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Is it the labor unions' fault? Dissecting the causes of the impaired technical efficiencies of the legacy carriers in the United States

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  • Greer, Mark

Abstract

Data envelopment analysis is used to evaluate the technical efficiencies of a number of major passenger airlines in the United States at transforming their inputs (labor, fuel and fleet-wide seating capacity) into available seat-miles. A tobit regression model is then used to identify the underlying drivers of airline efficiency, as measured by the data envelopment analysis efficiency score. The impact of unionization on airline efficiency is found to be statistically insignificant, controlling for the influences of other hypothesized determinants of airline efficiency: the average age of an airline's fleet, the average size of its aircraft, its average stage length, the extent to which the airline relies of hubbing within its route structure, the percent of its passenger enplanements that are international, and whether the airline is a legacy carrier. The statistically significant drivers of airline efficiency, at a ten percent level of significance, are average aircraft size, average stage length and the extent to which the airline relies on hubbing and connecting flights within its route structure. The stage length variable is not significant at a five percent level of significance, however. An increase in average aircraft size or in average stage length enhances an airline's efficiency whereas an increase in hubbing reduces it.

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  • Greer, Mark, 2009. "Is it the labor unions' fault? Dissecting the causes of the impaired technical efficiencies of the legacy carriers in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(9-10), pages 779-789, November.
  • Handle: RePEc:eee:transa:v:43:y:2009:i:9-10:p:779-789
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    6. Li, Ye & Wang, Yan-zhang & Cui, Qiang, 2016. "Has airline efficiency affected by the inclusion of aviation into European Union Emission Trading Scheme? Evidences from 22 airlines during 2008–2012," Energy, Elsevier, vol. 96(C), pages 8-22.
    7. Wanke, Peter & Barros, C.P., 2016. "Efficiency in Latin American airlines: A two-stage approach combining Virtual Frontier Dynamic DEA and Simplex Regression," Journal of Air Transport Management, Elsevier, vol. 54(C), pages 93-103.
    8. Mallikarjun, Sreekanth, 2015. "Efficiency of US airlines: A strategic operating model," Journal of Air Transport Management, Elsevier, vol. 43(C), pages 46-56.
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    13. Barros, Carlos P. & Liang, Qi Bin & Peypoch, Nicolas, 2013. "The technical efficiency of US Airlines," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 139-148.
    14. Ying Li & Tai‐Yu Lin & Yung‐ho Chiu & Shu‐Ning Lin & Tzu‐Han Chang, 2021. "Impact of alliances and delay rate on airline performance," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(6), pages 1607-1618, September.
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    18. Cui, Qiang & Li, Ye & Yu, Chen-lu & Wei, Yi-Ming, 2016. "Evaluating energy efficiency for airlines: An application of Virtual Frontier Dynamic Slacks Based Measure," Energy, Elsevier, vol. 113(C), pages 1231-1240.
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    22. Heshmati, Almas & C. Kumbhakar, Subal & Kim, Jungsuk, 2016. "Persistent and Transient Efficiency of International Airlines," Working Paper Series in Economics and Institutions of Innovation 444, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    23. Kuljanin, Jovana & Kalić, Milica & Caggiani, Leonardo & Ottomanelli, Michele, 2019. "A comparative efficiency and productivity analysis: Implication to airlines located in Central and South-East Europe," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 152-163.

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