1. An evaluation of European airlines’ operational performance

2. Outline Abstract Motivation and Objective Research on airline efficiency Methodology Data and results Discussion Conclusion

3. Abstract This paper uses data envelopment analysis (DEA) to evaluate the operational performance of a sample of AEA— Association 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, 31–64] is used.

4. Abstract In 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.

5. Motivation and Objective An 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.

6. Members of the Association of European Airlines and characteristics ※ Star alliance, One word alliance, Sky team alliance are some kind of airline strategic alliance

7. Research on airline efficiency Author Cost modelCavesetal.,1981,1984; Windle, 1991; Baltagi et al.,1995; Oum and You,1998; Liu and Lynk,1999. Total factor productivity approach Bauer,1990; Oum and You,1995; Barbot et al.,2008. Stochastic econometric frontier model Cornwell 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.

8. Methodology T characterized as the technology set, defined: N inputs denoted with x M outputs denoted with y z means weight of factor

9. Methodology Farrell/Debreu-type output-oriented TE measure: In practice, T is unobserved and so were place it with its DEA- estimate, :

10. Methodology In 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.

11. Methodology Regression analysis Replaced by its DEA estimate.

12. Data and results Used 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/).

13. Data and results Output RPK-operational revenue by passenger kilometer EBIT-earning before interest and taxes Input Number of employees Operational cost Number of planes Number 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.

14. Data and results

15. Trend 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.

16. Data and results-First stage

17. 1. There are significant differences in efficiency among the airlines analyzed. 2. Almost all European airlines operated at a high level of pure technical efficiency in the period. 3. All CRS(CCR) technically efficient airlines are also technically efficient in VRS(BBC), signifying that the dominant source of efficiency is scale. 4. According to the SE, almost all European airlines authorities are efficient, while a small number are not.

18. Data and results-Second stage Two-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.

19. Data and results-Second stage The 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.

20. Discussion 1. The efficiency is increase over the period, according to the trend. And it increases at a decreasing rate. 2. The population contributes to the efficiency of the airline. 3. Low costs companies promote the efficiency of the European airlines. 4. All alliances contribute to the technical efficiency, although the SkyTeam is insignificant in the second-stage.

21. Discussion The 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 should…  Adopt a benchmark management procedure. ‚ Upgrade the quality of management practices. ƒ Adopt human resources policies. „ Pursue market-oriented strategies.

22. Conclusion Use 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 airline’s home country is important, representing economies of scale and membership of a alliances is also important.