The Impact of Noisy Catch Data on Estimates of Fishing Capacity
Derived From DEA and Stochastic Frontier Models: A Monte Carlo Comparison
By S. Todd Lee and Daniel Holland
ABSTRACT
There is currently much national and international interest in measuring commercial fishing capacity. Due to data constraints, capacity measurement may be largely confined to estimates of technical capacity. Two quantitative methods that have been proposed for this purpose are data envelopment analysis (DEA) and stochastic frontier (SF) production functions. Although both methods can be used to estimate a production frontier, their underlying assumptions and method of solving for the frontier are quite different. One substantial difference is how each model handles noisy data. An understanding of the implications of this difference is important because random variation is likely to exist in commercial fishery catch data. This research uses Monte Carlo simulations to investigate possible finite sample biases in estimates of efficient output attributable to this type of noise. The results suggest that the mean bias associated with noisy data is often substantially larger for DEA than SF. However, the frequency distributions of the biases from each method show a wide variation in some cases.
KEYWORDS: Capacity, technical efficiency, data envelopment analysis, stochastic production frontier, monte carlo analysis
 Capacity: Data Envelope Analysis
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