Racial Disparities in Special Education      17 Even when supplemental resources follow higher rates of special education representation for minority students, troubling questions remain about the identification criteria for such disability categories as MR or ED and how they arc applied. More disturbing, however, are findings that even where certain groups of students are overrepresented as MR, the supplementary resources and services that are assumed necessary for these students often do not follow. Are minority students being under-funded and underserved in special education at the same time they are being overidentified? This chapter provides an overview of special education finance across the nation, followed by a description of the data sources and methods used in this analysis. Findings on special education funding in relation to over- and under- representation, the placements received, and the services provided by race are presented. The chapter concludes with a section on the implications for reform. FUNDING SPECIAL EDUCATION Over 12 percent of all public school-age children receive special education ser¬ vices. Special education has risen as a percentage of total enrolment every year since the passage of IDEA in 1975. The most current study of special education spending (Chambers et al., 2002) estimated total spending for the average spe¬ cial education student to be 1.9 times that of a non-special education student. The growth of special education enrollments, and consequently in special edu¬ cation spending, has become an increasingly contentious issue over the past sev¬ eral years (Parrish, 2000). Concern has also been expressed about the degree to which special education spending is taking resources from general education programs (Rothstein, 1997). Special education is funded through a mix of local, state, and federal funds, with the latter contributing about 9 percent of the total cost (Parrish & An¬ thony, 2002), while the remaining percentage of support comes from state and local contributions, with the relative shares varying considerably from state to state. For example, Oklahoma reported that 3 percent of the state's special edu¬ cation programs were supported through state revenues, while Wyoming re¬ ported 90 percent state support (Parrish & Anthony, 2002). For all states, spe¬ cial education spending that is not offset by federal revenues must be supported through state and local funds. The limited data available on national special education spending suggest that the local share of special education spending has been growing over the past few years. This has contributed to concerns that special education funds are increasing at the cost of other local education programming (Parrish & An¬ thony, 2002).

18      Racial Inequity in Special Education The allocation formula governing special education funding at the federal level has changed considerably over the past few years. Since the passage of the IDEA, federal special education funds have been allocated on the basis of a fixed dollar amount per student identified for special education. A new federal funding system, passed as a part of the IDEA reauthorization in 1997, allocates federal aid primarily on a state's total school-age population (70%) with an ad¬ justment for poverty (30%) (Parrish & Anthony, 2002). Under this new sys¬ term, the amounts of money allocated to states and to school districrs are the same regardless of the mix of services provided or disability category assigned. Two districts with the same total enrollment would be eligible to receive the same federal funding even though one may have much higher proportions of students in more severe categories of disability, such as autism. Allocation rules also vary considerably across the states. For example, while Wyoming now reimburses districts for 100 percent of all special education costs, most other states have more generic systems providing funding based on the number of students receiving special education services or on general enroll¬ ment. For the purposes of this paper, state special education funding systems are divided into three basic approaches: 1. Unlinked: Districts with high rates of student identification for special edu¬ cation do not generate more funds than districts with lower identification rates. 2. Service Linked: The amount of money received is a direct function of how many students are identified for services overall, but the amount of funding is not linked to students' category of disability. 3. Service and Category Linked: Funding differentials are directly linked to higher-cost categories of disability or placement. The analysis of these three approaches to funding yields a somewhat sur¬ prising answer to the question of whether additional special education funding is likely to accompany patterns of overrepresentation. This research shows that states that vary funding by category of disability are more likely to overrepresent and underfund minority students. For example, one might expect that students with what have generally been found to be higher-cost categories of disability would get the most financial assistance in states that use formulas that differen¬ tiate funding by type of disability. Conversely, one might predict that when supplemental funding does not follow a higher-cost designation, for example, MR, that the services MR children need may be less likely to be provided. Therefore, the findings that category-linked formulas correlate with both over- representation and underservice are counterintuitive, potentially disturbing, and require further investigation. This type of pattern also further intensifies

Racial Disparities in Special Education      19 concerns of overrepresentation; that is, classification into more severe categories of disability than are warranted without the receipt of appropriate supplemental services. DATA SOURCES AND METHODS An extensive analysis of spending on special education programs across the na¬ tion has recently been completed (Chambers et al., 2002), In addition to this national study, individual expenditure analyses were completed for eleven states during 2002.3 However, other than the data resulting from these eleven state studies, the national study does not include special education expenditure in¬ formation by individual state. Further, the last time all fifty states were asked to report special education spending (Parrish & Anthony, 2002), only thirteen states said they could do so with a high degree of confidence. Information on the amount of categorical funds made available by the state and the federal government for special education is generally more available.4 District-level analyses are required to understand the relationship between overidentification. in high-minority districts and the amount of categorical funds these students generate. Because federal funding is generally a fixed amount per special education student, federal special education funding at the district level can be fairly easily estimated based on a district's special education enrollment. In addition, the amount of federal and state special education funds that every district receives are data elements on the Annual Survey of Local Govern¬ ment Finances conducted by the U.S. Bureau of the Census. These data are col¬ lected for every school district in the nation in odd fiscal years (FY). The most current year for which these data are available is FY 1997= or the 1997-1998 school year. These data can be matched to the Common Core of School Dis¬ trict Data produced by the U.S. Department of Education's National Center for Education Statistics (NCES), which contains a variable "number of stu¬ dents with Individualized Education Programs (IEPs)." This dataset also pro¬ vides breakouts of district enrollments by race and ethnicity. By combining these data, it is possible to compare federal and state special education funding per special education student for districts of varying degrees of percent minority enrollment by state. Although all of the districts in the United States are included in this database, under scrutiny the data elements most relevant to this analysis are not available for all states. States were elimi¬ nated from this analysis if state and federal special education funding was miss¬ ing for large numbers of districts, or if the aggregate count of special education students listed by districts for a state did not reasonably approximate the official

20      Racial Inequity in Special Education statewide count of special education students submitted to the U.S. Depart¬ ment of Education, (Although the latter count of special education students is considered much more reliable than the NCES count, it is not reported by dis¬ trict.) Other sources of data used for this analysis are counts of special education students by race, by disability, by state, and the total count of school-age chil¬ dren by race and state for students ages six to twenty-one provided by the Washington, DC, research firm Westat. Westat collects these data from the states for the U.S. Department of Education, Office of Special Education Pro¬ grams. Although these data also are not reported at the district level, they allow analyses of the identification of students by race into categories of disability, in relation to the racial composition of the state's overall student population (ac¬ cording to Census data for ages 6-21). This allows state-level comparisons of identification practices that can be compared with funding practices. Where in¬ formation on state special education funding was lacking from the NCES data, state-level information was filled in to the extent available from data provided by the states through a survey recently administered by the National Associa¬ tion of State Directors of Special Education (NASDSE). Last, analyses of the services received by students, by disability, and by racial category within Cali¬ fornia were conducted using the student-level special education database main¬ tained by the California State Department of Education.5 To measure and compare identification patterns by race, by category of disability, and by state, "risk ratios" were calculated. Based on special education and overall population counts by race as used in this paper, this ratio compares the risk of a child in a particular minority group being labeled as having a cer¬ tain disability to the risk of a white child. Any racial group can be compared to any other using this procedure, but as white children are in the majority in forty-four states, this seemed to be the most logical comparison group. When the ratio is greater than 1 for a given racial group in a category of disability, the risk that a child of this race will be labeled as having the identified disability is greater than the risk of a white child being so labeled. The higher the ratio, the greater the likelihood of a minority child being labeled as having a certain dis¬ ability as compared with a white child.6 Given that the purpose of this paper is to explore possible relationships be¬ tween funding formulas and over- and underrepresemation of minority stu¬ dents, a relatively conservative standard has been adopted. Overrepresentation is defined as twice the risk of identification in relation to chat for white stu¬ dents, with underrepresentation defined as one-half the risk. However, it should not be inferred that these standards, which rely on extensive levels of over- and underrepresentation, should apply to general determinations of over-

Racial Disparities in Special Education      21 and underrepresentation, for which tests of statistical significance or other crite¬ ria are bettet suited. In addition, differing identification rates are only shown when statistically- significant at a .01 level of confidence. These are differences of sufficient mag¬ nitude that there is less than a 1 percent likelihood (.01) of their occurring by- chance. IDENTIFICATION RATE COMPARISONS Using the risk ratios described above, counts of students by category of disability and race were compared to those for white children by state. Table 1 shows the patterns that exist across racial groups and the disability categories of mental re¬ tardation, emotional disturbance, and specific learning disabilities. For each state, risk ratios are shown by race for identification for each of the three primary disability categories, MR, ED, and LD. Together, these three cognitive disabil¬ ity categories account for the largest number of children. They are also the cate¬ gories for which over- and underrepresentation seem most likely to occur. Using this high standard for disproportionate identification, the number of states in which black students were overrepresented in the disability catego¬ ries of mental retardation and emotional disturbance is shown, as well as the de¬ gree of racial disparity, For example, black children are overrepresented for mental retardation in thirty-eight states. Moreover, the risk of a black child be¬ ing labeled mentally retarded are more than four times greater than for a white child in the states of Connecticut, Mississippi, North Carolina, Nebraska, and South Carolina. Conversely, the risk of an Asian American/Pacific Islander child being designated as emotionally disturbed are less than one-fourth the chance of a white student being so designated in over one-half the states. It should be noted that the state-level comparisons present aggregated in¬ formation by state. Dramatic variations between individual districts and re¬ gions within a state might exist that are not revealed in the state-level aggre¬ gates. Therefore, the under- or overrepresentation shown for a state does not necessarily depict what is occurring in individual districts. An interesting observation is the greater likelihood of children in the vari¬ ous minority groups being placed in the category most prone to over- representation (MR) when the minority group constitutes a relatively large pro¬ portion of the state's population. Examples include Asian American/Pacific Islander in Hawaii, with a risk ratio of 3.48 for MR, compared to their national average of .54. Asian American/Pacific Islander children constitute nearly 59 percent of all school-age children in Hawaii, much higher than that found in any other state. Similarly, in Alaska, where American Indian children comprise

TABLE 1    Risk Ratios for Mental Retardation, Emotional Disturbance,, and Specific Learning Disabilites by Race and Ethnicity3
   United States Alaska Alabama Arkansas Arftona California Colorado Connecticut Delaware Florida Georgia Hawaii Iowa idahD Illinois Indiana Kansas Kentucky Louisiana Massachusetts Maryland Maine Michigan Minnesota Missouri Mississippi  American Indian 1.31* 2.AS" 1.13 1.12 1.50* 145 1.89 3.27 0.00 2.13* 0.31 1.60 1.69 2.11 0.59 0.90 1.47 0.17 1.35 0.89 2.74 0.44 1.39 1.76" 0.5B 0.46 Mental Retardation Asian/ Pacific 0.54* 1.49 0.38 0.97 0.70 0.39 0.98 D.66 0.68 0.B5* 0.43* 3.48* 0.54* 0.77 0.58* 0.43* 0.53 0.22* 0.49 0.29* 0.73 0.52 0.93 0.82 0.41* 0.63 Black  2.M* 2.07 3.89* 3.00* 2.97* 1.S9* 3.48* 4.7B* 3.61* 3.91* 3.09* 1.43 2.62* 1.34 3.09* 3.31* 2.93* 1.60* 3.31* 1.41' 3.29* 1.53 2.34* 2.48* 2.75* 4.31*  Hispanic  0.77* 1.21 0.43* 0.44* 1.65* 1.23* 1.89* 3.25* 2.01* 1.19* 0.82 0.67 0.90 1.77* 1.05 0.83 1.26 0.36" 0.39* 1.13 0.66 0.79 0.82 1.39 0.53* 0.20  American Indian 1.24* 1.54 0.76 0.00 0.59* 1.30 1.75. 2.88* 1.34 2.61* 0.60 1.90 1.99 1.10 0.61 0.58 1.40 0.17 1.24 0.87 1.96 0.12 1.52 3.33* 0.67 NA" Emotional Disturbance Asian/ Pacific 0.29* 0.15 0.17 0.93 0.23* 0.17* 0.61 0.23* 0.41 0.13* 0,25* 1.16 0.32* 0.27 0.19* 0.19* 0.16* 0.22 0.07 0.29* 0.22* 0.42 0.69 0.27* 0.21* NA*« Black  1.92" 2.12 1.27* 1.12 1.67* 2.56* 2.05* 2.62* 2.4S* 2.14* 1.38* 0.91 4.31* 0.65 2.16* 1.78" 2,20* 3,87* 2,99* 1,41* 2.13* 1,69 1.09 3.29* 2.45* 0,94  Hispanic  0.74" 0.36 0.24 0.25 0.43* 0.3&* 0.84 1.92* 0.45 0.63* 0.28* 0.38* 0.83 0.39 0.61* 0.35* 0.71 0.43 0.41 1.13 0.52* 1,14 0.58* 1.01 0.53* 0.22 Spec/Ac Learning Disabilities American Indian 1.50* 1.74* 2.09* 0.66 1.43* 1.78* 1.91* 1.72 1.11 1.96* 0.62 1.28 1.77* 1.97' 0.59 0.73 1.23 0.42 2.21* 0.88 1.42 0.66 1.41* 1.95* 0.54* 0.45 Asian/ Pacific 0.39* 0.69 0.22* 0.44* 0.29* 0.32* 0.40* 0.22* 0.16* 0.26* 0.24* 1.82* 0.39* 0.23* 0.22* 0.14* 0.35* 0.28* 0.19* 0.28* 0.20* 0.36* 0.84* 0.71* 0.20* 0.25* Black  1.32* 2.01* 0.97 1.10 1.59* 2.0S* 1.69* 1.49* 2.55* 1.20* 0.71* 0.83 1.87* 1.49 1.21* 0.96 1.41* 1.52* 1.86* 1.45* 1.31* 1.40 1.04 2.72* 1.52" 1.72* Hispanic  1.17* 0.88 0.46* 0.63* 1.20* 1.10* 1.25* 1.38* 1.43* 0.93* 0.61* 0.51* 0.92 1.13 0.94* 0.51* 0.93 0.54* 0.36* 1.14* 0.80* 0.74 0.92 1.38* 0.54* 0.33*

   Montana North Carolina North Dakota Nebraska New Hampshire New Jersey Mew Mexrco Nevada New York Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Virginia Vermont Washington Wisconsin West Virginia Wyoming  American Indian 2.00* 2.97* 1.45 2.31* 0.41 2.28 1AT 1.72 1.80 0.67 1.76* 1.57 1.28 0.78 1.41 1.25 0.41 1.33 1.28 0.47 1.50 2.89' 144 0.80 1.01 Mental Retardation Asian/ PadUe 0.77 0.55* 1.28 0.29 0.23 1.01 1.12 1.26 0.66* 0.27* 0.34* 0.57 0.41* 0.91 0.32 0.71 0.35* 0.70* 0.76 0,72 0.37 0.81 1.16 0.28 1.78 Black  3.18 4.08* 1.36 4.08* 0.95 3.60* 2.16* 3.13* 2.32* 2.59* 3.44* 2.48* 1.B6* 2.5B* 4.30* 2.49 3.46* 3.21* 1.75 3.02* 0.91 2.7S* 3.16* 1.52* 2,04  Hispanic  0.54 0.94 0.74 t.45* 0.45 2.39* 1.66* 1.20 1.72* 0.93 0.94 0.85 1.55* 1.98* D.47* 0.S4 0.38* f.42* 1.30 1.43* 0.00 1.68* 1.25 0.15 1.04  American Indian 1.31 0.68 1.24 4.83* 0.91 0.20 0,71 0.55 2.sr 1.21 1.02 1.71 2.2S" 1.05 0.93 2.30* 0.28 1.09 1.44 1.09 1.92 2.24* 2.63* 1.39 1.39 Emotional Disturbance Asian/ Pacific 0.81 0.18* 0.37 0.31 0.24 0.19* 0,14 0.32 0.27* 0.15* D.29 0.20* 0,21* 0.26 0.20 0.29 0.18 0.13* 0.66 0.27* 0.41 0,24* 0.21* 0.17 0.37 Black  3.75 2.76* 2.61 6.06* 1.14 2.40* 1.88* 1.74* 2.56* 2.6+* 1.90* 2.85* 2.3S" 1.89* 2.04* 2.36 1.47* 1.45" 3.73* 1.61* 1.61 2-89' 1.99* 2.77* 2.45  Hispanic  D.6D 0.46* 1.62 0.56 0.39 1.02 0.87 0.24* 2.33* 0.92 0.39* 0.36* 1.54* 0.84 0.46 1.S5 0.31 0.66* 1.06 1.08 0.25 0.52* 0.75 0.66 0.73 Specific Learning Disabilities American Indian 2.19* 0.96 1.32 2.94* 0.22 0.76 1.09 1.69* 1.39* 0.76 1.74* 1.43* 1.24 0.70 0.53 1,33* 0.40* 1.21 2.34* 0.84 0.50 2.25* 1.58' 0.67 1.50 Asian/ Pacific 0.67 0.34* 0.38 0.38* 0.13* 0.25* 0.47* 0.43* 0.36* 0.28* 0.27* 0.26* 0.2a* 0.28* 0.18* 0.69 0.21* 0.23* 0.65' 0.41* 0.29 0.55* 0.67* 0.14* 0.23 Black  5.44* 1.10* 1.19 1.69* 0.39 1.28* 1.93" 2.D9* 1.64* 0.87* 1.58* 1.08 1.29* 1.28* 1.26* 2.00' 1.09* 1.54* 2.25* 1.14* 1.36 2.10* 1.41* 1.14 1.84 Hispanic  0.29* 0.70* 0.92 1.04 0.32* 0.87* 1.27* 0.92 1.22* 0.73* 0.87 0.78* 1.43* 1.16 0.48* 0.74 0.40* 1.23* 1.3B" 1.37* 0.33 1.31* 0.97 0.52 1.03
a The data underlying this table are from the U.S. Department of Education (1998). Boldface indicates double the rate of whites. Asian/Pacific indicates Asian American/Pacific Islander * Indicates statistical significance at the .01 level ** NA, not applicabfe, is shown for Mississippi because no American Indian or Asian/Pacific students were identified for Emotional Disturbance in that state for the 1998-1999 school year.

24      Racial Inequity in Special Education TABLE 2 Mental Retardation Risk Ratios in High- and Low-Percentage Minority States by Race3
Percent Minority     Lowest Ten States Highest Ten States American Indian  Risk of MR com¬ pared to whites 1.07  1.75   % of total enrollment that are Am. lnd.b 0.2%  8.4%  Asian/Pacific  Risk of MR com¬ pared to whites 0.66  1.14   % of total enrollment that are Asian/Pac. 0.7%  10.8%  Block  Risk of MR com¬ pared to whites \.77  3.59   % of total enrollment that are black 0.8%  31.6%  Hispanic  Risk of MS com¬ pared to whites 0.42  1.55   % of total enrollment that are Hispanic 1.2%  25.6% 
a The data underlying this table are from the U.S. Department of Education (1998). b "Total enrollment" refers to special education and general education combined.
over 21 percent of student enrollment, the risk for American Indian children being identified as mentally retarded are considerably larger than the national average (2.43 vs. 1.31). For the disability category of mental retardation, this trend holds across all minority groups (see Table 2). For black children, the risk of being designated mentally retarded (in comparison to white children) range from 3.59 in the ten states in which the proportion is the highest to 1.77 in the ten states where the proportion is the lowest. Thus, the ratio is about twice as high for blacks in states where they are more heavily concentrated. For Hispanic children, the odds are more than three times as great in the ten states with the highest percentage of Hispanic students (1.55 vs. 0.42) as in the states with the lowest percentage of Hispanics, Many of the highest- percentage minority states are southern and southwestern states with histories of segregation.
identification Disparities in Soft versus Hard Categories of Disability One approach to attempting to isolate the effect of poverty as opposed to race on the disproportionate identification of black students for special education is to compare the identification patterns observed for black students in soft versus hard categories of disability. This division into soft and hard categories is some¬ what of an artificial distinction, as all categories of disability contain some form of medical determination in their definition. However, the categories of disabil¬ ity called specific learning disability, mental retardation, and emotional distur¬ bance are sometimes referred to as soft categories because they are more subjec-

Racial Disparities in Special Education      25 TABLE 3 Comparison of Risk Ratios between Blacks and Whites in Hard and Soft Disability Categories in the United States and in Selected States3
  Connecticut Mississippi South Carolina North Carolina Nebraska Florida Alabama Delaware New Jersey Colorado United States  MR 4.76 4.31 4.30 4.08 4.08 3.91 3,69 3.61 3.60 3.48 2.88 Soft Disabilities ED 2.62 0.94 2.04 2.76 6.06 2.14 1.27 2.45 2.40 2.05 1.92  SID 1.49 1.72 1.26 1.10 1.69 1.20 0.97 2.55 1.28 1.69 1.32 Hard Disabilities*1  1.49 1.07 1.30 1.03 1.50 1.09 1.11 1.30 2.18 1.34 1.18
Blacks were most overrepresented for mental retardation (MR) in the ten states selected. a All data are from 1998 U.S. Department of Education Office of Special Education Programs. See Table 1 for indications on statistical significance. b The hard disabilities category includes hearing impairments, visual impairments, orthopedic im¬ pairments, deaf-blindness, multiple disabilities, and traumatic brain injury.
lively and less medically determined than categories such as deafness or blindness, which are deemed hard because they are less prone to subjectivity and are readily diagnosed medically. Arguably, if the effects of poverty, includ¬ ing diminished nutrition and limited access to preventive health care, cause mote black students to be identified for the soft category of mental retardation, then these factors should have a similar effect on the incidence of the hard cate¬ gories of deafness and blindness. However, the data show substantial incongruence between the level of overrepresentation for hard and soft categories. Nationwide, blacks are almost exactly as likely as white students to be identified for the hard disability catego¬ ries, while they are nearly three times (2.88) more likely than whites to be iden¬ tified as mentally retarded and nearly twice as likely to be identified as ED (1.92). Furthermore, as Table 3 shows, we found substantially less racial dispro- portionality for hard categories in the ten states that were highest for overrepre- senting black students as MR. These data suggest that something more than the effects of poverty are causing black students to be disproportionately identified for some categories of disability.

26      Racial Inequity in Special Education TABLE 4 Placement and Service Characteristics by Race lor California
      American Indian Asian American/ Pacific Islander Black Hispanic White Percent in General Education with Related Services (o) 21% 32%  17% 22% 27% Percent in Resource Room   (b) 48% 32%  41% 44% 46% Percent in Private School   (c) 2% 1%  4% 1% 2% Percent in Special Education Self- Contained (<V 29% 35%  37% 33% 24% Percent Requiring Intensive Services  (e) 20% 27%  29% 25% 23%
Source: California Special Education Management System (CASEMIS).
PLACEMENTS AND SERVICES BY RACE A more extensive, detailed view of the mix of special education services received by individual students can be found in the California Special Education Man¬ agement Information System (CASEMIS). This file contains a broad range of information about the more than 600,000 children who receive special educa¬ tion services in California. Because California is so populous, these data provide a detailed snapshot of approximately one-eighth of the nation's students. Table 4 shows the types of placements and services that result from these patterns of identification by race and by disability. Column (e) shows that black students are more likely than white students to be designated as requiring inten¬ sive services (29% vs. 23%) and more likely to be placed in such restrictive set¬ tings as self-contained special education classrooms (37% vs. 24%) and private special education schools (4% vs. 2%). Also, the gap between restrictive settings and the need for intensive services is most pronounced for minorities. While this gap [the difference between columns (d) and (e)] equals 8-9 percent for minority students, it equals 1 percent for white students. This suggests that whites are generally only placed in more restrictive self-contained classes when they need intensive services. Minority students, however, may be more likely to be placed in the restrictive setting whether they require intensive services or not. Another view of the placements and services received by California stu¬ dents by race is presented in Table 5. It compares breakouts of total student en¬ rollment, special education enrollment, special education private school enroll-

Racial Disparities in Special Education      27 TABLE 5 Distribution by Race of California Students Overall, in Special Education, and in the California Youth Authority
 All Students Special Education Private Special Education Schools Residential Private Special Education California Youth Authority White 40% 43% 48% 58% 15% Hispanic 41% 37% 19% 15% 47% Black 9% 14% 30% 21% 29% Asian 9% 3% 1% 3% 7% Other 1% 3% 2% 3% 2% Total 100% 100% 100% 100% 100%
Source; California Special Education Management System (CASEMIS).
ments, and students under the jurisdiction of the California Youth Authority. While the distribution of special education by race is reasonably reflective of all students, this picture changes dramatically in the case of private special educa¬ tion schools, and particularly in the case of residential private special education schools. As these placements rise in cost, peaking at the very costly private spe¬ cial education residential placements, white participation increases dramatically in relation to the percentage of white children in the population (from 40% to 58%). Black enrollments also rise in relation to their percentage of the popula¬ tion (from 9% to 30% in private special education schools) until the most costly placement of special education residential schools, where black students drop to 21 percent representation. Hispanic students show a dramatic reverse pattern, with their representation dropping from 41 percent of all students to 15 percent of residential private special education schools. Most students (63%) in private special education schools in California are identified as having an emotional disturbance. Many of these children find themselves in these schools because they have demonstrated some form of anti¬ social behavior. Children exhibiting such behaviors sometimes end up in resi¬ dential private special education placements, where they are likely to receive in¬ tensive treatment, while in other cases they may be placed with the California Youth Authority, a placement that is more punishing than remedial. Black children are overrepresented in these three types of settings in rela¬ tion to their percentage of the population (9% vs. 21% vs. 29%). Very different placement patterns are seen for Hispanic and white children. Although nearly equally divided in the population (41% vs. 40%), Hispanics represent 47 per-

28      Racial Inequity in Special Education cent of the children under the jurisdiction of the California Youth Authority, compared to 15 percent for white children. These figures are close to being re¬ versed in private special education school settings, where Hispanic children constitute 15 percent of the population, compared to 58 percent for white chil¬ dren.7
SPECIAL EDUCATION FUNDING IN RELATION TO OVER- AND UNDERREPRESENTATION What is the relationship between special education identification rates for mi¬ nority students and special education funding? In the case of overrepresentation of minority students in more severe and generally higher-cost disability catego¬ ries, it might be expected that added special education funding would follow these higher rates of designation. But this was not the case. The following anal¬ ysis presents a complex pattern that suggests a possible link between identifica¬ tion rates and certain types of funding formulas. For the purpose of these analyses, state formulas are divided into three cate¬ gories: those direcdy linked to the overall number of students identified for spe¬ cial education and/or the services provided; the subset of these linked formulas in which the funding amounts vary by category of disability; and formulas that are not linked to any measure of special education incidence or service. Table 6a separates the states by the three approaches to special education funding described above [column (b)].s It also lists the state's risk ratio for mi¬ nority children as mentally retarded relative to that of white children [column (c)]. Table 6a also shows the percentage difference in special education fund¬ ing received by the highest and the lowest quartiles of districts in the state in terms of minority enrollment [column (d)]. For example, the amount shown for South Carolina indicates that the average amount of state special education funding per special education student in the highest-percentage-minority dis¬ tricts was 3.8 percent more than in the lowest-percentage-minority districts. In New Jersey, however, the opposite trend was observed, with the state's highest- minority districts receiving 33.2 percent less in average state funding per spe¬ cial education student than the lowest-minority districts. Is additional special education funding found in high-minority districts in states where minority students are overrepresented for mental retardation? Do racial disproportion and funding patterns vary by type of special education funding formula? Further funding comparisons were conducted to find out whether high- minority districts in states with high rates of overrepresentation for MR re¬ ceived additional special education funding. Although there was no uniform

Racial Disparities in Special Education      29
TABLE 6a Comparison of Special Education Funding Formula with Minority Risk Ratios and with Special Education Per Pupil Expenditures in High- and Low-Minority Districts
States Ranked by Minority Risk Compared to White (a)  South Carolina Connecticut Morth Carolina Delaware Louisiana Maryland New Jersey Arkansas Florida Indiana Virginia Nebraska Missouri Wisconsin Ohio Colorado Illinois Mississippi Kansas Texas Nevada Pennsylvania North Dakota California Utah South Dakota Wyoming Vermont Formula Type (b)     Service and Category Linked           < Service Linked Service Linked Service and Category Linked Service Linked Unlinked Service and Category Linked Service Linked Service and Category Linked Service and Category Linked Service Linked Service Linked Service Linked Service Linked Service and Category Linked Unlinked Service Linked                                  , Service Linked Service Linked Service Linked Service Linked Unlinked Service Linked Service Linked Unlinked Service Linked Service Linked Service Linked                              ( Risk Ratio for Mental Retardation fc)   1.06 3.65 3.61 s.15 3.03 2.74 2.71 2.65 2.62 2.55 2.52 2.36 2.31 2.26 2.23 2.10 J.07 .98 .88 .84 .71 .64 .33 .29 .23 .23 .16 ).46 Special Education Funding Differential per Student in High- versus Low-Minority Districts** (d) 3.8% 85.8% 5.5% -2.2% -2.1% 99.9% -33.2% 45.3% 31.8% -7.7% 0.6% -3.7% 41.9% 25.0% -18.9% 10.4% -38.1% 3.2% 77.3% -0.1% -9.2% -12.8% 41.1% 23,7% 13.3% 55.8% -2.0% -1.6%
3 All data are from 1998 U.S, Department of Education Office of Special Education Programs. See Table 1 for indications on statistical significance. D Annual Survey of Local Government Finances conducted by the U.S. Bureau of the Census and the Com¬ mon Core of School District Data produced by the U.S. Department of Education's National Center for Edu¬ cation Statistics (NCES) for the 1997-1998 school year. Service Linked: All students identified for special education and/or the services provided. Service and Category Linked: Subset of these linked formulas in which the funding amounts vary by cate¬ gory of disability. Unlinked: Formulas that are not linked to any measure of special education incidence or service (e.g., na¬ tional averages).

30      Racial Inequity in Special Education TABLE 6b Summary of Spending Differentials and MR Risk Ratios in High- versus Low-Minority Districts
Comparison Group (a)    Number of States (b)   Risk Ratio for Mental Retardation (c)  By Degree of Overrepresentation of Minority Students Highest Lowest By Formufo Type 14 14  Service and Category Linked             6 Service Linked Unlinked 18 4 2.87 1.58  2.89 2.07 1.93 Special Education Funding Differential, High- versus Low- Minority Districts (d)  20.8% 10.2%  -4.4% 19.4% 27.7%
Source: 1998 U.S. Department of Education Office of Special Education Programs.
pattern, the evidence generally suggests that some fiscal premium for high- minority districts appears to be associated with high rates of overrepresentation. Specifically, of the twenty states with sufficient financial data, sixteen showed a positive relationship between the percentage minority enrollment in a district and district special education funding per pupil. However, twelve states showed a negative relationship. Moreover, a comparison of the half of these states with the largest risk ratios to the half with the lowest risk ratios shows a 20.8 percent funding differential favoring high-minority districts among the states with the highest risk ratios. This compares with a 10.2 percent differen¬ tial for high-minority districts in states showing the lowest risk ratios. Thus, some fiscal premium for high-minority districts appears to be associated with higher degrees of overrepresentation.9 The clearest pattern of funding differentials between high- and low- per¬ centage-minority districts appears to be by type of funding formula, Interest¬ ingly, the six states with funding formulas that specifically place higher premi¬ ums on higher-cost disabilities, such as MR (Service and Category Linked), are much more likely to have minority students overrepresented for mental retarda¬ tion. At the same time, four of the six show negative special education funding differentials disfavoring high-minority districts. When the degree of minority overrepresenration is compared by formula type, the largest average risk ratio (2.89) is shown for the six states with formu¬ las that differentiate funding by category of disability partially or totally. This

Racial Disparities in Special Education      31 suggests that state formulas that place revenue premiums on more severe cate¬ gories of disability, such as MR, may somehow affect the overidentification of minority students. Interestingly, of the twenty-eight states, the four that have funding systems with no relationship to any measure of special education provision show the highest degree of resource targeting to high-minority districts (with a 27.7% differential). The eighteen states with funding systems linked to special educa¬ tion provision, but not specifically to category of disability, show special educa¬ tion funding favoring high-minority districts by 19.4 percent. Only the states basing funding on disability category show a negative relationship between spe¬ cial education funding and percentage of minority students, with an average of —4.4 percent. Two of the three states with the greatest disparity in special edu¬ cation funding for high- and low-minority districts, Ohio and New Jersey, have funding systems that vary by category of disability. It may not be wise to overspeculate about this rather counter-intuitive rela¬ tionship, especially when based on six states that show considerable variation in overidentifying and underfunding minority students. On the other hand, when taken as a whole, these states look substantially different from their counter¬ parts, which do not have systems that fund differently by category of disability. For example, although minority students in New Jersey are overrepre- sented in the mental retardation disability category, they are generally under- represented in special education overall. Thus, overidentification for MR does not lead to higher special education funding flowing to high-minority districts. In Ohio, although black students are overdesignated for mental retardation, all four categories of minority students are underrepresented in what is by far the largest special education category, learning disability. The policy implications of these findings in relation to disability-based for¬ mulas are not clear, but they challenge the assumption of equity associated with differentiating special education funding by category of disability. The strongest rationale underlying this type of funding system is that, by having higher dollar allocations associated with more severe categories of disability, special education funding will flow to where they are most needed. Based on the findings above, this assumption must be questioned unless it can be argued that, even though minority students are more likely to be designated mentally retarded, their over¬ all special education needs are less than those of their white counterparts. IMPLICATIONS FOR POLICY REFORM Clear patterns of overrepresentation of minority children are found, which vary dramatically by state, by category of disability, and by race. Across all disabili-

32      Racial Inequity in Special Education ties and states, Hispanic children are represented in special education at about the same rate as white students. However, at least one study that looked at dis¬ trict-level disparities suggest that Hispanic children may be substantially owe- rrepresented in some districts and underrepresented in others (Finn, 1982). Black and American Indian children are overrepresented, while Asian Ameri¬ can/Pacific Islander children are underrepresented. Overrepresentation in the mental retardation disability category is shown for each of the minority catego¬ ries in at least one state. The likelihood of overrepresentation in this category of disability for a minority group seems to be greater in states where the minority group is the largest. For example, Asian American/Pacific Islander students show overrepresentation foi mental retardation in Hawaii, where they comprise a large portion of the population, as compared to underrepresentation for Asian American/Pacific Islander children nationally. What policy interventions do these troubling findings suggest? One is that these kinds of data should be tracked on an ongoing basis, incorporated into the monitoring processes of state and federal agencies administering special educa¬ tion programs, and reported to policymakers, advocates, and the public at large. Can Connecticut, Mississippi, North Carolina, Nebraska, and South Carolina be in compliance with special education and civil rights law when black stu¬ dents are over four times more likely than white students to be designated men¬ tally retarded? One would expect that, where higher concentrations of disabilities do ex¬ ist, supplemental funds and services should also be found. However, special education formulas that differentiate funding by category of disability appear not to be working that way. A concern long associated with formulas that place funding premiums on certain categories of disability is that such categories may create fiscal incentives to overidentify students into these disability groups. Why would these incentives be more likely to pertain to minority stu¬ dents? Perhaps they have fewer advocates to protect them from these economic incentives. Poverty does not appear to explain the patterns of overrepresentation re¬ vealed in this chapter. For example, using data from the years 1976, 1980, and 1983, McLaughlin and Owings (1993) found a significant negative relation¬ ship between the percentage of school-age children living in poverty and identi¬ fication rates for learning-disabled students for two of these three years. These authors also found a significant negative relationship between poverty and identification rates for emotionally disturbed students in one of these three years. No significant relationship between poverty and overall special education identification rates was shown for any of the three years (Parrish & Verstegen, 1994).

Racial Disparities in Specidl Education      33 Parrish and Hikido (1998) examined the relationship between the percent¬ age of children living in poverty, the percentage of minority students, and over¬ all allocations of state special education funding across the nation's school dis¬ tricts. Their analysis suggests a much stronger relationship between special education and race than between special education and poverty. State special education funding was 17 percent more in the quartile of districts across the na¬ tion with the highest percentage of students in poverty than in the quartik of districts with the fewest students in poverty.10 However, special education funding was 41 percent higher in districts with the highest percentages of mi¬ nority students. In addition, the wide variations in over- and underrepresentation shown in this chapter suggest subjective identification and inconsistency in the identifi¬ cation process despite IDEA's clear guidelines. IDEA, Patt B, states: "The term 'children with learning disabilities', . , does not include children who have learning problems which are primarily the result of... environmental, cultural, or economic disadvantage," However, research has demonstrated that identifi¬ cation decisions for students with mild disabilities, who make up the vast ma¬ jority of the special education student population, are based on some combina¬ tion of objective criteria, subjective criteria, and local state and federal policies (Ysseldyke, Algozzine, Richey, & Graden, 1982; Ysseldyke, Algozzine, Shinn, & McGue, 1982). For these reasons, links shown to exist between at-risk con¬ ditions related to poverty and incidence levels of disabling conditions may not translate into a positive relationship between poverty and special education identification (Parrish & Verstegen, 1994). A majority of the states allocate more education resources and more special education resources to high-minority districts. However, this pattern seems only weakly linked to patterns of overrepresentation. Variation in the type of special education funding system suggests that funding systems based on category of disability are particularly prone to trou¬ bling patterns of minority overrepresentation and resource distribution. These systems appear much more likely to show over-representation of minority stu¬ dents into the disability category mental retardation, while at the same time providing greater special education funding to districts enrolling the lowest per¬ centages of minority students. Finally, in addition to special education funds not tracking well to the overidentification of minority students, data from California suggest that black students are more likely to be found in restrictive settings than their white counterparts, including the juvenile justice system. Hispanic students, who are generally underrepresented in special education in California, are also over- represented in the state's juvenile corrections system.

34      Racial Inequity in Special Education Finance systems that differentiate funding by the student's category of dis¬ ability are based on the assumption of a strong link between category of disabil¬ ity and cost. Numerous finance studies (e.g., Moore et al., 1988), however, have shown this connection to be much more tenuous than was commonly thought, with as much variation in cost found within categories of disability as across them. High-minority districts are much more reliant on categorical funding sources, such as special education, than low-minority districts (Parrish &C Hikido, 1998). While base funding is 10 percent lower per student in high- minority districts, categorical funding is twice as high. Until we achieve greater equity in base funds across districts, high-minority districts are likely to con¬ tinue to look to categorical programs such as special education for remedial ed¬ ucation support. A more fundamental concern associated with these formulas is that they tend to emphasize the wrong thing. They may be placing too much emphasis on category of disability at the expense of more appropriate foci for funding for¬ mulas, such as the equitable distribution of funds, accurate identification and labeling of students, and the delivery of appropriate services. Census-based funding systems, which have no link to variations in the identification of special education students or the provision of services, are predicated on an opposite notion of funding than those adjusted by category of disability. Census-based systems assume comparable distributions of disability across jurisdictions. Some variations of this model, such as the system recently adopted by the federal government, skew these Census-based distributions in favor of higher-poverty states and districts. This may be the best way to ensure that special education funds are allocated where they are most needed without creating incentives for overidentification. Perhaps this is why the states with census-type systems, designated in Table 4 as "Unlinked," were the least likely to overidentify minority students and the most likely to distribute more special education funds to high-minority districts. In addition to removing fiscal incentives for identifying special education students, and especially for identifying them into certain disability categories, other policy interventions are also likely to mediate the overidentification of minority children. One is equity in base funding for education. The more that base funds for education are inadequate in high-minority districts, the greater the pull of fiscal incentives to seek supplemental categorical funds such as spe¬ cial education (Parrish & Hikido, 1998). Another strategy is creating alterna¬ tives to special education. If special education is the only place where students with learning difficulties can receive supplemental help, the greater the attrac¬ tion of this program will be.

Racial Disparities in Special Education      35 Pressure to place more and more students in special education may also be increased by the recent emphasis on education, accountability. If high-minority districts are held to higher standards of performance without the supplemental resources needed to achieve them, minority children may increasingly be found to need remediation. Without alternatives, it may become even more likely in the future that these services will be provided through special education. This is unlikely to be the best way to assist many of these children and appears likely to further exacerbate minority overidentification. Although there may be agreement in principle that special education should be implemented, administered, and funded in a color-blind fashion, this does not seem to be the case. Clearer guidelines for identification, stricter moni¬ toring and enforcement, equitable base funding, the creation of remediation alternatives, and neutral funding formulas may not fully resolve the situation, but they seem likely to help and to be reasonable objectives for policymakers to pursue.
NOTES 1. "Soft" disability categories ate chose that are more subjectively and less medically deter¬ mined than "hard" disabilities, which are readily diagnosed medically. 2. LD is the category that is least likely to reflect overidentification. However, in states like California where the Larry P. v. Riles, 793 K2d 969 (9th Cir. 1984) case greatly re¬ stricted the placement of black students in MR, we do see black overidentification in LD (as well as in ED). A possible reason why black overrepresentation in LD is generally less common, however, may be that white and middle-class parents whose children ate eligible For services may be in a position to more actively contest the more stigmatizing labels of MR and ED in favor of LD. 3. Special education expenditure studies were completed by the Center For Special Educa¬ tion Finance at the American Institutes for Research for the states of New York, New Jersey, Rhode Island, Delaware, Alabama, Maryland, Ohio, Indiana, Kansas, Missouri, and Wyoming. 4. Funding difFers from spending. That is, while the amount of money made available to districts from state and federal sources is generally known, what is not known is how much comes from local sources to meet the full cost of providing special education pro¬ grams and services. 5. This database, the California Special Education Management Information System (CASEMIS), is updated for every special education student in California every year. 6. For A complete description of risk ratios, see Bohrnstedt and Knoke (1994, pp. 17S— 181). 7. It is important to keep in mind that Alfredo Artiles1 work (see Artiles et al., this volume) suggests that when these data are broken down by age, older Hispanic students are overidentified and younger students are underidentified. 8. As discussed, good data were only available for twenty-eight states.

36      Racial Inequity in Special Education
9. Although this pattern generally holds, it is not uniform. Maryland, with a strong posi¬ tive relationship favoring special education funding in high-poverty districts, and New- Jersey, with a strong negative relationship, overidentify minority students at about the same rate (2.74 vs. 2.71). Conversely, South Dakota shows relatively little overidenri- fication (1.23), but shows special education revenue allocations strongly favoring high- minoriry districts (by 55,8%), 10. However, special education funding was 41 percent higher in districts with the highest percentages of minority students. The data suggest that race is a much stronger predic¬ tor for special education than poverty.
REFERENCES Bohmstedt, G. W., &c Knoke, D. (1994). Statistics fir social science data analysis (3rd ed.). Itasca, IL: F. E. Peacock, Chambers, J. G. (1998). Development of a national geographic cost of education index (GCEI) for public schools. Palo Alto, CA: American Institutes for Research. Chambers, J., Parrish, T., &C Harr, J, {2002}, What are we spendingon special education services in the United States? Palo Alto, CA: American Institutes for Research, Center for Special Education Finance. Finn, J. D. (1982). Patterns in special education placement as revealed by the OCR surveys. In K. A. Heller, W. H. Holtmian, & S. Messick (Eds.), Placing children in special educa¬ tion: A strategy for equity. Washington, DC: National Academy Press. Hartman, W. T., &C Fay, T. A. {1996). Cost-effectiveness of instructional support teams in Penn¬ sylvania. Palo Alto, CA: American Institutes for Research, Center for Special Education Finance. McLaughlin, M. J., &c Owings, M. (1993). Relationships among states' fiscal and demo¬ graphic data and the implementation of PL 94-142. Exceptional Children, 59, 247-261. Moore, M. T., Sttang, E. W., Schwartz, M., & Braddock, M. (1988). Patterns in special edu¬ cation service delivery and cost. Washington, DC: Decisions Resources. Parrish, T. B. (2000). Special education: At what cost to general education? In CSEFResource (winter 1999-2000). Palo Alto, CA: American Institutes for Research, Center for Spe¬ cial Education Finance. Parrish, T. (2001). Who's paying the rising cost of special education? Journal ofSpecial Edu¬ cation Leadership, 14(1), 4-12. Parrish, T., & Anthony, J. J. (2002). State special education finance systems and expenditures, 1999-00. Palo Alto, CA: American Institutes for Research, Center for Special Educa¬ tion Finance. Parrish, T. B., & Hikido, C, (1998). Inequalities in public school district revenues (NCES 98- 210). Washington, DC: U.S. Department of Education, National Center for Education Statistics. Parrish, T. B., & Verstegen, D. (1994). Fiscal provisions of the Individuals with Disabilities Act: Policy issues and alternatives. Palo Alto, CA: American Institutes for Research, Cen- tet for Special Education Finance. Rothstein.R. (1997). Where's the money gos'n^Washington, DC: Economic Policy Institute. U.S. Department of Education. (1997), Nineteenth annual report to Congress to assure the five Appropriate public education of'all children with disabilities. Washington, DC: Author.

Racial Disparities in Special Education     37 Ysseldyke, J., Algozzine, B,, Richey, L-, & Graden, J. (1982). Declaring students efigible for learning disability services: Why bother with the data? Learning Disability Quarterly, 5, 37-A4. Ysseldyke, J., Algozzine, B,, Shinn, M-, & McGue, M, (1982). Similarities and differences between low achievers and students classified learning disabled. Journal of Special Educa¬ tion, 16(1), 73-S5.

CHAPTER  THREE
Double Jeopardy: An Exploration of Restrictiveness and Race in Special Education
EDWARD GARCIA FIERROS JAMES W. CONROY
SPECIAL EDUCATION: SUPPORT AND SERVICES, NOT A PLACE Following the 1954 U.S. Supreme Court decision in Brown v. Board of Educa¬ tion, segregated school districts often sought ways to circumvent school deseg¬ regation. As the National Research Council suggested in 1982, "One device to screen out minority students, which relied heavily on intelligence tests, may have been special education, especially classes for mildly mentally retarded stu¬ dents. For example, the repeal of the law in California excluding Mexican- Americans from white schools coincided with the legislative creation of pro¬ grams for EMR [the educationally mentally retarded] students" (National Re¬ search Council [NRC], 1982, p. 33). In 1975, Congress passed the Education for All Handicapped Children Act partly as a response to the fact that public schools often treated students with disabilities as second-class students, placing them in separate classrooms where they were unnecessarily isolated from the general education classroom. The Education for All Handicapped Act (PL 94-142), now the Individuals with Disabilities Education Act (IDEA), sought to remedy the denial of access to schools and the unjustified segregation of students with disabilities. The law has consistently required that schools provide a continuum of educational set¬ tings and services so that students with disabilities can be educated with their nondisabled peers in the least restrictive environment to the maximum extent appropriate. The term restrictive describes the extent to which, students with
39

40      Racial Inequity in Special Education disabilities are educated outside of regular classrooms and isolated from their nondisabled peers. Restrictiveness is measured by the percentage of the typical school day that a student spends in a regular or "general" education classroom.5 "Fully inclusive" and 100 percent outside the general classroom are opposite ends of the spectrum,2 This research reveals that special education students from minority racial groups are more likely than whites to be placed in restrictive educational set¬ tings. This disproportionate level of restrictiveness is most pronounced for Afri¬ can Americans and Hispanics. In addition to concerns about the segregative ef¬ fect of such placements, research has documented that students with disabilities generally benefit more from inclusion, although it also confirms that some stu¬ dents with disabilities make greater progress when they are educated in a sepa¬ rate setting for all or part of the school day when their individual academic and/ or behavioral needs call for it. In every state, however, once identified for special education services, minority students are more likely to be restricted than whites, suggesting a disturbing lack of effective mainstreaming programs and unwarranted isolation. Under IDEA, identifying a student as being eligible for special education services must be entirely separate from deciding the proper level of inclusion/re- strictiveness. At the heart of IDEA is the principle that each student will receive individualized services. Decisions to place any student in a given educational setting must be individually tailored to best meet the needs of the student, and not dictated by the administrative convenience of a school, district, or existing program of special education. The issue of placement is often confused with that of identification for special education services. Part of this confusion stems from the frequent and incorrect notion that special education is a place, rather than a system of supports and services. Disturbing patterns of isolation in special education—most pronounced for African American children—point to a large and complex ser of issues. As this research shows, this isolation phenomenon is not uncommon in urban schools, including those with predominantly minority populations. It is cer¬ tainly possible that the increased isolation of minority students disproportion¬ ately labeled for special education in some schools may be a product of racial bias. At the same time, racially isolated, high-poverty urban schools may be us¬ ing special education as triage because they lack supports for inclusive educa¬ tional placements. As a recent National Research Council report points out, some poorly prepared or supported teachers may refer students for special edu¬ cation evaluation as a way to deal with discipline problems and insufficient re¬ sources (NRC, 2002).

Double Jeopardy     4l Special education teachers may be ill trained or supported when it comes to providing high-quality inclusion for students. Furthermore, special education programs in urban schools generally suffer from the same resource shortfalls, in¬ experienced and highly mobile teacher corps, and poor administrative supports that plague their general education counterparts (NRC, 2002). Thus, inade¬ quacies within special education may be compounded by the difficulties in¬ volved with providing services in poorly functioning general education settings. This chapter seeks to add a fuller description to the growing body of re¬ search on minority overrepresentation in special education. To seek a remedy, we first need to identify the extent of the pattern and then explore its complex mechanisms. In earlier studies, the categories mental retardation (MR), emo¬ tional disturbance (ED), previously called severe emotional disturbance (SED), and specific learning disability (SLD) have been found to include a dispropor¬ tionate number of black students compared to their percentage in the overall student population—with the greatest levels of overrepresentation in die area of MR (Artiles & Trent, 1994; Conroy, 1999; Coutinho & Oswald, 2000; Finn, 1982; Stainback & Stainback, 1996). The variation in the disproportionality of black MR student placement has been attributed to teachers' perceptions and attitudes in identifying characteristics of special needs students (Grossman, 1995; Harry, 1992; Utley & Mortweet, 1999), high-stakes assessments (Gross¬ man, 1995; Harry, 1992), differing state-level special needs education funding formulas (Parrish, this volume), and differences among states in data collection procedures and terminology (Danielson & Bellamy, 1989). The research presented here describes restrictiveness and its relationship to overidentification, but it does not explore the possible causes or solutions. This research does indicate that, nationwide, the percentage of black students who receive their special education supports and services in restrictive educational settings is substantially higher than the percentage of similarly situated white students. At the time of this study there was no national dataset that described levels of restrictiveness by disability category witri race/ethnicity. However, there are two prevalent trends in the data: 1) once identified, minority students from every major racial group are more likely than white students with disabili¬ ties to be removed from the general education classroom for all or part of their school day; and 2) black students are most often overidentified in the disability categories that have the highest correlation -with isolation from the general edu¬ cation setting, mental retardation and emotional disturbance. Two additional levels of analysis further substantiate the national- and state-level findings: first, a survey of selected urban districts, and second, an in- depth analysis of comprehensive data from Connecticut that direcdy shows re-

42      Racial Inequity in Special Education strictiveness according to disability category by race/ethnicity. The examination of ten large urban school districts generally found rates of restrictiveness that were much higher than their home state's levels of restrictiveness in all three cat¬ egories. Most striking, the Connecticut study directly showed substantially higher rates of restrictiveness for every minority group within each category studied, along with extremely wide variations in labeling and placement rates for minority students across school districts. Viewed together, this national-, state-, and district-level research suggests not only widespread violations of IDEA's least restrictive environment requirements, but also that minority stu¬ dents within a given state are disproportionately subjected to possibly unlawful treatment.
RESTRICTIVENESS FROM THE REGULAR EDUCATION CLASSROOM SETTING3 The regular classroom setting has been described as an essential part of a full ed¬ ucational experience for all students (Coutinho & Rupp, 1999; Crockett & Kauffman, 1999; U.S. Department of Education, 1999). Research has shown that "social interactions between students with and without disabilities are en¬ hanced when students with disabilities are served in regular classes—and are beneficial for many students without disabilities" (U.S. Department of Educa¬ tion, 1999, p. 6). Rea, McLaughlin, and Walther-Thomas (2002) have found that students with specific learning disabilities served in inclusive classrooms earned higher grades, achieved higher or comparable scores on standardized tests, committed no more behavioral infractions, and attended more days of school than students served in the pullout program. The three most common instructional settings for students with disabilities are Inclusive, Resource Room, and Substantially Separate (see Figure 1), Although the national data have only become available recently, many prior studies concluded that minorities have continued to be disproportionately overrepresented relative to their white counterparts in resource rooms, separate classrooms, and separate school facilities (Harry, 1992; Grossman, 1995). These restrictive placements mean that minority special education students1 ed¬ ucational experiences are likely to be delivered in unequal and separate class¬ room environments (Crockett & Kauffman, 1999; Grossman, 1995). For ex¬ ample, Coutinho and Repp (1999) reported that, for the 1992-1993 school year, nearly 60 percent of students with disabilities ages three to twenty-one were taught outside the regular classroom. In addition, Lipsky and Gartner (1997) stated that "the negative consequences of the separate special education system are greater for students from racial minorities" (p. 33).

Double Jeopardy     43 FIGURE 1 Most Common Instructional Settings for Students with Disabilities Inclusive: Regular class includes students who receive the majority of their instruction in a regular classroom and receive special education and related services outside the classroom for less than 21 percent of the school day. Resource Room: Resource room includes students who receive special education and related services outside the general classroom for at least 21 percent but not more than 60 percent of the school day. This may include students placed in resource rooms with part-time instruction in a regular class. Substantially Separate: Separate class, separate school, and residential facilities includes students who receive special education and related services outside the general classroom for greater than 60 percent of the school day. The term separate class generally refers to placements in self-contained special classrooms with part-time instruction in general classes or placed in self-contained classes full time on a general education school campus. (United States Department of Education, 1996; U.S. Department of Education, 2000a)
DATA SOURCES AND METHODOLOGY This research examines restrictiveness rates and racial/ethnic proportionality for students deemed eligible for special education placements and identified as hav¬ ing mental retardation, emotional disturbance, and specific learning disability. First, national special education placement data from the Office of Special Edu¬ cation Programs (OSEP) will be examined to determine the potential risk for black and Hispanic students with disabilities to be restricted from the regular education classroom. Second, to achieve a basic understanding of the ramifica¬ tions of overrepresentation in each disability category (i.e., MR, ED, and SLD), national U.S. Office of Civil Rights (OCR) data will be explored to look at re¬ strictiveness rates. Specifically, the restrictiveness rate of each state by three cog¬ nitive disability categories was paired with an analysis of state-level identifica¬ tion rates for blacks and Hispanics, Third, a selection of urban, mostly high- minority school districts will be examined to learn how these urban districts' rates of restrictiveness align with that of their state. Finally, data from Connect¬ icut, where racial disproportionality in identification is high for most minority groups, is examined in depth to see whether inferences drawn from the national database are borne out by more complete data from a separate database.

44      Racial Inequity in Special Education The data for this study were drawn from three sources: the 2002 Office of Special Education Programs, Data Analysis System (U.S. Department of Educa¬ tion, 2001a); the Fall 1998 Elementary and Secondary School Civil Rights Com¬ pliance Report (U.S. Department of Education, 2000); and the Connecticut In¬ tegrated Special Student Information System (ISSIS), which was produced as part of the discovery process for examination by Dr. James Conroy, who testi¬ fied as an expert for the plaintiffs in C.A.R.C, v. State of Connecticut Board of Education, a class action lawsuit that reached settlement in 2001 (Conroy, 1997; Conroy, Nerney, & Bowen, 1998; Nerney, Conroy, & Bowen, 1997).4 Fal! 1998 Elementary and Secondary School Civil Rights Compliance Report Since 1968, the OCR has conducted a biannual compliance report to help en¬ force students' civil rights in public schools. Using a stratified random sample of 5,898 school districts, the 1998 dataset for che first time included all those districts under court orders initiated by the U.S. Justice Department (U.S. De¬ partment of Education, 2000). The reported results in the dataset are national and state projections based on a probability sample of public school districts and schools. The data used in the analysis for this study were national-, state-, and district-level projections of membership, mental retardation, emotional disturbance, and specific learning disability. OSEP Data The IDEA, requires that the Office of Special Education Programs Data Analy¬ sis System (DANS) collect race data from all states as posted on its website (IDEAdata.org). OSEP data were collected for race for all states in the 1998- 1999 and 1999-2000 school years (U.S. Dept. of Education, 2001b). The OSEP data differs from the OCR data in that all the OCR data are projections based on sampling, whereas the OSEP data are from nearly every district and based on an actual child count, not primarily on projections. The OSEP data were reported in separate tables as numbers of students with disabilities in each educational setting disaggregated by race. The percentages for the OSEP data as reported here were calculated on the basis of the total for all three educational settings so that approximately 100 percent of students with disabilities for each racial group were represented. The data available nationally allowed for two types of analysis. First, it was possible to determine the educational placement percentages for all students with disabilities disaggregated by race using the data provided by OSEP. This permitted a comparison of the level of restrictiveness for black, Hispanic, and

Double Jeopardy     45 FIGURE 2 National Racial Disparities in Inclusion for Blacks, Hispanics, and Whites Among students with disabilities, blacks and Hispanics are each more likely than whites to be educated in a substantially separate educational setting and denied access to inclusive educational settings,
0.60
0.00
Inclusive (less than 21% of the school day out)
Resource Room (between 21 and 60% of the school day out)
Substantially Separate (greater than 60% of the school day out)
TIME OUT OF THE GENERAL EDUCATION CLASSROOM | % of Blacks 0 % of Hispanics         Q % of Whites
Source: U.S. Department of Education, Office of Special Education Programs, Data Analysis System (2002).
white students with disabilities. Second, OCR data from 1998 enabled us to show the level of restrictiveness by disability category but not race. The OCR data also enabled us to calculate the risk that black or Hispanic students would be labeled for a given disability category compared to the risk for white stu¬ dents. From the OCR data we determined the risk ratio for blacks being labeled mentally retarded and the likelihood that students with mental retardation would be included in the general classroom. However, neither of the national datasets allowed for a comprehensive disaggregated picture of race with disabil¬ ity with educational setting. For example, at the national level we could not de¬ termine the risk that blacks with mental retardation would be included in the

46      Racial Inequity in Special Education general classroom compared to the risk for whites with mental retardation. This level of analysis, however, was achieved with data collected by the state of Con¬ necticut described at the end of this chapter. RESTRICTIVENESS IN SPECIAL EDUCATION BY RACE Risk indexes for levels of inclusion were calculated for blacks, Hispanics, and whites in order to compare their respective rates of representation in the fully inclusive setting (less than 21% time outside the regular classroom), resource room (21% to 60% time outside the regular classroom), and substantially sepa¬ rate placements (greater than 60% time outside the regular classroom). The risk level is calculated by dividing the number of students with special needs for each racial group within each placement category by the total number of stu¬ dents deemed eligible for special needs. Risk indexes were calculated for all mi¬ nority groups, but are only presented for blacks, whites, and Hispanics because of their greater aggregate numbers. NATIONAL DATA ANALYSIS Blacks and Hispanics are much less likely to be educated in an inclusive setting and more likely to be substantially separate from their white counterparts (Fig¬ ure 2). In short, inclusion in the regular classroom may be a benefit that black and Hispanic students with disabilities enjoy less often than whites once they are part of the special education system. RESTRICTIVENESS COMPARED WITH MINORITY OVERREPRESENTATION BY DISABILITY TYPE To compare overrepresentation with restrictiveness, we used OCR data to ex¬ amine placement rates for minority students who are deemed eligible for special education based on the labels of mental retardation, emotional disturbance, and specific learning disability. Although OCR data allowed further analysis by des¬ ignating three subgroups of mental retardation (i.e., mild MR, moderate MR, and severe MR), we aggregated these in a single category named MR.5 In a large majority of the states in the OCR dataset, the number of students designated as having mild MR represented the largest MR subgroup, while those students with moderate and severe MR accounted for a much smaller proportion of the MR group (U.S. Department of Education, 2000). The national- and state-level results and comparisons include data for all major minority groups in racial categories used by the OCR.6 However, further

Double Jeopardy     47
TABLE A National Special Education Percentages by Race and Ethnicity American Asian Hispanic   Black White Percentage Indian/ Amecf fnon- (nor!- of AW Alaska Pacific Hispanic) Hispanic) Students Native Islander Percentage* of students deemed eligible for three specific disability categories" Mental Retardation (MR)      1.28 0.64         0.92         2.64         1.18 1.37 Emotional 1.03 0.26         O.SS 1.45         0.91 0.93 Disturbance (ED) Specific Learning 7.45 2.23 6.44         6.49         6.02         6.02 Disability (SLD) Source: Department of Education, Office for Civil Rights, Fall 199a Elementary and Secondary School Civil Rights Compliance Report: Protected Values for the Nation and Individual States (2002). * Percentages are based on weighted projections to national totals from 1998 OCR survey data. ** MR, ED, and SLD were the special education categories used in the 1998 OCR survey data—the special education groups in this study do not include all students receiving special education.
analyses were limited to blacks and Hispanics because of their relatively larger numbers in the general population and their dominance in large urban school systems. Blacks and Hispanics are the two largest minority groups in the United States, together accounting for 32 percent of the 1998 U.S. student population. The percentage of students in a given racial group with a specific type of disability is calculated on the basis of each group's representation in the overall student population. The percentages in Table A are based on the number of students from a particular ethnic group with a given disability divided by their total enrollment. As shown in Table A, these percentages help reveal the pat¬ terns of overrepresentation of minorities relative to whites, given their represen¬ tation in the overall student population. For example, 2.64 percent of all enrolled blacks were labeled MR in 1998, compared to only 1.18 percent of en¬ rolled whites. To determine the likelihood for blacks compared to whites for being identified as having MR is a simple matter of comparing the percentage of each race that is labeled MR. As this study indicates, this comparison consis¬ tently shows that blacks are at substantially greater risk than whites of being la¬ beled MR nationally. But, as important, the risks vary dramatically from one state to the next. The findings that blacks are substantially overrepresented rela¬ tive to their white counterparts in MR and ED are numerically very similar to those found by Parrish in this volume.

48      Racial Inequity in Special Education Table A lists the percentage of students deemed eligible for three specific disability types within five ethnic groups, including whites for comparison. For example, Hispanics made up 15.01 percent of the overall student population and were identified for specific learning disabilities at a rate of 6.44 percent. In contrast, white students accounted for 62.66 percent of the overall population and had a lower rate of SLD identification (6.02%),7 To simplify the comparison between overrepresentation and restrictive- ness, the rates of restrictiveness were narrowed from the three placement cate¬ gories in the OCR database to two. OCR survey respondents were asked to pro¬ vide data based on the three restrictiveness categories described earlier in Figure 1: less than 21 percent (one day or less per week), 21-60 percent (one to three days per week), or greater than 60 percent (three days or more). For this re¬ search, the second and third levels were combined into one category, greater than 21 percent, and expressed as a percentage of the school day. The collapsing of the two highest levels is similar to the procedure employed by the U.S. De¬ partment of Education's Office of Special Education and Rehabilitative Ser¬ vices in differentiating fully inclusive special education from pullout services (U.S. Department of Education, 2001b). The decision was also based in part on the uncertainty of the amount of time students spend outside the regular ed¬ ucation classroom in the middle category. The finding that placement patterns for a large majority of states heavily favored either the first (less than 21%) or die third (greater than 60%) restrictiveness categories also factored into the de¬ cision to focus on two rather than three levels of restrictiveness. The data on educational setting disaggregated by disability type show that restrictiveness levels (greater than 21% of the day outside the regular education classroom) for the 1997—1998 school year were highest for the MR and ED cat¬ egories, and that students with MR wete rarely educated in inclusive settings. As Figure 3 shows, 82 percent of their placements fit the more restrictive de¬ scriptions of resource room or substantially separate. Students with ED were slightly less likely to be educated in such non-inclusive settings (70%). Students with SLD were the least likely to be removed from general classrooms, with just 56 percent not in fully inclusive educational settings (see Figure 3). According to recent data collected by OSEP for the 2000-2001 academic year, these OCR calculations based on data from the 1997-1998 school year may underestimate the degree of restrictiveness. The more recent figures from OSEP are: 86 per¬ cent for students with MR, 74.22 percent for students with ED, and 55 percent for students with SLD (U.S. Department of Education, 2001a). Please note that the datasets from OCR and OSEP are based on different questionnaires and responses. They are nonetheless consistent in depicting very high rates of restrictiveness.

a <u < 01 1
Double Jeopardy      49 FIGURE 3 MR, ED, and SLD Levels of Restrictiveness Greater than 21 percent time spent outside the regular education classroom (1997-1998 school year)
Mental Retardation (MR)
Emotional Disturbance (ED)
Specific Learning Disorder (SLD)
Source: U.S. Department of Education (2000).
This level of restrictiveness is of serious concern in light of both the re¬ quirement that students be educated in the least restrictive environment to the maximum extent appropriate and the overrepresentation of blacks in these cate¬ gories. Another way to think about under- and overrepresentation is to compare a racial group's representation in the total enrollment to their enrollment in a given disability category. For example, Table B shows that black students con¬ stituted 17. H percent of the enrolled students but 33.04 percent of all students with mental retardation. Conversely, Hispanic students constituted 15.01 per¬ cent of the enrolled students but only 10.04 percent of the students with men¬ tal retardation. Table B suggests that on a national scale black students are overrepresented for MR and ED, while Hispanic students are underrepresented in these two categories. Particularly problematic is the high level of restrictiveness (82%) in MR, a disability category in which blacks are substantially overrepresented compared to their overall enrollment (Table B), or when blacks' risk for mental retarda¬ tion is compared to the risk rate of whites (Table A). This latter analysis allows for direct comparisons of risk rates described in Table A. The OCR data show that blacks are nearly two and one-quarter times as likely to be so labeled.

5 0      Racial Inequity in Special Education TABLE B Representation by Race within Selected Disability Categories
 Percent of Overall Enrollment Percent of Students with Mental Retardation (MR) Percent of Students with Emotional Disturbance (ED) Percent of Students with Specific Learning Disability (SLD) American Indian/ Alaska Native 1.11 1.04 1.23 1.38 Asian Amer./ Pacific Islander 4.08 1.90 1,16 1.51 Hispanic 15.01 10.04 8.87 16,04 Black (non- Hispanic) 17.14 33.04 26.92 18.48 White (non- Hispanic) 62.66 53.97 61.82 62.60
Source; Department of Education, Office for Civil Rights, Fall 1998 Elementary and Secondary School Civil Rights Compliance Report: Projected Values for the Nation and Individual States (2002). Percentages are based on weighted projections to national totals from 1998 OCR survey data. MR, ED, and SLD were the special education categories used in the 1998 OCR survey data—the special education groups in this study do not include all students receiving special education.
In the next layer of analysis we compared risk indexes of minority students with white students for specific special education categories in every state (Fleiss, 1973). This comparison, sometimes referred to as odds ratios (NRC, 2002), is expressed as follows: a ratio of greater than 1.0 shows that the risk of designation are larger than for the comparison group; a ratio of 1 indicates that there is no difference between the two groups; and a ratio of less than one indi¬ cates the comparison group (i.e., white students) has a greater likelihood of des¬ ignation.
ANALYSES OF STATE LEVEL OF RESTRICTIVENESS AND OVERREPRESENTATION The national trends in rates of restrictiveness and rates of disproportionality of¬ ten mask more problematic state-level statistics. An examination of state-level MR, ED, and SLD restrictiveness and disproportionality rates as reported in OCR survey data help to identify those states that are serving more of their stu¬ dents in regular education classrooms and identifying special education stu¬ dents at a rate that is roughly proportional to their representation in the general population, compared to those that are not. This examination also helps iden-

Double Jeopardy     51 tify potential relationships between demographic variables and MR, ED, and SLD placement patterns for students in general and disproportionality rates for minority subgroups. To illuminate this relationship, Table 1 also includes an indicator or disproportionality index of those states with disproportionality rates greater than 1.5 and below 3.0 denoted by a /, and greater than 3.0 de¬ noted by /+. Although the range of restrictiveness for MR students is very wide for the fifty states, from a low of 46 percent in Utah to a high of 92 percent in Georgia., thirty-one states show restrictiveness levels for over 80 percent of their MR stu¬ dents; only two states restrict less than 60 percent of their MR students. The practical significance of these levels of restrictiveness is that vast numbers of special education students have their educational experiences outside the gen¬ eral education settings. Issues of racial discrimination aside, the rates of restrictiveness for students with MR in states across the country are troubling. Specifically, these indices suggest noncompliance with the IDEA requirement for placing students in the Least Restrictive Environment (LRE). The concern is even more troubling when you consider the concurrent high levels of restrictiveness and dispropor¬ tionality that minority MR students face. For example, in thirty-eight states that provided information to OCR, black students are more than one and one- half times as likely as their white counterparts to be deemed eligible for MR (and more than three times as likely in seven states; see Table 1). In nineteen states both black and Native American students are disproportionately repre¬ sented for MR. Hispanic students with MR experience disproportionate rates in six states. The Asian American and Pacific Islander MR students experience the smallest occurrence of disproportionality, in four states. The inequitable rates of disproportionality between minority groups once again find blacks with the greatest level of disproportionality relative to whites. The segregative effect of the disproportionality means less time in the regular class, less time with peers, and likely reduced access to the general curriculum. Foe students designated as having an emotional disorder, the national level of restrictiveness is over 70 percent (see Table 2). Although the national ED re¬ strictiveness rate is not as high as for MR, some troubling and familiar patterns persist. The restrictiveness rates range from a low of 36.45 in New Hampshire to a high of 89.06 in Delaware. Forty-three states place more than half of their ED students out of the regular education setting more than 21 percent of the school day. Not surprisingly, the rates of disproportionality for minority ED students were not as prevalent as for the MR students. Other research studies found in-

52      Racial Inequity in Special Education
TABLE 1 National and State Levels of Restrictiveness for Students Deemed Eligible for Mental Retardation (MR) Designation with Minority Disproportionality Indices
Sfate United States Georgia Maine Missouri Maryland Nevada Wisconsin Delaware New Jersey West Virginia Wyoming Arizona Pennsylvania Michigan New York New Mexico Illinois Hawaii Alaska Virginia Arkansas Indiana California Oklahoma Washington Mississippi Louisiana Connecticut North Carolina Alabama Minnesota Percentage of Students Restricted from Regular Classroom More Than 20% of Time 81.95 92.15 91.41 90.93 90.57 90.49 89.91 89.57 89.53 33.89 88.80 88.41 87.92 87.79 87.69 87.49 87.U 86.14 85.32 84,62 84.40 84.10 83.74 83.71 83.71 82.82 81.93 81.89 81.55 81.51 80.20 Black / /  / / / • • /  /   /   / / • S  S / / / /   + • /   + /   + •   +  Hispanic    /     /         /          / +    Asian Amer./ Pacific Islander         /  /     /  /              American Indian/ Alaska Native   S / • •     /        /    / /    /  / 

Double Jeopardy      53
State
Montana Ohio Tennessee Kentucky South Carolina
Percentage of Students Restricted from Regular Classroom More Than 20% of Time 80.16 79.72 79.52 78.54 77.62
Black         Hispanic
/
/   +
Asian American Amer./ Indian/ Pacific Alaska Islander Native /
Colorado Kansas North Dakota Nebraska South Dakota
76.85 75.40 73.84 72.39 71.73
/
/
Florida Oregon Rhode Island Iowa Massachusetts
69.81 67.86 67.23 64.81 64.26
/  + / / / /  +
/   +
Idaho New Hampshire Vermont Utah Texas*
64.01 63.13 52.86 45.84 0.00
/ / /
/   +
•
Source: Department of Education, Office for Civil Rights, Fall 1998 Elementary and Secondary School Civil Rights Compliance Report: Projected Values for the Nation and Individual States (2002). A "/+" designates a risk ratio greater than 3, where the subgroup was three or more times as likely as whites to be labeled MR; A V" designates a risk ratio greater than 1.5 but less than 3. Blank spaces are those states where the risk ratio was less than 1. S. *Texas did not provide Information on restrictiveness.
consistent placement patterns by race and by special need designation. Some studies suggest that the increased time spent in the regular education classroom is largely attributable to a special needs student's race (Conroy, 1999; Harry, 1992). The national average level of restrictiveness for students identified -with a specific learning disability was 56.03, which means that more than half the SLD students spend at least 21 percent of their time in school outside the regu-

54      Racial Inequity in Special Education
TABLE 2 National and State Levels of Restrictiveness for Students Deemed Eligible for Emotional Disturbance (ED) Designation with Minority Disproportionaltty Indices
State
United States
Percentage of Students Restricted from Regular Classroom More Than 20% of Time 70.30
Black        Hispanic
Asian Amer./ Pacific Islander   American Indian/ Alaska Native / /
Delaware Michigan New Mexico Kentucky Illinois
89.06 82.25 82.25 79.63 79.63
/ / /   +
Massachusetts Iowa New York Louisiana California
78.93 78.93 78.70 78.23 78.11
/   +
/
Pennsylvania Arkansas New Jersey Missouri Alaska
77.91 77.23 74.89 74.89 74.70
/ / /
Georgia Virginia Mississippi North Carolina Oklahoma
74.50 73.64 72.21 71,48 71.03
Tennessee Wyoming Indiana Ohio Montana
70.05 69.71 69.43 69.35 68.06
/ / / /   +
Wisconsin Maine Nevada West Virginia Connecticut
67.99 67.89 67.40 65.64 64.85
•
/

Double Jeopardy      55
State
Maryland South Carolina Kansas Arizona Washington
Percentage of Students Restricted from Regular Classroom More Than 20% of Time 64.5B 62.80 62.11 62.07 61.11
Black        Hispanic
Asian American Amer./ Indian/ Pacific Alaska islander Native
South Dakota Utah Nebraska Hawaii Rhode Island
60.26 59.83 59.76 58.78 56.03
/  + •  +
/
Florida Alaska Colorado Oregon Minnesota
56.02 55.77 50.34 48.07 47.51
S S
/   +
Idaho North Dakota New Hampshire Vermont Texas*
47.39 40.17 36.65 0.00 0.00
/ /
•
Source: Department oi Education, Office for Civil Rights, Fall 1998 Elementary and Secondary School Civil Rights Compliance Report: Projected Values for the Nation and Individual States (2002). A V+" designates a risk ratio greater than 3, where the subgroup was three or more times as likely as whites to be labeled ED; A"/" designates a risk ratio greater than 1.5 but less than 3. Blank spaces are those states where the risk ratio was less than 1.5. 'Texas did not provide information on restrlctiveness.
lar classroom setting (see Table 3). Once again, wide discrepancies in state-level restrictiveness rates were found, with Delaware (75-35%) on the high end and North Dakota (13.75%) on the low end. In twenty-four states, more than half of the SLD students spend at least 21 percent of their schooling outside the reg¬ ular education classroom setting. Interestingly, in only four states are more than three-quarters of students with SLD included in the regular education setting for at least 79 percent of the school day. The higher inclusion rate for SLD stu-

5 6      Racial Inequity in Special Education
TABLE 3 National and State Levels of Restrictiveness for Students Deemed Eligible for Specific Learning Disability (SLD) Designation with Minority Disproportionality Indices
State
United States
Percentage of Students Restricted from Regular Classroom More Than 20% of Time 56.03
Black        Hispanic
y
Asian American Amer./ Indian/ Pacific Alaska Islander Native
Delaware Pennsylvania Illinois New Mexico Mississippi
75.35 68.60 67.98 65.25 65.04
/
California Kentucky Georgia Michigan Louisiana
62.00 61.46 61.30 61.01 60.42
Wisconsin Arkansas New Jersey Ohio Maryland
60.18 59.36 59.03 58.01 56.98
Nevada West Virginia Hawaii Arizona Florida
56.96 56.70 54.56 54.31 54.11
Utah Alabama Indiana South Carolina Tennessee
53.32 52.19 51.89 51.37 49.40
/
/
Missouri Wyoming Kansas Washington New York
49.02 47.85 46.27 46.16 46.11
• /

Double Jeopardy      57
State
Maine Alaska Oklahoma Iowa Virginia
Percentage of Students Restricted from Regular Classroom More Than 20% of Time 45.17 43.82 42.56 41.31 40.98
Slack        Hispanic     Asian American Amer./ Indian/ Pacific Alaska Islander Native
•  +
Montana Connecticut Nebraska Rhode Island Massachusetts Colorado North Carolina Minnesota South Dakota Idaho New Hampshire Oregon Vermont North Dakota Texas* 39.67 37.98 37.29 33.59 33.37 33.31 32.99 29.81 29.27 26.11 21.85 21.81 16.37 13.75 0.00 /               /  / /    •  •    / 
Source: Department of Education, Office for CivlI Rights, Fall 1998 Elementary and Secondary School Civil Rights Compliance Report: Projected Values for the Nation and Individual States (2002). A "/+" designates a risk ratio greater than 3, where the subgroup was three or more times as likely as whites to be labeled SLD; A V" designates a risk ratio greater than 1.5 but less than 3. Blank spaces are those states where the risk ratio was less than 1.5. •Texas did not provide information on restrictiveness.
dents is concurrent with much of the research that shows that SLD students are less likely than MR and ED students to be restricted from the regular education classroom setting (Grossman, 1995; Lipsky & Gartner, 1997; U.S. Depart¬ ment of Education, 1999). Many states separate students with MR, ED, and SLD from their non- disabled peers at alarmingly high rates (Tables 1-3). Buc the high rate of vari¬ ability among states suggests that there might also be wide ranges of restrictive-

58      Racial Inequity in Special Education ness and disproportionality within each state. If this Is true, then even states showing relatively low degrees of restrictiveness and disproportionality in ag¬ gregated data may nonetheless have individual school districts with rates that are disturbingly high. The next section explores this possibility in detail. DISTRICT-LEVEL ANALYSES OF ENROLLMENT, RESTRICTIVENESS RATES, AND DISPROPORTIONALITY The examination of district-level data in ten urban school districts with high black or Hispanic populations found levels s/restrictiveness that were greatly masked in the national- and state-level analyses. Findings regarding rates of re¬ strictiveness for students overall are presented for large urban school districts in Atlanta, Baltimore, Detroit, Los Angeles, New Haven, Omaha, Philadelphia, San Diego, and Birmingham, Alabama. The district-level results also include the percentage of black and Hispanic enrollment relative to the overall student enrollment, and the percentages of blacks and Hispanics among MR, SIX), and ED designees. Of this list, only two of these cities were below theTJ.S. average. The most important pattern revealed by the district data is that minority students attending these high-minority school districts, if found eligible for spe¬ cial education, were also at very high risk of being placed in a restrictive educa¬ tional setting. In many high-minority districts the level of restrictiveness was substantially higher than average restrictiveness for the district's state and for the nation. Furthermore, the general findings of disproportionality in the dis¬ trict-level analysis are consistent with those reported by Parrish in this volume.* In these high-minority urban districts, the percentage of students with MR who spent more than 21 percent of their school days outside the regular educa¬ tion classroom ranged from 71.33 percent in Birmingham to 99.21 in Atlanta public schools (Table 4), Eight of the ten U.S. school districts identified in this study have rates of restrictiveness that are higher than the district's home state. For example, in Detroit, only 1.71 percent of students with MR were removed for less than 21 percent of their school day or more in the regular education set¬ ting. In contrast, for Michigan as a whole, a far larger percentage of students, 12.81, experienced inclusion. Table 4 reveals that in all ten urban school dis¬ tricts black students designated as MR make up a greater proportion of the cog¬ nitive disability group than would be expected. For example, in Atlanta, where 99.21 percent of students with MR are excluded from the regular education classroom more than 21 percent of the time, blacks represent 90.13 percent of the district's enrollment, yet they make up nearly all of Atlanta's MR popula¬ tion (96.98%).

Double Jeopardy      59 TABLE 4 Mental Retardation—Overall Level of Restrictiveness*, Percentage** of Overall Enrollment, and Percentage MR Designation in Ten U.S. School Districts Compared to Black and Hispanic Enrollment
District United States Atlanta Philadelphia Detroit Omaha Baltimore New Haven New Orleans Los Angeles San Diego Birmingham Percentage Students Spending More Than 21% Time Outside Regular Classroom 81.95 99.21 98.69 98.29 97.86 96.63 93.07 85.68 84.03 78.33 71.33 Percentage Block of Overall Enrollment** 17.14 90.13 64.55 91.35 31.17 29.23 57.67 90.31 14.03 16.58 95.40 Percentage of Blacks Among MR Designees — 96.98 71.27 93.03 50.66 38.30 68.65 95.00 17.81 20.38 97.89 Percentage Hispanic of Overall Enrollment** 15.01 2.22 12.17 3.26 9.90 1.32 27.27 1.44 68.75 36.22 0.29 Percentage of Hispanics Among MR Designees — 1.18 13.09 1.47 5.19 0.53 22.82 0.75 66.82 38,33 0.07
Source: Department of Education, Off ice for Civil Rights, Fall 1998 Elementary and Secondary School Civil Rights Compliance Report: Projected Values for the Nation and Individual States (2002). * Restrictiveness of student placement is determined by the number of hours or the extent to which students with disabilities are restricted from placement in a regular education classroom. ** Percentages are based on weighted projections to national and state totals from 199B OCR survey data.
The percentages of Hispanic students eligible for MR relative to their over¬ all enrollment numbers are opposite from the results for black students eligible for MR (Table 4). Eight often districts have fewer Hispanic students with MR than would be expected, given their home district's overall Hispanic enrollment. The rates of restrictiveness for students designated as having emotional dis¬ turbance ranged from 69.10 percent in Birmingham to 95.94 percent in Phila¬ delphia (see Table 5). In the ED category, all the selected urban school districts rates of restrictiveness were greater than the rate for their respective states. For nine often districts, more than 80 percent of students with the ED designation were restricted from the regular classroom. In effect, students with ED in all of

60      Racial Inequity in Special Education TABLE 5 Emotional Disorder—Overall Level of Restrictiveness*, Percentage** of Overall Enrollment, and Percentage ED Designation in Ten U.S. School Districts Compared to Black and Hispanic Enrollment
District United States Philadelphia Detroit Omaha Atlanta San Diego New Orleans Los Angeles Baltimore New Haven Birnrtingham Percentage Students Spending More Than 21% Time Outside Regular Classroom 70.30 95.94 94.97 94.83 91.00 87.29 as. 76 84.03 83.28 80.98 69.10 Percentage Black of Overall Enrollment** 17.14 64.55 91.35 31.17 90.13 16.58 90.31 14.03 29.23 57.67 95,40 Percentage of Blacks Among ED Designees — 67.32 89.93 50.88 87.72 34.80 97.14 42.09 3S.10 66.18 89.45 Percentage Hispanic of Overall Enrollment** 15.01 12.17 3.26 9.90 2.22 36.22 1.44 68.75 1.32 27.27 0.29 Percentage of Hispanics Among ED Designees  13.96 2.97 2.45 0.69 16.52 0.00 14.36 1.23 20.77 0.00
Source: Department of Education, Office for Civil Rights, Fall 1998 Elementary and Secondary School Civil Rights Compliance Report; Projected Values for the Nation and Individual States (2002). * Restrictiveness of student placement is determined by the number of hours or the extent to which students with disabilities are restricted from placement in a regular education classroom ** Percentages are based on weighted projections to national and state totals from 1998 OCR survey data.
these districts spent much of their time in resource rooms or separate classes, not in the regular classroom. Moreover, the percentage of black students with the ED designation was disproportionate in seven of the ten school districts; Hispanic students labeled ED were underrepresented in nine of the ten dis¬ tricts (see Table 5)- The underrepresentation was most notable in those cities with the greatest numbers of Hispanic students (i.e., Los Angeles, San Diego, and Chicago), This finding raises the concern of a lack of needed services for Hispanic students, due to their underidentification for eligibility from the spe¬ cial needs designation.

Double Jeopardy     61 TABLE 6 Specific Learning Disability—Overall Level of Restrictiveness*, Percentage** of Overall Enrollment, and Percentage SLD Designation in Ten US School Districts Compared to Black and Hispanic Enrollment
District
Percentage     Percentage     Percentage     Percentage     Percentage Students         Black of of Blacks        Hispanic of     of Hispanics Spending        Overall Among SLD    Overall Among SLD More Than     Enrollment**   Designees       Enrollment**  Designees 21% Time Outside Regular
 United States Philadelphia New Orleans Los Angeles 5an Diego Detroit Atlanta Omaha Birmingham New Haven Baltimore Classroom 56.03 93.21 90.58 88.35 87.29 83.46 79.38 70.33 59.35 58.40 55.10  17.14 64.55 90.31 14.03 16.58 91.35 90.13 31.17 95.40 57.67 29.23  — 58.63 94.45 40.15 25.78 91.41 89.12 37.73 93.58 60.55 33.29  15.01 12.17 1,44 68.75 36.22 3.26 2.22 9.90 0.29 27.27 1.32  — 16.25 0.94 37.63 34.89 2.73 2.68 6.98 0.18 27.83 1.12
Source: Department of Education, Office for Civil Rights, Fall 1998 Elementary and Secondary School Civil Rights Compliance Report: Projected Values for the Nation and Individual States (2002). * Restrictiveness of student placement is determined by the number of hours or the extent to which students with disabilities are restricted from placement in a regular education classroom. ** Percentages are based on weighted projections to national and state totals from 1998 OCR survey data.
The level of restrictiveness for students with the specific learning disability in the selected districts is unusually high—nine of them were considerably higher than the state average (see Table 6). The rates of restrictiveness ranged from 55.10 percent in Baltimore to 93.21 percent in Philadelphia. Although the percentage of black students with SLD was much closer to the expected number in the general population in all but two districts (San Diego and Los Angeles), the findings are disturbing for all students, given the high rates of ex¬ clusion from the regular classroom and the large numbers of students with the SLD label.

62      Racial Inequity in Special Education The percentage of Hispanic students with SLD was fairly close to the ex¬ pected average in nine of the ten districts (see Table 6). Los Angeles, the district with the largest Hispanic population, reveals a large underrepresentation of Hispanic SLD students relative to their percentage of enrollment in the school district.
ANALYSES OF INCLUSION BY RACE AND GENDER IN CONNECTICUT As mentioned in the introduction, no readily available dataset shows educa¬ tional placement disaggregated by race with disability type. However, IDEA re¬ quires states to report this information. Working for plaintiffs in a lawsuit, Jim Conroy obtained access to Connecticut's data through legal discovery proceed¬ ings. Connecticut was notably high in its overrepresentation of both blacks and Hispanics for special education. Specifically, blacks in Connecticut are four times as likely as whites to be identified as mentally retarded (Parrish, this vol¬ ume). But the Connecticut database, Connecticut Integrated Special Student Information System (ISSIS),3 did allow the complete analysis of placement disaggregated by race with disability category. The Connecticut state database contains one basic record for each student involved in special education. The data were collected on forms that contain in¬ formation about students' age, grade, gender, primary and secondary special ed¬ ucation identification, placement type, and a variety of other demographic and programmatic information (Conroy, 1999). According to the 1998-1999 Connecticut data, 95,442 children were assigned to special education pro¬ grams. Fot the purposes of this study, students labeled as uncategorized infant, gifted, art talented, or regular education were excluded, resulting in a sample of 69,549 children between the ages of six and twenty-one with a disability level. For the state- and district-level analyses of Connecticut, 1998-1999 school year student-level data were examined for district variation in the mental retar¬ dation designation, ethnic variations in MR labeling, and restrictiveness rates for MR students by race. Mental Retardation The/y.j etal. v. State of Connecticut, Board of Education, etal. lawsuit settle¬ ment on May 18, 2001, ended eight years of litigation over least restrictive en¬ vironment.10 The improvement in the rates of inclusion for Connecticut mi¬ norities found in Figure 4 may reflect the impact of the litigation (filed in 1993) and/or the change in the federal law in 1997—or some combination of the two. Moreover, the results suggest that improvement is possible.