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Efficiency measurement of higher education units using multilevel frontier analysis

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Abstract

This study aims at evaluating the crucial effects of incorporating heterogeneities and hierarchies in the efficiency evaluation of higher education units through conducting multilevel frontier analysis. Using two sets of data on academic departments nested within faculties, and faculties within colleges of a comprehensive university in Iran, we simultaneously evaluate efficiency scores of departments, faculties, colleges, and the university. It has been shown that: (1) there is a great degree of heterogeneities among departments, faculties, and colleges of the university; (2) incorporating the heterogeneities changes efficiency scores which accordingly questions the accuracy of standard DEA and SFA efficiency estimates; (3) multilevel modelling helps to uncover huge differences in behavior and performance through decomposing overall efficiency scores into multiple measures of efficiency; and (4) departments, in comparison with faculties and colleges, play a more significant role in the overall performance of the university. Resource allocation policy implications of the findings were also discussed.

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Notes

  1. See among others, Johnes and Taylor (1990), and Naderi (2016).

  2. See for example, Seiford (1996), Athanassopoulos and Shale (1997), Thanassoulis et al. (2016), and Askari et al. (2019).

  3. See for example, Kumbhakar and Lovel (2000), Huang (2004), Greene (2005), and Parmeter and Kumbhakar (2014).

  4. Some studies tend to satisfy homogeneity of DMUs by selecting academic departments at a university or specialized departments across different universities. In this research, we show that there is a great degree of heterogeneities among academic departments of a university.

  5. For a more detailed discussion on the evolved changes and heterogeneities initiated by government policies, see for example Rip and Kulati, (2015: 106–107).

  6. Flegg et al. (2004: 232) also argue that the standard DEA has the disadvantage that it cannot distinguish between changes in relative efficiency brought about by movements towards or away from the efficiency frontier in a given year and shifts in this frontier over time.

  7. For more detailed discussion, see among other, Kumbhakar and Lovell (2000) and Greene (2008).

  8. The issue of heteroscedasticity and its effects has attracted some attention in analysis of efficiency. See, for example, Kumbhakar and Lovell (2000: 115).

  9. Modified Iterated Generalized Least Squared (MIGLS) is another procedure to provide estimates of the technical efficiency using the IGLS residuals, exactly as in the CIGLS model. Nonetheless, due to space limitation, we confine our analysis to CIGLS.

  10. Goldstein (1995: 22) states that when the residuals have normal distributions, the IGLS yields maximum likelihood estimates. Nonetheless, it would be interesting to compare the measures of technical efficiency derived from CIGLS estimators with those of maximum likelihood estimates which merits further research. The results would help to see if the claim made by Coelli et al. (2005: 245) that maximum likelihood estimators under some distributional assumptions about the error terms provide a better solution than COLS estimators, is held in the context of multilevel assessment of technical efficiency of HEUs (i.e., CIGLS estimators).

  11. To examine the hypothesis that data used are dominated by a hierarchical structure, empirical analyses based on intra-unit correlation statistic were conducted. The results confirm the heterogeneities among departments, faculties, and colleges. Due to space limitations, the results are not reported here.

  12. It is very interesting to see if the relationship between efficiency scores of the units of analysis at different levels should be negative or positive which merits further research.

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  1. Economics of Education, Faculty of Psychology & Education, University of Tehran, Jalale Ale-Ahmad High Way, Gisha, Tehran, Iran

    Abolghasem Naderi

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Naderi, A. Efficiency measurement of higher education units using multilevel frontier analysis. J Prod Anal 57, 79–92 (2022). https://doi.org/10.1007/s11123-021-00621-0

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