Analyzing factors affecting the technical efficiency of small-scale seaweed farms using a two-stage double bootstrap DEA approach

References

• Amundsveen, R., O.-P. Kordahl, H. Kvile, and T. Langset (2014). Second stage adjustment for firm heterogeneity in DEA: A novel approach used in regulation of Norwegian Electricity DSOS. Recent Developments in Data Envelopment Analysis and its applications: Proceedings of the 12th International Conference of DEA, Kuala Lumpur, Malaysia. (pp. 334–40).
   Google Scholar
• Apao, E. R. (2018, April 4). Mariculture Seaweeds. (M. L. Orbeta, & L. N. Digal, Interviewers)
   Google Scholar
• Au, T., B. The, and S. Speelman. 2018. Analyzing the variations in cost-efficiency of marine cage lobster aquaculture in Vietnam: A two-stage bootstrap DEA approach. Aquaculture Economics, & Management. doi:10.1080/13657305.2018.1429032.
   Web of Science ®Google Scholar
• Au, T., J. Meensel, and S. Speelman. 2020. The factors influencing environmental performance of marine aquaculture: A combine material balance-based and meta-frontier approach. Journal of Cleaner Production 7–8. doi:10.1016/j.jclepro.2020.122342.
   Web of Science ®Google Scholar
• Bacaltos, D., N. Revilla, R. Castanaga, M. Laguting, G. Anguay, D. Ang, … G. Flamiano-Garde. 2013. Gender roles in the seaweed industry cluster of the southern Philippines: The DICCEP experience. Noumea: The Pacific Community.
   Google Scholar
• Badunenko, O., and H. Tauchmann. 2018. Simar and Wilson two-stage efficiency analysis for Stata. FAU Discussion Papers in Economics 1–37. No. 08/2018, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Institute for Economics, Erlangen
   Google Scholar
• Badunenko, O., and P. Mozharovskyi. 2016. Nonparametric frontier analysis using Stata. The Stata Journal 16 (3):550–89. doi:10.1177/1536867X1601600302.
   Web of Science ®Google Scholar
• Banton, C. (2019, April 26). Efficiency Definition. Accessed May 20, 2019. https://www.investopedia.com/terms/e/efficiency.asp
   Google Scholar
• Banyuriatiga, D., and L. Waluyati. 2017. Production efficiency of seaweed farming in Tarakan North Borneo. Agro Ekonomi 28 (1):80–94. Accessed November 18, 2019. doi:10.22146/jae.24316.
   Google Scholar
• Besta, N. (2013). Seaweed farming and intra-household gender relations on Songo Songo Island, Tanzania. Norwich, Norfolk, United Kingdom. Accessed February 5, 2020. https://core.ac.uk/download/pdf/19905462.pdf
   Google Scholar
• BFAR. 2010. Fisheries commodity road map: Seaweeds Bureau of fisheries and aquatic resources, fisheries policy and economics division. Quezon City: Bureau of Fisheries and Aquatic Resources.
   Google Scholar
• Bhagavath, V. Technical efficiency measurement by data envelopment analysis: An application in transportation. 2009. Alliance Journal of Business Research 60-72. Accessed May 24, 2019. http://www.ajbr.org/Archives/Technical%20Efficiency%20Measurement%20by%20Data%20Envelopment%20Analysis%20-%20An%20Application%20in%20Transportation.pdf
   Google Scholar
• Buschmann, A. H., C. Camus, J. Infante, A. Neori, A. Israel, M. C. Hernandez-Gonzalez, A. T. Critchley, J. L. Gomez-Pinchetti, A. Golberg, and N. Tadmor-Shalev. 2017. Seaweed production: Overview of the global state of exploitation, farming and emerging research activity. European Journal of Phycology 52 (4):391–406. doi:10.1080/09670262.2017.1365175.
   Web of Science ®Google Scholar
• Butardo, V. M., E. T. Ganzon-Fortes, V. A. Silvestre, M. B. Bacano-Maningas, M. E. Montaño, and A. O. Lluisma. 2003. Isolation and classification of culturable bacteria associated with ice-ice disease in kappaphycus and eucheuma (Rhodophyta, Solieriaceae). The Philippine Scientist Volume 40 226.
   Google Scholar
• CARE. (2018). Seaweeds value chain in IloIlo. Quezon City: CARE Philippines. Accessed March 14, 2019. https://care-philippines.org/wp-content/uploads/2018/07/Seaweeds-VC-brochure_final_revised.pdf
   Google Scholar
• Coelli, T. J., D. P. Rao, C. J. O’Donnell, and G. E. Battese. 2005. An introduction to efficiency and productivity analysis. Second ed. New York, New York, United States of America: Springer Science+Business Media, Inc.
   Google Scholar
• Cooper, W. W., L. M. Seiford, and K. Tone. 2007. Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software. Second ed. New York: Springer Science+Business Media, LLC.
   Google Scholar
• DTI. 2015. Sibugay seaweeds situationer. Ipil City: Department of Trade and Industry - Zamboanga Sibugay Provincial Office. Department of Trade and Industry - Region IX.
   Google Scholar
• DTI. 2017. Zamboanga sibugay seaweeds industry cluster meeting. Ipil, Zamboanga Sibugay, Philippines: Department of Trade and Industry. March 27. Accessed March 27, 2018.
   Google Scholar
• FAO. (2020a). Fishery and aquaculture statistics. Rome, Italy. Accessed September 11, 2020. www.fao.org/fishery/statistics/software/fishstatj/en
   Google Scholar
• FAO. 2020b. The state of world fisheries and aquaculture 2020. Sustainability in action. Rome: Food and Agriculture Organization of the United Nations. doi:10.4060/ca9229en.
   Google Scholar
• Fisheries Policy and Economics Division. 2010. Fisheries commodity road map: Seaweeds. Quezon City: Bureau of Fisheries and Aquatic Resources.
   Google Scholar
• Fitriana, R. 2017. Gendered participation in seaweed production - examples in Indonesia. Gender in Aquaculture and Fisheries: Engendering Security in Fisheries and Aquaculture.The Journal of the Asian Fisheries Society (Special Issue) 30S 245–64.
   Google Scholar
• Forleo, M., L. Romagnoli, N. Palmieri, and A. Di Nocera. 2018. Assessing the efficiency of aquaculture cooperatives: A country case study. Economia Agro-Alimantare (2):205–20. doi:10.3280/ECAG2018-002006.
   Google Scholar
• Golany, B., and Y. Roll. 1989. An application procedure for DEA. OMEGA International Journal of Management Science 17 (3):237–50. doi:10.1016/0305-0483(89)90029-7.
   Web of Science ®Google Scholar
• Hukom, V., R. Nielsen, M. Asmild, and M. Nielsen. 2020. Do aquaculture farmers have an incentive to maintain good water quality? The case of small-scale shrimp farming in Indonesia. Ecological Economics 1–9. doi:10.1016/j.ecolecon.2020.106717.
   Web of Science ®Google Scholar
• Hurtado, A. Q., N. J. Guanzon, T. De Castro-Mallare, and M. Luhan. (2002). Proceedings of the National Seaweed Planning Workshop held on August 2- 3, 2001. (pp. 40). Iloilo: Southeast Asian Fisheries Development Center.
   Google Scholar
• Iliyasu, A., and Z. Mohamed. 2015. Technical efficiency of tank culture systems in peninsular malaysia: An application of data envelopment analysis. Aquaculture, Economics, and Management 19 (4):372–86. doi:10.1080/13657305.2015.1082118.
   Web of Science ®Google Scholar
• Iliyasu, A., Z. Mohamed, M. Ismail, A. Abdullah, S. Kamarudin, and H. Mazuki. 2014. A review of production frontier research in aquaculture (2001-2011). Aquaculture, Economics & Management 18 (3):221–47. doi:10.1080/13657305.2014.926464.
   Web of Science ®Google Scholar
• Jain, H., and R. Jha. 2015. Measuring technical efficiency of agricultural inputs. Journal of Land and Rural Studies 3 (1):139–61. doi:10.1177/2321024914534032.
   Google Scholar
• Kambey, C. S., I. Campbell, C. F. Sondak, A. R. Nor, P. E. Lim, and E. J. Cottier-Cook. 2020. An analysis of the current status and future of biosecurity frameworks for the Indonesian seaweed industry. Journal of Applies Phycology 32 (4):2147–60. doi:10.1007/s10811-019-02020-3.
   Web of Science ®Google Scholar
• Kim, J. K., C. Yarish, E. Hwang, M. Park, and Y. Kim. 2017. Seaweed aquaculture: Cultivation technologies, challenges and its ecosystem services. Algae 31 (1):1–13. doi:10.4490/algae.2017.32.3.3.
   Google Scholar
• Kuala Lumpur.Angiz, M., A. Mustafa, and M. Kamali. 2013. Cross-ranking of decision making units in data envelopment analysis. Applied Mathematical Modelling 1. doi:10.1016/j.apm.2012.02.038.
   Google Scholar
• Le, K., V. Le, and T. Nguyen. 2020. An application of data envelopment analysis with the double bootstrapping technique to analyze cost and technical efficiency in aquaculture: Do credit constraints matter? Aquaculture 1–11. doi:10.1016/j.aquaculture.2020.735290.
   Web of Science ®Google Scholar
• Le, K., V. Le, T. Nguyen, and T. Pham. 2020. Data envelopment analysis for analyzing technical efficiency in aquaculture: The bootstrap methods. Aquaculture Economics, & Management. doi:10.1080/13657305.2019.1710876.
   Web of Science ®Google Scholar
• Mohammadi, S., S. Mirdehghan, and G. Jahanshaloo. 2016. Finding the most preferred decision-making unit in data envelopment analysis. Advances in Operations Research 8. November 20. doi:10.1155/2016/7171467.
   Web of Science ®Google Scholar
• Monzales, O. 2017. Seaweed industry of the philippines: Beginning, evolution, problems, and possible solutions. Seaweeds Industry Association of the Philippines. Philexport-Cebu LDM Building., Legaspi St., Cebu City.
   Google Scholar
• Mulholland, B. (2017, October 23). Productivity vs efficiency: How to analyze the performance of anything. Accessed May 20, 2019. https://www.process.st/productivity-vs-efficiency/
   Google Scholar
• Narvaez, T. A. 2015. Seaweeds jobs value-chain analysis. The World Bank. Washington DC: World Bank Publications.
   Google Scholar
• Narvaez, T. A. 2018. Seaweeds jobs value-chain analysis in Zamboanga Peninsula, Philippines, 2015. International Journal of Oceanography & Aquaculture 2(2):1–12. April 24. Accessed March 14, 2019. doi:10.23880/IJOAC-16000136.
   Google Scholar
• Neish, I. C. 2007. Assessment of the seaweed value chain in Indonesia. Jakarta: USAID.
   Google Scholar
• Nguyen, K. T., and T. C. Fisher. 2014. Efficiency analysis and the effect of pollution on shrimp farms in the mekong river delta. Aquaculture Economics & Management 18 (4):325–43. doi:10.1080/13657305.2014.959209.
   Web of Science ®Google Scholar
• Nguyen, L. A., T. B. Pham, R. Bosma, J. Verreth, R. Leemans, S. De Silva, and A. O. Lansink. 2017. Impact of climate change on the technical efficiency of Striped Catfish, Pangasianodon hypophthalmus, Farming in the Mekong Delta, Vietnam. Journal of the World Aquaculture Society November 30. doi:10.1111/jwas.12488.
   Web of Science ®Google Scholar
• Nobleza, J. S. (2015). Value chain analysis for seaweeds in Bohol, Cebu and Guimaras. DA - PRDP. Accessed March 20, 2018.
   Google Scholar
• Nor, A. M., T. S. Gray, G. S. Caldwell, and S. M. Stead. 2020. A value chain analysis of Malaysia’s seaweed industry. Journal of Applied Phycology 32, 2161–2171. https://doi.org/10.1007/s10811-019-02004-3
   Google Scholar
• Oluwatayo, I., and T. Adedeji. 2019. Comparative analysis of technical efficiency of catfish farms using different technologies in Lagos State, Nigeria: A Data Envelopment Analysis (DEA) Approach. Agriculture & Food Security 8 (1). doi:10.1186/s40066-019-0252-2.
   Google Scholar
• Owusu, R., and A. Hailu (2014). A two-stage double bootstrap data envelopment analysis of technical efficiency of rice farms in Ghana. Recent Developments in Data Envelopment Analysis and it’s Applications: Proceedings of the 12th International Conference of DEA, (pp. 50–55). Kuala Lumpur.
   Google Scholar
• Pant, J., B. Barman, K. Murshed-E-Jahan, B. Belton, and M. Beveridge. 2013. Can aquaculture benefit the extreme poor? A case study of landless and socially marginalized Adivasi (ethnic) communities in Bangladesh. Aquaculture 418.
   Web of Science ®Google Scholar
• Pascoe, S. 2007. Capacity analysis and fisheries policy: Theory versus practice. Marine Resource Economics 22 (1):83–87. doi:10.1086/mre.22.1.42629539.
   Web of Science ®Google Scholar
• Pedrosa, A. A. (2017, March 13). Current status of the philippine seaweed industry. Davao City, Davao del Sur, Philippines. Accessed September 2, 2018. file:///D:/murpleng/Downloads/4.%20Plenary%202_Mr.%20Pedrosa%20III.pdf
   Google Scholar
• Peñaflor, F. (2018, February 22). Carrageenan. Accessed September 12, 2020. https://boi.gov.ph/ufaqs/carrageenan/
   Google Scholar
• Pettinger, T. (2017, November 28). Allocative efficiency. https://www.economicshelp.org/blog/glossary/allocative-efficiency
   Google Scholar
• Philippine Statistics Authority. (2018, August 5). Aquaculture: Value of production by species, by region and by Province, 1996-2018 by Geolocation, Quarter, Type/Environment/Species and Year. Quezon, NCR, Philippines. Accessed September 2, 2018. http://countrystat.psa.gov.ph/?cont=10&pageid=1¯D80PNCAP
   Google Scholar
• Province of Zamboanga Sibugay. 2015. Provincial commodity investment plan (PCIP) 2016-2019. Ipil: DA - PRDP.
   Google Scholar
• PSA. (2018, August 14). Aquaculture: Volume of production by species, by region and by province, 1996-2018 by Type/Environment/Species, Geolocation, Year and Quarter. Quezon City, National Capital Region, Philippines. Accessed September 2, 2018. http://countrystat.psa.gov.ph/?cont=10&pageid=1¯D70PNVAP
   Google Scholar
• Ruiz, A. (2018, July 8). Business diary philippines. Accessed December 5, 2019. https://businessdiary.com.ph/1083/how-to-start-a-seaweed-production-business/#ixzz4Dgfe8UXl
   Google Scholar
• Saili, A., N. Ibrahim, F. Fatah, M. Muhamad, and N. Ruslan. 2019. Analysing determinant of the technical efficiency of seaweed operators in Semporna, Sabah, Malaysia. Journal of Engineering and Applied Sciences 14 (3):6066–71. doi:10.36478/jeasci.2019.6066.6071.
   Google Scholar
• Sharma, K. R., and P. Leung. 2008. A review of production frontier analysis for aquaculture management. Aquaculture, Economics, & Management 15–34. doi:10.1080/13657300309380329.
   Google Scholar
• Simar, L., and P. Wilson. 1998. Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science 44 (1):49–61. doi:10.1287/mnsc.44.1.49.
   Web of Science ®Google Scholar
• Simar, L., and P. Wilson. 2000. Statistical inference in nonparametric frontier models: The state of the art. Journal of Productivity Analysis 13 (1):49–78. doi:10.1023/A:1007864806704.
   Web of Science ®Google Scholar
• Simar, L., and P. Wilson. 2007. Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics 136 (1):31–64. doi:10.1016/j.jeconom.2005.07.009.
   Web of Science ®Google Scholar
• Simar, L., and P. W. Wilson. 2002. Non-parametric tests of returns to scale. European Journal of Operational Research 139 (1):115–32. doi:10.1016/S0377-2217(01)00167-9.
   Web of Science ®Google Scholar
• Theodoridis, A., A. Ragkos, and G. Koutouzidou. 2020. Revealing the profile of economically efficient mussel farms: A restricted data envelopment analysis application. Aquaculture International 28, 675–689. https://doi.org/10.1007/s10499-019-00490-3
   Google Scholar
• Theodoridis, A., C. Batzios, R. Athanasios, and P. Angelidis. 2016. Technical efficiency measurement of mussel aquaculture in Greece. Aquaculture International 1–13. doi:10.1007/s10499-016-0092-z.
   Web of Science ®Google Scholar
• Tung, D. 2016. Measuring the technical efficiency of livestock production in Vietnam. Outlook on Agriculture 45 (2):132–39. doi:10.1177/0030727016650771.
   Web of Science ®Google Scholar
• Tziogkidis, P. (2012). Bootstrap DEA and hypothesis testing. Cardiff Economic Working Papers, No. E2012/18.
   Google Scholar
• Valderrama, D., J. Cai, N. Hishamunda, and N. Ridler. 2013. Social and economic dimensions of carrageenan seaweed farming. Rome: FAO. Food and Agriculture Organization of the United Nations (FAO), Fisheries and Aquaculture Department.
   Google Scholar
• Valderrama, D., J. Cai, N. Hishamunda, N. Ridler, I. C. Neish, A. Q. Hurtado, J. Fraga, M. Krishnan, R. Narayanakumar, and M. Kronen. 2015. The economics of kappaphycus seaweed cultivation in developing countries: A comparative analysis of farming systems. Aquaculture, Economics, and Management 19 (2):251–77. doi:10.1080/13657305.2015.1024348.
   Web of Science ®Google Scholar
• Van den Burg, S. W., A. Van Dujin, H. Bartelings, M. Van Krimpen, and M. Poelman. 2016. The economic feasibility of seaweed production in the North Sea. Aquaculture Economics & Management 20 (3):235–52. doi:10.1080/13657305.2016.1177859.
   Web of Science ®Google Scholar
• Wilson, P. 2003. Testing independence in models of productive efficiency. Journal of Productivity Analysis 20 (3):361–90. doi:10.1023/A:1027355917855.
   Web of Science ®Google Scholar
• Yu, J., and W. Yu. 2018. The economic benefit of marine based on DEA model. International Journal of Low-Carbon Technologies 13 (4):364–68. July 2. doi:10.1093/ijlct/cty028.
   Web of Science ®Google Scholar
• Yu, Y.-S., A. Barros, M.-L. Yeh, M.-J. Lu, and C.-H. Tsai. 2012. A study estimating the technical efficiency of optoelectronic firms: An application of data envelopment analysis and tobit analysis. International Journal of Academic Research in Business and Social Sciences 192–99.
   Google Scholar
• Yusuf, S., and O. Malomo. 2007. Technical efficiency of poultry egg production in ogun state: A data envelopment analysis (DEA) approach. International Journal of Poultry Science 6 (9):622–29. doi:10.3923/ijps.2007.622.629.
   Google Scholar
• Zhu, J. 2016. Data envelopment analysis: A handbook of empirical studies and applications, Ed. J. Zhu, New York: Springer International Publishing AG Switzerland. doi:10.1007/978-1-4899-7684-0.
   Google Scholar