Author
Rolf Färe
Other affiliations: Southern Illinois University Carbondale, Portland State University, University of California ...read more
Bio: Rolf Färe is an academic researcher from Oregon State University. The author has contributed to research in topics: Productivity & Data envelopment analysis. The author has an hindex of 71, co-authored 382 publications receiving 32346 citations. Previous affiliations of Rolf Färe include Southern Illinois University Carbondale & Portland State University.
Papers published on a yearly basis
Papers
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TL;DR: In this article, a nonparametric programming method (activity analysis) is used to compute the Malmquist productivity indexes, which are decomposed into two component measures, namely, technical change and efficiency change.
Abstract: This paper analyzes productivity growth in 17 OECD countries over the period 1979-1988. A nonparametric programming method (activity analysis) is used to compute Malmquist productivity indexes. These are decomposed into two component measures, namely, technical change and efficiency change. We find that U.S. productivity growth is slightly higher than average, all of which is due to technical change. Japan's productivity growth is the highest in the sample, with almost half due to efficiency change. (JEL C43, D24) In this paper we apply recently developed
3,434 citations
TL;DR: In this paper, a directional distance function is used as a component in a new productivity index that readily models joint production of goods and bads, credits firms for reductions in bads and increases in goods, and does not require shadow prices of bad outputs.
2,003 citations
Book•
01 Jan 1985
TL;DR: The Structure of Production Technology as discussed by the authors, Radial Input Efficiency Measures, Hyperbolic Graph Efficiency Measures and Non-radial Efficiency Measures for Scale Efficiency and Toward Empirical Implementation.
Abstract: The Structure of Production Technology.- Radial Input Efficiency Measures.- Radial Output Efficiency Measures.- Hyperbolic Graph Efficiency Measures.- A Comparison of Input, Output, and Graph Efficiency Measures.- Nonradial Efficiency Measures.- Measures of Scale Efficiency.- Toward Empirical Implementation.
1,933 citations
Posted Content•
TL;DR: A directional distance function is introduced and used as a component in a new productivity index that readily models joint production of goods and bads, credits firms for reductions in bads and increases in goods, and does not require shadow prices of bad outputs.
Abstract: Undesirable outputs are often produced together with desirable outputs. This joint production of good and bad outputs brings about a difficulty for productivity measurement. Here we introduce a directional distance function and use it as a component in a new productivity index. This index, as an empirical example shows, seems to solve the problem caused by the joint production of good and bad outputs.
1,522 citations
Book•
31 Dec 1994
TL;DR: Theories of the Firm through duality as mentioned in this paper have been used in many applications, e.g. towards empirical applications, such as profit functions, cost functions, distance functions, and profit functions.
Abstract: List of Figures. Preface. 1. Theories of the Firm through Duality. 2. Distance Functions. 3. Cost and Revenue Functions. 4. Indirect Distance Functions. 5. Indirect Cost and Revenue Functions. 6. The Profit Function. 7. Towards Empirical Applications. References. Subject Index. Author Index.
1,259 citations
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TL;DR: 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 as mentioned in this paper.
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."
14,941 citations
Book•
30 Nov 1999TL;DR: In this article, the basic CCR model and DEA models with restricted multipliers are discussed. But they do not consider the effect of non-discretionary and categorical variables.
Abstract: List of Tables. List of Figures. Preface. 1. General Discussion. 2. The Basic CCR Model. 3. The CCR Model and Production Correspondence. 4. Alternative DEA Models. 5. Returns to Scale. 6. Models with Restricted Multipliers. 7. Discretionary, Non-Discretionary and Categorical Variables. 8. Allocation Models. 9. Data Variations. Appendices. Index.
4,395 citations
01 Jan 1994
TL;DR: In this article, the authors analyzed productivity growth in seventeen OECD countries over the period 1979-88 and found that U.S. productivity growth is slightly higher than average, all of which is due to technical change.
Abstract: This paper analyzes productivity growth in seventeen OECD countries over the period 1979-88. A nonparametric programming method (activity analysis) is used to compute Malmquist productivity indexes. These are decomposed into two component measures, namely, technical change and efficiency change. The authors find that U.S. productivity growth is slightly higher than average, all of which is due to technical change. Japan's productivity growth is the highest in the sample with almost half due to efficiency change. Copyright 1994 by American Economic Association.
3,851 citations
Posted Content•
TL;DR: In this article, a nonparametric programming method (activity analysis) is used to compute the Malmquist productivity indexes, which are decomposed into two component measures, namely, technical change and efficiency change.
Abstract: This paper analyzes productivity growth in 17 OECD countries over the period 1979-1988. A nonparametric programming method (activity analysis) is used to compute Malmquist productivity indexes. These are decomposed into two component measures, namely, technical change and efficiency change. We find that U.S. productivity growth is slightly higher than average, all of which is due to technical change. Japan's productivity growth is the highest in the sample, with almost half due to efficiency change. (JEL C43, D24) In this paper we apply recently developed
3,434 citations
TL;DR: In this paper, a modified version of DEA based upon comparison of efficient DMUs relative to a reference technology spanned by all other units is developed, which provides a framework for ranking efficient units and facilitates comparison with rankings based on parametric methods.
Abstract: Data Envelopment Analysis DEA evaluates the relative efficiency of decision-making units DMUs but does not allow for a ranking of the efficient units themselves. A modified version of DEA based upon comparison of efficient DMUs relative to a reference technology spanned by all other units is developed. The procedure provides a framework for ranking efficient units and facilitates comparison with rankings based on parametric methods.
3,320 citations