power. The results of the regression of on-time graduation for students who chose income segregated schools, but some attend income segregated schools and some attend income desegregated schools in last column of Table 12 display that there is not a statistically significant difference in the probability of graduating from high school on time for white, black, or Hispanic students. In sum, these results suggest that attendance at an income desegregated school is not associated with lower dropout rates or higher on-time graduation rates for black or Hispanic students. Black or Hispanic students who rank and attend income desegregated schools are more likely to drop out of high school than black or Hispanic students who attend income segregated schools. But white students who attend income desegregated schools are less likely to drop out of high school than white students in income segregated schools. Neither black, Hispanic, nor white students in income desegregated schools experience different on-time graduation rates than their counterparts. Therefore, these findings run counter to the hypothesis that minority students in Seattle Public Schools who attend income desegregated schools are less likely to drop out of high 66

72 school and more likely to graduate on time from high school on time than minority students who attend income segregated schools. Hypothesis #3 Because the Parents Involved case does not allow schools to use a race-conscious desegregation method and race desegregation has more statistically significant results than income desegregation, I examined whether an income desegregation method could ensure racial desegregation. I hypothesized that an income-based desegregation program could still create high levels of racial segregation. Examining the Free or Reduced-Price Lunch program enrollment rates by race reveals large differences in percentages of white and minority students enrolled in the program. For instance, Table 13 shows that in , 12.1% of all white students were enrolled in the FRPL program, while 63.2% of black students, 54.1% of Hispanic students, 60.4% of black or Hispanic students, and 53.9% of all non-white students were enrolled in the program. Table 13. Percentages of Students Enrolled in Free or Reduced-Price Lunch Program by Race Racial Group White 15.7% 14.8% 12.8% 11.8% 11.4% 11.7% 12.1% 12.4% 14.1% 12.7% 12.1% Black 53.9% 55.1% 53.9% 53.8% 53.9% 58.5% 57.2% 56.6% 64.6% 61.2% 63.2% Hispanic 50.5% 49.3% 48.6% 48.0% 48.6% 52.3% 52.4% 50.8% 51.7% 54.2% 54.1% Asian 50.4% 49.4% 50.4% 48.9% 46.4% 47.2% 45.6% 45.3% 46.0% 47.1% 45.6% Native American 37.6% 40.8% 35.7% 32.4% 32.7% 34.0% 33.4% 42.2% 45.6% 45.8% 44.1% Source: Seattle Public Schools Data to While these differences in percentages of students enrolled in the Free or Reduced-Price Lunch program between white and other students are large, the differences are not large enough to ensure racially desegregated schools using an income- 67

73 based measure. Table 14 shows the levels of two-group segregation possible when each school is required to have the same percentage of students enrolled in the Free or Reduced-Price Lunch program as the district. All but one of the V-values in Table 14 is above Values above 0.50 mean that using an income-based desegregation program, non-white students could attend high schools with less than half the percentage of white students expected, given the district demographics. This means that an income desegregation program could create an extremely racially segregated school system. Thus, if the income demographics for Seattle public high school students remain similar to what they have been in the last eight years, an income desegregation program can still produce racially segregated schools, confirming this hypothesis. Table 14. Maximum Possible V-Values* of Racial Segregation Using Income Desegregation from to Racial Groups Black-White Hispanic-White Black and Hispanic- White Non-White White Source: Seattle Public Schools Data to *V=1.00 is perfect segregation; V=0.50 is extremely segregated; V=0.00 is perfect desegregation 68

74 Conclusions & Policy Recommendations This study reveals three main results: 1) Hispanic students in Seattle Public Schools who are placed in racially desegregated schools are less likely to dropout of high school and more likely to graduate from high school on time than Hispanic students who attend racially segregated schools; 2) minority students in income desegregated schools in Seattle do not experience better educational attainment outcomes than minority students in income-segregated schools; and, 3) using income-based school assignment in Seattle could still create racially segregated schools. The results for Hispanic students in racially desegregated schools are somewhat promising; the race-based school desegregation program is associated with higher levels of minority student educational attainment. These results partially support the existing literature that finds a relationship between race-based school desegregation and minority student outcomes. However, the study does not reveal a relationship for black students, in contrast with the literature. This study does not show differences in high school dropout and on-time graduation rates for minority students in income desegregated and segregated schools, but it does not eliminate the possibility that a relationship exists. This result appears to conflict with the literature that attending an income desegregated school as opposed to an income segregated school is related to more positive student outcomes. But the relationship between income desegregation and minority student educational attainment should be studied further, potentially using either a continuous or ordinal variable with more than two levels of family income or using Free or Reduced-Price Lunch Program eligibility as opposed to enrollment. 69

75 The findings of this study suggest that an income-based assignment program could still create racially segregated schools. The maximum levels of segregation found here support the research findings in the literature of Reardon, Yun, & Kurlaender (2006) that income desegregation programs have the potential to create or maintain racially segregated schools. But a simulation should be conducted to determine the likely levels of racial segregation in income desegregation and not just the maximum levels of racial segregation found here. The overall results of this study have legal implications that suggest that the Supreme Court in the Parents Involved case may have prematurely dismissed Seattle s program or similar desegregation programs as unconstitutional. A line of cases in the Supreme Court require government programs that use race-conscious methods to serve a compelling government interest and be narrowly tailored to serve that interest in order to be constitutional under the 14 th Amendment (See Grutter v. Bollinger, 539 U.S. 306, 308 (2003); Shaw v. Hunt, 517 U.S. 899, 900 (1996); Fullilove v. Klutznick, 448 U.S. 448, 480 (1980)). In the Parents Involved opinion, the court implies that the educational or social benefits of a program could serve as a compelling government interest. But the court chooses not to discuss whether Seattle s plan has educational benefits (See 127 S. Ct. at 2755). Instead, the court claims the program is not narrowly tailored to the interest, because the plan is tied to [the] district s racial demographics rather than to any pedagogic concept of the level of diversity needed to obtain the asserted educational benefit (See id. at 2755). 70

76 The results in this study support the idea that Seattle s racial desegregation plan provides an educational benefit for its students. The lower probability of dropping out of high school and higher probability of graduating on time for Hispanic students in racially desegregated schools as compared to Hispanic students in racially segregated schools is a quantifiable educational benefit. Therefore, these improved outcomes on educational attainment measures can provide a compelling government interest for Seattle Public Schools use of race in school assignment. Additionally, the fact that a plan is linked to a district s racial demographics should not make the plan fail the narrowly tailored portion of the constitutional test. This study compares students in schools that are desegregated with students who are in segregated schools using an index that categorizes a school as desegregated or segregated based on how representative a school s population is of the district population. The court essentially states that regardless of the district demographics, plans that use district racial representation as a guide are unconstitutional (Parents Involved, 127 S. Ct. at 2755). However, the results from this study suggest that for Seattle, a plan that is tied to its current racial demographics in Seattle Public Schools can obtain the asserted educational benefit, making it narrowly tailored to meet the compelling government interest of improved educational outcomes for minority students. Also, attendance at a racially desegregated or income desegregated Seattle public school could also be related to higher levels of educational attainment such as college enrollment, college graduation, or on-time college graduation. If further research finds a difference in college outcomes, this could bolster the educational benefit of a 71

77 desegregation program and the need to find an alternative program that the Supreme Court would not consider unconstitutional. Therefore, more research should be conducted to study whether there is a relationship between college outcomes and attendance at a racially and income-desegregated Seattle public high school for black and Hispanic students. Even with the outcome in Parents Involved in Community Schools v. Seattle School District No. 1, Seattle Public Schools and other districts can and should still adopt programs that promote racially desegregated schools. Justice Kennedy s concurring opinion in the case permits school districts to track student populations and assign students to schools based on school needs and student characteristics that might include race as a component (Parents Involved, 127 S. Ct at 2745). Because the findings in this study suggest that racially desegregated schools are related to educational benefits for minority students, the school district could adopt an income-based assignment program that uses multiple factors, including race. The school district could use factors such as parental education level, the past academic achievement of a student, whether a student is an English language learner, whether a student is in special education, and other factors on which there is generally a disparity between white and minority students in order to assign students to schools. Alternatively, if the district can measure family wealth, which encompasses family assets and income, a desegregation program based on wealth might simultaneously desegregate the schools by income and race. Studies have found that the disparity in wealth between whites and minorities is much larger than the income 72

78 disparity (Smith, 1995). As this study suggests, the income disparities are not large enough to guarantee racial desegregation using an income desegregation plan. But because the wealth disparity between minorities and whites is larger, wealth desegregation could ensure racial desegregation. Therefore, a wealth desegregation program could provide a means to achieve racial desegregation and the educational benefits of such a program without bordering on unconstitutionality. In conclusion, the results in this study support a relationship between racial desegregation and minority student high school educational attainment for Hispanic students. The lack of a relationship between income-based school desegregation and high school educational attainment should be examined further using different variables to characterize low-income students. And more research should be conducted to determine whether race or income desegregation is positively associated with higher levels of educational attainment such as college enrollment and graduation. But the findings in this study have legal and policy implications for Seattle Public Schools that suggest the district should adopt a desegregation program to ensure the high schools are racially desegregated. 73

79 Appendix A Additional Tables & Statistics 74

80 Table 1. Race & Income Demographics for Seattle Public High Schools Students from to White 38.9% 39.2% 39.3% 39.4% 39.7% 40.5% 40.8% 41.0% 41.1% 40.4% 40.8% Black 23.6% 23.4% 23.4% 23.8% 23.6% 23.2% 23.0% 23.0% 22.9% 23.3% 23.1% Hispanic 7.7% 7.8% 8.0% 8.1% 9.1% 9.4% 9.7% 10.0% 10.0% 10.4% 10.6% Asian 27.2% 27.0% 26.6% 26.1% 25.1% 24.4% 24.1% 23.7% 23.6% 23.5% 23.1% Native American 2.7% 2.6% 2.6% 2.5% 2.5% 2.5% 2.5% 2.4% 2.3% 2.3% 2.4% Number of Students 13,374 13,544 13,401 13,727 13,615 13,865 14,256 14,313 14,313 14,241 14,064 Free or Reduced- Price Lunch Enrollment 37.4% 37.0% 35.9% 35.0% 34.1% 35.6% 34.9% 34.9% 37.7% 37.2% 36.9% Number of Students 13,374 13,544 13,401 13,727 13,615 13,865 14,234 14,313 14,313 14,241 14,064 Source: Seattle Public Schools Data ( to )

81 Table 2. Results from Probit Model Comparing Students in Racially Desegregated & Segregated Schools (1) Dropout (2) Graduate on Time Variable Constant (White) 4.41*** -6.35*** (0.24) (0.00) Black (0.09) (0.12) Hispanic (0.12) (0.15) Desegregated School * (White) (0.08) (0.10) Black x Desegregated School (0.12) (0.15) Hispanic x Desegregated * School (0.17) (0.24) FRPL Enrollment 0.15** -0.13* (0.06) (0.08) Female -0.25*** 0.45*** (0.05) (0.07) Attendance -0.06*** 0.08*** (0.00) (0.01) Expelled 0.63*** (0.11) (0.22) Math ITED -0.01*** 0.01*** (0.00) (0.00) Reading ITED (0.00) (0.00) Language ITED (0.00) (0.00) Number of Observations 8,298 6,088 Wald Chi 2 (16) Pseudo R Source: Seattle Public Schools Data to * = Statistically Significant at.10 level; ** = Statistically Significant at.05 level; *** = Statistically Significant at.01 level Robust Standard Errors in Parenthesis 76

82 Table 3. Results from Probit Model for Racial Desegregation Natural Experiment Variable Dropout Graduate on Time Desegregated School Choice (1) Segregated School Choice (2) Desegregated School Choice (3) Segregated School Choice (4) Constant (White) 5.27*** 4.89*** -6.24*** -6.62*** (0.41) (0.35) (0.67) (0.62) Black (0.16) (0.11) (0.17) (0.14) Hispanic (0.16 (0.13) (0.21) (0.18) Desegregated School *** Placement (White) (0.11) (0.11) (0.13) (0.12) Black x Desegregated School Placement (0.18) (0.17) (0.21) (0.22) Hispanic x Desegregated -0.43* -0.47* 0.57* 0.87** School Placement (0.25) (0.28) (0.34) (0.37) FRPL Enrollment * -0.25** -0.22** (0.08) (0.07) (0.10) (0.10) Female -0.32*** -0.22*** 0.56*** 0.56*** (0.07) (0.07) (0.09) (0.09) Attendance -0.07*** -0.07*** 0.08*** 0.08*** (0.00) (0.00) (0.01) (0.01) Expelled 0.52*** 0.52*** (0.16) (0.16) (0.35) (0.35) Math ITED -0.01*** -0.01*** 0.01*** 0.01*** (0.00) (0.00) (0.00) (0.00) Reading ITED (0.00) (0.00) (0.00) (0.00) Language ITED (0.00) (0.00) (0.00) (0.00) Number of Observations 5,040 6,038 3,984 4,723 Wald Chi 2 (16) Pseudo R Source: Seattle Public Schools Data to * = Statistically Significant at.10 level; ** = Statistically Significant at.05 level; *** = Statistically Significant at.01 level Robust Standard Errors in Parenthesis 77

83 Table 4. Results from Probit Model Comparing Students in Income Desegregated & Segregated Schools Variable (1) Dropout (2) Graduate on Time Constant (White) 5.07*** (0.21) (0.44) Black -0.45*** 0.17 (0.14) (0.25) Hispanic Income Desegregated School (White) Black x Income Desegregated (0.21) (0.59) -0.27** 0.18 (0.11) (0.20) 0.40*** School (0.14) (0.25) Hispanic x Income Desegregated School (0.22) (0.59) FRPL Enrollment 0.18*** -0.15** (0.04) (0.06) Female -0.21*** 0.39*** (0.04) (0.05) Attendance -0.07*** 0.08*** (0.00) (0.00) Expelled 0.69*** (0.09) (0.20) Math ITED 0.00*** 0.01*** (0.00) (0.00) Reading ITED (0.00) (0.00) Language ITED 0.00** 0.00 (0.00) (0.00) Number of Observations 17,114 10,322 Wald Chi 2 (16) Pseudo R Source: Seattle Public Schools Data to * = Statistically Significant at.10 level; ** = Statistically Significant at.05 level; *** = Statistically Significant at.01 level Robust Standard Errors in Parenthesis 78

84 Table 5. Results from Probit Model for Income Desegregation Natural Experiment Variable Dropout Graduate on Time Desegregated School Choice (1) Segregated School Choice (2) Desegregated School Choice (3) Segregated School Choice (4) Constant (White) 5.86*** 51.3*** -6.12*** -5.98*** (0.27) (0.00) (0.50) (0.88) Black -0.42*** -0.37** (0.15) (0.15) (0.25) (0.27) Hispanic -0.44* (0.23) (0.23) (0.38) (0.41) -0.42*** -0.31** Income Desegregated School Placement (White) (0.10) (0.13) (0.19) (0.24) Black x Income Desegregated School 0.37** Placement (0.15) (0.19) (0.26) (0.33) Hispanic x Income Desegregated School 0.42* Placement (0.24) (0.29) (0.39) (0.48) FRPL Enrollment 0.16*** *** 0.12 (0.05) (0.10) (0.07) (0.14) Female -0.20*** -0.14* 0.42*** 0.38*** (0.05) (0.08) (0.06) (0.13) Attendance -0.08*** -0.07*** 0.08*** 0.07*** (0.00) (0.01) (0.01) (0.01) Expelled 0.61*** 0.73*** (0.12) (0.22) (0.29) (0.69) Math ITED -0.00** *** 0.01 (0.00) (0.00) (0.00) (0.00) Reading ITED (0.00) (0.00) (0.00) (0.00) Language ITED * 0.01** (0.00) (0.00) (0.00) (0.01) Number of Observations 13,966 2,779 8,879 1,522 Wald Chi 2 (16) Pseudo R Source: Seattle Public Schools Data to * = Statistically Significant at.10 level; ** = Statistically Significant at.05 level; *** = Statistically Significant at.01 level Robust Standard Errors in Parenthesis 79

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