Abstract
Conflicting measures of state support for postsecondary education create confusion and misunderstanding that convolute debates about states’ postsecondary education funding. The use of multiple measures is largely unnecessary, though. A simple single measure is constructed that adequately quantifies both states’ postsecondary need and states’ ability to pay. Specifically, this study proposes measuring state support for postsecondary education as state postsecondary funding per high school graduate over the previous four years per dollar of per capita income.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
To provide a more concrete example, if a wealthy philanthropist were to make a one-time donation of $100 million to a public college in Maine while state appropriations and other revenues remained at their 2004 levels, Maine’s percentage of public postsecondary revenues coming state funding would fall from 30.5 percent to 26.8 percent (using data from Table 338 of the National Center for Education Statistics’ Digest of Education Statistics: 2006).
To be specific, the most commonly used price index, the Consumer Price Index (CPI), overstates the true rate of inflation for two reasons. Because of the way the CPI is constructed, it generally places too much emphasis on the items that experience the highest price increases and not enough emphasis on the items with the lowest price increases. The CPI also does not fully account for quality improvements and the introduction of new products. The Higher Education Cost Adjustment constructed by the State Higher Education Executive Officers (and all other price indices) also suffers from these types of problems.
Endogeneity bias can occur when a right-hand-side variable (i.e., supposedly an independent variable) is affected by the left-hand variable (the dependent variable) and hence there is two-way causation. For more information on this concept see Wooldridge (2006).
The development of the Higher Education Price Index (HEPI) and its subsequent replacement by the Higher Education Cost Adjustment (HECA) is an interesting case in point. HEPI is a more accurate and arguably a more precise adjustment for institutional purchasing power than is either HECA or CPI. However, as stated by SHEEO, “while the HEPI has been useful, it has not been universally accepted because 1) it is a privately developed analysis and 2) one of its main components, average faculty salaries, has been criticized as self-referential” (Iowa Board Office, 2004, Unpublished manuscript).
The HEPI experiment again provides a good case in point. SHEEO stated that HEPI is an unappealing measure because it “would be costly to update, refine, and maintain.” (Iowa Board Office 2004, Unpublished manuscript).
Using state personal income as the interstate yardstick of ability to pay is standard in literature on the economics of public finance. Indeed, state personal income is frequently reported along side government expenditure data in government finance reports such as the Census Bureau’s Government Finances: 1999–2000.
Halstead (1987) and State Higher Education Executive Officers (2004) attempt to account for disproportionate interstate (but not intertemporal) differences in public FTE enrollment levels (i.e., proportions that are in two-year, four-year, and graduate institutions) and in public higher education research needs by controlling for the differences in cost per FTE student at different Carnegie Classification institutions. This clearly adds a significant layer of complexity to the measure. It also to some extent centers the measure on education institutions instead of students. Moreover, it is not clear that disproportionate interstate differences in enrollment levels and research are due to differences in ‘need’ as opposed to different outcomes and/or choices. To illustrate, consider a state that has a disproportionately high number of graduate students and relatively high spending on university research per FTE student. Does this indicate that the state’s postsecondary education institutions are relatively geared toward research and graduate enrollment (i.e., outcomes)? Does this indicate that the people in the state have relatively high preferences for research and graduate studies (i.e., choices)? Or does it demonstrate that the state has a higher ‘need’ for state support for postsecondary education? The latter possibility seems the hardest to defend.
Given that changes in the number of high school graduates in a state are gradual (the degree of serial correlation in these data is 0.98), the following results are very similar when using high school graduates in the previous two years or the previous six years.
Data in Tables 207 and 208 of the National Center for Education Statistics’ Digest of Education Statistics: 2006 indicates that 65.8 percent of freshmen in degree-granting institutions in Fall 2004 had graduated high school in the previous 12 months. This proportion is essentially the same as in 1992, 65.6 percent. In most years in between, though, the proportion was slightly higher than these numbers and the average over the time period was 67.6 percent.
Combining Tables 190–192 of the Digest of Education Statistics: 2006 indicates that 85.6 percent of students in degree-granting institutions in Fall 2005 were undergraduates. This proportion is just slightly lower than it was 1980, 86.6 percent.
Table 176 of the Digest of Education Statistics: 2006 indicates that 62.9 percent of students in degree-granting institutions in Fall 2005 were in four-year institutions. This proportion is essentially the same as it was 1980, 62.6 percent (the proportion was much higher prior to 1980, though).
Combining Tables 99 and 103 of the Digest of Education Statistics: 2006 indicates that there were 7.5 high school diplomas for each GED in 2004, but this ratio varies considerably from year to year (e.g., the ratio was 4.4 to 1 in 2001 and 8.8 to 1 in 2002). The average ratio from 1976 to 2004 was 6.5 to 1, and there was no clear time trend over this period. Unfortunately, we are unable to quantify the extent that GED recipients go on to postsecondary education.
Per capita income is used to account for ability to pay rather than total income as in Eq. 1 because Eq. 5 is already normalized to be comparable across states. Thus, the ability-to-pay indicator must also be comparable across states. Lieberman (1998) also uses this adjustment in measuring state support for elementary and secondary education.
As a practical matter, to adequately incorporate intertemporal changes in need per potential student would probably require an elaborate econometric analysis. Simplicity, transparency, and replicate-ability would be sacrificed. In addition, quantifying growth in need per potential student would require continual modification of the measurements as new information becomes available.
The Census Bureau Governments Division has information on public postsecondary education, but their data are expenditures, not revenues. Charges (i.e., tuition, fees, and revenues from auxiliary activities) can be removed from these expenditures, but this still leaves some expenditures funded through gifts and endowment revenues. Thus, it is not appropriate to use the Census Bureau data to measure state support. The National Association of State Budget Officers collects data on public funding for postsecondary education, but their data are not collected consistently across states (e.g., some states include tuition and fees in their reporting of state funding). Data on public funding for postsecondary education is also collected in Illinois State University’s Grapevine project, but their data are not as comprehensive as SHEEO’s (e.g., local government funding, among others, is not included).
The values for FY 2005 are preliminary estimates because data on high school graduates are currently available only through 2003. Numbers of graduates in 2004 was forecast using a ten-year linear regression.
This is the issue of whether education produces human capital or is a signaling/screening mechanism. Although there is some evidence that obtaining degrees has labor-market effects independent of years in college, the evidence that it is time spent in education that matters is more compelling. On this issue see Groot and Oosterbeek (1994), and Chevalier et al. (2004).
Moreover, errors in the measurements of the numbers of states’ high school graduates in the previous fours years can upwardly bias the observed correlation. The number of high school graduates in the previous four years is in the denominator of both variables in Fig. 5. Thus, measurement error in the number of high school graduates can create some of the observed positive correlation.
Weights for the different degrees could also be derived from data on average earnings differentials (i.e., the average earnings differential between Associate’s degrees and high school diplomas is 46.5% as large as the average earnings differential between Bachelor’s degrees and high school diplomas, the earnings differential between Master’s degrees and Bachelor’s degrees is 39.5% as large as the average earnings differential between Bachelor’s degrees and high school diplomas, etc.). The results, however, were essentially the same when using this more complicated weighting scheme.
References
Archibald, R. B., & Feldman, D. H. (2006). State higher education spending and the tax revolt. The Journal of Higher Education, 77(4), 618.
Arnone, M. (2002). State spending on colleges increases at lowest rate in a decade. The Chronicle of Higher Education, 49(16), A28.
Blose, G. L., Porter, J. D., & Kokkelenberg, E. C. (2006). The effect of institutional funding cuts on baccalaureate graduation rates in public higher education. In R. G. Ehrenberg (Ed.), What’s happening to public higher education? (ACE/Praeger series on higher education). Washington, DC: Praeger Publishers.
Boskin, M. J., Dulberger, E. R., Gordon, R. J., Griliches, Z., & Jorgensen, D. W. (1998). Consumer prices, the consumer price index, and the cost of living. Journal of Economic Perspectives, 12(1), 3–26.
Bound, J., & Turner, S. (2006). Cohort crowding: How resources affect collegiate attainment (NBER Working Paper No. 12424). Cambridge, MA: National Bureau of Economic Research.
Chambers, M. M. (1961). Appropriations of state tax funds for operating expenses of higher education. Washington, DC: Joint Office of Institutional Research.
Chevalier, A., Harmon, C., Walker, I., & Zhu, Y. (2004). Does education raise productivity, or just reflect it? Economic Journal, 114(499), 499–517.
Council of Economic Advisors. (2006). Economic report of the President: 2006. Washington, DC: United States Government Printing Office.
Desrosières, A. (1998). The politics of large numbers: A history of statistical reasoning. Cambridge, MA: Harvard University Press.
Groot, W., & Oosterbeek, H. (1994). Earnings effects of different components of schooling: Human capital versus screening. Review of Economics and Statistics, 76(2), 317–321.
Halstead, K. (1974). Statewide planning in higher education. Washington, DC: U.S. Government Printing Office.
Halstead, K. (1987). State profiles: Financing public higher education 1978 to 1987 (10th ed.). Washington, DC: Research Associates of Washington.
Hand, D. J. (1996). Statistics and the theory of measurement. Journal of the Royal Statistical Society, Series A, 159, 445–492.
Jenny, N. W., & Arbak, E. (2004, March). Challenges for financing public higher education. State Fiscal News, 4(2), 1–6.
Kane, T. J., & Orszag, P. R. (2003). Funding restrictions at public universities: Effects and policy implications. Unpublished manuscript.
Kane, T. J., & Orszag, P. R. (2004). Financing public higher education: Short-term and long-term challenges. Ford Policy Forum 2004. Cambridge, MA: Forum for the Future of Higher Education.
Kane, T. J., Orszag, P. R., & Apostolov, E. (2005). Higher education appropriations and public universities: Role of Medicaid and the business cycle. In G. T. Burtless & J. R. Pack (Eds.), Brookings-Wharton Papers on Urban Affairs: 2005. Washington, DC: Brookings Institution Press.
Kelly, P. J., & Jones, D. P. (2005). A new look at the institutional component of higher education finance: A guide for evaluating performance relative to financial resources. Boulder, CO: National Center for Higher Education Management Systems.
Krantz, D. H., Luce, R. D., Suppes, P., & Tversky, A. (1971). Foundations of measurement, Volume I: Additive and polynomial representations. New York: Academic Press.
Lieberman, C. (1998). Educational expenditures and economic growth in the American states. Akron, OH: Midwest Press.
Longanecker, D. (2006). A tale of two pities. Change, 38(1), 14.
Luce, R. D., Krantz, D. H., Suppes, P., & Tversky, A. (1990). Foundations of measurement, Volume III: Representation, axiomatization, and invariance. New York: Academic Press.
Lyall, K. C., & Sell, K. R. (2006). The true genius of America at risk: Are we losing our public universities to de facto privatization? (ACE/Praeger series on higher education). Washington, DC: Praeger Publishers.
McPherson, M. S., & Schapiro, M. O. (2003). Funding roller coaster for public higher education. Science, 302(5648), 1157.
Mortenson, T. G. (2004). State tax fund appropriations for higher education: FY1961 to FY2004. Postsecondary Education Opportunity, 139, 1–12.
Moulton, B. R. (1996). Bias in the consumer price index. Journal of Economic Perspectives, 10(4), 159–177.
Musgrave, R. A. (1959). The theory of public finance. New York: McGraw-Hill.
National Association of State Budget Officers. (2005). State expenditure report 2004. Washington, DC: National Association of State Budget Officers.
National Center for Public Policy and Higher Education. (2002). Losing ground: A national status report on the affordability of higher education. San Jose, CA: National Center for Public Policy and Higher Education.
Organisation of Economic Co-operation and Development. (2001). Tax and the economy: A comparative assessment of OECD countries. Paris: Organisation of Economic Co-operation and Development.
Organisation of Economic Co-operation and Development. (2007). Revenue statistics: 1965–2006. Paris: Organisation of Economic Co-operation and Development.
Palmer, J. C. (2005). Grapevine survey of state higher education tax appropriations for fiscal year 2005. Normal, IL: Center for the Study of Education Policy.
Rafuse, R. W. (1990). A walk on the expenditure side: “Needs” and “fiscal capacity”. Intergovernmental Perspective, 16(4), 25–30.
Ryan, J. F. (2004). The relationship between institutional expenditures and degree attainment at baccalaureate colleges. Research in Higher Education, 45(2), 97–114.
Selingo, J. (2003, February 28). The disappearing state in public higher education. The Chronicle of Higher Education, 49, A25.
Shao, J. (2003). Mathematical statistics. New York: Springer.
State Higher Education Executive Officers. (2004). State higher education finance FY 2003. Boulder, CO: State Higher Education Executive Officers.
Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103, 677–680.
Stevens, S. S. (1951). Mathematics, measurement, and psychophysics. In S. S. Stevens (Ed.), Handbook of experimental psychology. New York: Wiley.
Stevens, S. S. (1959). Measurement. In C. W. Churchman (Ed.), Measurement: Definitions and theories. New York: Wiley.
Stevens, S. S. (1968). Measurement, statistics, and the schemapiric view. Science, 161, 849–856.
Stiglitz, J. E. (2000). Economics of the public sector (3rd ed.). New York: Norton.
Suppes, P., Krantz, D. H., Luce, R. D., & Tversky, A. (1989). Foundations of measurement, Volume II: Geometrical, threshold, and probabilistic representations. New York: Academic Press.
Tannenwald, R. (2002). Interstate fiscal disparity in 1997. New England Economic Review, 8(3), 17–33.
Tannenwald, R., & Turner, N. (2004). Interstate fiscal disparity in state fiscal year 1999 (No. 04–9). Boston, MA: Federal Reserve Bank of Boston.
Thelin, J. R. (2004). A history of American higher education. Baltimore: Johns Hopkins University Press.
Trombley, W. (2003). College affordability in jeopardy. San Jose, CA: The National Center for Public Policy and Higher Education.
Velleman, P. F., & Wilkinson, L. (1993). Nominal, ordinal, interval, and ratio typologies are misleading. The American Statistician, 47, 65–72.
Wagner, A. (1880). Finanzwissenschaft (Part II). Leipzig: C. F. Winter.
Wooldridge, J. M. (2006). Introductory econometrics: A modern approach (3rd ed.). Mason, OH: Thompson South-Western.
Yudof, M. G. (2002). Higher tuitions. Change, 34(2), 16.
Zhang, L. (2006). Does public funding for higher education matter? Unpublished manuscript.
Zumeta, W. (2001). State policy and private higher education: Past, present and future. In M. B. Paulsen & J. C. Smart (Eds.), The finance of higher education: Theory, Research, Policy, and Practice. New York, NY: Agathon Press.
Zumeta, W. (2004). State higher education financing: Demand imperatives meet structural, cyclical, and political constraints. In St. John, E. P., & Parsons, M. D. (Eds.), Public funding of higher education: Changing contexts and new rationales. Baltimore, MD: Johns Hopkins University Press.
Acknowledgments
For helpful comments we are grateful to Kate Clark, Sara Goldrick-Rab, Lee Hansen, Nik Hawkins, Bob Hanle, Katharine Lyall, Andy Reschovsky, David Weerts, and an anonymous referee. We are also grateful to State Higher Education Executive Officers for kindly making their data available to us.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
Rights and permissions
About this article
Cite this article
Trostel, P.A., Ronca, J.M. A Simple Unifying Measure of State Support for Postsecondary Education. Res High Educ 50, 215–247 (2009). https://doi.org/10.1007/s11162-008-9115-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11162-008-9115-6