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Transport firms’ inefficiency and managerial optimism: A stochastic frontier analysis

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  • Jarboui, Sami
  • Forget, Pascal
  • Boujelbene, Younes

Abstract

This paper presents an original essay that explains the correlation between transport firms’ technical efficiency and managerial optimism. We initiate a debate concerning the potential role of Chief Executive Officers’ (CEO) irrationalities in explaining the inefficiency of public transport operators, such as the shortfall between the optimal production function and the observed production level. Stochastic frontier analysis (SFA) methods are applied to our sample over a twelve-year period from 2000 to 2011, where we aim to detect the potential effect of a well-documented bias in behavioral economic and finance theory: the managerial optimism bias. Using two proxies of managerial optimism based on the 2005 work of Malmendier and Tate as well as following recommendations of cognitive psychology and using an SFA approach, we find strong evidence of the negative impact of CEOs’ optimism bias on transport firms’ technical efficiency, meaning that managerial optimism decreases transport firms’ technical efficiency.

Suggested Citation

  • Jarboui, Sami & Forget, Pascal & Boujelbene, Younes, 2014. "Transport firms’ inefficiency and managerial optimism: A stochastic frontier analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 3(C), pages 41-51.
    • Handle: RePEc:eee:beexfi:v:3:y:2014:i:c:p:41-51
    DOI: 10.1016/j.jbef.2014.07.003
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    References listed on IDEAS

    as
    1. Lin, Yueh-hsiang & Hu, Shing-yang & Chen, Ming-shen, 2005. "Managerial optimism and corporate investment: Some empirical evidence from Taiwan," Pacific-Basin Finance Journal, Elsevier, vol. 13(5), pages 523-546, November.
    2. Malmendier, Ulrike & Tate, Geoffrey, 2008. "Who makes acquisitions? CEO overconfidence and the market's reaction," Journal of Financial Economics, Elsevier, vol. 89(1), pages 20-43, July.
    3. Ulrike Malmendier & Geoffrey Tate, 2005. "Does Overconfidence Affect Corporate Investment? CEO Overconfidence Measures Revisited," European Financial Management, European Financial Management Association, vol. 11(5), pages 649-659, November.
    4. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    5. Karlaftis, Matthew G. & Tsamboulas, Dimitrios, 2012. "Efficiency measurement in public transport: Are findings specification sensitive?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 392-402.
    6. Jensen, Michael C. & Meckling, William H., 1976. "Theory of the firm: Managerial behavior, agency costs and ownership structure," Journal of Financial Economics, Elsevier, vol. 3(4), pages 305-360, October.
    7. Beniamina Margari & Fabrizio Erbetta & Carmelo Petraglia & Massimiliano Piacenza, 2007. "Regulatory and environmental effects on public transit efficiency: a mixed DEA-SFA approach," Journal of Regulatory Economics, Springer, vol. 32(2), pages 131-151, October.
    8. repec:bla:jfinan:v:53:y:1998:i:6:p:1839-1885 is not listed on IDEAS
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    Cited by:

    1. Mária Ďurišová & Emese Tokarčíková & Florina Oana Virlanuta & Zuzana Chodasová, 2019. "The Corporate Performance Measurement and Its Importance for the Pricing in a Transport Enterprise," Sustainability, MDPI, vol. 11(21), pages 1-17, November.

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    More about this item

    Keywords

    Transport firm; Technical efficiency; Stochastic frontier analysis; Managerial optimism;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L92 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Railroads and Other Surface Transportation
    • N70 - Economic History - - Economic History: Transport, International and Domestic Trade, Energy, and Other Services - - - General, International, or Comparative
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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