Ocean policy research (해양정책연구)

Korea Maritime Institute (한국해양수산개발원)
권호 표지

An Estimation of Competitive Power of Deep-Sea Fishing Industries by Using Cross Efficiency Analysis

교차효율분석을 활용한 원양어업의 업종별 경쟁력 추정

  • Published : 2009.06.30

⟨ Previous Next ⟩

Abstract

Currently, there is a need to develop scrap programs for deep-sea fishing industry of Korea, since the business environment becomes more uncertain and competitive. Accordingly, it is required to evaluate the competitive power of individual deep-sea fishing industry by considering multiple inputs and outputs of the deep-sea fishing industries. In the efficiency evaluation, Data Envelopment Analysis (DEA) can be used because it is able to handle multiple inputs and outputs in spite of the lack of weight coefficients. The DEA, however, has a drawback of generating 'maverick' decision making unit (DMU)s as well as producing too many efficient DMUs. Therefore, we applied cross efficiency analysis to estimate the relative efficiency of individual deep-sea fishing industry and explicitly rank the all DMUs. In the cross efficiency analysis for deep-sea fishing industries, we considered two outputs of the profitability and secured fishing quarters, while considering the impact of Korea-US FTA, fishing regulation of coastal countries, fishing charges, and competitive fishing conditions as input parameters. The results of our approach indicate that cross efficiency analysis can be successfully used to rank the deep-sea fishing industries. The results of the cross efficiency analysis indicate that Tuna purse seine, Indonesia trawl, and Falkland trawl fishery are expected to have better competitive efficiency.

Keywords

References

  1. 김재희․최강득․김수관, "원양어업의 효율성 평가를 위한 자료포락 분석 모형", 수산경영론집, Vol. 39, No. 3, 2008, pp. 49-65.
  2. 농림수산식품부, 한미FTA체결에 따른 직접피해 지원계획 수립 연구 용역 보고서, 2008.
  3. 한국해양수산개발원, 한미FTA 수산분야 예상피해 - 주요품목 설명자료, 2007, pp. 1-21.
  4. 해양수산부, 원양어업 경영실태조사에 관한 연구, 2005.
  5. 해양수산부, 한미FTA협상결과 및 국내대책, 2007.
  6. 홍현표․최성애․이헌동, 수산업의 구조변화와 정책방안에 관한 연구, 한국해양수산개발원, 2005.
  7. Adler, N., L. Friedman and Z. Sinuany-Stern, "Review of Ranking Methods in the Data Envelopment Analysis Context", European Journal of Operational Research, Vol. 140, Issue 2, 2002, pp. 249-265. https://doi.org/10.1016/S0377-2217(02)00068-1
  8. Appa, G. and H. P. Williams, "A New Framework for the solution of DEA Models", European Journal of Operational Research, Vol. 172, Issue 2, 2006, pp. 604-615. https://doi.org/10.1016/j.ejor.2004.09.051
  9. Banker, R. D., A. Charnes, and W. W. Cooper, "Some Models for the Estimation of Technical and Scale Efficiencies in Data Envelopment Analysis", Management Science, Vol. 30, No. 9, 1984, pp. 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078
  10. Charnes, A., W. W. Cooper, and E. Rhodes, "Measuring Efficiency of Decision Making Units", European Journal of Operations Research, Vol. 2, Issue 6, 1978, pp. 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
  11. Cooper, W. W., L. M. Seiford, and K. Tone, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Springer, 2007.
  12. Doyle, J. and R. Green, "Efficiency and Cross-Efficiency in DEA: Derivations, Meanings and Uses", The Journal of the Operational Research Society, Vol. 45, No. 5, 1994, pp. 567-578.
  13. Sexton, T. R., R. H, Silkman, A. J. Hogan, Data envelopment analysis: Critique and extensions, In: Silkman, R. H. (Ed.). Measuring Efficiency: An Assessment of Data Envelopment Analysis, pp. 73-105, Jossey-Bass, San Francisco, CA., 1986.
  14. Zhu, J., Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets, Kluwer Academic Publishers, Boston, 2002.