By Harold O. Fried, Shelton S. Schmidt, C. A. Knox Lovell
This paintings specializes in measuring and explaining manufacturer functionality. The authors view functionality as a functionality of the kingdom of know-how and fiscal potency, with the previous defining a frontier relation among inputs and outputs; the previous incorporating waste and misallocation relative to this frontier. They express that insights will be won through taking into consideration the potential of a divergence among the commercial aim and genuine functionality, and by means of associating this inefficiency with causal variables topic to managerial or coverage effect. Derived from a chain of lectures hung on suggestions and functions of the 3 techniques to the development of construction frontiers and degree of potency, this paintings could be a vital connection with students of various disciplines who're concerned with quantitative equipment or coverage.
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Additional resources for The Measurement of Productive Efficiency: Techniques and Applications
Sample text
If producers seek maximum profit, then the inequalities <-"*-ij are parameterized as and a host of hypotheses can be tested. Cost minimization for producer i is tested by testing the hypothesis that Bij = Bik for j, k = 1 , . . , n. Profit maximization for producer i is tested by testing the hypothesis that B^ = Bik = 1 for j, A: = 1 , . . , n. Equal relative and absolute allocative efficiency with respect to the cost minimization and the profit maximization goals across producers are tested in an analogous fashion.
Producers are assumed to use inputs x e R+ to produce outputs y e R™, for sale at prices p e -R++. Their objective is assumed to be the maximization of revenue, subject to the constraints imposed by output prices, input supplies, and the structure of production technology. Assuming constant returns to scale, strong disposability of inputs and outputs, and convexity, all of which can be relaxed, this problem has linear programming structure subject to The revenue maximization problem is solved separately for each producer in the sample.
This stochastic goal programming model is structurally similar to an "asymmetric criterion funtion" approach to the econometric estimation of production frontiers developed by Aigner, Amemiya, and Poirier (1976). It has been compared to the stochastic DBA model discussd above by Retzlaff-Roberts and Morey (1991). 1 are all enveloping techniques. Each of them treats technical efficiency in terms of distance to a production frontier, economic efficiency in terms of distance to an appropriate economic frontier, and allocative efficiency as a ratio of economic efficiency to technical efficiency.