State policy diffusion and Race to the Top: The impact of federal competitive grants on state policymaking

The U.S. Department of Education's Race to the Top program marked the first use of a competitive grant program by the federal government to influence state-level education policy. Using a directed state-dyad dependent variable and a piecewise linear growth model to capture states' policy differences and changes, this dissertation investigates the impact of Race to the Top on state-level education policy diffusion in teacher policy, college and career readiness policy, and persistently lowest achieving schools policy during the first two rounds of the competition, spanning March 2009 to August 2010.;The analysis suggests that states were most reactive to federal preferences in the teacher policy area; after the release of federal program guidance and the announcement of Round 1 winners, state policies converged and moved towards the policies of the Round 1 winners, adopting policies to tie teacher evaluations to students' academic growth, compensate based on performance, and incentivize teaching in high-need schools, among others. The effects of federal preferences were not as distinct in college and career readiness policy, in which state policies were generally converging during this time frame, or in persistently lowest achieving schools policy where other grant programs may have mediated the influence of RTTT. Nonetheless, this study provides evidence to suggest future use of competitive grant programs as a means for the federal government to influence state education policy.;This study adds to education policy research through the use of diffusion theory to understand the effects of federal competitive grants, rather than another policy tool, on state policy reforms. Further, this research contributes to policy and diffusion literature by demonstrating the value of piecewise linear growth models to simultaneously model the spread of policies across states and the influence of federal preferences on state policy decisions over time.