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

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  • 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

Suggested Citation

  • Chew Chua & Hsein Kew & Jongsay Yong, 2005. "Airline Code-share Alliances and Costs: Imposing Concavity on Translog Cost Function Estimation," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 26(4), pages 461-487, June.
  • Handle: RePEc:kap:revind:v:26:y:2005:i:4:p:461-487
    DOI: 10.1007/s11151-005-0223-6
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    References listed on IDEAS

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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:

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    11. Philip G. Gayle, 2007. "Airline Code-Share Alliances and Their Competitive Effects," Journal of Law and Economics, University of Chicago Press, vol. 50(4), pages 781-819.
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    15. 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.
    16. Zuidberg, Joost, 2014. "Identifying airline cost economies: An econometric analysis of the factors affecting aircraft operating costs," Journal of Air Transport Management, Elsevier, vol. 40(C), pages 86-95.
    17. Lijesen Mark G., 2013. "Solving the Endogeneity Problem in Empirical Cost Functions: An Application to US Banks," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 13(2), pages 1051-1070, September.
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    21. McMullen, B. Starr & Du, Yan, 2007. "The Economic Impact of the ATA/Southwest Airlines Code Share Alliance," 48th Annual Transportation Research Forum, Boston, Massachusetts, March 15-17, 2007 207914, Transportation Research Forum.
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