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Identifying Technically Efficient Fishing Vessels: A Non-Empty, Minimal Subset Approach

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Abstract

There is a growing resource economics literature, concerning the estimation of the technical efficiency of fishing vessels utilizing the stochastic frontier model. In these models, vessel output is regressed on a linear function of vessel inputs and a random composed error. Using parametric assumptions on the regression residual, estimates of vessel technical efficiency are calculated as the mean of a truncated normal distribution and are often reported in a rank statistic as a measure of a captain's skill and used to estimate excess capacity within fisheries. We demonstrate analytically that these measures are potentially flawed, and extend the results of Horrace (2005) to estimate captain skill for thirty-nine vessels in the Northeast Atlantic herring fleet, based on homogeneous and heterogeneous production functions within the fleet. When homogeneous production is assumed, we find inferential inconsistencies 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.

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  • Alfonso Flores-Lagunes & William C. Horrace & Kurt E. Schnier, 2006. "Identifying Technically Efficient Fishing Vessels: A Non-Empty, Minimal Subset Approach," Center for Policy Research Working Papers 78, Center for Policy Research, Maxwell School, Syracuse University.
  • Handle: RePEc:max:cprwps:78
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    1. 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.
    2. 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.
    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. 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.
    7. William C. Horrace & Michah W. Rothbart & Yi Yang, 2020. "Technical Efficiency of Public Middle Schools in New York City," Center for Policy Research Working Papers 235, Center for Policy Research, Maxwell School, Syracuse University.

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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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    1. Socio-economics of Fisheries and Aquaculture

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