Showing papers by "William W. Cooper published in 2005"

Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References

Abstract: General Discussion.- The Basic CCR Model.- The CCR Model and Production Correspondence.- Alternative Dea Models.- Returns To Scale.- Models with Restricted Multipliers.- Discretionary, non-Discretionary and Categorical Variables.- Allocation Models.- Data Variations.- Super-Efficiency Models.

Financial Intermediary Versus Production Approach to Efficiency of Marketing Distribution Systems and Organizational Structure of Insurance Companies

Abstract: An examination of the efficiency of the marketing distribution channel and organizational structure for insurance companies is presented from a framework that views the insurer as a financial intermediary rather than as a "production entity" which produces "value added" through loss payments. Within this financial intermediary approach, solvency can be a primary concern for regulators of insurance companies, claims-paying ability can be a primary concern for policyholders, and return on investment can be a primary concern for investors. These three variables (solvency, financial return, and claims-paying ability) are considered as outputs of the insurance firm. The financial intermediary approach acknowledges that interests potentially conflict, and the strategic decision makers for the firm must balance one concern versus another when managing the insurance company. Accordingly, we investigate the efficiency of insurance companies using data envelopment analysis (DEA) having as insurer output an appropriately selected (for the firm under investigation) combination of solvency, claims-paying ability, and return on investment as outputs. These efficiency evaluations are further examined to study stock versus mutual form of organizational structure and agency versus direct marketing arrangements, which are examined separately and in combination. Comparisons with the "value-added" or "production" approach to insurer efficiency are presented. A new DEA approach and interpretation is also presented. INTRODUCTION This article uses the nonparametric properties of data envelopment analysis (DEA) coupled with distribution-free rank-order statistics to study the relative efficiency of the different organizational structures used by U.S. property and liability insurance companies (cross classified by their marketing distribution systems). Additionally, this article extends the interpretation of DEA toward a goal-directing technique with the goals as outputs rather than simply having a "product" as an output. This provides another focus and interpretation for DEA analysis in the insurance literature. We also use a form of DEA (the Range-Adjusted Measure, or RAM, model), new to the insurance literature, which is able to provide ordinal level efficiency scoring that allows for subsequent nonparametric statistical analysis such as regression, rank statistical analysis, etc. to be performed incorporating efficiency score as an explanatory variable in subsequent analysis. (1) We dichotomize our results by organizational form into mutual versus stock companies to examine whether these two organizational structures might have differential managerial strategic focus in terms of goals, and have different efficiency and slack variables when using solvency propensity, return on investment, and claims-paying ability as output goals. One might expect potential differences in efficiency between stock and mutual insurers due to the different incentive structures inherent in the two types of organizational forms; in stock companies return on shareholder investment dominates incentives, whereas solvency and claims-paying ability considerations can dominate considerations of mutual insurance company decision makers. Possible efficiency differences between mutual and stock types of organization are intrinsically intertwined with the use of the agency versus direct sales type of marketing distribution systems (2) and these dichotomies are also correlated to emphasis in commercial versus personal lines of insurance. Finally, the differences that can occur by using different DEA formulations (production approach considering losses as the output versus the financial intermediary approach of this article) are explored and discussed. THE RAM DEA MODEL (3) There is a theoretical problem in using DEA efficiency numbers from the standard CCR or BCC models for subsequent statistical analysis because, while DEA evaluates the efficiency of each firm, the comparison set for each firm may be different producing potentially nonmetric level data. …

A note extending Grosskopf, Hayes, Taylor and Weber, "Anticipating the consequences of school reform: A new use of DEA"

Abstract: The article cited in the current paper's title shows how to extend Data Envelopment Analysis (DEA) to evaluate future, as well as past and present, performance. This is accomplished by relaxing some of the constraints that reflect the presence of imposed regulations, and then re-computing the evaluations under these alternate conditions. We here extend this approach to account for “long-run” and “short-run” behaviors, and the related bodies of theory that play prominent roles in microeconomics.

Financial Intermediary Versus Production Approach to Efficiency of Marketing Distribution Systems and Organizational Structure of Insurance Companies

Abstract: An examination of the efficiency of the marketing distribution channel and organizational structure for insurance companies is presented from a framework that views the insurer as a financial intermediary rather than as a "production entity" which produces "value added" through loss payments. Within this financial intermediary approach, solvency can be a primary concern for regulators of insurance companies, claims-paying ability can be a primary concern for policyholders, and return on investment can be a primary concern for investors. These three variables (solvency, financial return, and claims-paying ability) are considered as outputs of the insurance firm. The financial intermediary approach acknowledges that interests potentially conflict, and the strategic decision makers for the firm must balance one concern versus another when managing the insurance company. Accordingly, we investigate the efficiency of insurance companies using data envelopment analysis (DEA) having as insurer output an appropriately selected (for the firm under investigation) combination of solvency, claims-paying ability, and return on investment as outputs. These efficiency evaluations are further examined to study stock versus mutual form of organizational structure and agency versus direct marketing arrangements, which are examined separately and in combination. Comparisons with the "value-added" or "production" approach to insurer efficiency are presented. A new DEA approach and interpretation is also presented.

DEA: Past Accomplishments and Future Prospects

Abstract: This paper covers some of the past accomplishments of DEA and its future prospects. It starts with the engineering-science definitions of efficiency and uses the duality theory of linear programming to show how, in DEA, they can be related to the Pareto-Koopmens definitions used in welfare economics. Some of the models that have now been developed for implementing these concepts are then described and properties of these models are examined for weaknesses and strengths along with different measures of distance that may be used to determine their optimal values. Relations between the models are also demonstrated en route to delineating paths for future developments. These include extensions to different objectives such as satisfactory vs. full (or strong) efficiency. They also include extensions from efficiency to effectiveness evaluations of performances as well as extensions to evaluate social-economic performances of countries and other entities where inputs and outputs give way to other categories in which increases and decreases are located in the numerator or denominator of the ratio (=engineering-science) definition of efficiency in a manner analogous to the way output (in the numerator) and input (in the denominator) are usually positioned in the fractional programming form of DEA. Beginnings in each of these extensions are cited and the role of applications in bringing further possibilities to the fore is highlighted.