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object(Timber\Post)#3711 (44) { ["ImageClass"]=> string(12) "Timber\Image" ["PostClass"]=> string(11) "Timber\Post" ["TermClass"]=> string(11) "Timber\Term" ["object_type"]=> string(4) "post" ["custom"]=> array(5) { ["_wp_attached_file"]=> string(12) "R_209SLR.pdf" ["wpmf_size"]=> string(6) "528193" ["wpmf_filetype"]=> string(3) "pdf" ["wpmf_order"]=> string(1) "0" ["searchwp_content"]=> string(88620) "Resolving Special Education Disputes in California Stephen Lipscomb with research support from Karina Jaquet Supported with funding from The Special Hope Foundation and The William and Flora Hewlett Foundation February 2009 The Public Policy Institute of California is dedicated to informing and improving public policy in California through independent, objective, nonpartisan research on major economic, social, and political issues. The institute?s goal is to raise public awareness and to give elected representatives and other decisionmakers a more informed basis for developing policies and programs. The institute?s research focuses on the underlying forces shaping California's future, cutting across a wide range of public policy concerns, including economic development, education, environment and resources, governance, population, public finance, and social and health policy. PPIC is a private, nonprofit organization. It does not take or support positions on any ballot measures or on any local, state, or federal legislation, nor does it endorse, support, or oppose any political parties or candidates for public office. PPIC was established in 1994 with an endowment from William R. Hewlett. Mark Baldassare is President and Chief Executive Officer of PPIC. Thomas C. Sutton is Chair of the Board of Directors. Copyright ? 2009 by Public Policy Institute of California All rights reserved San Francisco, CA Short sections of text, not to exceed three paragraphs, may be quoted without written permission provided that full attribution is given to the source and the above copyright notice is included. Research publications reflect the views of the authors and do not necessarily reflect the views of the staff, officers, or Board of Directors of the Public Policy Institute of California. ii Contents  Summary iv  Acknowledgments vi  Introduction 1  1.  HOW OFTEN IS DISPUTE RESOLUTION REQUESTED? 2  The Process for Resolving Special Education Disputes in California 2  Mediation and Due Process Hearings in California 3  Dispute Resolution Requests Across States 7  Due Process Request Trends since 1991?92 9  2.  HOW AND WHY DO DISPUTE RESOLUTION REQUESTS VARY ACROSS CALIFORNIA? 12  Rates of Dispute Resolution Requests across California School Districts 12  Factors that Predict Higher Rates of Dispute Resolution by Filing Party 12  Patterns of Parent-Initiated Dispute Resolution Requests 13  Patterns of District-Initiated Dispute Resolution Requests 15  Differences in Filing Rates by School District Type 16  Overall Rates of Dispute Resolution in California School Districts 17  Conclusion 18  Appendix A: Supplementary Material and Results 19  Parental and District Rights Under IDEA 19  Data Sources 20  Estimation Methods 21  Appendix Tables 23  Glossary: Disability Categories and Definitions 29  List of Acronyms 31  References 32 iii iv Summary Children with disabilities in the United States are entitled to appropriate special education services to meet their individual needs. The responsibility for meeting these needs falls on local school districts. In 2006?07, California school districts spent $9.3 billion to provide special education to children with disabilities, who represent about 10 percent of the state?s total enrollment (Lipscomb, 2009). Although the financial obligation is on school districts, identifying disabilities and designing individualized educational programs for disabled students necessitates close cooperation between parents and schools. Most of the time, parents and schools agree on an educational program that best serves the interests of the child. However, collaborative processes inevitably result in disagreements in some cases. California?s procedures for resolving special education disputes provide a means for parents and school districts to address their differences. Federal law gives both parties the right to due process and mediation in special education matters. In 2007?08, requests for dispute resolution in California totaled 3.9 per 1,000 special education students, one of the highest rates in the country. Resolving disputes is critical to effectively serving disabled children. Yet pursuing dispute resolution can be costly and time consuming for both parties, raising the possibility that due process is more accessible to some Californians than to others. This report examines special education dispute resolution in California and its use by parents and school districts. The first chapter describes how mediation and due process hearings work. It also provides basic statistics about hearing requests in the state, recent trends, and alternative approaches that local districts are developing. Finally, the chapter explores factors that may relate to growth in hearing requests at the national level. The second chapter examines patterns of dispute resolution requests in California school districts, focusing on predictors of higher request rates by parents and school districts. A common misconception about special education disputes is that most involve administrative law judges who issue rulings after very legalistic hearings. The reality is that parties settle the majority of disputes and only 4 percent are ultimately adjudicated. Since 2005? 06, the number of hearing requests per special education student has declined both in California and nationally, possibly in response to several contemporaneous changes to special education law. This recent trend marks a reversal of a pattern of steady growth in hearing requests since at least 1991?92. The findings here suggest that part of the nationwide growth during this period relates to the adoption of a special education funding model by California and a number of other states. Requests for dispute resolution in California are concentrated in less than a third of school districts. Holding constant other observable characteristics, requests initiated by both parents and school districts are more common in larger, densely populated districts. District size and enrollment density are also significant predictors of higher autism rates, the disability that is most frequently the source of dispute. The findings also suggest that cost and budgetary factors bear on the decision by parents and school districts to pursue dispute resolution. In this regard, patterns of filing by each side differ because parents and school districts face different sets of relevant economic constraints. Parents initiate the vast majority of requests. Parent-initiated request rates are higher in school districts with lower enrollment rates in free or reduced price lunch, a variable that relates to the community poverty level. This suggests that lower-income parents might not have the resources necessary to initiate requests. Community income and poverty are weaker predictors of higher request rates initiated by districts. Instead, district-initiated request rates are higher in districts that receive less special education revenue per capita and in districts that operate in regions where educators can expect to earn higher wages. While the patterns of dispute resolution may arise through somewhat different processes for each party, the explanation for each pattern points toward the influence of relevant budgetary factors. California?s pattern of settling most cases before hearings suggests that the dispute resolution process operates efficiently. However, its high rate of dispute requests is evidence that more could be done to resolve disputes locally. School districts in many states, including California, are developing alternative dispute resolution (ADR) strategies that emphasize non- adversarial environments. ADR aims to increase efficiency, trust, and cost-effectiveness on both sides. For example, the technique of including a third party during the development of educational plans for disabled students emphasizes dispute prevention. Although the research on alternative techniques is currently limited, the findings from this and other existing work support the idea of expanding their availability across the state and analyzing their effectiveness. At the same time, increasing public awareness will be essential to addressing equity concerns and fostering effective utilization. v Acknowledgments I would like to thank several people who spoke with me about the dispute resolution process in California, including Paul Goldfinger, Sarge Kennedy, Bill Koski, Sherianne Laba, Jack Lucas, Steve Moyer, and John Namkung. In particular, I deeply appreciate the insightful comments that Kim Connor, Jaqui Guzman, and Tom Parrish provided on an earlier draft. This work would not be possible without their careful assistance. I would also like to thank members of PPIC?s Education Advisory Committee for their suggestions during presentations of the work in progress. At PPIC, Richard Greene, Hans Johnson, Eric Larsen, and Lynette Ubois provided thoughtful comments and suggestions that improved the study and Karina Jaquet provided outstanding research support. Research publications reflect the views of the author and do not necessarily reflect the views of the staff, officers, or Board of Directors of the Public Policy Institute of California. All errors are my own. vi 1 Introduction The Individuals with Disabilities Education Act (IDEA) of 2004 includes an extensive list of ?procedural safeguards? to help ensure that children with disabilities in the United States obtain a free, appropriate public education. Designing educational programs for disabled students is fundamentally a collaborative process between parents and schools (Lipscomb, 2009). In such an environment, it is only natural that disagreements occasionally arise. IDEA?s most important safeguards give families and school districts the right to resolve disputes through two avenues: mediation and due process. In 2007?08, requests for dispute resolution in California totaled 3.9 per 1,000 special education students. Almost all cases filed in California request both mediation and due process. The most recent national numbers show that parents and school districts across the United States filed 2.1 due-process requests and 1.2 mediation requests per 1,000 special education students in 2006?07. California?s request rates are among the highest in the country. Dispute resolution can involve substantial costs for both families and school districts. The best cost estimates come from Chambers, Harr, and Dhanani (2003), who conclude that U.S. school districts spent $8,160 to $12,200 per case on mediation and due process hearings in 1999? 2000. These estimates suggest high financial burdens on families as well. For most families, the burden is probably more substantial in relation to income than it is for school districts. Districts can also budget in advance for projected dispute resolution costs, and spread them over a larger number of students. For instance, Chambers, Harr, and Dhanani show that dispute resolution costs to districts in 1999?2000 converted to about $24 per special education student. In contrast, just affording the dispute resolution process, let alone navigating it, may be a barrier to some families. This report examines dispute resolution processes in California, to inform future special education policy and research. It describes how mediation and due process hearings work and presents current statistics about their implementation. It also discusses dispute resolution trends and the development of alternative strategies, and presents evidence suggesting an association between California?s type of special education funding system and higher request rates. Finally, this report examines request rates at the district level, with a particular focus on understanding which factors make parents and school districts each more likely to initiate the process. The findings suggest approaches California might consider to improve the accessibility of dispute resolution. 2 1. How Often Is Dispute Resolution Requested? Chapter highlights: x Most special education disputes settle before a due process hearing. Only 4 percent of disputes ended with a judicial decision in 2007?08. x In 2007?08, California parents and school districts filed 3.9 requests for dispute resolution per 1,000 special education students, among the highest rates in the U.S. x California?s system of special education finance is associated with growth in hearing requests per capita nationwide between 1991 and 2000. x The frequency of hearing requests grew steadily in California until 2005?06 and then declined, possibly due to several contemporaneous policy developments. The Process for Resolving Special Education Disputes in California Parents, legal guardians, and school districts can seek dispute resolution when disagreements about a child?s special needs and services arise. 1 In California, the Office of Administrative Hearings (OAH) facilitates the state?s dispute resolution process. This process is very legalistic when it involves formal hearings, but most disputes are resolved before that stage. Parents and school districts first make concerted efforts to reach an agreement themselves through cooperative, non-adversarial approaches. Parties that request dispute resolution through OAH choose between two main options: due process with mediation, and mediation only. 2 Due process with mediation is the predominant choice across the state. When OAH receives such a request, it schedules a due process hearing date for about 55 days later. The interval gives parents and school districts an opportunity first to resolve the dispute through a mandatory resolution session and then through mediation. Unless the parties agree otherwise, students remain in their current educational placements until the dispute is resolved. 3 School district representatives must convene a resolution session with parents to allow each side to discuss factual matters and to attempt a settlement. School districts may bring a lawyer only if the parents also bring one. If the dispute is resolved, the parties alert OAH and sign a legally binding settlement agreement. If an impasse remains, OAH schedules a mediation conference. 4 Mediation is a voluntary, non-adversarial process in which parties use a trained OAH mediator to help them find common ground. In practice in California, parties almost never waive mediation. OAH cannot appoint attorneys to represent parties, but federal law allows 1 This report refers to dispute resolution requests as either district-filed or parent-filed. 2 See www.oah.dgs.ca.gov for additional information. 3 IDEA requires that school districts provide the parents of disabled students a copy of their rights under special education law. 4 Parents and school districts can agree to waive the resolution session. 3 attorneys and advocates to attend mediations requested in conjunction with due process. 5 The parties keep all discussions and possible agreements confidential. If mediation does not resolve the dispute, OAH convenes a due process hearing before one of its Administrative Law Judges (ALJs). 6 Due process hearings resemble a civil trial. The ALJ presides over the hearing, rules on motions, considers evidence, and issues a binding legal decision. Parties involved in due process hearings have the right to legal counsel at their own expense and may present evidence. They may compel witnesses to testify and cross-examine witnesses called by the other side. OAH mediators are available for parents representing themselves, to answer questions about the process and to identify issues, so long as they do not give legal advice. Prevailing parties can attempt to recoup their legal fees through separate civil action. As well, the losing side can appeal in state or federal court. The hearings themselves typically last several days. Although they resolve the impasse, the outcome may not be mutually satisfactory. Chambers, Harr, and Dhanani (2003) find that almost all the school districts in their nationally representative sample view mediation as more cost-effective than due process. Cost- effectiveness is one reason why California offers ?mediation only? as an alternative to the due process framework. Mediation-only conferences operate like other mediation conferences with one key difference, that lawyers or advocates are not permitted. This approach can be an attractive way to use the services of an independent mediator, keep costs low, and maintain a non-adversarial setting. Of course, the downside of mediation only is that it is not always successful. However, federal law prohibits mediation from infringing on an individual?s right to due process in a follow-up request. Mediation and Due Process Hearings in California Figure 1.1 describes the 2,626 requests for dispute resolution that OAH received during 2007?08. According to federal disability count data, this is about 3.9 requests per 1,000 special education students ages 3?21. Over 90 percent of all cases filed request due process hearings. 7 Mediation-only requests, in contrast, account for only 5 percent of cases. The remainder are expedited hearings, such as to determine whether a student?s misbehavior was caused by a disability, or dual hearings that involve both expedited and non-expedited issues. Parents initiated 86 percent of the cases. 8 5 California requires that OAH maintain a list of special education advocates and attorneys offering free or reduced cost services. 6 Interpreters for mediation conferences and due process hearings are available if requested. 7 As described above, due process requests include a mediation conference prior to the hearing unless one of the parties opts out. The due process request rate is about 3.7 per 1,000 special education students. 8 ?Parent-initiated? requests include those filed by parents, legal guardians, and the student. Figure 1.1 Requests for Dispute Resolution by Filing Party, 2007?08 79% 5% 2% 11% 0% 3% ParentrDueProcessHearing[2,072] ParentrMediationOnly[139] ParentrExpedited&Dual[41] DistrictrDueProcessHearing[303] DistrictrMediationOnly[4] DistrictrExpedited&Dual[67] SOURCE: Office of Administrative Hearings (2008). Collectively, dispute resolution requests involve a variety of student disabilities and issues. They tend to involve disabilities with higher growth rates and wider prevalence. For example, Table 1.1 shows that 31 percent of all disputes concern students with autistic-like behaviors. Autism is the fastest growing disability in California, but it includes only 7 percent of disabled students (Lipscomb, 2009). That is, autistic-like behaviors are much more likely to lead to dispute resolution than other types of disabilities. 9 By comparison, the second and third most frequently cited categories are learning disabilities and speech or language impairments. 10 These categories account for more than 70 percent of student disabilities in California but only about 27 percent of all disputes. The Glossary provides disability definitions for each category. 4 9 Some of these cases may involve students believed to have, but not diagnosed with, autistic-like behaviors. 10 Learning disabilities are disorders in ??one or more of the basic psychological processes involved in understanding or in using language, spoken or written, that may manifest itself in an imperfect ability to listen, think, speak, read, write, spell, or to do mathematical calculations, including conditions such as perceptual disabilities, brain injury, minimal brain dysfunction, dyslexia, and developmental aphasia. Specific learning disability does not include learning problems that are primarily the result of visual, hearing, or motor disabilities, of mental retardation, of emotional disturbance, or of environmental, cultural, or economic disadvantage.? Source: 34 CFR Section 300.8 of the Federal Register. 5 Table 1.1 Disability Categories and Issues, 2007?08 Disability Categories Total Cases % of Total Issues Total Issues % of Total Autistic Like Behaviors 82731.5Placement 1612 61.4 Learning Disability 37814.4Assessment 1204 45.9 Not Identified 37314.2Compensatory Education 847 32.3 Speech or Language Impairment 34513.1Speech and Language 770 29.3 Other Health Impaired 29611.3Reimbursement 752 28.6 Emotional Disturbance 2479.4Designated Instruction 727 27.7 Mental Retardation 1294.9ABA/One-to-One Aid 11 601 22.9 Multiple Disabilities 853.2Goals and Objectives 568 21.6 Hearing Impaired 542.1Occupational Therapy 503 19.2 Orthopedic Impairment 511.9Eligibility 381 14.5 Visual Impairment 491.9Extended School Year 329 12.5 Deafness 271.0Mental Health Services 208 7.9 Traumatic Brain Injury 140.5Procedural 199 7.6 Deaf/Blind 00.0Private Services 185 7.0 Discipline 68 2.6 SOURCE: Office of Administrative Hearings (2008). NOTE: Totals sum to more than 100 percent because some cases involve more than one disability category or more than one issue. The Glossary provides disability definitions for each category. Disputes cite placement issues in 61 percent of cases. These relate to the educational environment in which schools serve students according to their Individualized Education Program (IEP). IEPs list the services that disabled students need in order to receive a free, appropriate public education. Cases may involve multiple issues as well. Assessment, the second most frequently mentioned issue, is cited 46 percent of the time. In contrast, student eligibility is among the least cited issues. A common misconception about the dispute resolution process is that most cases follow trial-like proceedings to eventual adjudication. In reality, very few disputes are resolved that way. Only 119?or 4 percent?of the 2,795 cases closed in 2007?08 ended with an ALJ?s decision (Figure 1.2). Settlement is far more likely. About 74 percent of all disputes settled through the resolution session, mediation conference, or outside the formal process altogether. 12 11 ABA stands for Applied Behavioral Analysis, a comprehensive autism treatment. 12 Ahearn (2002) finds that the number of cases that never went to a hearing rose from 52.8 percent to 72.7 percent in the United States between 1996 and 2000. According to a recent article in Education Week, 80 percent of current requests in the United States never result in a hearing (Samuels, 2008). Figure 1.2 Reasons Cases Closed, 2007?08 40% 29% 14% 5% 4% 8% SettledOutsideMediationor ResolutionSession[1,115] SettledatMediation[817] Withdrawn[392] SettledatResolutionSession [125] DecisionsIssued[119] Other[227] SOURCE: OAH (2008). NOTE: ?Other? includes cases closed due to inactivity, motions to dismiss, failure to file an amended complaint after receiving a notice of insufficiency, and other reasons. Table 1.2 describes the due process hearings that are adjudicated. 13 They last about 4.5 days on average. Each side has similar amounts of time for case presentation and calls 5 to 6 witnesses to testify. Districts prevailed either outright or in a split decision 90 percent of the time, which is more often than the 66 percent that Chambers, Harr, and Dhanani (2003) find for the nation in 1998?99. California students and parents won at least a split decision 51 percent of the time. Their success rate is lower than what parents report in the National Longitudinal Transition Study 2 (NLTS2), a recent survey of students 13?17 years old receiving special education in 2001. Parents report at least partially receiving what they requested 76 percent of the time (NLTS2, 2003). Table 1.2 Characteristics of Due Process Hearings, 2007?08 Average Length (days) 4.5 % Time for Student Case Presentation 46.7 % Time for District Case Presentation 53.4 # Average Student Witnesses 5.7 # Average District Witnesses 5.8 % District Prevailed 49 % Split Decision 41 % Student Prevailed 10 SOURCE: Office of Administrative Hearings (2008). 6 13 Decisions describe the issues, factual findings, legal conclusions, orders, and prevailing party. 7 Despite their infrequency, adjudicated hearings are important because they help set precedents for special education services that schools should provide. Parents and disability rights advocates also turn to past cases to gauge whether ALJs in the United States display bias or inadequate training (Golden, 2007). In California, a February 2008 federal class action lawsuit (since withdrawn) made these very allegations (Marshall, 2008). The high district ?win? rate in California, however, does not convincingly show that ALJs are biased in their favor because districts may pursue cases to adjudication only when they feel legally well positioned. Legal representation is common at due process hearings. In 2006?07, OAH reported more extensive information on legal representation than in 2007?08. The 2006?07 data show that an attorney or advocate represented parents in about two-thirds of due process hearings. 14 Parents were just as likely to have legal representation when they originally filed. District legal representation, on the other hand, appears to be an indicator of cases that eventually proceed to due process hearings. Districts used legal representation in about half of all cases filed, but in almost every case that went to a hearing. Dispute Resolution Requests Across States Zirkel and Gischlar (2008) recently explored trends in adjudicated decisions at the state level between 1991 and 2005. They found a steady upward trend in such decisions per capita through 1996, followed by a somewhat level trend since. The data they provide indicate that California ALJs issued 0.2 decisions per 1,000 special education students in 2005, the 15th highest rate in the country. 15 California?s due process filing rate is much higher than its rate of adjudicated decisions. As noted above, OAH received about 3.7 due process hearing requests per 1,000 students with disabilities in 2006?07. 16 This was the third highest rate among the 50 states and about 81 percent higher than the national average. California also has the second highest rate of mediation. Not all states hold mediation conferences before almost every due process hearing as California does. For instance, Table 1.3 shows that New York has the highest state filing rate for due process but a below-average rate for mediation. 14 Office of Administrative Hearings (2007). 15 This comes from 125 decisions issued and 676,318 special education students in California ages 3?21 in 2005?06. 16 2006?07 is the most recent year for which cross-state data is available. 8 Table 1.3 States with the 10 Largest Rates of Due Process Requests per 1,000 Students with Disabilities, 2006?07 State Hearing Requests Mediation Requests New York 13.3 1.0 Hawaii 6.6 0.3 California 3.7 4.1 Massachusetts 3.6 5.1 New Jersey 3.4 2.4 Maryland 3.0 3.1 Connecticut 2.9 3.3 Pennsylvania 2.5 1.0 New Hampshire 2.0 1.1 Vermont 1.9 2.9 U.S. Average 2.1 1.2 SOURCES: IDEA Dispute Resolution and Child Count Data for 2006?07. NOTES: Hearing, mediation, and special education enrollment data pertain to students 3?21 years old. Mediation counts include both mediation only requests and mediations requested in conjunction with due process. Table 1.3 excludes the District of Columbia, which has a higher request rate than New York. Different practices partially explain the variation across states. For example, some states have made large investments in alternative dispute resolution (ADR) strategies that IDEA does not mandate. ADR processes, such as IEP and resolution session facilitation, advisory councils, and resolution training in schools, aim to address potential issues locally before they become formal disputes. ADR strategies use less adversarial forums and try to maintain trust between parties. Ahearn (2002) suggests that ADR may have contributed to the decline in requests that made it to the hearing phase between 1996 and 2000. ADR may contribute to an overall reduction in hearing requests as well. Henderson (2008) recently surveyed 43 states about their ADR practices and then conducted in-depth interviews in eight states that reported using a variety of techniques. 17 She concludes that ADR is producing encouraging results. Appendix Table A.1 shows that six of the eight states chosen for follow-up interviews had due process request rates 20 percent of the national average or lower. Only Connecticut and Pennsylvania exceeded the national average. Despite its high due process request rate, California also uses a variety of ADR strategies at both the state and local level. Mediation-only is a prime statewide alternative to a formal hearing. Currently, 20 regional school district consortia known as Special Education Local Plan Areas (SELPAs) receive $15,000 each in funding to develop and test ADR programs. The districts use techniques that include phone expert assistance, facilitated IEPs, local mediation, and solutions panels that include both a local mediator and a third-party parent. 18 (The grant 17 The states chosen were Connecticut, Iowa, Minnesota, North Dakota, Oregon, Pennsylvania, Virginia, and Wisconsin. 18 California?s IDEA Annual Performance Report (2008) finds that some districts are also using early case review, resource parents, independent child advocates, and placement specialists. These strategies does not cover the full cost of these programs.) Other SELPAs are likely experimenting with ADR as well. As ADR is developed and tested, it will be important to share which practices best promote efficiency, cooperation, cost-effectiveness, and the student?s interest. At the same time, greater public awareness about effective ADR strategies will be necessary for parents to use them effectively. Due Process Request Trends since 1991?92 Until recently, due process hearing requests grew steadily in California relative to special education enrollment. Figure 1.3 shows that the trend features uninterrupted growth between 1991?92 and 2004?05 followed by a noticeable decline and leveling. Available national dispute resolution data suggest a similar trend, albeit at a lower level. Figure 1.3 Due Process Hearing Requests per 1,000 Special Education Students, 1991?92 to 2007?08 0.0 1.0 2.0 3.0 4.0 5.0 6.0 California U.S.Average SOURCES: Special Education Hearing Office (SEHO) Quarterly Reports, Ahearn (2002), OAH Quarterly Reports, Consortium for Appropriate Dispute Resolution in Special Education (CADRE), and IDEA Dispute Resolution and Child Count Data. NOTES: Due process requests and special education enrollment pertain to students 3?21 years old. California data for 2005?06 include only new cases, not the pipeline cases OAH inherited from SEHO. Part of a recent study by Dhuey and Lipscomb (2009) examines due process request rates in all 50 states from 1991?92 to 2000?01 to better understand the factors that contributed to growing request rates during this period. Their specific interest is to test whether a special education finance reform known as ?census funding? is associated with higher rates of due process requests per 1,000 special education students. California is one of nine states that adopted census funding during this period. Census-based models distribute special education 9 involve using third parties to assist the resolution process at the local level. Henderson (2008) provides details on specific ADR strategies. 10 funds by total enrollment, rather than the size or needs of the disabled population. Areas with equal enrollment, at least in theory, receive comparable grants under the assumption that disabilities are evenly distributed. Dhuey and Lipscomb propose that requests for dispute resolution may rise when states adopt census funding because school districts lose any ability they had to seek partial reimbursement from the state for providing placements that are more intensive or by classifying additional students with disabilities. The findings, which hold constant changes in a number of other factors as well, cannot rule out the possibility of no effect (Table A.2). 19 This report revisits the Dhuey and Lipscomb analysis using a slightly different approach. Whereas the earlier study treats states equally regardless of size, the analysis here attaches more weight to states with larger special education enrollments like California. The strategy helps researchers learn about changes in the nationwide rate of due process requesting because growth in California?s rate accounts for a larger part of growth nationwide. Table A.2 shows that the findings are mostly similar, except that census-based reforms become a meaningful predictor of more due process requests per capita. Specifically, they are associated with a 0.65 percentage point rise in requests per 1,000 special education students across the country over this 10-year span. The estimate translates into a meaningful 47 percent increase over the average national rate of 1.4 requests per 1,000 disabled students. 20 Neither analysis draws specific inferences about California in particular. To do so, we would need to study dispute resolution patterns in California school districts before and after the state reformed its finance system. The findings in this report provide evidence that census- funding policies are associated with more frequent due process requests per special education student nationwide between 1991 and 2000. Similar empirical evidence on factors associated with the recent downturn in filing rates is currently unavailable. Nevertheless, part of the new trend may reflect several relevant national and state policy developments that occurred around 2005. The first is the 2004 reauthorization of IDEA that became effective July 1, 2005. This added several new provisions regarding dispute resolution that may have influenced filing decisions. For instance, it reduced the statutory limitation on disputes from three to two years. IDEA also now requires that all requests for a hearing state facts about the case and propose a solution. This helps to motivate a resolution, potentially before formally submitting the request. Finally, legal fees can now be awarded to prevailing school districts if parents make frivolous claims. In the past, legal fees could only go to parents. In a separate development, the U.S. Supreme Court ruled in Schaffer v. Weast (2005) that the burden of proof in special education disputes lies with the party seeking relief. Jocelyn and Martin Schaffer of Montgomery County, Maryland had sought private school tuition 19 These factors include free lunch program participation, adoption of state accountability systems, racial composition, the unemployment rate, the maximum Supplemental Security Income support payment, and whether the state Supreme Court had ever ruled the school finance system unconstitutional. 20 Columns 3 and 4 hold constant changes in several additional variables: the log of total enrollment, special education revenue per pupil, non-special education revenue per pupil, the log of average teacher salaries, and the teacher pupil ratio. Adding these additional variables does not change the ?unweighted? effect of census funding that Dhuey and Lipscomb (2009) report. Holding these variables constant reduces the ?weighted? estimate by about 8 percent to 0.60 percentage points. 11 reimbursement through due process based on their view that their son?s IEP was inappropriate for his disability. They argued that school districts should have the burden of proof in special education disputes because districts have informational and resource advantages. This had been usual practice in several states, including California. 21 The Court disagreed. 22 The ruling may have contributed to a somewhat steeper reduction in California case filings than across the United States as a whole. Lastly, OAH took over dispute resolution services for California from the Special Education Hearing Office (SEHO) of the McGeorge School of Law in July 2005. CDE switched providers to comply with a California Supreme Court ruling that required the state to use civil servants for hearings. 23 Simply switching service providers should not influence dispute resolution requests, unless parties perceive that OAH is less favorable in some way. One cannot discount the possibility that some people were discouraged from pursuing a formal dispute resolution request based on their perception of OAH. Empirically isolating the separate influence of contemporaneous developments can be difficult, and in this case may not be possible. As a result, the above discussion highlights possible contributing factors to the recent decline in dispute resolution requesting but cannot offer conclusive empirical support for them. 21 Technically, parties seeking to change the status quo about a child?s disability status or placement had the burden of proof. School districts had the burden of proof in cases that did not involve a change in status quo. 22 See Lane and Aratani (2005) for further description about the case. 23 See Professional Engineers in California Government v. Department of Transportation (1997) and Professional Engineers in California Government v. Kempton (2007). 12 2. How and Why Do Dispute Resolution Requests Vary Across California? Chapter highlights: x Special education dispute resolution requests are concentrated in less than one third of California school districts. x Budgetary factors affecting parents and school districts are strong predictors of their respective filing rates. For instance, a district?s rate of free or reduced price lunch relates inversely to the rate of parent-initiated requests. x School districts request dispute resolution more often when they receive less special education revenue per student and are located in more expensive regional labor markets. x The filing rate for both parties tends to be higher in larger, densely populated school districts, and in districts that serve elementary students. Rates of Dispute Resolution Requests across California School Districts Requests for dispute resolution are distributed unevenly across California school districts, with large differences in the request rate per 1,000 special education students. In 2007? 08, OAH received dispute resolution requests from parties in less than one third of all school districts. Table A.3 lists the top 25 filing rates separately for unified, elementary, and high school districts. 24 Several rates exceeded 15 per 1,000 special education students. Among the school districts included in the table, elementary districts have the highest rates and high school districts have the lowest. In other words, request rates tend to be higher in districts that serve the lower grades, corresponding to when disability identification rates are highest. Los Angeles Unified accounted for 35 percent of all matters filed in the state, but had only the 17th highest filing rate per capita among unified districts with more than 25 special education students. Factors That Predict Higher Rates of Dispute Resolution by Filing Party As noted before, parents initiate 86 percent of all dispute resolution requests in California. One way to think about requests initiated by parents is that they signal a gap between the special education services that parents want school districts to provide for their children and the services that districts propose to provide them. That is, requests reflect a parent?s ?excess demand? for publicly provided special education services. 25 Part of the uneven distribution of request rates may point to differences in excess demand across the state. Basic economic theories about household and school district budgeting predict that the demand for services and the ability to provide them will differ geographically. For instance, 24 The table excludes districts with fewer than 25 special education students because even one case filed in these districts will greatly affect their filing rate. 25 This analogy works less well for some school district-initiated requests. Districts use the process to prove that its assessment is appropriate when parents request an independent one. They also use it to challenge unilateral private school placements where parents seek tuition reimbursement. Finally, due process is a way to evaluate a child for a disability when parents refuse to provide their consent. 13 wealthier families may elect to pursue dispute resolution more often because they can better afford legal representation. The available evidence, although limited, suggests that the monetary costs to families are substantial. Higher income families also may know more about navigating the process and have greater flexibility in their work schedules to see it through. A school district?s ability to provide special education services inevitably relates to budgetary factors as well. We might expect districts to push back more on the services that parents request when budgets are tighter, because additional special education spending means scaling back on other programs. Costs are also important. In particular, more expensive regional labor markets for educators increase the cost of providing services. Rose and Sengupta (2007) show that teacher compensation differs greatly across California labor markets, and that these differences affect the purchasing power of school districts. After adjusting for differences in district budget sizes, we might expect district-initiated requests to be more common in areas of California where professionals with similar qualifications as educators earn higher wages. Rates of requesting dispute resolution probably differ for non-budgetary reasons too. Table A.3 points to a lower dispute rate in high school districts than in elementary districts. Dispute rates may be higher in larger, urban districts as well. These districts may offer a broader range of services, and are probably closer to hospitals and therapy services in the community. As a result, they may attract more families of children with severe disabilities. 26 Patterns of Parent-Initiated Dispute Resolution Requests A regression analysis sheds light on how dispute resolution requests relate to factors such as community income, district revenue, and population density. The advantage of a regression framework is that it holds constant many of the differences across school districts. It addresses questions such as, ?If school districts were identical in many ways but differed in their community income levels, would parent-initiated requests be more common in the higher income districts?? The findings do not say whether higher income causes more disputes to occur. Instead, it predicts whether dispute resolution is more likely in a school district with a particular mix of characteristics than in one with a different mix of characteristics. The first part of the analysis examines patterns of parent-filed dispute resolution requests per 1,000 children with disabilities in 2007?08, and includes the types of requests listed in Figure 1.1. The sample includes data from 954 California school districts with complete information for all variables. Appendix A provides detailed information about the data sources and methods. Table 2.1 lists the strongest predictors of higher parent-initiated request rates based on the school district data. 27 The table illustrates how the predicted request rates differ across school districts. Consider a hypothetical district that has the statewide average value of each variable. Each table value shows the predicted filing rate in a school district that is like the hypothetical ?average? district except for a very low or very high value of one factor. 26 Parrish, Kaleba, Gerber, and McLaughlin (1998) find that differences in low-incidence and high-cost disability rates in California are too large to be due to chance alone. 27 There is suggestive evidence that higher median family income, lower percent English learner, and lower percent Hispanic are also predictors of higher parent-initiated request rates. Table 2.1 excludes these characteristics because the estimates are slightly less precise. 14 Table 2.1 Predicted Rates of Parent-Initiated Dispute Resolution Requests per 1,000 Children with Disabilities for California School Districts, 2007?08 School District Level 10th percentile 90th percentile Students with free or reduced price lunch (%) 4.2 2.4 Other revenue per ADA ($) 2.1 5.6 Asian students (%) 3.5 2.5 Total enrollment 1.1 8.3 Density (enrollment/km 2 ) 2.7 3.5 NOTES: Predictions come from the regression coefficients in column 1 of Table A.5. The findings predict that a school district with the average value of each characteristic will have 3.1 parent-filed requests per 1,000 children with disabilities. The 10th percentile includes the 10 percent of students in school districts with the lowest value for each respective characteristic. The 90th percentile includes the 10 percent of students in districts with the highest value for each characteristic. ?Other? revenue includes all non- special education categorical district funding. ADA stands for average daily attendance. Total enrollment is in natural log form. For example, the analysis predicts higher parent-initiated request rates in districts with lower enrollment rates in the free or reduced price lunch program. Table 2.1 uses the empirical results to show that they predict 4.2 requests per 1,000 children with disabilities in a district that differs from the average district only because of a very low rate of free or reduced price lunch. In contrast, the findings suggest that the ?average? district (not shown in the table) will have 3.1 requests, or 26 percent fewer. Moreover, a district with a very high rate of free or reduced price lunch will have only 2.4 parent-initiated requests per 1,000 children with disabilities. The inverse relationship between parent-initiated request rates and free or reduced price lunch participation supports the theory that family budgets influence parental decisions to pursue dispute resolution. Several other variables are important predictors as well. For instance, more non-special education categorical revenue per ADA is associated with a higher request rate. This appears to suggest that parents pursue dispute resolution more often when they know districts have more revenue to allocate. However, California?s numerous categorical programs and funding formulas make it hard to explain why some districts receive more of this funding than do others. Additional evidence should be collected to substantiate this finding before too much can be conclusively drawn from it. 28 The evidence also suggests that parent-initiated dispute resolution requests are more common in school districts with a lower percentage of Asian students, larger total enrollments, and more urban settings. Lipscomb (2009) shows that disability rates among Asian children in California are the lowest among any major racial or ethnic group. Their special education enrollment rate is just over half the statewide average, plausibly explaining the lower dispute rate as well. 28 The main concern is that some districts may receive higher funding amounts based on factors that also signal whether special education disputes are more likely to occur. In this case, the causality of the association described in Table 2.1 is suspect. 15 Higher predicted parent-initiated request rates in large, densely populated districts are consistent with the theory above that a larger share of children with severe or high-cost disabilities reside in these communities because they offer a greater range of services and care options. Table A.6 lends additional support by examining predictors of districts with higher autism rates, the disability that is most frequently the source of disputes. The data indicate that autism rates do tend to be higher in larger, more densely populated school districts. 29 The evidence suggests that district size and population density help characterize the geographic distribution of autism, a typically high-cost disability. Patterns of District-Initiated Dispute Resolution Requests School districts exhibit somewhat different patterns. Free or reduced price lunch participation and median family income levels relate less strongly to district request rates, holding constant other characteristics. Instead, higher regional wage levels and smaller special education revenue per capita are both strongly associated with a higher district-filed request rate. Although the patterns are different for parents and school districts, both support the theory that budgetary factors help motivate the decision to pursue dispute resolution because school district revenue corresponds to family income and regional wage levels are a proxy for the price of services. 30 Table 2.2 lists the strongest predictors of higher district-initiated request rates, as Table 2.1 does for parent-initiated requests. 31 The findings suggest that very high regional wage levels can raise district-initiated requests 63 percent above the predicted rate of 0.5 in the hypothetical ?average? district to 0.8 requests per 1,000 children with disabilities. Very high levels of special education revenue per capita can lower district-initiated requests by 24 percent, to 0.4 per 1,000 children with disabilities. 29 Higher median family income is also a strong predictor of higher autism rates. Table A.6 expresses the autism rate both as a percentage of special education enrollment and as a percentage of total enrollment. Findings are similar in both cases. 30 California school boards cannot raise property tax rates to augment school revenue. If they could, community income may be an important predictor of district-initiated request rates as well. 31 There is some evidence that a lower percent Hispanic predicts higher district-initiated request rates. 16 Table 2.2 Predicted Rates of District-Initiated Dispute Resolution Requests per 1,000 Children with Disabilities in California, 2007?08 10th percentile 90th percentile Regional wage index 0.3 0.8 Special ed. rev. per ADA ($?s) 0.6 0.4 American Indian students (%) 0.5 0.5 Total enrollment 0.2 1.8 Density (enrollment/km 2 ) 0.4 0.6 NOTES: Predictions come from the regression coefficients in column 3 of Table A.5. The findings predict that a school district with the average value of each characteristic will initiate 0.5 requests per 1,000 children with disabilities. The 10th percentile includes the 10 percent of students in school districts with the lowest value for each respective characteristic. The 90th percentile includes the 10 percent of students in districts with the highest value for each characteristic. Total enrollment is in natural log form. The filing patterns of parents and school districts are similar in a number of ways as well. Specifically, district-initiated dispute rates are also higher in larger, urban districts. Recall that these characteristics are strong predictors of higher autism rates too. The findings point to the possibility that educational programs for children with autism may be contentious from the perspective of both parents and school districts. Differences in Filing Rates by School District Type Another important similarity between the two parties is that their request rates are higher in districts serving the younger grades. Table 2.3 shows predicted request rates by filing party for unified, elementary, and high school districts. Table 2.3 Predicted Request Rates by Filing Party and School District Type in California, 2007?08 Parent-filed District-filed Unified 3.0 0.5 Elementary 3.8 0.6 High school 2.3 0.3 NOTES: Predictions come from Table A.5. The predicted rates are for a school district that has the statewide average value of the other characteristics in the analysis. District-filed rates in unified and elementary districts are not statistically different. All other filing rates are statistically different. For both parents and school districts, request rates are highest in elementary districts and lowest in high school districts. Dhuey and Lipscomb (2009) find that statewide disability rates between 1991?92 and 2003?04 in the United States are highest in grades four through six. The dispute resolution trends across district types in California correspond to the grade levels where most disability identification is occurring. 17 Overall Rates of Dispute Resolution in California School Districts Altogether, the findings support the conclusion that patterns of parent- and district- initiated request rates arise through somewhat different processes. The findings about district size and density highlight similarities as well. The differences arguably stem from the economic constraints facing each party. Overall patterns of dispute resolution requests reflect the combination of these separate processes. To provide a comparison with national estimates, it is nevertheless of interest to examine dispute resolution patterns across school districts without distinguishing the initiating party. The findings are in Table A.5 and Table A.7. 32 Not surprisingly, the results are similar to patterns of the dispute rates initiated by parents, who initiated 86 percent of all cases examined. However, the findings support national evidence from Chambers, Harr, and Dhanani (2003), who conclude that disputes are more common in higher income, larger, and urban school districts. 32 Table A.5 contains the regression results and Table A.7 provides predictions. The findings report separate estimates for hearing requests per 1,000 children with disabilities in 2007?08 and 2006?07. 18 Conclusion How best to structure dispute resolution processes is an ongoing special education policy issue in all states. California?s pattern of resolving most registered disputes before a hearing suggests that something about its process is working. Strategies like mediation, which evoke IDEA?s spirit of negotiation and compromise, are often successful in settling the disagreements that arise in the state. Yet California still faces important challenges. Despite the recent decline in hearing requests, California parents and school districts continue to request dispute resolution at a higher rate than in almost any other state. The state may find it beneficial to consider broadening the use and awareness of the ADR options that show the most potential for resolving disputes locally. For example, IEP facilitation is a way to bring in a third party when designing a child?s educational program, in an effort to head off future impasses before they occur. A solutions panel relies on the advice from third-party parents, special education advocates, or mediators to help reach a consensus. Local mediation resembles the state?s mediation process but tries to keep the atmosphere as non-adversarial as possible by not initiating formal dispute resolution procedures through OAH. The piloting for these strategies is largely underway already in the 20 California SELPAs that currently receive small grants to offer ADR. Expanding awareness and use to other areas would not necessitate changes to existing state law. Henderson (2008) finds that ADR offers a ?perception of neutrality? that helps parties communicate effectively and preserve trust. Her study suggests that ADR can work efficiently, fairly, cost-effectively, and in some cases preventatively. In these respects, parents and schools share common interests. Equity concerns pose additional challenges. The data provide strong evidence of a concentration of requests in wealthier areas. It is in California?s interest to avoid a system of dispute resolution that is open to all but accessible to few. The state has taken a number of steps to address equity concerns. School districts must provide parents with a list of their rights under special education law in their native language. OAH must keep a list of attorneys and advocates offering free or reduced cost legal services on its website. It also now provides outreach programs to parents. Finally, mediation-only is a low-cost approach that may be underutilized. Local ADR strategies should increase accessibility as well. Yet the capacity of families across the state to understand and navigate between dispute resolution options may pose as critical an equity concern for policymakers as does affording the process itself. Public awareness about the availability and effectiveness of various strategies is instrumental to ensuring their effective use. Finally, the results from this study suggest that schools are susceptible to economic factors in providing special education services. California school districts face the reality of both a fixed budget and a disabled student?s entitlement to free, appropriate services regardless of cost. The finding that districts file for dispute resolution more often when their special education budgets are smaller and their expected employee costs are higher suggests a role for economic factors in district decisionmaking. As economic conditions in California become less favorable, education funds may be more constrained. In such an environment, it is increasingly important that parents and schools can negotiate successfully over appropriate services for disabled students through a framework that is equitable, cost-effective, and efficient. 19 Appendix A: Supplementary Material and Results Parental and District Rights Under IDEA 33 x Parental right to an available list of procedural safeguards. x Parental right to receive information in their native language. x Parental right to participate in decision-making meetings and to serve on the IEP team. x Prior written notice when schools propose to initiate, refuse to initiate, or change the identification, evaluation, or educational placement of a child. x Parental consent before an initial assessment can proceed. x Right to a non-discriminatory assessment. x Right to obtain an independent assessment at public expense. x Right to examine all relevant records. x Opportunity to present a complaint to the state education agency related to any part of the process, or alleged violation, within the last two years. x Opportunity to attempt resolving disputes before an impartial mediator. x Right to resolve disputes through an impartial due process hearing. x Right to be accompanied by legal counsel in due process hearings. x Right to appeal the final decision of a due process hearing through civil action. 33 California Department of Education (2007). 20 Data Sources Dispute Resolution Requests Data on mediation and due process requests come from several sources. California data for 2005?06 through 2007?08 come from the OAH Quarterly Reports. California data for 1991? 92 to 2004?05 come from Ahearn (2002) and the Quarterly Reports filed by OAH?s predecessor, the Special Education Hearing Office (SEHO). National state-level data from 2006?07 come from the IDEA dispute resolution data file. Data for 2003?04 to 2005?06 come from the Consortium for Appropriate Dispute Resolution in Special Education (CADRE). Finally, national data for 1991?92 to 2000?01 come from Ahearn (2002). All data are currently available online from each source. Special Education Enrollment Data National special education enrollment data come from the IDEA child count data that are available by state for 1991?92 to 2006?07. California school district special education enrollment data come from a district-level version of the California Special Education Management Information System (CASEMIS) for 2007?08. The CASEMIS data are tabulated by the district of residence. Special education enrollment includes all children with disabilities who are 3?21 years old. Demographic and Financial Data for California School Districts Regional educator wage levels, district revenue, and enrollment density come from version 2.3 of the simulation model developed by Rose, Sengupta, Sonstelie, and Reinhard (2008) for the 2004?05 year. The regional wage level variable is an index developed by Rose and Sengupta (2007) that captures the wage of non-educators with similar qualifications as educators in each local labor market. The Rose et al (2008) study is arguably the most reliable source of financial data on California school districts because an advisory group from the Department of Education, the Legislative Analyst?s Office, and the Department of Finance guided its collection. Unrestricted revenue comprises the revenue programs they list as ?Funds allocated by the state but unrestricted in use.? Special education revenue comprises the revenue categories they list under ?Special education.?In California, special education revenue goes to each SELPA. Member districts allocate the revenue according to how they agree to operate programs. Because California allocates revenue to SELPAs based largely on their total ADA, Rose et al (2008) assign each SELPA?s per ADA funding amount to each member district. The third revenue category used in this report includes all remaining programs in the simulation. Median family income comes from the 2000 U.S. Census School District Tabulation. The remaining data are for 2006?07. Race/ethnicity, district type, English learner, and total enrollment data come from CDE. Free and reduced price lunch data come from the CalWORKS data files that CDE maintains. All monetary values are in 2006 dollars. Other Data The data for Table A.2, as well as an explanation of the sources and variable definitions, come from Dhuey and Lipscomb (2009). 21 Estimation Methods Fixed Effects Analysis : x Table A.2 The fixed effects model uses repeated observations on states over time to draw inference about how changes in factors like the adoption of new school finance policies are associated with changes in due process requc o like the following equation: est rates. The spe ificati n looks U ? ? L? 4 E? 5 T 5 ? ? ET ? ? ? EP ? EO ? E? ? ? . The variable y st is the due process request rate per 1,000 special education students. The subscript s denotes states from 1 to 50 and t denotes years from 1991?92 to 2000?2001. The variable of interest, x 1 indicates whether state s used a census-based funding system in year t. ? 1 is the effect of adopting census funding on the frequency of requesting dispute resolution. The other factors included in the analysis are described by x. The analysis also incorporates a few other factors. First, it includes a series of year indicators, t, to adjust for national time trends. Second, it includes a series of state indicators, s, that account for all time invariant factors about states between 1991?92 and 2000?2001. ? 0 is a constant. Finally, ? is an unobserved error term. Tobit Analysis : x Table A.5 x Tables 2.1-2.3 The findings in Chapter 2 come from a type of regression known as a Tobit model. The Tobit model is appropriate when a variable, such as dispute resolution requests per 1,000 children with disabilities, is zero in some school districts but could be any positive number in other districts. This is an example of what statisticians call a ?corner solution.? Related examples include retirement account contributions, life insurance purchases, and donations to charity. In cases like these, a Tobit model allows researchers to calculate the results accurately. Conceptually, an ordinary regression is inappropriate because it will predict that some school districts ought to have a negative filing rate. Addressing this problem is especially important here because requests are concentrated in less than one third of California school districts. The Tobit model estimates the following equation: U L ? ? ? 4 ET? E? ? U ? L ? 4 ET? E? ? ?U ? ? Pr r? U ? ? Qr . The outcome variable y i * is the request rate that we would observe in school district i if requests were not bound to be at least zero. Interpreting this variable in applications like this is seldom useful, because it is difficult to envision what a negative rate of dispute resolution requests might be. For example, it may signal districts where parents and schools are increasingly likely 22 to agree. The main interest is y i , the actual frequency of dispute resolution requests per capita. This variable equals zero whenever y i *?0 and equals y i * whenever y i *>0. The variables in x are predictors of why disputes are more common in some districts than in others. Regression Coefficients ? represents the regression coefficients. In an ordinary regression model like Table A.6, ? is the relationship between x and the outcome variable of interest. Researchers call this relationship a ?marginal effect.? Estimating ? also allows us to make predictions based on the model. The Tobit framework is different because estimates of ? are not the marginal effects we seek. They describe the relationship between x and y i *. We care about the relationship between x and y i . Estimates of ? are nevertheless important for two reasons: They allow us to make predictions based on the model and to calculate the relevant marginal effects. The top half of Table A.5 contains the regression coefficients. Predicted Dispute Resolution Request Rates Tables 2.1?2.3 use the regression coefficients to predict request rates for districts with different characteristics. The predictions come from the following formula: ' : U ? T ? ; L0 @ ? ?  A FT ? ? E? %@ ? ?   A ?@ ? ?   A G. The E(y i |x i ) is the expected number of dispute resolution requests per 1,000 children with disabilities. Like y i itself, zero is the minimum value possible. The predictions are for hypothetical school districts with the statewide average value of each characteristic in x except for a very low or very high value of one characteristic. The Marginal Effect of Each Characteristic The bottom half of Table A.5 uses the regression coefficients to calculate the relevant marginal effects. The marginal effect is the relationship between each characteristic in x and the observed rate of dispute resolution requests, yrm la for calculating them is: i . The fo u ! ? : ? ?  ? ? ; ! ? ? L? ? 0 @ ? ?  A . The subscript k identifies the components of x. The first part of the right hand side of the equation is the effect of factor k on y i *. The remainder of the expression is the probability that a school district with a particular mix of characteristics will have zero requests filed. The marginal effect is the product of these two pieces. The marginal effects listed in Table A.5 apply to a hypothetical school district with the statewide average value of each characteristic. 23 Appendix Tables Table A.1 Hearing and Mediation Requests per 1,000 Children with Disabilities, 2006?07 State Hearing Requests Mediation Requests State Hearing Requests Mediation Requests New York 13.250.96Florida 0.47 0.47 Hawaii 6.640.28Arizona 0.46 0.29 California 3.744.08New Mexico 0.42 0.40 Massachusetts 3.575.07Mississippi 0.41 0.37 New Jersey 3.442.44Virginia 0.40 0.76 Maryland 2.993.09Tennessee 0.40 0.29 Connecticut 2.863.34Idaho 0.39 0.11 Pennsylvania 2.521.04Oregon 0.37 0.89 New Hampshire 1.971.11Michigan 0.32 0.36 Vermont 1.862.86Minnesota 0.29 0.55 Rhode Island 1.553.01Colorado 0.29 0.38 Alaska 1.410.34West Virginia 0.29 0.12 Delaware 1.291.14North Carolina 0.25 0.57 Illinois 1.120.58Wisconsin 0.25 0.68 Maine 1.043.43Oklahoma 0.24 0.29 Washington 0.990.85Kentucky 0.23 0.17 Alabama 0.970.91Montana 0.22 0.16 Nevada 0.830.31Louisiana 0.19 0.16 Ohio 0.770.49South Carolina 0.13 0.02 Texas 0.670.48Arkansas 0.12 0.25 Kansas 0.590.64Utah 0.11 0.05 Wyoming 0.570.07South Dakota 0.11 0.00 Georgia 0.560.50Iowa 0.06 0.34 Missouri 0.50 0.19Nebraska 0.02 0.25 Indiana 0.490.23North Dakota 0.00 0.22 50 States and D.C. 2.071.17 SOURCE: IDEA Dispute Resolution and Child Count Data for 2006?07. NOTE: Hearing, mediation, and special education enrollment data pertain to 3?21 year old students. Mediation counts include both mediation only requests and mediations requested in conjunction with due process. The table omits the District of Columbia, which has a higher hearing request rate than any state. 24 Table A.2 Factors Associated with Growth in Hearings Requested per 1,000 Special Education Students in the United States, 1991?92 to 2000?01 Unweighted Weighted by special education enrollment Unweighted Weighted by special education enrollment Census funding {0,1} 0.25 0.65 0.25 0.60 (0.23) (0.20) (0.21) (0.23) Plaintiff victory {0,1} 0.26 -0.01 0.13 -0.11 (0.35) (0.25) (0.33) (0.29) Free lunch (%) 1.91 -0.76 2.00 -0.36 (1.61) (1.31) (1.33) (1.19) State accountability sys. {0,1} -0.17 -0.15 -0.14 -0.10 (0.13) (0.15) (0.12) (0.13) SSI payment ($?s) 0.21 -0.23 0.25 -0.60 (0.56) (0.67) (0.49) (0.61) Unemployment rate -0.08 -0.17 -0.05 -0.14 0.06 (0.09) (0.05) (0.08) Black (%) 0.30 0.31 0.20 0.25 (0.36) (0.41) (0.36) (0.38) Hispanic (%) 0.03 0.09 -0.10 -0.04 (0.05) (0.10) (0.10) (0.13) Other race (%) 0.07 0.03 -0.02 -0.04 (0.15) (0.19) (0.15) (0.16) Ln(total enrollment) 4.50 4.72 (2.26) (2.70) Special ed rev. per pupil ($?s) 0.49 -0.78 (0.70) (0.63) Other revenue per pupil ($?s) -0.05 -0.18 (0.18) (0.17) Ln(avg teacher salary) -0.32 -2.87 (1.10) (1.90) Teacher pupil ratio -0.05 -0.15 (0.10) (0.09) Dependent var. mean 1.2 1.4 1.2 1.4 Dependent var. sd 1.3 1.4 1.3 1.4 Observations 464 464 464 464 Adj. R-squared 0.8 0.9 0.8 0.9 SOURCE: Author?s calculation based on data from Dhuey and Lipscomb (2009) and Ahearn (2002). NOTE: Standard errors are in parentheses. Bold denotes statistical significance at the 5 percent level. Regressions include a constant, an indicator for missing free lunch data, and state and year fixed effects. SSI payments and revenue per pupil are reported in $1,000s. SSI data are statewide average monthly disability payments. Robust standard errors are clustered at the state level. Observations are less than 500 due to missing hearing data in some state-year cells. 25 Table A.3 California School Districts with Top 25 Dispute Resolution Filing Rates per 1,000 Children with Disabilities, 2007?08 Unified Districts Elementary School Districts High School Districts Rank District Filing rate District Filing rate District Filing rate 1 Alpine County 30.3 Upper Lake Union 78.4Acalanes Union 19.3 2 Laytonville 27.0 San Miguel Joint Union 42.9Chowchilla Union 14.7 3 Santa Monica-Malibu 26.8 Hermosa Beach City 41.3Los Gatos-Saratoga J.U. 9.4 4 Laguna Beach 23.1 Grant 35.7Sequoia Union 7.5 5 Mountain Valley 21.7 Larkspur 34.2San Rafael City 7.3 6 Shandon Joint 18.5 Pacifica School District 32.9Fallbrook Union 7.0 7 Berkeley 17.9 Oak Grove Union 27.0San Dieguito Union 6.2 8 Culver City 17.8 Pollock Pines 25.2San Mateo Union 5.8 9 Coronado 17.6 Woodside 23.8Tamalpais Union 5.3 10 Newport-Mesa 17.4 Ross 23.3Huntington Beach Union 4.5 11 Albany City 15.5 North County J.U. 23.3Galt Joint Union 4.3 12 Ripon 15.0 Elverta Joint 22.2Oxnard Union 3.9 13 Dublin 14.8 Burlingame 19.5West Sonoma C.U. 3.4 14 Oak Park 13.9 Cupertino Union 19.3Campbell Union 3.3 15 Irvine 13.2 Central Union 19.2San Benito 3.2 16 Kelseyville 11.6 Colfax 17.9Fullerton Joint Union 3.2 17 Los Angeles 11.3 Orinda Union 17.2El Dorado Union 2.9 18 Brea-Olinda 10.3 Jefferson 17.0Perris Union 2.9 19 Novato 10.3 Spencer Valley 15.4Mountain View-Los Altos 2.6 20 Mojave 9.6 Montecito Union 15.4Antelope Valley Union 2.6 21 Capistrano 9.1 Gravenstein Union 14.3William S. Hart Union 2.5 22 Beverly Hills 8.7 Exeter Union 13.6Fremont Union 2.5 23 Scotts Valley 8.7 Spreckels Union 13.5Anaheim Union 2.5 24 Castro Valley 8.3 Vallecito Union 13.3Salinas Union 2.4 25 Coast 8.0 Solana Beach 12.6 Liberty Union 2.3 SOURCES: OAH (2008) and CASEMIS. NOTE: Rankings exclude districts with fewer than 25 special education students. 26 Table A.4 Summary Statistics for California School District Characteristics Variable Average value Standard deviation 10th percentile 90th percentile Median family income ($?s) 65,744.722,618.244,401.5 95,492.8 Free or reduced price lunch (Fraction) 0.50.20.2 0.8 English learners (Fraction) 0.30.10.1 0.4 Regional wage index 1.00.10.9 1.1 Unrestricted revenue per ADA ($?s) 5,611.0710.55,260.8 6,186.1 Special ed revenue per ADA ($?s) 743.1140.8624.7 923.9 Other revenue per ADA ($?s) 1,441.4608.8856.3 2,679.4 Hispanic (%) 7.97.11.3 17.3 Asian (%) 49.123.516.6 77.8 African American (%) 11.711.42.6 25.2 American Indian (%) 0.82.00.2 1.3 Ln(total enrollment) 10.11.78.2 13.5 Density (enrollment / km 2 ) 307.6280.419.3 575.6 Elementary school district {0,1} 0.20.40.0 1.0 High school district {0,1} 0.10.30.0 0.0 NOTE: Statistics are weighted by special education enrollment. Monetary values are reported in 2006 dollars. The sample includes 954 California school districts. 27 Table A.5 Factors Associated with Higher Dispute Resolution Rates per 1,000 Children with Disabilities in California, 2007?08 and 2006?07 2007 ? 08 2006 ? 07 Parent filed District filed All cases All cases Coeff. Std. error Coeff. Std. error Coeff. Std. error Coeff. Std. error A. Regression Coefficients Median family income ($?s) 0.0220.0140.0080.005 0.034 0.015 0.066 0.019 Free/reduced price lunch (Frac.) -3.616 1.777-0.6371.034-3.4921.795 -3.198 1.377 English learners (Frac.) -2.9411.8062.0312.458-2.5552.689 -1.9053.026 Regional wage index 6.9394.562 8.108 3.57110.0265.453 1.1716.601 Unrestricted rev. per ADA ($?s) 0.2900.2290.2060.1790.4030.285 0.0510.237 Special ed. rev. per ADA ($?s) 0.2350.999 -1.804 0.748-0.5951.241 -2.7051.417 Other revenue per ADA ($?s) 2.286 0.455-0.6820.424 1.863 0.566 2.194 0.720 Hispanic (%) -0.0250.014-0.0320.018-0.0340.018 -0.0260.023 Asian (%) -0.056 0.019-0.0110.012 -0.055 0.019 -0.059 0.012 African American (%) 0.0120.029-0.0190.0210.00030.034 0.0190.046 American Indian (%) -0.1340.221 0.081 0.0390.0040.087 -0.0360.114 Ln(total enrollment) 1.635 0.170 0.636 0.179 1.788 0.200 1.988 0.328 Density (enrollment / km 2 ) 0.176 0.066 0.113 0.055 0.241 0.071 0.269 0.079 Elementary school district {0,1} 0.854 0.3830.2760.309 1.120 0.377 0.6540.369 High school district {0,1} -1.064 0.499 -0.686 0.327 -1.321 0.514 -1.461 0.652 Constant -23.363 3.533 -13.162 4.360 -27.615 4.681 -20.872 7.015 School districts with cases filed 308 143 331 323 Pseudo R-squared 0.146 0.096 0.121 0.063 B. Marginal Effects Estimated at the Statewide Average Value of Each Characteristic Median family income ($?s) 0.0180.0110.0030.002 0.027 0.013 0.045 0.017 Free/reduced price lunch (Frac.) -2.899 1.376-0.2480.402 -2.830 1.417 -2.197 0.963 English learners (Frac.) -2.3581.4540.7920.989-2.0702.165 -1.3092.057 Regional wage index 5.5633.698 3.161 1.2808.1234.467 0.8054.493 Unrestricted rev. per ADA ($?s) 0.2320.1870.0800.0720.3260.235 0.0350.163 Special ed. rev. per ADA ($?s) 0.1880.806 -0.703 0.293-0.4820.993 -1.8590.986 Other revenue per ADA ($?s) 1.833 0.420-0.2660.168 1.509 0.492 1.508 0.608 Hispanic (%) -0.0200.012-0.0130.007-0.0280.015 -0.0180.016 Asian (%) -0.045 0.014-0.0040.005 -0.044 0.015 -0.041 0.011 African American (%) 0.0100.023-0.0070.0080.00020.027 0.0130.032 American Indian (%) -0.1070.179 0.032 0.0150.0030.070 -0.0250.079 Ln(total enrollment) 1.311 0.151 0.248 0.064 1.449 0.175 1.366 0.138 Density (enrollment / km 2 ) 0.141 0.049 0.044 0.020 0.196 0.051 0.185 0.051 Elementary school district {0,1} 0.702 0.3260.1120.135 0.933 0.325 0.4570.279 High school district {0,1} -0.810 0.378 -0.229 0.092 -1.014 0.397 -0.951 0.439 NOTE: The estimates come from a Tobit model where the frequency of dispute resolution requests is either zero or positive. The marginal effects are for the E[y|x]. The regression is weighted by special education enrollment to be representative of disabled students in the state. The sample includes 954 districts with complete information. Heteroskedasticity-consistent standard errors are clustered by metropolitan statistical area. Revenue estimates are multiplied by 1,000. Density estimates are multiplied by 100. 28 Table A.6 Predictors of Higher Autism Rates in California School Districts, 2007?08 Variable Autism Rate per Special Education Enrollment Autism Rate per Total Enrollment Coeff. Std. dev. Coeff. Std. dev. Median family income ($?s) 0.027 0.011 0.004 0.002 Free/reduced price lunch (Frac.) -2.5751.481 -0.255 0.149 English learners (Frac.) -0.5682.193 0.133 0.226 Regional wage index 3.1723.859 0.464 0.407 Unrestricted rev. per ADA ($?s) 0.0170.111 0.006 0.014 Special ed. rev. per ADA ($?s) -2.4181.334 -0.169 0.136 Other revenue per ADA ($?s) 0.0570.395 0.049 0.042 Hispanic (%) -0.0290.025 -0.005 0.003 Asian (%) 0.0160.024 -0.002 0.002 African American (%) -0.0600.037 -0.006 0.004 American Indian (%) -0.0240.031 -0.002 0.003 Ln(total enrollment) 0.972 0.180 0.113 0.017 Density (enrollment / km 2 ) 0.256 0.034 0.020 0.005 Elementary school district {0,1} 2.503 0.445 0.274 0.058 High school district {0,1} -1.693 0.401 -0.196 0.048 Constant -4.1133.390 -0.751 0.372 Mean value 6.9380.745 R-squared 0.506 0.500 NOTE: Column 1 and 2 statistics are weighted by special education enrollment. Column 3 and 4 statistics are weighted by total enrollment. The sample includes 954 districts with complete information. Heteroskedasticity-consistent standard errors are clustered by metropolitan statistical area. Revenue estimates are multiplied by 1,000. Density estimates are multiplied by 100. Table A.7 Predicted Total Dispute Resolution Request Rates per 1,000 Children with Disabilities, 2007?08 and 2006?07 2007 ? 08 2006 ? 07 10th percentile 90th prcentile 10th percentile 90th percentile Median family income ($?s) 3.1 4.5 3.3 5.7 Free/reduced price lunch (Frac.) 4.7 3.0 5.0 3.7 Other revenue per ADA ($?s) 2.8 5.7 3.4 6.3 Asian (%) 4.1 3.1 4.6 3.7 Ln(total enrollment) 1.4 9.4 2.0 9.9 Density (enrollment/km 2 ) 3.1 4.2 3.7 4.7 Elementary school district {0,1} 3.5 4.4 4.1 4.6 High school district {0,1} 3.8 2.8 4.3 3.3 NOTES: Predictions come from the regression coefficients in specifications 3 and 4 of Table A.5. The predicted value for the average school district is 4.2 in 2006?07 and 3.7 in 2007?08. The 10th percentile is the value of a characteristic at which one in ten special education students attends a school district with a lower value for that characteristic. 29 Glossary: Disability Categories and Definitions Autism ??a developmental disability significantly affecting verbal and nonverbal communication and social interaction, generally evident before age three, that adversely affects educational performance. Other characteristics often associated with autism are engaging in repetitive activities and stereotyped movements, resistance to environmental change or change in daily routines, and unusual responses to sensory experiences.? Deaf- Blindness ??concomitant hearing and visual impairments, the combination of which causes such severe communication and other developmental and educational needs that they cannot be accommodated in special education programs solely for children with deafness or children with blindness.? Deafness ??a hearing impairment that is so severe that a child is impaired in processing linguistic information through hearing, with or without amplification that adversely affects a child's educational performance.? Emotional Disturbance ??a condition exhibiting one or more of the following characteristics over a long period of time and to a marked degree that adversely affects a child's educational performance: (a) An inability to learn that cannot be explained by intellectual, sensory, or health factors. (b) An inability to build or maintain satisfactory interpersonal relationships with peers and teachers. (c) Inappropriate types of behavior or feelings under normal circumstances. (d) A general pervasive mood of unhappiness or depression. (e) A tendency to develop physical symptoms or fears associated with personal or school problems. Emotional disturbance includes schizophrenia. The term does not apply to children who are socially maladjusted, unless it is determined that they have an emotional disturbance?? Hearing Impairment ??an impairment in hearing, whether permanent or fluctuating, that adversely affects a child?s educational performance but that is not included under the definition of deafness.? Mental Retardation ??significantly subaverage general intellectual functioning, existing concurrently with deficits in adaptive behavior and manifested during the developmental period, that adversely affects a child?s educational performance.? Multiple Disabilities ??concomitant impairments (such as mental retardation-blindness, mental retardation-orthopedic impairment), the combination of which causes such severe educational needs that they cannot be accommodated in special education programs solely for one of the impairments. Multiple disabilities does not include deaf-blindness.? Orthopedic Impairment ??a severe orthopedic impairment that adversely affects a child?s educational performance. The term includes impairments caused by a congenital anomaly, 30 impairments caused by disease (e.g. poliomyelitis, bone tuberculosis), and impairments from other causes (e.g., cerebral palsy, amputations, and fractures or burns that cause contractures).? Other Health Impairment ??having limited strength, vitality, or alertness, including a heightened alertness to environmental stimuli, that results in limited alertness with respect to the educational environment, that?(a) is due to chronic or acute health problems such as asthma, attention deficit disorder or attention deficit hyperactivity disorder, diabetes, epilepsy, a heart condition, hemophilia, lead poisoning, leukemia, nephritis, rheumatic fever, and sickle cell anemia; and (b) adversely affects a child?s educational performance.? Specific Learning Disability ??a disorder in one or more of the basic psychological processes involved in understanding or in using language, spoken or written, that may manifest itself in an imperfect ability to listen, think, speak, read, write, spell, or to do mathematical calculations, including conditions such as perceptual disabilities, brain injury, minimal brain dysfunction, dyslexia, and developmental aphasia. Specific learning disability does not include learning problems that are primarily the result of visual, hearing, or motor disabilities, of mental retardation, of emotional disturbance, or of environmental, cultural, or economic disadvantage.? Speech or Language Impairment ??a communication disorder such as stuttering, impaired articulation, a language impairment, or a voice impairment, that adversely affects a child?s educational performance.? Traumatic Brain Injury ??an acquired injury to the brain caused by an external physical force, resulting in total or partial functional disability or psychosocial impairment, or both, that adversely affects a child's educational performance. Traumatic brain injury applies to open or closed head injuries resulting in impairments in one or more areas, such as cognition; language; memory; attention; reasoning; abstract thinking; judgment; problem-solving; sensory, perceptual, and motor abilities; psychosocial behavior; physical functions; information processing; and speech. Traumatic brain injury does not apply to brain injuries that are congenital or degenerative, or to brain injuries induced by birth trauma.? Visual Impairment Including Blindness ??an impairment in vision that, even with correction, adversely affects a child?s educational performance. The term includes both partial sight and blindness.? SOURCE: 34 CFR Section 300.8 of the Federal Register (2006). 31 Acronyms ADA Average Daily Attendance ADR Alternative Dispute Resolution ALJ Administrative Law Judge CADRE Consortium for Appropriate Dispute Resolution in Special Education CASEMIS California Special Education Management Information System CDE California Department of Education IDEA Individuals with Disabilities Education Act IEP Individualized Education Program NLTS2 National Longitudinal Transition Study 2 OAH Office of Administrative Hearings SEHO Special Education Hearing Office SELPA Special Education Local Plan Area 32 References Ahearn, Eileen, ?Due Process Hearings: 2001 Update,? Project Forum, National Association of State Directors of Special Education, 2002, available at www.nasdse.org. California Department of Education, ?Notice of Procedural Safeguards,? 2007, available at www.cde.ca.gov/sp/se/qa/documents/pseng.pdf. Chambers, Jay G., Jenifer J. Harr, and Amynah Dhanani, ?What Are We Spending on Procedural Safeguards in Special Education, 1999-2000?? Special Education Expenditure Project Report 4, American Institutes for Research, Palo Alto, CA, 2003. Dhuey, Elizabeth and Stephen Lipscomb, ?The Effects of Fiscal Incentives in Special Education: Evidence from Capitation Finance Reforms,? PPIC working paper, 2009. Federal Register, ?34 CFR Parts 300 and 301: Assistance to States for the Education of Children With Disabilities and Preschool Grants for Children With Disabilities; Final Rule,? Federal Register, Vol. 71, No. 156, 2006. Golden, Daniel, ?Schools Beat Back Demands For Special Ed Services,? The Wall Street Journal, July 24, 2007. Henderson, Kelly, ?Optional IDEA Alternative Dispute Resolution,? Project Forum and The Consortium for Appropriate Dispute Resolution in Special Education, 2008, available at www.projectforum.org. Lane, Charles and Lori Aratani, ?High Court to Hear Md. Special-Ed Case,? Washington Post, October 5, 2005. Lipscomb, Stephen, ?Students with Disabilities and California?s Special Education Program,? 2009, Public Policy Institute of California, San Francisco, CA. Marshall, Scott, ?Lawsuit Challenges State Special Education Hearings,? North County Times, February 11, 2008. National Longitudinal Transition Study 2, NLTS2 Data Table 91, 2003, available at www.nlts2.org. Office of Administrative Hearings, ?Special Education Division Quarterly Report, Fourth Quarter 2007/2008 Fiscal Year,? 2008, www.oah.dgs.ca.gov. Office of Administrative Hearings, ?Special Education Division Quarterly Report, Fourth Quarter 2006/2007 Fiscal Year,? 2007, www.oah.dgs.ca.gov. Parrish, Thomas, Daniel Kaleba, Michael Gerber, Margaret McLaughlin, ?Special Education: Study of Incidence of Disabilities,? American Institutes for Research, Palo Alto, CA, 1998. Rose, Heather, and Ria Sengupta, ?Teacher Compensation and Local Labor Market Conditions in California: Implications for School Funding,? Public Policy Institute of California, Occasional Paper, 2007. 33 Rose, Heather, Ria Sengupta, Jon Sonstelie, and Ray Reinhard, ?Funding Formulas for California Schools: Simulations and Supporting Data,? Public Policy Institute of California, Occasional Paper, 2008. Samuels, Christina A., ?States Found Moving to Head Off Due-Process Hearings,? Education Week, Vol. 27, Issue 42, 2008, p. 12. State of California, ?Annual Performance Report for Federal Fiscal Year 2006,? 2008, available at www.cde.ca.gov. Zirkel, Perry A. and Karen L. Gischlar, ?Due Process Hearings Under the IDEA: A Longitudinal Frequency Analysis, Journal of Special Education Leadership, Vol. 21, No. 1, 2008, pp. 22-31. PUBLIC POLICY INSTITUTE OF CALIFORNIA Board of Directors Thomas C. Sutton, Chair Retired Chairman and Chief Executive Officer Pacific Life Insurance Company Mark Baldassare President and Chief Executive Officer Public Policy Institute of California Ruben Barrales President and Chief Executive Officer San Diego Regional Chamber of Commerce John Bryson Retired Chairman and CEO Edison International Edward K. Hamilton Chairman Hamilton, Rabinovitz & Associates, Inc. Gary K. Hart Former State Senator and Secretary of Education State of California Walter B. Hewlett Director Center for Computer Assisted Research in the Humanities Donna Lucas Chief Executive Officer Lucas Public Affairs Ki Suh Park Design and Managing Partner Gruen Associates Constance L. Rice Co-Director The Advancement Project Raymond L. Watson Vice Chairman of the Board Emeritus The Irvine Company Carol Whiteside President Emeritus Great Valley Center PUBLIC POLICY INSTITUTE OF CALIFORNIA 500 Washington Street, Suite 600 San Francisco, California 94111 phone: 415.291.4400 fax: 415.291.4401 PPIC SACRAMENTO CENTER Senator Office Building 1121 L Street, Suite 801 Sacramento, California 95814 phone: 916.440.1120 fax: 916.440.1121 www.ppic.org" } ["___content":protected]=> string(102) "

R 209SLR

" ["_permalink":protected]=> string(93) "https://www.ppic.org/publication/resolving-special-education-disputes-in-california/r_209slr/" ["_next":protected]=> array(0) { } ["_prev":protected]=> array(0) { } ["_css_class":protected]=> NULL ["id"]=> int(8706) ["ID"]=> int(8706) ["post_author"]=> string(1) "1" ["post_content"]=> string(0) "" ["post_date"]=> string(19) "2017-05-20 02:40:01" ["post_excerpt"]=> string(0) "" ["post_parent"]=> int(3992) ["post_status"]=> string(7) "inherit" ["post_title"]=> string(8) "R 209SLR" ["post_type"]=> string(10) "attachment" ["slug"]=> string(8) "r_209slr" ["__type":protected]=> NULL ["_wp_attached_file"]=> string(12) "R_209SLR.pdf" ["wpmf_size"]=> string(6) "528193" ["wpmf_filetype"]=> string(3) "pdf" ["wpmf_order"]=> string(1) "0" ["searchwp_content"]=> string(88620) "Resolving Special Education Disputes in California Stephen Lipscomb with research support from Karina Jaquet Supported with funding from The Special Hope Foundation and The William and Flora Hewlett Foundation February 2009 The Public Policy Institute of California is dedicated to informing and improving public policy in California through independent, objective, nonpartisan research on major economic, social, and political issues. The institute?s goal is to raise public awareness and to give elected representatives and other decisionmakers a more informed basis for developing policies and programs. The institute?s research focuses on the underlying forces shaping California's future, cutting across a wide range of public policy concerns, including economic development, education, environment and resources, governance, population, public finance, and social and health policy. PPIC is a private, nonprofit organization. It does not take or support positions on any ballot measures or on any local, state, or federal legislation, nor does it endorse, support, or oppose any political parties or candidates for public office. PPIC was established in 1994 with an endowment from William R. Hewlett. Mark Baldassare is President and Chief Executive Officer of PPIC. Thomas C. Sutton is Chair of the Board of Directors. Copyright ? 2009 by Public Policy Institute of California All rights reserved San Francisco, CA Short sections of text, not to exceed three paragraphs, may be quoted without written permission provided that full attribution is given to the source and the above copyright notice is included. Research publications reflect the views of the authors and do not necessarily reflect the views of the staff, officers, or Board of Directors of the Public Policy Institute of California. ii Contents  Summary iv  Acknowledgments vi  Introduction 1  1.  HOW OFTEN IS DISPUTE RESOLUTION REQUESTED? 2  The Process for Resolving Special Education Disputes in California 2  Mediation and Due Process Hearings in California 3  Dispute Resolution Requests Across States 7  Due Process Request Trends since 1991?92 9  2.  HOW AND WHY DO DISPUTE RESOLUTION REQUESTS VARY ACROSS CALIFORNIA? 12  Rates of Dispute Resolution Requests across California School Districts 12  Factors that Predict Higher Rates of Dispute Resolution by Filing Party 12  Patterns of Parent-Initiated Dispute Resolution Requests 13  Patterns of District-Initiated Dispute Resolution Requests 15  Differences in Filing Rates by School District Type 16  Overall Rates of Dispute Resolution in California School Districts 17  Conclusion 18  Appendix A: Supplementary Material and Results 19  Parental and District Rights Under IDEA 19  Data Sources 20  Estimation Methods 21  Appendix Tables 23  Glossary: Disability Categories and Definitions 29  List of Acronyms 31  References 32 iii iv Summary Children with disabilities in the United States are entitled to appropriate special education services to meet their individual needs. The responsibility for meeting these needs falls on local school districts. In 2006?07, California school districts spent $9.3 billion to provide special education to children with disabilities, who represent about 10 percent of the state?s total enrollment (Lipscomb, 2009). Although the financial obligation is on school districts, identifying disabilities and designing individualized educational programs for disabled students necessitates close cooperation between parents and schools. Most of the time, parents and schools agree on an educational program that best serves the interests of the child. However, collaborative processes inevitably result in disagreements in some cases. California?s procedures for resolving special education disputes provide a means for parents and school districts to address their differences. Federal law gives both parties the right to due process and mediation in special education matters. In 2007?08, requests for dispute resolution in California totaled 3.9 per 1,000 special education students, one of the highest rates in the country. Resolving disputes is critical to effectively serving disabled children. Yet pursuing dispute resolution can be costly and time consuming for both parties, raising the possibility that due process is more accessible to some Californians than to others. This report examines special education dispute resolution in California and its use by parents and school districts. The first chapter describes how mediation and due process hearings work. It also provides basic statistics about hearing requests in the state, recent trends, and alternative approaches that local districts are developing. Finally, the chapter explores factors that may relate to growth in hearing requests at the national level. The second chapter examines patterns of dispute resolution requests in California school districts, focusing on predictors of higher request rates by parents and school districts. A common misconception about special education disputes is that most involve administrative law judges who issue rulings after very legalistic hearings. The reality is that parties settle the majority of disputes and only 4 percent are ultimately adjudicated. Since 2005? 06, the number of hearing requests per special education student has declined both in California and nationally, possibly in response to several contemporaneous changes to special education law. This recent trend marks a reversal of a pattern of steady growth in hearing requests since at least 1991?92. The findings here suggest that part of the nationwide growth during this period relates to the adoption of a special education funding model by California and a number of other states. Requests for dispute resolution in California are concentrated in less than a third of school districts. Holding constant other observable characteristics, requests initiated by both parents and school districts are more common in larger, densely populated districts. District size and enrollment density are also significant predictors of higher autism rates, the disability that is most frequently the source of dispute. The findings also suggest that cost and budgetary factors bear on the decision by parents and school districts to pursue dispute resolution. In this regard, patterns of filing by each side differ because parents and school districts face different sets of relevant economic constraints. Parents initiate the vast majority of requests. Parent-initiated request rates are higher in school districts with lower enrollment rates in free or reduced price lunch, a variable that relates to the community poverty level. This suggests that lower-income parents might not have the resources necessary to initiate requests. Community income and poverty are weaker predictors of higher request rates initiated by districts. Instead, district-initiated request rates are higher in districts that receive less special education revenue per capita and in districts that operate in regions where educators can expect to earn higher wages. While the patterns of dispute resolution may arise through somewhat different processes for each party, the explanation for each pattern points toward the influence of relevant budgetary factors. California?s pattern of settling most cases before hearings suggests that the dispute resolution process operates efficiently. However, its high rate of dispute requests is evidence that more could be done to resolve disputes locally. School districts in many states, including California, are developing alternative dispute resolution (ADR) strategies that emphasize non- adversarial environments. ADR aims to increase efficiency, trust, and cost-effectiveness on both sides. For example, the technique of including a third party during the development of educational plans for disabled students emphasizes dispute prevention. Although the research on alternative techniques is currently limited, the findings from this and other existing work support the idea of expanding their availability across the state and analyzing their effectiveness. At the same time, increasing public awareness will be essential to addressing equity concerns and fostering effective utilization. v Acknowledgments I would like to thank several people who spoke with me about the dispute resolution process in California, including Paul Goldfinger, Sarge Kennedy, Bill Koski, Sherianne Laba, Jack Lucas, Steve Moyer, and John Namkung. In particular, I deeply appreciate the insightful comments that Kim Connor, Jaqui Guzman, and Tom Parrish provided on an earlier draft. This work would not be possible without their careful assistance. I would also like to thank members of PPIC?s Education Advisory Committee for their suggestions during presentations of the work in progress. At PPIC, Richard Greene, Hans Johnson, Eric Larsen, and Lynette Ubois provided thoughtful comments and suggestions that improved the study and Karina Jaquet provided outstanding research support. Research publications reflect the views of the author and do not necessarily reflect the views of the staff, officers, or Board of Directors of the Public Policy Institute of California. All errors are my own. vi 1 Introduction The Individuals with Disabilities Education Act (IDEA) of 2004 includes an extensive list of ?procedural safeguards? to help ensure that children with disabilities in the United States obtain a free, appropriate public education. Designing educational programs for disabled students is fundamentally a collaborative process between parents and schools (Lipscomb, 2009). In such an environment, it is only natural that disagreements occasionally arise. IDEA?s most important safeguards give families and school districts the right to resolve disputes through two avenues: mediation and due process. In 2007?08, requests for dispute resolution in California totaled 3.9 per 1,000 special education students. Almost all cases filed in California request both mediation and due process. The most recent national numbers show that parents and school districts across the United States filed 2.1 due-process requests and 1.2 mediation requests per 1,000 special education students in 2006?07. California?s request rates are among the highest in the country. Dispute resolution can involve substantial costs for both families and school districts. The best cost estimates come from Chambers, Harr, and Dhanani (2003), who conclude that U.S. school districts spent $8,160 to $12,200 per case on mediation and due process hearings in 1999? 2000. These estimates suggest high financial burdens on families as well. For most families, the burden is probably more substantial in relation to income than it is for school districts. Districts can also budget in advance for projected dispute resolution costs, and spread them over a larger number of students. For instance, Chambers, Harr, and Dhanani show that dispute resolution costs to districts in 1999?2000 converted to about $24 per special education student. In contrast, just affording the dispute resolution process, let alone navigating it, may be a barrier to some families. This report examines dispute resolution processes in California, to inform future special education policy and research. It describes how mediation and due process hearings work and presents current statistics about their implementation. It also discusses dispute resolution trends and the development of alternative strategies, and presents evidence suggesting an association between California?s type of special education funding system and higher request rates. Finally, this report examines request rates at the district level, with a particular focus on understanding which factors make parents and school districts each more likely to initiate the process. The findings suggest approaches California might consider to improve the accessibility of dispute resolution. 2 1. How Often Is Dispute Resolution Requested? Chapter highlights: x Most special education disputes settle before a due process hearing. Only 4 percent of disputes ended with a judicial decision in 2007?08. x In 2007?08, California parents and school districts filed 3.9 requests for dispute resolution per 1,000 special education students, among the highest rates in the U.S. x California?s system of special education finance is associated with growth in hearing requests per capita nationwide between 1991 and 2000. x The frequency of hearing requests grew steadily in California until 2005?06 and then declined, possibly due to several contemporaneous policy developments. The Process for Resolving Special Education Disputes in California Parents, legal guardians, and school districts can seek dispute resolution when disagreements about a child?s special needs and services arise. 1 In California, the Office of Administrative Hearings (OAH) facilitates the state?s dispute resolution process. This process is very legalistic when it involves formal hearings, but most disputes are resolved before that stage. Parents and school districts first make concerted efforts to reach an agreement themselves through cooperative, non-adversarial approaches. Parties that request dispute resolution through OAH choose between two main options: due process with mediation, and mediation only. 2 Due process with mediation is the predominant choice across the state. When OAH receives such a request, it schedules a due process hearing date for about 55 days later. The interval gives parents and school districts an opportunity first to resolve the dispute through a mandatory resolution session and then through mediation. Unless the parties agree otherwise, students remain in their current educational placements until the dispute is resolved. 3 School district representatives must convene a resolution session with parents to allow each side to discuss factual matters and to attempt a settlement. School districts may bring a lawyer only if the parents also bring one. If the dispute is resolved, the parties alert OAH and sign a legally binding settlement agreement. If an impasse remains, OAH schedules a mediation conference. 4 Mediation is a voluntary, non-adversarial process in which parties use a trained OAH mediator to help them find common ground. In practice in California, parties almost never waive mediation. OAH cannot appoint attorneys to represent parties, but federal law allows 1 This report refers to dispute resolution requests as either district-filed or parent-filed. 2 See www.oah.dgs.ca.gov for additional information. 3 IDEA requires that school districts provide the parents of disabled students a copy of their rights under special education law. 4 Parents and school districts can agree to waive the resolution session. 3 attorneys and advocates to attend mediations requested in conjunction with due process. 5 The parties keep all discussions and possible agreements confidential. If mediation does not resolve the dispute, OAH convenes a due process hearing before one of its Administrative Law Judges (ALJs). 6 Due process hearings resemble a civil trial. The ALJ presides over the hearing, rules on motions, considers evidence, and issues a binding legal decision. Parties involved in due process hearings have the right to legal counsel at their own expense and may present evidence. They may compel witnesses to testify and cross-examine witnesses called by the other side. OAH mediators are available for parents representing themselves, to answer questions about the process and to identify issues, so long as they do not give legal advice. Prevailing parties can attempt to recoup their legal fees through separate civil action. As well, the losing side can appeal in state or federal court. The hearings themselves typically last several days. Although they resolve the impasse, the outcome may not be mutually satisfactory. Chambers, Harr, and Dhanani (2003) find that almost all the school districts in their nationally representative sample view mediation as more cost-effective than due process. Cost- effectiveness is one reason why California offers ?mediation only? as an alternative to the due process framework. Mediation-only conferences operate like other mediation conferences with one key difference, that lawyers or advocates are not permitted. This approach can be an attractive way to use the services of an independent mediator, keep costs low, and maintain a non-adversarial setting. Of course, the downside of mediation only is that it is not always successful. However, federal law prohibits mediation from infringing on an individual?s right to due process in a follow-up request. Mediation and Due Process Hearings in California Figure 1.1 describes the 2,626 requests for dispute resolution that OAH received during 2007?08. According to federal disability count data, this is about 3.9 requests per 1,000 special education students ages 3?21. Over 90 percent of all cases filed request due process hearings. 7 Mediation-only requests, in contrast, account for only 5 percent of cases. The remainder are expedited hearings, such as to determine whether a student?s misbehavior was caused by a disability, or dual hearings that involve both expedited and non-expedited issues. Parents initiated 86 percent of the cases. 8 5 California requires that OAH maintain a list of special education advocates and attorneys offering free or reduced cost services. 6 Interpreters for mediation conferences and due process hearings are available if requested. 7 As described above, due process requests include a mediation conference prior to the hearing unless one of the parties opts out. The due process request rate is about 3.7 per 1,000 special education students. 8 ?Parent-initiated? requests include those filed by parents, legal guardians, and the student. Figure 1.1 Requests for Dispute Resolution by Filing Party, 2007?08 79% 5% 2% 11% 0% 3% ParentrDueProcessHearing[2,072] ParentrMediationOnly[139] ParentrExpedited&Dual[41] DistrictrDueProcessHearing[303] DistrictrMediationOnly[4] DistrictrExpedited&Dual[67] SOURCE: Office of Administrative Hearings (2008). Collectively, dispute resolution requests involve a variety of student disabilities and issues. They tend to involve disabilities with higher growth rates and wider prevalence. For example, Table 1.1 shows that 31 percent of all disputes concern students with autistic-like behaviors. Autism is the fastest growing disability in California, but it includes only 7 percent of disabled students (Lipscomb, 2009). That is, autistic-like behaviors are much more likely to lead to dispute resolution than other types of disabilities. 9 By comparison, the second and third most frequently cited categories are learning disabilities and speech or language impairments. 10 These categories account for more than 70 percent of student disabilities in California but only about 27 percent of all disputes. The Glossary provides disability definitions for each category. 4 9 Some of these cases may involve students believed to have, but not diagnosed with, autistic-like behaviors. 10 Learning disabilities are disorders in ??one or more of the basic psychological processes involved in understanding or in using language, spoken or written, that may manifest itself in an imperfect ability to listen, think, speak, read, write, spell, or to do mathematical calculations, including conditions such as perceptual disabilities, brain injury, minimal brain dysfunction, dyslexia, and developmental aphasia. Specific learning disability does not include learning problems that are primarily the result of visual, hearing, or motor disabilities, of mental retardation, of emotional disturbance, or of environmental, cultural, or economic disadvantage.? Source: 34 CFR Section 300.8 of the Federal Register. 5 Table 1.1 Disability Categories and Issues, 2007?08 Disability Categories Total Cases % of Total Issues Total Issues % of Total Autistic Like Behaviors 82731.5Placement 1612 61.4 Learning Disability 37814.4Assessment 1204 45.9 Not Identified 37314.2Compensatory Education 847 32.3 Speech or Language Impairment 34513.1Speech and Language 770 29.3 Other Health Impaired 29611.3Reimbursement 752 28.6 Emotional Disturbance 2479.4Designated Instruction 727 27.7 Mental Retardation 1294.9ABA/One-to-One Aid 11 601 22.9 Multiple Disabilities 853.2Goals and Objectives 568 21.6 Hearing Impaired 542.1Occupational Therapy 503 19.2 Orthopedic Impairment 511.9Eligibility 381 14.5 Visual Impairment 491.9Extended School Year 329 12.5 Deafness 271.0Mental Health Services 208 7.9 Traumatic Brain Injury 140.5Procedural 199 7.6 Deaf/Blind 00.0Private Services 185 7.0 Discipline 68 2.6 SOURCE: Office of Administrative Hearings (2008). NOTE: Totals sum to more than 100 percent because some cases involve more than one disability category or more than one issue. The Glossary provides disability definitions for each category. Disputes cite placement issues in 61 percent of cases. These relate to the educational environment in which schools serve students according to their Individualized Education Program (IEP). IEPs list the services that disabled students need in order to receive a free, appropriate public education. Cases may involve multiple issues as well. Assessment, the second most frequently mentioned issue, is cited 46 percent of the time. In contrast, student eligibility is among the least cited issues. A common misconception about the dispute resolution process is that most cases follow trial-like proceedings to eventual adjudication. In reality, very few disputes are resolved that way. Only 119?or 4 percent?of the 2,795 cases closed in 2007?08 ended with an ALJ?s decision (Figure 1.2). Settlement is far more likely. About 74 percent of all disputes settled through the resolution session, mediation conference, or outside the formal process altogether. 12 11 ABA stands for Applied Behavioral Analysis, a comprehensive autism treatment. 12 Ahearn (2002) finds that the number of cases that never went to a hearing rose from 52.8 percent to 72.7 percent in the United States between 1996 and 2000. According to a recent article in Education Week, 80 percent of current requests in the United States never result in a hearing (Samuels, 2008). Figure 1.2 Reasons Cases Closed, 2007?08 40% 29% 14% 5% 4% 8% SettledOutsideMediationor ResolutionSession[1,115] SettledatMediation[817] Withdrawn[392] SettledatResolutionSession [125] DecisionsIssued[119] Other[227] SOURCE: OAH (2008). NOTE: ?Other? includes cases closed due to inactivity, motions to dismiss, failure to file an amended complaint after receiving a notice of insufficiency, and other reasons. Table 1.2 describes the due process hearings that are adjudicated. 13 They last about 4.5 days on average. Each side has similar amounts of time for case presentation and calls 5 to 6 witnesses to testify. Districts prevailed either outright or in a split decision 90 percent of the time, which is more often than the 66 percent that Chambers, Harr, and Dhanani (2003) find for the nation in 1998?99. California students and parents won at least a split decision 51 percent of the time. Their success rate is lower than what parents report in the National Longitudinal Transition Study 2 (NLTS2), a recent survey of students 13?17 years old receiving special education in 2001. Parents report at least partially receiving what they requested 76 percent of the time (NLTS2, 2003). Table 1.2 Characteristics of Due Process Hearings, 2007?08 Average Length (days) 4.5 % Time for Student Case Presentation 46.7 % Time for District Case Presentation 53.4 # Average Student Witnesses 5.7 # Average District Witnesses 5.8 % District Prevailed 49 % Split Decision 41 % Student Prevailed 10 SOURCE: Office of Administrative Hearings (2008). 6 13 Decisions describe the issues, factual findings, legal conclusions, orders, and prevailing party. 7 Despite their infrequency, adjudicated hearings are important because they help set precedents for special education services that schools should provide. Parents and disability rights advocates also turn to past cases to gauge whether ALJs in the United States display bias or inadequate training (Golden, 2007). In California, a February 2008 federal class action lawsuit (since withdrawn) made these very allegations (Marshall, 2008). The high district ?win? rate in California, however, does not convincingly show that ALJs are biased in their favor because districts may pursue cases to adjudication only when they feel legally well positioned. Legal representation is common at due process hearings. In 2006?07, OAH reported more extensive information on legal representation than in 2007?08. The 2006?07 data show that an attorney or advocate represented parents in about two-thirds of due process hearings. 14 Parents were just as likely to have legal representation when they originally filed. District legal representation, on the other hand, appears to be an indicator of cases that eventually proceed to due process hearings. Districts used legal representation in about half of all cases filed, but in almost every case that went to a hearing. Dispute Resolution Requests Across States Zirkel and Gischlar (2008) recently explored trends in adjudicated decisions at the state level between 1991 and 2005. They found a steady upward trend in such decisions per capita through 1996, followed by a somewhat level trend since. The data they provide indicate that California ALJs issued 0.2 decisions per 1,000 special education students in 2005, the 15th highest rate in the country. 15 California?s due process filing rate is much higher than its rate of adjudicated decisions. As noted above, OAH received about 3.7 due process hearing requests per 1,000 students with disabilities in 2006?07. 16 This was the third highest rate among the 50 states and about 81 percent higher than the national average. California also has the second highest rate of mediation. Not all states hold mediation conferences before almost every due process hearing as California does. For instance, Table 1.3 shows that New York has the highest state filing rate for due process but a below-average rate for mediation. 14 Office of Administrative Hearings (2007). 15 This comes from 125 decisions issued and 676,318 special education students in California ages 3?21 in 2005?06. 16 2006?07 is the most recent year for which cross-state data is available. 8 Table 1.3 States with the 10 Largest Rates of Due Process Requests per 1,000 Students with Disabilities, 2006?07 State Hearing Requests Mediation Requests New York 13.3 1.0 Hawaii 6.6 0.3 California 3.7 4.1 Massachusetts 3.6 5.1 New Jersey 3.4 2.4 Maryland 3.0 3.1 Connecticut 2.9 3.3 Pennsylvania 2.5 1.0 New Hampshire 2.0 1.1 Vermont 1.9 2.9 U.S. Average 2.1 1.2 SOURCES: IDEA Dispute Resolution and Child Count Data for 2006?07. NOTES: Hearing, mediation, and special education enrollment data pertain to students 3?21 years old. Mediation counts include both mediation only requests and mediations requested in conjunction with due process. Table 1.3 excludes the District of Columbia, which has a higher request rate than New York. Different practices partially explain the variation across states. For example, some states have made large investments in alternative dispute resolution (ADR) strategies that IDEA does not mandate. ADR processes, such as IEP and resolution session facilitation, advisory councils, and resolution training in schools, aim to address potential issues locally before they become formal disputes. ADR strategies use less adversarial forums and try to maintain trust between parties. Ahearn (2002) suggests that ADR may have contributed to the decline in requests that made it to the hearing phase between 1996 and 2000. ADR may contribute to an overall reduction in hearing requests as well. Henderson (2008) recently surveyed 43 states about their ADR practices and then conducted in-depth interviews in eight states that reported using a variety of techniques. 17 She concludes that ADR is producing encouraging results. Appendix Table A.1 shows that six of the eight states chosen for follow-up interviews had due process request rates 20 percent of the national average or lower. Only Connecticut and Pennsylvania exceeded the national average. Despite its high due process request rate, California also uses a variety of ADR strategies at both the state and local level. Mediation-only is a prime statewide alternative to a formal hearing. Currently, 20 regional school district consortia known as Special Education Local Plan Areas (SELPAs) receive $15,000 each in funding to develop and test ADR programs. The districts use techniques that include phone expert assistance, facilitated IEPs, local mediation, and solutions panels that include both a local mediator and a third-party parent. 18 (The grant 17 The states chosen were Connecticut, Iowa, Minnesota, North Dakota, Oregon, Pennsylvania, Virginia, and Wisconsin. 18 California?s IDEA Annual Performance Report (2008) finds that some districts are also using early case review, resource parents, independent child advocates, and placement specialists. These strategies does not cover the full cost of these programs.) Other SELPAs are likely experimenting with ADR as well. As ADR is developed and tested, it will be important to share which practices best promote efficiency, cooperation, cost-effectiveness, and the student?s interest. At the same time, greater public awareness about effective ADR strategies will be necessary for parents to use them effectively. Due Process Request Trends since 1991?92 Until recently, due process hearing requests grew steadily in California relative to special education enrollment. Figure 1.3 shows that the trend features uninterrupted growth between 1991?92 and 2004?05 followed by a noticeable decline and leveling. Available national dispute resolution data suggest a similar trend, albeit at a lower level. Figure 1.3 Due Process Hearing Requests per 1,000 Special Education Students, 1991?92 to 2007?08 0.0 1.0 2.0 3.0 4.0 5.0 6.0 California U.S.Average SOURCES: Special Education Hearing Office (SEHO) Quarterly Reports, Ahearn (2002), OAH Quarterly Reports, Consortium for Appropriate Dispute Resolution in Special Education (CADRE), and IDEA Dispute Resolution and Child Count Data. NOTES: Due process requests and special education enrollment pertain to students 3?21 years old. California data for 2005?06 include only new cases, not the pipeline cases OAH inherited from SEHO. Part of a recent study by Dhuey and Lipscomb (2009) examines due process request rates in all 50 states from 1991?92 to 2000?01 to better understand the factors that contributed to growing request rates during this period. Their specific interest is to test whether a special education finance reform known as ?census funding? is associated with higher rates of due process requests per 1,000 special education students. California is one of nine states that adopted census funding during this period. Census-based models distribute special education 9 involve using third parties to assist the resolution process at the local level. Henderson (2008) provides details on specific ADR strategies. 10 funds by total enrollment, rather than the size or needs of the disabled population. Areas with equal enrollment, at least in theory, receive comparable grants under the assumption that disabilities are evenly distributed. Dhuey and Lipscomb propose that requests for dispute resolution may rise when states adopt census funding because school districts lose any ability they had to seek partial reimbursement from the state for providing placements that are more intensive or by classifying additional students with disabilities. The findings, which hold constant changes in a number of other factors as well, cannot rule out the possibility of no effect (Table A.2). 19 This report revisits the Dhuey and Lipscomb analysis using a slightly different approach. Whereas the earlier study treats states equally regardless of size, the analysis here attaches more weight to states with larger special education enrollments like California. The strategy helps researchers learn about changes in the nationwide rate of due process requesting because growth in California?s rate accounts for a larger part of growth nationwide. Table A.2 shows that the findings are mostly similar, except that census-based reforms become a meaningful predictor of more due process requests per capita. Specifically, they are associated with a 0.65 percentage point rise in requests per 1,000 special education students across the country over this 10-year span. The estimate translates into a meaningful 47 percent increase over the average national rate of 1.4 requests per 1,000 disabled students. 20 Neither analysis draws specific inferences about California in particular. To do so, we would need to study dispute resolution patterns in California school districts before and after the state reformed its finance system. The findings in this report provide evidence that census- funding policies are associated with more frequent due process requests per special education student nationwide between 1991 and 2000. Similar empirical evidence on factors associated with the recent downturn in filing rates is currently unavailable. Nevertheless, part of the new trend may reflect several relevant national and state policy developments that occurred around 2005. The first is the 2004 reauthorization of IDEA that became effective July 1, 2005. This added several new provisions regarding dispute resolution that may have influenced filing decisions. For instance, it reduced the statutory limitation on disputes from three to two years. IDEA also now requires that all requests for a hearing state facts about the case and propose a solution. This helps to motivate a resolution, potentially before formally submitting the request. Finally, legal fees can now be awarded to prevailing school districts if parents make frivolous claims. In the past, legal fees could only go to parents. In a separate development, the U.S. Supreme Court ruled in Schaffer v. Weast (2005) that the burden of proof in special education disputes lies with the party seeking relief. Jocelyn and Martin Schaffer of Montgomery County, Maryland had sought private school tuition 19 These factors include free lunch program participation, adoption of state accountability systems, racial composition, the unemployment rate, the maximum Supplemental Security Income support payment, and whether the state Supreme Court had ever ruled the school finance system unconstitutional. 20 Columns 3 and 4 hold constant changes in several additional variables: the log of total enrollment, special education revenue per pupil, non-special education revenue per pupil, the log of average teacher salaries, and the teacher pupil ratio. Adding these additional variables does not change the ?unweighted? effect of census funding that Dhuey and Lipscomb (2009) report. Holding these variables constant reduces the ?weighted? estimate by about 8 percent to 0.60 percentage points. 11 reimbursement through due process based on their view that their son?s IEP was inappropriate for his disability. They argued that school districts should have the burden of proof in special education disputes because districts have informational and resource advantages. This had been usual practice in several states, including California. 21 The Court disagreed. 22 The ruling may have contributed to a somewhat steeper reduction in California case filings than across the United States as a whole. Lastly, OAH took over dispute resolution services for California from the Special Education Hearing Office (SEHO) of the McGeorge School of Law in July 2005. CDE switched providers to comply with a California Supreme Court ruling that required the state to use civil servants for hearings. 23 Simply switching service providers should not influence dispute resolution requests, unless parties perceive that OAH is less favorable in some way. One cannot discount the possibility that some people were discouraged from pursuing a formal dispute resolution request based on their perception of OAH. Empirically isolating the separate influence of contemporaneous developments can be difficult, and in this case may not be possible. As a result, the above discussion highlights possible contributing factors to the recent decline in dispute resolution requesting but cannot offer conclusive empirical support for them. 21 Technically, parties seeking to change the status quo about a child?s disability status or placement had the burden of proof. School districts had the burden of proof in cases that did not involve a change in status quo. 22 See Lane and Aratani (2005) for further description about the case. 23 See Professional Engineers in California Government v. Department of Transportation (1997) and Professional Engineers in California Government v. Kempton (2007). 12 2. How and Why Do Dispute Resolution Requests Vary Across California? Chapter highlights: x Special education dispute resolution requests are concentrated in less than one third of California school districts. x Budgetary factors affecting parents and school districts are strong predictors of their respective filing rates. For instance, a district?s rate of free or reduced price lunch relates inversely to the rate of parent-initiated requests. x School districts request dispute resolution more often when they receive less special education revenue per student and are located in more expensive regional labor markets. x The filing rate for both parties tends to be higher in larger, densely populated school districts, and in districts that serve elementary students. Rates of Dispute Resolution Requests across California School Districts Requests for dispute resolution are distributed unevenly across California school districts, with large differences in the request rate per 1,000 special education students. In 2007? 08, OAH received dispute resolution requests from parties in less than one third of all school districts. Table A.3 lists the top 25 filing rates separately for unified, elementary, and high school districts. 24 Several rates exceeded 15 per 1,000 special education students. Among the school districts included in the table, elementary districts have the highest rates and high school districts have the lowest. In other words, request rates tend to be higher in districts that serve the lower grades, corresponding to when disability identification rates are highest. Los Angeles Unified accounted for 35 percent of all matters filed in the state, but had only the 17th highest filing rate per capita among unified districts with more than 25 special education students. Factors That Predict Higher Rates of Dispute Resolution by Filing Party As noted before, parents initiate 86 percent of all dispute resolution requests in California. One way to think about requests initiated by parents is that they signal a gap between the special education services that parents want school districts to provide for their children and the services that districts propose to provide them. That is, requests reflect a parent?s ?excess demand? for publicly provided special education services. 25 Part of the uneven distribution of request rates may point to differences in excess demand across the state. Basic economic theories about household and school district budgeting predict that the demand for services and the ability to provide them will differ geographically. For instance, 24 The table excludes districts with fewer than 25 special education students because even one case filed in these districts will greatly affect their filing rate. 25 This analogy works less well for some school district-initiated requests. Districts use the process to prove that its assessment is appropriate when parents request an independent one. They also use it to challenge unilateral private school placements where parents seek tuition reimbursement. Finally, due process is a way to evaluate a child for a disability when parents refuse to provide their consent. 13 wealthier families may elect to pursue dispute resolution more often because they can better afford legal representation. The available evidence, although limited, suggests that the monetary costs to families are substantial. Higher income families also may know more about navigating the process and have greater flexibility in their work schedules to see it through. A school district?s ability to provide special education services inevitably relates to budgetary factors as well. We might expect districts to push back more on the services that parents request when budgets are tighter, because additional special education spending means scaling back on other programs. Costs are also important. In particular, more expensive regional labor markets for educators increase the cost of providing services. Rose and Sengupta (2007) show that teacher compensation differs greatly across California labor markets, and that these differences affect the purchasing power of school districts. After adjusting for differences in district budget sizes, we might expect district-initiated requests to be more common in areas of California where professionals with similar qualifications as educators earn higher wages. Rates of requesting dispute resolution probably differ for non-budgetary reasons too. Table A.3 points to a lower dispute rate in high school districts than in elementary districts. Dispute rates may be higher in larger, urban districts as well. These districts may offer a broader range of services, and are probably closer to hospitals and therapy services in the community. As a result, they may attract more families of children with severe disabilities. 26 Patterns of Parent-Initiated Dispute Resolution Requests A regression analysis sheds light on how dispute resolution requests relate to factors such as community income, district revenue, and population density. The advantage of a regression framework is that it holds constant many of the differences across school districts. It addresses questions such as, ?If school districts were identical in many ways but differed in their community income levels, would parent-initiated requests be more common in the higher income districts?? The findings do not say whether higher income causes more disputes to occur. Instead, it predicts whether dispute resolution is more likely in a school district with a particular mix of characteristics than in one with a different mix of characteristics. The first part of the analysis examines patterns of parent-filed dispute resolution requests per 1,000 children with disabilities in 2007?08, and includes the types of requests listed in Figure 1.1. The sample includes data from 954 California school districts with complete information for all variables. Appendix A provides detailed information about the data sources and methods. Table 2.1 lists the strongest predictors of higher parent-initiated request rates based on the school district data. 27 The table illustrates how the predicted request rates differ across school districts. Consider a hypothetical district that has the statewide average value of each variable. Each table value shows the predicted filing rate in a school district that is like the hypothetical ?average? district except for a very low or very high value of one factor. 26 Parrish, Kaleba, Gerber, and McLaughlin (1998) find that differences in low-incidence and high-cost disability rates in California are too large to be due to chance alone. 27 There is suggestive evidence that higher median family income, lower percent English learner, and lower percent Hispanic are also predictors of higher parent-initiated request rates. Table 2.1 excludes these characteristics because the estimates are slightly less precise. 14 Table 2.1 Predicted Rates of Parent-Initiated Dispute Resolution Requests per 1,000 Children with Disabilities for California School Districts, 2007?08 School District Level 10th percentile 90th percentile Students with free or reduced price lunch (%) 4.2 2.4 Other revenue per ADA ($) 2.1 5.6 Asian students (%) 3.5 2.5 Total enrollment 1.1 8.3 Density (enrollment/km 2 ) 2.7 3.5 NOTES: Predictions come from the regression coefficients in column 1 of Table A.5. The findings predict that a school district with the average value of each characteristic will have 3.1 parent-filed requests per 1,000 children with disabilities. The 10th percentile includes the 10 percent of students in school districts with the lowest value for each respective characteristic. The 90th percentile includes the 10 percent of students in districts with the highest value for each characteristic. ?Other? revenue includes all non- special education categorical district funding. ADA stands for average daily attendance. Total enrollment is in natural log form. For example, the analysis predicts higher parent-initiated request rates in districts with lower enrollment rates in the free or reduced price lunch program. Table 2.1 uses the empirical results to show that they predict 4.2 requests per 1,000 children with disabilities in a district that differs from the average district only because of a very low rate of free or reduced price lunch. In contrast, the findings suggest that the ?average? district (not shown in the table) will have 3.1 requests, or 26 percent fewer. Moreover, a district with a very high rate of free or reduced price lunch will have only 2.4 parent-initiated requests per 1,000 children with disabilities. The inverse relationship between parent-initiated request rates and free or reduced price lunch participation supports the theory that family budgets influence parental decisions to pursue dispute resolution. Several other variables are important predictors as well. For instance, more non-special education categorical revenue per ADA is associated with a higher request rate. This appears to suggest that parents pursue dispute resolution more often when they know districts have more revenue to allocate. However, California?s numerous categorical programs and funding formulas make it hard to explain why some districts receive more of this funding than do others. Additional evidence should be collected to substantiate this finding before too much can be conclusively drawn from it. 28 The evidence also suggests that parent-initiated dispute resolution requests are more common in school districts with a lower percentage of Asian students, larger total enrollments, and more urban settings. Lipscomb (2009) shows that disability rates among Asian children in California are the lowest among any major racial or ethnic group. Their special education enrollment rate is just over half the statewide average, plausibly explaining the lower dispute rate as well. 28 The main concern is that some districts may receive higher funding amounts based on factors that also signal whether special education disputes are more likely to occur. In this case, the causality of the association described in Table 2.1 is suspect. 15 Higher predicted parent-initiated request rates in large, densely populated districts are consistent with the theory above that a larger share of children with severe or high-cost disabilities reside in these communities because they offer a greater range of services and care options. Table A.6 lends additional support by examining predictors of districts with higher autism rates, the disability that is most frequently the source of disputes. The data indicate that autism rates do tend to be higher in larger, more densely populated school districts. 29 The evidence suggests that district size and population density help characterize the geographic distribution of autism, a typically high-cost disability. Patterns of District-Initiated Dispute Resolution Requests School districts exhibit somewhat different patterns. Free or reduced price lunch participation and median family income levels relate less strongly to district request rates, holding constant other characteristics. Instead, higher regional wage levels and smaller special education revenue per capita are both strongly associated with a higher district-filed request rate. Although the patterns are different for parents and school districts, both support the theory that budgetary factors help motivate the decision to pursue dispute resolution because school district revenue corresponds to family income and regional wage levels are a proxy for the price of services. 30 Table 2.2 lists the strongest predictors of higher district-initiated request rates, as Table 2.1 does for parent-initiated requests. 31 The findings suggest that very high regional wage levels can raise district-initiated requests 63 percent above the predicted rate of 0.5 in the hypothetical ?average? district to 0.8 requests per 1,000 children with disabilities. Very high levels of special education revenue per capita can lower district-initiated requests by 24 percent, to 0.4 per 1,000 children with disabilities. 29 Higher median family income is also a strong predictor of higher autism rates. Table A.6 expresses the autism rate both as a percentage of special education enrollment and as a percentage of total enrollment. Findings are similar in both cases. 30 California school boards cannot raise property tax rates to augment school revenue. If they could, community income may be an important predictor of district-initiated request rates as well. 31 There is some evidence that a lower percent Hispanic predicts higher district-initiated request rates. 16 Table 2.2 Predicted Rates of District-Initiated Dispute Resolution Requests per 1,000 Children with Disabilities in California, 2007?08 10th percentile 90th percentile Regional wage index 0.3 0.8 Special ed. rev. per ADA ($?s) 0.6 0.4 American Indian students (%) 0.5 0.5 Total enrollment 0.2 1.8 Density (enrollment/km 2 ) 0.4 0.6 NOTES: Predictions come from the regression coefficients in column 3 of Table A.5. The findings predict that a school district with the average value of each characteristic will initiate 0.5 requests per 1,000 children with disabilities. The 10th percentile includes the 10 percent of students in school districts with the lowest value for each respective characteristic. The 90th percentile includes the 10 percent of students in districts with the highest value for each characteristic. Total enrollment is in natural log form. The filing patterns of parents and school districts are similar in a number of ways as well. Specifically, district-initiated dispute rates are also higher in larger, urban districts. Recall that these characteristics are strong predictors of higher autism rates too. The findings point to the possibility that educational programs for children with autism may be contentious from the perspective of both parents and school districts. Differences in Filing Rates by School District Type Another important similarity between the two parties is that their request rates are higher in districts serving the younger grades. Table 2.3 shows predicted request rates by filing party for unified, elementary, and high school districts. Table 2.3 Predicted Request Rates by Filing Party and School District Type in California, 2007?08 Parent-filed District-filed Unified 3.0 0.5 Elementary 3.8 0.6 High school 2.3 0.3 NOTES: Predictions come from Table A.5. The predicted rates are for a school district that has the statewide average value of the other characteristics in the analysis. District-filed rates in unified and elementary districts are not statistically different. All other filing rates are statistically different. For both parents and school districts, request rates are highest in elementary districts and lowest in high school districts. Dhuey and Lipscomb (2009) find that statewide disability rates between 1991?92 and 2003?04 in the United States are highest in grades four through six. The dispute resolution trends across district types in California correspond to the grade levels where most disability identification is occurring. 17 Overall Rates of Dispute Resolution in California School Districts Altogether, the findings support the conclusion that patterns of parent- and district- initiated request rates arise through somewhat different processes. The findings about district size and density highlight similarities as well. The differences arguably stem from the economic constraints facing each party. Overall patterns of dispute resolution requests reflect the combination of these separate processes. To provide a comparison with national estimates, it is nevertheless of interest to examine dispute resolution patterns across school districts without distinguishing the initiating party. The findings are in Table A.5 and Table A.7. 32 Not surprisingly, the results are similar to patterns of the dispute rates initiated by parents, who initiated 86 percent of all cases examined. However, the findings support national evidence from Chambers, Harr, and Dhanani (2003), who conclude that disputes are more common in higher income, larger, and urban school districts. 32 Table A.5 contains the regression results and Table A.7 provides predictions. The findings report separate estimates for hearing requests per 1,000 children with disabilities in 2007?08 and 2006?07. 18 Conclusion How best to structure dispute resolution processes is an ongoing special education policy issue in all states. California?s pattern of resolving most registered disputes before a hearing suggests that something about its process is working. Strategies like mediation, which evoke IDEA?s spirit of negotiation and compromise, are often successful in settling the disagreements that arise in the state. Yet California still faces important challenges. Despite the recent decline in hearing requests, California parents and school districts continue to request dispute resolution at a higher rate than in almost any other state. The state may find it beneficial to consider broadening the use and awareness of the ADR options that show the most potential for resolving disputes locally. For example, IEP facilitation is a way to bring in a third party when designing a child?s educational program, in an effort to head off future impasses before they occur. A solutions panel relies on the advice from third-party parents, special education advocates, or mediators to help reach a consensus. Local mediation resembles the state?s mediation process but tries to keep the atmosphere as non-adversarial as possible by not initiating formal dispute resolution procedures through OAH. The piloting for these strategies is largely underway already in the 20 California SELPAs that currently receive small grants to offer ADR. Expanding awareness and use to other areas would not necessitate changes to existing state law. Henderson (2008) finds that ADR offers a ?perception of neutrality? that helps parties communicate effectively and preserve trust. Her study suggests that ADR can work efficiently, fairly, cost-effectively, and in some cases preventatively. In these respects, parents and schools share common interests. Equity concerns pose additional challenges. The data provide strong evidence of a concentration of requests in wealthier areas. It is in California?s interest to avoid a system of dispute resolution that is open to all but accessible to few. The state has taken a number of steps to address equity concerns. School districts must provide parents with a list of their rights under special education law in their native language. OAH must keep a list of attorneys and advocates offering free or reduced cost legal services on its website. It also now provides outreach programs to parents. Finally, mediation-only is a low-cost approach that may be underutilized. Local ADR strategies should increase accessibility as well. Yet the capacity of families across the state to understand and navigate between dispute resolution options may pose as critical an equity concern for policymakers as does affording the process itself. Public awareness about the availability and effectiveness of various strategies is instrumental to ensuring their effective use. Finally, the results from this study suggest that schools are susceptible to economic factors in providing special education services. California school districts face the reality of both a fixed budget and a disabled student?s entitlement to free, appropriate services regardless of cost. The finding that districts file for dispute resolution more often when their special education budgets are smaller and their expected employee costs are higher suggests a role for economic factors in district decisionmaking. As economic conditions in California become less favorable, education funds may be more constrained. In such an environment, it is increasingly important that parents and schools can negotiate successfully over appropriate services for disabled students through a framework that is equitable, cost-effective, and efficient. 19 Appendix A: Supplementary Material and Results Parental and District Rights Under IDEA 33 x Parental right to an available list of procedural safeguards. x Parental right to receive information in their native language. x Parental right to participate in decision-making meetings and to serve on the IEP team. x Prior written notice when schools propose to initiate, refuse to initiate, or change the identification, evaluation, or educational placement of a child. x Parental consent before an initial assessment can proceed. x Right to a non-discriminatory assessment. x Right to obtain an independent assessment at public expense. x Right to examine all relevant records. x Opportunity to present a complaint to the state education agency related to any part of the process, or alleged violation, within the last two years. x Opportunity to attempt resolving disputes before an impartial mediator. x Right to resolve disputes through an impartial due process hearing. x Right to be accompanied by legal counsel in due process hearings. x Right to appeal the final decision of a due process hearing through civil action. 33 California Department of Education (2007). 20 Data Sources Dispute Resolution Requests Data on mediation and due process requests come from several sources. California data for 2005?06 through 2007?08 come from the OAH Quarterly Reports. California data for 1991? 92 to 2004?05 come from Ahearn (2002) and the Quarterly Reports filed by OAH?s predecessor, the Special Education Hearing Office (SEHO). National state-level data from 2006?07 come from the IDEA dispute resolution data file. Data for 2003?04 to 2005?06 come from the Consortium for Appropriate Dispute Resolution in Special Education (CADRE). Finally, national data for 1991?92 to 2000?01 come from Ahearn (2002). All data are currently available online from each source. Special Education Enrollment Data National special education enrollment data come from the IDEA child count data that are available by state for 1991?92 to 2006?07. California school district special education enrollment data come from a district-level version of the California Special Education Management Information System (CASEMIS) for 2007?08. The CASEMIS data are tabulated by the district of residence. Special education enrollment includes all children with disabilities who are 3?21 years old. Demographic and Financial Data for California School Districts Regional educator wage levels, district revenue, and enrollment density come from version 2.3 of the simulation model developed by Rose, Sengupta, Sonstelie, and Reinhard (2008) for the 2004?05 year. The regional wage level variable is an index developed by Rose and Sengupta (2007) that captures the wage of non-educators with similar qualifications as educators in each local labor market. The Rose et al (2008) study is arguably the most reliable source of financial data on California school districts because an advisory group from the Department of Education, the Legislative Analyst?s Office, and the Department of Finance guided its collection. Unrestricted revenue comprises the revenue programs they list as ?Funds allocated by the state but unrestricted in use.? Special education revenue comprises the revenue categories they list under ?Special education.?In California, special education revenue goes to each SELPA. Member districts allocate the revenue according to how they agree to operate programs. Because California allocates revenue to SELPAs based largely on their total ADA, Rose et al (2008) assign each SELPA?s per ADA funding amount to each member district. The third revenue category used in this report includes all remaining programs in the simulation. Median family income comes from the 2000 U.S. Census School District Tabulation. The remaining data are for 2006?07. Race/ethnicity, district type, English learner, and total enrollment data come from CDE. Free and reduced price lunch data come from the CalWORKS data files that CDE maintains. All monetary values are in 2006 dollars. Other Data The data for Table A.2, as well as an explanation of the sources and variable definitions, come from Dhuey and Lipscomb (2009). 21 Estimation Methods Fixed Effects Analysis : x Table A.2 The fixed effects model uses repeated observations on states over time to draw inference about how changes in factors like the adoption of new school finance policies are associated with changes in due process requc o like the following equation: est rates. The spe ificati n looks U ? ? L? 4 E? 5 T 5 ? ? ET ? ? ? EP ? EO ? E? ? ? . The variable y st is the due process request rate per 1,000 special education students. The subscript s denotes states from 1 to 50 and t denotes years from 1991?92 to 2000?2001. The variable of interest, x 1 indicates whether state s used a census-based funding system in year t. ? 1 is the effect of adopting census funding on the frequency of requesting dispute resolution. The other factors included in the analysis are described by x. The analysis also incorporates a few other factors. First, it includes a series of year indicators, t, to adjust for national time trends. Second, it includes a series of state indicators, s, that account for all time invariant factors about states between 1991?92 and 2000?2001. ? 0 is a constant. Finally, ? is an unobserved error term. Tobit Analysis : x Table A.5 x Tables 2.1-2.3 The findings in Chapter 2 come from a type of regression known as a Tobit model. The Tobit model is appropriate when a variable, such as dispute resolution requests per 1,000 children with disabilities, is zero in some school districts but could be any positive number in other districts. This is an example of what statisticians call a ?corner solution.? Related examples include retirement account contributions, life insurance purchases, and donations to charity. In cases like these, a Tobit model allows researchers to calculate the results accurately. Conceptually, an ordinary regression is inappropriate because it will predict that some school districts ought to have a negative filing rate. Addressing this problem is especially important here because requests are concentrated in less than one third of California school districts. The Tobit model estimates the following equation: U L ? ? ? 4 ET? E? ? U ? L ? 4 ET? E? ? ?U ? ? Pr r? U ? ? Qr . The outcome variable y i * is the request rate that we would observe in school district i if requests were not bound to be at least zero. Interpreting this variable in applications like this is seldom useful, because it is difficult to envision what a negative rate of dispute resolution requests might be. For example, it may signal districts where parents and schools are increasingly likely 22 to agree. The main interest is y i , the actual frequency of dispute resolution requests per capita. This variable equals zero whenever y i *?0 and equals y i * whenever y i *>0. The variables in x are predictors of why disputes are more common in some districts than in others. Regression Coefficients ? represents the regression coefficients. In an ordinary regression model like Table A.6, ? is the relationship between x and the outcome variable of interest. Researchers call this relationship a ?marginal effect.? Estimating ? also allows us to make predictions based on the model. The Tobit framework is different because estimates of ? are not the marginal effects we seek. They describe the relationship between x and y i *. We care about the relationship between x and y i . Estimates of ? are nevertheless important for two reasons: They allow us to make predictions based on the model and to calculate the relevant marginal effects. The top half of Table A.5 contains the regression coefficients. Predicted Dispute Resolution Request Rates Tables 2.1?2.3 use the regression coefficients to predict request rates for districts with different characteristics. The predictions come from the following formula: ' : U ? T ? ; L0 @ ? ?  A FT ? ? E? %@ ? ?   A ?@ ? ?   A G. The E(y i |x i ) is the expected number of dispute resolution requests per 1,000 children with disabilities. Like y i itself, zero is the minimum value possible. The predictions are for hypothetical school districts with the statewide average value of each characteristic in x except for a very low or very high value of one characteristic. The Marginal Effect of Each Characteristic The bottom half of Table A.5 uses the regression coefficients to calculate the relevant marginal effects. The marginal effect is the relationship between each characteristic in x and the observed rate of dispute resolution requests, yrm la for calculating them is: i . The fo u ! ? : ? ?  ? ? ; ! ? ? L? ? 0 @ ? ?  A . The subscript k identifies the components of x. The first part of the right hand side of the equation is the effect of factor k on y i *. The remainder of the expression is the probability that a school district with a particular mix of characteristics will have zero requests filed. The marginal effect is the product of these two pieces. The marginal effects listed in Table A.5 apply to a hypothetical school district with the statewide average value of each characteristic. 23 Appendix Tables Table A.1 Hearing and Mediation Requests per 1,000 Children with Disabilities, 2006?07 State Hearing Requests Mediation Requests State Hearing Requests Mediation Requests New York 13.250.96Florida 0.47 0.47 Hawaii 6.640.28Arizona 0.46 0.29 California 3.744.08New Mexico 0.42 0.40 Massachusetts 3.575.07Mississippi 0.41 0.37 New Jersey 3.442.44Virginia 0.40 0.76 Maryland 2.993.09Tennessee 0.40 0.29 Connecticut 2.863.34Idaho 0.39 0.11 Pennsylvania 2.521.04Oregon 0.37 0.89 New Hampshire 1.971.11Michigan 0.32 0.36 Vermont 1.862.86Minnesota 0.29 0.55 Rhode Island 1.553.01Colorado 0.29 0.38 Alaska 1.410.34West Virginia 0.29 0.12 Delaware 1.291.14North Carolina 0.25 0.57 Illinois 1.120.58Wisconsin 0.25 0.68 Maine 1.043.43Oklahoma 0.24 0.29 Washington 0.990.85Kentucky 0.23 0.17 Alabama 0.970.91Montana 0.22 0.16 Nevada 0.830.31Louisiana 0.19 0.16 Ohio 0.770.49South Carolina 0.13 0.02 Texas 0.670.48Arkansas 0.12 0.25 Kansas 0.590.64Utah 0.11 0.05 Wyoming 0.570.07South Dakota 0.11 0.00 Georgia 0.560.50Iowa 0.06 0.34 Missouri 0.50 0.19Nebraska 0.02 0.25 Indiana 0.490.23North Dakota 0.00 0.22 50 States and D.C. 2.071.17 SOURCE: IDEA Dispute Resolution and Child Count Data for 2006?07. NOTE: Hearing, mediation, and special education enrollment data pertain to 3?21 year old students. Mediation counts include both mediation only requests and mediations requested in conjunction with due process. The table omits the District of Columbia, which has a higher hearing request rate than any state. 24 Table A.2 Factors Associated with Growth in Hearings Requested per 1,000 Special Education Students in the United States, 1991?92 to 2000?01 Unweighted Weighted by special education enrollment Unweighted Weighted by special education enrollment Census funding {0,1} 0.25 0.65 0.25 0.60 (0.23) (0.20) (0.21) (0.23) Plaintiff victory {0,1} 0.26 -0.01 0.13 -0.11 (0.35) (0.25) (0.33) (0.29) Free lunch (%) 1.91 -0.76 2.00 -0.36 (1.61) (1.31) (1.33) (1.19) State accountability sys. {0,1} -0.17 -0.15 -0.14 -0.10 (0.13) (0.15) (0.12) (0.13) SSI payment ($?s) 0.21 -0.23 0.25 -0.60 (0.56) (0.67) (0.49) (0.61) Unemployment rate -0.08 -0.17 -0.05 -0.14 0.06 (0.09) (0.05) (0.08) Black (%) 0.30 0.31 0.20 0.25 (0.36) (0.41) (0.36) (0.38) Hispanic (%) 0.03 0.09 -0.10 -0.04 (0.05) (0.10) (0.10) (0.13) Other race (%) 0.07 0.03 -0.02 -0.04 (0.15) (0.19) (0.15) (0.16) Ln(total enrollment) 4.50 4.72 (2.26) (2.70) Special ed rev. per pupil ($?s) 0.49 -0.78 (0.70) (0.63) Other revenue per pupil ($?s) -0.05 -0.18 (0.18) (0.17) Ln(avg teacher salary) -0.32 -2.87 (1.10) (1.90) Teacher pupil ratio -0.05 -0.15 (0.10) (0.09) Dependent var. mean 1.2 1.4 1.2 1.4 Dependent var. sd 1.3 1.4 1.3 1.4 Observations 464 464 464 464 Adj. R-squared 0.8 0.9 0.8 0.9 SOURCE: Author?s calculation based on data from Dhuey and Lipscomb (2009) and Ahearn (2002). NOTE: Standard errors are in parentheses. Bold denotes statistical significance at the 5 percent level. Regressions include a constant, an indicator for missing free lunch data, and state and year fixed effects. SSI payments and revenue per pupil are reported in $1,000s. SSI data are statewide average monthly disability payments. Robust standard errors are clustered at the state level. Observations are less than 500 due to missing hearing data in some state-year cells. 25 Table A.3 California School Districts with Top 25 Dispute Resolution Filing Rates per 1,000 Children with Disabilities, 2007?08 Unified Districts Elementary School Districts High School Districts Rank District Filing rate District Filing rate District Filing rate 1 Alpine County 30.3 Upper Lake Union 78.4Acalanes Union 19.3 2 Laytonville 27.0 San Miguel Joint Union 42.9Chowchilla Union 14.7 3 Santa Monica-Malibu 26.8 Hermosa Beach City 41.3Los Gatos-Saratoga J.U. 9.4 4 Laguna Beach 23.1 Grant 35.7Sequoia Union 7.5 5 Mountain Valley 21.7 Larkspur 34.2San Rafael City 7.3 6 Shandon Joint 18.5 Pacifica School District 32.9Fallbrook Union 7.0 7 Berkeley 17.9 Oak Grove Union 27.0San Dieguito Union 6.2 8 Culver City 17.8 Pollock Pines 25.2San Mateo Union 5.8 9 Coronado 17.6 Woodside 23.8Tamalpais Union 5.3 10 Newport-Mesa 17.4 Ross 23.3Huntington Beach Union 4.5 11 Albany City 15.5 North County J.U. 23.3Galt Joint Union 4.3 12 Ripon 15.0 Elverta Joint 22.2Oxnard Union 3.9 13 Dublin 14.8 Burlingame 19.5West Sonoma C.U. 3.4 14 Oak Park 13.9 Cupertino Union 19.3Campbell Union 3.3 15 Irvine 13.2 Central Union 19.2San Benito 3.2 16 Kelseyville 11.6 Colfax 17.9Fullerton Joint Union 3.2 17 Los Angeles 11.3 Orinda Union 17.2El Dorado Union 2.9 18 Brea-Olinda 10.3 Jefferson 17.0Perris Union 2.9 19 Novato 10.3 Spencer Valley 15.4Mountain View-Los Altos 2.6 20 Mojave 9.6 Montecito Union 15.4Antelope Valley Union 2.6 21 Capistrano 9.1 Gravenstein Union 14.3William S. Hart Union 2.5 22 Beverly Hills 8.7 Exeter Union 13.6Fremont Union 2.5 23 Scotts Valley 8.7 Spreckels Union 13.5Anaheim Union 2.5 24 Castro Valley 8.3 Vallecito Union 13.3Salinas Union 2.4 25 Coast 8.0 Solana Beach 12.6 Liberty Union 2.3 SOURCES: OAH (2008) and CASEMIS. NOTE: Rankings exclude districts with fewer than 25 special education students. 26 Table A.4 Summary Statistics for California School District Characteristics Variable Average value Standard deviation 10th percentile 90th percentile Median family income ($?s) 65,744.722,618.244,401.5 95,492.8 Free or reduced price lunch (Fraction) 0.50.20.2 0.8 English learners (Fraction) 0.30.10.1 0.4 Regional wage index 1.00.10.9 1.1 Unrestricted revenue per ADA ($?s) 5,611.0710.55,260.8 6,186.1 Special ed revenue per ADA ($?s) 743.1140.8624.7 923.9 Other revenue per ADA ($?s) 1,441.4608.8856.3 2,679.4 Hispanic (%) 7.97.11.3 17.3 Asian (%) 49.123.516.6 77.8 African American (%) 11.711.42.6 25.2 American Indian (%) 0.82.00.2 1.3 Ln(total enrollment) 10.11.78.2 13.5 Density (enrollment / km 2 ) 307.6280.419.3 575.6 Elementary school district {0,1} 0.20.40.0 1.0 High school district {0,1} 0.10.30.0 0.0 NOTE: Statistics are weighted by special education enrollment. Monetary values are reported in 2006 dollars. The sample includes 954 California school districts. 27 Table A.5 Factors Associated with Higher Dispute Resolution Rates per 1,000 Children with Disabilities in California, 2007?08 and 2006?07 2007 ? 08 2006 ? 07 Parent filed District filed All cases All cases Coeff. Std. error Coeff. Std. error Coeff. Std. error Coeff. Std. error A. Regression Coefficients Median family income ($?s) 0.0220.0140.0080.005 0.034 0.015 0.066 0.019 Free/reduced price lunch (Frac.) -3.616 1.777-0.6371.034-3.4921.795 -3.198 1.377 English learners (Frac.) -2.9411.8062.0312.458-2.5552.689 -1.9053.026 Regional wage index 6.9394.562 8.108 3.57110.0265.453 1.1716.601 Unrestricted rev. per ADA ($?s) 0.2900.2290.2060.1790.4030.285 0.0510.237 Special ed. rev. per ADA ($?s) 0.2350.999 -1.804 0.748-0.5951.241 -2.7051.417 Other revenue per ADA ($?s) 2.286 0.455-0.6820.424 1.863 0.566 2.194 0.720 Hispanic (%) -0.0250.014-0.0320.018-0.0340.018 -0.0260.023 Asian (%) -0.056 0.019-0.0110.012 -0.055 0.019 -0.059 0.012 African American (%) 0.0120.029-0.0190.0210.00030.034 0.0190.046 American Indian (%) -0.1340.221 0.081 0.0390.0040.087 -0.0360.114 Ln(total enrollment) 1.635 0.170 0.636 0.179 1.788 0.200 1.988 0.328 Density (enrollment / km 2 ) 0.176 0.066 0.113 0.055 0.241 0.071 0.269 0.079 Elementary school district {0,1} 0.854 0.3830.2760.309 1.120 0.377 0.6540.369 High school district {0,1} -1.064 0.499 -0.686 0.327 -1.321 0.514 -1.461 0.652 Constant -23.363 3.533 -13.162 4.360 -27.615 4.681 -20.872 7.015 School districts with cases filed 308 143 331 323 Pseudo R-squared 0.146 0.096 0.121 0.063 B. Marginal Effects Estimated at the Statewide Average Value of Each Characteristic Median family income ($?s) 0.0180.0110.0030.002 0.027 0.013 0.045 0.017 Free/reduced price lunch (Frac.) -2.899 1.376-0.2480.402 -2.830 1.417 -2.197 0.963 English learners (Frac.) -2.3581.4540.7920.989-2.0702.165 -1.3092.057 Regional wage index 5.5633.698 3.161 1.2808.1234.467 0.8054.493 Unrestricted rev. per ADA ($?s) 0.2320.1870.0800.0720.3260.235 0.0350.163 Special ed. rev. per ADA ($?s) 0.1880.806 -0.703 0.293-0.4820.993 -1.8590.986 Other revenue per ADA ($?s) 1.833 0.420-0.2660.168 1.509 0.492 1.508 0.608 Hispanic (%) -0.0200.012-0.0130.007-0.0280.015 -0.0180.016 Asian (%) -0.045 0.014-0.0040.005 -0.044 0.015 -0.041 0.011 African American (%) 0.0100.023-0.0070.0080.00020.027 0.0130.032 American Indian (%) -0.1070.179 0.032 0.0150.0030.070 -0.0250.079 Ln(total enrollment) 1.311 0.151 0.248 0.064 1.449 0.175 1.366 0.138 Density (enrollment / km 2 ) 0.141 0.049 0.044 0.020 0.196 0.051 0.185 0.051 Elementary school district {0,1} 0.702 0.3260.1120.135 0.933 0.325 0.4570.279 High school district {0,1} -0.810 0.378 -0.229 0.092 -1.014 0.397 -0.951 0.439 NOTE: The estimates come from a Tobit model where the frequency of dispute resolution requests is either zero or positive. The marginal effects are for the E[y|x]. The regression is weighted by special education enrollment to be representative of disabled students in the state. The sample includes 954 districts with complete information. Heteroskedasticity-consistent standard errors are clustered by metropolitan statistical area. Revenue estimates are multiplied by 1,000. Density estimates are multiplied by 100. 28 Table A.6 Predictors of Higher Autism Rates in California School Districts, 2007?08 Variable Autism Rate per Special Education Enrollment Autism Rate per Total Enrollment Coeff. Std. dev. Coeff. Std. dev. Median family income ($?s) 0.027 0.011 0.004 0.002 Free/reduced price lunch (Frac.) -2.5751.481 -0.255 0.149 English learners (Frac.) -0.5682.193 0.133 0.226 Regional wage index 3.1723.859 0.464 0.407 Unrestricted rev. per ADA ($?s) 0.0170.111 0.006 0.014 Special ed. rev. per ADA ($?s) -2.4181.334 -0.169 0.136 Other revenue per ADA ($?s) 0.0570.395 0.049 0.042 Hispanic (%) -0.0290.025 -0.005 0.003 Asian (%) 0.0160.024 -0.002 0.002 African American (%) -0.0600.037 -0.006 0.004 American Indian (%) -0.0240.031 -0.002 0.003 Ln(total enrollment) 0.972 0.180 0.113 0.017 Density (enrollment / km 2 ) 0.256 0.034 0.020 0.005 Elementary school district {0,1} 2.503 0.445 0.274 0.058 High school district {0,1} -1.693 0.401 -0.196 0.048 Constant -4.1133.390 -0.751 0.372 Mean value 6.9380.745 R-squared 0.506 0.500 NOTE: Column 1 and 2 statistics are weighted by special education enrollment. Column 3 and 4 statistics are weighted by total enrollment. The sample includes 954 districts with complete information. Heteroskedasticity-consistent standard errors are clustered by metropolitan statistical area. Revenue estimates are multiplied by 1,000. Density estimates are multiplied by 100. Table A.7 Predicted Total Dispute Resolution Request Rates per 1,000 Children with Disabilities, 2007?08 and 2006?07 2007 ? 08 2006 ? 07 10th percentile 90th prcentile 10th percentile 90th percentile Median family income ($?s) 3.1 4.5 3.3 5.7 Free/reduced price lunch (Frac.) 4.7 3.0 5.0 3.7 Other revenue per ADA ($?s) 2.8 5.7 3.4 6.3 Asian (%) 4.1 3.1 4.6 3.7 Ln(total enrollment) 1.4 9.4 2.0 9.9 Density (enrollment/km 2 ) 3.1 4.2 3.7 4.7 Elementary school district {0,1} 3.5 4.4 4.1 4.6 High school district {0,1} 3.8 2.8 4.3 3.3 NOTES: Predictions come from the regression coefficients in specifications 3 and 4 of Table A.5. The predicted value for the average school district is 4.2 in 2006?07 and 3.7 in 2007?08. The 10th percentile is the value of a characteristic at which one in ten special education students attends a school district with a lower value for that characteristic. 29 Glossary: Disability Categories and Definitions Autism ??a developmental disability significantly affecting verbal and nonverbal communication and social interaction, generally evident before age three, that adversely affects educational performance. Other characteristics often associated with autism are engaging in repetitive activities and stereotyped movements, resistance to environmental change or change in daily routines, and unusual responses to sensory experiences.? Deaf- Blindness ??concomitant hearing and visual impairments, the combination of which causes such severe communication and other developmental and educational needs that they cannot be accommodated in special education programs solely for children with deafness or children with blindness.? Deafness ??a hearing impairment that is so severe that a child is impaired in processing linguistic information through hearing, with or without amplification that adversely affects a child's educational performance.? Emotional Disturbance ??a condition exhibiting one or more of the following characteristics over a long period of time and to a marked degree that adversely affects a child's educational performance: (a) An inability to learn that cannot be explained by intellectual, sensory, or health factors. (b) An inability to build or maintain satisfactory interpersonal relationships with peers and teachers. (c) Inappropriate types of behavior or feelings under normal circumstances. (d) A general pervasive mood of unhappiness or depression. (e) A tendency to develop physical symptoms or fears associated with personal or school problems. Emotional disturbance includes schizophrenia. The term does not apply to children who are socially maladjusted, unless it is determined that they have an emotional disturbance?? Hearing Impairment ??an impairment in hearing, whether permanent or fluctuating, that adversely affects a child?s educational performance but that is not included under the definition of deafness.? Mental Retardation ??significantly subaverage general intellectual functioning, existing concurrently with deficits in adaptive behavior and manifested during the developmental period, that adversely affects a child?s educational performance.? Multiple Disabilities ??concomitant impairments (such as mental retardation-blindness, mental retardation-orthopedic impairment), the combination of which causes such severe educational needs that they cannot be accommodated in special education programs solely for one of the impairments. Multiple disabilities does not include deaf-blindness.? Orthopedic Impairment ??a severe orthopedic impairment that adversely affects a child?s educational performance. The term includes impairments caused by a congenital anomaly, 30 impairments caused by disease (e.g. poliomyelitis, bone tuberculosis), and impairments from other causes (e.g., cerebral palsy, amputations, and fractures or burns that cause contractures).? Other Health Impairment ??having limited strength, vitality, or alertness, including a heightened alertness to environmental stimuli, that results in limited alertness with respect to the educational environment, that?(a) is due to chronic or acute health problems such as asthma, attention deficit disorder or attention deficit hyperactivity disorder, diabetes, epilepsy, a heart condition, hemophilia, lead poisoning, leukemia, nephritis, rheumatic fever, and sickle cell anemia; and (b) adversely affects a child?s educational performance.? Specific Learning Disability ??a disorder in one or more of the basic psychological processes involved in understanding or in using language, spoken or written, that may manifest itself in an imperfect ability to listen, think, speak, read, write, spell, or to do mathematical calculations, including conditions such as perceptual disabilities, brain injury, minimal brain dysfunction, dyslexia, and developmental aphasia. Specific learning disability does not include learning problems that are primarily the result of visual, hearing, or motor disabilities, of mental retardation, of emotional disturbance, or of environmental, cultural, or economic disadvantage.? Speech or Language Impairment ??a communication disorder such as stuttering, impaired articulation, a language impairment, or a voice impairment, that adversely affects a child?s educational performance.? Traumatic Brain Injury ??an acquired injury to the brain caused by an external physical force, resulting in total or partial functional disability or psychosocial impairment, or both, that adversely affects a child's educational performance. Traumatic brain injury applies to open or closed head injuries resulting in impairments in one or more areas, such as cognition; language; memory; attention; reasoning; abstract thinking; judgment; problem-solving; sensory, perceptual, and motor abilities; psychosocial behavior; physical functions; information processing; and speech. Traumatic brain injury does not apply to brain injuries that are congenital or degenerative, or to brain injuries induced by birth trauma.? Visual Impairment Including Blindness ??an impairment in vision that, even with correction, adversely affects a child?s educational performance. The term includes both partial sight and blindness.? SOURCE: 34 CFR Section 300.8 of the Federal Register (2006). 31 Acronyms ADA Average Daily Attendance ADR Alternative Dispute Resolution ALJ Administrative Law Judge CADRE Consortium for Appropriate Dispute Resolution in Special Education CASEMIS California Special Education Management Information System CDE California Department of Education IDEA Individuals with Disabilities Education Act IEP Individualized Education Program NLTS2 National Longitudinal Transition Study 2 OAH Office of Administrative Hearings SEHO Special Education Hearing Office SELPA Special Education Local Plan Area 32 References Ahearn, Eileen, ?Due Process Hearings: 2001 Update,? Project Forum, National Association of State Directors of Special Education, 2002, available at www.nasdse.org. California Department of Education, ?Notice of Procedural Safeguards,? 2007, available at www.cde.ca.gov/sp/se/qa/documents/pseng.pdf. Chambers, Jay G., Jenifer J. Harr, and Amynah Dhanani, ?What Are We Spending on Procedural Safeguards in Special Education, 1999-2000?? Special Education Expenditure Project Report 4, American Institutes for Research, Palo Alto, CA, 2003. Dhuey, Elizabeth and Stephen Lipscomb, ?The Effects of Fiscal Incentives in Special Education: Evidence from Capitation Finance Reforms,? PPIC working paper, 2009. Federal Register, ?34 CFR Parts 300 and 301: Assistance to States for the Education of Children With Disabilities and Preschool Grants for Children With Disabilities; Final Rule,? Federal Register, Vol. 71, No. 156, 2006. Golden, Daniel, ?Schools Beat Back Demands For Special Ed Services,? The Wall Street Journal, July 24, 2007. Henderson, Kelly, ?Optional IDEA Alternative Dispute Resolution,? Project Forum and The Consortium for Appropriate Dispute Resolution in Special Education, 2008, available at www.projectforum.org. Lane, Charles and Lori Aratani, ?High Court to Hear Md. Special-Ed Case,? Washington Post, October 5, 2005. Lipscomb, Stephen, ?Students with Disabilities and California?s Special Education Program,? 2009, Public Policy Institute of California, San Francisco, CA. Marshall, Scott, ?Lawsuit Challenges State Special Education Hearings,? North County Times, February 11, 2008. National Longitudinal Transition Study 2, NLTS2 Data Table 91, 2003, available at www.nlts2.org. Office of Administrative Hearings, ?Special Education Division Quarterly Report, Fourth Quarter 2007/2008 Fiscal Year,? 2008, www.oah.dgs.ca.gov. Office of Administrative Hearings, ?Special Education Division Quarterly Report, Fourth Quarter 2006/2007 Fiscal Year,? 2007, www.oah.dgs.ca.gov. Parrish, Thomas, Daniel Kaleba, Michael Gerber, Margaret McLaughlin, ?Special Education: Study of Incidence of Disabilities,? American Institutes for Research, Palo Alto, CA, 1998. Rose, Heather, and Ria Sengupta, ?Teacher Compensation and Local Labor Market Conditions in California: Implications for School Funding,? Public Policy Institute of California, Occasional Paper, 2007. 33 Rose, Heather, Ria Sengupta, Jon Sonstelie, and Ray Reinhard, ?Funding Formulas for California Schools: Simulations and Supporting Data,? Public Policy Institute of California, Occasional Paper, 2008. Samuels, Christina A., ?States Found Moving to Head Off Due-Process Hearings,? Education Week, Vol. 27, Issue 42, 2008, p. 12. State of California, ?Annual Performance Report for Federal Fiscal Year 2006,? 2008, available at www.cde.ca.gov. Zirkel, Perry A. and Karen L. Gischlar, ?Due Process Hearings Under the IDEA: A Longitudinal Frequency Analysis, Journal of Special Education Leadership, Vol. 21, No. 1, 2008, pp. 22-31. PUBLIC POLICY INSTITUTE OF CALIFORNIA Board of Directors Thomas C. Sutton, Chair Retired Chairman and Chief Executive Officer Pacific Life Insurance Company Mark Baldassare President and Chief Executive Officer Public Policy Institute of California Ruben Barrales President and Chief Executive Officer San Diego Regional Chamber of Commerce John Bryson Retired Chairman and CEO Edison International Edward K. Hamilton Chairman Hamilton, Rabinovitz & Associates, Inc. Gary K. Hart Former State Senator and Secretary of Education State of California Walter B. Hewlett Director Center for Computer Assisted Research in the Humanities Donna Lucas Chief Executive Officer Lucas Public Affairs Ki Suh Park Design and Managing Partner Gruen Associates Constance L. Rice Co-Director The Advancement Project Raymond L. Watson Vice Chairman of the Board Emeritus The Irvine Company Carol Whiteside President Emeritus Great Valley Center PUBLIC POLICY INSTITUTE OF CALIFORNIA 500 Washington Street, Suite 600 San Francisco, California 94111 phone: 415.291.4400 fax: 415.291.4401 PPIC SACRAMENTO CENTER Senator Office Building 1121 L Street, Suite 801 Sacramento, California 95814 phone: 916.440.1120 fax: 916.440.1121 www.ppic.org" ["post_date_gmt"]=> string(19) "2017-05-20 09:40:01" ["comment_status"]=> string(4) "open" ["ping_status"]=> string(6) "closed" ["post_password"]=> string(0) "" ["post_name"]=> string(8) "r_209slr" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2017-05-20 02:40:01" ["post_modified_gmt"]=> string(19) "2017-05-20 09:40:01" ["post_content_filtered"]=> string(0) "" ["guid"]=> string(50) "http://148.62.4.17/wp-content/uploads/R_209SLR.pdf" ["menu_order"]=> int(0) ["post_mime_type"]=> string(15) "application/pdf" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" ["status"]=> string(7) "inherit" ["attachment_authors"]=> bool(false) }