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Predatory reputation in US airline markets

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  • Kim, Sung-Hwan

Abstract

Despite its long history in antitrust policy, predation remains a poorly understood phenomenon. The main difficulty is in empirically identifying predatory intent. I propose a method for measuring the effect of reputation, whose significance would enable us to infer predatory intents. Maximum likelihood estimation using simulated annealing algorithm is conducted with a sample of US airline markets. The results provide some support for the presence of entrants learning and reputation effect. As an application, I discuss how the outcomes of my empirical model could help analyze such antitrust cases as the American Airlines Case.

Suggested Citation

  • Kim, Sung-Hwan, 2009. "Predatory reputation in US airline markets," International Journal of Industrial Organization, Elsevier, vol. 27(5), pages 592-604, September.
    • Handle: RePEc:eee:indorg:v:27:y:2009:i:5:p:592-604
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    Cited by:

    1. Bettini, Humberto F.A.J. & Silveira, José Maria F.J. & Oliveira, Alessandro V.M., 2018. "Estimating strategic responses to the march of a low cost carrier to primary airports," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 190-204.
    2. Kappes, Jan Willem & Merkert, Rico, 2013. "Barriers to entry into European aviation markets revisited: A review and analysis of managerial perceptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 57(C), pages 58-69.
    3. Aguirregabiria, Victor & Ho, Chun-Yu, 2010. "A dynamic game of airline network competition: Hub-and-spoke networks and entry deterrence," International Journal of Industrial Organization, Elsevier, vol. 28(4), pages 377-382, July.
    4. John Kwoka & Birzhan Batkeyev, 2019. "Strategic Responses to Competitive Threats: Airlines in Action," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 54(1), pages 83-109, February.
    5. Nathan Yang, 2011. "An Empirical Model of Industry Dynamics with Common Uncertainty and Learning from the Actions of Competitors," Working Papers 11-16, NET Institute.

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