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1 of 16 A peer-reviewed scholarly journal Editor: Gene V Glass College of Education Arizona State University Copyright is retained by the first or sole author, who grants right of first publication to the EDUCATION POLICY ANALYSIS ARCHIVES EPAA is a project of the Education Policy Studies Laboratory. Articles appearing in EPAA are abstracted in the Current Index to Journals in Education by the ERIC Clearinghouse on Assessment and Evaluation and are permanently archived in Resources in Education Volume 12 Number 11March 16, 2004ISSN 1068-2341Public Policy and the Shaping of Disability: Incide nce Growth in Educational Autism Dana Lee Baker University of Missouri-ColumbiaCitation: Baker, D. L., (2004, March 16). Public Po licy and the Shaping of Disability: Incidence Growt h in Educational Autism. Education Policy Analysis Archives, 12 (11). Retrieved [Date] from a/v12n11/.AbstractAutism has gained the attention of policy makers an d public administrators in recent years. The surge in preval ence, in tandem with a growing social preference for community incl usion of individuals with disabilities, strains a variety of policy infrastructures. Autism and related disorders, whic h were first described in 1943, were originally thought to be ex tremely low incidence and usually coincident with mental retard ation. In accordance with the disability policy paradigm of t he era, public services for autism were provided predominantly in institutional settings. Since then, however, autism and related d isorders have come to be understood as more common than was origi nally thought and more rarely associated with mental reta rdation. In this article, shift-share analysis is used to gain insig ht into how the growth in autism incidence is being differentially experienced and recorded within a single arena of policy across the United States. The challenges associated with a sudden growth in s upply (that is the number of children with autism), while unique t o autism in some respects, include aspects that are similar for other disabilities and in policy challenges in other aren as. Especially since the implementation of the Government Performa nce Results Act of 1996, there is increased pressure to create public policy


2 of 16 infrastructures that are anchored by clearly cut ca tegorical service delivery. If the categories themselves leave signif icant room for interpretation and their use actually has a shaping effect on the target population, then it is important to administ ration and policy evaluation to understand how the effect is playing out. The increased prevalence of autism spectrum disorde rs has come to the attention of a variety of public agencies over the past decade (Be rtrand et al, 2001; Croen et al., 2000). A dramatic rise in incidence of a developmental disab ility over a short period of time is a pervasive administrative and policy challenge in an d of itself. However, many circumstances surrounding the surge in autism make an effective public response elusive at best. These circumstances include: largely uncertai n (and highly contested) causality; politicized and somewhat polarized treatments; an e ra of increasing reliance on community and civil rights based policy responses to disabili ty; and, for the time being at least, oftentimes unknown prognosis (Feinberg and Vacca, 2 000, p. 130). Such circumstances beg not only for more attention toward autism on government policy agendas, but also beg the question of potential pat terns in observable growth in autism in light of varying public infrastructures. In order t o best address the recorded growth in autism, it is important to directly consider the hy pothesis that the recorded growth is a matter of observation as much as it is a matter of proliferation. Furthermore, since autism and its related disorders are viewed by most as a c ontinuum and, to some degree, a construction, the patterns of growth that cannot be immediately explained by obvious environmental or socio-economic factors are of part icular interest to policymakers (Bargerhuff, 2003; Simpson, 2003; Tinge, 2002).There have been efforts to reshape the public polic y infrastructure directed at autism in recent years (Feinberg and Vacca, 2000). For exampl e, states such as Maryland and Indiana have Medicaid waivers directed specifically at autism. Nevertheless, as the children that are part of the autism baby boom have yet to r each their teenage years, the work necessary for effectively adapting public infrastru ctures to respond to the autism challenge is far from complete. As terms such as educational autism—which sometimes serve to differentiate what a school considers to be autism from the opinions of the medical community--suggest, the definition and nature of au tism itself has not even been solidified to the general satisfaction of stakeholders. Unders tanding how the incidence of autism is being differentially recorded across the states is helpful to the management of public policy challenges associated with autism. It is also among the first steps on a path toward understanding any potentially bi-causal relationshi ps between developing public structures and the incidence of autism as recorded in the educ ational environments of individual states.In this article, a shift-share analysis of the inci dence of autism and related disorders (hereinafter referred to as autism) reported as par t of the Individuals with Disabilities Education Act (IDEA) is used to gain insight into t he nature of the growth patterns of autism as it is being experienced in the public education system. Since the public education system most comprehensively touches the lives of children in the United States and the observed incidence of autism is currently highest among youn g children, the public administration of autism happens most frequently within the public sc hools. A better understanding of the growth pattern of the recorded incidence of autism is likely to help in the development of public policy that more appropriately addresses soc iety’s challenges associated with the incidence of autism.Autism and the Public ContextThe word “autism” was first coined in 1911 by Eugen Bleuler, a Swiss psychiatrist (Williams


3 of 16 2000) who described it as a temporary disorder rela ted to schizophrenia. However, Leo Kanner, who studied a group of children with what c ame to be known as early onset autism, more (in)famously reinterpreted autism as non-tempo rary disorder and emphasized a connection to mental retardation and the need for i nstitutionalization (Kanner, 1943). One of the causes often assigned to autism in the early ye ars was the supposed “refrigerator mother” phenomena. Essentially, it was believed tha t children with autism chose to withdraw into an internal world because they were b urdened with emotionally shutdown or cruel—and almost invariably WASP--mothers who did n ot show them enough or appropriate affection to allow the children to deve lop normally. As a result, a common early treatment for autism was therapy for the child’s mo ther. This understanding of autism, combined with the pre vailing tenor of disability policy in the middle part of the twentieth century that encourage d separation of individuals with disabilities from the general public (O’Briend, 200 3; Jongbloed, 2003), meant that autism fell from most policy agendas. However, during the last part of the twentieth century, a dramatic change in the general perception of autism began to take place. Autism came to be understood as a complex disorder that was not ca used solely by external factors, at least not external factors are simple as having a parent that did not provide enough love (Stokstad, 2001, Rutter, 2000). The causality of au tism is now an open question toward which significant resources and research time are c urrently being directed. A crucial component of the modern reformation of th e understanding of autism was the reconsideration of the question “What exactly is au tism?” During the 1990s, international efforts were made to specify the definition of auti sm. For example, the definition from the Diagnostic and Statistical Manual of Mental Disorde rs, Fourth Edition (DSM-IV), published in 1994, includes criteria in three categories: qua litative impairment in social interaction; qualitative impairments in communication; and delay s or abnormal functioning in either social interaction language as used in social commu nications or symbolic or imaginative play with onset prior to age three. In order to be diagnosed as having autism, a person must have a set number of characteristics in these categ ories from a defined list of possible symptoms.According to Dr. Deborah Hirtz of the National Inst itute of Neurological Disorders and Stroke, autism is “a complex, life-long, developmen tal disability that results in difficulty with social interactions, problems in communication, and restrictive or repetitive interests and behavioral challenges. There is considerable variab ility in the severity of the symptoms, and intellectual function can range from profound menta l retardation to above mean performance on IQ tests” (Hirtz 2000). However, the specifics of the definition of autism are hotly debated. For example, the Michigan Departmen t of Education reported in 2002 that there was a lack of agreement on the proposed defin ition of autism and that despite the fact that the original criteria were retained, further d evelopment should be anticipated (Michigan Department of Education 2002).Public policy that provides for services on the bas is of diagnosis categories is, therefore, a difficult administrative match for autism because t he service needs of children with autism spectrum disorder vary dramatically between childre n with autism spectrum disorders and during the life of a child with an autism spectrum disorder over time (Feinberg and Vacca, 2000). Neither the exact needs nor the expected pro gnosis can be easily estimated on a case-by-case basis given diagnosis. By the same tok en, since establishing accurate prognosis for individual children is nearly impossi ble and the most effective treatment highly debated (presumably because the treatments have not well understood differential effects on different individuals), public policy that provi des services on the basis of individual demands or rights can be equally and uniquely diffi cult in managing the social challenges associated with autism.


4 of 16 This type of challenge, while unique to autism in s ome respects, includes aspects that are similar in other disabilities and in policy challen ges in other arenas. Especially since the implementation of the Government Performance Result s Act of 1996, there is pressure to create public policy infrastructures that are ancho red by clearly cut categorical service delivery. If the categories themselves leave signif icant room for interpretation and their differential implementation has a shaping effect on the target population, then the distribution of incidence reflects variance in the (broadly cast) environment, including public infrastructures. Examining this potential is especi ally important in policy arenas that have complex fiscal federalism structures, such as is fo und in Medicaid and the provision of special education under the Individuals with Disabi lities Education Act or Section 504 of the Rehabilitation Act.Autism IncidenceThe incidence of autism was once believed to be 1 t o 2 per 10,000 people. More recently, reported incidence has climbed drastically—to aroun d 1 in 500 in most estimates (Mandell et al., 2002). However, specific estimates of the prevalence of autism as recorded in the research and reported to the public vary. For examp le, in 2001, Bertrand et al, who studied the prevalence of autism in Brick Township, New Jer sey, reported, “the prevalence of all autism spectrum disorders was 6.7 cases per 1000 ch ildren. The prevalence for children whose condition met full diagnostic criteria for au tistic disorder was 4.0 cases per 1000 children, and the prevalence for PDD-NOS and Asperg er disorder was 2.7 cases per 1000 children” (Bertrand et al., 2001). However, in a st udy of the incidence of autism in children born in California between 1987 and 1994, it was fo und that “a total of 5038 children with full symptom autism were identified from 4,590,333 California births, a prevalence of 11.0 per 10,000. During the study period, the prevalence increased from 5.8 to 14.9 per 10,000, for an absolute change of 9.1 per 10,000” (Croen 20 02). Outside of the academic literature, the range of reported and suspected incidence is ev en wider. The causality of the rise of incidence in autism is highly debated and politicized. Class action suits, such as the one discussed on vaccinea, are arising that seek to place blame on particular events or practices such as mercury poisoning in infants during childhood vaccination. The most widely accepted exp lanations are of complex causes: “recent research reports show that autism spectrum disorders may actually be more common than previously believed. General awareness and clinical knowledge of these disorders have increased, and the criteria in the I CD-10 and the DSM-IV are also now more detailed” (Kielinen 2000). As this quote suggests, there are two core cause groups—a better professional understanding of autism or chan ges in the (broadly defined) environment.Shattock et al. describe four basic reasons that au tism might have a perceived increase in recorded incidence independent of any actual increa se in the raw rate of autism disorders in children. Their reasons include: “the increased awa reness and skill in diagnosis which has developed, the changing diagnostic criteria, the la ck of appropriate and available records and the increased number of associated disorders wh ich may formerly have been included within the ‘autism’ diagnosis” (Shattock, 2001). E ven though these authors are writing from the United Kingdom, these types of issues are expec ted to arise by those who are professionally or personally connected to autism re lated issues in the United States as well. Suggestions abound for reasons related to autism’s rise and—to the extent that it has been noted—variance within this recorded rise across spa ce. Searching the web for “autism incidence” using a basic search engine brings up in excess of 21,000 hits. Autism tends to be popularly intriguing for reasons including the f act that autism is much more common in boys, with an incidence rate that is 3 to 4 times t he rate found in girls (Hirtz 2000, Miles


5 of 16 2003), that it was once blamed on traumatic experie nce or perverse behavior on the part of parents, and the way in which autism is manifested, particularly in the case of the so-called savants.These elements of fascination, in combination with the position stakeholders find themselves in trying to manage a specific case of a utism in an era of rapidly shifting ground make the nature of the growth of autism a crucial c oncern for policy development and administration. Three major policy and administrati ve challenges are: identification, the distribution and selection of treatment options, an d the creation of appropriate policy and administrative goals that will effectively address the autism baby boom in the long term. The effective management of these factors could be expe cted to be easier with information about how autism is being differentially recorded a cross the country. Many observers have hypothesized that the reasons f or this recorded rise might be expected to have as much to do with changing servic e systems and increased awareness as with an epidemiological growth in the general po pulation (DeFrancesco, 2001; Barbaresi, W.J., 2002). To the extent that the rise in recorde d incidence is the result of a change in broad based professional practice and in public awa reness, if the rise in incidence is not occurring quite similarly across the country (if no t world), then the structure of the public policy and socioeconomic conditions of states, as a defining region policy arenas such as health and education, could be having a shaping eff ect on the growth of autism. Especially because individuals with autism and their families are currently expected to require very different services from a variety of public agencie s over the course of a lifetime than are individuals without autism, it is important to cons ider coincidences and correlations between observed patterns of growth in recorded incidence o f autism and socioeconomic and political factors.There is no centralized place or database to which all cases of autism are reported. However, the incidence of autism in children is arg uably recorded most comprehensively by the public education system under the provisions of the Individuals with Disability Education Act. Autism is one of thirteen categories in which children with disabilities are currently entitled to special education services under the In dividuals with Disabilities Education Act. In accordance with this key structural policy of sp ecial education in the United States, states and regions are required to report the number of ch ildren with autism (and in twelve other disability categories) served to the Office of Spec ial Education and Rehabilitation Services on a yearly basis. Whereas the categories are defin ed at the federal level, states, regions and, to a certain extent, individual districts, hol d the responsibility to define exactly which children will be included in counts within individu al school systems (Feinberg and Vacca, 2000).The rise in autism incidence is catching the attent ion of public administrators—perhaps particularly those involved in special education se rvice planning and delivery. In fact, in the data appendix of the 2001 Report to Congress on the implementation of IDEA, it is explained, “twelve states commented that the increa ses in counts of students with autism were a result of better diagnosis and identificatio n of the disorder, continued reclassification of students, and improved training in methods and a ssessment of autism” (OSERS 2002). The twelve states are Alabama, California, Colorado Connecticut, Georgia, Indiana, Kansas, Kentucky, Minnesota, Missouri, Washington a nd Wisconsin. In conducting the shift-share analysis a hypothesis was that these st ates would be those with both the highest rise in incidence and the highest relative growth a s compared to the growth in incidence experienced by these states in the other disability categories.MethodShift-share analysis is most commonly used in regio nal economics. In that context,


6 of 16 “shift-share analysis produces results that can be valuable for diagnosing, describing and building understanding of major differences between the industry pattern of employment growth locally and nationwide trends” (Washington S tate University 2002). In the context of the incidence of autism as recorded in the public e ducation system, this technique can be used to build understanding of the differences betw een the pattern of growth in autism as compared to the other diagnosis categories locally and in nationwide trends. In this article, a shift-share analysis of special education diagnosis categories are conducted using data reported by the Office of Special Education and Reh abilitative Services (OSERS) to Congress.For the special education shift-share analysis, the local regions of interest are the states. The aggregate level is the national level. For this article, a shift-share analysis of the changes in diagnosis incidences in autism as record ed by public schools between the 1995-1996 and 2001-2002 school years was conducted. This period is of particular interest. The DSM-IV standards were developed and released du ring this era and the dawn of widespread public attention to the perceived rise i n incidence in autism dates to at least the late 1990s (though perhaps up to ten years earlier in some locations). Shift-share analysis is fundamentally a technique o f arithmetic decomposition. In regional economics the purpose of shift-share analysis is to allow for comparison of differences in growth in selected industries in smaller regions (s uch as states or localities) with one another and with a larger, encompassing region (suc h as the nation). Through relatively simple calculations, shift-share analysis produces two measures of interest, which are typically called competitive and mix components in regional economics. The arithmetic decomposition in shift share proceed s as follows. First, in traditional shift-share analysis, employment data is collected on a chosen number of industries for two time periods of interest for both the encompassing region (hereinafter referred to as national) and smaller regions of interest (hereinaf ter referred to as locality). The percent increase in total employment at the national level is first calculated. For each locality, the national growth share is then calculated. This is t he increase that would have been expected in a given industry if that industry had g rown at the regional level at exactly the same rate as the overall, national employment growt h rate. That is, a number for each industry is calculated using the following formula: National Growth Share = (Industry Employment, year 1) (overall growth rate). Not surprisingly, industries in given regions very rarely grow at exactly the growth rate observed on the national level. The national growth share is not generally observed in practice. In this article, the national growth shar e shows the increase that would have been seen in a given diagnosis category at the state lev el if that diagnosis category had grown in the state at the same rate as disability in general in the United States. In traditional shift-share analysis, the expected g rowth in employment in a particular industry is calculated, using the national growth rate in th at industry. This component, which is called the industrial mix component in traditional shift-s hare analysis, is calculated as follows: Industrial Mix = (Local industry employment, year 1 ) (National Industry Growth Rate). In the context of disability as explored in this ar ticle, this is the number of additional individuals with a particular diagnosis one would e xpect to see at the state level if the category had grown at the same rate as the overall national rate. Finally, the local share or competitive regional sh ift is calculated. This is the measure of particular interest in most shift-share analysis. I n traditional shift-share analysis this


7 of 16 demonstrates the extent to which factors unique to the local area have caused growth or decline in regional employment of an industrial gro up. It is calculated as follows: Regional Shift = (Local industry employment, year 1 ) (Percent local growth in industrypercent national growth in industry) In the context of disability as explored in this ar ticle, the result of this equation is the number of individuals (or lack of individuals if the numbe r is negative) with a particular diagnosis attributable to a growth pattern unique to the stat e. Once these three calculations have been performed, the results are examined for growth patterns within and between states. In the context of the administration of challenges and services associated with autism, the traditional shift-share language is somewhat awkwar d. Competition for children with specific disability types does not typically take place in s tate education systems in the same spirit as competition for businesses and industries takes pla ce between regional economies. Nevertheless, the shift-share components are potent ially very useful indicators in the diagnosis of growth patterns in educational autism because they provide a way to compare across states and between diagnosis categories. The refore, in applying this technique to recorded incidence of disability it is helpful to e mploy language that better describes the measures of interest in the disability context. In this article “diagnosis mix” is hereinafter used instead of “industrial mix” to describe the ex pected growth in individual diagnostic categories and “state-specific label growth” is use d in place of local share or competitive regional shift.As is described above, in shift-share analysis nati onal (or another larger, encompassing region) growth patterns are used as a reference poi nt (Hoover and Giarantti 2002). At the national level, disability incidence on the whole i s growing at any given time at a certain rate, but it is to be expected that the rate of inc idence of individual diagnoses will be growing at different rates. That is, mental retarda tion incidence is not expected to be growing at exactly the same rate as deaf-blindness, for example. The diagnosis mix component shows how categories would have grown at the more local level if the growth pattern at the national level held uniformly in the localities. In the context of regional economics, a region is said to have a favorable gro wth mix if economic activity in a region is growing quickly (or more quickly) in a set of indus tries that are also growing quickly at the national level (Hoover and Giarantti 2002). Though the story and its implications are more complex when it comes to comparing the mix of slow and fast growing diagnosis in a state vis--vis a nation, the diagnostic mix component is of course, of interest in public administration since a state that has a diagnostic mix pattern that is different from the national trend will face unique administration chal lenges (and, perhaps, opportunities), especially in an arena as flush with federalist ten sion as special education. In regional economics, the competitive regional sha re or local share component is understood by “imagining a case of a region that ha s exactly the same mix of activities, as does the nation (and) its percentage share is the s ame for all activities” (Hoover and Giarantti 2002). A competitive advantage, in the co ntext of regional economics, is found in regions that increase their share, or, as Hoover an d Giarantti explained, “if most activities grow faster in the region than in the nation.” In the context of special education, and specifically autism, the state-specific label growt h component examines how the growth in autism in individual states compare relative to eac h other and to the national growth rate.ResultsThe increase in incidence of all disabilities as re corded through the system of special education was just over 15% between the 1995-1996 a nd 2001-2002 school years. During this period, autism grew faster than that in all fi fty states, the District of Columbia and


8 of 16 Puerto Rico. In fact, of the twelve disability cate gories that were recorded in these years, autism was the fastest growing disability category in 32 states. The range of incidence growth rate during this period was between 53.7 per cent (Puerto Rico) and 1,413.37 percent (Ohio) with a mean growth of 306.80 percent and a standard of deviation of 218.70 percent. The states’ ranked growths are shown in T able 1 below. Table 1. Ranked Autism Percent Growth Rates State RankRate State RankRate State RankRate Alabama36201Louisiana48104Ohio11413Alaska19321Maine16364Oklahoma24283Arizona21313Maryland15365Oregon5064Arkansas25279Massachusetts12377Pennsylvania32227California18333Michigan43168Puerto Rico5154Colorado4573Minnesota10392Rhode Island7419Connecticut27268Mississippi40185South Carolina6438Delaware47118Missouri31229South Dakota26279Florida35211Montana42170Tennessee45137Georgia9394Nebraska23288Texas38193Hawaii17352Nevada5517Utah20318Idaho30233N. Hampshire2936Vermont14368Illinois11379New Jersey28268Virginia41182Indiana29250New Mexico37194Washington3650Iowa4976New York46126West Virginia39188Kansas34214North Carolina44151Wisconsin8397Kentucky13373North Dakota33220Wyoming22303 As Table 1 demonstrates, the recorded growth of aut ism around the nation was far from uniform across the country. As is mentioned above, from a public administration and policy standpoint, in the context of marble cake federalis t special education policy, to the extent the policies and administrative react to prevailing growth rates, states with outlying growth rates may have administrative and other policy rela ted challenges. When a 95% confidence interval is drawn around the mean growth rate, the states that are found Ohio and New Hampshire are found to have statistically significa ntly higher growth rates. No states had statistically significantly lower growth rates (a s tate would have had to have a decrease in the reported incidence of autism for this to be the case). The national growth rate for autism during this per iod was almost 240%. It is not surprising that this national growth rate is different from th e mean growth rate since the populations of states vary dramatically and, therefore, the change in growth in a small state will have much less effect on the overall change in growth than wi ll a similar (or even smaller) change in a


9 of 16 large state. It is interesting to note, however, th at the national growth rate is less than one standard deviation away from the mean state growth rate. States that self identified in their 2002 reports t hat increased incidence was due to better identification might be expected to have both a rep orted incidence rate that was relatively high when compared to other states and to have the highest growth of all disability categories have taken place in autism. However, nei ther of the outlying states—Ohio and New Hampshire—were in this group. When the percenta ge growth rates were examined, the ranks from highest growth to lowest of the twel ve states that self-identified as improving their diagnoses mechanisms were: Alabama (36th); Ca lifornia (18th); Colorado (4th); Connecticut (27th); Georgia (9th); Indiana (29th); Kansas (34th); Kentucky (13th); Minnesota (10th); Missouri (31st); Washington (3rd) ; and Wisconsin (8th). As can be seen from these ranks, the states that self-identified a s more aggressively diagnosing autism were almost as likely to be in the bottom half of t he ranked growth rates and in the top half. Furthermore, autism was the highest growth category in only six (50%) of these self-identifying states (less than the 62% of all s tates or regions). This evidence does not support the hypothesis that exceeding rapid growth rate is caused by institutionalized overenthusiastic discovery of new cases of autism.The diagnosis mix and state-specific label growth f or each of the states and regions is shown in Table 2. As is described above, the number s generated in the shift share analysis refer to the number of cases of autism. Diagnosis m ix refers to the number of additional cases of autism one would expect in the school syst em’s population if the state’s growth in autism had exactly matched the national growth rate in autism. A larger number, therefore, means that the state had a larger population of chi ldren with autism in the mid-1990s. The state-specific label growth refers to the number of cases of autism above or below what would have been expected as an observed growth in a utism once the overall growth in autism at the national level is controlled for. The state-specific label growth reports the absolute increase (or decrease) in the number of re corded cases of autism once the growth attributed to the national growth in autism has bee n controlled for. States that did not grow at at least the national rate would have a negative state-specific label growth. In other words, for example, a state with a very negative nu mber has much less autism than would be expected given the number of cases they began wi th and the growth experienced in autism nation wide. Table 2. Diagnosis Mix (DM) and State-Specific Labe l Growth (SSLG) State DMSSLG State DMSSLG State DMSSLG Alabama672-115Louisiana1427-866Ohio4532371Alaska11943Maine267148Oklahoma45989Arizona730241Maryland1154647Oregon3887-3045Arkansas45781Massachusetts1259772Pennsylvania2722-1 57 California68652852Michigan3948-1264Puerto Rico755-6 26 Colorado179266Minnesota14881015Rhode Island166133Connecticut894115Mississippi363-89S. Carolina421374Delaware302-164Missouri1331-64S. Dakota14826Florida3121-403Montana164-51Tennessee1042-476


10 of 16 Georgia1116771Nebraska24052Texas5424-1123Hawaii18895Nevada188233Utah388136Idaho240-7N. Hampshire87272Vermont11968Illinois17771109New Jersey2149269Virginia1878-481Indiana208897New Mexico202-41Washington5871079Iowa706-516New York6975-3549W. Virginia291-67Kansas531-62N. Carolina2765-1096Wisconsin1013712Kentucky484288North Dakota101-9Wyoming6519 The numbers presented in Table 2 are not controlled for the population size of the state or for the general population growth experienced by th e state during the time frame. It is arguable, then, that except for very small deviatio ns which may not be (statistically) significantly different from zero, that from a stan dpoint of public policy and public management, the most telling aspect of the state sp ecific label growth is whether the number generated by the shift-share analysis is pos itive or negative. The examination of state-specific label growth (SSL G) and of emergent patterns of growth demonstrated therein, also sheds light on the growt h pattern of autism. The median SSLG was 52 recorded cases (with the mean, as would be e xpected, indistinguishable from zero). The largest SSLG was in California, which had 2852 cases. The four other states that had SSLGs in the top five were: Ohio (2371 cases); Illi nois (1109 cases); Washington (1079 cases); and Minnesota (1015 cases). Autism grew th e fastest of all the disability categories in each of these states except Washington.The lowest SSLG was in New York, which had an SSLG of –3549 cases. The four other states that had local shares in the bottom five wer e: Oregon (-3045 cases); Michigan (-1265 cases); Texas (-1123 cases) and North Carolina (-10 96 cases). As can be seen from Table 1, in some cases these absolute growths were also r anked in the same categories in terms of percentage growth. Of these states, autism grew the fastest of all the disability categories in Michigan, Texas and North Carolina. Other states however, such as California, Texas and New York had changes that were large due to the size of the states population rather than the size of the percentage change. In addition to states with outlying growth rates me ntioned above, states or regions with growth rates closest to the mean, that is the most average states, are interesting from a standpoint of public policy and management. The sta tes whose growth most closely matched the states’ mean growth in autism included Idaho, Indiana, Missouri, North Dakota and Pennsylvania. The growth rate in each of these states was within 20 percentage points of the national mean. Autism was the fastest growin g disability category in only two of these states—Idaho and North Dakota.When the growths of other disability categories in these states are examined, we find that the state-specific label growth patterns on the who le is not overly similar across these states. We also find that the states were not avera ge across all disability categories. For example, Pennsylvania had a very high state specifi c label growth in specific learning disabilities, whereas Missouri had a quite low (and negative) SSLG in the same category. This is interesting especially because learning dis abilities are another type of disability sometimes regarded as potentially trendy. Also, Nor th Dakota reported no children in the multiple disabilities category, whereas Pennsylvani a had a large SSLG in that category as


11 of 16 well. Given autism’s historical connection with men tal retardation, it is worth noting that all but one of the average growth states (Indiana) had a negative state-specific label growth in autism. Finally, since autism and speech and langua ge impairments are sometimes confounded or combined, one might expect that state s that are average in autism would be similarly average in speech or language impairments However, as Table 3 shows, only North Dakota was close to average in that category. Table 3. State-Specific Label Growths in States wit h Average Autism Growth IdahoIndianaMissouriNorth Dakota Pennsylvania Specific Learning Disabilities4133562-1441-79315899Speech or Language Impairment 749-993202771-5795 Mental Retardation-1078883-620-122-564Emotional Disturbance3233464-13643451978Multiple Disabilities-4119043N/A588Hearing Impairments-342382220-346Orthopedic Impairments-46258-225-10-298Other Health Impairments-195161124931142084Visual Impairments2360856-207Autism-797-64-9-158Deaf-Blindness2-47-51-5021Traumatic Brain Injury-88-147-172-17-1612ConclusionState environment—including perhaps public infrastr ucture--seems clearly to have had a role in the shaping of the autism baby boom in the United States. Presumably in the recording of any phenomena by agencies in the publi c infrastructure (such as the system of formal education) there will be always be some vari ation in growth rates. The range of growth rates recorded by the public education syste ms as measured by shift-share analysis is too large to be explained away through pure chan ce or variation in a phenomenon of the physical environment that remains unnoticed. Diffe rences in the implementation patterns of special education policy between states are a far m ore likely causal element. As can be seen by the range of growth rates, to the extent th at identification by the school district can be connected with appropriate educational (and, to some degree other) services, the willingness on the part of states to provide servic es for children with autism is perhaps remarkably different in different states.Furthermore, as can be seen from the shift-share re sults, autism’s growth pattern as measured by the system of formal education does not appear to be spatially correlated. Whereas the states that are growing at close to the national rate are basically midwestern, the states that grew most quickly or most slowly ha ve no such proximity. Neither can


12 of 16 population or state wealth explain the distribution of growth as measured in the shift share analysis. After all whereas California experienced the largest share of growth in educational autism during this era, New York’s share indicated that educational autism grew much less than was expected.This lack of a preeminent environmental or regional causality suggests that there is a relationship between the recorded growth of autism and the public infrastructure. In his description of shift-share analysis, Martin Sheilds states that among the many questions to consider in the interpretation of results from shif t-share analysis, two of the most important are: Compared to other regions, does the community seem highly competitive in any particular industry? What is the source of this co mpetitiveness? 1. Does this information support popular perceptions? Or, does the analysis uncover “surprising” areas of economic growth? (Located onl ine at: http://radburn.rutgers .edu/lahr/509/ ) 2. This study has thus far focused on the first of the se questions, looking at a single diagnosis (the “industry” for our purposes). As is mentioned above there are several states that appear to be highly competitive when it comes to th e recording of autism in their educational system and part of the source of this c ompetitiveness is most likely connected to the public infrastructure (but not to a reported enthusiasm for diagnosing autism cases). As far as the second question is concerned, the inf ormation leads several surprises, both from the standpoint of growth in autism specificall y and in the way in which the development and administration of policy is more generally unde rstood. First of all, the popular perception is that autism is growing very rapidly, but presuma bly relatively evenly nationwide. Furthermore, the most oft discussed clustering of a utism is in Brick Township, New Jersey. New Jersey did not rank among the top growths of st ates. A nationwide surge in incidence is a much less complex (and arguably less troubleso me) occurrence than a surge with a magnitude that varies dramatically from state to st ate. From the standpoint of public policy and administra tion, these findings call for a sustained look at the relationship between the unfolding of s ocial policy problems and the public infrastructure. Shift-share analysis, after all, pr ovides only a two-period snapshot of growth that is continuous in nature. To the extent that th is variance in growth is due to street and state level bureaucracy, public policy has a level of responsibility for the shaping of the public challenge. Especially when this challenge is so intimately connected to the development of children and to the unfolding of the new conception of civil rights being forged through modern disability policy, there shou ld be more direct examination of behavior within public infrastructures that account s for wide differences in observation and in understandings of a federally defined public mis sion.ReferencesBarbaresi, W.J. et al. (2002). Explaining the Appar ent Increase in the Incidence of Autism in Olmsted County, Minnesota, from 1976 to 1997. Journal of Developmental & Behavioral Pediatrics. 23(5), p. 398-400. Bargerhuff, Mary Ellen. (2003). Exploring the Spect rum of Autism and Pervasive Developmental Disorders (Book Review). Remedial and Special Education 24(4), p. 255-256. Bertrand, J., A. Mars, C. Boyle, F. Bove, M. Yeargi n-Allsopp and P. Decoufle. (2001). Prevalence of Autism in a United States Population: the Brick Township, New J ersey, Investigation. Pediatrics 108(5): 1155-61. Bureau of Economic Analysis, U.S. Department of Con gress. (2002). Regional Accounts Data, Local Area Personal Income. Located online at: http://w Accessed on September 17, 2002. Braddock, David (editor). (2002). Disability at th e Dawn of the 21st Century and the State of the Sta tes.


13 of 16 Washington, D.C.: American Association of Mental Re tardation. Croen, Lisa A., Judith K. Grether, Jenny Hoogstrate and Steve Selvin. (2002). The Changing Prevalence of Autism in California. Journal of Autism and Develo pmental Disorders 32(3): 207-215. DeFrancesco, Laura. (2001). Scientists Question Ris e in Autism. Nature Medicine. 7(6), 1. DeFrancesco, Laura. (1994). Diagnostic and Statisti cal Manual of Mental Disorders, Fourth Edition. DeFrancesco, Laura. Exploring Autism. (2002). Locat ed online at: http://www.exploringautism. org Accessed on September 3, 2002. Feinberg, Edward and John Vacca (2000). The Drama a nd Trauma of Creating Policies on Autism: Critical Issues to Consider in the New Millennium. Focus on Autism and Other Developmental Disabilitie s. 15(3), p. 130. Hirtz, Deborah. (2000). Testimony on ‘The Challeng es of Autism—Why the Increased Rates?’ Testimony be fore the House Committee on Government Reform. April 6, 2000. Located online at: http://www.hhs .gov/asl/testify/t000406b.html Accessed on September 10, 2002. Hoover, Edgar M. and Frank Giarratani. (2002). An Introduction to Regional Economics. West Virginia University: Regional Research Institute. Located online at: http://www.rri.wv Accessed on September 4, 2002. Jongbloed, Lyn. (2003). Disability Policy in Canada : An Overview. Journal of Disability Policy Studies. 13(4), p. 203-210. Kanner, Leo. (1943). Autistic Disturbances of Effec tive Contact. Nervous Child 2, p. 217-250. Kielinen, M., SL Linna and I Moilanen. (2000). Aut ism in Northern Finland. European Child and Adoles cent Psychiatry 9(3): 162-7. Mandell, David S., et al. (2002). Race Differences in Age at Diagnosis among Medicaid-Eligible Childre n with Autism. Journal of the American Academy of Child and Adoles cent Psychiatry. 41(2), p. 1447-1454. Michigan Department of Education, Office of Special Education and Early Intervention Services. (2002) Summary of Revised Rules for Special Education. Loc ated online at: EP6_Rules Pres entation SpeakingNotes_15687_7.pdf Accessed on September 11, 2002. O’Brien, Ruth. (2003). From a Doctor’s to a Judge’s Gaze: Epistemic Communities and the History of Dis ability Rights Policy in the Workplace. Polity. 35(3), p. 325-347. Rimland, Bernard. (2002). The Autism Explosion. Aut ism Research Institute. Located online at: http:// Accessed on September 21, 2002. Rutter, Michael. (2000). Genetic Studies of Autism: From the 1970s into the Millennium. Journal of Abnormal Child Psychology 28(il): p. 3. Shattock, Paul, Paul Whiteley, James Roger and Lynd a Todd. (2001). Incidence Rates in Autism: A Brie f Overview. Presented at Durham Conference. Shields, Martin. (2003). Tool 4. Shift-share Analy sis Helps Identify Local Growth Engines. Located o nline at: http://radburn.rutgers .edu/lahr/509/ Shields_Shift_Share.PDF Accessed on February 3, 2003. Simpson, David. (2003). Autism Spectrum Disorder is Not as Certain as Implied. British Medical Journal 326(7396), p. 986. Stokstad, Erik. (2001). New Hints into the Biologic al Basis of Autism. Science 294(5540), p. 34. Tinge, Bruce. (2002). Autism, Autistic Spectrum and the Need for Better Definition. The Medical Journal of Australia. 176(9), p. 412-414. United States Census Bureau. (2000). Statistical Abstract of the United States (selected tables). L ocated online at: http://www.censu Accessed on September 17, 2002. Washington State University. (2002). Shift-share An alysis of Employment Growth, 1969-2000. Located on line at: http://niip.wsu .edu/washington/ssharewa.htm Accessed on September 4, 2002. Williams, Robert Jr. (2000). Autism Through the Age s Baffles Science. Located online at: http://ww Accessed on September 21, 2002.


14 of 16 About the AuthorDana Lee Baker Assistant Professor Harry S Truman School of Public Affairs 118 Middlebush Hall University of Missouri-Columbia Columbia, MO 65203 Email: (573) 882-0363Baker’s primary research interests are in disabilit y and children’s policy with a comparative focus. She is also fascinated by the study of publi c policy particularly disability policy and agenda setting. She earned her bachelor’s degree in History and Religious Studies at Rice University and her Masters of Public Policy at the University of Southern California. While working on her Masters degree, Baker was a casework er for the Alliance for Children’s Rights in Los Angeles and interned with the Los Ang eles chapter of Physicians for Social Responsibility. Baker did her Ph.D. work at the Lyn don Baines Johnson School of Public Affairs at the University of Texas at Austin. The World Wide Web address for the Education Policy Analysis Archives is Editor: Gene V Glass, Arizona State UniversityProduction Assistant: Chris Murrell, Arizona State University General questions about appropriateness of topics o r particular articles may be addressed to the Editor, Gene V Glass, or reach him at College of Education, Arizona State Universi ty, Tempe, AZ 85287-2411. The Commentary Editor is Casey D. Cobb: .EPAA Editorial Board Michael W. Apple University of Wisconsin David C. Berliner Arizona State University Greg Camilli Rutgers University Linda Darling-Hammond Stanford University Sherman Dorn University of South Florida Mark E. Fetler California Commission on TeacherCredentialing Gustavo E. Fischman Arizona State Univeristy Richard Garlikov Birmingham, Alabama Thomas F. Green Syracuse University Aimee Howley Ohio University Craig B. Howley Appalachia Educational Laboratory William Hunter University of Ontario Institute ofTechnology Patricia Fey Jarvis Seattle, Washington Daniel Kalls Ume University


15 of 16 Benjamin Levin University of Manitoba Thomas Mauhs-Pugh Green Mountain College Les McLean University of Toronto Heinrich Mintrop University of California, Los Angeles Michele Moses Arizona State University Gary Orfield Harvard University Anthony G. Rud Jr. Purdue University Jay Paredes Scribner University of Missouri Michael Scriven University of Auckland Lorrie A. Shepard University of Colorado, Boulder Robert E. Stake University of Illinois—UC Kevin Welner University of Colorado, Boulder Terrence G. Wiley Arizona State University John Willinsky University of British ColumbiaEPAA Spanish and Portuguese Language Editorial BoardAssociate Editors for Spanish & Portuguese Gustavo E. Fischman Arizona State Universityfischman@asu.eduPablo Gentili Laboratrio de Polticas Pblicas Universidade do Estado do Rio de Janeiro pablo@lpp-uerj.netFounding Associate Editor for Spanish Language (199 8-2003) Roberto Rodrguez Gmez Universidad Nacional Autnoma de Mxico Adrin Acosta (Mxico) Universidad de J. Flix Angulo Rasco (Spain) Universidad de Teresa Bracho (Mxico) Centro de Investigacin y DocenciaEconmica-CIDEbracho Alejandro Canales (Mxico) Universidad Nacional Autnoma Ursula Casanova (U.S.A.) Arizona State Jos Contreras Domingo Universitat de Barcelona Erwin Epstein (U.S.A.) Loyola University of Josu Gonzlez (U.S.A.) Arizona State Rollin Kent (Mxico) Universidad Autnoma de Puebla Mara Beatriz Luce (Brazil) Universidad Federal de Rio Grande do


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