IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v44y2010i3p161-173.html
   My bibliography  Save this article

Causes of inefficiency in Japanese railways: Application of DEA for managers and policymakers

Author

Listed:
  • Jitsuzumi, Toshiya
  • Nakamura, Akihiro

Abstract

Although railway services have been suffering financially due to modal shifts and aging populations, they have been, and will continue to be, an essential component of nations' basic social infrastructures. Since railway firms generate positive externalities, and are required to operate in pre-determined licensed areas, governmental intervention/support may, in some cases, be justified. Indeed, many types of subsidies are created and offered for railway operations in Japan; while some are meant to cover large investments, others are used as compensation for regional disparities. However, thus far, no attempt has been made to analyze the reasons for the underperformance of Japanese railway services. In other words, it is unclear whether this underperformance can be attributed to exogenous and uncontrollable causes, or endogenous phenomena and, hence, capable of being handled by managers. The optimal degree of intervention is thus not sufficiently known. In the current paper, we propose a method based on data envelopment analysis (DEA) to analyze the causes of inefficiency in Japanese railway operations, and, further, to calculate optimal subsidy levels. The latter are designed to compensate for railways' lack of complete discretion in changing location of their operations and/or increasing/decreasing these operations since they are a regulated service. Our proposed method was applied to 53 Japanese railway operators. In so doing, we identified several key characteristics related to their inefficiencies, and developed optimal subsidies designed to improve performance.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:soceps:v:44:y:2010:i:3:p:161-173
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038-0121(09)00045-7
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mitomo, Hitoshi & Jitsuzumi, Toshiya, 1999. "Impact of telecommuting on mass transit congestion: the Tokyo case," Telecommunications Policy, Elsevier, vol. 23(10-11), pages 741-751, November.
    2. Banker, Rajiv D., 1984. "Estimating most productive scale size using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 17(1), pages 35-44, July.
    3. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    4. Fumitoshi Mizutani, 2004. "Privately Owned Railways' Cost Function, Organization Size and Ownership," Journal of Regulatory Economics, Springer, vol. 25(3), pages 297-322, May.
    5. 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.
    6. Mark Armstrong & Simon Cowan & John Vickers, 1994. "Regulatory Reform: Economic Analysis and British Experience," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262510790, April.
    7. Tone, Kaoru & Tsutsui, Miki, 2007. "Decomposition of cost efficiency and its application to Japanese-US electric utility comparisons," Socio-Economic Planning Sciences, Elsevier, vol. 41(2), pages 91-106, June.
    8. Liu, Junming & Tone, Kaoru, 2008. "A multistage method to measure efficiency and its application to Japanese banking industry," Socio-Economic Planning Sciences, Elsevier, vol. 42(2), pages 75-91, June.
    9. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2007. "Data Envelopment Analysis," Springer Books, Springer, edition 0, number 978-0-387-45283-8, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hashem Omrani & Mehdi Keshavarz, 2016. "A performance evaluation model for supply chain of shipping company in Iran: an application of the relational network DEA," Maritime Policy & Management, Taylor & Francis Journals, vol. 43(1), pages 121-135, January.
    2. 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.
    3. Le, Yiping & Oka, Minami & Kato, Hironori, 2022. "Efficiencies of the urban railway lines incorporating financial performance and in-vehicle congestion in the Tokyo Metropolitan Area," Transport Policy, Elsevier, vol. 116(C), pages 343-354.
    4. 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.
    5. Bo Hsiao & LihChyun Shu & Ming-Miin Yu & Li-Kang Shen & Ding-Jiun Wang, 2017. "Performance evaluation of the Taiwan railway administration," Annals of Operations Research, Springer, vol. 259(1), pages 119-156, December.
    6. 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.
    7. Liu, Junqing & Fukushige, Mototsugu, 2020. "Efficiency and pricing of water supply and sewerage services in Japan," Utilities Policy, Elsevier, vol. 62(C).
    8. Esther Jose & Puneet Agarwal & Jun Zhuang & Jose Swaminathan, 2023. "A multi-criteria decision making approach to evaluating the performance of Indian railway zones," Annals of Operations Research, Springer, vol. 325(2), pages 1133-1168, June.
    9. Mahmoudi, Reza & Emrouznejad, Ali & Shetab-Boushehri, Seyyed-Nader & Hejazi, Seyed Reza, 2020. "The origins, development and future directions of data envelopment analysis approach in transportation systems," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    10. 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.
    11. Holvad, Torben, 2020. "Efficiency analyses for the railway sector: An overview of key issues," Research in Transportation Economics, Elsevier, vol. 82(C).
    12. Tomikawa, Tadaaki & Goto, Mika, 2022. "Efficiency assessment of Japanese National Railways before and after privatization and divestiture using data envelopment analysis," Transport Policy, Elsevier, vol. 118(C), pages 44-55.
    13. Feras Tayeh & Osman Ghanem, 2023. "Decision-Making Support of Sustainable and Efficiency of Railway Project: Case Study China-Pakistan," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 15(1), pages 1-25, January.
    14. Bhatia, Vinod & Sharma, Seema, 2021. "Expense based performance analysis and resource rationalization: Case of Indian Railways," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matthias Klumpp & Dominic Loske, 2021. "Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    2. Ole Bent Olesen & Niels Christian Petersen & Victor V. Podinovski, 2022. "Scale characteristics of variable returns-to-scale production technologies with ratio inputs and outputs," Annals of Operations Research, Springer, vol. 318(1), pages 383-423, November.
    3. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    4. Alonso, José M. & Clifton, Judith & Díaz-Fuentes, Daniel, 2015. "The impact of New Public Management on efficiency: An analysis of Madrid's hospitals," Health Policy, Elsevier, vol. 119(3), pages 333-340.
    5. Eshagh Esfandiar & Robabeh Eslami & Mohammad Khoveyni & Alireza Gilani, 2023. "Identifying the closest most productive scale size unit in data envelopment analysis," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(2), pages 623-660, June.
    6. Jamal Ouenniche & Skarleth Carrales, 2018. "Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback," Annals of Operations Research, Springer, vol. 266(1), pages 551-587, July.
    7. Yun Liao, 2024. "Super-efficiency and Stock Market Valuation: Evidence from Listed Banks in China (2006 to 2023)," Papers 2407.14734, arXiv.org.
    8. Torben Schubert & Guoliang Yang, 2016. "Institutional change and the optimal size of universities," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1129-1153, September.
    9. Victor V. Podinovski & Robert G. Chambers & Kazim Baris Atici & Iryna D. Deineko, 2016. "Marginal Values and Returns to Scale for Nonparametric Production Frontiers," Operations Research, INFORMS, vol. 64(1), pages 236-250, February.
    10. Viera Mendelová, 2021. "Decomposition of cost efficiency with adjustable prices: an application of data envelopment analysis," Operational Research, Springer, vol. 21(4), pages 2739-2770, December.
    11. Miki Tsutsui & Kaoru Tone, 2007. "Network DEA: A slacks-based measure approach," GRIPS Discussion Papers 07-08, National Graduate Institute for Policy Studies.
    12. Bakhtiari, Sasan, 2018. "Coming Out Clean: Australian Carbon Pricing and Clean Technology Adoption," Ecological Economics, Elsevier, vol. 154(C), pages 238-246.
    13. Pérez, Karen & González-Araya, Marcela C. & Iriarte, Alfredo, 2017. "Energy and GHG emission efficiency in the Chilean manufacturing industry: Sectoral and regional analysis by DEA and Malmquist indexes," Energy Economics, Elsevier, vol. 66(C), pages 290-302.
    14. Blagojević, Mladenka & Ralević, Predrag & Šarac, Dragana, 2020. "An integrated approach to analysing the cost efficiency of postal networks," Utilities Policy, Elsevier, vol. 62(C).
    15. Barnabé Walheer, 2020. "Output, input, and undesirable output interconnections in data envelopment analysis: convexity and returns-to-scale," Annals of Operations Research, Springer, vol. 284(1), pages 447-467, January.
    16. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, April.
    17. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    18. Peter Fernandes Wanke & Rebecca de Mattos, 2014. "Capacity Issues and Efficiency Drivers in Brazilian Bulk Terminals," Brazilian Business Review, Fucape Business School, vol. 11(5), pages 72-98, October.
    19. George Halkos & Roman Matousek & Nickolaos Tzeremes, 2016. "Pre-evaluating technical efficiency gains from possible mergers and acquisitions: evidence from Japanese regional banks," Review of Quantitative Finance and Accounting, Springer, vol. 46(1), pages 47-77, January.
    20. da Silva, Aneirson Francisco & Miranda, Rafael de Carvalho & Marins, Fernando Augusto Silva & Dias, Erica Ximenes, 2024. "A new multiple criteria data envelopment analysis with variable return to scale: Applying bi-dimensional representation and super-efficiency analysis," European Journal of Operational Research, Elsevier, vol. 314(1), pages 308-322.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:soceps:v:44:y:2010:i:3:p:161-173. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.