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Identifying technically efficient fishing vessels: a non‐empty, minimal subset approach
Author(s) -
FloresLagunes Alfonso,
Horrace William C.,
Schnier Kurt E.
Publication year - 2007
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.942
Subject(s) - inefficiency , ranking (information retrieval) , selection (genetic algorithm) , production (economics) , econometrics , yield (engineering) , fishing , computer science , homogeneous , economics , mathematics , fishery , artificial intelligence , microeconomics , biology , materials science , combinatorics , metallurgy
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.