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1 of 49 Education Policy Analysis Archives Volume 9 Number 1January 1, 2001ISSN 1068-2341 A peer-reviewed scholarly journal Editor: Gene V Glass, College of Education Arizona State University Copyright 2001, the EDUCATION POLICY ANALYSIS ARCHIVES. Permission is hereby granted to copy any article if EPAA is credited and copies are not sold. Articles appearing in EPAA are abstracted in the Current Index to Journals in Education by the ERIC Clearinghouse on Assessment and Evaluation and are permanently archived in Resources in Education .School Segregation of Children Who Migrate to the United States From Puerto Rico Luis M. Laosa Educational Testing Service Princeton, New JerseyAbstract This study examined patterns of school segregation (ethnic/racial, linguistic, and socioeconomic) and other ecological characteristics of the schools that preadolescent children who migrate fro m Puerto Rico to the United States (New Jersey) attend in this country d uring the first two years following their arrival ( N = 89 schools). The data show that Hispanics/Latinos are the majority of the student b ody in 43% of the schools; African Americans, in 30% of the schools; and European Americans, in 12% of the schools. Native speakers o f Spanish are the majority of the student body in 29% of the schools. Approximately one half of the schools are in economically depressed, highly urbanized areas. Although the schools are on average large, 4 4% of them enroll above capacity. In most schools the majority of the student body is from economically impoverished families with low levels of parental education. There are, however, wide differences amo ng the schools on

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2 of 49each of these variables. Correlations show that the higher a student body's proportion of Hispanics/Latinos or native sp eakers of Spanish, the higher is the student body's proportion of pupils f rom economically impoverished households with low levels of parental education, and the higher the school's likelihood of being crowded and of being located in a poor inner-city area. Similarly, the higher a stude nt body's proportion of African Americans, the higher is the student body's proportion of pupils from low-income families, and the higher the school 's likelihood of being in a poor inner-city area. The findings are d iscussed with regard to implications for policy and hypotheses in need of r esearch concerning possible consequences of school segregation for stu dents' academic, linguistic, social, and emotional development. Also presented is a historical overview, to the present, and discussion of U.S. policies and judicial decisions concerning school segregation, w ith particular reference to segregation of Hispanics/Latinos. Introduction Schools are social institutions ecologicall y niched in individual communities that are in turn embedded in larger, layered systems. Th us, each school functions as part of a social, cultural, political, and economic environme nt. What each school is like will be determined in part by this ecology. In the United S tates, vast ecological differences exist among schools. This subject raises a broad range of issues, including questions about resource allocation, the distribution of power in s ociety, and educational ideologies (see, e.g., Barton, Coley, & Goertz, 1991; Cobb & Glass, 1999; Kennedy, Jung, & Orland, 1986; Laosa, 1984; Minuchin & Shapiro, 1983; Orland 1994; Puma, Jones, Rock, & Fernandez, 1993; Rutter, Maughan, Mortimore, & Oust on, 1979; Southern Education Foundation, 1995; U.S. Department of Education, 199 3b, 1996, 1997). The subject also raises serious questions about the role of schools in creating or maintaining socioeconomic stratification and ethnolinguistic is olation. These considerations bear especially on children from immigrant and other eth nocultural and linguistic minority groups. For many of these children, the school is t he first—and perhaps the only—influential point of direct experience with a "mainstream" socializing institution. In recent years, many reformers and critics of the U.S. system of education have stressed the importance of academic standards, acco untability, and student assessment, whereas less attention has been given to other crit ical dimensions of the ecology of schools. In contrast, ecological approaches stress the context of events and encourage the search for recurrent patterns that describe the cha racteristics of a system. From this perspective, no unit is considered separable from t he system as a whole (see, e.g., Bronfenbrenner, 1979, 1995; Laosa, 1999; Laosa & He nderson, 1991; Minuchin & Shapiro, 1983). The study reported here examines specific d imensions of the ecology of schools, focusing particularly on the schools attended by ch ildren who migrate to the United States from Puerto Rico. Puerto Ricans are the larg est Hispanic/Latino population in the Northeast of the United States (Prez & Martnez, 1 993; U.S. Bureau of the Census, 1992, 1996). Because of the special sociopolitical relationship between the two countries, (Note 1) making Puerto Ricans U.S. citiz ens by birth, Puerto Ricans are not, technically speaking, "immigrants" in the same sens e as are entrants from nations under

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3 of 49the jurisdiction of U.S. immigration laws. Yet, Pue rto Ricans who migrate to the United States possess all the characteristics of an immigr ant group, including a distinct culture and a different language—Spanish. Puerto Ricans in this country, as a group, fare worse than does the U.S. Hispanic/Latino population as a whole—and far less well than the U.S. non-Hispanic/non-Latino White population—on ma ny socioeconomic characteristics, including varied measures of emplo yment, income, and academic achievement (Prez & Martnez, 1993; U.S. Bureau of the Census, 1994a, b, 1996). The study reported here is guided by the view that in o rder to gain a better understanding of children's development and adaptation, one must fir st describe the attributes of the human environments they face. Particularl y in the United States, critica l ecological attributes of schools include the student body's ethnic/racial, linguistic, and socio economic composition. National trends show that school segregation of African American ch ildren declined dramatically from the mid-1960s through the early 1970s; it then rema ined to a large extent stable until the late 1980s when, in a reversal of this trend, it be gan to rise. In sharp contrast, school segregation of Hispanic/Latino children has continu ed to increase steadily since at least the mid-1960s, when national data on the subject we re first collected (Orfield, 1993; Orfield, Bachmeier, James, & Eitle, 1997; Orfield & Yun, 1999; U.S. Department of Education, 1995). The level of school segregation for Hispani c/Latino children is high across the country; it is highest for the substantially Puerto Rican population of the Northeast, although it is rapidly rising in other regions with significant concentrations of Hispanics/Latinos. African Americans, too, face the highest segregation levels in the Northeast, although they encounter rising levels in other regions because of resegregation trends (Orfield, 1993; Orfield et al. 1997; Orfield & Yun, 1999). The highest levels of school segregation occur in urban areas, particularly in the inner core of cities. Of greatest concern, national data further show a relationship of ethnic/racial segregation to poverty: Both Hispanic/Latino and Af rican American children are much more likely than European American children to find themselves in schools of concentrated poverty (Orfield, 1993; Orfield, Eaton & the Harvard Project on School Desegregation, 1996; Orfield et al., 1997; Orfield & Yun, 1999; Orland, 1994; Puma et al., 1993; U.S. Department of Education, 1993b, 199 6, 1997). Although socioeconomic status (SES) typically refers to the background of indivi duals, a growing body of research suggests that the SES of a child's school may be as critical an influence on the child's academic achievement as is the SES of the c hild. Individual differences in children's academic performance have been shown to correlate not only with the children's household SES but also with the SES of t heir schools' student bodies (Kennedy et al., 1986; Orland, 1994; Puma et al., 1 993; U.S. Department of Education, 1993b, 1996, 1997; U.S. General Accounting Office, 1992). For example, on the basis of a nationally representative sample of U.S. elementa ry students, Kennedy et al. (1986) and Orland (1994) concluded that the higher a schoo l's concentration of economically impoverished students, the higher tends to be the i ncidence of low academic achievers. This relationship held even after statistically con trolling for demographic characteristics of the individual students and of their families (K ennedy et al., 1986, chap. 2; Myers, 1985; Orland, 1994). Other studies lead to similar conclusions (e.g., Puma et al., 1993; U.S. Department of Education, 1993b, 1996, 1997; U. S. General Accounting Office, 1992). Unlike previous research, the present study focuses on a specific Hispanic/Latino population and follows it longitudinally, centering on a specific chronological age period

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4 of 49and a specific stage in the migration process. The target age is preadolescence, an age when children typically position themselves for the marked physiological and psychological changes of adolescence. Informal obse rvations suggest that academic and psychosocial problems experienced by many Hispanic/ Latino and other ethnic/racial minority students emerge during this developmental stage. The target phase of the process of migration and settlement is the first tw o-year span immediately following arrival in the United States, a phase when stressfu l demands are often placed on the individual for personal change and adaptation (Laos a, 1990, 1997, 1999). Specifically, this study examines the follo wing ecological attributes of the schools that preadolescents who migrate from Puerto Rico to the United States (New Jersey) attend in this country during the first two years f ollowing their arrival: the ethnic/racial, linguistic, and socioeconomic mix of the schools' s tudent bodies; the degree of urbanness and the economic status of the neighborho ods in which the schools are located; and the schools' size and density-overcrow dedness. Also examined are the associations among these attributes. The data and a nalyses sought answers to the following questions concerning these schools: • What is the ethnic/racial composition of the scho ols' student bodies? • What is the linguistic composition of the schools student bodies? • What are the socioeconomic characteristics of the schools' student bodies? • In what types of neighborhoods are the schools lo cated? • Are the schools overcrowded? What is the size of the schools? • What, if any, are the relationships of the studen t body's (a) ethnic/racial composition and (b) linguistic composition to the s tudent body's family socioeconomic characteristics? to characteristics o f the school's neighborhood? to school crowdedness and school size ? Here I examine several issues pertaining to these questions; it is organized as follows: After a section that briefly notes certain sociohistorical circumstances bearing on the present relationship between the United Stat es and Puerto Rico and on contemporary characteristics of the Puerto Rican po pulation, the next section describes the study's research method and procedures. Next is the presentation of the data analysis results, answering each research question. An exten ded Discussion section summarizes conclusions from the answers to these questions and considers implications for policy and for students' academic, linguistic, social, and emotional development, identifying hypotheses in need of research; that section also i ncludes a historical overview, to the present, and discussion of U.S. policies and judici al decisions concerning school segregation, with particular reference to segregati on of Hispanics/Latinos. Sociohistorical Context Puerto Rico was under the colonial rule of Spain for four centuries. Spanish is the language generally spoken in Puerto Rico; it is als o the language used as the medium of instruction in Puerto Rico's public schools. The population of Puerto Rico is composed l argely of the descendants of three groups: the Spanish colonizers, the original Amerin dian inhabitants—the Arawak people who developed the Tano culture—and African slaves imported by the colonizers (Mathews & Tata, 1992; Wagenheim, 1970). Sizeable m inorities of the three races constitute the extremes of the skin-color spectrum, which blend in the predominant middle. Most Puerto Ricans, therefore, are generall y considered "colored" by European Americans. In Puerto Rico, fuzzy lines between raci al groups discourage color

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5 of 49discrimination, although the U.S. presence and cert ain attitudes and practices it has brought to the island appear to have heightened the awareness of racial differences among Puerto Ricans (Rodrguez, 1991; Wagenheim, 19 70). Once slavery was abolished in 1873, the law in Puerto Rico opened public place s to all (Wagenheim, 1970). Thus, unlike the U.S. mainland with its de jure segregation, Puerto Rico did not have racially separate public facilities such as rest rooms, wate r fountains, or rear sections of public vehicles. In the second half of the nineteenth centur y, the United States plunged into international politics and took the road to imperia lism—a foreign-policy direction with far-reaching and lasting consequences. These overse as incursions brought under the nation's jurisdiction some eight million people of color in the Caribbean basin, other parts of Latin America, and the Pacific region (Lew is, 1963; Link, 1992; Morison, 1972; Woodward, 1966). (Note 2) U.S. involvement in Puerto Rico began with the Spanish-American War, a short and relatively bloodless war that ended with the Tr eaty of Paris in 1898, by which Spain ceded Puerto Rico to the United States. U.S. involv ement in the Caribbean region grew in the early part of the twentieth century. U.S. mi litary bases in that area have served to protect U.S. and European interests (e.g., during W orld War Two) but also provide investment opportunities, often leading to the expl oitation of the peoples of the Caribbean and of other parts of Latin America and h ence to dependency and resentment (Carr, 1984; Lewis, 1963; Mathews & Tata, 1992; Mor ison, 1972). In 1917 the U.S. Congress passed the Jones Act, which gave limited self-government to Puerto Rico and conferred U.S. c itizenship collectively on its inhabitants (Carr, 1984; Wagenheim, 1970). U.S. cit izens of Puerto Rico elect a representative (i.e., a "resident commissioner") to the U.S. House of Representatives, who may speak but cannot vote except in committees. These citizens are automatically involved in wars declared by the U.S. Congress and led by the U.S. President, in whose elections they cannot participate. Although Puerto Ricans had migrated to the continental United States before the nineteenth century, only after 1900 did they begin doing so in significant numbers. Annual inflows reached their peaks during the two d ecades following the end of World War Two, a period when Puerto Rico's agricultural e conomy was radically transformed into one based on industrial production, as U.S. ta x laws encouraged the establishment of new industries (Rodrguez, 1991; U.S. Commission on Civil Rights, 1976; Wagenheim, 1970). Because the number of small farms had been sharply reduced by the introduction of large-scale, single-crop corporate agribusiness, the island had virtually lost its subsistence farming system that could have enabled many families to return to individually self-supporting farming (Moore & Pacho n, 1985). Numerous workers left the agricultural sector and moved into cities along the island's coast in search for jobs. Many also migrated to large metropolitan centers in the northeastern United States, responding to those areas' expanding economies and consequent demand for low-skill work, and taking advantage of the low-cost island-t o-mainland passenger flights that commercial airlines then began offering (Mathews & Tata, 1992; Wagenheim, 1970). Although annual inflows are currently below the lev els reached in the 1950s and 1960s, migration from Puerto Rico to the continental Unite d States inevitably continues, and by all indications will continue into the foreseeable future. Method Preparatory Demographic Studies

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6 of 49 To inform the development of the sampling p lan, a series of empirical demographic studies (e.g., Laosa, 1998) had been conducted rega rding children's migratory movements between Puerto Rico and New Jersey. Those studies were necessary because the needed demographic information was not availabl e from centralized sources. The U.S. Immigration and Naturalization Service, a sour ce of statistics on immigration, does not monitor Puerto Rican migration because of the s pecial U.S.-P.R. relationship. The U.S. Bureau of the Census routinely provides demogr aphic information on the Puerto Rican stateside population and on the population of Puerto Rico but no information bearing specifically on the present investigation's more detailed focus. Similar difficulties arose with data from other agencies an d organizations that provide national and state statistics. Sample Selection Based on those demographic studies, a sampl e of 241 public elementary (Note 3) schools (27 school districts) was drawn to yield a sample as representative as possible of children migrating from Puerto Rico to urban and su burban areas and small towns in the state of New Jersey. The enrollment records of each of these schools were then continually monitored during two full, consecutive academic years (i.e., two annual migration waves). All the children who transferred in from Puerto Rico (regardless of prior migration history) to the third and fourth gr ades (or the equivalent for ungraded programs) in these schools at any time during those two years were identified within approximately two months of their arrival. Those wh o met these sample-eligibility criteria and gave informed consents (self and paren tal) became research participants (i.e., focal children). Each focal child was then followed long itudinally (from the date of his or her transfer-in from Puerto Rico), regardless of destination, for two consecutive academic years. Considerable care, time, and effort were devoted to sample identification, recruitment, and longitudinal follo w-up. Consequently, as reported elsewhere (Laosa, n.d.), both the participant conse nt rate and the sample retention rate were quite adequate with respect to scientific samp ling standards; there is no reason to suspect significant sample bias. The children who met the sample-eligibility criteria were found widely and thinly scattered across the sample schools; many of the sc hools received no children who met these criteria. (Note 4) The analyses reported here are based on the schools that received the focal children directly from Puerto Rico plus t he schools that these children subsequently attended stateside during their respec tive two-year longitudinal spans ( N = 89 schools). Almost all are New Jersey public schoo ls because the vast majority of the focal children who transfered out of their initial receiving schools did so either to other New Jersey public schools or back to Puerto Rico. Variables and Measures Measurements were taken on each school that focal children attended, as described below. (Note 5) Student body's ethnic/racial composition. A student body's ethnic/racial composition is indexed by the following seven varia bles (a school's measurement on a variable is the percentage (Note 6) of the sch ool's student body belonging to the corresponding ethnic/racial category): African American (i.e., Black), Asian/Pacific Islander American European American (i.e., White/Caucasian), Hispanic/Latino and other ethnic/racial groups Puerto Rican and other Hispanic/Latino disaggregate the Hispanic/Latino category. The fir st, second, third, and fifth ethnic/racial categories include o nly non-Hispanics/nonLatinos. Student body's linguistic composition. A student body's linguistic composition

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7 of 49is indexed by four variables (a school's measuremen t on a variable is the percentage (Note 7) of the school's student body be longing to the corresponding linguistic category). Three of them divide the stud ent body by native language: monolingual native speakers of English native speakers of Spanish and native speakers of other languages The fourth linguistic category is limited-Englishproficient/English-language learners (LEP/ELL) ; it identifies the pupils whom the school's officials formally classified as "limitedEnglish-proficient (LEP);" also called "English-language learners (ELL)," this clas sification can be applied only to pupils who are not native speakers of English. Student body's family socioeconomic characteristics To gain a deeper understanding of the construct socioeconomic status as it applies to the focal issues—and thus add to its relevance for policy, pr actice, and theory—the present study examines seven variables that respectively me asure particular social, economic, and educational characteristics of the st udent bodies' families. Previous studies have typically included only one of these v ariables as a proxy index or else have combined them into a single measure of socioec onomic status or social class. Although these variables are expected to be interco rrelated, it was deemed important for the purposes of the present study to measure and analyze them individually: Unemployment level is the percentage (Note 8) of the school's student body living in households in which the householder (Note 9) is unemployed. Public assistance dependence level is the percentage (Note 10) of the school's student body living in households receivin g public assistance (i.e., welfare). A student body's average family economic status is measured on a 5-point scale (1 = low income; 5 = affluent). A school's fully subsidized lunch eligibility level is the percentage (Note 11) of the student body eligible for free lunches. Partly subsidized lunch eligibility level is the percentage (Note 12) of the student body eligible for reduced-price lunches. Subsidized lunch eligibility level (fully + partly) is the aggregate of the last two variables (i.e., the percentage of the student body eligible for fully subsidized lunch plus the percentage eligible for p artly subsidized lunch). (Note 13) Finally, maternal schooling level is the average level of formal education attained by the student body's mothers or female gu ardians, measured on a 9-point scale (1 = six years of schooling or less; 9 = doctor's degree). School neighborhood's urbanness and economic status Two variables describe the area, or neighborhood, in which the school is l ocated: urbanness a 5-point scale (1 = rural; 5 = inner core of a city), and economic status also a 5-point scale (1 = low-income area; 5 = affluent area). School size and crowdedness. Four variables pertain to school size and crowdedness: A school's enrollment size is the total number of students enrolled in the school in late spring. Enrollment capacity is the number of students for which the school was built. A school's density-overcrowdedness level is indexed by subtracting the school's enrollment capacity from i ts enrollment size (thus, a higher positive value signifies denser crowdedness than does a lower positive value). The crowdedness dichotomy is a dichotomous variable: 1 = the school is not crowded (i.e., densityovercrowdedness level is z ero or negative); 2 = the school is crowded (i.e., density-overcrowdedness le vel is greater than zero).

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8 of 49 Data Sources The data, including the scale ratings, were obtained directly from the schools' principals, primarily through structured questionna ires; however, when necessary the questionnaire approach was supplemented or replaced by telephone calls and by site visits in order to examine school records and to in terview principals and other school staff. Statistical Analyses The unit of analysis is the (unweighted) in dividual school. The school is not weighted (i.e., by the number of focal children att ending it) in the analyses, since the present focus is on the schools that focal children attend rather than on the focal children per se. (Footnote 4 shows the frequency distributio n of focal children on the schools.) The analyses examine individual differences that oc cur among the schools on the variables. To this end, computed were the frequency distribution of the schools on each variable, its mean, standard deviation, standard er ror of the mean, and skewness value. Also computed were matrices of correlation coeffici ents. (Notes 14 & 15) For the purposes of exposition only, the frequency distribu tion on any variable with a very wide range is summarized in the tables or text below by collapsing the range into a suitable number of grouping intervals; however, for the purp oses of computing the statistics and performing the statistical analyses, all the variab les are based on the actual detailed data. Results The presentation of the analysis results is organized by the research questions. 1. What is the ethnic/racial composition of the sch ools' student bodies? The schools attended by the focal children have, on average, a student body that is nearly one-half Hispanic/Latino, one-third African American, 17% European American, 2% Asian/Pacific Islander American, and 2% "other." Specifically, Table 1 shows that of the five broad ethnic/racial composition variables, Hispanic/Latino has the highest mean percentage (i.e., 46.5), signifying that the school s have, on average, a student body that is 46.5% Hispanic/Latino. In finer detail, this tab le shows that the vast majority of the Hispanic/Latino students in these schools are Puert o Rican. Indeed, the schools have, on average, a student body that is 38% Puerto Rican. N ext in descending order of size is the African American mean percentage (i.e., 32.4), foll owed in turn by the European American (i.e., 17.1) and Asian/Pacific Islander Am erican (i.e., 1.9) mean percentages. (The mean percentage for other ethnic/racial groups is 1.9; this variable is excluded from subsequent analyses.) Table 1 Student Body's Ethnic/Racial Composition Variables: Means, Standard Deviations, Standard Errors of the Mean, a nd Skewness ValuesVariableMSDSEMeanSkewnessAfrican American 32.428.73.080.58 Asian/Pacific Islander American 1.94.10.443.64

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9 of 49 European American 17.126.82.901.91 Hispanic/Latino 46.528.83.140.16 Puerto Rican (37.5) (25.9)(3.05)(0.37) Other Hispanic/Latino (9.0) (17.8)(2.14)(2.94) Other ethnic/racial groups 1.96.40.694.97Note. N = 84–87 schools. A school's measurement on a varia ble in this table is the percentage of the student body described by the var iable. Percentages are within rounding error. a Estimated mean. It also should be noted that the schools di ffer widely around these averages, as the standard deviations in Table 1 and the summary freq uency distributions in Table 2 demonstrate. For example, Table 2 shows the followi ng: About one fourth of the schools have a student body that is over 74% Hispanic/Latin o, but at the other end of the distribution, another one fourth of the schools hav e a student body that is less than 25% Hispanic/Latino. About one third of the schools hav e a student body with an African American majority, but about one half of the school s have a student body that is less than 25% African American. About one tenth of the s chools have a student body with a European American majority, but about three fourths of the schools have a student body that is less than 25% European American.Table 2 Summary Frequency Distributions of Schools with respect to Student Body's Ethnic/Racial Compos ition AfricanAmerican a Asian/ Pacific Islander American b EuropeanAmerican c Hispanic/ Latino d Percent of the school's student body Percent of schools 75% to 99% 10%0%7%23% 50% to 74% 220623 25% to 49% 171929 0% to 24% 51997826 Note. N = 84–87 schools. The footnotes to this table descr ibe the extremes of the tails of the distributions and other details. Percentages are wi thin rounding error. aIn 1% of the schools, the student body is 0.2% African American; in another 1% of the schools, the student body is 94.5% African American. In 30% of t he schools, the majority (i.e., over 50%) of the student body is African American. bIn 48% of the schools, the number of Asian/Pacific Islander American students is zero; i n 1% of the schools, the student body is

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10 of 49 27% Asian/Pacific Islander American. In 99% of the schools, Asian/Pacific Islander Americans account for less than 15% of the student body. c In 7% of the schools, the number of European American students is zero; in 1% of the schools, the student body is 97.4% European American. In 12% of the schools, the majority of the student body is European American. dIn 1% of the schools, the student body is 1.4% Hisp anic/Latino; in another 1% of the schools, it is 98.7% Hispanic/Lat ino. In 43% of the schools, the majority of the student body is Hispanic/Latino. 2. What is the linguistic composition of the school s' student bodies? The focal children attend schools in which, on average, monolingual native speakers of English constitute 58% of the student b ody; native speakers of Spanish, 36%; and native speakers of other languages, the re maining 5% (Table 3). The correlation coefficients in Table 4 add to the evidence that schools tend to isolate students on the basis of both ethnicity/rac e and language. The focal children attend schools in which, on average, students formally classified as limited-English-proficient (or English-language learners; LEP/ELL) constitute 18.5% of the student body (Table 3). This figure, when co nsidered in relation to the mean percentages for the other linguisticcomposition v ariables, shows that, on average in these schools, approximately 45% of the students wh o are not monolingual native speakers of English are formally classified as LEP/ ELL.Table 3 Student Body's Linguistic Composition Variables: Means, Standard Deviations, Standard Errors of the Mean, and Skewness VariableMSDSEMeanSkewnessNative speakers of Spanish 35.927.32.980.53 Monolingual native speakers of English 57.729.23.21-0.32 Native speakers of other languages 5.212.51.384.88 Classified as LEP/ELL 18.513.31.440.74Note. N = 82–86 schools. A school's measurement on a varia ble in this table is the percentage of the student body described by the var iable. Percentages are within rounding error.Table 4 Correlations among the Student Body's Ethnic/Racial and Linguistic Composition VariablesVariable23456 Ethnic/racial composition

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11 of 49 1: African American —— -.25 ** .73 *** -.30 ** 2: European American —— -.21 -.10.05 3: Hispanic/Latino — .89 *** -.28 ** .80 *** Linguistic composition 4: Native speakers of Spanish — -.38 *** .74 *** 5: Monolingual native speakers ofEnglish — -.32 ** 6: Classified as LEP/ELL —Note. N = 80–86 schools. The coefficients among the lingui stic composition variables and the coefficients of variable 5 with variables 2 and 3 are Spearman rank-order correlations; the other coefficients in this table are Pearson pr oduct-moment correlations. The coefficients in this table are based on the variabl es measured in counts. p < .05 ** p < .01 *** p < .001 (1-tailed tests) It also should be noted that the schools ag ain vary widely around the mean percentages, as the standard deviations in Table 3 and the summary frequency distributions in Table 5 show. For example, native speakers of Spanish are the majority of the student body in about one third of the schoo ls, but less than 25% of the student body in another one third of the schools. Similarly monolingual native speakers of English constitute 75% or more of the student body in about one third of the schools, but less than 50% in another one third of the schools ( Table 5).Table 5 Summary Frequency Distributions of Schools on the Student Body's Linguistic Composition Variab les Native speakers of Spanish a Monolingual native speakers of English b Native speakers of other languages c Classified as LEP/ELL d Percent of the school's student body Percent of schools 75% to 99% 12%36%1%0% 50% to 74% 192714 25% to 49% 3018223

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12 of 49 0% to 24% 39199573Note. N = 82–86 schools. The footnotes to this table descr ibe the extremes of the tails of the distributions and other details. Percentages are wi thin rounding error. a In 1% of the schools, the student body is 0.2% native speaker of Spanish; in another 1% of the schools, the student body is 96.4% native speaker of Spanish In 29% of the schools, the majority (i.e., over 50%) of the student body is native spea ker of Spanish. bIn 1% of the schools, the student body is 1.6% monolingual native speaker of English; in another 1% of the schools, it is 98.6% monolingual native speaker of English. In 58% of the schools, the majority of the student body is monolingual native speaker of E nglish. c In 21% of the schools, there are zero native speakers of languages other than Spanis h and English; in 1% of the schools, the student body is 88.7% native speakers of languages other than Spanish and English. dIn 1% of the schools, there are zero students formally cl assified as LEP/ELL; in another 1% of the schools, 58% of the student body is formally classi fied as LEP/ELL. 3. What are the family socioeconomic characteristic s of the schools' student bodies? The schools have, on average, a student bod y composed largely of students who live in poverty and whose parents have very limited formal education, as Table 6 shows. Specifically, the mean percentages indicate that th e schools have, on average, a student body characterized as follows: 42% of the students live in households in which the householder is unemployed; 45%, in households recei ving public assistance (i.e., welfare); 60% of the students are eligible for full y subsidized lunch; and 68%, eligible for either fully or partly subsidized lunch. The me an for maternal education shows that the schools have, on average, a student body of whi ch the average formal education level of the students' mothers or female guardians is bel ow high school graduation (and below a General Education Diploma [GED]).Table 6 Student Body's Family Socioeconomic Status Variable s: Means, Standard Deviations, Standard Errors of the Mean, and Skewness ValuesVariable MSDSEMeanSkewnessUnemployment level 41.627.42.970.33 Public assistance dependencelevel 44.928.23.020.20 Economic status scale 1.430.600.061.41 Fully subsidized lunch eligibilitylevel 59.825.92.83-0.47 Partly subsidized lunch eligibilitylevel 8.66.60.721.38 Subsidized lunch eligibility level(fully + partly) 68.426.82.94-0.75

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13 of 49 Maternal schooling scale 2.701.000.110.56Note. N = 83–89 schools. A school's family unemployment level is the percentage of the student body living in households in which the hous eholder is unemployed. Public assistance dependence level is the percentage of the student body from househo lds receiving public assistance (i.e, welfare). The ave rage family economic status of a school's student body is measured on a 5-point scale: 1 = lo w income; 2 = between middle and low income; 3 = middle income; 4 = between middle incom e and affluent; 5 = affluent. A school's fully subsidized lunch eligibility level is the percentage of the student body eligible for fully subsidized lunch. Partly subsidized lunch eligibility level is the percentage of the student body eligible for partly subsidized lunch. Subsidized lunch eligibility level (fully + partly) is the percentage of the student body eligible for fully subsidized lunch plus the percentage eligible for partly subsidized lunch. Maternal schooling level is the average level of formal education attained by the student b ody's mothers or female guardians, measured on a 9-point scale: 1 = six years of schoo ling or less; 2 = 7 to 9 years of schooling; 3 = 10 to 11 years; 4 = high school grad uate or General Education Diploma (GED); 5 = post-high-school vocational or trade tra ining; 6 = some college; 7 = college graduate; 8 = master's degree; 9 = doctor's degree. Around each of these means is a wide range of differences among the schools, manifested in Tables 7 through 10. For example, in about two fifths of the schools, the student body is over 74% eligible for fully subsidi zed lunch, but at the other end of the distribution, in about one tenth of the schools, th e student body is less than 25% thus eligible (Table 8). In one fifth of the schools, th e student body is over 74% from homes with unemployed householders, but the student body is less than 25% from such homes in about one third of the schools (Table 7). In 8% of the schools, the student body's average maternal schooling level is less than a 7th -grade education, but in 17% of the schools it is high school graduation or a GED (Tabl e 10).Table 7 Summary Frequency Distributions of Schools on the S tudent Body's Family Unemployment Level and Public Assistance Dep endence LevelUnemployed householder a Household on public assistance b Percent of the school's student body Percent of schools 75% to 95% 20%25% 50% to 74% 2421 25% to 49% 2223 1% to 24% 34 31Note. N = 85–87 schools. The footnotes to this table descr ibe the extremes of the tails of the distributions and other details. Percentages are wi thin rounding error. a In 1% of the schools, the student body is 1% from households in which the householder is unemployed; in another 1% of the schools, the student body is 9 5% from such households. In 31% of the schools, the majority (i.e., over 50%) of the stude nt body is from households in which the

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14 of 49 householder is unemployed. b In 2% of the schools, the student body is 1% from h ouseholds receiving public assistance; in 1% of the schools, the student body is 95% from such households. In 37% of the schools, the majority of the student body is from households receiving public assistance.Table 8 Summary Frequency Distributions of Schools on the Student Body's Subsidized Lunch Eligibility VariablesEligible for fully subsidized lunch a Eligible for partly subsidized lunch b Eligible for subsidized lunch (fully + partly) c Percent of the school'sstudent body Percent of schools 75% to 100% 39%0%52% 50% to 74% 26025 25% to 49% 21512 0% to 24% 139511Note. N = 83–84 schools. The footnotes to this table descr ibe the extremes of the tails of the distributions and other details. Percentages are wi thin rounding error. a In 1% of the schools, 2% of the student body is eligible for ful ly subsidized lunch; in another 1% of the schools, 99% of the student body is so. In 65% of t he schools, the majority (i.e., over 50%) of the student body is eligible for fully subsidize d lunch. b In 1% of the schools, 0.1% of the student body is eligible for partly subsidized lunc h; in another 1% of the schools, 31% of the student body is so. c In 1% of the schools, 3% of the student body is eli gible for either fully or partly subsidized lunch; in 8% of the scho ols, 100% of the student body is so. In 77% of the schools, the majority of the student bod y is eligible for either fully or partly subsidized lunch.Table 9 Frequency Distribution of Schools on the Student Body's Family Economic Status ScaleStudent body's average family economic statusPercen t of schools Affluent 0% Between middle income and affluent 1 Middle income 2 Between middle and low income 35 Low income 62

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15 of 49 Note. N = 89 schools. Percentages are within rounding erro r.Table 10 Frequency Distribution of Schools on the Student Body's Maternal Schooling ScaleStudent body's average maternalschooling level Percent of schools Cumulative percent Doctor's degree 0%0% Master's degree 00 College graduate 00 Some college 11 Post-high school vocational or trade training 23 High school graduate or General Educ.Diploma (GED) 1720 10 to 11 years 3252 7 to 9 years 3991 6 years or less 8100Note. N = 87 schools. Percentages are within rounding erro r. The intercorrelations among the student bod y's family socioeconomic variables show the expected pattern of consistency among meas ures of social, economic, and educational status (Table 11); these results add to the evidence supporting the data's construct validity.Table 11 Intercorrelations among the Student Body's Family V ariables23456 1: Unemployment level .92 *** -.58 *** .75 *** .74 *** -.29 ** 2: Public assistancedependence level --.60 *** .80 *** .80 *** -.34 *** 3: Economic status scale --.52 *** -.52 *** .46 *** 4: Fully subsidized luncheligibility level -.98 *** -.36 ***

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16 of 49 5: Subsidized lunch eligibility level (fully +partly) --.36 *** 6: Maternal schooling scale --Note. N = 82–87 schools. The coefficients of variable 3 wi th variables 1 and 2 are Spearman rank-order correlations; the other coeffic ients in this table are Pearson product-moment correlations. Variables 1, 2, 4, and 5 are measured in counts for the purpose of computing their intercorrelations; they are measured in percentages for the purpose of computing their correlations with variab les 3 and 6. p < .05 ** p < .01 *** p < .001 (1-tailed tests) 4. In what types of neighborhoods are the schools l ocated? The schools are located mostly in highly ur banized areas—areas that are largely poor (Tables 12 and 13). Specifically, 60% of the s chools are in the inner core of cities; 28%, in other urban parts of cities; 10%, in suburb an neighborhoods; and 1%, in small towns. Forty-six percent (46%) of the schools are i n low-income areas; 44%, in neighborhoods of a type characterized by a mix of l ow and middle income; 7%, in middle-income areas; and the remaining 3%, in neigh borhoods comprising a mix of middle income and affluence (Table 13).Table 12 School's Neighborhood Variables and School's Size a nd Crowdedness Variables: Means, Standard Deviations, Standard Err ors of the Mean, and Skewness ValuesMSDSEMeanSkewness School's neighborhood Urbanness scale 4.480.730.081.21 Economic status scale 1.670.750.081.11 School's size and crowdedness Enrollment size 677.2295.831.40.39 Enrollment capacity 661.7 a 265.829.20.38 Density-overcrowdedness level 15.5205.222.50.44Note. N = 88–89 schools for the school's neighborhood vari ables; N = 83–89 schools for the school's size and crowdedness variables. Urbanness is a 5-point scale: 1 = the school is in a rural area; 2 = small town (not suburban); 3 = subu rban; 4 = urban part of a city other than its inner core; 5 = inner core of a city. The economic status of the neighborhood in which a school is located is measured on a 5-point scale: 1 = low income; 2 = mix of low and middle income; 3 = middle income; 4 = mix of middle income and affluent; 5 = affluent. A school's enrollment size is the total number of students enrolled in the sc hool in late spring. Enrollment capacity is the number of students for which a school was b uilt. A school's density-overcrowdedness level is measured by subtracting the enrollment capacity from the

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17 of 49 enrollment size; thus, a higher positive value sign ifies denser crowdedness than does a lower positive value. aMean adjusted for missing data.Table 13 Frequency Distributions of Schools on the Neighborh ood Urbanness Scale and Neighborhood Economic Status ScaleNeighborhood urbanness scaleNeighborhood economic s tatus scale School's location Percent of schools School's location Percent of schools Inner core of a city 60% Affluent area 0% Urban part of a city other than its inner core 28 Mix of middle income and affluent 3 Suburban 10 Middle income 7 Small town (notsuburban) 1 Mix of low and middle income 44 Rural 0 Low-income area 46Note. N = 88–89 schools. Percentages are within rounding e rror. The correlations reported in Tables 14 and 15 show the following relationships: The more highly urbanized a school's neighborhood, the higher is the likelihood of the neighborhood's being poor. The lower a student body 's average family economic status and parental schooling level, the higher is the lik elihood of the school's being in an economically depressed and highly urbanized neighbo rhood. Table 14 Correlations among the School's Neighborhood Variab les and School's Size and Crowdedness Variables23456 School's neighborhood 1: Urbanness scale -.63 *** .36 *** .34 *** .10.07 2: Economic status scale — -.25 ** -.16-.16-.16 School's size and crowdedness 3: Enrollment size — .75 *** .50 *** .48 *** 4: Enrollment capacity — -.20 -.04

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18 of 49 5: Density-overcrowdedness level — .76 *** 6: Crowdedness dichotomy —Note. N = 83–89 schools. Pearson product-moment correlatio ns. Urbanness is a 5-point scale: 1 = the school is in a rural area; 2 = small town (not suburban); 3 = suburban; 4 = urban part of a city other than its inner core; 5 = inner core of a city. The economic status of the neighborhood in which a school is located is me asured on a 5-point scale: 1 = low income; 2 = mix of low and middle income; 3 = middl e income; 4 = mix of middle income and affluent; 5 = affluent. A school's enrollment size is the total number of students enrolled in the school in late spring. Enrollment capacity is the number of students for which a school was built. A school's densityovercrowdedness level is measured by subtracting the enrollment capacity from the enroll ment size; thus, a higher positive value signifies denser crowdedness than does a lower posi tive value. Crowdedness dichotomy is a dichotomous variable: 1 = the school is not crowded (i.e., density-overcrowdedness level is 0 or lower); 2 = the school is crowded (i.e., densi tyovercrowdedness level is greater than 0). p < .05 ** p < .01 *** p < .001 (1-tailed tests)Table 15 Correlations of the Student Body's Family Variables with the School's Neighborhood VariablesSchool's neighborhood variable Family variable Urbanness scale Economic status scale Unemployment level .62 *** -.58 *** Public assistance dependence level .53 *** -.60 *** Economic status scale -.54 *** .74 *** Fully subsidized lunch eligibility level .59 *** -.56 *** Subsidized lunch eligibility level(fully + partly) .53 *** -.54 *** Maternal schooling scale -.42 *** .42 ***Note. N = 84–89 schools for the correlations of the school 's neighborhood variables with the unemployment, public assistance, family economi c status, and maternal schooling variables; N = 82–84 schools for the correlations of the neighb orhood variables with the subsidized lunch variables. The coefficients of une mployment level and public assistance dependence level with the school's neighborhood var iables are Spearman rank-order correlations; the other coefficients in this table are Pearson product-moment correlations. The unemployment, public assistance, and both subsi dized lunch variables are measured in percentages. p < .05 ** p < .01 *** p < .001 (1-tailed tests)

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19 of 49 5. What is the size of the schools? Are the school facilities crowded? The schools have an average physical enroll ment capacity for 662 students but enroll an average of 677 students (Tables 12 and 16 ). Forty-four percent (44%) of the schools enroll above capacity; that is, they enroll a higher number of students than the number for which the school was built (Table 17).Table 16 Summary Frequency Distributions of Schools on Enrollment Size and Enrollment Capacity Enrollment sizeEnrollment capacity Number of studentsPercent of schools1,200 to 1,400 4%5% 1,000 to 1,199 178 800 to 999 1423 600 to 799 1724 400 to 599 2723 200 to 399 2016 86 to 199 11Note. N = 83–89 schools. Percentages are within rounding e rror.Table 17 Summary Frequency Distribution of Schools on Density-Overcrowdedness LevelSchool's density-overcrowdedness level Percent of schools Cumulative percent 600 to 680 2%2% 400 to 599 02 200 to 399 1719 1 to 199 2544 0 549 -1 to -199 4089 -200 to -399 1099 -400 to -515 1100

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20 of 49 Note. N = 83 schools. A school's density-overcrowdedness level is measured by subtracting the enrollment capacity from the enrollment size; t hus, a higher positive value signifies denser crowdedness than does a lower positive value Percentages are within rounding error. There are, however, wide differences among the schools on each of these variables, as Tables 16 and 17 show. For example, 13% of the s chools have a capacity for as many as 1,000 to 1,400 students, but 17% of the schools, for fewer than 400. Twenty-one percent (21%) of the schools enroll as many as 1,00 0 to 1,400 students, but another 21%, fewer than 400 (Table 16). Nineteen percent (19%) o f the schools enroll 200 or more students above capacity, but 51% of the schools enr oll below capacity (Table 17). The correlations in Tables 14 and 18 show t he following: The larger a school, the higher is the likelihood of its being located in a highly urbanized, economically impoverished area. Also, the larger a school, the l ower is its student body's average parental schooling level, and the higher is its stu dent body's family unemployment rate.Table 18 Correlations of the Student Body's Family Character istics with the School's Size and CrowdednessSchool's size and crowdedness Family variable Enrollment size Enrollment capacity Density-overcrowd level Crowdedness dichotomy Unemployment level .18 .20 .06.02 Public assistance dependence level .12.15.02.03 Economic status scale -.16-.13-.06-.12 Fully subsidized lunch eligibility level .09.06.06.06 Subsidized lunch eligibility level(fully + partly) .10.02.12.11 Maternal schooling scale -.24 ** -.27 ** .01-.04Note. N = 77–89 schools. Pearson product-moment correlatio ns. The unemployment, public assistance, and both subsidized lunch variables are measured in percentages. p < .05 ** p < .01 *** p < .001 (1-tailed tests)

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21 of 49 6. Correlates of the student body's ethnic/racial c omposition: 6.1. What are the relationships of the student body 's ethnic/racial composition to the student body's family socioeconomic characte ristics? The relative concentration of Hispanics/Lat inos in the student body correlates positively with the student body's family unemploym ent level, public assistance dependence level, and subsidized lunch eligibility level and, congruent with these relationships, negatively with the student body's f amily economic status scale and maternal schooling scale. This pattern of correlati ons is largely similar to the pattern of relationships between the relative concentration of African American students and these measures of the student body's socioeconomic charac teristics. These correlations are in a direction opposite to that of the correlations between the relative c oncentration of European American students and these measures of th e student body's socioeconomic characteristics. In short, these analysis results, reported in Table 19, signify the following: The higher a school's concentration of Hisp anic/Latino pupils, the lower is the student body's average family socioeconomic status and parental schooling level. Similarly, the higher the concentration of African American pupils, the lower is the student body's average family socioeconomic status. In contrast, the higher the concentration of European American students, the mo re affluent and the more highly educated, on average, are the student body's famili es.Table 19 Correlations of the Student Body's Ethnic/Racial Co mposition with the Student Body's Family, School's Neighborhood, and S chool's Size and Crowdedness CharacteristicsAfrican American European American Hispanic/Latino Family a Unemployment level .47 *** -.41 *** .52 *** Public assistance dependence level .47 *** -.38 *** .55 *** Economic status scale -.21 .58 *** -.38 *** Fully subsidized lunch eligibility level .32 ** -.30 ** .61 *** Subsidized lunch eligibility level (fully + partly) .31 ** -.24 .64 *** Maternal schooling scale .04 .39 *** -.43 *** School's neighborhood b

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22 of 49 Urbanness scale .25 ** -.69 *** .46 *** Economic status scale -.22 .54 *** -.34 *** School's size and crowdedness c Enrollment size -.11-.16 .25 ** Enrollment capacity .00 -.18 .08 Density-overcrowd level -.24 -.02 .30 ** Crowdedness dichotomy -.19 -.10 .28 **a N = 79–87 schools for the coefficients involving the family variables. The coefficients of the African American variable with the family varia bles, and the coefficients of the ethnic/racial composition variables with the family economic status scale and the maternal schooling scale are Pearson product-moment correlat ions; the coefficients of the ethnic/racial composition variables with the other family variables are Spearman rank-order correlations. The unemployment, public assistance, and both subsidized lunch variables are measured in counts for the purpose of computing the ir correlations in this table; likewise, the ethnic/racial composition variables are measure d in counts for the purpose of computing their correlations with the unemployment, public assistance, and both subsidized lunch variables. The ethnic/racial composition vari ables are measured in percentages for the purpose of computing their correlations with th e other variables in this table. b N = 83–87 schools for the coefficients involving the sc hool's neighborhood variables. The coefficients of the ethnic/racial composition varia bles with the school's neighborhood variables are Pearson product-moment correlations. c N = 78–87 schools for the coefficients involving the school's size and crowdedness variabl es. The coefficients of the ethnic/racial composition variables with the crowdedness dichotom y are Pearson product-moment correlations; the coefficients of the ethnic/racial composition variables with the other school size and crowdedness variables are Spearman rank-order correlations. p < .05 ** p < .01 *** p < .001 (1-tailed tests) 6.2. What are the relationships of the student body 's ethnic/racial composition to the characteristics of the school's neighborhood ? The correlations in Table 19 show the follo wing: The higher the concentration of Hispanic/Latino students in a school, the higher is the likelihood of the school's location being an economically depressed and highly urbanize d area. An association similar to this occurs between the relative concentration of A frican American students and these school neighborhood characteristics. In contrast, t he higher the concentration of European American students in a school, the lower is the likelihood of the school's being located in a poor or highly urbanized neighborhood. 6.3. Is the student body's ethnic/racial composition rel ated to school size and crowdedness? There is little or no relationship between ethnic/racial composition and school size. On the other hand, the student body's percentage of Hispanics/Latinos correlates positively and significantly with the school crowde dness dichotomy (Table 19). These analyses thus show that schools with higher proport ions of Hispanic/Latino students are more likely to be crowded (i.e., more likely to enr oll in excess of the number of pupils

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23 of 49 for which the school was built) than schools with l ower proportions of this ethnic/racial group. 7. Correlates of the student body's linguistic comp osition: 7.1. What are the relationships of the student body 's linguistic composition to the student body's family socioeconomic characteris tics? The student body's relative concentration o f native speakers of Spanish correlates positively with the student body's family unemploym ent level, public assistance dependence level, and subsidized lunch eligibility level and, consistent with these associations, negatively with the student body's fa mily economic status scale and maternal schooling scale. These correlations are si milar to those between the student body's relative concentration of LEP/ELL students a nd these measures of the student body's socioeconomic characteristics. In contrast, the student body's relative concentration of monolingual native speakers of Eng lish correlates positively with the student body's family economic status scale and mat ernal schooling scale. These results, presented in Table 20, signify the following: The higher a school's concentration of pupi ls who are native speakers of Spanish, the lower is the student body's average family soci oeconomic status and parental schooling level. Similarly, the higher a school's c oncentration of LEP/ELL pupils, the lower is the student body's average family socioeco nomic status and parental schooling level. In contradistinction, the higher a school's concentration of pupils who are monolingual native speakers of English, the higher is the student body's average family economic status and parental schooling level.Table 20 Correlations of the Student Body's Linguistic Compo sition with the Student Body's Family, School's Neighborhood, and S chool's Size and Crowdedness CharacteristicsNative speakers of Spanish Monolingual native speakers of English Classified as LEP/ELL Family a Unemployment level .54 *** .12 .38 *** Public assistance dependence level .57 *** .10 .40 *** Economic status scale -.35 *** .25 ** -.25 ** Fully subsidized lunch eligibility level .62 *** .13 .53 *** Subsidized lunch eligibility level (fully + partly) .65 *** .10 .54 *** Maternal schooling scale -.35 *** .33 *** -.25 **

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24 of 49 School's neighborhood b Urbanness scale .38 *** -.34 *** .42 *** Economic status scale -.32 *** .24 -.28 ** School's size and crowdedness c Enrollment size .18 -.28 ** .12 Enrollment capacity .07-.08.04 Density-overcrowd level .25 ** -.37 *** .19 Crowdedness dichotomy .24 -.33 ** .08a N = 79–86 schools for the coefficients involving the family variables. The coefficients of the linguistic composition variables with the famil y economic status scale and the maternal schooling scale are Pearson product-moment correlat ions; the coefficients of the linguistic composition variables with the other family variabl es are Spearman rank-order correlations. The unemployment, public assistance, and both subsi dized lunch variables are measured in counts for the purpose of computing their correlati ons in this table; likewise, the linguistic composition variables are measured in counts for th e purpose of computing their correlations with the unemployment, public assistan ce, and both subsidized lunch variables. The linguistic composition variables are measured i n percentages for the purpose of computing their correlations with the other variabl es in this table. b N = 82–86 schools for the coefficients involving the school's neighborhoo d variables. The coefficients of the linguistic composition variables with the school's neighborhood variables are Pearson productmoment correlations. c N = 79–86 schools for the coefficients of the lingui stic composition variables with the school's size and cr owdedness variables. The coefficients of the linguistic composition variables with the crowd edness dichotomy are Pearson product-moment correlations; the coefficients of th e linguistic composition variables with the other school size and crowdedness variables are Spearman rank-order correlations. p < .05 ** p < .01 *** p < .001 (1-tailed tests) 7.2. What are the relationships of the student body 's linguistic composition to the characteristics of the school's neighborhood? Table 20 shows the following relationships: The higher a school's concentration of students who are native speakers of Spanish, the hi gher is the likelihood of the school's location being a low-income, inner-city area. Simil arly, the higher a school's concentration of LEP/ELL students, the higher is th e likelihood of its location being a poor, highly urbanized area. In contrast, the highe r a school's concentration of students who are monolingual native speakers of English, the higher is the likelihood that its location is in the more affluent and less urbanized neighborhoods. 7.3. Is the student body's linguistic composition r elated to school size and crowdedness? Table 20 shows that the school crowdedness dichotomy correlates positively with the student body's percentage of native speakers of Spanish, but negatively with the student body's percentage of monolingual native spe akers of English. Enrollment capacity is not related to the student body's lingu istic composition. These results

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25 of 49demonstrate the following relationships: The larger a school's proportion of pupils who are native speakers of Spanish, the higher is the s chool's likelihood of being crowded. In contrast, the larger a school's proportion of pupil s who are monolingual native speakers of English, the lower is its likelihood of being cr owded.Discussion In this century, few issues in North Americ a have aroused more intense and bitter controversy, or caused more renting and sustained c onflict, than those surrounding ethnic/racial integration generally and school dese gregation in particular (see, e.g., Lukas, 1986; Woodward, 1966). At present, more than a century after Plessy v. Ferguson and almost half a century after Brown v. Board of Education the fundamental concerns remain unresolved in practice; indeed, the y have grown in complexity. In 1896, in the Plessy decision, the U.S. Supreme Court codified racial s egregation, making it the law of the land. In 1954, in the Brown decision, the Court reversed the Plessy decision. Current trends, however, point to a de facto return to widespread segregated schooling, as the present study shows. In recent years, the public debate concerni ng education reform in the United States has given relatively little attention to certain cr itical attributes of the ecology of schooling, particularly to attributes that bear on the isolation of students by ethnicity/race, language, and family socioeconomic characteristics. These attributes of schooling—and their interrelationships—were examine d in the present study, focusing specifically on the schools that children who migra te from Puerto Rico to New Jersey (i.e., focal children) attend in the United States during the first two years following their arrival in this country. This study shows that there is considerable ethnic/racial segregation of students in many of the schools attended by focal children. His panics/Latinos are the majority of the student body in 43% of the schools. European Americ ans are the majority of the student body in only 12% of the schools. This study further shows that there is considerable isolation by language. Native speakers of Spanish a re the majority of the student body in nearly one third of the schools. Economic impoverishment and low parental ed ucation are also salient attributes of the student body in many of the schools. In 65% of the schools, the majority of the student body is eligible for fully subsidized lunch In addition, many of the schools are located in highly urbanized and economically depres sed areas. Nearly two thirds of the schools are in the inner core of cities; most of th e remaining third, in other urban parts of cities. Almost one half are in low-income areas. As used here in reference to the present st udy's findings, the term school segregation or school isolation does not necessarily imply that the school boards or other public school officials caused the ethnic/rac ial, linguistic, or socioeconomic segregation of students observed in the present stu dy. Regardless of the causes, however, the observed patterns of segregation do not bode we ll. Insofar as a school does not provide adequate occasions for interethnic interact ions, it deprives students of the opportunity to develop the sociocultural knowledge, shared understandings, and behavior patterns that they will need as adults in order to function harmoniously and productively in ethnically heterogeneous settings ( Laosa, 1999)—a serious problem for a society as increasingly diverse as ours. Other pote ntial consequences of the observed patterns of ethnic/racial and linguistic isolation are discussed in subsequent sections of

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26 of 49this article. The present findings gain in significance i n the light of previous research suggesting an influence of the student body's socio economic status on scholastic achievement (Kennedy et al., 1986, chap. 2; Myers, 1985; Orland, 1994; Puma et al., 1993; U.S. Department of Education, 1993b, 1996, 19 97). One may further hypothesize that the ecology of schools can affect not only a c hild's academic achievement but also his or her long-term social development. For instan ce, a neighborhood with a high unemployment rate will likely provide limited expos ure to successfully employed role models (Brooks-Gunn, Denner, & Klebanov, 1995; Laos a, 1999; Wilson, 1995). Children in such schools are largely cut off from a range of options and opportunities commonly available in middle-class schools. Based on the available research evidence, a U.S. Department of Education (1993b) report concluded that "teachers in high-poverty sch ools face special challenges that often undermine their effectiveness" (p. 31). Although st udies clearly confirm a relationship between student body poverty and academic achieveme nt, the evidence is weaker concerning the mechanisms, or processes, that may e xplain this relationship (see, e.g., Barton et al., 1991; Taylor & Pich, 1991; and U.S. Department of Education, 1993b, 1996, 1997, for reviews of research). The data coll ected in the larger investigation of which the present study is a part will permit analy ses to illuminate these processes. A large size and crowdedness are additional attributes of many schools attended by focal children. The schools attended by the focal c hildren enroll an average of 677 pupils—a much larger figure than the estimated aver age number of pupils per public elementary school for the United States nationwide, for New Jersey and New York statewide, and for Puerto Rico island-wide; respect ively they are 458, 419, 582, and 298 (U.S. Department of Education, 1993a, Table 96). Mo reover, 44% of the focal children's schools enroll in excess of the number of pupils fo r which they were built. These findings must be considered in light of the potenti al effects of school size and crowdedness on the focal children's academic perfor mance and socioemotional adjustment—an issue for future research. Also neede d is research concerning the effects on the focal children of the dramatic size differen ce between the schools they attend in this country and those in Puerto Rico. Additional i ssues for future research are considered later. Separation and Inequality The student body's ethnic/racial compositio n and linguistic composition were found to correlate with the student body's socioeconomic characteristics, with school crowdedness, and with the school neighborhood's cha racteristics. The larger a school's proportion of pupils who are Hispanic/Latino or nat ive speakers of Spanish, the higher is the school's concentration of pupils from economica lly impoverished and poorly educated parents, and the higher its likelihood of being crowded and of being located in an economically depressed and highly urbanized area Similarly, the larger a school's proportion of African American pupils, the higher i s its concentration of pupils from low-income families and the higher its likelihood o f being in a poor inner-city area. In contrast, the larger a school's proportion of Europ ean American pupils, the lower is its concentration of pupils from economically impoveris hed and poorly educated parents, and the lower its likelihood of being in an economically depress ed and highly urbanized area. The correlational analyses thus clearly sho w that separate is not equal. School segregation by ethnicity/race is closely associated with school segregation by poverty and by parental education. Similarly, school segreg ation by language is closely

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27 of 49associated with school segregation by poverty and b y parental education. Furthermore, ethnic/racial segregation and linguistic segregatio n are associated with crowded schools. A focal child in a school with a relatively high concentration of pupils who are Hispanic/Latino or native speakers of Spanish is li kely in a school with a high concentration of pupils from economically impoveris hed and poorly educated families, a crowded school located in a poor inner-city area. I n contrast, a focal child in a school with a relatively high proportion of European Ameri can pupils is likely in a school with relatively few students from economically impoverished or poorly educated families, a school that is not located in an economically depressed or highly urb anized area. The present findings raise crucial question s concerning equality of educational opportunity, fairness, and social justice— concerns that urgently need the attention of educators, parents, and policy makers. Equal educat ional opportunity is the fundamental American answer to social and economic inequality, but school segregation by ethnicity/race or language does in effect concentra te poverty and low academic achievement in schools that are not equal—a histori cal and contemporary fact (e.g., Barton et al., 1991; Bremner, Barnard, Hareven, & M ennel, 1970, 1971, 1974; Forehand, Ragosta, & Rock, 1976; Kennedy et al., 19 86; Laosa, 1984; Orfield, 1993; Orland, 1994; Puma et al., 1993; Taylor & Pich, 19 91; U.S. Department of Education, 1993b, 1996, 1997). Such schools are often vulnerab le to becoming overwhelmed with problems of economically impoverished and poorly ed ucated families isolated in neighborhoods lacking many of the opportunities typ ically available in other schools. The challenging task of providing access for these children to appropriate and effective schooling so that every student can have a fair cha nce of becoming a full participant in American society demands high priority (Crdenas, 1 995, 1996; Donato et al., 1991; Network of Regional Desegregation Assistance Center s, 1989; Orfield, 1993; Orfield et al., 1996; Orfield & Yun, 1999). Differences Among the Schools It is also important to note that substanti al differences among the focal children's schools occur on almost all the variables. The scho ols differ widely in student body ethnic/racial composition. For example, in about on e fourth of the schools, Hispanics/Latinos constitute between 75% and 99% of the student body; yet at the other end of the distribution, in another one fourth of t he schools, they constitute less than 25% of the student body. In about one tenth of the schools, European Americans constitute 50% to 98% of the student body, although in about three quarters of the schools they are less than 25% of the student body. Similarly, the schools differ widely in lin guistic composition. For instance, in about one third of the schools, native speakers of Spanis h are the majority of the student body, but in about two fifths of the schools they are les s than 25% of the student body. The schools also differ widely in student b ody socioeconomic characteristics, school size, and density-overcrowdedness. In additi on, although to a lesser extent, the schools differ with regard to quality of location. Needed Research From the perspective of scientific inquiry, the observed differences among the focal children's schools constitute a series of naturally occurring experiments, raising a compelling question: Will these differences among t he schools explain, or statistically predict, individual differences in focal children's learning and adaptation? The present findings point to specific hypotheses in need of sy stematic research, as next steps in the larger longitudinal investigation of which this stu dy is a part. For example, concerning

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28 of 49the potential influence of the observed ecological attributes of schools on particular dimensions of child outcome, the following hypothes es focus on language development: The second-language motivation hypothesis predicts that the strength of the motivation to acquire a second language will vary a s a function of the need to communicate through that language. If this hypothes is is correct, then the larger a school's concentration of pupils who are native spe akers of Spanish, the weaker will be a focal child's need to use English to communicate wi th peers, hence the lower the child's motivation to learn English, and hence the slower t he child's English-language development rate. The second-language exposure hypothesis predicts that the rate of learning a second language will depend on the exposure to that language (i.e., on the frequency, or probability, of opportunities to hear and use the l anguage in functional situations). This hypothesis predicts a relatively slow rate of Engli sh-language development in the schools with relatively small proportions of pupils who are monolingual speakers of English. Thus, both hypotheses make the same predic tion, namely, a negative relationship between the student body's proportion of native speakers of Spanish and focal children's English-language development rate. On the other side of the coin is the native-language loss hypothesis According to it, second-language learners will, to the extent th at they have limited opportunity to use their native language actively, lose native-languag e skills (Laosa, 1999). If this hypothesis is accurate, then the smaller a school's proportion of Spanish-speaking students, the fewer will be the focal child's oppor tunities to use Spanish, and hence the faster the rate of Spanishlanguage loss. Especially for the focal population, develo pment of both languages is vitally important: English-language development is, of cour se, critically important for children's academic achievement and psychosocial adaptation in the United States. Because of the special relationship between the two countries, man y focal children return to Puerto Rico—establishing a "circular migration" pattern—wh ere they must compete (in school and eventually in the workplace) through the Spanis h language. Thus, especially for them, continued Spanish-language development is as critically important as English-language acquisition. Language development and academic achieveme nt are not the only child outcomes that the school ecology may influence. Psychosocial /affective outcomes may also be influenced. Various hypotheses bear on this point. For instance, according to the intercultural stress hypothesis the cultural "distance" (i.e., the degree of diff erence) between ecological settings bears on psychosocial a daptation (Laosa, 1999). This hypothesis predicts that the wider the difference b etween the child's primary culture/language and the school context, the more e xacting and hence the more stressful and anxiety-producing will be the school experience In turn, these high levels of psychological distress will raise the probability o f behavioral/emotional problems. If this hypothesis is valid, then focal children in schools with relatively few Hispanic/Latino pupils who are native speakers of Spanish will show a higher prevalence of symptoms of behavioral/affective maladjustment than will the fo cal children in schools with larger proportions of such pupils. In short, for focal children, the consequen ces of relatively intense levels of ethnolinguistic segregation (i.e., high concentrati ons of Hispanic/Latino, native-Spanish-speaking pupils) may include relativ ely slow rates of Englishlanguage development, but little or no loss of Spanish, and a relatively high probability of healthy behavioral/emotional adjustment. These hypotheses t hus illustrate some of the difficult dilemmas that one must confront when addressing the question, What is best for a focal

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29 of 49child? These and other hypotheses can be tested usi ng the longitudinal data from the larger investigation of which this study is a part— an investigation uniquely designed to permit this important and urgently needed scientifi c research. School Segregation Policies and Judicial Trends in the United States According to some historians (e.g., Woodwar d, 1966), the doctrines of Anglo-Saxon superiority by which some intellectuals and politicians justified and rationalized U.S. imperialism in the Caribbean, Lat in America, and the Pacific did not differ in essentials from the race theories espouse d by those who sought to justify White supremacy over African Americans. In 1896, two year s before the United States acquired Puerto Rico, the U.S. Supreme Court's ruli ng in the case of Plessy v. Ferguson affirmed a vision of a rigidly segregated society. Homer Plessy—of mixed African and European ancestry—had taken an East Louisiana Railw ay train car seat reserved for Whites; (Note 16) as a consequence, he was jailed f or violating a segregation statute that forbade members of either race to occupy accommodat ions set aside for the other—with the exception of "nurses attending the children of the other race" (as quoted in Kunen, 1996, p. 40). Segregation statutes, or "Jim Crow" l aws, constituted a strict code that, as Woodward (1966) noted, "lent the sanction of law to a racial ostracism that extended to churches and schools, to housing and jobs, to eatin g and drinking. Whether by law or by custom, that ostracism extended to virtually all fo rms of public transportation, to sports and recreations, to hospitals, orphanages, prisons, and asylums, and ultimately to funeral homes, morgues, and cemeteries" (p. 7). In a nearly unanimous decision on Plessy the Supreme Court declared that laws mandating "equal b ut separate" treatment of the races "do not necessarily imply the inferiority of either race," and cited the widely accepted propriety of separate schools for White and "colore d" children. In lone dissent, Justice John Harlan remarked, "The thin disguise of 'equal' accommodations . will not mislead anyone, nor atone for the wrong this day do ne" (as quoted in Kunen, 1996, p. 40). From 1896 to 1954 northern and southern sta te policies and practices confirmed the prediction that Justice Harlan had made in his diss enting opinion in Plessy : that the Court's decision would place "in a condition of leg al inferiority a large body of American citizens" (as quoted in F. C. Jones, 1981, p. 72). The thin disguise to which he referred endured for a half century until African A merican plaintiffs in a series of court cases challenged the constitutionality of school se gregation (Orfield et al., 1996; Woodward, 1966). The plaintiffs in these cases were attacking not only inequality, but segregation itself (Woodward, 1966). These cases cu lminated in the 1954 Supreme Court's landmark decision in Oliver Brown et al. v. Board of Education of Topeka Kansas (Note 17) which reversed a constitutional trend b egun long before Plessy The new Chief Justice, Earl Warren, delivered the Court 's unanimous opinion in favor of the African American plaintiffs: "We conclude," said th e Chief Justice, "that in the field of public education, the doctrine of `separate but equ al' has no place. Separate educational facilities are inherently unequal." The plaintiffs had therefore been "deprived of the equal protection of the laws guaranteed by the Four teenth Amendment" of the U.S. Constitution; consequently, intentional segregation in public schools was unconstitutional (as quoted in Woodward, 1966, p. 1 47). By thus ruling that de jure segregation was unlawful, the Brown decision reversed the Plessy decision, which rested on the principle that there could be "separate-butequal" treatment of people (Laosa, 1984; Sitkoff, 1993; Woodward, 1966). Central to the promise inherent in the Brown decision is the belief that ethnic/racial segregation in public education has a detrimental e ffect on children and "may affect their

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30 of 49hearts and minds in a way unlikely ever to be undon e" (as quoted in Woodward, 1966, p. 147)—not because ethnically/racially segre gated institutions are inherently inferior but due to continuing structural inequitie s directly attributable to ethnic/racial prejudice and discrimination (E. R. Jones, 1996). In the first decade after Brown very little desegregation occurred in the South (Rist, 1979). There was open defiance and massive r esistance against attempts to implement the Brown mandate (Motley, 1995; Sitkoff, 1993; Woodward, 19 66). The federal government and the federal district courts in the South did little to pressure the states or the school districts to comply with the c onstitutional requirements of the Brown decision (Orfield et al., 1996; van Geel, 1982, p. 980; Zashin, 1978). Moreover, segregation in the North remained virtually untouch ed until the 1970s. According to Orfield et al. (1996, p. 8), "Most Northern distric ts even refused to provide racial data that could be used to measure segregation." For nea rly two decades following Brown the Supreme Court denied hearings to school desegre gation cases from the North (Note 18) (Orfield et al., 1996), a historical fact illus trating that the legal meaning of desegregation has evolved (see, e.g., Kirp, 1977; L andsberg, 1995; Orfield, 1978; Orfield et al., 1996; van Geel, 1982). Although the Supreme Court's decision in Brown greatly encouraged many Hispanics/Latinos, it did not offer definitive guid ance on how to combat discrimination against them (Gonzlez, 1982; Laosa, 1984). Various issues have arisen in desegregation litigation involving this ethnic/racial group, all hinging on the identifiability of the group and of its members (Levin, Castaneda, & von Euler, 1977; Orfield, 1978; Orfield et al., 1996; Roos, 1977). A central question the courts ha ve asked in judging whether the isolation of Hispanic/Latino students violates the equal protection clause of the Fourteenth Amendment is whether Hispanics/Latinos c onstitute a group (i.e., a "class") that should be legally treated in the same manner a s African Americans (Levin et al., 1977; Roos, 1977). In other words, Are Hispanics/La tinos a group such that discrimination against them violates the equal prot ection clause? Schools, courts, and policy makers were uncertain how to categorize Hisp anics/Latinos for the purposes of civil rights (Gonzlez, 1982). In the mid1960s momentous changes began t o occur: Martin Luther King, Jr., and his organization marched in the early 1960s, and in so doing raised the moral conscience of the nation (Laosa, 1984; Oates, 1982; van Geel, 1982). The administrations of presidents John F. Kennedy and Lyndon B. Johnson pr ovided executive leadership in the battle for civil rights. In 1964 the U.S. Congress passed the Civil Rights Act, which required cutting off federal funds to school distri cts and other institutions that discriminate: Title VI of the Act states, "No perso n in the United States shall, on the ground of race, color, or national origin, be exclu ded from participation in, be denied the benefits of, or be subjected to discrimination unde r any program or activity receiving Federal financial assistance" (78 Stat. 252 [1964]; 42 U.S.C. 2000d [1965]). An important key to questions of how to com bat discrimination against Hispanic/Latino students appeared in the Civil Righ ts Act of 1964. This law and the authorization it vested on federal agencies to enfo rce it "by issuing rules, regulations, or orders of general applicability" established a lega l basis to regulate matters pertaining to national origin discrimination in addition to race (Civil Rights Act of 1964, as quoted in Gonzlez, 1982, p. II-3). This law gave federal edu cation officials responsibilities for working with the courts to enforce the Brown decision and subsequent decisions requiring racial desegregation. To this end, the th en Office of Education (OE) of the U.S. Department of Health, Education, and Welfare (HEW) developed guidelines to ensure compliance with Title VI. Aiding OE's efforts, Cong ress passed the Elementary and

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31 of 49Secondary Education Act of 1965, which substantiall y increased the amount of federal assistance to public education, thereby making fund cutoffs a more serious threat (Laosa, 1984; Zashin, 1978). The Supreme Court, too, provided strong lea dership on desegregation during that period. For example, in 1968, the Court declared th at discrimination must be "eliminated root and branch" ( Green v. County School Board of New Kent County as quoted in Orfield et al., 1996, p. xxii). In 1971, the Court held in Swann v. Charlotte-Mecklenburg Board of Education and in North Carolina State Board of Education v. Swann that the federal courts could order busing to desegregate sc hools (Orfield, 1978; Orfield et al., 1996; Zirkel, Richardson, & Goldberg, 1995). Despite this country's long history of pers istent school segregation and other forms of discrimination against Hispanic/Latino students (see, e.g., Carter & Segura, 1979; Donato, Menchaca, & Valencia, 1991; Gonzlez, 1982; Laosa, 1984; U.S. Commission on Civil Rights, 1971, 1972; Weinberg, 1977), the t ask of proving to the courts that these discriminatory practices are de jure rather than de facto was frequently more difficult for this ethnic/racial group than for Afr ican Americans. (Note 19) In cases involving discrimination against African Americans in the South, previous state statutes or constitutional provisions requiring segregation of this group had usually existed, and they were widely known and understood and could be readily documented (Laosa, 1984; Orfield, 1978). In order to establish a case of unl awful segregation, therefore, African American plaintiffs have needed merely to show the continued presence of school segregation in school systems formerly segregated b y law (Levin et al., 1977; van Geel, 1982). In contrast, Hispanic/Latino plaintiffs have frequently been hindered by a lack of systematic documentation concerning the magnitude o f educational exclusion of their group and by unclear understandings of the policies underlying the group's disenfranchisement (Gonzlez, 1982). In the absence of a statutory history of de jure segregation, Hispanic/Latino plaintiffs in segregation cases have been required to show that they are segregated and that the segregation is attributable to intentional action by school officials or other state authorities. In other words, proving to the courts that the isolation of Hispanic/Latino students constitutes a violation of the equal prote ction clause has required a showing of de jure segregation attributable not to statute but instea d to the action of school officials (Levin et al., 1977; Roos, 1977). For example, in United States v. Texas Education Agency (1972, as cited in Levin et al., 1977) the circuit court found intentional segregative action by the school district, particul arly in the choice of school sites, construction of schools, drawing of attendance zone s, and student assignment and transfer policies. The court thus found de jure segregation of Hispanic/Latino students despite the absence of a previous statute requiring segregation of this ethnic/racial group, and stated that discrimination in this case was "no different from any other school desegregation case" (as quoted in Levin et al., 197 7, p. 76). (Note 20) The U.S. Supreme Court did not begin to try to untangle the problem of school segregation as it relates to Hispanics/Latinos unti l 1973, when it tried the case of Keyes v. School District No. 1 (Denver, Colorado). In Keyes the Supreme Court recognized the problem but did not solve it entirely, seemingly sa ying that at least some Hispanics/Latinos, in some regions, under some cond itions, should be recognized as a distinct class: There is also much evidence that in the Southwest H ispanos and Negroes have a great many things in common. . Though of different origins, Negroes and Hispanos in Denver suffer identical dis crimination in treatment

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32 of 49when compared with the treatment afforded Anglo stu dents. In that circumstance, we think petitioners are entitled to have schools with a combined predominance of Negroes and Hispanos inclu ded in the category of "segregated" schools. ( Keyes 413 U.S. 189 [1973], as quoted in Gonzlez, 1982, p. II-7) In multi-ethnic areas, this recognition has often meant that the degree of segregation in a school depends on the ratio of Eur opean American students to the combined number of identified "minority" students i n that school (Levin et al., 1977; Roos, 1977). Issues left unresolved by the Supreme Court's ruling in Keyes were articulated by Orfield (1978, pp. 203-204): The [ Keyes ] decision mentions conditions prevailing in the So uthwest. It is unclear whether the same rights extend to MexicanAmericans in cities outside the Southwest. Would evidence that social c onditions had changed in a part of the Southwest remove this special cons titutional protection for Mexican-American children? Conditions in the region vary greatly on matters ranging from residential segregation to int ermarriage, socioeconomic mobility to educational achievement. It is not clear what factors would determine how a particular Hispanic g roup in a given part of the country should be treated for desegregation pur poses. Although a narrow reading could indeed limi t applicability to Mexican Americans/Chicanos in the Southwest, in applying Keyes the courts have often "interpreted this aspect of the holding expansively neither restricting application of the term Hispanic to Chicanos in the Southwest nor requ iring a showing of `identical discrimination'" (Teitelbaum & Hiller, 1977, p. 165 ). Subsequent to Keyes courts in school desegregation cases have typically treated c hildren from other Hispanic/Latino groups—and from certain other ethnic/racial groups as well—as "minority" students (Teitelbaum & Hiller, 1977, p. 165). For example, f ederal judges in New York and Boston decided that desegregation could be extended to Hispanic/Latino groups that were primarily Puerto Rican (Orfield, 1978, p. 204; Teitelbaum & Hiller, 1977, p. 165). More broadly, Keyes is also significant because, as the Supreme Court' s first case on desegregation in the "North," it expanded desegr egation requirements to the North and West (Orfield et al., 1996). (Note 21) Before 1 970, legal developments had not affected racial segregation patterns outside the So uth because such patterns had usually been characterized as de facto. In the 1970s, howev er, the courts were finding—as the Supreme Court did in the Keyes case in Denver—that much northern urban segregatio n was de jure segregation based not on statute but instead on sp ecific acts or policies of school boards and other school officials (Brown, 19 95; Orfield, 1978). In the early 1970s, public protests intensi fied over the potential expansion of school desegregation and over forced transportation (i.e., busing) of students as a means to desegregate. Accordingly, the leadership that the e xecutive and legislative branches of government were providing in desegregation efforts waned. Moreover, by this time, as a consequence of demographic alterations in the ethni c/racial composition of the U.S. population and shifts in residential patterns, many Northern urban school districts, which seldom extend beyond city limits, lacked sufficient numbers of European American children to desegregate (Kunen, 1996; Orfield, 1978 ). By the time of President Richard Nixon's second term of office, significant progress toward school desegregation had virtually stopped (Orfield et al., 1996; Orfield, 1 978; Orfield & Monfort, 1992).

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33 of 49 In 1974, the Supreme Court began issuing a series of decisions limiting Brown 's reach. For example, in Milliken v. Bradley [1974] the Supreme Court erected serious barriers to interdistrict, city-suburban desegregat ion plans; such plans have aimed to desegregate racially isolated schools that are loca ted in urban areas by drawing students from the surrounding suburban districts. In this De troit metropolitan case, the Supreme Court prohibited such plans unless plaintiffs could demonstrate that the suburbs or the state took actions that contributed to segregation in the city. Because obtaining such legal proof is often difficult, Milliken seriously limits access to the option of drawing students from largely European American suburbs in order to desegregate urban districts that enroll high concentrations of students of colo r (Orfield et al., 1996). That unconstitutional segregation existed in Detroit was not questioned in this case; in question was the constitutionality of the courtor dered desegregation plan's extending to outlying districts with no history of segregative a ction on the part of their school boards or local governments (Zirkel et al., 1995). Through out the country, large numbers of students of color are segregated in urban areas; he nce, insofar as Milliken puts suburban schools out of reach of these students, it practica lly ensures their isolation in the cities (Orfield et al., 1997; Orfield & Monfort, 1992; van Geel, 1982). During the 1980s, the executive branch of t he federal government worked actively against mandatory school desegregation; and Congres s accepted a proposal from President Ronald Reagan's administration to slash t he budget for federal desegregation assistance programs (Orfield et al., 1996). In rece nt years, neither branch has made a significant school desegregation initiative. In Milliken v. Bradley II [1977] the Supreme Court, facing the challenge of providing a remedy for the Detroit schools, where Milliken I had made long-term integration practically impossible, had ruled that a court could order a state to pay for educational programs to repair the harms caused by segregation (Orfield et al., 1996; Zirkel et al., 1995). More recently, however, in Missouri v. Jenkins [1995], the Supreme Court ruled that the court-ordered programs designe d to improve the quality of education in predominantly poor, predominantly non-White scho ols in order to make them educationally more equal to other schools, and to i ncrease the attractiveness of schools in order to accomplish desegregation through volunt ary choices, should be temporary, and that school districts need not show any actual correction of the educational harms of segregation before such programs can be discontinue d (Orfield et al., 1996, 1997). Analyzing this court decision, Orfield and his coll eagues (1996, p. xv) concluded that the Supreme Court by allowing, as it did in this ca se, for the dismantling of the special educational programming that the district had estab lished as a remedy for students in segregated schools, may have signaled that in the f uture the Court may not even support enforcement of the "separate but equal" doctrine th at Brown overturned. That is, it seems reasonable to conclude from the apparent underlying philosophy in the Supreme Court's rulings in Jenkins and in two other recent cases (i.e., Board of Education of Oklahoma City v. Dowell in 1991 and Freeman v. Pitts in 1992) that, in issues of school desegregation, the U.S. Supreme Court as presently constituted is pursuing the twin goals of minimizing judicial involvement in educati on and quickly restoring authority to local and state government, "whatever the consequen ces" (Orfield et al., 1996, p. 3). In sum, the urgent focus of public opinion on civil rights lasted only two years, from 1963 to 1965. Vigorous and effective enforceme nt of school desegregation by the executive branch of the federal government began in 1965 and lasted four years (Gonzlez, 1982; Laosa, 1984; Orfield et al., 1996) The Supreme Court continued to provide strong leadership on desegregation for four more years, in a series of sweeping decisions from 1969 to 1973—decisions that launched busing as a remedy, extended

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34 of 49desegregation requirements from the South to northe rn cities, established the right of Hispanic/Latino children to desegregated schools, a nd declared that it was no longer permissible to delay implementing the Court's manda te to desegregate (Gonzlez, 1982; Orfield, 1978; Orfield & Monfort, 1992; Rist, 1979; Zirkel et al., 1995). Congressional leadership on civil rights weakened after 1965 as p ublic opinion changed. Efforts toward school desegregation then waned on the part of the three branches of government. Political and legal forces have converged in recent years to effect movement in a direction opposite to that of efforts to desegregat e public education (Orfield et al., 1996, 1997; Orfield & Yun, 1999). School Segregation Trends in the United States A clear correspondence can be seen, on the one hand, between the foregoing chronology of events pertaining to efforts to deseg regate American schools and, on the other, the annual national statistics on the segreg ation of African American students: During the 1964-1972 period of active enforcement i n the southern and border states, a major decline occurred in the segregation of those regions' African American students. The South changed from almost total segregation in 1963 to become the most desegregated region of the country by 1970 (Orfield & Monfort, 1988; Rist, 1979). (Note 22) In the early 1970s the trend toward incre ased desegregation of African American students virtually stopped. Then, in 1988, a drift toward increased segregation of African American students began (Orfield, 1993; Orfield et al., 1996, 1997; Orfield & Yun, 1999). The corresponding national statistics o n the segregation of Hispanic/Latino students show, however, a strikingly different tren d, as noted below. Studies by Orfield and his colleagues and b y other researchers show a steady trend in the United States toward increased school segreg ation of Hispanic/Latino children. This trend is evident since national data on the su bject were first collected, in the 1960s. Indeed, since 1980 Hispanics/Latinos have been more likely than African Americans to attend predominantly minority schools. (Note 23) Sp ecifically, nationwide in the 196869 academic year, 77% of African American students and 55% of Hispanic/Latino students attended predominantly minority schools; i n 1972-73 these figures were 64% and 57%; by 1980-81 they had switched to 63% and 68 %. In 1996-97, 69% of African American students and 75% of Hispanic/Latino studen ts attended predominantly minority schools (Orfield, 1993; Orfield et al., 19 97; Orfield & Yun, 1999). A similar trend can be observed in other measures of segregat ion, namely, the percentage of children of each ethnic/racial group in schools wit h a 90% to 100% minority enrollment (Orfield, 1993; Orfield et al., 1997; Orfield & Yun 1999; U.S. Department of Education, 1995), and the weighted average percenta ge of European American students in the schools attended by children of a particular ethnic/racial group (Orfield, 1993; Orfield et al., 1997; Orfield & Yun, 1999). Needed: Public Awareness, Policies, and Leadership Some advocates of bilingual education for H ispanic/Latino children have sometimes objected to efforts to desegregate studen ts from this ethnolinguistic group, fearing that such desegregation may weaken support for the bilingual/bicultural education programs that many of these children need Other advocates and experts on the subject have argued that there is no inherent conflict between bilingual/multicultural education and desegregation, that under certain con ditions both can be effectively realized—indeed, and that with sufficient will and effort, the aims of both can be achieved synergistically to produce educationally s uccessful, integrated communities. There is an urgent need to inform parents, educator s, and policy makers of the reality,

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35 of 49the issues, the potential consequences, and the asyetunanswered questions about the existing segregation of ethnolinguistic minority ch ildren in our nation's schools. Heretofore, solutions to the problems of sc hool segregation have been sought almost exclusively through the courts. Certainly, t he most significant advances toward desegregation of African American students have bee n achieved with the considerable help of judicial decisions. At present, however, th e problems of school segregation are even more complex and difficult than those of the p ast. There is also growing evidence that these problems affect multiple ethnic/racial a nd linguistic groups (perhaps in different ways), including children who migrate fro m Puerto Rico, as this study shows. Some observers have questioned whether the courts ( particularly as they are presently constituted), and the adversarial system on which t he judicial structure rests, are still the most effective and appropriate means possible for p olicy formation in an area as complex as school segregation (cf. Crdenas, 1995; Fischer, 1982). Be that as it may, it is now painfully evident that desegregation does no t guarantee integration, nor ensure full equality of educational opportunity (Brown, 19 95; Crdenas, 1995; Laosa, 1984, 1999; Teitelbaum & Hiller, 1977). It seems clear, considering the statistical trends and the history of school desegregation efforts, that significant advances in solving problems of school segregation cannot in the foreseeable future be ach ieved through the courts alone Urgently needed are creative, informed efforts towa rd the formulation of comprehensive solutions, and concerted leadership to implement th em effectively.NotesFor editorial simplicity, the term country is used here as if Puerto Rico and the United States were two distinct countries. Followin g this usage, the terms United States (U.S.) and American(s) are used exclusively in reference to the 50 states (and the District of Columbia) of the United States and the people therein. Similarly, the term Hispanic/Latino is used exclusi vely to refer to the Hispanic/Latino population of the 50 states (and th e District of Columbia). The present usage does not imply any view regarding Pue rto Rico's sociopolitical status, which at present is neither that of an inde pendent nation nor that of a state of the United States. Of the 50 states, New Jersey has the highest Puerto Rican population density and the second-largest proportio n of the total Puerto Rican population that resides stateside (Prez & Martnez 1993; U.S. Bureau of the Census, 1992, 1993). 1. Giving rise to these developments were several sign ificant ideological, economic, and political currents in the United States: As the end of the nineteenth century approached, there were changes in thought about the nation's mission and its destiny. The nation had become a world power becaus e of its prodigious economic growth (Link, 1992; Morison, 1972). After the disap pearance of the "American frontier," the conviction grew that the country nee ded to find new outlets for an ever increasing population and agricultural and ind ustrial production. Advocates of sea power argued that "future national security and greatness" depended upon a large navy supported by bases throughout the world (Link, 1992, p. 248). Social Darwinists advanced the view that the world is a ju ngle, with international rivalries inevitable, and that only a strong nation could survive (Link, 1992; Morison, 1972). Added to these arguments were those of idealists and religious 2.

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36 of 49 leaders who believed that Americans had a duty to take up the White man's burden" and to carry their assertedly superior cult ure "to the backward peoples of the world" (Link, 1992, p. 248; Morison, 1972; Wood ward, 1966). It was against this background that the Spanish-American War of 18 98 propelled the United States along the road to war and empire (Lewis, 196 3; Link, 1992; Morison, 1972)—a war that, although brief and relatively blo odless, had farreaching and long-lasting political and diplomatic consequences. These overseas incursions brought under the nation's jurisdiction some eight million people of color, "a varied assortment of inferior races," as the Nation described them, "which, of course, could not be allowed to vote" (1898, as quo ted in Woodward, 1966, p. 72). More specifically, schools with at least one thirdor fourth-grade class (or the equivalent for ungraded programs). This study focus es on public and not private schools because a previous study (Laosa, 1998) show ed that of the total population of elementary-school transfers-in from P uerto Rico to New Jersey, only a tiny proportion are transfers-in to non-public sc hools. 3. Below are the annual distributions of children tran sferring in from Puerto Rico to the third and fourth grades (or the equivalent for ungraded programs) in the sample of New Jersey schools. To avoid inflating th ese counts, if a child transferred in from Puerto Rico more than once duri ng the course of the investigation, the child was counted only once. Number of children Number of schools Year 1 Year 2 0169 1771 27 212 1683 984 595 446 547 338 039 2110 0 211 0 112 0 013 00 4.

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37 of 49 14 10 The data describe the school at the time that focal children attended it; if the school had focal children more than one academic ye ar, then the analyses selected the data corresponding to the first academic year t hat the school had focal children. 5. Counts rather than percentages were used in computi ng this variable's correlations with certain other variables; see footnote 15. 6. Counts rather than percentages were used in computi ng this variable's correlations with certain other variables; see footnote 15. 7. Counts rather than percentages were used in computi ng this variable's correlations with certain other variables; see footnote 15. 8. Consistent with the usage adopted by the U.S. Burea u of the Census, the term householder (rather than head of household) is used in the presentation of data that had previously been presented with the designation head (e.g., U.S. Bureau of the Census, 1994b, p. A-2). 9. Counts rather than percentages were used in computi ng this variable's correlations with certain other variables; see footnote 15. 10. Counts rather than percentages were used in computi ng this variable's correlations with certain other variables; see footnote 15. 11. Counts rather than percentages were used in computi ng this variable's correlations with certain other variables; see footnote 15. 12. Counts rather than percentages were used in computi ng this variable's correlations with certain other variables; see footnote 15. 13. Two matrices of correlation coefficients were compu ted: a matrix of Pearson product-moment correlations and a matrix of Spearma n rank-order correlations; depending on the shape of the observed frequency di stributions on a given pair of variables, either one type of coefficient or the ot her is reported; the two coefficients are very similar or practically identi cal to each other for the vast majority of the pairs of variables. Variables with distributions too skewed to yield meaningful coefficients were excluded from the corr elation matrices. 14. To avoid the spurious correlation that may occur be tween variables that share in common the same variable denominator (McNemar, 1969 pp. 180-182), whenever two variables shared in common the same va riable denominator, the correlation between them was computed using counts rather than percentages. The Appendix presents the descriptive statistics based on counts for these variables. 15. In the United States, persons of mixed European and African ancestry are generally considered Black/African American (i.e., "non-White"). This system of racial classification differs from the predominant conceptions of race and of racial identification in Puerto Rico; for a discussion of these conceptions see Rodrguez 16.

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38 of 49(1991).Four separate cases from Kansas, South Carolina, Vi rginia, and Delaware were consolidated and decided in the 1954 case of Brown v. Board of Education. In each case, African Americans sought admission to th e public schools of their community on a nonsegregated basis. Kansas, by stat e law, permitted but did not require segregated schools. The other three states had state constitutional and statutory provisions that required the segregation of Blacks and Whites in public schools (Zirkel, Richardson, & Goldberg, 1995). 17. The nature of racial segregation in the North diffe red from that in the South: Typically in the South, school segregation was requ ired by state constitutional or statutory provisions. 18. The term de jure segregation" generally refers to segregation that has had the sanction of law; that is, segregation directly inte nded by law or otherwise issuing from an official racial classification. The term co mprehends situations in which the activities of school authorities have had a rac ially discriminatory impact contributing to the establishment or continuation o f school segregation. The term de facto segregation" is limited to what is "inadvertent an d without the assistance or collusion of school authorities" and not caused by state action (Black, Nolan, Nolan-Haley, Connolly, Hicks, & Alibrandi, 1990, pp 416, 425). State action refers to action by the government, including actio n by a public school system or its agents (Zirkel et al., 1995, p. 208). 19. Similarly, in Cisneros v. Corpus Christi Independen t School District (1970, Texas), the circuit court had found de jure segregation to exist, noting that the de jure nature of the existing pattern of segregati on within the Corpus Christi Independent School District has as its basi s state action of a non-statutory variety—that is, the school board's a ctive pursuit of policies that not only do nothing to counteract the effect of existing patterns of residential segregation in view of viab le alternatives of significant integrative value, but, in fact, increa se and exacerbate the district's racial and ethnic imbalance. There has b een a history of official school board acts which have had such a se gregative effect. (Cisneros, 1970, as quoted in Levin et al., 1977, p 76) Thus, once the necessary intentional segregative ac tions were found, coupled with a high concentration of Hispanic/Latino students in some schools, a prima facie case of unlawful segregation was established (Levin et al., 1977). Cisneros is the first circuit court cas e to hold that Hispanics/Latinos must be considered an identifiable minority group for pu rposes of desegregation; that is to say, that the principles enunciated in Brown v. Board of Education apply to Hispanics/Latinos as well as to African Americans. This decision prevented school officials in Corpus Christi from claiming th at they had desegregated a school by placing in it only African American and H ispanic/Latino (i.e., Mexican American) students (Gonzlez, 1982; Levin et al., 1 977). 20. Keyes is the first Supreme Court opinion addressing de jure segregation in a city (Denver, Colorado) located in a state where at the time of Brown v. Board of 21.

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39 of 49Education the public schools were not segregated pu rsuant to state statutory authority (Brown, 1995, p. 650). Many of Denver's p ublic schools were segregated, although the city's school system had n ever been operated under a state constitutional provision or law that mandated or permitted school segregation (Zirkel et al., 1995, p. 113).Significantly, prior to 1964 no systematic data on the implementation of Brown were collected. The general consensus among those w ho studied this period is that fewer than 1% of all African American students in t he eleven southern states attended desegregated schools (i.e., schools that W hite/European American students also attended; Rist, 1979, p. 4). In the s ame academic year (1964-65) of the passage of the Civil Rights Act, the first priv ate efforts at collecting desegregation data on these states began. The findi ngs from those efforts suggest that 2% of all African American students in these s tates were in desegregated schools. In 1965-66 the federal government began to collect data; that year, 7% of the South's African American students were in deseg regated schools (Rist, 1979, p. 4). Then the pace of desegregation in the South quickened: The first national statistics on school desegregation became available with the 1968-69 academic year. That year 23% of African American students na tionwide were in majority-White schools, in contrast with 18% in the South alone. Within two years the shift was dramatic as the South had 39% of its African American students in majority-White schools, compared with 28% in the no rthern and western states (Orfield, 1978, pp. 56-57; Orfield & Monfort, 1992, p. 13; Rist, 1979, p. 4). 22. A predominantly minority school is one in which mor e than half of the school's combined enrollment is African American, American I ndian/Native American, Asian/Pacific Islander American, or Hispanic/Latino (Orfield, 1993, p. 5). 23.ReferencesBarton, P. E., Coley, R. J., & Goertz, M. E. (1991 ). The state of inequality (Policy Information Report). Princeton, NJ: Educational Tes ting Service. Black, H. C., Nolan, J. R., Nolan-Haley, J. M., Con nolly, M. J., Hicks, S. C., & Alibrandi, M. N. (1990). Black's law dictionary (6th ed.). Saint Paul, MN: West Publishing. Bremner, R. H., Barnard, J., Hareven, T. K., & Menn el, R. M. (Eds.). (1970). Children and youth in America: A documentary history: Vol 1. 1600-1865. Cambridge, MA: Harvard University Press.Bremner, R. H., Barnard, J., Hareven, T. K., & Menn el, R. M. (Eds.). (1971). Children and youth in America: A documentary history: Vol 2. 1866-1932. Cambridge, MA: Harvard University Press.Bremner, R. H., Barnard, J., Hareven, T. K., & Menn el, R. M. (Eds.). (1974). Children and youth in America: A documentary history: Vol 3. 1933-1973. Cambridge, MA: Harvard University Press.Bronfenbrenner, U. (1979). The ecology of human development: Experiments by na ture

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40 of 49and design. Cambridge, MA: Harvard University Press. Bronfenbrenner, U. (1995). Developmental ecology th rough space and time: A future perspective. In P. Moen, G. H. Elder, Jr., & K. Ls cher (Eds.), Examining lives in context: Perspectives on the ecology of human devel opment (pp. 619-647). Washington, DC: American Psychological Association.Brooks-Gunn, J., Denner, J., & Klebanov, P. (1995). Families and neighborhoods as contexts for education. In E. Flaxman & A. H. Passo w (Eds.), Changing populations, changing schools. Ninety-fourth yearbook of the Nat ional Society for the Study of Education (Part 2, pp. 233-252). Chicago: University of Chic ago Press. Brown, K. (1995). Revisiting the Supreme Court's op inion in Brown v. Board of Education from a multiculturalist perspective. Teachers College Record, 96, 644-653. Crdenas, J. A. (1995). Multicultural education: A generation of advocacy. Needham Heights, MA: Simon & Schuster Custom Publishing.Crdenas, J. A. (1996). Ending the crisis in the K12 system. In L. I. Rendn & R. O. Hope and associates (Eds.), Educating a new majority: Transforming America's educational system for diversity (pp. 51-70). San Francisco: Jossey-Bass. Carr, R. (1984). Puerto Rico: A colonial experiment. New York: Random House. Carter, T. P., & Segura, R. D. (1979). Mexican Americans in school: A decade of change. New York: College Entrance Examination Board. Cobb, C. D., & Glass, G. V (1999). Ethnic segregati on in Arizona charter schools. Education Policy Analysis Archives [On-line journal], 7 (1). Available: http://epaa.asu.edu/epaa/v7n1/ Donato, R., Menchaca, M., & Valencia, R. R. (1991). Segregation, desegregation, and integration of Chicano students: Problems and prosp ects. In R. R. Valencia (Ed.), Chicano school failure and success: Research and po licy agendas for the 1990s (pp. 27-63). London: Falmer.Fischer, L. (1982). The courts and educational poli cy. In A. Lieberman & M. W. McLaughlin (Eds.), Policy making in education. Eighty-first yearbook o f the National Society for the Study of Education (pp. 56-79). Chicago: University of Chicago Press. Forehand, G. A., Ragosta, M., & Rock, D. A. (1976). Conditions and processes of effective school desegregation (Report No. PR-76-23. Final report prepared for th e Office of Education, U.S. Department of Health, Edu cation, and Welfare). Princeton, NJ: Educational Testing Service.Gonzlez, J. M. (1982). Hispanics, bilingual education and desegregation: A review of major issues and policy directions. Unpublished report to the U.S. Commission on Civil Rights. Jones, E. R. (1996). Foreword. In G. Orfield, S. E. Eaton, & the Harvard Project on School Desegregation, Dismantling desegregation: The quiet reversal of Br own v. Board of Education (pp. vii-ix). New York: The New Press/Norton.

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41 of 49Jones, F. C. (1981, Spring). External crosscurrents and internal diversity: An assessment of black progress, 19601980. Daedalus, 71-101. Kennedy, M. M., Jung, R. K., & Orland, M. E. (1986) Poverty, achievement, and the distribution of compensatory education services. Washington, DC: U.S. Government Printing Office.Kirp, D. L. (1977). Law, politics, and equal educat ional opportunity: The limits of judicial involvement Harvard Educational Review, 47 117-137. Kunen, J. S. (1996, April 29). The end of integrati on. Time, pp. 39-45. Landsberg, B. K. (1995). The federal government and the promise of Brown Teachers College Record, 96, 627-636. Laosa, L. M. (1984). Social policies toward childre n of diverse ethnic, racial, and language groups in the United States. In H. W. Stev enson & A. E. Siegel (Eds.), Child development research and social policy (pp. 1109). Chicago: University of Chicago Press.Laosa, L. M. (1990). Psychosocial stress, coping, a nd development of Hispanic immigrant children. In F. C. Serafica, A. I. Schweb el, R. K. Russell, P. D. Isaac, & L. B. Myers (Eds.), Mental health of ethnic minorities (pp. 38-65). New York: Praeger. Laosa, L. M. (1997). Research perspectives on const ructs of change: Intercultural migration and developmental transitions. In A. Boot h, A. Crouter, & N. Landale (Eds.), Immigration and the family: Research and policy on U.S. immigrants (pp. 133-148). Mahwah, NJ: Erlbaum.Laosa, L. M. (1998). Child migration from Puerto Rico to public and priv ate schools in the United States: Sampling a difficult-to-reach po pulation (Research Rep. No. 98-24). Princeton, NJ: Educational Testing Service.Laosa, L. M. (1999). Intercultural transitions in h uman development and education. Journal of Applied Developmental Psychology, 20 (3), 355-406. Laosa, L. M. (n.d.). Psychosocial stress and Hispanic immigrant children 's coping and adaptation to their role as students: Puerto Rican migration. Progress report. Princeton, NJ: Educational Testing Service. Laosa, L. M., & Henderson, R. W. (1991). Cognitive socialization and competence: The academic development of Chicanos. In R. R. Valencia (Ed.), Chicano school failure and success: Research and policy agendas for the 1990s (pp. 164-199). New York: Falmer. Levin, B., Castaneda, S., & von Euler, M. (1977). L egal issues related to school desegregation and the educational concerns of the H ispanic community. Desegregation and education concerns of the Hispanic community. C onference report (pp. 75-89). Washington, DC: National Institute of Education, U. S. Department of Health, Education, and Welfare.Lewis, G. K. (1963). Puerto Rico: Freedom and power in the Caribbean. New York:

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43 of 49Dismantling desegregation: The quiet reversal of Br own v. Board of Education. New York: The New Press/Norton.Orfield, G., & Monfort, F. (1988). Racial change an d desegregation in large school districts: Trends through the 1986-1987 school year Alexandria, VA: Council of Urban Boards of Education, National School Boards Associa tion. Orfield, G., & Monfort, F. (1992). Status of school desegregation: The next generation. Alexandria, VA: Council of Urban Boards of Educatio n, National School Boards Association. Orfield, G., & Yun, J. T. (1999). Resegregation in American schools. Cambridge, MA: Civil Rights Project, Harvard University.Orland, M. E. (1994). Demographics of disadvantage: Intensity of childhood poverty and its relationship to educational achievement. In J. I. Goodlad & P. Keating (Eds.), Access to knowledge: The continuing agenda for our nation' s schools (Rev. ed., pp. 4358). New York: College Entrance Examination Board.Prez, S. M., & Martnez, D. (1993). State of Hispa nic America 1993: Toward a Latino anti-poverty agenda. Washington, DC: National Counc il of La Raza. Puma, M., Jones, C. C., Rock, D., & Fernandez, R. ( 1993). Prospects: The congressionally mandated study of educational growt h and opportunity (Interim report prepared for the U.S. Department of Education, Plan ning and Evaluation Service). Bethesda, MD: Abt Associates.Rist, R. C. (1979). Introduction. In R. C. Rist (Ed .), Desegregated schools: Appraisals of an American experiment (pp. 1-11). New York: Academic Press. Rodrguez, C. E. (1991). Puerto Ricans born in the U.S.A. Boulder, CO: Westview Press. Roos, P. (1977). Issues in desegregation remedial o rder for Hispanos. Desegregation and education concerns of the Hispanic community. Confe rence report (pp. 29-35). Washington, DC: National Institute of Education, U. S. Department of Health, Education, and Welfare. Rutter, M., Maughan, B., Mortimore, P., & Ouston, J (1979). Fifteen thousand hours: Secondary schools and their effects on children. Cambridge, MA: Harvard University Press. Sitkoff, H. (1993). The struggle for Black equality: 1954-1992 (Rev. ed.). New York: Hill and Wang.Southern Education Foundation, Panel on Educational Opportunity and Postsecondary Desegregation. (1995). Redeeming the American promi se: Report of the Panel on Educational Opportunity and Postsecondary Desegrega tion. Atlanta, GA: Author. Taylor, W. L., & Pich, D. M. (1991). Shortchanging children: The impact of fiscal inequity on the education of students at risk (Repo rt prepared for the Committee on Education and Labor of the U.S. House of Representa tives; Serial No. 102-O).

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44 of 49Washington, DC: U.S. Government Printing Office.Teitelbaum, H., & Hiller, R. J. (1977). Bilingual e ducation: The legal mandate. Harvard Educational Review, 47, 138-170. U.S. Bureau of the Census. (1992). 1990 Census of p opulation. General population characteristics: United States (1990 CP-1-1). Washi ngton, DC: U.S. Government Printing Office.U.S. Bureau of the Census. (1993). 1990 Census of p opulation and housing. Population and housing unit counts: United States (1990 CPH-21). Washington, DC: U.S. Government Printing Office.U.S. Bureau of the Census. (1994a). Educational att ainment in the United States: March 1993 and 1992 (Current Population Reports, Series P 20-476; by R. Kominski & A. Adams). Washington, DC: U.S. Government Printing Of fice. U.S. Bureau of the Census. (1994b). The Hispanic po pulation in the United States: March 1993 (Current Population Reports, Series P20475; by P. A. Montgomery). Washington, DC: U.S. Government Printing Office. U.S. Bureau of the Census. (1996). Statistical abst ract of the United States: 1996 (116th ed.). Washington, DC: U.S. Government Printing Offi ce. U.S. Commission on Civil Rights. (1971). Ethnic iso lation of Mexican Americans in the public schools of the Southwest (Report 1 of the Me xican American Education Study). Washington, DC: U.S. Government Printing Office.U.S. Commission on Civil Rights. (1972). The exclud ed student: Educational practices affecting Mexican Americans in the Southwest (Repor t 3 of the Mexican American Education Study). Washington, DC: U.S. Government P rinting Office. U.S. Commission on Civil Rights. (1976). Puerto Ric ans in the continental United States: An uncertain future. Washington, DC: Author U.S. Department of Education. (1993a). Digest of ed ucation statistics, 1993 (NCES 93-292). Washington, DC: National Center for Educat ion Statistics, U.S. Government Printing Office.U.S. Department of Education. (1993b). Reinventing Chapter 1: The current Chapter 1 program and new directions (Rep. of the National As sessment of Chapter 1 Independent Review Panel). Washington, DC: U.S. Department of E ducation, Office of Policy and Planning. U.S. Department of Education. (1995). Findings from The Condition of Education 1995: No. 4. The educational progress of Hispanic students (NCES 95-767, by T. M. Smith). Washington, DC: National Center fo r Education Statistics, U.S. Government Printing Office. U.S. Department of Education. (1996). Urban schools : The challenge of location and poverty. Executive summary (NCES 96-864, by L. Lipp man, S. Burns, & E. McArthur, with contributions by R. Burton, T. M. Smith, & P. Kaufman). Washington, DC:

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45 of 49National Center for Education Statistics, U.S. Gove rnment Printing Office. U.S. Department of Education. (1997). Findings from The Condition of Education 1997: No. 10. The social context of educa tion (NCES 97-981, by B. A. Young & T. M. Smith). Washington, DC: National Cent er for Education Statistics, U.S. Government Printing Office. U.S. General Accounting Office. (1992). Remedial ed ucation: Modifying Chapter 1 formula would target more funds to those most in ne ed (GAO/HRD-92-16). Washington, DC: Author.van Geel, T. (1982). Judicial decisions. In H. E. M itzel, J. H. Best, & W. Rabinowitz (Eds.), Encyclopedia of educational research (5th ed., Vol. 2, pp. 974982). New York: The Free Press.Wagenheim, K. (1970). Puerto Rico: A profile. New York: Praeger. Weinberg, M. (1977). A chance to learn: A history of race and education in the United States. New York: Cambridge University Press. Wilson, W. J. (1995). Jobless ghettos and the socia l outcome of youngsters. In P. Moen, G. H. Elder, Jr., & K. Lscher (Eds.), Examining lives in context: Perspectives on the ecology of human development (pp. 527-543). Washington, DC: American Psychological Association.Woodward, C. V. (1966). The strange career of Jim Crow (2nd Rev. ed.). Oxford, England: Oxford University Press.Zashin, E. (1978, Winter). The progress of Black Am ericans in civil rights: The past two decades assessed. Daedalus, 239-262. Zirkel, P. A., Richardson, S. N., & Goldberg, S. S. (1995). A digest of Supreme Court decisions affecting education (3rd ed.). Bloomington, IN: Phi Delta Kappa Educat ional Foundation. About the AuthorLuis M. Laosa Principal Research ScientistEducational Testing Service Turnbull Hall, 8-RRosedale RoadPrinceton, New Jersey 08541 Email: llaosa@ets.org Phone: (609) 734-5524Luis M. Laosa has conducted extensive research in v aried Hispanic/Latino communities (Chicano/Mexican American, Puerto Rican, Cuban Amer ican) throughout the United States and in Mexico and South America. His current studies include a large-scale longitudinal project focusing on child migration, s upported in part by the William T.

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46 of 49 Grant Foundation and the Spencer Foundation. He is the author of numerous scientific and scholarly publications and is often sought as s cientific and technical advisor to government agencies, universities, research centers and philanthropic foundations. He is a fellow of the American Psychological Society and of the American Psychological Association (in the divisions of developmental psyc hology, educational psychology, general psychology, and ethnic-minority psychology) Other honors include a Martin Luther King, Jr./Csar Chvez/Rosa Parks Visiting P rofessorship at the University of Michigan, receipt of the Educational Testing Servic e's Senior Scientist Award, and induction into the Phi Kappa Phi Honor Society. Dr. Laosa has served on the editorial boards of Review of Educational Research, Child Development, Developmental Psychology, Journal of Educational Psychology, Psyc hological Bulletin the Journal of Applied Developmental Psychology, Early Education a nd Development and Journal of School Psychology He received his Ph.D. (1971) from the University of Texas at Austin (specializing in cross-cultural psychology, persona lity/social development, educational psychology, measurement, and research methodology); completed a postdoctoral residency in clinical and community psychology at t he University of Texas Medical School, Health Sciences Center, at San Antonio; and received his certification in school psychology and his professional certification and l icense in general psychology. He was chief school psychologist in a large school distric t and has served on the clinical faculty of the Department of Psychiatry of the University o f Texas Medical School and on the faculty of the Graduate School of Education of the University of California, Los Angeles. He has been on the research staff of Educa tional Testing Service in Princeton, New Jersey, since 1976, where at present he holds t he post of principal research scientist. AcknowledgmentThe research presented here was made possible in pa rt by a research grant from the William T. Grant Foundation to the author. The data presented, the statements made, and the views expressed are solely the responsibili ty of the author. This study is part of the author's large-scale longitudinal research proj ect focusing on children who migrate to the United States from Puerto RicoAppendix Descriptive Statistics for Variables Measured in Co unts: Means, Standard Deviations, Standard Errors of the Mean, a nd Skewness ValuesVariableMSDSEMeanSkewnessStudent body's ethnic/racial compositionAfrican American 216.1231.224.791.42 European American 99.4164.617.753.24 Hispanic/Latino 336.4287.631.381.27 Student body's linguistic compositionNative speakers of Spanish 253.1248.627.121.41

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47 of 49 Monolingual native speakers of English 360.5244.426.821.06 Classified as LEP/ELL 130.7127.213.721.84 Student body's family socioeconomic statusUnemployment level 293.5249.227.031.21 Public assistance dependence level 315.9250.026.801.04 Fully subsidized lunch eligibility level 404.8252.027.500.66 Subsidized lunch eligibility level (fully + partly) 461.7276.130.310.59Note. N = 83–87 schools. The figures in this appendix are based on the variables measured in counts.Copyright 2001 by the Education Policy Analysis ArchivesThe World Wide Web address for the Education Policy Analysis Archives is epaa.asu.edu General questions about appropriateness of topics o r particular articles may be addressed to the Editor, Gene V Glass, glass@asu.edu or reach him at College of Education, Arizona State University, Tempe, AZ 8 5287-0211. (602-965-9644). The Commentary Editor is Casey D. C obb: casey.cobb@unh.edu .EPAA Editorial Board Michael W. Apple University of Wisconsin Greg Camilli Rutgers University John Covaleskie Northern Michigan University Alan Davis University of Colorado, Denver Sherman Dorn University of South Florida Mark E. Fetler California Commission on Teacher Credentialing Richard Garlikov hmwkhelp@scott.net Thomas F. Green Syracuse University Alison I. Griffith York University Arlen Gullickson Western Michigan University Ernest R. House University of Colorado Aimee Howley Ohio University Craig B. Howley Appalachia Educational Laboratory William Hunter University of Calgary Daniel Kalls Ume University Benjamin Levin University of Manitoba Thomas Mauhs-Pugh Green Mountain College Dewayne Matthews Western Interstate Commission for HigherEducation

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48 of 49 William McInerney Purdue University Mary McKeown-Moak MGT of America (Austin, TX) Les McLean University of Toronto Susan Bobbitt Nolen University of Washington Anne L. Pemberton apembert@pen.k12.va.us Hugh G. Petrie SUNY Buffalo Richard C. Richardson New York University Anthony G. Rud Jr. Purdue University Dennis Sayers Ann Leavenworth Centerfor Accelerated Learning Jay D. Scribner University of Texas at Austin Michael Scriven scriven@aol.com Robert E. Stake University of Illinois—UC Robert Stonehill U.S. Department of Education David D. Williams Brigham Young UniversityEPAA Spanish Language Editorial BoardAssociate Editor for Spanish Language Roberto Rodrguez Gmez Universidad Nacional Autnoma de Mxico roberto@servidor.unam.mx Adrin Acosta (Mxico) Universidad de Guadalajaraadrianacosta@compuserve.com J. Flix Angulo Rasco (Spain) Universidad de Cdizfelix.angulo@uca.es Teresa Bracho (Mxico) Centro de Investigacin y DocenciaEconmica-CIDEbracho dis1.cide.mx Alejandro Canales (Mxico) Universidad Nacional Autnoma deMxicocanalesa@servidor.unam.mx Ursula Casanova (U.S.A.) Arizona State Universitycasanova@asu.edu Jos Contreras Domingo Universitat de Barcelona Jose.Contreras@doe.d5.ub.es Erwin Epstein (U.S.A.) Loyola University of ChicagoEepstein@luc.edu Josu Gonzlez (U.S.A.) Arizona State Universityjosue@asu.edu Rollin Kent (Mxico)Departamento de InvestigacinEducativa-DIE/CINVESTAVrkent@gemtel.com.mx kentr@data.net.mx Mara Beatriz Luce (Brazil)Universidad Federal de Rio Grande do Sul-UFRGSlucemb@orion.ufrgs.brJavier Mendoza Rojas (Mxico)Universidad Nacional Autnoma deMxicojaviermr@servidor.unam.mxMarcela Mollis (Argentina)Universidad de Buenos Airesmmollis@filo.uba.ar

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49 of 49 Humberto Muoz Garca (Mxico) Universidad Nacional Autnoma deMxicohumberto@servidor.unam.mxAngel Ignacio Prez Gmez (Spain)Universidad de Mlagaaiperez@uma.es Daniel Schugurensky (Argentina-Canad)OISE/UT, Canadadschugurensky@oise.utoronto.ca Simon Schwartzman (Brazil)Fundao Instituto Brasileiro e Geografiae Estatstica simon@openlink.com.br Jurjo Torres Santom (Spain)Universidad de A Coruajurjo@udc.es Carlos Alberto Torres (U.S.A.)University of California, Los Angelestorres@gseisucla.edu


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