Journal ArticleDOI
Productive Efficiency under Capitalism and State Socialism: An Empirical Inquiry Using Chance-Constrained Data Envelopment Analysis
TLDR
In this article, the authors employ the techniques of chance-constrained data envelopment analysis to conduct a comparison of the productive efficiency of a set of West European market economies and East European planned economies and find the market economies to have been much more efficient in their allocation of resources.About:
This article is published in Technological Forecasting and Social Change.The article was published on 1994-06-01. It has received 71 citations till now. The article focuses on the topics: Economic efficiency & Productive efficiency.read more
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Data envelopment analysis (DEA) - Thirty years on
Wade D. Cook,Lawrence M. Seiford +1 more
TL;DR: A sketch of some of the major research thrusts in data envelopment analysis (DEA) over the three decades since the appearance of the seminal work of Charnes et al. is provided.
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Chapter 13 Satisficing DEA models under chance constraints
TL;DR: DEA (Data Envelopment Analysis) models and concepts are formulated here in terms of the "P-Models" of Chance Constrained Programming, which are modified to contact the "satisficing concepts" of H.A. Simon, adding as a third category to the efficiency/inefficiency dichotomies that have heretofore prevailed in DEA.
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Chance Constrained Programming Formulations for Stochastic Characterizations of Efficiency and Dominance in DEA
TL;DR: In this article, the P-model of chance constrained programming is extended to stochastic inputs and outputs via probabilistic input-output vector comparisons in a given empirical production (possibility) set.
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Chance constrained programming approaches to congestion in stochastic data envelopment analysis
TL;DR: It is shown to be possible to avoid some of the need for dealing with non-linear problems of congestion in DEA by identifying conditions under which they can be replaced by ordinary (deterministic) DEA models.
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Stochastic models and variable returns to scales in data envelopment analysis
TL;DR: The stochastic efficiency measure of a decision-making unit (DMU) is defined via joint probabilistic comparisons of inputs and outputs with other DMUs, and can be characterized by solving a chance constrained programming problem as mentioned in this paper.
References
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Measuring the efficiency of decision making units
TL;DR: 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 and methods for objectively determining weights by reference to the observational data for the multiple outputs and multiple inputs that characterize such programs.
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Chance-Constrained Programming
TL;DR: The paper presents a method of attack which splits the problem into two non-linear or linear programming parts, i determining optimal probability distributions, ii approximating the optimal distributions as closely as possible by decision rules of prescribed form.
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Recent developments in dea : the mathematical programming approach to frontier analysis
TL;DR: In this paper, the authors discuss the mathematical programming approach to frontier estimation known as Data Envelopment Analysis (DEA) and examine the effect of model orientation on the efficient frontier.
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Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions
TL;DR: The construction and analysis of Pareto-efficient frontier production functions by a new Data Envelopment Analysis method is presented in the context of new theoretical characterizations of the inherent structure and capabilities of such empirical production functions.