Sensitivity analysis of network DEA illustrated in branch banking

Users of data envelopment analysis (DEA) often presume efficiency estimates to be robust. While traditional DEA has been exposed to various sensitivity studies, network DEA (NDEA) has so far escaped similar scrutiny. Thus, there is a need to investigate the sensitivity of NDEA, further compounded by the recent attention it has been receiving in literature. NDEA captures the underlying performance information found in a firm?s interacting divisions or sub-processes that would otherwise remain unknown. Furthermore, network efficiency estimates that account for divisional interactions are more representative of a dynamic business. Following various data perturbations overall findings indicate positive and significant rank correlations when new results are compared against baseline results - suggesting resilience. Key findings show that, (a) as in traditional DEA, greater sample size brings greater discrimination, (b) removing a relevant input improves discrimination, (c) introducing an extraneous input leads to a moderate loss of discrimination, (d) simultaneously adjusting data in opposite directions for inefficient versus efficient branches shows a mostly stable NDEA, (e) swapping divisional weights produces a substantial drop in discrimination, (f) stacking perturbations has the greatest impact on efficiency estimates with substantial loss of discrimination, and (g) layering suggests that the core inefficient cohort is resilient against omission of benchmark branches. Various managerial implications that follow from empirical findings are discussed in conclusions.

A nonparametric approach to productive efficiency measurement : an application of bootstrap DEA to gold mining

In this dissertation the technical efficiency in gold mining is investigated. To the best available knowledge, this is the first such study on gold mining, whether on a localised (one country) or for a cross-section of countries. Since the work by Farrell (1957), much work has been done using nonparametric methods such as DEA. Although extensions in DEA technique, such as bootstrapping have been available for some time, their use has been limited in comparison with the number of overall DEA studies carried out. In this dissertation both DEA and bootstrap DEA are applied to two gold mining cross sectional samples, one on Zimbabwe consisting of thirty-four mines, and the an international one which also included some Zimbabwean mines which comprise fifty-nine observations.The main reason for carrying out the study is an interest in gold mining in general and its importance to Zimbabwe in particular. As will be noted in Chapter 2, the economic development of Zimbabwe has been linked, to a varying extent over the ages, to its growth of the gold mining sector.The results of the dissertation provide some useful insights into the relative performances of gold mines and also some characteristics of the Zimbabwean gold mining sector. The main results indicate that gold mining is characterised mainly by technical efficiency dominating scale efficiency. This is particular relevant when the Zimbabwean mines are compared with their international counterparts. Zimbabwean mines are found to be relatively technically efficient but less so when overall efficiency is considered. In fact they have the lowest overall efficiency scores in the international sample. The results also indicate that mines from the so-called developed mining economies, Australia, Canada, the US and South Africa are the benchmarks in terms of optimal operations. It is mines from these countries which define the overall efficiency frontier.The results of both the samples highlight potential shortcomings in applying DEA and bootstrap extension to gold mining, both for single country and for cross-country cases. Additionally, there are possibilities, with adequate data, of relating country-specific characteristics to differences in overall efficiency among countries.Finally there are indications that including mineralogical factors such as the recovery rate in the production technology has an effect on technical efficiency. Mines with low recovery rates tend to exhibit comparatively higher technical efficiency. The study does have some limitations, mainly because of lack of data. In particular, there were problems in coming with attributing the contribution of capital services to efficiency with the result that a different measure for the flow of capital services is used for each sample. In addition, the two samples are for different time periods. This limits comparative analysis

Testing the Statistical Significance of Linear Programming Estimators

Linear programming-based estimation procedures are used in a variety of arenas. Two notable areas are multiattribute utility models (LINMAP) and production frontiers (data envelopment analysis (DEA)). Both LINMAP and DEA have theoretical and managerial advantages. For example, LINMAP treats ordinal-scaled preference data as such in uncovering individual-level attribute weights, while regression treats these preferences as interval scaled. DEA produces easy-to-understand efficiency measures, which allow for improved productivity benchmarking. However, acceptance of these techniques is hindered by the lack of statistical significance tests for their parameter estimates. In this paper, we propose and evaluate such parameter significance tests. Two types of tests are forwarded. The first examines whether a model's fit is significantly reduced when an explanatory variable is deleted. The second is based on generating a standard deviation or distribution for the parameter estimate using nonparametric jackknife or bootstrap techniques. We demonstrate through simulations that both types of tests reliably identify both significant and insignificant parameters. The availability of these tests, especially the relatively simple and easy-to-use tests of the first type, should enhance the utilization of linear programming-based estimation.attribute weights, DEA, linear programming, LINMAP