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Airline Code-share Alliances and Costs: Imposing Concavity on Translog Cost Function Estimation

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Author Info

  • Chew Chua
  • Hsein Kew
  • Jongsay Yong

    ()

Abstract

This paper provides an assessment of how airline code-share alliances affect the costs of the airline industry. It makes two contributions to the literature. First, it measures the effects of airline alliances by estimating a translog cost function using a panel dataset of 10 major U.S.-based airlines over 29 quarters. Secondly, it ensures concavity of the estimated cost function by using the procedure suggested by Ryan and Wales (2000, Economics Letters 67, 253–260). A conventional translog cost function is first estimated and scale estimates are computed. Unfortunately, the estimated function fails the curvature requirement, which makes interpreting the estimated effects of alliances somewhat dubious. Hence, we re-estimate the cost function by imposing local concavity restrictions. We find that large alliance partners have a small negative effect on airlines’ costs, but small alliance partners’ effect on costs appear to be positive, although the magnitude is negligible. We also find material differences in the estimates of scale economies after imposing local concavity. Copyright Springer 2005

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File URL: http://hdl.handle.net/10.1007/s11151-005-0223-6
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Bibliographic Info

Article provided by Springer in its journal Review of Industrial Organization.

Volume (Year): 26 (2005)
Issue (Month): 4 (06)
Pages: 461-487

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Handle: RePEc:kap:revind:v:26:y:2005:i:4:p:461-487

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Web page: http://www.springerlink.com/link.asp?id=100336

Related research

Keywords: Airline alliances; concavity; costs; translog;

References

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  1. Goh, Mark & Yong, Jongsay, 2006. "Impacts of code-share alliances on airline cost structure: A truncated third-order translog estimation," International Journal of Industrial Organization, Elsevier, vol. 24(4), pages 835-866, July.
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Cited by:
  1. Zou, Bo & Hansen, Mark, 2012. "Impact of operational performance on air carrier cost structure: Evidence from US airlines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(5), pages 1032-1048.
  2. Kazuo Ogawa, 2008. "Why Are Concavity Conditions Not Satisfied in the Cost Function? The Case of Japanese Manufacturing Firms during the Bubble Period," ISER Discussion Paper 0719, Institute of Social and Economic Research, Osaka University.
  3. Linz, Teresa & Tsegai, Daniel W., 2009. "Industrial Water Demand analysis in the Middle Olifants sub-basin of South Africa: The case of Mining," Discussion Papers 49927, University of Bonn, Center for Development Research (ZEF).
  4. Ryerson, Megan S., 2010. "Optimal Intercity Transportation Services with Heterogeneous Demand and Variable Fuel Price," University of California Transportation Center, Working Papers qt8696z26t, University of California Transportation Center.

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