Super-efficiency Approach Research Articles

An Enhanced Russell Measure of Super-Efficiency for Ranking Efficient Units in Data Envelopment Analysis

Problem statement: Conventional Data Envelopment Analysis (DEA) helps decision makers to discriminate between efficient and inefficient Decision Making Units (DMUs). However, DEA does not provide more information about the efficient DMUs. Super-efficiency DEA model can be used in ranking the performance of efficient DMUs. Because of the possible infeasibility of radial super-efficiency DEA model, the ranking has been limited to the model under the assumption of Constant Returns to Scale (CRS). Approach: This study proposes a super-efficiency model based on the Enhanced Russell Measure (ERM) of efficiency. This is a non-radial measure and appropriate for ranking the efficient DMUs when inputs and outputs may change non-proportionally. Results: Theoretical results show that the new super-efficiency model is always feasible under the assumption of non-CRS. Also, numerical examples from the literature are provided to test the new super-efficiency approach. Conclusion: This study provides a non-radial measure of super-efficiency based on the ERM model to discriminate among the efficient DMUs resulting different efficiency scores greater than one. Unlike the traditional radial super-efficiency models, the proposed method is always feasible.

Asset Quality Based Ranking of Indian Commercial Banks: A Superefficiency Approach

The present paper tries to make an asset quality based ranking of select (28) Indian commercial banks for four years period 2001-2002 to 2004-2005 using the super efficiency approach - a non-parametric tool. The super efficiency approach due to Andersen and Petersen (1993), allows rank order technically efficient firms. In this approach, a firm which is under evaluation is not included in the reference set of the relative envelopment model. A firm is called super efficient if its observed output exceeds what is necessary for the firm to be considered efficient relative to other firms in the sample. Out of the 28 observed commercial banks, the number found to be super efficient was only in the range between 5 to 8 during observed years. The rest were found to be inefficient. Further, the results obtained from the exercise indicate gradual improvement in mean technical efficiency scores (excepting 2003-2004) over the observed years. The observed private sector banks exhibit higher mean technical efficiency relative to the observed public sector banks for all the observed years.

Performance Evaluation of Indian Commercial Banks Using the Super Efficiency Approach

In view of the changed scenario in the Indian banking sector during the reform period, this paper tries to make a comparison of fund and non-fund based operating performance of select (28) Indian commercial banks (20 public sector, eight private sector) for the period 2002–03 to 2004–05 using the super efficiency approach. The results available from the study indicate a decline in mean technical efficiency (implying greater heterogeneity in performance across banks) during the period under consideration. This divergence in performance during the later years could be due to the influence of idiosyncratic factors. The study also indicates that the observed private sector banks have higher mean technical efficiency than the observed public sector banks.

Sensitivity analysis of efficient units in the presence of non-discretionary inputs

Discretionary models of data envelopment analysis (DEA) assume that all inputs and outputs can be varied at the discretion of management or other users. In any realistic situation, however, there may exist “exogenously fixed” or non-discretionary factors that are beyond the control of a DMU’s management, which also need to be considered. This paper discusses and reviews the use of super-efficiency approach in data envelopment analysis (DEA) sensitivity analyses when some inputs are exogenously fixed. Super-efficiency data envelopment analysis (DEA) model is obtained when a decision making unit (DMU) under evaluation is excluded from the reference set. In this paper by means of modified Banker and Morey’s (BM hereafter) model [R.D. Banker, R. Morey, Efficiency analysis for exogenously fixed inputs and outputs, Operations Research 34 (1986) 513–521], in which the test DMU is excluded from the reference set, we are able to determine what perturbations of discretionary data can be tolerated before frontier DMUs become nonfrontier.

The benefits of non-radial vs. radial super-efficiency DEA: an application to burden-sharing amongst NATO member nations

Using radial super-efficiency data envelopment analysis (DEA) has improved the discriminating performance across efficient decision-making units (DMUs). This paper extends the super-efficiency approach to a non-radial super-efficiency DEA (NRSE-DEA) index. NRSE-DEA is shown to be invariant to units of input (output) measurement. NRSE-DEA is illustrated here via an application to NATO burden-sharing assessment in which the DMUs are the member nations of NATO. The NRSE-DEA provides additional insights into the ranking of efficient countries, suggesting which are absorbing a particularly large share of NATO responsibilities. The NRSE-DEA generates a smaller set of efficient DMUs. This, in turn, provides more discriminatory power, a more accurate measure of super-efficiency, a more meaningful ranking of the efficient burden sharing countries, and a more reliable assessment of contributions by NATO members, amongst other policy issues.

Super-efficiency and DEA sensitivity analysis

This paper discusses and reviews the use of super-efficiency approach in data envelopment analysis (DEA) sensitivity analyses. It is shown that super-efficiency score can be decomposed into two data perturbation components of a particular test frontier decision making unit (DMU) and the remaining DMUs. As a result, DEA sensitivity analysis can be done in (1) a general situation where data for a test DMU and data for the remaining DMUs are allowed to vary simultaneously and unequally and (2) the worst-case scenario where the efficiency of the test DMU is deteriorating while the efficiencies of the other DMUs are improving. The sensitivity analysis approach developed in this paper can be applied to DMUs on the entire frontier and to all basic DEA models. Necessary and sufficient conditions for preserving a DMU’s efficiency classification are developed when various data changes are applied to all DMUs. Possible infeasibility of super-efficiency DEA models is only associated with extreme-efficient DMUs and indicates efficiency stability to data perturbations in all DMUs.