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Efficiency Selection Procedures for Capacity Utilization Estimation

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Festschrift in Honor of Peter Schmidt

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Abstract

A production function is estimated for a sample of vessels in the U.S. Bering Sea flatfish fishery. Panel data identify vessel-specific individual effects, which are estimates of unobserved vessel efficiency. These efficiency effects are ranked, and subset selection procedures determine tiers of vessels differentiated by efficiency rank. Maximal efficiency is then estimated within each tier. Tier-level maximal efficiency is used to estimate capacity and capacity utilization within the fishery.

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Notes

  1. 1.

    This presumes the existence of technical inefficiency, where different vessels employing the same inputs, technology, and ‘luck’ may have different output.

  2. 2.

    Numerical approximation of critical values is also an option. See Hsu 1996, Sect. 7.2.1 and Appendix A.

  3. 3.

    Since the differences in the fixed-effects are the usual measure of inefficiencies (see Schmidt and Sickles 1984), inefficiency in the sole source of production heterogeneity in this simple model.

  4. 4.

    Our purpose here is not to discuss specification issues, but to demonstrate a unique application of selection procedures, so for parsimony we consider only the most restrictive specification in Eq. (12.1).

  5. 5.

    It is also possible to calculate aggregate or average maximal output over the entire period, but the annual calculation seems more intuitive, particularly if interest centers on how capacity utilization changes over time.

  6. 6.

    This will be true if, say, the errors v it are normal. Relaxing this assumption is not beyond the realm of possibilities. In particular bootstrapping the procedure seems like a promising alternative. However, we leave that for future research.

  7. 7.

    It would be useful to develop a procedure that automatically controls for the error rate. For large J the Bonferroni inequality will be too conservative and the procedure will not work well.

  8. 8.

    Note that this is not an exercise in modeling and estimating heterogeneous frontiers for a single fishery; we are simply proposing an alternative estimator of potential output that incorporates statistical noise though the error rate and that nests the usual estimate.

  9. 9.

    We would like to thank an anonymous referee for pointing out this alternative interpretation of γ.

  10. 10.

    An unbalanced panel has t = 1,.., T i for every i, and introduces estimation computational complexities that are beyond the scope of this exercise.

  11. 11.

    In practice a capacity utilization exercise might include all vessels, but then specification of the production function becomes difficult. This is beyond the scope of this exercise.

  12. 12.

    An alternative specification would explicitly account for different species. See, for example Orea et~al. (2005) or Felthoven (2002).

  13. 13.

    For maximal and potential output estimation, the net-tons coefficient is set to zero.

  14. 14.

    Maximal output results are available from the authors. Standard errors on the capacity measures could be bootstrapped without much complication, but we do not attempt that here.

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Author information

Authors and Affiliations

  1. Department of Economics, Syracuse University, 426 Eggers Hall, Syracuse, NY, 13244, USA

    William C. Horrace

  2. Department of Economics, Andrew Young School of Policy Studies, Georgia State University, 438 Andrew Yong School, 14 Marietta St NW, Atlanta, GA, 30303, USA

    Kurt E. Schnier

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  1. William C. Horrace
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Correspondence to William C. Horrace .

Editor information

Editors and Affiliations

  1. Department of Economics, Rice University, Houston, Texas, USA

    Robin C. Sickles

  2. Department of Economics, Syracuse University, Syracuse, New York, USA

    William C. Horrace

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Horrace, W.C., Schnier, K.E. (2014). Efficiency Selection Procedures for Capacity Utilization Estimation. In: Sickles, R., Horrace, W. (eds) Festschrift in Honor of Peter Schmidt. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-8008-3_12

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  • DOI: https://doi.org/10.1007/978-1-4899-8008-3_12

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