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Technical efficiency of Connecticut Long Island Sound lobster fishery: a nonparametric approach to aggregate frontier analysis

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Listed:
  • Lei Chen

    (Jianghan University)

  • Rangan Gupta

    (University of Pretoria)

  • Zinnia Mukherjee

    (Simmons College)

  • Peter Wanke

    (Federal University of Rio de Janeiro)

Abstract

In this paper, we address the question whether the technical efficiency of a fishing industry is affected by the determinants of ambient water quality of the aquatic ecosystem. Using zone-specific data from 1998 to 2007 for the Connecticut Long Island Sound lobster fishery and an approach combining a bootstrapping technique with data envelopment analysis, we obtained the DEA estimates of technical efficiency for each fishing zone. We then used the bootstrapped DEA results and censored quantile regression to assess the impact of the environmental variables on different efficiency percentiles. A key result indicates when environmental conditionals are favorable (high dissolved oxygen levels), efficiency is low, and when environmental conditionals are less favorable (high levels of nitrogen), efficiency is high. The results show that the intensity of significant impacts given the contextual variables may vary among high- and low-efficiency periods.

Suggested Citation

  • Lei Chen & Rangan Gupta & Zinnia Mukherjee & Peter Wanke, 2016. "Technical efficiency of Connecticut Long Island Sound lobster fishery: a nonparametric approach to aggregate frontier analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1533-1548, April.
  • Handle: RePEc:spr:nathaz:v:81:y:2016:i:3:d:10.1007_s11069-015-2144-5
    DOI: 10.1007/s11069-015-2144-5
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    1. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
    2. Tom Kompas & Tuong Nhu Che & R. Quentin Grafton, 2004. "Technical efficiency effects of input controls: evidence from Australia's banana prawn fishery," Applied Economics, Taylor & Francis Journals, vol. 36(15), pages 1631-1641.
    3. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
    4. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    5. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    6. M. Zarepisheh & E. Khorram & G. Jahanshahloo, 2010. "Returns to scale in multiplicative models in data envelopment analysis," Annals of Operations Research, Springer, vol. 173(1), pages 195-206, January.
    7. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    8. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    9. James E. Kirkley & Dale Squires & Ivar E. Strand, 1995. "Assessing Technical Efficiency in Commercial Fisheries: The Mid-Atlantic Sea Scallop Fishery," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(3), pages 686-697.
    10. Niels Vestergaard & Dale Squires & Frank Jensen & Jesper L. Andersen, 2002. "Technical Efficiency of the Danish Trawl fleet: Are the Industrial Vessels Better than Others?," Working Papers 32/02, University of Southern Denmark, Department of Sociology, Environmental and Business Economics.
    11. Curi, Claudia & Gitto, Simone & Mancuso, Paolo, 2011. "New evidence on the efficiency of Italian airports: A bootstrapped DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 45(2), pages 84-93, June.
    12. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    13. Subhash C. Ray, 2010. "A One-Step Procedure for Returns to Scale Classification of Decision Making Units in Data Envelopment Analysis," Working papers 2010-07, University of Connecticut, Department of Economics.
    14. Ferdinand D. Vinuya, 2010. "Technical efficiency of shrimp fishery in South Carolina, USA," Applied Economics Letters, Taylor & Francis Journals, vol. 17(1), pages 1-5, January.
    15. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, September.
    16. Hong H. & Chernozhukov V., 2002. "Three-Step Censored Quantile Regression and Extramarital Affairs," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 872-882, September.
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    Cited by:

    1. Mohammed Al-Siyabi & Gholam R. Amin & Shekar Bose & Hussein Al-Masroori, 2019. "Peer-judgment risk minimization using DEA cross-evaluation with an application in fishery," Annals of Operations Research, Springer, vol. 274(1), pages 39-55, March.
    2. Rangan Gupta & Zinnia Mukherjee & Mike G. Tsionas & Peter Wanke, 2016. "Productive Efficiency of Connecticut Long Island Lobster Fishery Using a Finite Mixture Model," Working Papers 201614, University of Pretoria, Department of Economics.
    3. Zhiwen Su & Mingyu Zhang & Jianjun Sun & Wenbing Wu, 2023. "Agribusiness diversification and technological innovation efficiency: A U‐shaped relationship," Agribusiness, John Wiley & Sons, Ltd., vol. 39(2), pages 322-346, March.

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    More about this item

    Keywords

    Technical efficiency; Data envelopment analysis; Censored quantile regression; Lobster; Harvest; Long Island Sound;
    All these keywords.

    JEL classification:

    • Q22 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Fishery
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics

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