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Efficiency in rail transport: Evaluation of the main drivers through meta-analysis with resampling


  • Marchetti, Dalmo
  • Wanke, Peter F.


Meta-analysis is a statistical method used to make a systematic review of the literature to integrate the results of a series of studies. It is increasingly adopted in social sciences but according to our best knowledge used for the first time to aggregate and contrast findings on rail transport efficiency. The experiment adopted a permutation test to evaluate the influence of variables discussed in the literature in the mean efficiency scores. The results suggest that railways located in Japan and in the US have characteristics that push them toward increasing efficiency. The passenger rail systems reached significantly higher estimates than conventional cargo systems. Estimates from parametric and nonparametric models showed significant difference, while from nonparametric models including Data Envelopment Analysis (DEA) and from Network DEA did not. The number of variables and the ratio between the number of decision making units and the number of variables employed significantly influenced the scores. Unexpectedly, different data structures did not. Validation methods are presented. Public policies based on the empirical results are commented.

Suggested Citation

  • Marchetti, Dalmo & Wanke, Peter F., 2019. "Efficiency in rail transport: Evaluation of the main drivers through meta-analysis with resampling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 120(C), pages 83-100.
  • Handle: RePEc:eee:transa:v:120:y:2019:i:c:p:83-100
    DOI: 10.1016/j.tra.2018.12.005

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    References listed on IDEAS

    1. Howard, II., James P., 2016. "Meta-Analysis with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(b01).
    2. Castillo-Manzano, José I. & Castro-Nuño, Mercedes, 2012. "Driving licenses based on points systems: Efficient road safety strategy or latest fashion in global transport policy? A worldwide meta-analysis," Transport Policy, Elsevier, vol. 21(C), pages 191-201.
    3. Antonio Couto & Daniel Graham, 2008. "The contributions of technical and allocative efficiency to the economic performance of European railways," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 7(2), pages 125-153, August.
    4. 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.
    5. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    6. Koetter, Michael & Karmann, Alexander & Fiorentino, Elisabetta, 2006. "The cost efficiency of German banks: a comparison of SFA and DEA," Discussion Paper Series 2: Banking and Financial Studies 2006,10, Deutsche Bundesbank.
    7. Yu, Ming-Miin, 2008. "Assessing the technical efficiency, service effectiveness, and technical effectiveness of the world's railways through NDEA analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(10), pages 1283-1294, December.
    8. Dervaux, Benoît & Kerstens, Kristiaan & Vanden Eeckaut, Philippe, 1998. "Radial and nonradial static efficiency decompositions: a focus on congestion measurement," Transportation Research Part B: Methodological, Elsevier, vol. 32(5), pages 299-312, June.
    9. Dimitropoulos, Alexandros & Rietveld, Piet & van Ommeren, Jos N., 2013. "Consumer valuation of changes in driving range: A meta-analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 55(C), pages 27-45.
    10. Crafts, Nicholas & Mills, Terence C. & Mulatu, Abay, 2007. "Total factor productivity growth on Britain's railways, 1852-1912: A reappraisal of the evidence," Explorations in Economic History, Elsevier, vol. 44(4), pages 608-634, October.
    11. Oum, Tae Hoon & Pathomsiri, Somchai & Yoshida, Yuichiro, 2013. "Limitations of DEA-based approach and alternative methods in the measurement and comparison of social efficiency across firms in different transport modes: An empirical study in Japan," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 57(C), pages 16-26.
    12. Yu, Ming-Miin & Lin, Erwin T.J., 2008. "Efficiency and effectiveness in railway performance using a multi-activity network DEA model," Omega, Elsevier, vol. 36(6), pages 1005-1017, December.
    13. 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.
    14. Nicholas Crafts & Timothy Leunig & Abay Mulatu, 2008. "Were British railway companies well managed in the early twentieth century?1," Economic History Review, Economic History Society, vol. 61(4), pages 842-866, November.
    15. Aziz Kutlar & Ali Kabasakal & Murat Sarikaya, 2013. "Determination of the efficiency of the world railway companies by method of DEA and comparison of their efficiency by Tobit analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(6), pages 3575-3602, October.
    16. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
    17. Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
    18. Mohammad, Sara I. & Graham, Daniel J. & Melo, Patricia C. & Anderson, Richard J., 2013. "A meta-analysis of the impact of rail projects on land and property values," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 158-170.
    19. Suzuki, Soushi & Nijkamp, Peter & Rietveld, Piet & Pels, Eric, 2010. "A distance friction minimization approach in data envelopment analysis: A comparative study on airport efficiency," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1104-1115, December.
    20. Graham, Daniel J., 2008. "Productivity and efficiency in urban railways: Parametric and non-parametric estimates," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(1), pages 84-99, January.
    21. Dean A. Follmann & Michael A. Proschan, 1999. "Valid Inference in Random Effects Meta-Analysis," Biometrics, The International Biometric Society, vol. 55(3), pages 732-737, September.
    22. Olli-Pekka Hilmola, 2007. "European railway freight transportation and adaptation to demand decline: Efficiency and partial productivity analysis from period of 1980-2003," International Journal of Productivity and Performance Management, Emerald Group Publishing, vol. 56(3), pages 205-225, March.
    23. Wardman, Mark & Chintakayala, V. Phani K. & de Jong, Gerard, 2016. "Values of travel time in Europe: Review and meta-analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 93-111.
    24. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    25. Subal Kumbhakar & Luis Orea & Ana Rodríguez-Álvarez & Efthymios Tsionas, 2007. "Do we estimate an input or an output distance function? An application of the mixture approach to European railways," Journal of Productivity Analysis, Springer, vol. 27(2), pages 87-100, April.
    26. Holmgren, Johan, 2007. "Meta-analysis of public transport demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(10), pages 1021-1035, December.
    27. Adler, Nicole & Martini, Gianmaria & Volta, Nicola, 2013. "Measuring the environmental efficiency of the global aviation fleet," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 82-100.
    28. Mallikarjun, Sreekanth & Lewis, Herbert F. & Sexton, Thomas R., 2014. "Operational performance of U.S. public rail transit and implications for public policy," Socio-Economic Planning Sciences, Elsevier, vol. 48(1), pages 74-88.
    29. Peter Wanke & Barros, 2015. "Slacks determinants in Brazilian railways: a distance friction minimization approach with fixed factors," Applied Economics, Taylor & Francis Journals, vol. 47(47), pages 5103-5120, October.
    30. Feli X. Shi, 2011. "Railroad productivity analysis: case of the American Class I railroads," International Journal of Productivity and Performance Management, Emerald Group Publishing, vol. 60(4), pages 372-386, April.
    31. Jitsuzumi, Toshiya & Nakamura, Akihiro, 2010. "Causes of inefficiency in Japanese railways: Application of DEA for managers and policymakers," Socio-Economic Planning Sciences, Elsevier, vol. 44(3), pages 161-173, September.
    32. Fare, Rolf & Knox Lovell, C. A., 1978. "Measuring the technical efficiency of production," Journal of Economic Theory, Elsevier, vol. 19(1), pages 150-162, October.
    33. Cavill, Nick & Kahlmeier, Sonja & Rutter, Harry & Racioppi, Francesca & Oja, Pekka, 2008. "Economic analyses of transport infrastructure and policies including health effects related to cycling and walking: A systematic review," Transport Policy, Elsevier, vol. 15(5), pages 291-304, September.
    34. Martijn Brons & Peter Nijkamp & Eric Pels & Piet Rietveld, 2005. "Efficiency of urban public transit: A meta analysis," Transportation, Springer, vol. 32(1), pages 1-21, January.
    35. Nocera, Silvio & Tonin, Stefania & Cavallaro, Federico, 2015. "The economic impact of greenhouse gas abatement through a meta-analysis: Valuation, consequences and implications in terms of transport policy," Transport Policy, Elsevier, vol. 37(C), pages 31-43.
    36. Thiam, Abdourahmane & Bravo-Ureta, Boris E. & Rivas, Teodoro E., 2001. "Technical efficiency in developing country agriculture: a meta-analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 235-243, September.
    37. Peter Bogetoft & Lars Otto, 2011. "Benchmarking with DEA, SFA, and R," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7961-2, December.
    38. Viton, Philip A., 1997. "Technical efficiency in multi-mode bus transit: A production frontier analysis," Transportation Research Part B: Methodological, Elsevier, vol. 31(1), pages 23-39, February.
    39. R. G. Chambers & Y. Chung & R. Färe, 1998. "Profit, Directional Distance Functions, and Nerlovian Efficiency," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 351-364, August.
    40. Odeck, James & Bråthen, Svein, 2012. "A meta-analysis of DEA and SFA studies of the technical efficiency of seaports: A comparison of fixed and random-effects regression models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1574-1585.
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    Cited by:

    1. Francisco Gildemir Ferreira da Silva & Renata Lúcia Magalhães de Oliveira & Marin Marinov, 2020. "An Analysis of the Effects on Rail Operational Efficiency Due to a Merger between Brazilian Rail Companies: The Case of RUMO-ALL," Sustainability, MDPI, Open Access Journal, vol. 12(12), pages 1-21, June.
    2. Daraio, Cinzia & Kerstens, Kristiaan & Nepomuceno, Thyago & Sickles, Robin C., 2019. "Empirical Surveys of Frontier Applications: A Meta-Review," Working Papers 19-005, Rice University, Department of Economics.
    3. Weidong Li & Olli-Pekka Hilmola, 2019. "Belt and Road Initiative and Railway Sector Efficiency—Application of Networked Benchmarking Analysis," Sustainability, MDPI, Open Access Journal, vol. 11(7), pages 1-21, April.


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