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Costs and efficiency of higher education institutions in England: a DEA analysis

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Journal of the Operational Research Society

Abstract

As student numbers in higher education in the UK have expanded during recent years, it has become increasingly important to understand its cost structure. This study applies Data Envelopment Analysis (DEA) to higher education institutions in England to assess their cost structure, efficiency and productivity. The paper complements an earlier study that used parametric methods to analyse the same panel data. Interestingly, DEA provides estimates of subject-specific unit costs that are in the same ballpark as those provided by the parametric methods. The paper then extends the previous analysis and finds that further student number increases of the order of 20–27% are feasible through exploiting operating and scale efficiency gains and also adjusting student mix. Finally the paper uses a Malmquist index approach to assess productivity change in the UK higher education. The results reveal that for a majority of institutions productivity has actually decreased during the study period.

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References

  • Agasisti S and Salerno C (2007). Assessing the cost efficiency of Italian universities. Educ Econ 15 : 455–471.

    Article  Google Scholar 

  • Aigner DJ, Lovell CAK and Schmidt P (1977). Formulation and estimation of stochastic frontier production function models. J Econometrics 6: 21–37.

    Article  Google Scholar 

  • Andersen P and Petersen NC (1993). A procedure for ranking efficient units in data envelopment analysis. Mngt Sci 39: 1261–1264.

    Article  Google Scholar 

  • Athanassopoulos AD and Shale E (1997). Assessing the comparative efficiency of higher education institutions in the UK by the means of data envelopment analysis. Educ Econ 5: 117–134.

    Article  Google Scholar 

  • Banker R and Natarajan R (2004). Statistical tests based on DEA efficiency scores. Chapter 11. In: Cooper WW, Seiford L and Zhu J (eds). Handbook on Data Envelopment Analysis. Kluwer Academic Publishers: Norwell, MA, pp 299–321.

    Chapter  Google Scholar 

  • Banker R, Charnes A, Cooper WW and Rhodes E (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Mngt Sci 30 : 1078–1092.

    Article  Google Scholar 

  • Beasley J (1995). Determining teaching and research efficiencies. J Opl Res Soc 46 : 441–452.

    Article  Google Scholar 

  • Beasley J and Wong Y-HB (1990). Restricting weight flexibility in data envelopment analysis. J Opl Res Soc 41 : 829–835.

    Article  Google Scholar 

  • Caves DW, Christensen LR and Diewert WE (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica 50: 1393–1414.

    Article  Google Scholar 

  • Charnes A, Cooper WW and Rhodes E (1978). Measuring the efficiency of decision making units. Eur J Opl Res 2: 429–444.

    Article  Google Scholar 

  • De Groot H, McMahon WW and Volkwein JF (1991). The cost structure of American research universities. Rev Econ Stat 73 : 424–431.

    Article  Google Scholar 

  • Färe R, Grosskopf S, Lindgren B and Roos B (1994a). Productivity developments in Swedish hospitals: A Malmquist output index approach. In: Charnes A, Cooper WW, Levin AY and Seiford LM (eds). Data Envelopment Analysis: Theory, Methodology and Applications. Kluwer Academic Publishers: Boston, MA, pp 253–272.

    Chapter  Google Scholar 

  • Färe R, Grosskopf S, Norris M and Zhang Z (1994b). Productivity growth, technical progress and efficiency change in industrialized countries. Am Econ Rev 84: 66–83.

    Google Scholar 

  • Farrell MJ (1957). The measurement of productive efficiency. J R Stat Soc Ser A 120: 253–290.

    Article  Google Scholar 

  • Flegg AT, Allen DO, Field K and Thurlow TW (2004). Measuring the efficiency of British universities: A multi-period data envelopment analysis. Educ Econ 12 : 231–249.

    Article  Google Scholar 

  • Grifell-Tatjé E and Lovell CAK (1995). A note on the Malmquist productivity index. Econ Letters 47: 169–175.

    Article  Google Scholar 

  • Herrero I and Pascoe S (2004). Analysing the effect of technical change on individual outputs using modified quasi-Malmquist indexes. J Opl Res Soc 55 : 1081–1089.

    Article  Google Scholar 

  • Izadi H, Johnes G, Oskrochi R and Crouchley R (2002). Stochastic frontier estimation of a CES cost function: The case of higher education in Britain. Econ Educ Rev 21: 63–71.

    Article  Google Scholar 

  • Johnes G and Johnes J (2009). Higher education institutions' costs and efficiency: Taking the decomposition a further step. Econ Educ Rev 28: 107–113.

    Article  Google Scholar 

  • Johnes G, Johnes J and Thanassoulis E (2008). An analysis of costs in institutions of higher education in England. Stud High Educ 33 : 527–549.

    Article  Google Scholar 

  • Johnes J (2008). Efficiency and productivity change in the English higher education sector from 1996/97 to 2004/05. Manch Sch 76 : 653–674.

    Article  Google Scholar 

  • Johnes J and Johnes G (1995). Research funding and performance in UK university departments of economics: A frontier analysis. Econ Educ Rev 14: 301–314.

    Article  Google Scholar 

  • Kuosmanen T and Kortelainen M (2007). Stochastic nonparametric envelopment of data: Cross-sectional frontier estimation subject to shape constraints. Discussion Paper No: 46, Department of Economics and Business Administration, University of Joensuu.

  • Lovell CAK (2003). The decomposition of Malmquist productivity indexes. J Prod Anal 20: 437–458.

    Article  Google Scholar 

  • Meeusen W and van den Broeck J (1977). Efficiency estimation from Cobb-Douglas production function with composed error. Int Econ Rev 8: 435–444.

    Article  Google Scholar 

  • Podinovski V (2004). Bridging the gap between the constant and variable returns-to-scale models: Selective proportionality in data envelopment analysis. J Opl Res Soc 55: 265–276.

    Article  Google Scholar 

  • Simar L and Wilson PW (2008). Statistical inference in nonparametric frontier models: Recent developments and perspectives. In: Fried H, Lovell CAK and Schmidt S (eds). The Measurement of Productive Efficiency and Productivity Growth. Oxford University Press: New York.

    Google Scholar 

  • Stevens PA (2005). A stochastic frontier analysis of English and Welsh universities. Educ Econ 13 : 355–374.

    Article  Google Scholar 

  • Thanassoulis E (1996). A data envelopment analysis approach to clustering operating units for resource allocation purposes. Omega: Int J Mngt S 24 : 463–476.

    Article  Google Scholar 

  • Thanassoulis E (1999). Setting achievement targets for school children. Educ Econ 7/2: 101–119.

    Article  Google Scholar 

  • Thanassoulis E (2001). Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software. Kluwer Academic Publishers: Boston.

    Book  Google Scholar 

  • Thanassoulis E and Dyson RG (1992). Estimating preferred input-output levels using data envelopment analysis. Eur J Opl Res 56: 80–97.

    Article  Google Scholar 

  • Thanassoulis E, Portela MCAS and Allen R (2004). Incorporating value judgments in DEA. In: Cooper WW, Seiford LW and Zhu J (eds). Handbook on Data Envelopment Analysis. Kluwer Academic Publishers: Norwell, MA, pp 99–138.

    Chapter  Google Scholar 

  • Thanassoulis E, Portela M and Despic O (2008). The mathematical programming approach to efficiency analysis. In: Fried H, Lovell K, Schmidt S (eds). The Measurement of Productive Efficiency and Productivity Growth. Oxford University Press: New York pp 251–420.

    Chapter  Google Scholar 

  • Verry DW and Layard PRG (1975). Cost functions for university teaching and research. Econ J 85: 55–74.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Aston Business School, Aston University, Birmingham, UK

    E Thanassoulis

  2. University of Manchester, Manchester, UK

    M Kortelainen

  3. Lancaster University Management School, Lancaster, UK

    G Johnes & J Johnes

Authors
  1. E Thanassoulis
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  2. M Kortelainen
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  3. G Johnes
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  4. J Johnes
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Corresponding author

Correspondence to E Thanassoulis.

Additional information

This paper draws on research carried out on behalf of the UK Department for Education and Skills, now Department for Innovation, Universities and Skills, DIUS. The views expressed in the paper are those of the authors and no representation is being made that they are necessarily shared by the DIUS.

Appendix

Appendix

Mathematical presentation of DEA under VRS and CRS

The original DEA model of Charnes et al (1978) assumes constant returns to scale (CRS) under which the DEA-derived input- and output-oriented measures of efficiency for DMU are identical. The CRS assumption can be relaxed and the DEA model can be easily modified to incorporate variable returns to scale (VRS) (Banker et al, 1984). The set of DMUs identified as inefficient under VRS will be the same whether an input- or output-oriented approach is taken. In contrast to the CRS framework, however, the actual values of the efficiency scores for the inefficient DMUs vary with the orientation adopted.

In practice, DMUs may produce many outputs from their resources, in which case programming techniques have to be used to identify the piecewise linear frontier joining up all efficient DMUs. Suppose DMUs use m inputs to produce s outputs. Under VRS the following linear programming problem must be solved for each of the n DMUs (k=1,  …, n):

illustration

figure a

Overall efficiency of DMU k is measured by E k =1/φ k in the output-oriented framework or E k =θ k in the input-oriented framework. Further, scale efficiency can be identified by calculating the following ratio for DMU k:

where the numerator and denominator include efficiency scores calculated under CRS and VRS, respectively. Note that the CRS efficiency score can be calculated simply by deleting the constraint Σ j=1 n λ j =1 from model 1 (A.1) or (A.2).

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Thanassoulis, E., Kortelainen, M., Johnes, G. et al. Costs and efficiency of higher education institutions in England: a DEA analysis. J Oper Res Soc 62, 1282–1297 (2011). https://doi.org/10.1057/jors.2010.68

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  • DOI: https://doi.org/10.1057/jors.2010.68

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