Abstract
Non-parametric methods for efficiency evaluation were designed to analyse industries comprising multi-input multi-output producers and lacking data on market prices. Education is a typical example. In this chapter, we review applications of DEA in secondary and tertiary education, focusing on the opportunities that this offers for benchmarking at institutional level. At secondary level, we investigate also the disaggregation of efficiency measures into pupil-level and school-level effects. For higher education, while many analyses concern overall institutional efficiency, we examine also studies that take a more disaggregated approach, centred either around the performance of specific functional areas or that of individual employees.
Part of this research has been co-financed by the European Union (European Social Fund ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) – Research Funding Program: THALES. Investing in Knowledge Society through the European Social Fund. The views expressed in this chapter are those of the authors and no representation is made that they are shared by any funding body.
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- 1.
Clearly qualitative studies are of utmost importance in education, but we address here only quantitative studies.
- 2.
Note that parametric frontier models have also been widely used in the educational context, but these will not be detailed in this chapter (examples of pupil-level studies through stochastic frontier models can be seen amongst others in Cordero-Ferrera et al. (2011), Deutsch et al. (2013), Perelman and Santín (2011) or Crespo-Cebada et al. (2014)).
- 3.
In education settings, where the variables used in the analysis are grades obtained in national exams, it is unlikely that many changes happen from year to year, except if the syllabus of the course changes. Therefore, when a reasonable number of time períods is included in constructing a meta-frontier it is unlikely that new time periods will imply big changes in that frontier.
- 4.
- 5.
- 6.
The t statistics should be treated with caution. They are high because the regression fits a line through a scatterplot that comprises observations that lie perfectly on piecewise linear segments.
- 7.
- 8.
We should note, however, that, when the panel is broken into several sub-periods and models estimated on each sub-period separately, the magnitude of some parameters varies widely across sub-periods suggesting that the results should be treated with caution. Moreover, the latent classes determined by the data are puzzling: one might expect a priori that each class would comprise HEIs with common characteristics (perhaps with research intensive institutions, and other institutions in another). But this is not the case, and the common factor relating the HEIs in a group is not obvious.
- 9.
Earlier studies using DEA to estimate output distance functions for higher education include Athanassopoulos and Shale (1997), Flegg et al. (2004) and Johnes (2006a). The last is noteworthy for its pioneering application of statistical tests for comparing nested DEA models (Pastor et al. 2002) and for testing for differences in production frontiers of distinct groups of DMUs (Charnes et al. 1981).
- 10.
Data were obtained from the Higher Education Statistics Agency (HESA).
- 11.
The productivity of institutions on the frontier in Italy slipped back over this time period , but the gain in efficiency of other institutions more than compensated for this, yielding an average efficiency increase across the country of a little under 10 %.
- 12.
Since available data show that more senior academic staff have more, better and highly valued (cited) publications, department or university rankings based on uniform labour input will favour units with greater concentration at higher academic ranks.
- 13.
A priori the quality of a publication is independent of the number of collaborators and thus we have to adjust publications counts by both factors.
- 14.
Hagen (2014) also provided the corresponding formula for harmonic counting in fields like medicine where senior authorship is usually assigned to the first and last collaborator, who are respectively the leader of the specific research and the leader of the entire research group.
- 15.
For example, for a two-author paper, the first author receives 2/3 and the second 1/3 of credit. For a paper where three authors are involved, the first author receives 6/11, the second 3/11 and the third 2/11 of credit.
- 16.
- 17.
Right-hand skewness implies that most papers are relatively little cited and there are only few papers with many citations, and that the vast majority of papers is published in relatively low impact journals.
- 18.
- 19.
The other two research productivity evaluation methods, namely peer review and bibliometrics, rely respectively on a priori weights reflecting experts or stakeholders opinions or use equal weights and appropriate normalizations/standardization to obtain comparable metrics.
- 20.
- 21.
By consistency here we mean that the resulting aggregate measure has exactly the same intuitive interpretation as the individual efficiency scores.
- 22.
Another advantage of common weights is that they can be applied to calculate performance indices for DMUs not in the sample (Kao and Hung 2007).
- 23.
A productivity index is multiplicatively complete if it can be written in a ratio form of input/output indices that are non-negative, non-decreasing, linearly homogenous scalar functions (O’Donnell 2012).
- 24.
This section is based on De Witte and Hudrlikova (2013).
- 25.
This section is based on De Witte and Rogge (2010).
- 26.
For completeness, we mention that BoD alternatively allows for a ‘worst-case’ perspective in which entities receive their worst set of weights, hence, high (low) weights on performance indicators on which they perform relative weak (strong) (Zhou et al. 2007).
- 27.
See Kuosmanen (2002) for a more comprehensive discussion.
- 28.
This section is based on De Witte and Rogge (2011).
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Thanassoulis, E., De Witte, K., Johnes, J., Johnes, G., Karagiannis, G., Portela, C.S. (2016). Applications of Data Envelopment Analysis in Education. In: Zhu, J. (eds) Data Envelopment Analysis. International Series in Operations Research & Management Science, vol 238. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7684-0_12
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Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-7682-6
Online ISBN: 978-1-4899-7684-0
eBook Packages: Business and ManagementBusiness and Management (R0)