Technical Efficiency of Cotton Production in Iran Using Panel Data Models

Document Type : Original Article

Authors

1 PhD Graduate in Agricultural Economics, University of Tabriz, Iran

2 Professor of Agricultural Economics, University of Tabriz, Iran

3 Associate Professor of Agricultural Economics, University of Tabriz, Iran

Abstract

Increasing the efficiency of agricultural products is an important factor affecting the high performance without additional cost. According to the limitations of the agricultural sector to increase production through the development of production factors, it seems the best way to achieve appreciate growth in agricultural production is to improve and increase the efficiency of agricultural crop producing provinces. In this regard, this paper applied a stochastic frontier analysis to measure technical efficiency of Iran’s cotton production using panel data for the period of 2000-2012. For all the models, the estimated output elasticity of inputs as pesticide, chemical fertilizer and labor was positive and significant. Results indicated that production process was applied in the economic zone. According to most of the models, Khorasan, Ardebil and East Azerbaijan were the most efficient provinces, standing on the first to third ranks, respectively. Time distribution of technical efficiency indicated that the technical efficiency of provinces had decreased during the study period. Differences between minimum and maximum efficiency rates of provinces reflected the fact that it was possible to reallocate proper input usage and management. On the other hand, due to suitable climate, fertile soil and favorable moisture conditions of Ardebil province for cotton production, this province has to be considered as one of the material and immaterial resources distribution indices by government. In addition, the results showed that machinery led to a significant increase in technical efficiency in cotton production; therefore, the usage of new technology and machinery was recommended in cotton production. Finally, it was recommended for further studies to use model twelve in efficiency measurement; furthermore, investigation of sources of technical inefficiency revealed that inorganic fertilizer resulted in reduction in the technical efficiency.

Keywords


  1. Ahn, S.C., Young, A.E., Lee, Y.H. and Schmidt, P. (2007). Stochastic frontier models with multiple time-varying individual effects. Journal of Productivity Analysis, 27(2): 1-12.
  2. Aigner, D.J., Lovell, C.A.K. and Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6: 21-37.
  3. Battese, G. E. and Coelli, T. J. (1988).Prediction of firm level technical efficiencies with a generalized frontier production function and panel data. Journal of Econometrics, 38(3): 387–399.
  4. Battese, G. E. and Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2): 325–332.
  5. Battese, G. E. and Coelli, T. J. (1992). Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. Journal of Productivity Analysis, 3: 153-169.
  6. Behrouz, A.S. and Imami, Meybodi, AS. (2014). Measurement of technical, allocation, economic and productivity of Iran's subsection of agriculture through non-parametric method (with emphasis on watermelon production). Journal of Agricultural Economics Research, 6(3): 66-43. (Persian)
  7. Chen, Y.-T., Wang, H.-J. (2012). Centered-residuals-based moment tests forstochastic frontier models. Economic Review, 31: 625–653.
  8. Cornwell, C., Schmidt, P. and Sickles, R.C. (1990). Production frontiers with cross-sectional and time-series variation in efficiency levels. Journal of Econometrics, 46: 185-200.
  9. Dorandish A.,Kohansaal, M. R., Shah Nushi Salehini, N. and Hossein Zadeh, M. (2013). Survey of technical efficiency of barberry producers in Southern Khorasan province. Journal of Agricultural Economics, 6 (2): 120-101. (Persian)
  10.  Greene, W. (2005a).Fixed and random effects in stochastic frontier models. Journal of Productivity Analysis, 23: 7–32.
  11.  Greene, W. (2005b).Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126: 269–303.
  12.  Greene, W.H. (1980). On the estimation of a flexible frontier production model. Journal of Econometrics, 13(1):101–115.
  13.  Greene, W.H. (1990). A gamma distributed stochastic frontier model. Journal of Econometrics, 46(1): 141–164.
  14.  Haeri, A.S. and Asayesh, A.(2009). Study of cotton status in Iran and the world. Office of Statistical Studies and Strategic Studies of the Iranian Textile Industry. (Persian)
  15.  Hallam, D. and Machado, F. (1995). Efficiency analysis with panel data: A study of Portuguese dairy farms. European Review of Agricultural Economics, 23: 79-93.
  16.  Hosseinpoor, A., Moghadasi, R. and Yazdani, S.(2013). Study of technical efficiency and its effective factors in the industry of glasgowin Kashan. Two-Letter paper-Exploration of the Facts about the Market, 30 (1): 56-42. (Persian)
  17.  Jondrow, J., Lovell, C.A.K., Materov, I.S. and Schmidt, P. (1982). On the estimation of technicalinefficiency in the stochastic frontier production function model. Journal of Econometrics, 19(2-3): 233-238.
  18.  Kumbhakar, S.C. (1987). Production frontiers and panel data: an application to U.S. class 1 railroads. Journal of Business and Economics Statistics, 5: 249-255.
  19.  Kumbhakar, S.C. (1990). Production frontiers, panel data, and time-varying technical inefficiency. Journal of Econometrics, 46: 201-212.
  20.  Kumbhakar, S.C. and Lovell, C.A.K. (2000).Stochastic frontier analysis. Cambridge University Press, Cambridge.
  21.  Kumbhakar, S.C. and Wang, H.J. (2005).Estimation of growth convergence using a stochastic production function approach.Economic Letters, 88: 300-305.
  22.  Kumbhakar, S.C., Lien, G. and Hardaker, J.H. (2014). Technical efficiency in competing panel data models: a study of Norwegian grain farming. Journal of Productivity Analysis, 14(2): 321-337.
  23.  Lambarraa, F. (2012). The Spanish horticulture sector: a dynamic efficiency analysis of outdoor and greenhouse farms. Selected Paper Prepared for Presentation at the International Association of Agricultural Economists (IAAE) Triennial Conference, Foz do Iguaçu, Brazil, 18-24 August, 2012.
  24.  Lee, Y.H. and Schmidt, P. (1993). A production frontier model with flexible temporal variation in technical efficiency. Chapter 8, in the Measurement of Productive Efficiency Techniques and Applications, eds., Fried, H., C.A.K.
  25.  Meeusen, W. and van den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2):435– 444.
  26.  Ministry of Jihad-e-Agriculture. (2013). The first volume Agricultural crops Statistics, Deputy Director of Planning and Economics, ICT Center. (Persian)
  27.  Mohammed, R. and Saghaian, S. (2014). Technical efficiency estimation of rice production in South Korea. Selected Paper Prepared for Presentation at the 2014 Southern Agricultural Economics Association (SAEA) Annual Meetings in Dallas.
  28.  Pitt, M. and Lee, L.F. (1981).The measurement and sources of technical inefficiency in the Indonesian weaving industry. Journal of Development Economics, 9: 43-64.
  29.  Richmond, J. (1974). Estimating the efficiency of production. International Economic Review, 15(2): 515–521.
  30.  Schmidt, P. and Lovell, C.A.K. (1979). Estimating technical and allocative inefficiency relative to stochastic production and cost frontiers. Journal of Econometrics, 9: 343-366.
  31.  Schmidt, P. and Sickles, R.C. (1984). Production frontiers and panel data.Journal of Business and Economic Statistics, 4: 367-374.
  32.  Stevenson, R.E. (1980). Likelihood functions for generalized stochastic frontier estimation. Journal of Econometrics, 13(1): 57–66.