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Dramatic Declines in Higher Education Appropriations: State Conditions for Budget Punctuations

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Abstract

Public colleges and universities depend heavily on state appropriations and legislatures must decide how much to fund higher education. This study applies punctuated equilibrium theory to characterize the distribution of annual changes in higher education appropriations and defines the threshold for a dramatic budget cut. Using data for the 50 states from years 1980 to 2009, this study investigates the relationship between such unique policy events and state characteristics using a Cox proportional hazards model. Results show that economic and political conditions are most predicative of dramatic budget cuts. High unemployment rates increase the probability of cuts while rapid increases in tax revenue and wider income inequality are protective against cuts. Unified Republican and unified Democratic governments are both more likely to cut spending compared to a divided government. Sensitivity analyses of state characteristics associated with small budget cuts demonstrates that large cuts are indeed unique events catalyzed by different conditions.

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Notes

  1. The State Higher Education Executive Officers (SHEEO) State Higher Education Finance (SHEF) report in 2013 shows that tuition increases in these circumstances rarely offset entirely the effects of state funding cuts.

  2. Appropriations are per fall enrollment, not per FTE. I conduct diagnostics on the alternative outcome of total appropriations. It shows comparable descriptive patterns. I retain the per-student measure, which is widely accepted Tandberg and Griffith (2013).

  3. State-years of large cuts are coded “1”, otherwise they are coded “0”.

  4. I run analyses with NY, NJ, and PA in a separate region. I choose to keep these three states in the New England compact, as Doyle (2006) does, and because substantive findings do not change.

  5. Total tax revenues is comprised of both the tax base and the tax rate. Base captures underlying wealth and rate captures willingness to tax. Total tax revenues measures a combination of the two concepts.

  6. I include Nebraska by coding a unified government as one where the governor and the unicameral legislatures have the same party affiliation.

  7. This variable is used because it is highly correlated with an alternative composite measure that captures added factors such as time demands and staff resources–the Squire index–and has more years of available data.

  8. Larger enrollments logically require greater financial support so a per-student variable assesses the adequacy of funding.

  9. A relatively large number of funding cuts occurred in 2009. I conduct analyses excluding data from 2009, which resulted in conclusions substantively the same as those discussed in the results section.

  10. In this case, a “tied event” is when more than one state makes a funding cut in the same year. The Efron method computes an approximation of the exact marginal calculation. A strict exact marginal calculation assumes continuous times, which make it mathematically impossible for events to occur at the same time. Efron’s is a balance between the slightly less precise Breslow method and the conservative and computationally intensive Exact method.

  11. These types of data are known as a multiple risk set or multiple failure-time data.

  12. I consider the advice of Allison (2010), who states that a violation of the proportional hazards assumption for a variable simply means that the coefficient estimated on this variable represents an average effect over the period of observation.

  13. Comparisons between survival analysis models can be made using the AIC. The AIC penalizes models for the number of covariates included. Lower AIC values indicate a better fit with the data. Note that the AIC is not intended to compare nested models (Allison 2010).

  14. Applying one-year lags of economic, demographic and higher education variables decrease explanatory power and worsen model fit. Applied, prior year financial indicators are not associated with current year funding cuts. Similar non-findings occur when the percent changes are lagged by one year. That is, the rate of change in economic conditions from 2000 to 2001 do not relate to funding cuts in 2002. From here on, results for same year variables are reported.

  15. Efforts are made to include Nebraska in the analysis. Budget cuts exceeding 14 % do not occur in Nebraska, though these non-event years still contribute statistically to the hazard function. Nebraska is coded as unified (“1”) when the governor and the unicameral legislature are of the same party. In a separate analysis, I code Nebraska as “0.66” during these years to represent the slightly reduced version of a unified government with two power centers versus three. I also listwise delete Nebraska. Substantive results do not change.

  16. It is possible for an interaction term to reveal a significant relationship with an outcome even if main effects are absent.

  17. Researchers have questioned the relationship between higher education and subcategories. For example, Kane et al. (2003) demonstrated that higher education spending must fall if Medicaid spending rises because of federal mandates or incentives. However, as Archibald and Feldman (2006) proposed, if the budget itself is endogenous, different expenditure categories may instead be complements. If total revenues fall, higher education and Medicaid spending may both increase (or decrease).

  18. I also examine the relationship between higher education funding and whether a state has tax and expenditure limits (TELs) (Archibald and Feldman 2006; New 2010). I use data from New (2010) to create a TEL dummy variable indicating whether a state had a TEL during each year. I run models including and excluding TELs, which indicate that they do not predict large funding cuts, nor do they change results of existing covariates.

  19. It is worth noting though, some states have separate funding mechanisms for their merit aid programs such as through a lottery.

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  1. Department of Leadership, Policy, & Development, University of Northern Colorado, Greeley, CO, USA

    Amy Y. Li

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An earlier version of this manuscript was presented at the 2015 Association for Education Finance and Policy (AEFP) conference in Washington, DC. The author thanks William Zumeta and Christopher Adolph for comments on earlier drafts. This research was supported in part by the U.S. Department of Education, Institute of Education Sciences, Grant No. R305B090012.

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Li, A.Y. Dramatic Declines in Higher Education Appropriations: State Conditions for Budget Punctuations. Res High Educ 58, 395–429 (2017). https://doi.org/10.1007/s11162-016-9432-0

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