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Identifying the Best Buys in U.S. Higher Education

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Abstract

Which U.S. institutions of higher education offer the best value to consumers? To answer this question, we evaluate U.S. institutions relative to a data envelopment analysis (DEA) multi-factor frontier based on 2000–2001 data for 1,179 4-year institutions. The resulting DEA “best buy” scores allow the ranking of institutions by a weighted sum of institutional characteristics per dollar of average net price. The net price is calculated as tuition, fees, room, and board less per student financial aid. Institutional characteristics include SAT score, athletic expenditures, instructional expenditures, value of buildings, dorm capacity, and student body characteristics. The DEA scores indicate the distance of each institution from the “best buy” frontier for the chosen characteristics, providing an objective means of ranking institutions as the best values in higher education.

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Notes

  1. DEA was first introduced in a Ph.D. dissertation evaluating educational programs for disadvantaged students. See Charnes et al. (1994) and Coelli et al. (1998).

  2. Even by 2005 the financial data section of the survey was only available for the 2001 academic year.

  3. The analysis modifies Rosen’s (1974) approach for the special characteristics of higher education institutions identified by Tiffany and Ankrom (1998), Clotfelter (1999), Winston (2003, 2004), Epple et al. (2003), and Martin (2004).

  4. IPEDS was created in 1986 and is accessible through the NCES Web site (http://nces.ed.gov/) for the years 1996 to the present. Earlier years are archived at the University of Michigan (http://webapp.icpsr.umich.edu/cocoon/IAED-SERIES/00102.xml). The predecessor to IPEDS was Higher Education General Information Survey or HEGIS which dates to 1967 with limited data back to 1965. These data are also archived at the International Archive of Education Data at the University of Michigan (http://webapp.icpsr.umich.edu/cocoon/IAED-SERIES/00030.xml). Some, though not all, of these data can be downloaded from the Michigan Web site.

  5. Using dollar denominated items per FTE will cause smaller institutions with less impressive totals to appear more comparable to large institutions with large totals. Using total dollars would bias the results in favor of large institutions, regardless of the quality of the attribute offered. In the absence of an ideal quality measure—an indicator of absolute quality independent of expenditures—expenditures per FTE was chosen to eliminate the pure size effect. Note that FTE includes graduate students. As a practical matter it would be virtually impossible to parse the portion of most of the measures used between graduate and undergraduate students.

  6. The Carnegie classification is an ordinal measure of research intensity. This is perhaps not ideal, but is preferable to multiple zero–one categorical values which miss the ordinal aspect altogether. DEA will allow tradeoffs between higher Carnegie classifications and lower values of other outputs (and vice versa) using the ordinal values. Use of separate dummy variables for each category will not allow these tradeoffs and results in institutions in each Carnegie category being ranked only against others in the same category.

  7. Our dataset is for the 2000–2001 academic year and was originally collected for a different research project. Even by 2005 the financial data section of the survey was only available for the 2001 academic year. Given that a primary motivation for this research was to explore the application of the DEA methodology the fact that the data might seem dated is of lesser consequence for present purposes.

  8. Federal financial aid was deliberately excluded, for two reasons. First, federal aid is distributed on the basis of uniform criteria to all participating institutions. Second, when we did attempt to include it, in more than a few instances the net price was negative.

  9. One might ask why private institutions in general are not identified explicitly. First, we reason that consumers have no basis to prefer a private to a public institution, other than for the differences in the observed characteristics that are offered. Otherwise, one must believe that there is a common unobserved characteristic set possessed by all private institutions and not offered by any public institution. Second, the methodology identifies the output of an institution per dollar of student expenditure. We argue that this fact accounts for a major difference between public and private institutions, i.e., the net price to the student consumer. Finally, the religious affiliation measure does identify a set of unobserved variables that consumers may value.

  10. The implicit assumption is, of course, that there were no substantive changes in relative entrance scores between the fall of 2000 and fall 2003.

  11. DEA may be susceptible to measurement error, but the use of data averaged over several time periods has been shown to smooth out this measurement error (Ruggiero 2006, 2007). Unfortunately, data limitations prevent the implementation of this technique here.

  12. Only 1,065 of the 1,179 schools had at least 10 schools within a 150 mile radius; only these schools were used for these calculations.

  13. There were nine estimations dropping one output; 36 estimations dropping two outputs; 84 estimations dropping three outputs; and 126 estimations dropping four outputs.

  14. A complete listing of institutions, best buy scores and attribute weights is available from the authors on request.

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Authors and Affiliations

  1. Department of Economics and Finance, Middle Tennessee State University, Murfreesboro, TN, USA

    E. Anthon Eff, Christopher C. Klein & Reuben Kyle

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  1. E. Anthon Eff
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  2. Christopher C. Klein
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Correspondence to Christopher C. Klein.

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Eff, E.A., Klein, C.C. & Kyle, R. Identifying the Best Buys in U.S. Higher Education. Res High Educ 53, 860–887 (2012). https://doi.org/10.1007/s11162-012-9259-2

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