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Educational policy analysis archives.
n Vol. 12, no. 26 (June 15, 2004).
Tempe, Ariz. :
b Arizona State University ;
Tampa, Fla. :
University of South Florida.
c June 15, 2004
Do EMO-operated Charter Schools Serve Disadvantaged Students? The Influence of State Policies / Natalie Lacireno-Paquet.
Arizona State University.
University of South Florida.
t Education Policy Analysis Archives (EPAA)
1 of 27 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 26June 15, 2004ISSN 1068-2341Do EMO-operated Charter Schools Serve Disadvantaged Students? The Influence of State Policies Natalie Lacireno-Paquet The George Washington UniversityCitation: Lacireno-Paquet, N., (2004, June 15). Do EMO-operated charter schools serve disadvantaged students? The influence of state poli cies. Education Policy Analysis Archives, 12 (26). Retrieved [Date] from http://epaa.asu.edu/epa a/v12n26/.AbstractThere is a paucity of research about how the polici es enacted by states either foster or hinder charter schools ser vice to disadvantaged students or how the characteristics o f charter schools themselves affect this outcome. By combinin g data from the US Department of Educations Schools and Staffi ng Survey with data on the characteristics of state charter s chool policies, this article examines how different types of charte r schools respond to the policy and market signals establishe d by state charter legislation, and the impact of such signals on the willingness and ability of charter schools to serve disadvantaged student populations. With a sample of 533 charter s chools in 13 states, models are estimated to discern whether spe cific state policies and whether being managed by two types of for-profit educational management organizations (EMOs)large a nd small
2 of 27 onesencourages or discourages schools from enrolli ng low-income and minority students. The results sugge st that certain policy characteristics are important for en couraging schools to serve low-income and minority students. Specifically, having multiple chartering authorities and requirin g the transportation of students are important for explai ning charter schools service to low-income and minority student s. Being managed by a large-EMO was positively but not signi ficantly related to charter schools enrollment of low-income and minority students. The results differed for small-EMO manage d schools. Small-EMOs served significantly lower percentages o f minority students. The results suggest that not all charter schools are the same and that policy design and organizational form matters for determining whom charter schools will serve. We are at a crossroads in charter school policy and practice. We are more than ten years from the first law, many state legislatur es continue to make amendments to their state charter school laws, and many schools across the nation are having their charters come up for review and renewal. Thus the moment is timely to examine equity issues in charte r schools. Forty states and the District of Columbia have enacted charter schoo ls legislation. The extent of charter school activity varies both between and wit hin states. Characteristics of charter schools legislation also vary greatly by st ates, providing enough natural variation to examine patterns of charter schools se rvice to disadvantaged students. Unlike many education reforms, the charte r school idea is a structural reform, not a pedagogical one (Miron and Nelson 200 2; Vergari 2002). Proponents of charter schools and the legislators t hat have adopted variations of this policy believe that by changing the structu re within which schools operate, numerous desirable outcomes, such as incre ased student performance will occur. School choice is also incre asingly important because it is one of the cornerstones of the school accountabi lity provisions of the No Child Left Behind Act of 2001. In addition to emphasizing choice as an alternative to students in failing Title 1 school s, the federal budget for fiscal year 2002 allocated $200 million in grants for exp anding the number of high-quality charter schools available to students across the Nation (No Child Left Behind Act of 2001 § 5201(3)).Proponents of charter schools argue that they will provide havens for students who have been poorly served by traditional public s chools, promote parental involvement and satisfaction, improve academic achi evement, and save public education (Gill, Timpane et al. 2001, p. xii). The evidence regarding the ability of charter schools to deliver on these promises is quite mixed. No study has proved conclusively that the performance of charter school students is substantially better (or worse) than that of studen ts in traditional public schools. The question of access for underserved populations has generally been examined at an aggregated level. Aggregated at the national level, there tends to be little difference between charter and traditi onal schools in terms of the enrollment of minority and low-income students (RPP International 2000). Though findings from state and local studies someti mes paint a different portrait. For example, Gill et al. (2001) reported that in 11 out of 27 charter school states, charter schools served a population that had significantly lower
3 of 27 income than the states public school population, w hile in six out of 27 states the opposite was true. With regard to racial and ethnic segregation in charter schools, for example, Cobb and Glass (1999) found t hat nearly half of the schools in Arizona displayed substantial ethnic seg regation. But the research has not yet examined how the policies enacted by st ates either foster or hinder charter schools service to disadvantaged students or how the characteristics of charter schools themselves affect this outcome. As charter schools continue to proliferate, it is important to examine the equity implications of this policy and its ability to improve the learning opportunities of th e most vulnerable students in the public school system. The aim of this research is to begin to fill part of this information gap by examining how different types of charter schools respond to the policy and market signals established by state charter legislation, and the impact of such signals on the willingness and abili ty of charter schools to serve disadvantaged student populations.The Charter Schools IdeaCharter schools are an idea with several contested meanings and goals (Wells, Grutzik et al. 1999). The founders of the charter m ovement viewed charter schools as a structural reform that went beyond sit e-based management to create independent schools that shatter the idea th e a one-size fits all model that predominates public school systems (Nathan 199 6). But as policymakers took up the idea, the market metaphor for choice an d competition came to dominate the discussion (Wells 2002). Thus, along w ith supply-side arguments for charter schools (innovation, serving diverse ne eds, increased accountability, etc) are market-based ideas of competition, parenta l choice and shopping for schools.The most ardent supporters of school choice base th eir support in ideas about the operation of the market (Chubb and Moe 1990). O thers base their support in providing poor and minority children with option s out of poor performing neighborhood schools, in other reform ideas such as site-based management, or in the idea that public schools fail to meet the needs of diverse students (Nathan 1996; Peterson and Greene 1998; Peterson 19 99). Much of the discussion about charter schools, indeed, school ch oice in general, is based on principles of microeconomic theory: that the privat e market can determine the appropriate quantity and quality of a good by reach ing an equilibrium between consumers and producers that optimizes the utility of consumers and the profit of producers. It is also based in the idea that the bureaucracy of governments leads to ineffective and inefficient institutions ( Chubb and Moe 1990). These ideas are evident in the definition of charter scho ols provided by the Center for Education Reform, a pro-charter and school choice o rganization: Charter schools are independent public schools, des igned and operated by educators, parents, community leaders, educational entrepreneurs and others. They are sponsored by designated local or state edu cational organizations who monitor their quality and integrity, but allow them to operate freed from the traditional bureaucratic and regulatory red tape th at hog-ties public schools. Freed from such micromanagement, charter schools de sign and deliver programs tailored to educational excellence and com munity needs. Because they are schools of choice, they are held to the hi ghest level of accountability
4 of 27 consumer demand. (Center for Education Reform 2002)Proponents of charter schools make numerous claims about the effect of the increased autonomy and accountability that constitu te the charter school idea. They argue that autonomy and accountability will le ad to schools that improve student performance as measured by standardized tes t scores and other measures; this will happen because if schools fail to improve student performance and meet other goals as set out in thei r charter, then their permission to operate can be revoked and/or parents will take their children out of the school signaling a need to change (Nathan 19 96). Proponents also recognize that the very idea of charter schools rai ses equity issues, especially surrounding race, class, and abilityabout who will choose or get chosen, about what they will be choosing, and who will be l eft behind. Some proponents argue that charter schools can reduce class and rac ial segregation, which they rightly point out is quite extensive in the traditi onal system, by dissolving the geographic boundaries that result in schools that a re reflective of the largely racially and economically segregated neighborhoods in which they are located (Peterson and Greene 1998; Hassel 1999; Viteritti 1 999; Greene 2000). Others suggest that allowing schools the autonomy to devel op creative and different programs is more equitable than trying to serve all children with similar schools (Nathan 1996).Those who are wary of school choice, however, fear that it will result in schools selecting students rather than parents selecting sc hools (Rothstein 1998; Fiske and Ladd 2000; Kahlenberg 2001). Opponents fear tha t the combination of financial pressures of keeping costs low with the p ressure of being increasingly judged by academic performance will lead schools to select a student population that is relatively easy and inexpensive to serve. Those who are wary of choice are also concerned that many parents, esp ecially low-income and immigrant parents, may not have sufficient informat ion to allow them effectively use the mechanism of choice (Henig 1994; Rothstein 1998, Teske and Schneider 2001). There is also some concern that th e values and preferences of racial and ethnic minorities may lead them to vo luntarily segregate into particular schools. Wells and her colleagues fear t hat charter schools policies may result in the further isolation of disadvantage d communities and/or that they will fail to redistribute resources to disadva ntaged communities resulting in the hardening of race and class hierarchies, thus r einforcing social inequalities (Wells, Lopez et al. 1999).Educational Management Organizations and Charter Sc hoolsMost of the research about the impact of charter sc hools on social stratification and equity treats them as a single class of institu tion and the results have been mixed (Gill et al. 2001). But there are good reason s to believe that charter schools vary greatly in their orientation to the ma rket and in their missions. Those with a strong orientation to the market tend to have ties to educational management organizations (EMOs), but those that are more mission oriented tend to focus on at-risk populations and often have ties to social service or nonprofit organizations (Lacireno-Paquet, et al. 20 02; Henig, et al. 2003). We might expect different equity outcomes from differe nt types of charter schools in diverse policy and market environments. I believe t hat disaggregating charter
5 of 27 schools according to their orientation to the marke t will provide a more nuanced picture of charter schools overall.Categorizing charter schools according to whether o r not they are operated by a for-profit educational management organization is o ne useful way to gauge market orientation (Miron and Nelson 2002; Lacireno -Paquet, et al. 2002; Brown, et al. 2003). This distinction is useful bec ause while most states require charter schools to take non-profit status as their legal form, most also allow schools to enter into contracts with firms to manag e schools and many are choosing to do so. The literature on nonprofit orga nizations suggests that there are important organizational distinctions between f orand non-profits (Weisbrod 1998; Frumkin and Andre-Clark 2000). While contract ing with for-profits is not new to education, what is new is the extent of the contracting arrangements, with new firms sprouting to manage multiple schools across the country (Plank, Arsen et al. 2000; Miron and Nelson 2002). Indeed, the founders of the charter school movement did not envision corporate involvem ent in charter schools (Nathan 1996).The concern about EMOs is that the profit motive wi ll lead firms to cut costs, which may negatively affect educational quality and equity (Nelson, et al. 2000; Plank, et al. 2000; Miron and Nelson 2002). To date only one large-EMO working with charter schools has reported a profit and that is a Michigan based firm called National Heritage Academies (NHA). Howe ver, the increasing number of EMOs operating charters schools, suggest s that many more anticipate that profits are just over the horizon (Miron and Nelson 2002: p.170). There is some evidence that EMO-managed schools ten d to serve a different population than mission oriented or more independen t schoolsone that is less disadvantaged and less expensive to serve (Miron an d Nelson 2002; Lacireno-Paquet, et al. 2002).In their review of school choice outcomes, Teske an d Schneider (2001) suggest that stratification in terms of race and class is the most important question related to school choice (p. 624). Stratification, they suggest, can be controlled, to some extent, by the institutional mechanisms of forms of choice that are implemented. More importantly, we need to understan d the relationship between organizational characteristics of the schoo ls, like EMO-management status, and the types of students served because po licymakers can control whether schools are permitted to contract with EMOs and the terms of such contracts.The Importance of State PoliciesWhile charter schools can be thought of as a single policy innovation, the way they have been designed and implemented varies from state to state. Studies have shown that certain dimensions of charter schoo l legislation can be important for the emergence of charter schools (Buc kley and Kuscova 2003; Witte, Shober et al. 2003). Among the dimensions of variation in state charter school policy regimes that may be important for det ermining charter schools service to disadvantaged students include: (1) fund ing; (2) transportation; (3) the number and type of schools permitted; and (4) t he number and type of chartering authorities.
6 of 27 Funding is perhaps the most important policy charac teristic because the structure of funding formulas creates incentives an d disincentives for charter schools to target particular types of students. Gen erous formulas not differentiated by grade level or not targeted to th e types of student served might lead some schools to target high achieving and lowcost students, especially by those schools hoping to earn a profit because a gre ater profit can be extracted from a fixed per-pupil allotment if easier and/or l ess expensive students are served (Plank, et al. 2000; Miron and Nelson 2002). Alternatively, targeted or progressive funding formulas, where schools are rei mbursed for additional services, may entice charter operators to target th ese groups and lead to more equitable service. Funding structures for charter s chools are directly under the control of state legislators thus it is important t o understand how different funding structures affect equity.Transportation of students is an important policy v ariable in understanding how charter schools serve disadvantaged students. If tr ansportation is a family responsibility, it may act as a barrier to low-inco me families from exercising their choice options. Low-income families may not have th e resources (time and/or money) to transport their children to schools, whic h may be located far from their homes. In states where the transportation of charter students is required, states usually require that either the charter scho ol or the host school district provide it.In terms of the number of schools permitted, a cap on the total number of charter schools allowed could restrict their impact on traditional schools. Permitting only a small number of schools may actua lly impede true competition, a key part of the charter concept. Whi le policymakers may try to manage the competition potentially introduced by ch arter schools, for example, by restricting the total number of schools, these t ypes of policies may result in artificial constraints on the market with unintende d consequences for access. Some charter school policies allow only one set of actors, such as local school districts to grant charters. Others allow a wider v ariety of public actors to do so. Michigan, for example, permits state universities, community colleges, intermediate and local school districts to grant ch arters. Buckley and Kuscova (2003) find that:The institutional environment that states create fo r their school choice initiatives (or, more accurately, the environment created by po litical conflict and compromise) can have a profound effect on the perfo rmance of policies and programs, including market-based reforms. In our ex amination of the effect of charter school legislation on market share, we find that one particular set of provisions regarding who can grant charters has a s ubstantial effect on their share of a market for education (p.16).The number and types of actors that can grant chart ers also likely influences the types of schools that get approved and can also affect the issue of stratification. Local school districts have been wa ry of authorizing charter schools, which they see as their competitors (Hasse l 1999). Some authorizers, such as the university authorizers in Michigan, hav e favored granting charters to schools that partner with EMOs but school district authorizers there have the
7 of 27 opposite tendency (Miron and Nelson 2002).Research QuestionsEmpirical evidence about charter schools and the su rrounding public school districts can be used to answer questions about whe ther the market will exacerbate or ameliorate race and class based segre gation in certain policy environments. Here I begin to address these questio ns by examining how different types of charter schoolsspecifically EMO and non-EMO operated schoolsrespond to the policy and market signals es tablished by state charter legislation and how this affects their service to d isadvantaged student populations (Note 1) By combining data on state charter school laws wi th data from the 1999-2000 Schools and Staffing Survey (SAS S), a National Center for Education Statistics survey, I begin to discern imp ortant variations in policy features and in charter school organizational chara cteristics that influence charter schools service to disadvantaged students. Finding that different policy characteristics and/or school organizational charac teristics are associated with the under-serving or under-enrollment of traditiona lly disadvantaged students would suggest policy alternatives to manage competi tion while safeguarding the public values of equity and choice.Data and MethodsThe data for this research come from multiple sourc es. School level data on charter schools and data on the districts in which these schools are located (host districts) come from the 1999-2000 Schools and Staffing Survey. The SASS is a periodic survey conducted by the National Center for Education Statistics of the US Department of Education. SASS included a charter school component for the first time in the 1999-2000 data collection, when the population of charter schools was surveyed. The SAS S also included a sample of public school districts from each state in the n ation. Using the Common Core of Data, a yearly census survey of schools and dist ricts in the nation, I used charter school zip codes to identify the host distr icts for all charter schools responding to the SASS. Because districts were only sampled, not all charter school host districts were included in the SASS. Ab out half of the host districts were identified, but these represented the host dis tricts of more than 78 percent of all of the charter schools in the sample. Only c harter schools with host districts identified were included in this analysis These school and district level data form the base of the analysis file. After excl uding schools in states that did not have an EMO operated school responding to the s urvey, 587 schools in 13 states remained in the analysis file. (Note 2) Another 54 schools with missing data for the dependent variables were excluded, res ulting in 533 charter schools in 216 districts being included in the anal ysis. Table 1 lists the states that were included in this study, as well as the nu mber of charter schools responding to the SASS in the 1999-2000 school year and the number of host districts identified in SASS. Table 1. States in Analysis Sample
8 of 27 StateTotal Number of Charter Schools Number of Charter Schools Responding to SASS (1999-2000) Number of Charter Schools with Host Districts Identified*(Sample) Year Charter School Legislation Passed Arizona1801731151994California154113841992Colorado6052361993District of Columbia 1916151996 Florida7259571996Illinois2010101996Massachusetts3427161993Michigan131119721993Minnesota3829241991New Jersey3024101996North Carolina5642291996Pennsylvania3123141997Texas10971511996Total914748533 Excludes charter schools with missing data for th e number of FARL-eligible and minority students.Sources: RPP International 2000; Authors calculations using the 1999-2000 Schools and Staffing Survey Data.Data on the characteristics of state charter school legislation come from two sources, both commissioned by the US Department of Education. Data on the financial aspects of state charter legislation come s from Nelson, Muir and Drowns (2000) Venturesome Capital and data on the caps and other non-financial characteristics are from RPP Internat ionals (1999) A Comparison of Charter School Legislation Table 2 below tabulates the number of states in this study having each policy variable of interest.Variables included in the models come from the stat e, district, and school levels. Each level and the variables included are e xplicated in more detail below. OLS regression models with schools clustered by their host district were estimated for the percent of students eligible for Free and Reduced Price Lunch (Note 3) and the percent of minority students in charter sc hools. (Note 4) Each
9 of 27 model will be discussed in detail below. Table 2: Frequency of State Policy CharacteristicsPolicyNumber (Percent) of States Without Policy Feature Number (Percent) of States With Policy Feature Cap on number of charter schools in state 6 (46.15) 7 (53.85) Multiple Authorizers7 (53.85) 6 (46.15) State Level Authorizer Only 11 (84.61) 2 (15.38) Local School District Authorizers Only 9 (69.23) 4 (30.77) Funding for Charter Schools Varies by Grade Levels Served 6 (46.15) 7 (53.85) State Provides Funding for At-Risk or Low-Income Students 4 (30.77) 9 (69.23) Transportation Not Required for Charter Students 7 (53.85) 6 (46.15) Sources: RPP International 1999; Nelson, Muir et al 2000State Policy CharacteristicsAs noted above, some of the significant dimensions of variation in state charter school policy regimes represented by variables in t he model include: (1) funding; (2) transportation; (3) number of schools permitted; and (4) number and types of chartering authorities. More specifica lly, the funding variables included in the models are whether the state provid es additional funding for at-risk or low-income students and another to indic ate whether funding varies by the grade levels served. A variable is included to indicate whether transportation of charter students is required (either by the scho ol or the district) Another variable is included to indicate whether or not a state has a cap on the number of charter schools permitted. Also, a variab le is included to indicate whether the state has multiple chartering authoriti es compared to having only one authorizer. In cases where there is only one ch artering authority, it is usually local school districts, though a few states have a state-level charter authorizing board or agency. States with multiple a uthorities usually allow local school districts and other, usually state level, en tities to charter schools, such as public universities or state boards of education.
10 of 27 District CharacteristicsCharacteristics of the local school district in whi ch charter schools are located are also important because they affect both whether and what types of charter school are likely to operate in a district and what charter schools are likely to do in terms of targeting students once they open. Impo rtant explanatory variables at this level include the percentage of students in the district who are eligible for the federal Free and Reduced Price Lunch (FARL) pro gram and the percentage of minority students (all calculated excluding char ter schools) because they are populations considered at-risk for educational fail ure or considered underserved by traditional schools. These variables are also im portant because charter schools primarily draw students from their host dis trict and consequently their enrollments may be strongly influenced by the compo sition of student population of their districts. Miron, Neslon, and R isley (2002) found, for example, that the average charter school student in Pennsylvania travels 5.5 miles to attend a charter school. A variable for th e size of the district (measured as the natural log of total district enrollment) wa s included to control for the size of the market in which a charter school operates.School CharacteristicsThe main school level variable that is predicted to influence charter school service to disadvantaged students is charter school type. (Note 5) Charter schools are disaggregated into schools managed by l arge-EMOsdefined as those managing 10 or more schoolsand those managed by small-EMOsdefined as those managing at least three but fewer than 10 schools. The category large-EMO includes the foll owing for-profit management firms in existence at the time of the SASS data col lection: Advantage, Beacon, Edison, Leona, Mosaica, National Heritage Academies and SABIS. Small-EMOs include firms such as Smart Schools, Inc ., Designs for Learning, and others similar private, for-profit firms that o perate fewer than 10 schools, usually in only one state. These two EMO categories are contrasted against the excluded category of non-EMO managed charter school s. The differences in market oriented and more independent charter school s are important because each type is likely to respond differently to both the policy environment and the market for education in terms of where they locate, the programs they offer, and the students they serve.Other variables that may affect a schools service to disadvantaged students include whether the school is specifically for at-r isk students, whether the school has admissions criteria, whether the school require s parents to volunteer, and the age of the school. The grade levels offered by a school (elementary, elementary/middle, and high school) are also contro lled for in the models in order to see whether the patterns are different by grade level. School size is controlled for by including a variable of the total number of students enrolled in the school. The urban location of schools is also c ontrolled for. Charter schools in this sub-sample are all rather urban but a varia ble is used to indicate whether the school is located in the central city of a larg e or mid-sized city. (Note 6)
11 of 27 ResultsAs noted above each dependent variable of interest was modeled as a function of school, district, and state policy characteristi cs. Variables are defined in Table 3. The basic model is shown below. For a scho ol i in district j and state k: (Note 7) % subgroupijk = f (schoolijk characteristics, districtjk characteristics, statek policy characteristics) Table 3: Variable DefinitionsVariable NameDefinitionMinority EnrollmentThe percent of students who are Black, Hispanic, American Indian or Alaska Native American; continuous variable. Percent FARL-eligible Students in School Percent of K-12 plus ungraded students who are eligible to participate in the Free and Reduced Price Lunch Program; continuousvariable. Large-EMO-managed School Dichotomous variable equals 1 if school is operated by a large Educational Management Organization (one that operates 10 or more charter schools). Small-EMO-managed School Dichotomous variable equals 1 if school is operated by a small Educational Management Organization (one that operates at least 3 but fewer than 10 charterschools). Non-EMO Charter SchoolOmitted variable. Reference c ategory which includes all charter schools not managed by an EMO. At-Risk SchoolDichotomous variable equals 1 if scho ol is exclusively for at-risk or expelled students or those involved in the criminal justice system Admissions CriteriaDichotomous variable equals 1 if the school uses one or more admissions criteria (such as standardized test scores) for admissionsdecisions Number of Years in Operation Continuous variable indicating the total number of years a school has been offering classes as a charter school. Total EnrollmentTotal number of students in grades K-12 and any ungraded students
12 of 27 Parents Required to Volunteer Dichotomous variable equals 1 if the school requires parents to volunteer some amount of time at the school as a requirement forstudent enrollment. Middle SchoolDichotomous variable equals 1 if the s chool exclusively serves the middle school grades. High SchoolDichotomous variable equals 1 if the sch ool exclusively serves the high school grades. Other GradesDichotomous variable equals 1 if the sc hool serves other or combined grade configurations (i.e. k-12). Elementary GradesOmitted category to which other gr ade levels are compared. School Located in Central City of Large or Mid-Sized City Dichotomous variables equals 1 if the school is located in the central city of a large or mid-sized city, as compared to the urbanfringe of a city or rural areas. District Percent MinorityPercent of K-12 plus ungra ded students in a district who are Black, Hispanic, Native American or Alaska Native; Continuousvariable. District Percent FARL-eligible Percent of K-12 plus ungraded students in a district who are eligible for the Free and Reduced Lunch Program; continuousvariable. Log of District EnrollmentNatural log of total numb er of students enrolled in the host district. CapDichotomous variable equals 1 if state policy includes a limit or cap on the number of charter schools allowed statewide. Multiple AuthorizersDichotomous variable equals 1 i f state policy allows more than one type of agency to grant charters. Contrasted against policy thatallows only one authorizer (either state or local school districts). Funding Varies by Grade Level Dichotomous variable equals 1 if the state policy includes a funding formula that varies the per pupil amount by the grade level ofthe student. State Funds for At-RiskDichotomous variable equals 1 if the state provides additional funds for at-risk or low-income students in addition to anyfederal funds for which a school may be
13 of 27 eligible. Transportation not requiredDichotomous variable equ als 1 if the state policy does not specify or require that either the charter school or the host district providetransportation to charter school students. The results of each estimated model are described b elow. Tables 4 and 5 show the means and standard deviations for continuous va riables, and the tabulations for the dummy variables. Table 6 presents the resul ts of the multivariate regressions. Table 4: Tabulations for Variables of InterestVariable Number of Schools Total Number of EMO-managed Schools69Number of Large-EMO-managed Schools49Number of Small-EMO-managed Schools23Number of Non-EMO-managed Schools464Number of Schools for At-Risk or Expelled Students or Students who Dropped Out 30 Number of Schools with Admissions Criteria138Number of Schools Requiring Parents to Volunteer236Number of Elementary Schools278Number of Middle Schools 46 Number of High Schools 115 Number of Schools Serving Other Grades, or Other Grade Configurations 94 Number of Schools in Central City of Large or Mid-S ized Central Cities 347 Table 5: Table of Means and Standard DeviationsVariableMeanStandard Deviation Minimum Maximum Full Sample (N= 533)Percent FARL-eligible in school50.5431.320.22 100Minority Enrollment59.1535.360 100Years in Operation2.611.541 8Total Enrollment290.13326.1212 2,653
14 of 27 Log of District Enrollment10.241.595.30 13.47District Percent FARL-eligible students (N=532) 46.0521.760.57 100 District Percent minority students 56.5829.990.39 99.92 Non-EMO managed Schools (N= 464)Percent FARL-eligible in school49.9631.620.22 100Minority Enrollment58.6635.420 100Years in Operation2.691.571 8Log of District Enrollment10.241.615.30 13.47District Percent FARL-eligible students (N=463) 45.4422.350.57 100 District Percent minority students 55.7530.080.39 99.92 Large-EMO-Managed Schools (N= 23)Percent FARL-eligible in school63.0026.971.82 100Minority Enrollment68.2034.141.54 100Years in Operation1.850.921 4Log of District Enrollment10.041.327.57 12.93District Percent FARL-eligible students 51.6016.3111.85 86.45 District Percent minority students 62.2328.513.71 99.40 Small-EMO-Managed Schools (N=23)Percent FARL-eligible in school50.1626.776.52 94. 34 Minority Enrollment50.9834.623 100Years in Operation2.351.371 6Log of District Enrollment10.561.676.57 12.37District Percent FARL-eligible students 47.3718.118.95 79.94 District Percent minority students 61.9830.588.77 95.74 Table 6: OLS Regression With Robust Standard Errors Clustered by Host District
15 of 27 VariablePercent of FARL-eligible Students in School Percent of minority students in School School Level VariablesLarge-EMO school4.49 (4.57) 3.94 (3.40) Small-EMO School-0.30 (5.66) -10.44** (3.90) Non-EMO Charter SchoolOmittedOmittedSchool for at-risk students only 11.81 (6.50) -1.34 (4.64) Admission Criteria-5.85* (2.87) -1.92 (3.22) Parents Required to Volunteer -3.77 (2.95) 2.35 (2.59) Number of Years in Operation 1.12 (0.99) -0.20 (0.78) Middle School Grades Only-0.94 (5.19) 2.11 (4.68) High School Grades Only-3.52 (2.80) -0.33 (2.57) Combined Grades2.64 (3.68) -0.87 (3.29) Elementary GradesOmittedOmittedTotal Enrollment-0.005 (0.004) 0.003 (0.003) School Located in Central City of Large or Mid-sized City 6.35* (3.15) 5.70 (3.37) District Level VariablesPercent FARL-Eligible in District 0.45*** (0.06) -Percent Minority in District--0.72*** (0.05) Log of District Enrollment0.54 (1.01) 0.18 (0.96) State Level Policy VariablesCap on Number of Schools-1.57 (3.43) -5.40* (2.74)
16 of 27 Multiple Authorizers17.79*** (4.86) 14.11*** (3.33) Funding Varies by Grade Level Served -5.60 (4.15) -5.50 (2.92) State Funding for At-risk Students 5.98 (5.39) 1.21 (3.24) Transportation Not Required -22.29*** (4.84) -19.89*** (3.85) ConstantConstant23.40 (10.29) 20.47 (10.08) Model PropertiesNumber of Observations532533Number of Clusters215216F StatisticF (18, 214) = 9.99***F (18, 215) = 48.91*** R-Squared0.21560.5102 Note: Standard error in parentheses. p.05, ** p .01, *** p 0.001Percent of Free and Reduced Lunch Eligible StudentsThe average percentage of students eligible for Fre e and Reduced Lunch, in all charter schools combined, is 50.54 percent, but the re are differences by school type. For EMO-managed schools as a group the averag e is 56.52 percent, for large-EMO-managed schools it is 63.00 percent and 5 0.16 percent for small-EMO-managed schools. For all other charter sc hools the average is 49.96 percent. Do these patterns of FARL-eligible enrollm ent hold in a multivariate analysis?The multivariate regression is significant overall, with an F-statistic of 9.99. The model explains a fair amount of variation in our de pendent variable (R-squared 0.2156), the percentage of FARL-eligible students i n a school. The coefficient on the variable indicating management by a large-EM O is positive but not statistically significant, suggesting no difference in FARL-eligible enrollment between large-EMO and non-EMO operated charter scho ols. Small-EMO charter schools also do not enroll significantly di fferent percentages of FARL-eligible students, when controlling for all of the other variables. Only two school level variables reach statistical significan ce: having admissions criteria and urban location. Schools that have admission req uirements tend to enroll a lower percentage of FARL-eligible students, indicat ed by the significant coefficient of 5.85. Charter schools in urban area s enroll about 6.35 percentage points more FARL-eligible students, all else constant. One district characteristic variable is important i n explaining the variation in the
17 of 27 percent of FARL-eligible students in charter school s. The coefficient (0.45) though small, suggests that the higher the percenta ge of FARL-eligible students in the district, the higher the percentage in chart er schools. It is not surprising that, all else constant, the percentage of FARL-eli gible students in a districts is related to the percentage of FARL-eligible students in charter schools as charters likely draw most of their students from th e district in which they are located.The variables representing state policy characteris tics present some interesting results. While only two of the variables have signi ficant coefficients, the magnitudes are strikingly large. Having multiple ch artering authorities, as opposed to only one type of authorizerusually a st ate level agency or local school districtsis positively and significantly re lated to the percentage of FARL-eligible students served in a school; the coef ficient of 17.79 suggests that schools in states with multiple charter authorities enroll about 18 percentage points more Free and Reduced Lunch-eligible student s than schools in states with only one authorizer. There has been speculatio n that local school districts are wary of authorizing charter schools that they s ee as competitors and so when they do authorize a charter school, it is usua lly for a school with a special program or serving a special population, which may not encourage service to low-income students.Not surprisingly, the variable indicating that the transportation of charter school students (either by the schools or districts) is not required was also significant but with a large negative effect on the percentage of low-income students in a school, with a coefficient of 22.29. Schools locat ed in states that do not require the transportation of students to charter schools e nroll about 22 percentage points fewer low-income students than schools in st ates that do require transportation of charter students.Minority EnrollmentMinority enrollment in charter schools averages abo ut 59.15 percent. Enrollment, however, varies greatly by charter scho ol type. Minority students comprise about 68.20 percent of large-EMO managed s chools but only 50.98 percent of small-EMO managed schools. The minority enrollment of non-EMO charter schools falls in between at 58.66 percent. Again, the multivariate analysis is performed to see whether these differen ces remain when policy variables, and other school and district variables are taken into account. Minority enrollment is defined as the percentage of all students in a school who are Asian or Pacific Islander, Black or African Ame rican, Hispanic, or Native American or Alaska Native, or the percentage of non -White students. This model was estimated similarly to the model of FARLeligibility except with a variable controlling for the percent of minority st udents in the host district. The model is significant overall with an F-statistic of 48.91 and an R-squared of 0.5102.With regard to the school level variables, we see t hat EMO status is important for explaining the variation in minority enrollment Charter schools managed by small-EMO firms enroll a significantly lower percen tage of minority students,
18 of 27 about 10 percentage points lower, even controlling for the other school, district, and state policy variables. No other school level v ariables are significant in this model.At the district level the variable controlling for the minority enrollment of the host district is significant. The coefficient of 0.72 su ggests that as the minority enrollment of a charter schools host district incr eases, so too does that of the charter school. This is not surprising given the fa ct that charter schools tend to draw most of their students from their surrounding host district. In this model, the state level policy variables als o present dramatic results. Consistent with the FARL-eligible model above, the variables multiple authorizers and not requiring transportation have l arge and significant coefficients. Additionally, the variable having a c ap on the total number of charter schools has a significant and negative coef ficient. The coefficient of 5.40 suggests that schools in states with caps hav e minority enrollments that are about 5 percentage points lower than those in s tates without caps. The coefficient on multiple authorizers is 14.11 sugges ting that this policy characteristic plays a large role in encouraging ch arter schools to enroll minority students. Again, we find that not requiring transpo rtation has a large negative effect. In this case, not requiring the transportat ion of charter school students reduces the percent of minority students in charter schools by about 20 points compared to schools in states that require transpor tation, all else constant.ConclusionsThe findings presented here suggest that certain po licy characteristics are important for encouraging all types of charter scho ols to enroll low-income and minority students. Most notably, having multiple au thorizers, rather than only local school districts or only state level authoriz ers, and requiring the transportation of charter school students appear to lead schools to serve higher percentages of FARL-eligible and minority students. Diverse chartering authorities are likely to authorize diverse types o f schools across a state, whereas the biases and preferences of a single auth orizing body are likely to dictate the types of schools that open. In states w here local school districts are the only authorizers, for example, districts may be wary of the competition brought by charter schools or they may use charter schools as a place to channel more difficult or expensive to serve studen ts. Policymakers need to realize that their choice of charter authorizers wi ll influence the types of students served by charter schools.States where transportation for charter school stud ents is not required not surprisingly, keeps the percentage of FARL-eligible and minority students lower than in schools in states where some kind of transp ortation of charter students is required. Indeed, it has been a concern of those wary of school choice that transportation would be a barrier to low-income fam ilies to execute their choice options. Low-income families may not have the resou rces (time and/or money) to transport their children to schools, which may b e located far from their homes. Policymakers concerned about access to chart er schools need to ensure that the transportation of charter school st udents is provided.
19 of 27 The organizational form of the schoolthat is assoc iation with a large or small-EMOappears to be important in explaining a s chools service to minority students. Disaggregating the category of EMO operat ed schools into largeand small-EMOs proved to be important because the patte rns were not consistent between the two types of firms. Large-EMOs, for ins tance, do not serve significantly higher or lower percentages of low-in come and minority students than all other types of charter schools. But school s managed by small-EMO schools served much lower percentages of minority s tudents than all other charter schools.Policy ImplicationsPolicy matters for who gets served by charter schoo ls and the devil is indeed in the details. Policymakers, educational professional s, and interested citizens need to be thoughtful and deliberate about the kind s of charter schools they want. If equity concerns are the most prominent, th en policymakers need to be sure those aspects of the law dealing with the auth orization of schools, transportation, and others, have the desired effect s. The results for EMO operated schools are mixed. Lar ge-EMOs do not appear to underor over-enroll low-income and minority stude nts whereas small-EMO operated charter schools enroll significantly lower percentages of minority students. But recall that the data used in this stu dy are cross-sectional and it is important to monitor the constantly evolving charte r school landscape to determine if the findings reported here become tren ds or not. The number of EMOs is growing, as is the number of schools they o perate, and the number of students they serve. Indeed, six of the largest EMO s (Charter Schools USA, Chancellor Beacon Academies, Edison Schools Inc., M osaica, National Heritage Academies, and White Hat Management) recen tly formed a Washington, DC based interest group called the Nati onal Council of Education Providers (National Council 2004). The organization plans to use its collective clout as the employers of more than 14,000 employee s and the educators of more than 140,000 students in 24 states to increase public funds for charter schools and to influence state and federal legislat ion affecting charter schools (Archer 2004). In this analysis, only about 8 perce nt of the charter schools are EMO-operated. It remains to be seen what will happe n if EMOs come to manage more and more charter schools. Policymakers have the power to decide whether EMOs can operate in their state. Sch ool founders also have control over the organizational form they choose, i ncluding partnering with for-profit EMOs. School leaders need to think about their missions and goals in designing their schools, and policymakers need to t hink about the purposes and goals of the reform in designing or amending the po licy.AcknowledgmentThis research was supported by a grant from the Ame rican Educational Research Association which receives funds for its AERA Grants Program from the U.S. Department of Educations National Center for Education Statistics, and the Institute for Education Sciences, and the N ational Science Foundation under NSF Grant #REC-9980573. Opinions reflect thos e of the author and do
20 of 27 not necessarily reflect those of the granting agenc ies. The author would like to thank the anonymous reviewers, as well as Jeffrey H enig, Christopher Nelson, and Linda Renzulli for their helpful comments on ea rlier drafts. The author is responsible for any errors presented here. An earli er version of this paper was presented at the Annual Research Conference of the Association for Public Policy and Management, November 7-9, 2002, Dallas, TX.ReferencesArcher, Jeff (2004). Private Charter Managers Team Up. Education Week, 23 (21), 1, 15 (February 4, 2004). Brown, H., J.R. Henig, N. Lacireno-Paquet, T.T. Hol yoke, and M. Moser (2003). Scale of Operations and Locus of Control in Marketvs. Mission-Oriente d Charter Schools. Buckley, J. and S. Kuscova (2003). The Effects of I nstitutional Variation on Policy Outcomes: The Case of Charter Schools in the States. NY, Teacher' s College, Columbia University, National Center for the Study of Privatization in Education : Occasional Paper #79. Chubb, J. E. and T. M. Moe (1990). Politics, Markets, and America's Schools Washington, DC, Brookings Institution Press. Cobb, C. D. and G. V. Glass (1999). Ethnic Segregat ion in Arizona Charter Schools. Education Policy Analysis Archives, 7 (1). Retrieved June 15, 2004, from http://epaa.asu. edu/epaa/v7n1/. Fiske, E. B. and H. F. Ladd (2000). When Schools Compete: A Cautionary Tale Washington, DC, Brookings Institution Press. Frumkin, P. and A. Andre-Clark (2000). When Mission s, Markets, and Politics Collide: Values and Strategies in the Nonprofit Human Services. Nonprofit and Voluntary Sector Quarterly, 29 (1, Supplement), 141-163. Gill, B. P., P. M. Timpane, et al. (2001). Rhetoric Versus Reality: What we Know and What We N eed to Know About Vouchers and Charter Schools Santa Monica, CA, RAND. Greene, J. P. (2000). Why School Choice Can Promote Integration. Education Week, 19 (31), 72, 52. Hassel, B. C. (1999). The Charter School Challenge Washington, DC, Brookings Institution Press. Henig, J. R., T. T. Holyoke, et al. (2003). Privati zation, Politics, and Urban Services: The Political Behavior of Charter Schools. Journal of Urban Affairs, 25 (1), 37-54. Henig, J. R. (1994). Rethinking School Choice: Limits of the Market Meta phor Princeton, NJ, Princeton University Press. Kahlenberg, R. D. (2001). All Together Now: Creating Middle-Class Schools thr ough Public School Choice Washington, DC, Brookings Institution Press. Lacireno-Paquet, N., T. T. Holyoke, et al. (2002). Creaming versus Cropping: Charter School Enrollment Practices in Response to Market Incentiv es. Educational Evaluation and Policy Analysis, 24 (2), 145-158. Miron, G. and C. Nelson (2002). What's Public about Charter Schools? Lessons Learne d About Choice and Accountability Thousand Oaks, CA, Corwin Press, Inc. Miron, G., C. Neslon, and J. Risley. 2002. Strength ening Pennsylvania's Charter School Reform: Findings from the Statewide Evaluation and Discussi on of Relevant Policy Issues. Vol. 2003: The Evaluation Center, Western Michigan University.Nathan, J. (1996). Charter Schools: Creating Hope and Opportunity for American Education San Francisco, Jossey-Bass Publishers. National Council of Education Providers (2004). Int ernet Homepage. Retrieved February 2, 2004
21 of 27 from http://www.educationproviders.org. Nelson, F. H., E. Muir, et al. (2000). Venturesome Capital: State Charter School Finance S ystems Washington, DC, Office of Educational Research and Improvement, U.S. Department of Education. No Child Left Behind Act of 2001 § 5201(3) Peterson, P. and J. P. Greene (1998). Race Relation s & Central City Schools: It's time for an Experiment with Vouchers. Brookings Review, 16 (2), 33-37. Peterson, P. (1999). A Liberal Case for Vouchers. The New Republic, 221 (14), 29-30. Plank, D. N., D. Arsen, et al. (2000). Charter Scho ols and Private Profits. The School Administrator (Web Edition) : 1-9. Reform, Center for Education (2002). Answers to Fre quently Asked Questions About Charter Schools. Rothstein, R. (1998). Charter Conundrum. The American Prospect, 9 (39). RPP International (1999). A Comparison of Charter S chool Legislation: Thirty Three States and the District of Columbia Incorporating Changes Through October, 1998, U.S. Department of Education. RPP International (2000). The State of Charter Scho ols 2000: Fourth Year Report. Washington, DC, Office of Educational Research and Improvement, U.S ./ Department of Education. Teske, P. and M. Schneider (2001). What Research Ca n Tell Policymakers about School Choice. Journal of Policy Analysis and Management, 20 (4), 609-631. Vergari, S., Ed. (2002). The Charter School Landscape Pittsburgh, PA, University of Pittsburgh Press. Viteritti, J. P. (1999). Choosing Equality: School Choice, the Constitution, and Civil Society Washington, DC, Brookings Institution Press. Weisbrod, B. A. (1998). Institutional Form and Inst itutional Behavior. Private Action and the Public Good W. W. Powell and E. S. Clemens. New Haven, CT, Ya le University Press: 69-84. Wells, A. S., C. Grutzik, et al. (1999). Underlying Policy Assumptions of Charter school Reform: The Multiple Meanings of a Movement. Teachers College Record 100 (3): 513-535. Wells, A. S., A. Lopez, et al. (1999). Charter Scho ols as Postmodern Paradox: Rethinking Social Stratification in an Age of Deregulated School Choi ce. Harvard Educational Review 69 (2): 172-204. Wells, A. S., Ed. (2002). Where Charter School Policy Fails: The Problems of Accountability and Equity Sociology of Education. New York, Teachers Colleg e Press. Witte, J. F., A. F. Shober, et al. (2003). Analyzin g State Charter School Laws and Their Influence on the Formation of Charter Schools in the United Stat es. Paper prepared for the American Political Science Association 2003 Annual Meeting, Philadelph ia, PA.About the AuthorNatalie Lacireno-PaquetThe George Washington University805 21st Street NW, Suite 601Washington, DC 20052Email: email@example.com Natalie Lacireno-Paquet recently received her Ph.D. in Public Policy from The
22 of 27 George Washington University. Her research interest s include the equity implications of education reform, specifically scho ol choice policies, and the role of management companies in public education.Notes 1. While service to disadvantaged students is a mult ifaceted concept, in this paper I examine just one facetaccess. Other r elated and perhaps more important facets of service include examinatio n of the quality of the schools attended by disadvantaged students, as well as their achievement in their schools of choice. 2. The excluded states are Connecticut, Georgia, Kansa s, Ohio, and Wisconsin. 3. Eligibility for the Free and Reduced Price Lunch Pr ogram is used as a proxy for low-income status. 4. BRR-based standard errors are the recommended metho d of the National Center for Education Statistics for use wi th the SASS data because they adjust standard errors to account for nonresponse and for the complex design of the SASS. However, because th e charter schools were selected with certainty the use of the weights associated with the BRR method are unnecessary. Moreover, because the a ssumption that the error terms are independent is violated (84 of the school districts have more than one charter school), I corrected for the correlated error terms by clustering charter schools in their host distric ts, using the robust cluster option in STATA. 5. Here EMO is defined as firm that offers comprehensi ve school management services in three or more schools (see B ulkley 2001). This definition will exclude those EMOs that were set up to manage one particular school, such as some EMOs in Michigan wh ere school leaders incorporated to avoid participation in the state te achers retirement plan. 6. A caveat is important here because while this artic le looks at what types of students are served by charter schools it is imp ortant to note that there is difficulty in distinguishing between the effects of which parents are choosing charter schools and which students are cho sen by charter schools. States do not allow charter schools to dis criminate, but some states allow charter schools to specify geographic boundaries for service, specify admissions criteria or to give siblings pre ference or to give the children of founders and staff preference over othe rs who may want to attend, and these could allow schools to actively s hape their student populations. Additionally, charters may influence t heir student populations through the way they advertise or describe their of ferings. 7. While the nested nature of the data (charter school s in districts in states) appears to be a natural fit for Hierarchica l Linear Modeling techniques, the peculiarities of this dataset did n ot permit it. First, there was not enough variation in some of the key variabl es at the different levels of analysis. For example, some states have f ewer than 5 EMO operated schools, making the estimation of coeffici ents for these variables unreliable or even impossible. Second, some distric ts have only one charter school, and few have 5 or more, making the technical estimation at this level impossible. 8. Because the BRR-based standard errors are the prefe rred method of
23 of 27 the NCES, I want to note the results of the model u sing the BRR-based standard errors are somewhat different, though not contradictory. Using the BRR standard errors, a number of additional var iables become significant, including large-EMO (3.89), and all of the policy variables. The coefficients of variables that were significant in the cluster analysis remain significant with virtually identical magnitudes in the BRR-based standard errors model. The results of this model are availab le upon request from the author. 9. Again, for comparison purposes, I want to note the differences and similarities with results for the same model but us ing BRR-based standard errors. In the BRR model, the standard errors tend to be smaller and thus more variables reach statistical significance. Whil e all of the variables in the cluster analysis remain significant, with virtu ally identical coefficients, variables that are newly significant include: paren tal service requirement (2.33), total enrollment (0.003), and funding varie s by grade level (-5.58). The results of this model are available upon reques t from the author. The World Wide Web address for the Education Policy Analysis Archives is epaa.asu.edu 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, firstname.lastname@example.org or reach him at College of Education, Arizona State Un iversity, Tempe, AZ 85287-2411. The Commentary Editor is Casey D. Cobb: email@example.com .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
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