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Feasible Estimation of Firm-Specific Allocative Inefficiency through Bayesian Numerical Methods

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  • Atkinson, Scott E.
  • Dorfman, Jeffrey H.

Abstract

The estimation of allocative and technical inefficiency has grown to an enormous body of literature, both theoretical and empirical. Ideally, one would estimate time-varying firm and input-specific parameters describing allocative inefficiency in order to minimize aggregation bias. However, this has never been previously accomplished. Typically, only industry-wide allocative efficiency parameters have been empirically identified. Our proposed solution is to employ Gibbs sampling to approximate posterior distributions from a Bayesian limited information model, embedding GMM moment conditions imposed via an instrumental variables step to obtain plant-specific parameters estimates that vary flexibly over time. For a panel of Chilean hydroelectric power plants, posterior distributions of these estimates display substantial differences in location and precision. By contrast, the standard GMM approach which produces industry-wide, time-varying allocative inefficiency parameters, not only fails to reveal the inter-plant differences by construction, but does not even produce posterior medians that approximate a weighted average of the plant-specific posterior medians.

Suggested Citation

  • Atkinson, Scott E. & Dorfman, Jeffrey H., 2005. "Feasible Estimation of Firm-Specific Allocative Inefficiency through Bayesian Numerical Methods," 2005 Annual meeting, July 24-27, Providence, RI 19402, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea05:19402
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    Cited by:

    1. Kerstens, Kristiaan & Van de Woestyne, Ignace, 2014. "Comparing Malmquist and Hicks–Moorsteen productivity indices: Exploring the impact of unbalanced vs. balanced panel data," European Journal of Operational Research, Elsevier, vol. 233(3), pages 749-758.
    2. Bruno De Borger & Kristiaan Kerstens & Diego Prior & Ignace Van de Woestyne, 2013. "Static efficiency decompositions and capacity utilization: integrating economic and technical capacity notions," Applied Economics, Taylor & Francis Journals, vol. 45(24), pages 3529-3529, August.
    3. Tsionas, Efthymios & Assaf, A. George & Gillen, David & Mattila, Anna S., 2017. "Modeling technical and service efficiency," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 113-125.
    4. Briec, Walter & Kerstens, Kristiaan & Prior, Diego & Van de Woestyne, Ignace, 2010. "Tangency capacity notions based upon the profit and cost functions: A non-parametric approach and a general comparison," Economic Modelling, Elsevier, vol. 27(5), pages 1156-1166, September.
    5. Oum, Tae H. & Yan, Jia & Yu, Chunyan, 2008. "Ownership forms matter for airport efficiency: A stochastic frontier investigation of worldwide airports," Journal of Urban Economics, Elsevier, vol. 64(2), pages 422-435, September.
    6. Walter Briec & Kristiaan Kerstens & Ignace Van de Woestyne, 2013. "Nonparametric cost and revenue functions under constant economies of scale: An enumeration approach for the single output or input case," Working Papers 2013-ECO-22, IESEG School of Management.
    7. Briec, Walter & Kerstens, Kristiaan & Van de Woestyne, Ignace, 2011. "Nonparametric cost and revenue functions under constant economies of scale: A simplification for the single output or input case," Working Papers 2011/12, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.

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