EXAMINING HOW STATE FUNDING FOR HIGHER EDUCATION IS INFLUENCED BY LEGISLATOR POLICY PREFERENCES: A DYNAMIC PANEL ANALYSIS
EXAMINING HOW STATE FUNDING FOR HIGHER EDUCATION IS INFLUENCED BY LEGISLATOR POLICY PREFERENCES: A DYNAMIC PANEL ANALYSIS
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Date
2014
Authors
Williams, Joseph
Advisor
Titus, Marvin A
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Abstract
Guided by the spatial theory of voting, this study examines the influence of state legislator policy preferences, higher education interest groups, and other variables on state funding to higher education. Using data from all 50 U.S. states over a period of twelve years, this study utilizes a dynamic panel model with Generalized Methods of Moments (GMM) techniques to examine how a number of independent variables influence state funding for higher education. Dynamic panel modeling with GMM techniques addresses methodological limitations of prior research by accurately examining a lag of the dependent variable included as an independent variable while accounting for unobserved state-specific fixed-effects, time-related fixed-effects, and possible endogeneity of one or more of the independent variables.
The results of this study indicate that more conservative state legislatures are associated with lower levels of funding to higher education while more liberal legislatures are associated with higher levels of funding. Other variables, including prior year higher education appropriations, K-12 appropriations, gubernatorial strength, and the share of enrollment in private higher education are related to current year state appropriations to higher education.
The results from this study have a number of implications. First, this study utilizes the spatial theory of voting, a theory which has never been previously utilized in higher education research, to guide the selection of political variables, including state legislator policy preferences. Future research within political science and other academic disciplines can employ the spatial theory of voting to examine the influence of state legislator policy preferences on different policy outcomes. Second, future researchers may consider employing dynamic fixed-effects panel modeling with GMM techniques when including a lag of the dependent as an independent variable. Third, future studies can utilize the newly developed measures of policy preferences to understand the influence of state legislator policy preferences on a variety of state level policy outcomes. Fourth, an understanding of the influences of higher education funding will allow policymakers and administrators to better predict funding levels and plan future budgets.