State Characteristics and Charter School Legislation Strength ( ) To what extent are internal and external characteristics of states related to the passage of charter school authorization laws? Background As of 2005, 40 states and the District of Colombia had passed charter school legislation. providing the legal and financial framework to shapes the operation of charter schools. Components of charter school laws can make that law stronger or weaker, with a “strong” law defined as one that provides less supervision from state or local authorities (Center for Education Reform, 2011). The relative strength of a charter school law is related to both the number of charter schools and charter school quality. Regardless of the demographics of a particular district, more charter schools will open in districts that have stronger charter legislation (Renzulli, 2005). Two important components that strengthen charter school laws are allowing for multiple authorizers and places caps on the number of charter schools allowed in a state. By 2005, 24 states had passed laws that originally included a cap on the number of charter schools permitted and 25 had passed laws allowing multiple authorizers. Witte et al. (2003) find that having multiple authorizers lowered barriers for authorization and resulted in more charter schools being authorized within a state. Allowing multiple authorizers may also increase a school’s autonomy ((SRI International, 2000; Wells et al.,1998). States can restrict charter school growth by implementing caps on the number of schools that are allowed to open across the state, in each district, or in a given year. CREDO (2009) reports that states with caps limiting the number of charter schools showed significantly lower academic results than states sans caps. As charter school legislation moves from a yes or no question to a best practices question, it is important to continue to understand how legislation is formed. We seek to test hypotheses that are outlined in the policy rationale section. These motivating factors allow us to employ Event History Analysis to explore patterns across states by controlling for internal and external factors (demographic, political, and theoretical) that are related to the passing of legislation. Jonathon M. Attridge & Daniela Torre The data for this analysis are state level political, economic, and demographic characteristics. The data comes from the original analysis performed by Wong & Lavengin (2006). It is a censored, time series data that has each state from 1991 after the passage of the first charter school law in Minnesota. We have added Berry & Berry’s (1998) ideology index to mimic the process from Doyle (2006). We test the climate for teachers’ unions by taking legal status of unions from Moe (2011). To test for punctuated equilibrium, we control for the governor’s party if it is the first year in office, taken from the National Governors’ Association (Doyle, 2006). Demographics were taken from the US Census. Due to the nature of our data, survival analysis may produce biased results because observations leave the sample when they pass a law, not when they pass the characteristic of interest. To test whether all schools that do not pass laws and states that pass a law without that characteristic, we run one set of models on all states (329 possible observations) and reanalyze models 2 and 3 restricting data to states that have passed charter legislation (203 possible observations). Sample/ Data Research Question Results We fail to confirm the hypotheses listed above as seen to the left. However, the results for our diffusion models are found below. We test two pathways of diffusion, regional diffusion, using census regions, and neighboring state diffusion, both of which were found to not be significant in an important earlier work, Wong & Lavengin (2006). However, we find that, on average, a state which shares a border to another state with a law featuring multiple authorizers increases the likelihood of passage of a law featuring multiple authorizers. Models 7-10 restrict the sample to states that have passed charter legislation, however, with more states continuing to pass charter laws, we feel more confident in our results of Models 1-6, which find no relationship between regional characteristics in charter legislation. Discussion Statistical Model Hypotheses 1: A state bordering more states that have passed charter legislation will be more likely to enact strong charter policies. Hypotheses 2: : A state in a region with more states that have passed charter legislation will be more likely to enact strong charter policies Hypotheses 3: A states whose governor is in his first year will be more likely to enact strong charter policies. Hypothesis 4: States with a larger urban population will be more likely to pass charter legislation. Hypothesis 5: States with a higher percentage of Republican house members will be more likely to pass strong charter legislation. Hypothesis 6: States that prohibit collective bargaining will be more likely to pass charter strong charter legislation. Hypothesis 7: States with higher percentage of classroom spending are less likely to pass a charter law, but more likely to pass a law with caps. Hypotheses In this research we explored the relationship between particular state characteristics, the passage of charter school law, and the strength of that law. We did not find strong empirical evidence to support diffusion as a framework for understanding how laws are passed and how strong those laws were. In our supporting case studies of the political process preceding passage of charter school in 6 locations (California, Massachusetts, Ohio, Indiana, Tennessee, Washington, D.C.), we found that the interplay of policy makers, advocacy coalitions, and contextual factors better explain why and how charter school law are initially created. In our empirical analysis, we find that more conservative states may be more likely to pass legislation that allows multiple authorizers. Our case studies illustrated however, that the influence of a policy entrepreneur or particular advocacy collations could lead to passage even in more liberal states. Our work adds to the literature by showing that the initial passage of charter school law was most likely caused by factors unexplained by a diffusion model. Additionally, we know that many state characteristics are not strong predictors of passage of certain provisions of a charter school law. Our case study work shows that the relationship between factors at the state level offers a more nuanced explanation for passage. This is to be expected with a new law, when policy makers are overcoming the initial hurdle of enacting institutional change and working within state specific policy contexts. Our study is limited in that we look at predictors of the initial passage of charter school law. All charter school laws have evolved since their initial passage as interest groups battle over certain aspects of the law. This analysis leads us to believe that there may be a pathway which leads neighboring states adopt similar types of laws. Outcome:Likelihood of Charter Law (Model 1 and 2) Likelihood of Mult. Auth. Law (Model 3 and 4) Likelihood of Cap on Number of Schools (Model 5 and 6) Likelihood of Mult Auth. Law (CS Only) (Model 7 and 8) Likelihood of Cap on Schools (CS Only) (Model 9 and 10) Neighbor’s Adoption1.04 (Figure 1)3.46* (Figure 2)1.48 (Figure 3)4.72*4.68* (0.66)(1.47)(1.13)(1.81)(2.05) Regional Adoption (0.98)(5.37)(1.96)(7.52)(2.57) Diffusion: Diffusion occurs when one government’s decision to adopt a policy innovation is influenced by previous choices by other governments. Diffusion can work through three pathways: mimicry, competition, or through social networks. (Berry & Berry, 1990) Punctuated Equilibrium: A political science theory that posits that long standing policy monopolies may change quickly in the face of events such as electoral change, financial crises, or the influence of a policy entrepreneur. (Baumgartner and Jones, 1993) Multiple Authorizers: Institutions designated by state law to approve and monitor a school’s charter. These include universities, non-profit organizations, independent education agencies, mayors, local school districts, and state education agencies. Caps on Charter Schools: For this analysis, caps limit the number of charter schools that can open in a given city or district, how many can be authorized by a certain type of authorizer, or the kind of charter school that can be opened (start-up, conversion, virtual). Log[-log(1-λ it )]= β 1 RepublicanGovernor it +β 2 FirstYear it +β 3 PercRepHouse it +β 4 GovernmentIde ology it +β 5 PctStateRevenue it +β 6 ProhibitsCB it +β 7 NoCBlaw it +β 8 PupilTeacherR atio it +β 9 PctClassroomSpending it +β 10 CitizenIdeology it +β 11 PerCapitaIncome it +β 12 NeighborsAdoption it +β 13 DurationDependency it +e it -Where λ represents the log odds ratio of the probability of adopting a CS policy to the probability of prior event non-occurrence (Model 1), including policy with multiple authorizers (Model 3-4, 7-8), or probability of adopting a policy that caps the number of schools (Model 5-6, 9-10). Robust standard errors clustered at state level. We run a models for all states (Models 1-6 (329 possible observations)) and then limit to states that pass charter legislation (7-10 (203 possible observations)). Definitions Work Cited: Baumgartner, F., and Jones, B. (1993). Agendas and Instability in American Politics. Chicago: University of Chicago Press Berry, F. S., Berry, W. D., & Sabatier, P. A. (1999). Innovation and diffusion models in policy research. Theories of the policy process. Center for Education Reform. (2011). Charter school laws: Scorecard and rankings. Renzulli, L. A. (2005). Organizational environments and the emergence of charter schools in the United States. Sociology of Education, 78(1), 1–26. SRI International. (2000). Evaluation of the public charter schools program: Year one evaluation report. Washington, DC. Witte, J. F., Shober, A. F., & Manna, P. (2003). Analyzing state charter school laws and their influence on the formation of charter schools in the United States. American Political Science Association, 2003 Annual Meeting, Philadelphia, PA, August (pp. 28–31). Raymond, M. (2009). Multiple choice: Charter school performance in 16 states. Center for Research on Education Outcomes Report. We have chosen to graphically display the confidence intervals for the predicted likelihood of a state passing a charter law. Figure 1 shows the likelihood of passing a charter school law. We find no evidence of diffusion relating to the passage of charter school law and no evidence that particular state characteristics are related to the passage charter law in our model. Figure 2 displays the likelihood of a state adopting a charter school law allowing for multiple authorizers. We find that bordering a state that has adopted a law with multiple authorizers, having a collective bargaining law, having a republican senate, and having a republican governor, are significantly increase the likelihood of passing charter school legislation that allows multiple authorizers. Figure 3 displays the likelihood of a state adopting a charter school law that includes caps. We find that having a Republican governor in office increases the likelihood of a state passing a charter legislation that includes caps. We reject our hypothesis that a Republican governor would legislate more “charter friendly” laws. Moreover, we find no evidence of diffusion and no other significant predictors of a state passing laws with caps on charter schools. Results