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Charter schools legislation and the element of race.

Charter schools emerged in the early 1990's as a new idea for public school reform. Charter schools were presented as an alternative school choice option that can circumvent state and national bureaucratic controls that were perceived as hindering public schools reform efforts. The thinking behind the charter school idea is that the more autonomous a school is, the more the school will become an effective organization that is free to innovate and more attuned to the needs of the students (Chubb and Moe, 1990). In essence, charter schools have more independence and can allow parents and teachers to have greater say in how they are run. The "charter" establishing each such school is a performance contract detailing the school's goals. The charter may be granted by a local school board, a state board of education, or a public institution of higher education, depending upon the state. Within three to five years time, if the school's goals (fiscal and academic) are not met, then the school is at risk of losing its charter and can be forced to shut down.

The charter school initiative emerged as part of the school choice movement that gained momentum after the publication of a study by The National Commission on Excellence in Education (NCEE) in 1983 called "A Nation at Risk: The Imperative For Educational Reform" that underlined key weaknesses in the American public schooling system (NCEE, 1983). By the mid 1980's, an increased number of parents began to take their children out of public schools and enroll them in alternative schools. Among the alternatives were magnet and charter schools. More controversial alternatives also emerged such as voucher based education and home schooling (Van Galen and Pittman, 1991).

Minnesota passed the nation's first charter school law in 1991. Between 1991 and 2003 forty other states and the District of Columbia passed charter school laws. As of January 2007, 3,600 charter schools, enrolling more than 1 million students, were operating across the United States (Center for Education Reform, 2007). The diffusion of charter school laws across the United States happened rapidly. The speed of diffusion of innovation was calculated based on the average rate of diffusion (53 years) of 12 innovations examined by Gray (1973).

Besides autonomy and choice, proponents of charter schools legislation note that charter schools are also designed to increase opportunities for learning and provide access to quality education for students, provide a system of accountability for results in public education, encourage innovative teaching practices, create new professional opportunities for teachers, and encourage community and parent involvement in public education (Chen, 2006). In order to avoid a situation where charter schools become segregated schools, the majority of states that passed charter school legislation also have provisions in the legislation that calls for racial integration. The legislation calls for either the state or a local education agencies to enforce the regulations (Frankenberg and Lee, 2003).

It appears that charter schools, in general, are doing a good job in meeting many of their goals. A majority of studies on charter school reviewed by the Center for Education Reform (CER) between the mid 1990's and the Fall of 2000 show that "charters are doing the job they were designed to do, with 88 major reports now showing that charter schools are improving education for American kids" (CER 2003, 1). There are dissenting voices, however, that claim that although it is difficult to compare charter schools to public schools due to different educational approaches and enrollment mechanisms, careful analysis shows that there is no evidence that charter schools perform better than public schools (Frankenberg and Lee, 2003). On average, because many charter schools accept a higher percentage of low achieving public school students, charter schools students are not performing as well as public schools students on state and national tests (Collins, 2001; Toppo, 2002). The Role of Race in Charter Schools Legislation

In an extensive study conducted by the Civil Rights Project at Harvard University, Frankenberg and Lee (2003) show that charter schools actually exacerbate racial segregation. The study explored whether charter schools are more or less segregated than public schools. The authors note:
   In the sixteen states with charter school populations
   greater than 5,000 ... charter schools in most
   of these states enroll disproportionately high
   percentage of minority students resulting in students
   of all races being more likely to attend schools that.
   on average, have a higher percentage of minority
   students. However, white charter school students
   still less likely than other racial groups to be in
   heavily minority schools. (p. 7)

One of their most significant finding is that 70% of African American students in charter schools attend intensely segregated minority schools compared to 34% of African American public school students. The high level of segregation can be explained according to the authors by the free market orientation of charter schools. Incorporating schools into the free market system yields the same segregationist consequences that are found in areas like housing, employment, health care, etc. (Frankenberg and Lee, 2003).

In this study I will examine a possible correlation between public school desegregation policies that started in the 1960's and the passage of charter school laws as a way to elude desegregation. According to Orfield and Lee (2004) the desegregation process in public schools continued unabated until 1988 for all the regions of the United States except the Northeast (See Table 1).

The level of public school integration reached its peak for most regions in the 1980s and early 1990s time periods; the same time period when charter school legislations across the states began gathering momentum (see Table 2). I surmise that it is conceivable that charter school legislation began to flourish as a reaction to increased public school integration.

Diffusion Research

The key question that I will explore in this study is did states adopt charter school laws in response to desegration in public schools? To answer the question, I use diffusion analysis. Diffusion analysis explores the spread of innovative policies and sets out and tests hypotheses regarding why and how such spreading occurs. It also can illuminate differences in state level economic, social, and political characteristics, changes in these over time, and how these result in differences in policies among states. Analysis of the diffusion of policy innovations can explain how and why certain legislation is approved at a certain period or is not approved (Savage, 1985).

In his book Diffusion of Innovation, Rogers (1983) argues that diffusion "is the process by which innovation is communicated through certain channels over time among the members of a social system." (p.5) Diffusion research has its roots in European social research traditions. One of the early scholars who used diffusion research to study society was the French sociologist Gabriel Tarde. Tarde studied how new ideas are incorporated into the French society. He wanted to understand why certain innovations in areas like mythological ideas and industrial processes catch on while others fail to do so. Tarde observed that the adoption rate of a new innovation usually followed an S shaped curve over time. He argued that the S-curve of diffusion occurs because ideas must pass a stage in which opinion leaders (people who are respected in society) embrace it before it becomes popular in society. He also added that the diffusion of innovation is at the root of explaining human behavioral change (p.7). An S-curve cumulative frequency estimate of the diffusion of charter school legislation is provided in Figure 1.

To learn why certain states adopted charter school legislation I looked first at the pattern of state adoptions of charter school laws. I then incorporated demographic, political, and geographical variables into the study. This study will complement other studies on the diffusion of innovation (Cannon and Baum, 1981; Gray, 1973; Savage, 1978; Scott, 1968; Walker, 1969; Welch and Thompson, 1980).

Research Questions and Event History Analysis

To analyze whether segregation in public schools is a significant variable in the adoption of charter school laws, I use Event History Analysis (EHA) in my diffusion study. According to Blossfeld, Hamerle, and Mayer (1989), EHA is a "statistical method used to analyze time intervals between successive state transitions or events" (p. 11). EHA evolved as the preferred method of analysis because of the limitations of other techniques used for time interval analysis. In the case of regional diffusion, factor analysis was used in the past. For example, Walker (1969) used this technique in political science. Looking at a select list of 88 policies, he applied the method to establish groupings of adopting states that shared common political characteristics. He concluded that regional groupings of states do exist and that adjacent states tend to adopt similar policies at the same time period. A different approach was used by Gray (1973) to study the diffusion of adoption. Gray used a national interaction model that is based on the assumption that communication networks (internal determinants) among state officials are a key variable in determining the likelihood for a state to adopt a policy.

Berry (1994) called for the use of EHA because it is general, and incorporates both models used by Walker (1969) and Gray (1973). Furthermore, EHA also provides protection against possible spurious relationships when analyzing the adoption years of states and their neighbors. Berry and Berry (1990) explains that a spurious relationship is controlled because "the estimated effects of terms representing the behavior of nearby states would diminish to zero in the EHA equation ... as these regional effects would be 'controlled' for the impacts of internal characteristics" (p. 399).

In their study of state lottery adoptions, Berry and Berry (1990) used a specific type of EHA (which will be used in my study as well) called "discrete time EHA." In discrete time EHA the time period used in the analysis is divided into distinctive units of analysis like months or years. One of the most important elements of the EHA is the risk set. Similar to Berry and Berry's study, the risk set in my study is composed of the states that are at risk of experiencing the event of adoption of an innovation. Another important variable is the hazard rate. The hazard rate (also known as the transition rate or intensity) is defined by Mayer and Tuma (1990) as the "probability per unit of time that an event or transition occurs in an infinitesimal interval of time among those at risk during a particular time interval in question" (p. 11). It is defined by the probability Pi,t that an individual i (in this case a state) will experience the event at time t assuming that i is at risk for such experience. The hazard rate is calculated using a set of independent variables usually denoted by the letter x and x1, x2 x3 etc. for each independent variable (examples of independent variables for states include economic conditions, population characteristics etc,). The linear functions of the explanatory variables are written as follows:

Pi,t = _(b1xl i,t + b2x2 i,t + b3x3 i,t...) [1]


The equation shows the probability that state i will adopt a lottery t. The association depends on the assumption that state i has not adopted the innovation prior to the year t and that--denotes the cumulative distribution function (Berry, 1994).

Using EHA involves assigning a separate observational record for each unit of time that each state is known to be at risk. The data set is a pooled cross-sectional time series in which the cases are designated as "state-years". For each state year, the dependent variable is coded 1 if a state adopted the innovation in a specific year and zero if it did not. The explanatory variables (independent variables) are assigned the values they take on in each state-year. The next step is to pool all the state-years into a single sample. The final step is to estimate logit models for dichotomous dependent variable using the method of maximum likelihood (Allison, 1984). To do that, I used SPSS (SPSS Inc. 2000) statistical software.


Using EHA, I analyzed alternative adoption characteristics of charter school laws besides segregation in order to identify similarities and differences in diffusion.

The following are the hypotheses that are used in the EHA:

HYPOTHESIS 1. The higher the level of per capita income in a state, the greater the odds that a state will adopt a charter school law, controlling for the effects of other independent variables.

Rationale: Per capita income is a good measure of the wealth of a state. It seems reasonable to assume that a wealthier population that is not satisfied with how the public and private school systems run (parents may feel they have little influence over curriculum, amount of homework provided, teaching methodologies, and the quality of teachers) will opt for alternative forms of education like charter schools. Such parents will lobby state representatives for the legalization of charter schools. This hypothesis is supported by a study for increased propensity for charter school legislation among wealthier states (Hassel, 1999).

HYPOTHESIS 2. The higher the percentage of African Americans in a state, the greater the odds that a state will enact a charter school law, controlling for the effects of other independent variables.

Rationale: Opponents of charter school legislation claim that charter schools create a two tier educational system where more affluent whites attend affluent charter schools and poorer minorities attend poor charter schools (Hatt-Echeverria, 2005; Molnar, 1996; and O'Neil, 1996). Although states may hesitate to oppose the charter school option due to racial segregation, so far charter school advocates have been successful in lobbying for charter school laws. Hence, it is plausible that the higher the percentage of African Americans in a state, the more likely it is that white families will lobby the state to legalize charter schools.

HYPOTHESIS 3. The higher the racial segregation levels in public schools" in a state, the lower the odds' that a state will enact a charter school law, controlling for the effects of other independent variables.

Rationale: Racial segregation in public schools can have an effect on the inclination of some states to adopt charter school statutes. If some states are going through racial desegregation in public schools, it is possible that alternative charter schools could be used to circumvent the process and allow for increased white flight (Crowder, 2000).

HYPOTHESIS 4. When the Republican Party controls the governorship and both houses of the legislature, the odds that the state will enact charter school law is greater than when the government is under divided partisan control or is controlled by the Democratic party, controlling for the effects" of other independent variables.

Rationale: The Republican Party is supported primarily by wealthier and white voters. This demographic is more likely to feel threatened by school desegregation and would therefore more likely to lobby for school choice alternatives like charter schools. The Democratic Party is more allied with minority and union voters, it is less likely to pass legislation that will weaken the public schools or undermine teacher unions.

HYPOTHESIS 5. The higher the NEA membership in a state, the lower the odds that a state will enact a charter school law, controlling for the effects of other independent variables.

Rationale: Teachers unions oppose the legalization of charter schools. One possible political reason is that the growth of charter schools will undermine union membership and strength. It will also reduce the control that public school teachers have over education. Unions can block lobbying for charter schools as a method for alternative school segregation. As more states passed charter school legislation, NEA officials issued a comprehensive report that served as the basis for the Association's current policy regarding charter schools. It sets out a long list of criteria that would constrain the autonomy of charter schools and their freedom to experiment with new modes of education (NEA, 2001). I did not use the American Federation of Teachers (AFT) in my hypothesis due to lack of complete statistical data for the EHA.

HYPOTHESIS 6. The higher the high school graduation rate in a state the lower the odds' that a state will enact a charter school law, controlling for the effects of other independent variables.

Rationale: Another way to judge the quality of education is to measure the average high school graduation rate in a state. A state with a low graduation rate should be more inclined to experiment with charter schools despite resistance by teachers unions or racial minorities who might feel that it will undermine the public school system.

HYPOTHESIS 7. The probability that a state will adopt a charter school legislation is directly related to the number of states in the region that have passed charter school legislation, controlling for the effects of other independent variables.

Rationale: Regional culture and politics can also have a role in predisposition of certain states to adopt charter school legislation. Family relations and cross border commuting help spread ideas from one state to another. A state that passes charter school legislation as a way to circumvent federal desegregations laws might cause a neighboring state to do the same despite different economic and demographic characteristics. Since dividing the United States into regions is subjective (Berry and Berry, 1990), I decided to use the regional parameters provided by the Civil Rights Project at Harvard University. This decision was made in order to avoid confusion when I incorporate data about high school segregation (see Hypothesis 3). A full list of states for each region is available in Appendix B.

The Event History Analysis Model

The 7 hypotheses provided are amalgamated to produce the following EHA model equation:

[??] (ADOPT i,t) = bo (b1PCI2000 i,t + b2AAPER i,t +b3SEGREGAT i,t + b4PTYCONTR i,t + b5NEA i,t + b6HSGRADRT i,t + b7REGION i,t). [1]

[??] (ADOPT i,t) is the predicted logit or the log odds of a state adopting charter school legislation in a particular year (probability that a state (i) will adopt a charter school statute at year (t)). The independent variables are as follows: PCI2000 stands for the per capita income of the population in a state in a particular year adjusted for inflation (year 2000 dollars). AAPER is the African-American percentage of a state's population; SEGREGAT measures the public school segregation level in a state. PTYCONTR is the party in control of both the state legislature and the governorship in a particular year. The variable NEA is the percentage of the teachers in a state who are members of the National Education Association. For a specific definition of NEA membership guidelines see Appendix A. HSGRADRT is the estimated state high school graduation rate. The rate was calculated by dividing the number of public high school graduates in each state by the total of students in grades 9 through 12. Finally, REGION measures the number of states in a specific region (Northeast, Border, South, Midwest, and West) that have passed charter school legislation and is thus a measure of regional diffusion. More detailed explanations of measures and data sources can be found in Appendix A.

Table 3 shows the results of a logistic regression analysis of the 7 independent variables that were hypothesized as possible explanations for the enactment of charter school legislation in a particular state in a particular year. The table includes columns for the estimated coefficient "b", its standard error "s.e.", "Wald" which determines if "b" is significantly different from zero and therefore is making a significant contribution to the prediction of the outcome, the two-tailed significance level of the estimated coefficient, and "exp(b)" or the odds ratio. The dependent variable, L, is the log odds of the dependent variable (Y = 1)--that is, the log odds that a particular state will adopt a charter school legislation in a particular year, given its values on the independent variables.


The EHA results from Table 3 are as follows: Two independent variables--NEA membership and high school graduation rate--as hypothesized, lessens the odds of enacting charter schools. Two other independent variables- per capita income and segregation--have effects that increase the odds of enacting charter schools. The Northeast region had lesser odds of enacting charter schools. The effects of other independent variables upon the enactment of charter schools were negligible and statistically insignificant.


The results suggest some interesting possible explanations for the passage of charter schools laws. It appears that the enactment of charter school laws is considerably influenced by teacher unions and a state's wealth as gauged by per capita income. Poorer states with more powerful teachers' unions lobbying groups are less likely to pass charter school legislation. Nine of the ten states that did not pass charter school legislation have a below average per capita income and eight out of the ten have a higher than average NEA union membership. Income has been shown to be an important variable in determining the passage of charter school legislation. This result support previous research indicating that higher income populations are more likely to organize and successfully lobby for charter school legislation despite resistance from teachers' unions (Hassel, 1999).

The variable of high school graduation rate proved to be significant as well. The negative coefficient suggests that a higher than average graduation rate in a state lessen the odds of enacting a charter school law. A high graduation rate may be associated with better performing schools, making parents less inclined to consider other alternatives to public schools. The percentage of African Americans in a state proved to be irrelevant. One possible explanation is that it is not the demographic makeup of a state that determines the probability for charter school laws but the degree of integration within the public school system. In terms of region as a factor, only the coefficient for the Northeast proved to be statistically significant. It seems that states in the Northeast had a greater propensity to enact charter school laws than any other region in the U.S., even controlling for level of segregation. States in this region may be most reactive to attempts to integrate the public schools. One possible explanation is that the Northeast did not face the same federal pressures to integrate the public schools as the South region for example. Other regions, specifically most of the Midwest and West have a relatively low percentage of African Americans and therefore might had an easier time to integrate their public schools. The percentage of Latinos was not considered in this dataset although studies show that the pattern for Latino segregation in charter schools varies and that Latino segregation levels are not as acute as that of African Americans (Frankenberg and Lee, 2003).

My primary emphasis, the segregation variable, proved to have a significant positive effect on charter school adoption. It appears that charter school legislation is more likely to be enacted in states where school segregation is greater. It seems that the opening of charter schools is perceived as a solution to the problem of increased political pressure towards school integration.

The study by the Harvard Civil Rights Project shows that charter schools are more segregated than public schools. The segregation in charter schools may reflect state policies, judgments in enforcement, or the methodology of approving schools for charters. This level of segregation in charter schools exists despite the fact that more than half of all states with charter school legislation also have laws requiring the enforcement of desegregation policies (Frankenberg and Lee, 2003).

One problem with the current charter schools system is that it essentially allows for the return to the "separate but equal" doctrine of schooling that existed in the South. Since charter schools are not tied to geographically specific residential boundaries as public schools, parents can choose to what school they will send their children. This system perpetuates the all too common "white flight" from schools with a high percentage of minority students since the states are not enforcing desegregation policies (Fuller and Elmore, 1996).

Even if charter schools are a "politically correct" way to escape desegregation, it is important to note that a segregationist momentum in public education may not be a function of white flight alone. Cultural and demographic elements might also help explain the segregation trend. Some charter schools offer more afro-centric curriculum that attracts more African American students. Also, many charter schools are also located in inner city areas where it is much easier for minority inner city residents to reach than suburban residents. Finally, it is likely that parents of students in inner city schools are not worried as much about integration as they are about the quality of their local public schools. Charter schools are an alternative to the failing inner city schools for many inner city families. Since minorities attend more failing public schools they have a higher propensity to switch to charter schools even if those inner city charter schools are segregated as well (Frankenberg and Lee, 2003).

Overall, it is clear that in order to avoid the perpetuation of this level of segregation in charter schools in the future, it is imperative that states strictly enforce state and federal desegregation laws regarding charter schools. Another option to control for segregation in charter schools is to fix the geographical boundaries (not necessarily contiguous boundaries) of charter schools. The boundaries should incorporate a mix of different ethnic neighborhoods, allowing for a higher level of integration.


A. Measurement of Variables and Data Sources

Dependent Variable: The data for the years that states passed charter school legislation was obtained from the Center for Education Reform website at Once I obtained the dates, I translated the data into state-years for my EHA. As an EHA measure (ADOPT i, t), I developed a dichotomous (0,1) consequence variable. I designated a state-year variable equal to 0 for every year before the passage of charter school legislation and 1 for the year of passage of the legislation and the remaining years up to the year 2000.

Note: Since all the data for the EHA was updated only to the year 2000, I designated adoption years for Indiana (200 I), Tennessee (2002), Iowa (2002) and Maryland (2003) as if they were passed in the year 2000.

Independent Variables: I obtained the Per Capita Income (PCI2000 i,t) for each state for the years 1980 to 2000 from the Department of Commerce web site at I then adjusted the data to year 2000 dollars by multiplying the data by the Consumer Price Index (CPI) for the year 2000. I used Census data for the percentage of the population in each state that was classified as African American (AAPER i,t) for the years 1980 to 2000. Data for public school segregation levels (SEGREGAT i,t) for African Americans was obtained from the Civil Rights Project (CRP) at Harvard University. The CRP, however, only had data for approximately every decade since 1968. To control for this problem I divided the difference between data available (years provided) by the number of missing years to obtain an estimated percentage for each year throughout each missing time span.

I acquired data for party control (PTYCONTR i,t) in each state and governorship from the following sources: Tim Storey of The National Conference of State Legislators, Prof. Donald Miller at St. Mary's College, and database of historic American political biography. To translate into an EHA variable, I developed the following coding scheme: Office of governor and control of the upper and lower chambers of the state legislature were coded 0 for Republican control, 1 for Democratic control, and 0.5 for evenly divided. The sum is then divided by 3 to produce a 0 to 1 range for the EHA.

Data on the National Education Association (NEA i,t) membership was collected from the NEA Handbook for the years 1980 to 2000. NEA membership data include active teachers, retired, life-long, student, substitute, reserve, staff, and associate categories. The expanded membership can lead to a NEA membership in some states that are higher than the total active teachers in a state. The high school graduation data (HSGRADRT i,t) was derived from dividing the number of graduates by the total enrollment data (the number of high school students grades 9 through 12). This method was suggested to me by a data consultant at the National Center for Education Statistics (NCES) given that there are significant variations in the ways states determine the flow of students through the grades. The error rate in this method is significantly lower then just dividing the number of graduates by grade 9 enrollment 4 years ago. The graduation and enrollment data for the years 1980 to 2000 were obtained from the following sources: Digest of Educational Statistics, Statistics of Public Elementary and Secondary School Systems, Statistical Abstract of the United States, Statistics of State School Systems and from

B. Division of States into Regions

To divide the United States into regions (REGION i,t), I decided to incorporate the definition of six regions provided by the Civil Rights Project at Harvard University. The six regional groupings include the following states:

Note: States mentioned below with an asterisk have passed charter school laws. Northeast: Connecticut*, Maine, Massachusetts*, New Hampshire*, New Jersey*, New York*, Pennsylvania*, Rhode Island*, and Vermont.

Border: Delaware*, Kentucky, Maryland, Missouri*, Oklahoma*, and West Virginia.

South: Alabama, Arkansas*, Florida*, Georgia*, Louisiana*, Mississippi*, North Carolina*, South Carolina*, Tennessee*, Texas*, and Virginia*.

Midwest: Illinois*, Indiana*, Iowa*, Kansas*, Michigan*, Minnesota*, Nebraska, North Dakota, Ohio*, South Dakota, and Wisconsin*.

West: Arizona*, California*, Colorado*, Montana, Nevada*, New Mexico*, Oregon*, Utah*, Washington, and Wyoming*.

Other: Hawaii* and Alaska, which have very distinctive populations, are treated differently.


Allison, D. (1984). Event history analysis: Regression for longitudinal event data. Newbury Park, CA: SAGE Publications.

Berry, S. (1994). Sizing up state policy innovation research. Policy Studies Journal 22 (3), 442-456.

Berry, S. & Berry W. (1990). State lottery adoptions as policy innovations: An event history analysis. American Political Science Review 84, 395-415.

Blossfeld, H., Hamerle, A. & Mayer, K. (1989). Event history analysis: Statistical theory and application in the social sciences. Hillsdale, NJ: Lawrence Earlbaum Associates.

Canon, B., & Baum, L. (1981). Patterns of adoption of tort law innovations: An application of diffusion theory to judicial doctrines. American Political Science Review, 75, 975-987.

Center for Education Reform. (2003). What the research reveals about charter schools. Retrieved March 22, 2005 from

Chen, G. (2006) What is a charter school? Public School Review. Retrieved July 15, 2006 from

Collins, G. (2001, May 19). Charter schools rife with tales of failure. Dallas Morning News, p. 29A

Crowder, K. (2000). The racial context of white mobility: An individual-level assessment of the white flight hypothesis. Social Science Research, 29, 223-57.

Frankenberg, E., & Lee, C. (2003). Charter schools and race: A lost opportunity for integrated education. Retrieved July 15, 2005 from www.civilrightsproject, Charter_Schools03.pdf

Fuller, B. & Elmore, R. (1996). Who chooses, who looses? New York: Teacher College Press.

Gray, V. (1973). Innovation in the states: A diffusion study. American Political Science Review 67, 1174-85.

Hassel, B. (1999). The charter school challenge: Avoiding the pitfalls, fulfilling the promise. Washington, DC: Brookings Institution Press.

Hatt-Echeverria, B., & Jo, J. (2005). Understanding the "new" racism through an urban charter school. Educational Foundations, 19, 51-65.

Mayer, K., & Tuma, N. (1990). Event history analysis in life course research. Madison, WI: University of Wisconsin Press.

Molnar, A. (1996). Charter schools: The smiling face of disinvestment. Educational Leadership, 54(2), 9-15.

National Commission on Excellence in Education. (1983). A nation at risk: The imperative for educational reform. Washington, D.C.: U.S. Government Printing Office.

National Education Association. (2001). NEA policy on charter schools: statement adopted by the 2001 representative assembly. Retrieved July 18, 2009 from

O'Neil, J. (1996). New options, old concerns. Educational Leadership, 54 (2), 6- 8.

Orfield, G., & Lee, C. (2004, January 7). Brown at 50: King's dream or Plessy's nightmare? Harvard's Civil Rights Project. Retrieved July 15, 2005 from

Rogers, E. (1983). Diffusion of innovation. New York, NY: Free Press.

Savage, R. (1978). Policy innovativeness as a trait of American states. Journal of Politics, 40, 212-224.

Savage, R. (1985). Diffusion research traditions and the spread of policy innovations in a federal system. Publius: The Journal of Federalism 15, 1-27.

Scott, M. (1968). A study of the diffusion process of an educational innovation at eleven elementary schools in a medium-sized school district." Master's thesis. California State College, Long Beach.

Toppo, G. (2002, September 3). Study: Charter students score lower. Washington Post, p. 7A.

Walker, J. (1969). The diffusion of innovation among the American states. American Political Science Review 63, 880-99.

Welch, S., & Thompson, K. (1980). The impact of federal incentives on state policy innovation. American Journal of Political Science, 24, 715-29.


Tal Levy, PhD, is an assistant professor in the Department of Political Science at Marygrove College in Detroit, Michigan. This research follows his primary interest in education as well as institutionalized racism. His other research interests include civic engagement and leadership studies and urban and interest group politics.
Table 1 Percentage of African American Students in 50-100%
Minority Schools for the Years 1968, 1988, 1991, and 2001

            1968    1988    1991    2001

South       80.9    56.5    60.1    69.8
Border      71.6    59.6    59.3    67.9
Northeast   66.8    77.3    75.2    78.4
Midwest     77.3    70.1    69.7    72.9
West        72.2    67.1    69.2    75.8

Source: Orfield, Gary and Chungmei Lee. 2004. "Brown at 50:
King's Dream or Plessy's Nightmare?" Cambridge MA: The Civil
Rights Project at Harvard University. p. 20.

Table 2
Years of Passage of Charter Schools Legislation

Minnesota       1991   Delaware          1995
California      1992   Texas             1995
Michigan        1993   Louisiana         1995
Massachusetts   1993   New Hampshire     1995
Colorado        1993   Alaska            1995
Georgia         1993   Wyoming           1995
New Mexico      1993   Arkansas          1995
Wisconsin       1993   Rhode Island      1995
Hawaii          1994   Washington D.C.   1996
Kansas          1994   Florida           1996
Arizona         1994   North Carolina    1996

South Carolina  1996   Missouri          1998
New Jersey      1996   Virginia          1998
Illinois        1996   Oregon            1999
Connecticut     1996   Oklahoma          1999
Pennsylvania    1997   Indiana           2001
Ohio            1997   Tennessee         2002
Nevada          1997   Iowa              2002
Mississippi     1997   Maryland          2003
Idaho           1998
Utah            1998
New York        1998

Source: Center for Education Reform [CER] Website (2006)

Table 3
Logistic Regression Analysis of the Adoption of Charter
School Legislation

Variable (a)          b       s.e.     Wald       sig.      exp (b)

Constant            -14.15    5.904   5.7443    .0165
Per Capita           .0001    .0001   4.2503    .0392 *      1.0001
Income (2000)
African              .0390    .0326   1.4384    .2304        1.0398
American in
Segregation          .1996    .0864   5.3353    .0209 *      1.2209
Political Party     -.6899    .7093    .9460    .3307         .5016
in Control
Union               -.0150    .0067   4.7912    .0286 *       .9851
High School         -.2173    .1309   2.7558    .0969 **      .8047
Region: h            .3520    .5827    .3669    .5447        1.4233
Region:              .7575    .8643    .7682    .3808        2.1330
Region:             -2.496    1.019   5.9962    .1430         .0823
Region:              .1687    .6457    .0683    .7938        1.1838

n = 385 -2LL = 213.659 Chi-square = 36.459 Sig. = .0001

% correctly predicted= 89.84% Cox & Snell = .093 Nagelkerke = .190

* p [less than or equal to] 0.05, 2 tail

** p [less than or equal to] 0.05, 1 tail

(a.) The late adopting states of Indiana (2001), Tennessee
and Iowa (2002), Maryland (2003) were assumed to have
adopted charter school laws in 2000, the last year that I
have data for all variables.

(b.) There are four regional dummy variables given. I
deleted one dummy variable (Midwest) to avoid a perfect
linear dependency between the constant term (=1) and the
five dummy regional variables -their sum will always be
equal to I unless 1 delete a region. The intercept for the
Midwest is the constant term. The intercept for the other
regions is the constant term plus the coefficient on the
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Author:Levy, Tal
Publication:The Western Journal of Black Studies
Article Type:Report
Geographic Code:1USA
Date:Mar 22, 2010
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