Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in taiwan

A survey of DEA applications

Abstract: The literature of data envelopment analysis (DEA) encompasses many surveys, yet all either emphasize methodologies or do not make a distinction between methodological and application papers. This study is the first literature survey that focuses on DEA applications, covering DEA papers published in journals indexed by the Web of Science database from 1978 through August 2010. The results show that on the whole around two-thirds (63.6%) of DEA papers embed empirical data, while the remaining one-third are purely-methodological. Purely-methodological articles dominated the first 20 years of DEA development, but the accumulated number of application-embedded papers caught up to purely-methodological papers in 1999. Among the multifaceted applications, the top-five industries addressed are: banking, health care, agriculture and farm, transportation, and education. The applications that have the highest growth momentum recently are energy and environment as well as finance. In addition to the basic statistics, we uncover the development trajectory in each application area through the main path analysis. An observation from these works suggests that the two-step contextual analysis and network DEA are the recent trends across applications and that the two-step contextual analysis is the prevailing approach.

Additive efficiency decomposition in two-stage DEA

Abstract: Kao and Hwang (2008) [Kao, C., Hwang, S.-N., 2008. Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research 185 (1), 418–429] develop a data envelopment analysis (DEA) approach for measuring efficiency of decision processes which can be divided into two stages. The first stage uses inputs to generate outputs which become the inputs to the second stage. The first stage outputs are referred to as intermediate measures. The second stage then uses these intermediate measures to produce outputs. Kao and Huang represent the efficiency of the overall process as the product of the efficiencies of the two stages. A major limitation of this model is its applicability to only constant returns to scale (CRS) situations. The current paper develops an additive efficiency decomposition approach wherein the overall efficiency is expressed as a (weighted) sum of the efficiencies of the individual stages. This approach can be applied under both CRS and variable returns to scale (VRS) assumptions. The case of Taiwanese non-life insurance companies is revisited using this newly developed approach.

Data envelopment analysis 1978-2010: A citation-based literature survey

Abstract: This study surveys the data envelopment analysis (DEA) literature by applying a citation-based approach. The main goals are to find a set of papers playing the central role in DEA development and to discover the latest active DEA subareas. A directional network is constructed based on citation relationships among academic papers. After assigning an importance index to each link in the citation network, main DEA development paths emerge. We examine various types of main paths, including local main path, global main path, and multiple main paths. The analysis result suggests, as expected, that Charnes et al. (1978) [Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978; 2(6): 429–444] is the most influential DEA paper. The five most active DEA subareas in recent years are identified; among them the “two-stage contextual factor evaluation framework” is relatively more active. Aside from the main path analysis, we summarize basic statistics on DEA journals and researchers. A growth curve analysis hints that the DEA literature’s size will eventually grow to at least double the size of the existing literature.

Efficiency decomposition in network data envelopment analysis: A relational model

Abstract: Traditional studies in data envelopment analysis (DEA) view systems as a whole when measuring the efficiency, ignoring the operation of individual processes within a system. This paper builds a relational network DEA model, taking into account the interrelationship of the processes within the system, to measure the efficiency of the system and those of the processes at the same time. The system efficiency thus measured more properly represents the aggregate performance of the component processes. By introducing dummy processes, the original network system can be transformed into a series system where each stage in the series is of a parallel structure. Based on these series and parallel structures, the efficiency of the system is decomposed into the product of the efficiencies of the stages in the series and the inefficiency slack of each stage into the sum of the inefficiency slacks of its component processes connected in parallel. With efficiency decomposition, the process which causes the inefficient operation of the system can be identified for future improvement. An example of the non-life insurance industry in Taiwan illustrates the whole idea.

Measuring performance of two-stage network structures by DEA: A review and future perspective

Abstract: Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). An important area of development in recent years has been devoted to applications wherein DMUs represent two-stage or network processes. One particular subset of such processes is those in which all the outputs from the first stage are the only inputs to the second stage. The current paper reviews these models and establishes relations among various approaches. We show that all the existing approaches can be categorized as using either Stackelberg (leader-follower), or cooperative game concepts. Future perspectives and challenges are discussed.

Measuring the efficiency of decision making units

Abstract: A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs. A scalar measure of the efficiency of each participating unit is thereby provided, along with methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs. Equivalences are established to ordinary linear programming models for effecting computations. The duals to these linear programming models provide a new way for estimating extremal relations from observational data. Connections between engineering and economic approaches to efficiency are delineated along with new interpretations and ways of using them in evaluating and controlling managerial behavior in public programs.

Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis

Abstract: In management contexts, mathematical programming is usually used to evaluate a collection of possible alternative courses of action en route to selecting one which is best. In this capacity, mathematical programming serves as a planning aid to management. Data Envelopment Analysis reverses this role and employs mathematical programming to obtain ex post facto evaluations of the relative efficiency of management accomplishments, however they may have been planned or executed. Mathematical programming is thereby extended for use as a tool for control and evaluation of past accomplishments as well as a tool to aid in planning future activities. The CCR ratio form introduced by Charnes, Cooper and Rhodes, as part of their Data Envelopment Analysis approach, comprehends both technical and scale inefficiencies via the optimal value of the ratio form, as obtained directly from the data without requiring a priori specification of weights and/or explicit delineation of assumed functional forms of relations between inputs and outputs. A separation into technical and scale efficiencies is accomplished by the methods developed in this paper without altering the latter conditions for use of DEA directly on observational data. Technical inefficiencies are identified with failures to achieve best possible output levels and/or usage of excessive amounts of inputs. Methods for identifying and correcting the magnitudes of these inefficiencies, as supplied in prior work, are illustrated. In the present paper, a new separate variable is introduced which makes it possible to determine whether operations were conducted in regions of increasing, constant or decreasing returns to scale in multiple input and multiple output situations. The results are discussed and related not only to classical single output economics but also to more modern versions of economics which are identified with "contestable market theories."

Applied nonparametric statistics

Abstract: Applied nonparametric statistics , Applied nonparametric statistics , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Applied nonparametric statistics

Abstract: Applied nonparametric statistics , Applied nonparametric statistics , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

"Data Envelopment Analysis: Theory, Methodology, and Applications"

Abstract: Preface G. Kozmetsky. Part I: Concepts, Models & Computation. 1. Introduction. 2. Basic DEA Models. 3. Extensions to DEA Models. 4. Computational Aspects of DEA A. Iqbal Ali. 5. DEA Software Packages. Part II: Novel Applications. 6. Evaluating the Impacts of Operating Strategies on Efficiency in the U.S. Airline Industry R.D. Banker, H.H. Johnston. 7. Analyzing Technical and Allocative Efficiency of Hospitals P. Byrnes, V. Valdmanis. 8. A Multi Period Analysis of Market Segments and Brand Efficiency in the Competitive Carbonated Beverage Industry A. Charnes, W.w. Cooper, B. Golanyi, F.Y. Phillips, J.J. Rousseau. 9. Exploring why Some Physicians' Hospital Practices are More Efficient: Taking DEA Inside the Hospital J. Chilingerian. 10. On the Measurement and Monitoring of Relative Efficiency of Highway Maintenance Patrols W.D. Cook, A. Kazakov, Y. Roll. 11. Strategic Leaders in the U.S. Brewing Industry: a Longitudinal Analysis of Outliers D. Day, A.Y. Lewin, R. Salazar, Hongyu Li. 12. A Spatial Efficiency Framework for the Support of Locational Decision A. Desai, K. Haynes, J. Storbeck. 13. Productivity Developments in Swedish Hospitals: a Malmquist Output Index Approach R. Fare, S. Grosskopf, B. Lindgren, P. Roos. 14. Ownership Type, Property Rights and Relative Efficiency G. Ferrier. 15. A Comparative Analysis of Ferry Transport in Norway F.R. Forsund, E. Hernaes. 16. Incorporating Standards via Data Envelopment Analysis B. Golany, Y. Roll. 17.Stratified Models of Education Production Using Modified DEA and Regression Analysis C.A. Knox Lovell, L.C. Walters, L.L. Woods. 18. The Problems of New and Disappearing Commodities in the Construction of Price Indexes C.A. Knox Lovell, K.D. Zieschang. 19. Evaluating the Relative Efficiency of Baseball Players? M.J. Mazur. 20. Sensitivity Analysis of Efficiency Measures with Applications to Kansas Farming and Illinois Coal Mining R. Thompson, P.S. Dharmapala, R.M. Thrall. Part III: Epilogue: Process and Bibliography. 21. The DEA Process, Usages and Interpretations. 22. DEA Bibliography L.M. Seiford. References. Index.