کاربرد روش‌های ناپارامتریک اصلاح شده در ارزیابی کارایی فنی تولید چغندرقند ایران

نوع مقاله : مقالات پژوهشی

نویسندگان

دانشگاه تبریز

چکیده

علی‌رغم کاربرد فراوان روش‌های ناپارامتریک نظیر تحلیل پوششی داده‌ها (DEA) و تحلیل رویه آزاد (FDH)در محاسبه کارایی، اما این روش‌ها به دلیل ماهیت قطعی بودن این دو روش بوده زیرا نتایج نسبت به مشاهدات پرت و خطای اندازه‌گیری ناکارآمد تلقی می‌شود.در این راستا پیشرفت-های اخیر در ایجاد مرزهای جزئی تولید، نظیر رتبه-m و رتبه- ضعف‌های مربوط به الگوهای ناپارامتریک را رفع کرده است. بدین منظور در مطالعه حاضر جهت مقایسهتجربی چهار الگوی ناپارامتریک مذکور، کارایی فنی تولید چغندرقند در کشور با استفاده از داده‌های پانل مربوط به یازده استان‌ عمده تولیدکننده این محصول طیدوره زمانی91-1379 و بر اساس تحلیل پنجره‌ای مورد مطالعه قرار گرفت.به استناد یافته‌های حاصل و بر اساس الگوهای رتبه-m و رتبه- استان‌ کرمان به عنوان استان‌ فوق‌ کاراطبقه‌بندی شده و استان‌ اصفهان ناکاراترین استان‌ در تولید چغندرقند کشور می‌باشد. مطابق الگوهای تحلیل پوششی داده‌های پنجره‌ای و رویه دسترس آزاد پنجره‌ای نیز، استان‌های آذربایجان‌غربی، خراسان و لرستان جزء کاراترین استان‌ها در تولید چغندرقند کشور می‌باشند و استان فارس ناکاراترین استان در تولید این محصول محسوب می‌شود. با توجه به این‌که الگوهای مرزی کامل نسبت به الگوهای مرزی جزئی دارای فروضی می‌باشند که به واقعیت نزدیک‌تر است بنابراین نتایج حاصل از این الگوها مطلوب‌تر می‌باشند. از این رو پیشنهاد می‌گردد که در مطالعات آتی جهت اندازه‌گیری کارایی فنی و رتبه‌بندی واحدهای تصمیم‌گیرنده از این الگوها بهره گرفته شود.

کلیدواژه‌ها


عنوان مقاله [English]

Application of Modified Non-Parametric Models in Technical Efficiency of Iran’s Sugar Beet Production

نویسندگان [English]

  • G. Dashti
  • masoomeh rashidghalam
  • E. Pishbahar
University of Tabriz
چکیده [English]

Introduction
Despite their advantages, the DEA and FDH approaches have been criticized by econometricians for being deterministic, lacking a well-defined data generating process and for being highly sensitive to outliers and measurement errors. This objection was recently addressed by so-called partial frontier approaches, namely order-m and order-αefficiency measurement methods. To examine technical efficiency over time we used four non-parametric models using Window analysis which include Window-DEA, Window-FDH, Window order-α and Window order-m models. Window analysis uses moving average patterns in a way that every provincial observation in each period treated as if it is a different observation. Window analysis leads to a higher number of observations. The aim is to shed light on temporal patterns and heterogeneity in efficiency, time dependency and sensitivity of the results attributed to different efficiency measurement methodologies that are applied using the same dataset. In this study, we analyze the performance of cotton producing provinces in Iran over 2000-12.
Materials and Methods
Efficiency can be measured using parametric, semi-parametric and non-parametric approaches. In this study non-parametric approach was selected.The main contributions of this study are related to useing of four different Windows-based models to estimate technical efficiency using the same data and investigating the consistency of efficiency rankings. The DEA method is a non-parametric mathematical programming approach used to evaluate a set of comparable decision-making units (DMUs). Another method which has received a considerable amount of research attention is the FDH model which first formulated by Deprins et al., (1984).FDH estimator is both a deterministic and non-parametric tool for measuring productive efficiency. It is deterministic due to its inability to accommodate stochastic properties, it’s non- parametric nature arise from its lack of functional form specification. Like the DEA estimator, the FDH is also very sensitive to outliers/ extreme observations, susceptible to dimensionality problems and highly sensitive to noise. The basic motivation in FDH is to ensure that efficiency evaluations are affected only from actually observed performances. All the nonparametric envelopment estimators of frontiers are particularly sensitive to extreme observations, or outliers. These extreme points may disproportionately, and perhaps misleadingly, influence the evaluation of the performance of other firms. This drawback also plagues parametric frontier estimators when deterministic frontier models are considered. Then we use two partial frontier models: order-m and Order-α. According to Daraio and Simar (2007), order-m generalizes FDH by adding a layer of randomness to the computation of efficiency scores. Order-α is also a generalization of the FDH estimator which employs the (100-α)th percentile approach to minimize input consumption among available peers for benchmarking. At the end of the paper, we calculated the Spearman rank-order correlation coefficients (r) to determine how close the implied rankings of provinces were in each of the models.

Results and Discussion
This study analyzes the technical efficiency of Iran's 13 major sugar beet producing provinces over the period 2000-2012. According to the results, mean technical efficiency of models Window-DEA, Window-FDH, Window order-m and Window order- were 0.67, 0.94, 0.80 and 0.77 respectively. According to Window order-α and Window order-m, Kerman is supper efficient province and Isfahan is the most inefficient province. Also with respect to Window-DEA and Window-FDH, West-Azerbayjan, Khorasan and Lorestan provinces are the most efficient provinces in Iran’s sugar beet production. When we drop the convexity assumption (that is, move from Window-DEA to Window-FDH), the estimated efficiency scores become higher (as expected since the best practice frontier then wraps itself closer around the data). According to Spearman rank-order correlation coefficient results there is a strong correlation between full frontier models. Technical efficiency correlation between model one and models three and four is positive but insignificant. A very strong correlation also is between model three and four, therefore these models could be substituted in technical efficiency studies.
Conclusions
According to the results and almost all of the models, Khorasan province is one of the most efficient provinces in Iran’s sugar beet production. Therefore it is recommended that policy makers have a special attention to this province as a potential to increase sugar beet production. It is also recommended that it would be better if experience could be transferred from efficient to non-efficient provinces. On the other hand, due to the fact that full frontier models have more realistic assumption than partial frontier models, it is recommended that the former ones be utilized in future studies to get more reliable efficiency scores.

کلیدواژه‌ها [English]

  • Window-DEA
  • Window-FDH
  • Window order-alpha
  • Window order-m
1- Abrishami H., and Niakan L. 2010. Measuring the technical efficiency of Iranian power plants using stochastic frontier analysis (SFA) and comparison with selected developing countries. Quarterly Energy Economics Review, 26: 153-175. (in Persian with English abstract).
2- Afriat S.N., 1972. Efficiency estimation of production functions. International Economics Review, 13, 568-598.
3- 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.
4- Amadeh H., EmamiMeibodi A. and Azadinezhad A. 2009. Ranking the Iranian provinces by technical efficiency of industrial sector by applying DEA method. Journal of Science and Development, 29: 162-180. (in Persian with English abstract)
5- Aragon Y., Daouia A. and Thomas-Agnan C. 2005. Nonparametric frontier estimation: a conditional quantile based approach. Econometric Theory, 21: 358-389.
6- Asmild M.,ParadiJ.C.,AggarwallV. and Schaffnit C.2004. Combining DE AwindowanalysiswiththeMalmquistindexapproachinastudyoftheCanadian bankingindustry. Journal ofProductivityAnalysis, 21: 67–89.
7- Babaipur M., Rastegri F. and Sabuhi M. 2012. Analysing efficiency of cucumber greenhouses using distnce envelopment analysis. Journal of Agricultural Economics and Development, 26(2):117-125. (In Persian).
8- Banker R.D., Charnes A. and Cooper W.W. 1984. Some models for estimating technical and scale inefficiency in Data Envelopment Analysis, Management Science, 30:1078-92.
9- Bauer P.W., Berger A.N., Ferrier G.D. and Humphrey D.B. 1997. Consistency conditions for regulatory analysis of financial institutions: A comparison of frontier efficiency methods, US Federal Reserve Financial Services, Working Paper 02(97).
10- Boles J.N. 1996. Efficiency squared- efficient computation of efficiency indexes, Proceedings of the 39th Annual Meeting of the Western Farm Economic Association, 137-142.
11- Borimnejd V. and Mohtashami T. 2009. Technical efficiency of wheat production in Iran: case study. Journal of Agricultural Economics, 1:75-94. (In Persian).
12- Carbone T.A. 2000. Measuringefficiencyofsemiconductormanufacturingoperationsusingdataenvelopmentanalysis(DEA). In IEEE/SEMI advancedsemiconductormanufacturingconference, 56–62.
13- Cazals C., Florens J.P., and Simar L. 2002. Nonparametric Frontier Estimation: A Robust Approach. Journal of Econometrics, 106: 1-25.
14- Charnes A. and Cooper W.W. 1985. Preface to topics in data envelopment analysis, Annals of Operations Research, 2, 59-94.
15- Charnes A., Cooper W.W., and Rhodes E. 1978. Measuring the efficiency of decision making units. European Journal of Operations Research, 2:429-444.
16- Chiami B.C. 2011. Determinants of technical efficiency in smallholder sorghum farming in Zambia. MSc Thesis, Graduate School of The Ohio State University
17- Coelli T., 2008. Aguide to DEAP version 2.1: A data envelopment analysis (computer) program, CEPA Working Paper 96/08.
18- Daraio C. and Simar L. 2005. Conditional nonparametric frontier models for convex and non-convex technologies: A unifying approach, Discussion Paper #0502, Institute de Statistique, Universit´eCatholique de Louvain, Louvain-la-Neuve, Belgium.
19- Daraio C., and Simar L. 2007. Advanced Robust and Nonparametric Methods in Efficiency Analysis: Methodology and Applications, New York: Springer
20- DeBorger B., Kerstens K., Moesen W., and Vanneste J. 1994. A non-parametric free disposal hull (FDH) approach to technical efficiency: An illustration of radial and graph efficiency measures and some sensitivity results. Swiss Journal of Economics and Statistics, 130(4): 647-667.
21- Deprins D., and Tulkens H., 1984. Measuring labour efficiency in post offices. In Marchand, M. and Tulkens, H. (eds.) the Performance of Public Enterprises: Concepts and Measurement North-Holland, 243-267.
22- De Witte K., and Marques R.C., 2010. Influential observations in frontier models, a robust non-oriented approach to the water sector. Ann Oper Res. http://dx.doi.org/10.1007/s10479-010-0754-6
23- Esari A., Sadegi H., Sokhanvar M., Mehregan M. and Yavari K. 2011. Applying window data envelopment analysis to measure structure and trend of efficiency in Iranian power plants. Journal of Economic Growth and Development Research, 1(4): 145-182. (in Persian).
24- Farrell M.J., 1957. The Measurement of Productive Efficiency. Journal of the Royal Statistical Society, Series A (General), 120(3): 253-290.
25- Gabdo B.H., Abdlatif I.B., Abidin Mohammed Z.A. and Shamsuddin M.N. 2014. Comparative estimation of technical efficiency in livestock-oil palm integration in Johor, Malaysia: Evidence from full and partial frontier estimators. Journal of Agricultural Science, 6(3):140-150.
26- Karami A., Eftekhari S.F. and Abdshahi A. 2012. Anlyzing technical efficiency of firms in Kohgiluye Va Boyerahmad province (milk cow, meat chiken and fish farming). Journal of Agricultural Economics, 4(3):59-76.(in Persian)
27- Ministry of Agriculture Jihad. 2013. Agricultural statistics volume I- crops. Department of economic and planning.Center for Information and Communication Technology.
28- Pjevcevic D., Radonji A., Hrle Z. and Colic V. 2012. DEA Window Analysis for measuring port efficiencies in Serbia. Traffic & Transportation, 24(1):63-72.
29- Řepkova I., 2014. Efficiency of the Czech banking sector employing the DEA window analysis approach. Procedia Economics and Finance, 12:587 – 596.
30- Ross A. and Droge C.2002.AnintegratedbenchmarkingapproachtodistributioncenterperformanceusingDEAmodeling. Journal ofOperationsManagement, 20:19–32.
31- SeyedSharifi R., 2013. Industrial Crops. Amidi, University of MohagheghArdabili, Ardebil, Iran.
32- SueyoshiT., andAokiS.2001.Auseof a nonparametricstatisticforDEAfrontiershift:TheKruskalandWallisranktest. Omega Interntional Journal of Management Science, 29:1–18.
33- Tauchmann H. 2011. Partial frontier efficiency analysis for Stata. Discussion Paper, SF 823.
34- Yaghubi M., Shahrki J., and Karbasi A., 2009. Efficiency survey of cooperative and non-cooperative shrimp razing units in Chabahar using data envelopment analysis (application of CCR and FDH models). Cooperation Journl, 21(4):71-95. (In Persian)
35- Yang H.H., and Chang C.Y. 2009. Using DEA window analysis to measure efficiencies of Taiwan’s integrated telecommunication firms. Telecommunications Policy, 33:98-108.