Case 4

An evaluation of European airlines operational performance1OutlineAbstractMotivation and ObjectiveResearch on airline efficiencyMethodologyData and resultsDiscussionConclusion

2Abstract This paper uses data envelopment analysis (DEA) to evaluate the operational performance of a sample of AEAAssociation of European Airlines from 2000 to 2005, combining operational and financial variables.In this paper an innovative DEA two-stage procedure proposed by Simar and Wilson [2007.Estimationandinferencein two stage, semi-parametric models of productive efficiency. Journal of Econometrics 136, 3164] is used.

3AbstractIn the first stage a DEA model isused to rank the airlines by their overall efficiency.In the second stage a bootstrapped truncated regression is used to evaluate the drivers of efficiency. The regressions test the roles played, respectively, by population and network alliance in the efficiency of the airlines. The implications of this research for managerial purposes are then drawn.

4Motivation and ObjectiveAn intensification of the battle for European passengers between the national carriers and the low-cost airlines.To investigate the policies adopted by the AEA airlines to respond to the new competitive environment.To analyze the role played by national markets aspects in explaining differences revealed in the efficiency rankings.5Members of the Association of European Airlines and characteristics Star alliance, One word alliance, Sky team alliance are some kind of airline strategic alliance

6Research on airline efficiencyAuthorCost modelCavesetal.,1981,1984;Windle, 1991;Baltagi et al.,1995;Oum and You,1998;Liu and Lynk,1999.Total factor productivity approachBauer,1990; Oum and You,1995;Barbot et al.,2008.Stochastic econometric frontier modelCornwell et al.,1990;Good et al.,1993;Sickles,1985;Sickles et al.,1986;Captain and Sickles,1997;Coelli et al.,1999;Inglada et al.,2006.DEA modelDistexhe and Perelman,1994;Good et al.,1995;Adler and Golany,2001;Fethi et al.,2001;Scheraga,2004;Greer,2008;Bhadra,2008. The DEA permits the use of multiple inputs and outputs and does not impose any functional form on the data.Many papers adopt both the DEA and econometric frontier approaches simultaneously.7MethodologyT characterized as the technology set, defined:

N inputs denoted with xM outputs denoted with yz means weight of factor

8MethodologyFarrell/Debreu-type output-oriented TE measure:

In practice, T is unobserved and so were place it with its DEA-estimate, :

9MethodologyIn this paper, we assume constant returns to scale to gain more discriminatory power in comparison between DMUs and then analyze the returns-to-scale component in the second stage.

10MethodologyRegression analysis

Replaced by its DEA estimate .

11Data and resultsUsed data on European airline companies in the years from 2000 to 2005 (29 airline companies in 6 years = 174 observations), obtained in the AEA year book available in (http://www.aea.be/). 12Data and resultsOutputRPK-operational revenue by passenger kilometerEBIT-earning before interest and taxesInputNumber of employeesOperational costNumber of planesNumber of DMU is greater than three times the number of inputs plus outputs.Using an output-orientation to determine whether an airline is capable of producing the same level of output with less input.13Data and results

14Data and resultsTrend is a yearly trend.Population is the country population of origin obtained in the Eurostat statistics.Low cost are the low cost companies that are member of AEA.National airlines measure the influence on efficiency of being a long-established, national flagship carrier.

15Data and results-First stage

16Data and results-First stageThere are significant differences in efficiency among the airlines analyzed.Almost all European airlines operated at a high level of pure technical efficiency in the period.All CRS(CCR) technically efficient airlines are also technically efficient in VRS(BBC), signifying that the dominant source of efficiency is scale.According to the SE, almost all European airlines authorities are efficient, while a small number are not.17Data and results-Second stageTwo-stage DEA as suggested by Coelli et al.(1998).Truncated bootstrapped regression by Simar and Wilson (2007).

represent the CCR efficient score of the airline i in period t.

18Data and results-Second stageThe truncated model 2 drops the statistically insignificant variable NationalAirlines.Model 3 drops the variable SkyTeam, with the positive t-statistics which are statically significant for all parameters.

19DiscussionThe efficiency is increase over the period, according to the trend. And it increases at a decreasing rate.The population contributes to the efficiency of the airline.Low costs companies promote the efficiency of the European airlines.All alliances contribute to the technical efficiency, although the SkyTeam is insignificant in the second-stage.

20DiscussionThe companies with poor performance should adjust their management process based on pure TE.The variation in efficiency score may caused from the existence of strategic groups and their differences in resources.The manager of inefficient companies shouldAdopt a benchmark management procedure.Upgrade the quality of management practices.Adopt human resources policies.Pursue market-oriented strategies.

21ConclusionUse DEA-CCR model to determine relative efficient and the bootstrapped truncated regression model explains the efficiency drivers.There is a growth trend in the. The demographic dimension of the airlines home country is important, representing economies of scale and membership of a alliances is also important.

22