Constructing "Winners and Losers": An Analysis of Higher Education Performance Funding Policy Designs in Colorado and Texas

• In Event: Promising Scholarship in Education: Dissertation Fellows and Their Research

Abstract

In recent years, numerous state-level policies intended to improve the efficiency and effectiveness of higher education have emerged (Snyder, 2015) and, in some cases, re-emerged (e.g., McLendon & Hearn, 2013). This trend reflects a shift, resembling the move in K-12 education policy (Elmore, Abelmann, & Fuhrman, 1996), from input and process-based accountability mechanisms to those based on outcomes, such as student performance. One such policy that has gained traction in states is performance-based funding—a method of tying state funding for public higher education institutions directly to institutions’ performance on pre-specified metrics (Burke, 2002). This approach represents a departure from the traditional, input-based method of allocating state funds to public colleges and universities, which has historically relied on enrollment counts.

In recent years, policymakers across the states have increasingly taken an interest in performance-based funding. Signaling the policy’s revived popularity and rapid diffusion, in 2015, ten states were in the process of developing a new performance funding model (Snyder, 2015). Given the prominence of performance funding policies across the states, the heterogeneity across policy designs, and these policies’ potential intended and unintended impacts, this study examined an overlooked aspect of performance funding policies: their designs.

Grounded in a theory of policy design that draws attention to the value-laden elements of the policy process (Schneider & Ingram, 1993, 1997), this analysis employed a multiple case study research design and focused on two states: Colorado and Texas. These cases exhibited substantial variation in their proposed policy content, which facilitated an analysis of factors, such as political power, that explain variation across policy designs. Specifically, this study, which drew on interviews with 34 policy actors, over a dozen observations of legislative and state higher education agency proceedings, and over three hundred documents—including financial data—addressed the following research questions:

1. What populations (e.g., types of higher education institutions or students) are targeted through performance funding policy designs?

2.How are burdens and benefits distributed to various target populations through performance funding policy designs?

3. To what extent do the following factors explain performance funding policy design decisions:
a. target populations’ social constructions (as deserving or undeserving of policy benefits or burdens),
b. target populations’ relative levels of political power resources, and
c. the role of knowledge and information in the policy design process?

Findings from this study indicate that performance funding model designs are overwhelmingly a function of higher education institutions’ self-interest, particularly in contexts where institutional representatives have substantial authority over the model design process. The social construction of certain students (e.g., ethnic minority students) as deserving or undeserving of policy benefits, particularly during policy formulation, also contributes to model designs. Finally, institutional representatives’ political power resources are directly associated with the distribution of benefits or burdens to their institution. Tables 1 and 2 present institutions and students as target populations, respectively.

In addition to the theoretical findings, this study revealed that performance funding policies are manifestations of varying goals (e.g., equitable funding or accountability). Evaluation studies should pay attention to both the manifest and latent goals of the policies they examine. Second, model designers overwhelmingly sought to design models that fit their pre-conceived notions of who should benefit from the new model. The extent to which metrics are chosen for their substance as opposed to “statistical convenience[s]” might have important implications for performance funding policy impacts and policy sustainability. This study also revealed that institutional representatives may calculate that performance funding is not worthy of their attention, particularly when there are alternative sources of revenue available. Further, some alternative funding pools may represent competing goals (e.g., research excellence) to those of performance funding (e.g., student outcomes). Evaluations of performance funding policies should consider competing priorities in financial inducement policy tools.

By deconstructing performance funding policy designs to their constituent parts, this study focused on how and why, given myriad options for performance funding policy designs, certain policy elements were chosen instead of others. This analysis of designs is especially critical given evidence that costly failures in some instances of performance funding may be attributed to poor design, including the use of inappropriate metrics (Dougherty & Reddy, 2013; Gorbunov, 2013). Moreover, this study of performance-based funding policy design, which evaluates who gains and who loses from certain policy designs, sheds light on what public higher education policy actors in the states value and prioritize. Further, by employing a policy design theory, this study makes important contributions to the educational policy literature, which has focused overwhelmingly on policy adoption and policy evaluation.

Author

• Denisa Gandara, University of Georgia - Athens