Dynamic Efficiency Analysis of World Railway Firms: A DEA-Window Analysis with Malmquist Index

Yıl 2015, Cilt: 15 Sayı: 1, 27 - 41, 01.03.2015

Aziz Kutlar , Ali Kabasakal , Pınar Torun

https://doi.org/10.18037/ausbd.57515

Öz

Bu çalışmada, 2000-2009 döneminde faaliyette bulunan 31 adet dünya demiryolu şirketinin VZA- Pencere Analizi ile dinamik Teknik Etkinlik ve Tahsis Etkinliği ve Ortalama Etkinlik skorları CCR ve BCC metotlarıyla elde edilmek istenmiştir. CCR modeli ile yapılan analizde ilk yıl için toplam 17 firma etkin iken 2009 yılı için bu sayı 18 firmaya çıkmıştır. BCC modeli ile girdi yönelimli ve değişken getirili analizde dönem başında teknik etkinliğe sahip firma sayısı 20 iken dönem sonunda bu rakam 24’e çıkmaktadır. Pencere Analizi bulguları tüm firmaların dengeli bir ortalama verimlilik oranı ve benzer yapıda standart sapma değerlerine sahip olduklarını göstermektedir. Toplam Faktör Verimliliği için Malmquist Endeksi kullanılarak yıllar boyunca tüm işletmeler için Toplam Faktör Verimliliğinin sadece binde üç düzeyinde arttığı tespit edilmiştir.

Anahtar Kelimeler

Panel Data, VZA, TFV, Demiryolu

Dünya Demiryolu Firmalarının Dinamik Etkinlik Analizi: Malmquist Endeksli bir VZA-Pencere Analizi

Öz

This study attempts to obtain Technical Efficiency (TE), Allocative Efficiency (AE) and Average Efficiency of dynamic DEA-Window analysis scores of 31 railway firms operating worldwide. The data set covering the period of 2000 to 2009 is analyzed by CCR and BCC methods. In the analysis conducted by use of the CCR model, while total 17 firms are efficient in the first year, this figure reaches to 18 firms for the last year with one increment. With input oriented and variable return analysis conducted by use of the BCC model, the firms having TE at the beginning of the period were 20 in number. At the end of the period, the figure reaches to 24. Window analysis suggests that all firms in general have stable average efficiency rate and inefficiencies of the firms and standard deviations of their efficiency scores exhibit a similar pattern. Malmquist Index (MI) analysis also suggests that Total Factor Productivity has increased by 0.03% for the entire period.

Anahtar Kelimeler

Panel Data, DEA, TFP, Railway

Kaynakça

• Atkinson, S.E. & Cornwell, C. (1998). Estimating Ra- dial Measures of Productivity Growth: Frontier vs Non-Frontier Approaches. Journal of Productivity Analysis, 10, 35-46.
• Balgati, B.H. (2008). Econometrics. Berlin: Springer.
• Banker, R.D., Charnes, A. & Cooper, WW (1984). Some Models for Estimating Technical and Sca- le Inefficiency in DEA. Management Science, 30, 1078-1092.
• Cantos, P., Pastor, ],M. & Serrano, L. (1999). Produc- tivity EfŞciency and Technical Change in the Eu- ropean Railways: A Non-Parametric Approach. Transportation, 26 (4), 337-357.
• Caves, D., Christiensen, L.R. & Diewert, WE. (1982). The Economic Theory of Index Numbers and the Measurement of Input Output and Productivity. Econometrica, 50,1393-1414.
• Charnes, A., Cooper, WW & Rhodes, E. (1978). Me- asuring the Efficiency of Decision Making Units. European Journal of Operational Research, 2,429- 444.
• Charnes, A., Cooper, WW & Rhodes, E. (1979). Short Communication: Measuring the EfŞciency of De- cision Making Units. European Journal of Operati- onal Research, 3(4), 339.
• Coelli, T.A. & Perelman, S. (1999). A Comparison of Parametric and Non-Parametric Distance Functi- ons with Application to European Railways. Euro- pean Journal of Operational Research, 117, 326-339.
• Cooper, WW, Seiford, L.M. & Tone, K. (2006). Intro- duction to Data Envelopment Analysis and its Uses. New York : Springer.
• Cowie, J. (1999). The Technical EfŞciency of Public and Private Ownership in the Rail Industry: The Case of Swiss. Journal of Transport Economics and Policy, 33(3), 241-252.
• Çekerol, G.S. & Nalçakan, M. (2011). Lojistik Sektörü Içerisinde Türkiye Demiryolu Yurtiçi Yük Taşıma Talebinin Ridge Regresyonla Analizi. Marmara Üniversitesi İİBF Dergisi, 31(2), 321-344.
• Debreu, G. (1951). The Coeşicient of Resource Utili- zation. Econometrica, 19(3), 273—292.
• Duman, İ. (2006, 13-16 December). Demiryollarının Yeniden Yapılanma Ihtiyacı ve TCDD’deki Geliş- meler. Paper presented at International Railway Symposium, 2, 13-15. Ankara: Ortadoğu.
• Estache, A., Fe, B.T. & Trujillo, L. (2004). Sources of EfŞciency Gains in Port Reform, a DEA Decom- position of a Malmquist TFP Indeks for Mexico. Utulities Policy, 12, 221-230.
• Farrell, M.J. (1957). The Measurement of Productive EfŞciency. Journal of the Royal Statistical Society, 120(3), 253-281.
• Gujarati, D.N. (2004). Basic Econometrics. New York: McGraw Hill.
• Jitsuzumi, T. & Nakamura, A. (2010). Causes of InefŞ- ciency in Japanese Railways: Application of DEA for Managers and Policymakers. Socio-econornic Planning Sciences, 44, 161-173.
• Kabasakal, A., Kutlar, A. & Sarikaya, M. (2013). EfŞ- ciency Determinations of the Worldwide Railway Firms via DEA and Contributions of the Outputs to the EfŞciency and TFP by Panel Regression. Central European Journal of Operations Research. doi:10.1007/810100-013-0303-X
• Kim, H., Choi, C., Woo, J., Choi, Y., Kim, K. & Wu, D. (2010). Efficiency of the Modal Shift and Envi- ronmental Policy on the Korean Railroad. Stochas- tic Environmental Research & Risk Assessment, 25, 305-322.
• Koopmans, T.C. (1951). An Analysis of Production as an EfŞcient Combination of Activities. In T.C. Ko- opmans (Ed.), Activity Analysis Of Production And Allocation. New Jersey: John Wiley and Sons.
• Kumbhakar, S.C. & Lovell, C.A.K. (2000). Stochastic Frontier Analysis. Cambridge: University Press.
• Liu, F.F. & Wang, RH. (2008). DEA Malmquist Pro- ductivity Measure: Taiwanese Semiconductor Firms. International Journal of Production Econo- mics, 112, 367-379.
• Malmquist, S. (1953). Indeks Number and Indifferen- ces Surfaces. Trabajos de Estatistica, 4, 209-242.
• Nashand, A.S.J., & Nash. C.A. (2010). Benchmarking of Train Operating Firms: A Transaction Cost EfŞ- ciency Analysis. Transportation Planning and Tech- nology, 33(1), 35-53.
• Sabri, K., Colson, G.E. & Mbangala, A.M. (2008). Mul- tiple Criteria and Multiple Periods Performance Analysis: The Comparison of North African Rail- ways. Computing Anticipatory Systems: CASYS’07- Eighth International Conference, AIP Conference Proceedings, 1051, 351-365.
• TCDD (2011). Türkiye Cumhuriyeti Devlet Demir- yolları Istatistik Yıllığı: 2006-2010. http://www. tcdd.gov.tr/Upload/Files/ContentFiles/2010/ istatistik/20062010yillik.pdf.
• Tulkens, H. & Eeckaut , P. V. (1995). Non-Parametric Efficiency, Progress and Regress Measures for Panel Data: Methodological Aspects. European Journal of Operational Research, 80(3), 474-499. doi:IO.1016/0377-2217(94)00132-V
• UIC (2010). Railway Time-Series Data 1970-2009. http://www.uic.org/etf/publication/publication- detailphp?code_pub=302/70-09x1.
• Wang, S. & Liao, C. (2006). Cost Structure and Pro- ductivity Growth of the Taiwan Railway. Transpor- tation Research Part E, 42, 317—339.
• Yu, M. (2008). Assessing the Technical EfŞciency Ser- vice Effectiveness and Technical Effectiveness of The World’s Railways Through NDEA Analysis. Transportation Research Part A, 42, 1283—1294.