A Policy Analysis Of Performance Based Funding In The State University System Of Florida PDF Download

Over the past 40 years, performance-based funding has become a common tool of state legislatures to hold institutions accountable for student outcomes. Performance funding allocates state money to institutions based on a specific set of measures. However, states vary in the measures they select, methods for determining funding allocations, and the proportion of state funds reserved for performance funding. Common measures include retention rates, completion rates, labor market outcomes, and equity measures, but legislators may alter standardized measures to fit the political needs of their state. Although recent research estimates that over 35 states have adopted performance funding policies, the effectiveness of these policies is inconclusive and the unintended consequences have led to institutional mission narrowing and decreased acceptance rates for underserved students. This study analyzed the adoption of performance-based funding polices with three models. First, a state's likelihood to adopt a performance funding 1.0 policy. Second, a state's likelihood to adopt a performance funding 2.0 policy. Third, a state's likelihood to adopt any performance funding policy. Event history analysis was utilized to explore factors that impacted a state's likelihood to adopt these policies, focusing on both the influence of adoption through policy diffusion (regional accrediting agency groups) and internal determinants (political factors, education/unemployment factors, and higher education factors). This study extended the work of previous researchers in three important ways. First, by comparing results for the adoption of performance funding 1.0 and 2.0 policies independently. Second, by analyzing the adoption of performance funding policies through geographic regions, identified as regional accrediting organizations. Finally, by including new internal determinants not utilized in previous studies focusing on the adoption of performance funding policies. Data were collected from a variety of state-level sources and the dataset included information on 47 states from 1993 to 2013. All three models utilized similar syntax for the event history analysis and while there were statistically significant results, they were not consistent across all three models. For state adoption of a performance funding 1.0 policy, a Republican controlled legislature, consolidated governing board, unemployment rate, percent change in Fall term enrollment, and number of doctoral institutions were found to have a significant impact. For state adoption of a performance funding 2.0 policy, the percent of states in the accrediting region that already adopted a performance funding 2.0 policy and the educational attainment rate were found to have a significant impact on policy adoption. Finally, for state adoption of any policy, a Republican controlled legislature, legislative term limits, consolidated governing board, and percent change in Fall term enrollment were found to have a significant impact. In the variables listed above, a Republican legislature, legislative term limits, and states with more doctoral institutions increased the likelihood of policy adoption, while the percent of states in the accrediting region that already adopted a performance funding policy, the presence of a consolidated governing board, increases in a state's educational attainment rate, and increases in the percent change in Fall term enrollment decreased the likelihood of policy adoption. This study adds to the higher education literature by separately analyzing the adoption of performance funding 1.0 and 2.0 policies and finding differences in the factors that impact adoption. These differences indicate the importance of viewing performance funding 1.0 and 2.0 policies as distinct, rather than under an umbrella term of performance-based funding.