Agric. Econ. - Czech, 2018, 64(5):227-240 | DOI: 10.17221/19/2016-AGRICECON

Measuring technical efficiency of Thai rubber production using the three-stage data envelopment analysisOriginal Paper

Surakiat PARICHATNON1, Kamonthip MAICHUM1, Ke-Chung PENG*,2
1 Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Taiwan
2 Department of Agribusiness Management, National Pingtung University of Science and Technology, Taiwan

The study investigated the technical efficiency of rubber production in Thailand. Secondary data were collected from the Thai rubber plantations in four regions from 2005 to 2014 by using a three-stage data envelopment analysis (DEA) model. The DEA was used to evaluate the technical efficiency levels and to remove undesirable environmental impacts. Furthermore, the Malmquist productivity index was used to measure the changes in the rubber production efficiency and estimate the rubber productivity trend. The findings indicate that the efficiency scores obtained using adjusted inputs in stage 3 of the DEA approach were higher than the efficiency scores in stage 1 of the DEA approach. Moreover, the results also showed that the Northern region has the worst scores of technical efficiency and declination of productivity among the four regions. However, the technical performance of the Thai rubber production has shown a good performance, an upward productivity trend, and has demonstrated the advantages of the method used. Findings from the study could provide crucial information to farmers, the Thai government, and agricultural planners for formulating effective strategies or plans to improve their technology and efficiency levels.

Keywords: Malmquist productivity index, technical efficiency, Thai rubber production, three-stage DEA

Published: May 31, 2018  Show citation

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PARICHATNON S, MAICHUM K, PENG K. Measuring technical efficiency of Thai rubber production using the three-stage data envelopment analysis. Agric. Econ. - Czech. 2018;64(5):227-240. doi: 10.17221/19/2016-AGRICECON.
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    References

    1. Aigner D., Lovell C.K., Schmidt P. (1977): Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6: 21-37. Go to original source...
    2. Amini S., Kazemi N., Marzban A. (2015): Evaluation of energy consumption and economic analysis for traditional and modern farms of broiler production. Biological Forum, Research Trend, 7: 905-911.
    3. Areetrakul P., Wongchai A. (2015): Project analysis of the rubber purchasing center in Chiang Rai. Applied Mechanics and Materials, 799: 1445-1449. Go to original source...
    4. Asmild M., Paradi J.C., Aggarwall V., Schaffnit C. (2004): Combining DEA window analysis with the malmquist index approach in a study of the Canadian banking industry. Journal of Productivity Analysis, 21: 67-89. Go to original source...
    5. Assaf A.G., Barros C.P., Managi S. (2011): Cost efficiency of Japanese steam power generation companies: a Bayesian comparison of random and fixed frontier models. Applied Energy, 88: 1441-1446. Go to original source...
    6. Avkiran N.K. (2009): Opening the black box of efficiency analysis: an illustration with UAE banks. Omega, 37: 930-941. Go to original source...
    7. Banker R.D., Charnes A., Cooper W.W. (1984): Models for the estimation of technical and scale inefficiencies in DEA. Management Science, 30: 1078-1092. Go to original source...
    8. Banker R.D., Morey R.C. (1986): Efficiency analysis for exogenously fixed inputs and outputs. Operations Research, 34: 513-521. Go to original source...
    9. Barros C.P., Leach S. (2006): Performance evaluation of the English premier football league with data envelopment analysis. Applied Economics, 38: 1449-1458. Go to original source...
    10. Caves D.W., Christensen L.R., Diewert W.E. (1982): Multilateral comparisons of output, input, and productivity using superlative index numbers. The Economic Journal, 92: 73-86. Go to original source...
    11. Chang Y.T., Zhang N., Danao D., Zhang N. (2013): Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energy Policy, 58: 277-283. Go to original source...
    12. Charnes A., Cooper W.W., Lewin A.Y., Seiford L.M. (2013): Data Envelopment Analysis: Theory, Methodology, and Applications. Springer Science and Business Media.
    13. Charnes A., Cooper W.W., Rhodes E. (1978): Measuring the efficiency of decision making units. European Journal of Operational Research, 2: 429-444. Go to original source...
    14. Charnes A., Cooper W.W., Rhodes E. (1981): Evaluating program and managerial efficiency: an application of data envelopment analysis to program follow through. Management Science, 27: 668-697. Go to original source...
    15. Chen P.C., Yu M.M., Chang C.C., Hsu S.H. (2007): Productivity change in Taiwan's farmers' credit unions: a nonparametric risk-adjusted Malmquist approach. Agricultural Economics, 36: 221-231. Go to original source...
    16. Chung W. (2011): Review of building energy-use performance benchmarking methodologies. Applied Energy, 88: 1470-1479. Go to original source...
    17. Coelli T. (1996): A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer Program). Department of Econometrics, University of New England, Armidale, Australia.
    18. Coelli T.J., Rao D.S.P., O'Donnell C.J., Battese G.E. (2005): An Introduction to Efficiency and Productivity Analysis. Springer Science and Business Media.
    19. Coelli T.J., Rao D. (2005): Total factor productivity growth in agriculture: a Malmquist index analysis of 93 countries, 1980-2000. Agricultural Economics, 32: 115-134. Go to original source...
    20. Cook W.D., Seiford L.M. (2009): Data envelopment analysis (DEA) - thirty years on. European Journal of Operational Research, 192: 1-17. Go to original source...
    21. Cooper W.W., Seiford L.M., Tone K. (2006): Introduction to Data Envelopment Analysis and its Uses: With DEASolver Software and References. Springer Science and Business Media. Go to original source...
    22. Cui Q., Li Y. (2014): The evaluation of transportation energy efficiency: an application of three-stage virtual frontier DEA. Transportation Research Part D: Transport and Environment, 29: 1-11. Go to original source...
    23. De Koeijer T.J., Wossink G.A.A., Struik P.C., Renkema J.A. (2002): Measuring agricultural sustainability in terms of efficiency: the case of Dutch sugar beet growers. Journal of Environmental Management, 66: 9-17. Go to original source... Go to PubMed...
    24. Estache A., de la Fe B.T., Trujillo L. (2004): Sources of efficiency gains in port reform: a DEA decomposition of a Malmquist TFP index for Mexico. Utilities Policy, 12: 221-230. Go to original source...
    25. Fare R., Grosskopf S., Norris M., Zhang Z. (1994): Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84: 66-83.
    26. Ferrier G.D., Lovell C.K. (1990): Measuring cost efficiency in banking: econometric and linear programming evidence. Journal of Econometrics, 46: 229-245. Go to original source...
    27. Fried H.O., Lovell C.K., Schmidt S.S., Yaisawarng S. (2002): Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis, 17: 157-174. Go to original source...
    28. Fried H.O., Schmidt S.S., Yaisawarng S. (1999): Incorporating the operating environment into a nonparametric measure of technical efficiency. Journal of Productivity Analysis, 12: 249-267. Go to original source...
    29. Fuentes R., Fuster B., Lillo-Bañuls A. (2016): A three-stage DEA model to evaluate learning-teaching technical efficiency: key performance indicators and contextual variables. Expert Systems with Applications, 48: 89-99. Go to original source...
    30. Gorman M.F., Ruggiero J. (2008): Evaluating US state police performance using data envelopment analysis. International Journal of Production Economics, 113: 1031-1037. Go to original source...
    31. He F., Zhang Q., Lei J., Fu W., Xu X. (2013): Energy efficiency and productivity change of China's iron and steel industry: accounting for undesirable outputs. Energy Policy, 54: 204-213. Go to original source...
    32. Ji X., Wu J., Zhu Q. (2015): Eco-design of transportation in sustainable supply chain management: a DEA-like method. Transportation Research Part D: Transport and Environment, 48: 451-459. Go to original source...
    33. Kocisova K. (2015): Application of the DEA on the measurement of efficiency in the EU countries. Agricultural Economics - Czech, 61: 51-62. Go to original source...
    34. Kumarasinghe H., Edirisinghe J.C., Patalee M.A.B. (2012): Role of human capital in efficiency increases: evidence from a data envelopment analysis of rubber smallholdings in Gampaha district. Journal of the Rubber Research Institute of Sri Lanka, 92: 12-21. Go to original source...
    35. Lee B.L., Worthington A.C. (2016): A network DEA quantity and quality-orientated production model: an application to Australian university research services. Omega, 60: 26-33. Go to original source...
    36. Lee J.Y. (2008): Application of the three-stage DEA in measuring efficiency - an empirical evidence. Applied Economics Letters, 15: 49-52. Go to original source...
    37. Liu J.S., Lu L.Y., Lu W.M., Lin B.J. (2013): Data envelopment analysis 1978-2010: a citation-based literature survey. Omega, 41: 3-15. Go to original source...
    38. Liu Y., Mu C., Jiang K., Zhao J., Li Y., Zhang L., Li Z., Lai J.Y.L., Hu H., Ma T. (2015): A tetraphenylethylene corebased 3D structure small molecular acceptor enabling efficient non-fullerene organic solar cells. Advanced Materials, 27: 1015-1020. Go to original source... Go to PubMed...
    39. Longpichai O., Perret S.R., Shivakoti G.P. (2012): Role of livelihood capital in shaping the farming strategies and outcomes of smallholder rubber producers in southern Thailand. Outlook on Agriculture, 41: 117-124. Go to original source...
    40. Lygnerud K., Peltola-Ojala P. (2010): Factors impacting district heating companies' decision to provide small house customers with heat. Applied Energy, 87: 185-190. Go to original source...
    41. Meeusen W., Van den Broeck J. (1977): Technical efficiency and dimension of the firm: some results on the use of frontier production functions. Empirical Economics, 2: 109-122. Go to original source...
    42. Mesike C., Esekhade T. (2014): Rainfall variability and rubber production in Nigeria. African Journal of Environmental Science and Technology, 8: 54-57. Go to original source...
    43. Mustapha N.H.N. (2011): Technical efficiency for rubber smallholders under RISDA's supervisory system using stochastic frontier analysis. Journal of Sustainability Science and Management, 6: 156-168.
    44. National Statistical Office (2016): Statistical Yearbook Thailand. National Statistical Office, Office of the Prime Minister, Bangkok, Thailand.
    45. Nguyen B.T., Dang M.K. (2016): Temperature dependence of natural rubber productivity in the southeastern Vietnam. Industrial Crops and Products, 83: 24-30. Go to original source...
    46. Nomikos N.K., Pouliasis P.K. (2011): Forecasting petroleum futures markets volatility: the role of regimes and market conditions. Energy Economics, 33: 321-337. Go to original source...
    47. Nowak A., Kijek T., Domanska K. (2015): Technical efficiency and its determinants in the European Union agriculture. Agricultural Economics - Czech, 6: 275-283. Go to original source...
    48. Odeck J. (2009): Statistical precision of DEA and malmquist indices: a bootstrap application to Norwegian grain producers. Omega, 37: 1007-1017. Go to original source...
    49. Portela M.S., Thanassoulis E., Simpson G. (2004): Negative data in DEA: a directional distance approach applied to bank branches. Journal of the Operational Research Society, 55: 1111-1121. Go to original source...
    50. Reig-Martinez E., Picazo-Tadeo A.J. (2004): Analysing farming systems with data envelopment analysis: citrus farming in Spain. Agricultural Systems, 82: 17-30. Go to original source...
    51. Rho S., An J. (2007): Evaluating the efficiency of a two-stage production process using data envelopment analysis. International Transactions in Operational Research, 14: 395-410. Go to original source...
    52. Rubber Research Institute of Thailand (2016): Thai Rubber Statistics. Ministry of Agriculture and Cooperative, Bangkok, Thailand. Available at http://www.thainr.com/en/index.php (accessed July, 2016).
    53. Rubber Research Institute of Thailand (2005): Natural Rubber in Thailand. Presentation Held in the Appraisal Meeting on Improving the Productivity of Rubber Smallholdings through Rubber Agroforestry Systems, Hat Yai, Thailand.
    54. Sahin G., Gokdemir L., Ozturk D. (2016): Global crisis and its effect on Turkish banking sector: a study with data envelopment analysis. Procedia Economics and Finance, 38: 38-48. Go to original source...
    55. Sdoodee S., Rongsawat S. (2012): Impact of Climate Change on Smallholders' Rubber Production in Songkhla Province, Southern Thailand. In: Proceedings International and National Conference for the Sustainable Community Development of Local Community: The Foundation of Development in the ASEAN Economic Community (AEC).
    56. Shang J.K., Hung W.T., Wang F.C. (2008): Service outsourcing and hotel performance: three-stage DEA analysis. Applied Economics Letters, 15: 1053-1057. Go to original source...
    57. Shrestha R.B., Huang W.C., Gautam S., Johnson T.G. (2016): Efficiency of small scale vegetable farms: policy implications for the rural poverty reduction in Nepal. Agricultural Economics - Czech, 62: 181-195. Go to original source...
    58. Shwartz M., Burgess J.F., Zhu J. (2016): A DEA based composite measure of quality and its associated data uncertainty interval for health care provider profiling and pay for performance. European Journal of Operational Research, 253: 489-502. Go to original source...
    59. Silva E., Arzubi A., Berbel J. (2013): An Application of DEA in Azores Dairy Farms. In Efficiency Measures in the Agricultural Sector, Springer Netherlands.
    60. Stewart C., Matousek R., Nguyen T.N. (2016): Efficiency in the Vietnamese banking system: a DEA double bootstrap approach. Research in International Business and Finance, 36: 96-111. Go to original source...
    61. Tavana M., Khalili-Damghani K. (2014): A new two-stage stackelberg fuzzy data envelopment analysis model. Measurement, 53: 277-296. Go to original source...
    62. Torres-Jiménez M., García-Alonso C.R., Salvador-Carulla L., Fernández-Rodríguez V. (2015): Evaluation of system efficiency using the Monte Carlo DEA: the case of small health areas. European Journal of Operational Research, 242: 525-535. Go to original source...
    63. Tsay W.J., Huang C.J., Fu T.T., Ho I.L. (2009): Maximum Likelihood Estimation of Censored Stochastic Frontier Models: An Application to the Three-Stage DEA Method. Institute of Economics, Academia Sinica, Taipei, Taiwan.
    64. Tsolas I.E., Charles V. (2015): Incorporating risk into bank efficiency: a satisficing DEA approach to assess the Greek banking crisis. Expert Systems with Applications, 42: 3491-3500. Go to original source...
    65. Van Passel S., Van Huylenbroeck G., Lauwers L., Mathijs E. (2009): Sustainable value assessment of farms using frontier efficiency benchmarks. Journal of Environmental Management, 90: 3057-3069. Go to original source... Go to PubMed...
    66. Waduge T., Edirisnghe J., Fernando A., Herath H., Jayasinghe-Mudalige U. (2015): Labour and weather related risks in smallholder rubber production: evidence from Kalutara district. Tropical Agricultural Research and Extension, 16: 88-92. Go to original source...
    67. Wei Y.M., Liao H., Fan Y. (2007): An empirical analysis of energy efficiency in China's iron and steel sector. Energy, 32: 2262-2270. Go to original source...
    68. Xue X., Shen Q., Wang Y., Lu J. (2008): Measuring the productivity of the construction industry in China by using DEA-based Malmquist productivity indices. Journal of Construction Engineering and Management, 134: 64-71. Go to original source...
    69. Yu H., Hammond J., Ling S., Zhou S., Mortimer P.E., Xu J. (2014): Greater diurnal temperature difference, an overlooked but important climatic driver of rubber yield. Industrial Crops and Products, 62: 14-21. Go to original source...
    70. Zamanian G.R., Shahabinejad V., Yaghoubi M. (2013): Application of DEA and SFA on the measurement of agricultural technical efficiency in MENA countries. International Journal of Applied Operational Research, 3: 43-51.

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