• Anis Atiqah Abd Rais
  • Siti Shaliza Mohd Khairi
  • Zalina Zahid
  • Siti Aida Sheikh Hussin
Keywords: Efficiency, Panel Tobit Regression and Fishery production


Fishery contributes to a wide range of employment and economic benefits for individuals and the country. Unfortunately, the global fish stock has been fully exploited and the sign of recovery from it is tremendously low. It can be observed that the current condition of the fishery production should be taken seriously since there is a problem in the efficiency level. Therefore, this research is conducted to investigate the relationship of exogenous variables towards the efficiency level of fishery production. The Panel Tobit Regression is applied. Based on the data collected which involve 13 states in Malaysia from the year 2004 until 2014, efficiency result shows that Perak is the most efficient state throughout year 2004 to 2014 with a mean score of 0.9440, followed by Perlis and Pahang with efficiency mean scores of 0.9036 and 0.8001 respectively. The least efficient state for the 11 years period was Negeri Sembilan with efficiency mean score of 0.1470. Panel Tobit Regression is applied to find the relationship of efficiency score for 13 states in Malaysia with exogenous variables. Tobit characteristic applied in the model as the efficiency score is based on censored data with lower and upper limit of zero and one. The result shows temperature and total rainfall are positively affecting the efficiency score. Meanwhile, the wind speed shows a negative relationship with the efficiency score. Overall, this research has successfully demonstrated the effect of exogenous variables on the efficiency score of fishery production. The findings may give some reference for the policy maker to revise the current policy by considering the exogenous factors in calculating fish revenue.


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Department of Fisheries, 2015. “Landing of capture fisheries, 2009-2014 Fisheries Statisticsâ€, Retrieved from http://www.dof.gov.my/dof2/resources/Perangkaan/Perangkaan2014/2014baru/3.Jadual_Pendaratan_Ikan_Laut_.pdf

Fishing Future Organization, 2015. “Fishing for A Future. Getting to Eden. Building the ideal future for the global fish foodsystem through the collective actionsâ€, Retrieve fromhttp://www.fishingfuture.org/fileadmin/redaktion/PDF/FFF-02-15_LondonPaper_wen_RZ.pdf

Chen, Y., Li, Y., Liang, L., Salo, A., & Wu, H. (2016). “Frontier projection and efficiency decomposition in two-stage processes with slacks-based measuresâ€, European Journal of Operational Research, 250(2), 543-554.

Shen, S., & Shen, Z. (2013). “Analysis of fishery production efficiency based on the three-stage DEAâ€, In Proceedings of the 2nd International Conference on Green Communications and Networks 2012 (GCN 2012): Volume 1 (pp. 289-298). Springer Berlin Heidelberg.

Vázquez-Rowe, I., Iribarren, D., Moreira, M. T., &Feijoo, G. (2010). “Combined application of life cycle assessment and data envelopment analysis as a methodological approach for the assessment of fisheriesâ€, The International Journal of Life Cycle Assessment, 15(3), 272-283

Aisyah, N., Arumugam, N., Hussein, M. A., &Latiff, I. (2012). “Factors affecting the technical efficiency level of inshore fisheries in Kuala Terengganuâ€, Malaysia. International Journal of Agricluture Management & Development ,2, 49-56.

Lim, G. T., & Hussein, M. (2011). “Technical efficiency analysis for Penang trawl fishery, Malaysia: Applying DEA approachâ€, Australian Journal of Basic and Applied Sciences, 5(12), 1518-1523.

Rahman, R., Zahid, Z., Khairi, S. S. M., & Hussin, S. A. S. (2016). “Modeling technical efficiency of inshore fishery using data envelopment analysisâ€, In 4th International Conference on Quantitative Sciences and Its Applications, ICOQSIA 2016 (Vol. 1782). [040014] American Institute of Physics Inc.. DOI: 10.1063/1.4966081

Pascoe, S., & Tingley, D. (2007). “Capacity and technical efficiency estimation in fisheries: Parametric and non-parametric techniquesâ€, In Handbook Of Operations Research In Natural Resources (pp. 273-294). Springer US.

Ramli, N. A., & Munisamy, S. (2013). “Modeling undesirable factors in efficiency measurement using data envelopment analysis: A reviewâ€, Journal of Sustainability Science and Management, 8(1), 126-135.

Selim, S., & BursalıoÄŸlu, S. A. (2015). “Efficiency of Higher Education in Turkey: A Bootstrapped Two-Stage DEA Approach 1â€, International Journal of Statistics and Applications, 5(2), 56-67.

Muda, M., Shaharuddin, A., & Embaya, A. (2013). “Determinants Of Banks’ Efficiencyâ€, A Panel Regression Analysis of Islamic Banks in Malaysia Economics and Finance Review, Vol. 3(03) pp. 19 – 28,

Garza-García, J. G. (2012). “Determinants of bank efficiency in Mexico: a two-stage analysisâ€, Applied Economics Letters, 19(17), 1679-1682.

Zahid, Z. & Mokhtar, M. (2007). “Estimating technical efficiency of Malaysian manufacturing small and medium enterprises: A stochastic frontier modellingâ€, The 4th SMEs in a Global Economy Conference, University of Wollongong (pp. 9-10).

Ceyhan, V., & Gene, H. (2014). “Productive efficiency of commercial fishing: evidence from the Samsun Province of Black Sea, Turkeyâ€, Turkish Journal of Fisheries and Aquatic Sciences, 14, 309-320.

Verma, P. D., Dangar, R. D., & Suhagia, B. N. (2013). Evaluation of Anti-inflammatory Activity of Capparis decidua Edgew. Stem. June International Journal of Pharmacy Research and Technology (Vol. 3, pp. 16–19).

How to Cite
Abd Rais, A. A., Mohd Khairi, S. S., Zahid, Z., & Sheikh Hussin, S. A. (2018). EFFICIENCY ANALYSIS TOWARDS EXOGENOUS VARIABLES: FISHERY PRODUCTION IN MALAYSIA. COMPUSOFT: An International Journal of Advanced Computer Technology, 7(11). Retrieved from https://ijact.in/index.php/ijact/article/view/803