Presenting a New Technique to Assess the Efficiency of Farms with Window-DEA and Malmquist Productivity Index: The Case of Barley Farms In Khash County, Iran
محورهای موضوعی : Strategic planningعلی سردارشهرکی 1 , ندا علی احمدی 2
1 - استادیار اقتصاد کشاورزی ، دانشگاه سیستان و بلوچستان، ایران
2 - دانشجو دکتری اقتصاد کشاورزی ، دانشگاه سیستان و بلوچستان، ایران
کلید واژه: Data envelopment analysis, Malmquist Index, Productivity, Efficiency, Window-DEA,
چکیده مقاله :
As a major source of income in most countries, the agricultural sector is of a key importance among all economic activities. The improvement in productivity and efficiency is one of the main goals to accomplish economic growth and prosperity. Productivity enhancement has always been a major concern for all economic enterprises that produce commodities and services, and it is imperative to consider all of its aspects when planning for development. The present study aims at analyzing the variations of the productivity of production factors and measuring technical efficiency and productivity of farmers in Khash County, Iran using window data envelopment analysis (WDEA) method. So, the technical efficiency and productivity of the farmers were examined over the 2013-2016 period. The results show that the studied farmers have an average technical efficiency of 0.99, which is relatively high and indicates that the barley farmers are efficient. Indeed, the Malmquist productivity index reveals that the average variation of total productivity in the studied county was 1.95 over the studied period. One of the most effective factors influencing total productivity variations in agriculture is technological change. It is suggested that the new technology of agricultural technology (field integration and use of new irrigation) be used to increase the productivity and productivity of barley crops in the region.
بخش کشاورزی به عنوان منبع اصلی در آمد اکثر کشورهای جهان در مجموعه فعالیتهای اقتصادی از اهمیتی کلیدی برخوردار است. ارتقاء بهرهوری و کارایی یکی از اساسی‌ترین اهداف در دستیابی به رشد و شکوفایی اقتصادی به‌شمار می‌آید. مساله افزایش بهرهوری از اصلی‌ترین دغدغه‌هایی است که هر بنگاه اقتصادی تولید کننده کالا و خدمات با آن مواجه بوده و ضروری است به‌هنگام برنامه‌ریزی برای توسعه هر بخشی جوانب مختلف آن در نظر گرفته شود. تحقیق حاضر با هدف تحلیل تغییرات بهرهوری عوامل تولید و اندازهگیری کارایی فنی و همچنین بهرهوری زارعین شهرستان خاش با استفاده از تکنیک تحلیل پوششی پنجرهای دادهها است. بدین منظور وضعیت کارایی فنی، و بهرهوری زارعین طی دوره 95-1392 بررسی شد. با توجه به نتایج بدست آمده میانگین کارایی فنی بهرهبرداران شهرستان خاش، 99/0میباشد که نسبتاً بالا است که نشاندهنده کارا بودن بهرهبردارن جوکار میباشد. همچنین با توجه به مقادیر شاخص بهرهوری مالمکوئیست نشان میدهد که میانگین تغییرات بهره وری کل برای شهرستان با مقدار 95/1 در طی دوره مذکور است. یکی از تأثیرگذارترین عامل در تغییرات بهرهوری کل در کشاورزی، تغییرات تکنولوژی بوده است. پیشنهاد میشود که برای افزایش کارایی و بهرهوری محصول جو در منطقه از فناوری جدید استفاده شود.
Abbas, A., Yang, M., Yousal, K., Ahmad, M., Elahi, E., & Iqbal, T. (2018). Improving energy use efficiency of corn production by using data envelopment analysis (A non-parametric approach). Fresenius Environmental Bulletin, 274725-4733.
Abbasian, A., & Mehrgan, N. (2007). Measurement of production factors of different economic sectors by data envelopment analysis. Journal of Economic Research, 78, 153-176. (In Persian)
Abrishami, H., & Niakan, L. (2010). Measurement of technical productivity of power plants of Iran using stochastic frontier analysis (SFA) and its comparison with developing countries. Journal of Energy Economics, 26, 153-175. (In Persian)
AKamin, A., Bidogeza, J., Rene Minkoua, J., & Afari-sefa, V. (2017). Efficiency and productivity analysis of vegetable farming within root and tuber-based systems in the humid tropics of Cameroon. Journal of Integrative Agriculture, 16(8), 1865-1873.
Alirezaei, M., Keshvari, A., & Hashemi, M. (2005). Assessment of productivity growth with the Malmquist productivity index with data envelopment analysis approach. International Journal of Engineering Science, 16 (2), 145-154. (In Persian)
Altier, M. (2018). Agroecology: The Science of sustainable agriculture. Second Edition, Boca Raton, CRC Press.
Amegnaglo, C. J. (2018). Determinants of maize farmers' performance in Benin, West Africa. Kasetsart Journal of Social Sciences,8(1), 1-7.
Amiri, H., Googerdchian, A., Kiani, G., & Alizadeh, A. (2014). Adaptive efficiency of agricultural cooperatives in providing input sector; With emphasis on transaction cost economics approach (Case study: South Khorasan province). Journal of Iran's Economic Essays, 11 (21), 9-38. (In Persian with English abstract)
Anonymous. (2016). Statistics book of agricultural crops for 2014-2015 growing season. Office of Statistics and IT. Tehran, Iran: Deputy of Planning and Economics, Ministry of Jihad-e Agriculture. (In Persian)
Asmild, M.; Paradi, J. C.; Aggarwall, V.; and C. Schaffnit (2004). Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry. Journal of ProductivityAnalysis, 21, 67-89.
Azimian, M., Shahin, A., Alinaghian, M., & Badri, M. (2013). Developing an integrated approach to data envelopment analysis and Malmquist productivity index to measure the performance of the projects. Industrial Management, 5 (1), 43-62. (In Persian with English abstract)
Bakhtiari, S., Dehghanizadeh, M., & Hosseinipoor, S. (2014). Analysis of labor productivity and efficiency in the cooperative sector: A case study of the industrial cooperative sector in the province of Yazd. Journal of Management and Development Process, 27 (3), 45-73. (In Persian with English abstract)
Berimnejad, V., & Mohtashami, T. (2009). Technical efficiency of wheat production in Iran: A case study. Journal of Agricultural Economics, 1, 75-94. (In Persian)
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of OperationalResearch, 2(6), 429–444.
Chebil, A.; Abbas, K., &Frija, A. (2014) Water use efficiency in irrigated wheat production systems in Central Tunisia: A stochastic data envelopment approach. Journal of Agricultural Science, 6(2),63-71.
Chen, L. & Jia, G. (2017). Environmental efficiency analysis of China's regional industry: a data envelopment analysis (DEA) based approach. Journal of Cleaner Production, 142 (2), 846-853.
Chen, P. C., Yu, M. M., Chang, C. C., & Hsu, S. H. (2008.Total factor productivity growth in China’s agricultural sector. China EconomicReview. 112, 147-159.
Chen,Y., AghaIqbal, A (2004). DEA malmquist productivity measure: New insights with an application to computer industry. EuropeanJournal of Operational Research, 159, 239-249.
Coelli, T., Rao, D.S., O'Donnell, C., & Battese, G.E., (2005). An introduction to efficiency andproductivity analysis. Second Ed. Springer.
Dashti, G., Rashidghalam, M., & Pishbahar, E. (2017). Application of modified non-parametric models in technical efficiency of Iran's sugar beet production. Agricultural Economics & Development, 31 (2), 157-169. (In Persian)
Dashti, N., Yavari, K., & Sabbagh, M. (2009). Analysis of the total productivity growth of production factors in Iranian industries using the econometrics approach. Journal of Quantitative Economics, 6 (1), 101-128. (In Persian with English abstract)
Diao, P., Zhang, Z. & Jin, Z. (2018). Dynamic and static analysis of agricultural productivity in China, Browse Journals & Books. China Agricultural Economic Review , 10 (2), 293-312.
Díaz-Chao, A., Sainz-González, J., & Torrent-Sellens, J. (2015). ICT, innovation, and firm productivity: New evidence from small local firms. Journal of Business Research, 68(7):1439-1444
Diewert, W. & Fox, K. (2017). Decomposing productivity indexes into explanatory factors. European Journal of Operational Research, 256(1), 275-291.
Doaei, M., Shavazipour, B., & Zamani Sabzi, M. (2016). Efficiency and productivity in Tehran Stock Exchange based on diversification strategy. Journal of Investment Knowledge, 5 (18), 229-251. (In Persian with English Abstract)
Ekin, D. H., & Lovely, M. E. (2017). Scale economies, returns to variety, and the productivity of public infrastructure. World Scientific Studies in International Economics, 7, 73-91.
Fare, R., Grosskopf, S & Lovell, C.A.K. (1994). Production Frontirs. University of North Carolina, Chapel Hill.
Farrell, M.J. (1957), The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253-290.
Fazel Yazdi, A., & Moeeinoddin, M. (2018). Ranking and assessing the efficacy of insurance in Iran using the dynamic approach of data window analysis. Journal of Productivity Management, 9 (35), 131-150. (In Persian with English abstract)
Fazeli, A. (2012). Measurement of productivity of water industry with data envelopment analysis approach. Proceedings of 3rd National Conference of Data Envelopment Analysis (2011). Firuzkuh, Iran. (In Persian)
Ghosh, R., & Kathuria ,V.,(2016). The effect of regulatory governance on efficiency of thermal power generation in India: A stochastic frontier analysis. Energy Policy, 11-24 ,.
Guesmi, B., Serra, T., Radwan, A., & Gil, J. M. (2017). Efficiency of Egyptian organic agriculture: A local maximum likelihood approach, Agribusiness an International Journal, 34(2), 441-455.
Guo, X., Cheng Lu, C., Lee, J. H., & Chiu, Y. H. (2017). Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China. Energy, 134, 392-399.
Guzmán, I., & Arcas, N. (2008). The usefulness of accounting information in measurement of technical efficiency in agricultural cooperatives. Annals of Public and Cooperative Economics,79(1), 107-131.
Haynes, K. E., Ratick, S., & Cummings, S. J. (1997). Pollution prevention frontiers: A data envelopment simulation. In: Knaup GL, Kim TJ, editors. environmental program evaluation: a primer. Urbana, IL.
Hazneci, E., & Ceyhan, V. (2015). Measuring the productive efficiency and identifying the inefficiency determinants of farms in Amasya Province, Turkey. Journal of Agriculture and Environmental Sciences, 4( 1), 100-107.
Huang, Z., Fu, Y., Liang, Q., Song, Y., & Xu, X. (2013). The efficiency of agricultural marketing cooperatives in China's Zhejiang Province. Managerial and Decision Economics, 13(2), 108-127.
Idris, J., & Ramaha, I. (2009),Technical progress and labour productivity in small and medium scale industry in Malaysia. European Journal ofEconomics, Finance and Administrative Sciences, 15, 199-210.
Isazadeh, S., & Soufimajidpour, M. (2018). TFP growth, technological progress, efficiency changes: Empirical evidence from Iranian manufacturing industries. Journal of Economical Modeling, 11 (40), 29-48. (In Persian with English Abstract)
Jesus, T., Pastor, C. A., & Knox, L. (2005). A global Malmquist productivity index. EconomicsLetters, 88(2), 266-271.
Ji, A., Hong-Jie, K & Lin H.) 2015(. Data envelopment analysis with interactive variables. Management Decision, 53 ( 10), 2390-2406, https://doi.org/10.1108/MD-11-2014-0631.
Kao, C., & Hwang, S.N. (2008). Efficiency decomposition in twostage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of OperationalResearch, 185(1), 418-429.
Kneip, A., Simar, L., & Wilson, P. (2018). Inference in dynamic, nonparametric models of production: Central limit theorems for malmquist indices. Universite Catholique de Louvain, 10, 1-59.
Koupaei, M. (2010). The principles of agriculture economy. Tehran, Iran: Science & Technology University Press. (In Persian)
Krishna, P., Bhawani, M., & Thomas, J. (2018). Farm efficiency and its determinants in different farm size and altitude categories: A Case of organic coffee cultivation in Nepal. Journal of Economic Insight , 43 (1), 1-18.
Lakner, S., Munoz, T. B., & Brummer, B. (2017). Technical efficiency in chilean agribusiness industry: A Metafrontier Approach, Agribusiness an International Journal, 33(3), 302-323.
Li, Z., Liu, Q., Mao, T., & Che, S. (2010). Participation in agricultural cooperatives on the household income: As the danyang city dantu district an example. Rural Economy andTechnology, 21(07), 52-53.
Ma, J., Liu, J., & Sriboonchitta, S. (2018). Technical efficiency analysis of China’s Agricultural Industry: A Stochastic Frontier Model with Panel Data, International Conference of the Thailand Econometrics SocietyPredictive Econometrics and Big Data, vol 753, pp: 454-463.
Mahate, A., Hamidi, S., & Akinci, F. (2017). Measuring the effect of size on technical efficiency of the United Arab Emirates Hospitals. Global Journal of Health Science, 9( 3), 116-127.
Molaei, M., Jahanshahi, H., & Hosseini, A. (2011). Assessment of the efficiency of R&D centers: The application of window approach for data analysis. Proceedings of the Third National Conference of Data Envelopment Analysis (2011). (In Persian)
Mortazavi, S., Alipour, A., & Ghorbani, M. (2016). Examining factors affecting the sub-vector water efficiency in wheat production: A radial basis function artificial neural network and the Tobit model. The Economic Research, 16 (2), 117-135 (In Persian).
Mu, w., Kanellopoulos, C; Milddelaar, E; Stilmant, D & Bloemhof, J. M. (2018). Assessing the impact of uncertainty on benchmarking the eco-efficiency of dairy farming using fuzzy data envelopment analysis. Journal of Cleaner Production, 189, 709-717.
Muhammad, A., Rao, T., & Farooq, Q. (2018). DEA window analysis with slack-based measure of efficiency in Indian Cement Industry. Statitics, Optimization and Information Computing, 6, 292–302.
Parsa, P., Sadeghi, Z., & Jalaee, A. (2015). Decomposition of environmental total factor productivity growth using distance function in the provinces of Iran. Journal of Applied Economics Studies in Iran, 4 (16), 1-24. (In Persian with English Abstract)
Patra, A., & Ray, P. K. (2017). Measurement of efficiency and productivity growth of hospital systems: A Indian Case study, healthcare systems management: Methodologies and Applications. Managing the Asian Century. Springer, Singapore, pp 13-22.
Paul, S., & Shankar, S. (2018). On estimating efficiency effects in a stochastic frontier model. European Journal of Operational Research, 271(2), 769-774.
Rajabi, E. (2010). The optimum scale activity and stability of pure technical efficiency in the commercial banking system of Iran with DEA window analysis (1991-2006). Knowledge & Development, 17 (34), 91-125. (In Persian with English Abstract)
Ram Mohan, T T and Ray, S.C. (2004), ‘Comparing Performance of Public and Private Sector Banks: A Revenue Maximisation Efficiency Approach. Economic and Political Weekly, 39 (12), 1271-76.
Rao, D.S.P., Odonnell, C.J. & Battese, J. (2005). Metafrontier Functions for the study of Inter regional Productivity Differences.
Rezaee, J., Tavakkoli Baghdadabad, M., & Faghih Nasiri, M. (2008). An evaluation of changes in total productivity of factors of production in Iran's agricultural sector using nonparametric methods. Village and Development, 11 (3), 97-122. (In Persian with English abstract)
Sarica, K., Or, I. )2007(. Efficiency assessment of Turkish power plants using data envelopment analysis. Energy 32, 1484–1499.
Sengupta, J.K. (1995). Dynamics of data envelopment analysis:Theory of systems efficiency. Kluwer Academic Publishers, Boston.
Shakibai, A., & Golmohammadi, M. (2014). The impact of corporate listing in Tehran Stock Exchange on productivity: DEA approach. Journal of Economic Research and Policies, 22 (70), 103-118. (In Persian)
Shoja, N., Fallah Jelodar, M., & Darvish Motavali, M. (2009). Measuring productivity in the academic units and ranking them based on data envelopment analysis models and Malmquist. Journal of Economical Modeling, 3 (9), 159-176. (In Persian with English abstract)
Sokhanvar, M., Sadeghi, H., Assari, A., Yavari, K., & Mehregan, N. (2012). Structural analysis and efficiency trend of electricity distribution companies in Iran by using window data envelopment analysis. Journal of Economic Growth and Development Research, 1 (4), 145-182. (In Persian with English abstract)
Taeb, Z., Hosseinzadeh Lotfi, F., & Abbasbandy, S. (2017). Determine the Efficiency of Time Depended Units by Using Data Envelopment Analysis, International Journal of Research in Industrial Engineering, 6(3), 193-201.
Taghizadeh Mehrjardi, R., Yazdi, A., & Moheby, R. (2013). Modeling and predicting the efficiency of public and private banks in Iran using an artificial neural network model, fuzzy neural networks, and genetic algorithms. Asset management & Financing, 1 (2), 103-126. (In Persian with English abstract)
Tahamipour, M., Saleh, I., & Nemati, M. (2014). Measure and decompose total factor productivity growth in varieties of rice in Iran. Agronomy Journal (Pajouhesh & Sazandegi), 103, 96-104. (In Persian with English abstract)
Thanassoulis, E., & Silva, M. C. (2018). Measuring efficiency through data envelopment analysis, Journal Impact, 1, 37-41.
Toma, O., Miglietta, P., Zurlini, G., Valente, D., & Petrosillo, I. (2017). A non-parametric bootstrap-data envelopment analysis approach for environmental policy planning and management of agricultural efficiency in EU countries, Ecological Indicators, 83, 132-143.
Torabi Dastgerooyi, S., & Bakhshoudeh, M. (2007). A study on the trend of productivity variation of the agricultural sector in Islamic countries. Development and Productivity, 2 (6), 10-16. (In Persian)
Tozer, P.)2010). Measuring the efficiency of wheat production of western australian growers. Paper presented to the 54th annual meeting of the Australian Agricultural and Resource Economics Society, Adelaide (2009), SA. Australia.
Villano, V., & Fleming, E.( 2006). Technical inefficiency and production risk in rice farming: Evidence from Central Luzon Philippines. Asian Economic Journal, 20(1), 29-46,.
Wang, X., Sun, L., & Zhang, Y. (2012). The empirical study on operating efficiency of agricultural cooperatives in Langao. International Journal of Business and Management, 7(17), 60-74.
Ximing, Cai ., Yi-chen, E., Claudia, R., Zhao, J., & You, L. (2011) Agricultural water productivity assessment for the Yellow River Basin. Agricultural Water Management, 98, 1297-1306.
Xu, Q., Ding, S., & An, J. (2017). Empirical study on total factor productive energy efficiency in Beijing-Tianjin-Hebei Region-Analysis based on Malmquist Index and Window Model, IOP Conference Series: Earth and Environmental Science, 104, conference 1.
Zeranejad, M., & Yousefi Hajiabad, R. (2009). Assessment of the productivity of production factors in Maskan Bank using the Malmquist productivity index. Journal of Money and Economics, 2, 117-144. (In Persian)
