Abstract
The ultimate goal of this chapter is to provide future researchers interested in predicting and explaining state support of higher education with the tools they need to advance the field’s understanding of this important topic. In so doing, this chapter analyzes the various data sources and measures of state funding of higher education; reviews and synthesizes relevant theories which, when properly utilized, will help scholars understand the factors impacting state funding of higher education; reviews the relevant research; discusses several specific factors that ought to be consider when explaining state support of higher education; and reviews recent data and methodological advancements in this area of scholarship. The chapter concludes with a discussion of possible future directions for research in the area of state support of higher education.
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- 1.
Researchers have also gathered state funding of higher education data from The Statistical Abstracts of the United States (the country’s data book). However, since The Statistical Abstracts rely on other data sources for their funding figures (including recently SHEF for state funding of higher education data and NASBO for total state expenditure data), they are not discussed here.
- 2.
The reporting instructions have remained consistent since 1990. In 1989, states were given very general guidance (i.e., to exclude federal research grants and to include tuition and fees and support for community colleges). In the first 2 years (1987 and 1988), states were asked to exclude tuition and fees and federal research grants.
- 3.
For additional details and to view examples of NASBO’s State Expenditure Reports, visit their website here: http://nasbo.org/
- 4.
For example, Zumeta (1992, 1996) reported that in 1988, 21 states provided direct financial support to private colleges and universities. NASBO reports that in 1988, 20 states excluded data on funding for private colleges and universities, meaning 30 states reported those data. However, some may have reported 4s.
- 5.
As indicated, NASBO does track which states leave out what elements, which helps when attempting to make cross-state comparisons.
- 6.
Additional information and the Grapevine data can be found at the project website here: http://grapevine.illinoisstate.edu/
- 7.
For those years in which American Recovery and Reinvestment Act (ARRA) dollars were provided to states to support higher education, states were asked by SSHED to report:
-
“education stabilization funds used to restore the level of state support for public higher education;
-
government services funds used for public higher education (excluding modernization, renovation, or repair); and
-
government services funds used for modernization, renovation, or repair of higher education institutions (public and private).
Government services funds used for modernization, renovation, or repair of higher education institutions were excluded from Grapevine analyses.”
-
- 8.
Using data from the State Support for Higher Education Database and available from SHEEO, a consistent State Tax Effort measure can be constructed.
- 9.
For additional information and for examples of the SHEF reports, please visit SHEEO’s website at http://www.sheeo.org/
- 10.
Additional details and the Census data can be found here: http://www.census.gov/govs/estimate/
- 11.
Researchers have also gathered state funding of higher education data from The Statistical Abstracts of the United States (the country’s data book). However, since The Statistical Abstracts rely on other data sources for their funding figures (including, recently, SHEF for state funding of higher education data and NASBO for total state expenditure data), they are not discussed here.
- 12.
Additional information and the extensive IPEDS data can be found here: http://nces.ed.gov/ipeds/datacenter/
- 13.
Institutions report data using the accounting standards they employ at their institutions (FASBE or GASBE); therefore, the categories vary slight depending on the chosen standard. The Delta Cost Project has developed a useful crosswalk to merge across the standards.
- 14.
The full name is The Delta Project on Postsecondary Education Costs, Productivity, and Accountability. Additional information and the data can be found on its website found here: http://www.deltacostproject.org/
- 15.
Starting in 2012, NCES will take over maintenance of the Delta Cost Project Database.
- 16.
Data from the Census are not included in the comparison as the most comparable Census measure (not including auxiliary enterprises, capital, or local expenditures) indicates that there was $135 billion in state higher education expenditures in 2008. The closest of the other four sources (Grapevine) shows only $73 billion in state higher education appropriations. The difference is most likely due to the Census data including tuition- and fee-based expenditures.
- 17.
Grapevine data are not included in the second chart because the organization does not include a complete measure of total spending for higher education.
- 18.
As Grapevine does not include a “complete” measure of state support they are not included in this comparison.
- 19.
- 20.
The mid-1970s represented a high point for this measure. In 1960, the states appropriated just over $3.00 for every $1,000 of personal income.
- 21.
Rizzo (2004) uses a similar measure(s) however his conceptualization led him to develop three dependent variables:
-
1.
EDShare – Education’s share of total state expenditures
-
2.
HEShare – Higher education’s share of total state education expenditures
-
3.
InShare – Institution’s share of total state higher education expenditures
-
1.
- 22.
For a full discussion of their concerns, please see Trostel and Ronca (2009).
- 23.
For a full discussion, please see Trostel and Ronca (2009).
- 24.
For an extensive review of principal-agent theory and its application to higher education, see Lane and Kivisto (2008).
- 25.
Lindeen and Willis’s (1975) primary dependent variable was total expenditures per tax payer, and their data source was the precursor to the IPEDS survey, the Higher Education General Information Survey. Peterson’s (1976) primary dependent variables were appropriations per capita and per student, and his data source was also the Higher Education General Information Survey.
- 26.
Hossler et al. (1997) used levels of state appropriations to public four-year institutions. The data were from the Grapevine surveys.
- 27.
Toutkoushian and Hollis (1998) used the natural log of state appropriation levels as their dependent variable. Their data source was the precursor of the SHEEO SHEF compilation, the State Profiles: Financing Public Higher Education data collected by Kent Halstead.
- 28.
McLendon, Hearn et al. (2009) employed state tax appropriations per $1,000 of personal income as their dependent variable (Grapevine data). Tandberg (2010b) likewise used the same variable and Grapevine data. Tandberg (2010a) employed higher education’s share of total state general fund expenditures as his dependent variable (NASBO data).
- 29.
- 30.
We apologize for any studies we missed and for any inaccuracies in Appendix A. They were not intentional.
- 31.
For a detailed discussion of interest groups and state higher education policy research, see Ness et al. (2008).
- 32.
See Gray and Lowery’s (various years) extensive discussions on the use of interest group density measures.
- 33.
Michigan does not have a traditional state-level coordinating or governing agency for postsecondary education. However, the State Board of Education has very limited state postsecondary coordinating functions. While its primary responsibility is for elementary and secondary education, the board does have limited responsibility for the coordination of services for public two-year and four-year colleges and universities. Vermont likewise does not have a traditional structure. Instead, it has a voluntary state higher education coordinating system plus two system level boards (McGuinness, 2003).
- 34.
See Zhang (2010) for a full discussion of the use of panel data in higher education research.
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Acknowledgements
The authors wish to thank William M. Zumeta, Shouping Hu, and Michael B. Paulsen for their helpful suggestions and edits which greatly improved this chapter. The authors would also like to thank Andy Carlson from SHEEO, Brian Sigritz from NASBO, Allison Bell from NCES and formerly with SHEEO, Colleen Lenihan from NCES, and Jane Wellman formerly with the Delta Cost Project who all provided excellent suggestions and corrections to the first two sections of this chapter. Finally, we would like to thank Luciana Dar for her willingness to share her exceptional work with us. Of course, all errors are the responsibility of the authors alone.
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Appendix A: Studies of State Appropriation to Higher Educationa
Appendix A: Studies of State Appropriation to Higher Educationa
Authors | Year | Citation | Dependent variable(s) (RE: state support)b | Dependent source | Time period and empirical approach | Sample | Significant independent variables (+/−) | |
|---|---|---|---|---|---|---|---|---|
1. | Archibald, R. B., & Feldman, D. H. | Archibald, R. B., & Feldman, D. H. (2006). State higher education spending and the tax revolt. Journal of Higher Education, 77(4), 618–643 | State appropriations to higher ed. per $1,000 of personal income (excluding federal and lottery funds) | Grapevine, Census, Book of the States | 1961–2001 | 47 States | Democratic Governor, + Democratic Strength, + Super Majority Requirement, + Corrections Spending, + Health Spending, + Tax and Expenditure Limits, − | |
Panel data, fixed Zeffects | ||||||||
2. | Bailey, M. E., Rom, M. C., & Taylor, M. | Bailey, M. E., Rom, M. C., & Taylor, M. (2004). State competition in higher education: A race to the top or a race to the bottom? Economics of Governance, 5(1), 53–75 | Change in state support for higher education, annually (higher ed. exp. per state resident; higher ed. exp. FTE, CPI adjusted) | IPEDS, ICPSR, state finances | 1986–1987 | 48 States | Democratic Strength, − Competition (spending between states and neighbors), − Convergence (policy measure between states and neighbors), − Personal Income per Capita, − Student Aged Pop. (18–24), + Elderly Pop. (65<), + | |
Panel data, two-way fixed effects | ||||||||
3. | Cheslock, J., J., & Gianneschi, M. | Cheslock, J., & Gianneschi, M. (2008). Replacing state appropriations with alternative revenue sources: The case of voluntary support. Journal of Higher Education, 79, 208+ | State appropriations per student (adjusted CPI, HEPI, FTE) | IPEDS ICPSR, state finances | 1994–2004 Panel data | All public four-year institutions that offer undergraduate degrees, have a 2000 Carnegie Classification of Research/Doctoral, Masters, or Baccalaureate, 47 states | Barron’s Selectivity Ranking, + Enrollment, − Research/Doctoral Carnegie Classification, + US News Ranking, − Personal Income per Capita, + State Appropriations Previous Year, + Unemployment Rate, − | |
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7. | Delaney, J. A., & Doyle, W. R. | Delaney, J. A., & Doyle, W. R. (2011). State spending on higher education: Testing the balance wheel over time. Journal of Education Finance, 36(4), 343–368 | State appropriations for higher education (CPI adjusted) (1) Absolute levels of state funding for higher ed. (2) Year-to-year funding for Higher ed. by state and by year data evaluated by decade and business cycle | Grapevine, http://www.grapevine.ilstu.edu/historical/index.htm | 1985–2004 Panel Data | 49 States | Enrollment, + Private Enrollment FTE, − Share of Public, 2 year Enrollment, - Share of Private, 2 year Enrollment, − Share of Public, 4 year Enrollment, − Gross State Product, + Total Expenditure all Budget Categories other than HE, + | |
8. | Doyle, W. R. | Doyle, W. R. (2007 ). The political economy of redistribution through higher education subsidies. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. XXII, pp. 335–409). Dordrecht, The Netherlands: Springer | State tax appropriations for higher education (CPI adjusted) | Center for the Study of Education Policy, US Census Bureau, Grapevine | 1985–1989 1951–2007 (no data for 1973) Panel data, two-stage least squares estimation | 50 States 48 States | Private Enrollment FTE, + Student Aged Pop. (18–24), + | |
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15. | Lindeen, J. W., & Willis, G. L. | Lindeen, J. W., & Willis, G. L. (1975). Political, socioeconomic and demographic patterns of support for public higher education. The Western Political Quarterly, 28(3), 528–541 | (1) Public Financial Support: Total Amount per Taxpayer; Taxpayer Effort (2) Increase in state Support. 1960–70: gross net Percentage; Net Percentage Increase | Statistical Abstract of the United States(1962),Statistical Abstract of the United States, (1972), Digest of Educational Statistics (1971), Ohio Basic Data Series: Higher Education (1971) | 1960–1970 Correlation analysis, OLS | 48 States | Gross % Increase State Support, + Taxpayer Effort, − | |
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17. | McLendon, M. K., Hearn, J. C., & Mokher, C. G. | McLendon, M. K., Hearn, J. C., & Mokher, C. G. (2009). Partisans, professionals, and power: The role of political factors in state higher education funding. The Journal of Higher Education, 80(6), 686–713 | State appropriations per $1,000 of personal income (CPI adjusted 2004) | Grapevine, Postsecondary Opportunity | 1984–2004 Panel Data, regression model, fixed effects | 49 States (e.g., Nebraska) | Private Enrollment FTE, − Share of Public, 2 year Enrollment, + Gubernatorial Power, − HE Interest Groups, + Legislative Professionalism, + Republican Governor, − Republican Strength, − Term Limits, + Student Aged Pop. (18–24), − Elderly Pop. (65<), − Unemployment Rate, − | |
18. | McLendon, M. K., Mokher, C. G., & Doyle, W. | McLendon, M. K., Mokher, C. G., & Doyle, W. (2009). “Privileging” public research universities: An empirical analysis of the distribution of state appropriations across research and non-research universities. Journal of Education Finance, 34(4), 372–401 | State appropriations per FTE for each institution | IPEDS | 2003–2004 | 501 Institutions in 46 states, excluding institutions with missing data | Graduate & Professional Enrollment, + Proportion Completion in STEM, + Democratic Strength, + Gubernatorial Budget Powers, − Inst. Located in State Capital, + of Appropriations Comm. Members Graduating from Inst, + Term Limits, − Student Aged Pop. (18–24), − | |
Random effects model conditioned on the mean of individual-level variables | ||||||||
19. | Morgan, D., Kickham, K., & LaPlant, J. | Morgan, D., Kickham, K., & LaPlant, J. (2001). State support for higher education: A political economy approach. The Policy Studies Journal, 29(3), 359–371 | State and general education expenditures for higher ed. divided by population per capita income | Digest of Education Statistics, Census | 1986–1995 | 49 States (e.g., Arizona) | Enrollment, + Federal Aid, − Faculty Size, + Tuition per FTE, + Personal Income per Capita, − | |
Panel data | ||||||||
20. | Nicholson-Crotty, J., & Meier, K. J. | Nicholson-Crotty, J., & Meier, K. J. (2003). Politics, structure, and public policy: The case of higher education. Educational Policy, 17(1), 80–97 | State/local appropriations per Student | Digest of Education | 1989–1996 Panel data, fixed effects | 47 States (e.g., Nebraska, Michigan, Delaware) | Citizen Ideology (Berry Data), − HE Governance Structure, − Government Ideology, + Legislative Professionalism, − | |
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22. | Peterson, R. G. | Peterson, R. G. (1976). Environmental and political determinants of state higher education appropriations policies. The Journal of Higher Education, 47(5), 523–542 | Appropriations for both per capita and per student, for: (1) All public institutions (2) Public 4 year (3) Public 2 year | US Office of Education, US Bureau of the Census | 1960, 1969 Panel data, 2 cross-sectional studies | 50 States | Enrollment, + Share of Public, 2 year Enrollment, + Share of Private, 2 year Enrollment, − Share of Public, 4 year Enrollment, + Adults w/College Degree, + Hofferbert’s Influence Factor Scores, + Hofferbert’s Industrialization Factor Scores, − Median Yrs. School completed by Pop, + | |
23. | Rabovsky, T. | Rabovsky, T. M. (2012). Accountability in higher education: Exploring impacts on state budgets and institutional spending patterns. Journal of Public Administration Research and Theory | State appropriations, measured in constant dollars | SHEEO | 1999–2008 for stage one; 1998–2008 for stage two Panel data | 50 States | Graduate & Professional Enrollment, + Graduation Rate, + % Black, + % Hispanic, − Performance Funding, + Research/Doctoral Carnegie Classification, + Selectivity, + Undergrad Enrollment, + | |
24. | Rizzo, M. J. | Rizzo, M. J. (2004). State preferences for higher education spending: A panel data analysis, 1977–2001. Federal Reserve Bank of Cleveland’s Conference on Education and Economic Development | (1) Share of the public general fund budget allocated to education (2) Share of the education budget allocated to higher education (3) Share of the higher education budget allocated to institutions | US Bureau of the Census, State Government Finance Files(1972–2001), IPEDS, HEGIS, NASSGAP, Grapevine | 1977–2001 Panel data | 50 States | Giving, − PhD/BA Degrees Awarded, − Regional Non-Resident Tuition, − Share of Public, 2 year Enrollment, + State-Based Merit Scholarship, − Assembly Seats Per Capita, − Voter Turn Out, − Court Reform State, − Crime Rate, − Gross In-Migration, − Gross Out-Migration, − Median Household Income, − Median Household Income Squared, + Student Aged Pop. (18–24), + Race Interact, + Revenue Corporate Income Tax, + Revenue from Fuels, − Revenue Income Tax. + Revenue Lottery, − School Race Ratio, − Share of GSP (Ag, Fishing, Mining), + Share of GSP (Construction, Manufacturing, Trans. And Utilities), + Share of GSP (Government), + Share of GSP (Trade), + Unemployment Rate, − Unemployment Rate Non-White, − | |
25. | Strathman, J. G. | Strathman, J. G. (1994). Migration, benefit spillovers and state support of higher education. Urban Studies, 31(6), 913–920 | State and local appropriations per FTE student | Digest of Education Statistics, Statistical Abstract of the US Census | 1989–1990 Three-stage least squares parameter estimates | 48 States | Gross Out-Migration, − Personal Income per Capita, + | |
26. | Tandberg, D. A. | Tandberg, D. A. (2008). The politics of state higher education funding. Higher Education Review, 5, 1 | Higher education appropriation as a % of state general fund expenditures | Grapevine, Census | 1971–2001 Panel data, fixed effects | 50 States | Giving, − In State Tuition (lagged), − Private Enrollment FTE, + Regional Non-Resident Tuition, + Democratic Governor, + Democratic Strength, + Electoral Competition, + HE Interest Ratio, + Legislative Unity, − Appropriations to K-12, − Gross State Product, + Health (Medical CPI) Share of pop. > 65 year, − Inequality, + Medicaid, − Medicaid CPI, − Student Aged Pop. (18–24), − Elderly Pop. (65<), − Population Below PELL, − Race Interact, + Unemployment Rate, − | |
27. | Tandberg, D. | Tandberg, D. (2010). Interest groups and governmental institutions: The politics of state funding of public higher education. Educational Policy, 24(5), 44 | State appropriations per US$1,000 personal income | Grapevine, Postsecondary Opportunity | 1976–2004 Panel data, fixed effects | 50 States | Private Enrollment FTE, + Share of Public, 2 year Enrollment, − State uses Formula Funding, + Tuition Avg 4 year, − Democratic Governor, + Democratic Strength, + HE Governance Structure, − Government Ideology, + HE Interest Ratio, + Legislative Professionalism, + Legislative Unity, − Gini Coefficient, − Gross State Product, + Medicaid, − Student Aged Pop. (18–24), − Population Below PELL, + Unemployment Rate, + | |
28. | Tandberg, D. A. | Tandberg, D. A. (2010). Politics, interest groups and state funding of public higher education. Research in Higher Education, 51(5), 416–450 | State expenditure on higher education as a % of total state expenditures | NASBO | 1986–2004 Panel data, fixed effects | 50 States | Private Enrollment FTE, + Tuition Avg 4 year, − Citizen Ideology (Berry Data), + Democratic Governor, − Interest Group Density, − Legislative Professionalism, + Legislative Unity, − Political Culture, + Gross State Product, − | |
29. | Tandberg, D. A. & Ness, Eric | Tandberg, D. A. & Ness, E. (2011). State capital expenditures for higher education: “Where the real politics happens.”Journal of Education Finance, 36(4), 394–423 | Natural log of state capital expenditures | NASBO | 1988–2004 Panel data | 50 States | Giving, + State uses Formula Funding, + Tuition Avg 4 year, − Electoral Competition, + HE Governance Structure, − Gubernatorial Budget Powers, − HE Interest Ratio, + Legislative Professionalism, + Political Culture, + Voter Turn Out, − Student Aged Pop. (18–24), − | |
30. | Toutkoushian, R. K., & Hollis, P. | Toutkoushian, R. K., & Hollis, P. (1998). Using panel data to examine legislative demand for higher education. Education Economics, 6(2), 141–157 | Natural log of Level of appropriations for higher education in each state | Halstead data (State Profiles: Financing Public Higher Education) | 1982–1996 Panel data, OLS, 2SLS, fixed effects | 50 States | Mean Faculty Compensation, + Median Household Income, + Unemployment Rate, − | |
31. | Weerts, D. J., & Ronca, J. M. | Weerts, D. J., & Ronca, J. M. (2008). Determinants of state appropriations for higher education from 1985–2005: An organizational theory analysis. Madison, WI: Wisconsin Center for the Advanced of Postsecondary Education | First difference of the natural log of total restricted plus unrestricted state appropriations converted to 2004 dollars | US Bureau of Labor Statistics, IPEDS | 1985–2004 Panel data, random effects | 50 States, 1,000 institutions | Carnegie Classification, − # of Pub Inst. in State, + Republican Governor, + Voter Turn Out, + Appropriations to K-12, − Court Reform State, − Health Spending, − Personal Income per Capita, − Student Aged Pop. (18–24), − Unemployment Rate, − |
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Tandberg, D.A., Griffith, C. (2013). State Support of Higher Education: Data, Measures, Findings, and Directions for Future Research. In: Paulsen, M. (eds) Higher Education: Handbook of Theory and Research. Higher Education: Handbook of Theory and Research, vol 28. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5836-0_13
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