Identifying technically efficient fishing vessels: a non-empty, minimal subset approach (replication data)

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 estimat...

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Bibliographic Details
Main Authors: Flores-Lagunes, Alfonso, Horrace, William C., Schnier, Kurt E.
Format: Other/Unknown Material
Language:English
Published: ZBW - Leibniz Informationszentrum Wirtschaft 2007
Subjects:
Online Access:https://doi.org/10.15456/jae.2022319.0715027961
Description
Summary: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.