Sensitivity analysis in models of data envelopment analysis

Sensitivity analysis in Data Envelopment Analysis (DEA) is studied in this paper for the Charnes-Cooper-Rhodes (CCR) ratio model and for the additive model. Different cases of additive or proportionate changes of inputs or/and of outputs of an efficient Decision Making Unit (DMU) according to the CCR model or according to the additive model are considered. Sufficient conditions for an efficient DMU to preserve its efficiency after the corresponding changes of its inputs or/and of outputs are presented for these cases. Similar results for arbitrary (or nonnegative) additive perturbations of data of all DMUs in the additive model are described, too

Creating Composite Indicators with DEA and Robustness Analysis: the case of the Technology Achievement Index

Composite indicators are regularly used for benchmarking countries’ performance, but equally often stir controversies about the unavoidable subjectivity that is connected with their construction. Data Envelopment Analysis helps to overcome some key limitations, viz., the undesirable dependence of final results from the preliminary normalization of sub-indicators, and, more cogently, from the subjective nature of the weights used for aggregating. Still, subjective decisions remain, and such modelling uncertainty propagates onto countries’ composite indicator values and relative rankings. Uncertainty and sensitivity analysis are therefore needed to assess robustness of final results and to analyze how much each individual source of uncertainty contributes to the output variance. The current paper reports on these issues, using the Technology Achievement Index as an illustration.factor is more important in explaining the observed progress.composite indicators, aggregation, weighting, Internal Market

Creating composite indicators with DEA and robustness analysis: The case of the technology achievement index.

Composite indicators are regularly used for benchmarking countries’ performance, but equally often stir controversies about the unavoidable subjectivity that is connected with their construction. Data Envelopment Analysis helps to overcome some key limitations, viz., the undesirable dependence of final results from the preliminary normalization of sub-indicators, and, more cogently, from the subjective nature of the weights used for aggregating. Still, subjective decisions remain, and such modelling uncertainty propagates onto countries’ composite indicator values and relative rankings. Uncertainty and sensitivity analysis are therefore needed to assess robustness of final results and to analyze how much each individual source of uncertainty contributes to the output variance. The current paper reports on these issues, using the Technology Achievement Index as an illustration.Indexes; Indicators; Robustness; Technology;

Scale and Technical Efficiency of Islamic Banks in Sudan: Data Envelopment Analysis

This paper employs several efficiency measures and productivity changes using Data Envelopment Analysis (DEA) to investigate efficiency performance of Islamic banks in Sudan. Our results indicate, among twelve banks included in our sample only two banks, (the largest bank in the group which is government owned, and middle sized, private bank), score technical efficiency level (i.e. scale and pure technical efficiency). While the smallest bank in the group (private owned), score pure technical efficiency (i.e., managerial efficiency), but scale inefficient. This result adds additional evidence to the existing literature that ownership (government versus private) is not a constraint of managerial and scale efficiency but bank’s size is important factor for scale efficiency.DEA;Banks efficiency;scale efficiency

New tools for dealing with errors-in-variables in DEA.

Errors in variables; Tool;

New Tools for Dealing with Errors-in-Variables in DEA.

We develop a series of novel conceptual tools to systematically account for errors-in-variables in Data Envelopment Analysis (DEA). These tools allow for statistical inference while requiring minimal statistical distribution assumptions, and therefore constitute a valuable addition to the tools currently available for dealing with errors-in-variables. An empirical application for large European Union financial institutions illustrates the proposed approach.Data Envelopment Analysis (DEA), errors-in-variables, efficiency depth, robust reference sets, financial institutions

Hospital Efficiency: An Empirical Analysis of District and Grant-in-Aid Hospitals in Gujarat

This study focuses on analysing the hospital efficiency of district level government hospitals and grant-in-aid hospitals in Gujarat. The study makes an attempt to provide an overview of the general status of the health care services provided by hospitals in the state of Gujarat in terms of their technical and allocative efficiency. One of the two thrusts behind addressing the issue of efficiency was to take stock of the state of healthcare services (in terms of efficiency) provided by grant-in-aid hospitals and district hospitals in Gujarat. The motivation behind addressing the efficiency issue is to provide empirical analysis of governments policy to provide grants to not-for-profit making institutions which in turn provide hospital care in the state. The study addresses the issue whether grant-in-aid hospitals are relatively more efficient than public hospitals. This comparison between grant-in-aid hospitals and district hospitals in terms of their efficiency has been of interest to many researchers in countries other than India, and no consensus has been reached so far as to which category is more efficient. The relative efficiency of government and not-for-profit sector has been reviewed in this paper. It is expected that the findings of the study would be useful to evaluate this policy and help policy makers to develop benchmarks in providing the grants to such institutions.