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Efficiency determinations of the worldwide railway companies via DEA and contributions of the outputs to the efficiency and TFP by panel regression

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  • Ali Kabasakal
  • Aziz Kutlar
  • Murat Sarikaya

Abstract

This study attempts to obtain efficiency scores of thirty-one railway companies operating worldwide by using data envelopment analysis. To assess the data covering a term from 2000 to 2009, we use CCR and BCC methods. According to the results of the CCR model analysis, 17 firms are found to have technical efficiency in the first year, whereas this figure goes up to 18 companies in the last year. Input oriented and variable return analysis in the concept of the BCC model, the number of the firms found technically efficient at the beginning of the period are 20. At the end of the period, this figure is up to 24. We implement panel regression analysis to estimate the effects of the companies’ output on their efficiency. Results imply that CCR models provide us more meaningful explanations. Malmquist Index analysis also indicates that total factor productivity increases by 0.03 % for the entire period. Copyright Springer-Verlag Berlin Heidelberg 2015

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  • Ali Kabasakal & Aziz Kutlar & Murat Sarikaya, 2015. "Efficiency determinations of the worldwide railway companies via DEA and contributions of the outputs to the efficiency and TFP by panel regression," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(1), pages 69-88, March.
    • Handle: RePEc:spr:cejnor:v:23:y:2015:i:1:p:69-88
    DOI: 10.1007/s10100-013-0303-x
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    Cited by:

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    2. Boldanova, Elena V. & Bakalenko, Natalya P., 2020. "Concession agreements as a means of attracting investment in transport infrastructure," Economic Consultant, Roman I. Ostapenko, vol. 29(1), pages 4-11.
    3. Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2018. "Optimizing headways for urban rail transit services using adaptive particle swarm algorithms," Public Transport, Springer, vol. 10(1), pages 23-62, May.
    4. Marchetti, Dalmo & Wanke, Peter, 2017. "Brazil's rail freight transport: Efficiency analysis using two-stage DEA and cluster-driven public policies," Socio-Economic Planning Sciences, Elsevier, vol. 59(C), pages 26-42.
    5. Hanauerová, Eliška, 2019. "Assessing the technical efficiency of public procurements in the bus transportation sector in the Czech Republic," Socio-Economic Planning Sciences, Elsevier, vol. 66(C), pages 105-111.

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    Keywords

    Panel data; DEA; TFP; Railway;
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