The Governor and the State Higher Education Executive Officer: How the Relationship Shapes State Financial Support for Higher Education

ABSTRACT

Researchers have shown renewed interest during the past decade in the relationships among politics, policy, finance, and governance of higher education at the state level. Little attention, however, has been paid to state higher education executive officers (SHEEOs), the individuals responsible for leading the agencies that oversee higher education in the 50 states. Of noteworthy interest is the fact that the states vary in regard to the nature of the institutional relationship between the SHEEO and the governor, who has been shown in the literature to exert a strong influence over state higher education policy and finance. To explore the relationship, we developed measures that capture relevant dimensions of the relationship between the 2 actors and tested the impact of these measures on state spending for higher education using a unique panel of state-level data spanning more than 2 decades. We found that the institutional relationship between the SHEEO and the governor has a significant impact on state support for higher education.

KEYWORDS:

• Governance
• governor
• higher education
• policy
• politics
• SHEEO
• state funding of higher education
• state higher education executive officer

Acknowledgments

This article is based on a research paper presented at the annual meeting of the Association for the Study of Higher Education in St. Louis, MO, on November 15, 2013.

Notes

^1. Specifically, this measure used a Bayesian latent variable model with fully informed priors. The 19 items in the model focused on a governing body’s coverage over public institutions and programmatic and budgetary powers, and the informed priors utilized the preexisting typology of planning agency, coordinating board, and consolidated governing board. They are listed in the Appendix. For additional details, see A. Lacy (2011).

^2. Because our key independent variables were limited in the extent to which they vary within states, we also estimated all models with a random-effects specification for the state effect, thereby producing estimates that consider both within and across variation in these variables. The results are substantively identical to those presented in the manuscript and are available from the authors upon request.

^3. Logged real median household income was dropped as a control variable from Model 3 because income is a component of the model’s dependent variable. Logged state population was dropped from Model 2 because population is a component of that model’s dependent variable.