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Identifying technically efficient fishing vessels: a non-empty, minimal subset approach

Author

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  • Alfonso Flores-Lagunes

    (Department of Economics, University of Arizona, Tucson, Arizona, USA)

  • William C. Horrace

    (Center for Policy Research, Syracuse University, Syracuse, New York, USA)

  • Kurt E. Schnier

    (Department of Environmental and Natural Resource Economics, University of Rhode Island, Kingston, Rhode Island, USA)

Abstract

Stochastic frontier models are often employed to estimate fishing vessel technical efficiency. Under certain assumptions, these models yield efficiency measures that are means of truncated normal distributions. We argue that these measures are flawed, and use the results of Horrace (2005) to estimate efficiency for 39 vessels in the Northeast Atlantic herring fleet, based on each vessel's probability of being efficient. We develop a subset selection technique to identify groups of efficient vessels at pre-specified probability levels. When homogeneous production is assumed, inferential inconsistencies exist between our methods and the methods of ranking the means of the technical inefficiency distributions for each vessel. When production is allowed to be heterogeneous, these inconsistencies are mitigated. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Alfonso Flores-Lagunes & William C. Horrace & Kurt E. Schnier, 2007. "Identifying technically efficient fishing vessels: a non-empty, minimal subset approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 729-745.
    • Handle: RePEc:jae:japmet:v:22:y:2007:i:4:p:729-745
    DOI: 10.1002/jae.942
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    Cited by:

    1. William C. Horrace & Hyunseok Jung & Yi Yang, 2023. "The conditional mode in parametric frontier models," Journal of Productivity Analysis, Springer, vol. 60(3), pages 333-343, December.
    2. William Horrace & Seth Richards-Shubik & Ian Wright, 2015. "Expected efficiency ranks from parametric stochastic frontier models," Empirical Economics, Springer, vol. 48(2), pages 829-848, March.
    3. William C. Horrace & Christopher F. Parmeter, 2018. "A Laplace stochastic frontier model," Econometric Reviews, Taylor & Francis Journals, vol. 37(3), pages 260-280, March.
    4. William C. Horrace & Christopher F. Parmeter, 2017. "Accounting for Multiplicity in Inference on Economics Journal Rankings," Southern Economic Journal, John Wiley & Sons, vol. 84(1), pages 337-347, July.
    5. Ronald Felthoven & William Horrace & Kurt Schnier, 2009. "Estimating heterogeneous capacity and capacity utilization in a multi-species fishery," Journal of Productivity Analysis, Springer, vol. 32(3), pages 173-189, December.
    6. Horrace, William C. & Rothbart, Michah W. & Yang, Yi, 2022. "Technical efficiency of public middle schools in New York City," Economics of Education Review, Elsevier, vol. 86(C).
    7. Abbott, Joshua K. & Wilen, James E., 2009. "Regulation of fisheries bycatch with common-pool output quotas," Journal of Environmental Economics and Management, Elsevier, vol. 57(2), pages 195-204, March.
    8. Phill Wheat & William Greene & Andrew Smith, 2014. "Understanding prediction intervals for firm specific inefficiency scores from parametric stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 42(1), pages 55-65, August.

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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