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Measuring and Decomposing Productivity Change: Stochastic Distance Function Estimation VS. DEA

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  • Lastrapes, W.D.
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    Abstract

    Linear programming techniques have been widely used to compute Malmquist indices of productivity change as ratios of fitted distances from a convex hull frontier. These indices are then decomposed into technical and efficiency change. However, since this approach is non-stochastic, inference is problematic. Further, although the Malmquist index is valid for any degree of returns to scale, productivity change is measured relative to a constant returns to scale frontier. As an alternative, we propose a exible, stochastic input distance frontier which allows for statistical inference and imposes no restrictions on returns to scale. Using this distance frontier, we decompose productivity change into technical and efficiency change. Comparisons are drawn between the stochastic and non-stochastic methods based on a panel of electric utilities. We estimate our model by the generalized method of moments with a variety of instrument sets to gauge the sensitivity of productivity change calculations to changes in the underlying moment conditions.

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    Bibliographic Info

    Paper provided by Georgia - College of Business Administration, Department of Economics in its series Papers with number 99-478.

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    Length: 24 pages
    Date of creation: 2000
    Date of revision:
    Handle: RePEc:fth:georec:99-478

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    Postal: U.S.A.; The University of Georgia, College of Business Administration, Department of Economics, Athens, GA 30602
    Web page: http://www.terry.uga.edu/
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    Related research

    Keywords: TECHNICAL CHANGE ; EVALUATION ; ECONOMIC MODELS;

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    Cited by:
    1. Dorfman, Jeffrey H. & Atkinson, Scott E., 2002. "Multiple Comparisons With The Best: Bayesian Precision Measures Of Efficiency Rankings," 2002 Annual meeting, July 28-31, Long Beach, CA 19800, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

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