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Efficiency Persistence of Bank and Thrift CEOs Using Data Envelopment Analysis Read chapter Read first chapter

2016 | OriginalPaper | Chapter

12. Applications of Data Envelopment Analysis in Education

Authors : Emmanuel Thanassoulis, Kristof De Witte, Jill Johnes, Geraint Johnes, Giannis Karagiannis, Conceição S. Portela

Published in: Data Envelopment Analysis

Publisher: Springer US

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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.

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previous chapter Measuring Environmental Efficiency: An Application to U.S. Electric Utilities
next chapter Performance Benchmarking of School Districts in New York State
Footnotes
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
This follows earlier work by, for example, Athanassopoulos and Shale (1997) and Johnes (1998, 1999).
 
5
Following Thanassoulis (1999), the outliers have super-efficiencies (Andersen and Petersen 1993) in excess of 100 % and there is at least a 10 percentage point gap between the super-efficiencies of the outliers and the other observations.
 
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
The index developed by Malmquist (1953) was adapted for use in a DEA context by several researchers in the 1990s. See, for example, Førsund (1993) and Färe and Grosskopf (1996a).
 
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
There is also a disagreement on whether the choice of counting method affects more papers or citations counts. Lin et al. (2013) found that it impacts citation counts more than paper counts while Abramo et al. (2013b) reached the opposite conclusion.
 
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
Abramo et al. (2012a, b) also provided empirical evidence indicating that rankings of individual researchers obtained under different scaling factors (i.e., average, median, cited papers average, cited papers median) do not show significant discrepancies.
 
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
More on the radial DEA models with a single constant input can be found in Lovell and Pastor (1999), Caporaletti et al. (1999) and Liu et al. (2011). Notice also that unitary input DEA models are equivalent to DEA models without explicit inputs.
 
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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Metadata
Title
Applications of Data Envelopment Analysis in Education
Authors
Emmanuel Thanassoulis
Kristof De Witte
Jill Johnes
Geraint Johnes
Giannis Karagiannis
Conceição S. Portela
Copyright Year
2016
Publisher
Springer US
Book
Data Envelopment Analysis

Print ISBN: 978-1-4899-7682-6

Electronic ISBN: 978-1-4899-7684-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-1-4899-7684-0_12