Technical efficiency based on cost gradient measure

This study introduces a new scheme of data envelopment analysis (DEA) named cost gradient measure (CGM) to evaluate technical efficiency. In this model, we can obtain more cost conscious technical efficiency than those by other traditional DEA models such as CCR[7] and slacks-based measure (SBM) [19]. In addition, the CGM can avoid shortcomings of these traditional models, i.e. factor inefficiency scores can be measured for each input as opposed to CCR and SBM models. In this study, we show the generality of CGM that it includes CCR as a special case; and compare the CGM result with those of the other DEA models using illustrative data, and clarify favorite features of this model. In addition, we also apply these models to Japanese electric utilities and explain the characteristics of their results.http://www.grips.ac.jp/list/facultyinfo/tone_kaoru/http://www.grips.ac.jp/list/jp/facultyinfo/yoshida_yuichiro

Does it matter How We Measure Congestion?

This paper examines three alternative methods of measuring congestion, from both theoretical and empirical perspectives. These methods are the conventional approach of Färe and Grosskopf, the alternative proposed by Cooper et al., and a new method developed by Tone and Sahoo. Each method is found to have merits and demerits. The properties of the different methods are examined using data for 41 ‘new’ British universities in the period 1995/6 to 2003/4. Contrary to expectations, Färe and Grosskopf’s approach generally indicates substantially more congestion than do the other procedures. The main reason for this is identified as being its use of CRS rather than VRS as the assumed technology. While the alternative measures of congestion are found to be positively correlated, the correlations are not strong enough for them to be regarded as substitutes. All methods suggest the existence of a widespread problem of congestion in the new universities, although they differ noticeably as regards its severity.Length: 37 pagesData envelopment analysis; Education; Congestion;

Calculating Super Efficiency of DMUs for Ranking Units in Data Envelopment Analysis Based on SBM Model

There are a number of methods for ranking decision making units (DMUs), among which calculating super efficiency and then ranking the units based on the obtained amount of super efficiency are both valid and efficient. Since most of the proposed models do not provide the projection of Pareto efficiency, a model is developed and presented through this paper based on which in the projection of Pareto-efficient is obtained, in addition to calculating the amount of super efficiency. Moreover, the model is unit invariant, and is always feasible and makes the amount of inefficiency effective in ranking

Achieving MAX-MIN Fair Cross-efficiency scores in Data Envelopment Analysis

Algorithmic decision making is gaining popularity in today's business. The need for fast, accurate, and complex decisions forces decision-makers to take advantage of algorithms. However, algorithms can create unwanted bias or undesired consequences that can be averted. In this paper, we propose a MAX-MIN fair cross-efficiency data envelopment analysis (DEA) model that solves the problem of high variance cross-efficiency scores. The MAX-MIN cross-efficiency procedure is in accordance with John Rawls’s Theory of justice by allowing efficiency and cross-efficiency estimation such that the greatest benefit of the least-advantaged decision making unit is achieved. The proposed mathematical model is tested on a healthcare related dataset. The results suggest that the proposed method solves several issues of cross-efficiency scores. First, it enables full rankings by having the ability to discriminate between the efficiency scores of DMUs. Second, the variance of cross-efficiency scores is reduced, and finally, fairness is introduced through optimization of the minimal efficiency scores

Congestion in the Chinese automobile and textile industries revisited

This paper re-examines a problem of congested inputs in the Chinese automobile and textile industries, which was identified by Cooper et al. (Socio-Economic Planning Sciences 35 (2001) 227-242). These authors employed a single approach to measuring congestion, however, so it is of interest to see whether other approaches would yield very different answers as regards the severity of this problem. Indeed, the measurement of congestion is an area where there has been much theoretical debate but relatively little empirical work. Here we use the data set assembled by Cooper et al. for the period 1981-1997 to compare and contrast the measurements of congestion generated by three alternative approaches. We find that these measurements are indeed very different.Monetary Policy;

Efficiency evaluation of South Africa tertiary education institutions using data envelopment analysis

With an increasing number of students enrolling at higher education institutions in South Africa, it has become important to investigate whether these institutions are using their resources adequately. This study uses data envelopment analysis (DEA) to estimate the efficiency of 23 South African tertiary education institutions based on both teaching and research outputs. Using DEA we are able to rank South African universities according to their use of resources in these two areas. These rankings can identify institutions which are performing well and also those which require improvement. The effect that merging institutions has on this efficiency is also determined. Owing to the limited sample size, variable reduction techniques, including the efficiency contribution measure (ECM) and principal components analysis (PCA-DEA), were used to improve the discrimination of the analysis

Ranking Efficient DMUs Using the Variation Coefficient of Weights in DEA

Abstract One of the difficulties of Data Envelopment Analysis(DEA) is the problem of de_ciency discrimination among efficient Decision Making Units (DMUs) and hence, yielding large number of DMUs as efficient ones. The main purpose of this paper is to overcome this inability. One of the methods for ranking efficient DMUs is minimizing the Coefficient of Variation (CV) for inputs-outputs weights. In this paper, it is introduced a nonlinear model for ranking efficient DMUs based on the minimizing the mean absolute deviation of weights and then we convert the nonlinear model proposed into a linear programming form