European vs. U.S. airlines: Performance comparison in a dynamic market

This study measures and compares the efficiency and productivity of European and U.S. airlines, over the period from 2001 to 2008. We measure efficiency by estimating a Bayesian distance frontier model subject to regularity constraints. Productivity estimates are also derived parametrically, based on the estimates of the distance frontier model. We estimate both a constrained (i.e. subject to regularity conditions) and an unconstrained model and we show the importance of imposing the monotonicity and curvature conditions on the distance function. The efficiency and productivity results based on the constrained model indicate that European airlines have slightly higher efficiency and productivity growth than U.S. airlines. A comparison based on the type of airlines indicates that low-cost airlines are on average more productive and efficient than full-service airlines. The decomposition of productivity growth and related market discussions are also provided.

[1]  Zhiqiang Liu,et al.  Productivity Growth and Firm Ownership: An Analytical and Empirical Investigation , 1994, Journal of Political Economy.

[2]  Carl A. Scheraga,et al.  Operational efficiency versus financial mobility in the global airline industry: a data envelopment and Tobit analysis , 2004 .

[3]  D. Aigner,et al.  P. Schmidt, 1977,?Formulation and estimation of stochastic frontier production function models,? , 1977 .

[4]  C. Morley.  Impacts of International Airline Alliances on Tourism , 2003 .

[5]  D. Primont,et al.  Multi-Output Production and Duality: Theory and Applications , 1994 .

[6]  P. Bauer.  Recent developments in the econometric estimation of frontiers , 1990 .

[7]  Tae Hoon Oum,et al.  Airline Cost and Performance: Implications for Public and Industry Policies , 1986 .

[8]  Chenjie Yu,et al.  A PRODUCTIVITY COMPARISON OF THE WORLD'S MAJOR AIRLINES. IN: AIR TRANSPORT , 1995 .

[9]  Mark F. J. Steel,et al.  On the use of panel data in stochastic frontier models with improper priors , 1997 .

[10]  Robin C. Sickles,et al.  Allocative distortions and the regulatory transition of the U.S. airline industry , 1986 .

[11]  P. Schmidt,et al.  Production Frontiers and Panel Data , 1984 .

[12]  Apostolos Serletis,et al.  Efficiency, technical change, and returns to scale in large US banks: Panel data evidence from an output distance function satisfying theoretical regularity , 2010 .

[13]  Badi H. Baltagi,et al.  Airline Deregulation: The Cost Pieces of the Puzzle , 1995 .

[14]  C. Lovell,et al.  RESOURCES AND FUNCTIONINGS: A NEW VIEW OF INEQUALITY IN AUSTRALIA , 1994 .

[15]  Robin C. Sickles,et al.  Competition and market power in the European airline industry: 1976-90 , 1997 .

[16]  Álvaro Costa,et al.  Airlines performance in the new market context: A comparative productivity and efficiency analysis. , 2008 .

[17]  Robin C. Sickles,et al.  Specification of Distance Functions Using Semi- and Nonparametric Methods with an Application to the Dynamic Performance of Eastern and Western European Air Carriers , 2002 .

[18]  Philip A. Viton,et al.  TECHNICAL EFFICIENCY IN MULTI-MODE BUS TRANSIT: A PRODUCTION FRONTIER ANALYSIS , 1997 .

[19]  Sergio Perelman,et al.  Technical Efficiency and Productivity Growth in an Era of Deregulation: the Case of Airlines , 1994 .

[20]  Sergio Perelman,et al.  TECHNICAL EFFICIENCY IN AIRLINES UNDER REGULATED AND DEREGULATED ENVIRONMENTS , 1989 .

[21]  Christopher S. McIntosh,et al.  Imposing inequality restrictions: efficiency gains from economic theory , 2001 .

[22]  Tae Hoon Oum,et al.  Airline cost structure and policy implications. , 1990 .

[23]  P. Schmidt,et al.  Production frontiers with cross-sectional and time-series variation in efficiency levels , 1990 .

[24]  T. Coelli,et al.  Accounting for Environmental Influences in Stochastic Frontier Models: With Application to International Airlines , 1999 .

[25]  Paul W. Bauer,et al.  Decomposing TFP growth in the presence of cost inefficiency, nonconstant returns to scale, and technological progress , 1990 .

[26]  S. Perelman,et al.  Etude Comparative Des Performances Des Societes De Chemins De Fer , 1989 .

[27]  L. R. Christensen,et al.  Economies of Density versus Economies of Scale: Why Trunk and Local Service Airline Costs Differ , 1984 .

[28]  Timothy Coelli,et al.  An Introduction to Efficiency and Productivity Analysis , 1997 .

[29]  Gary Koop,et al.  Bayesian efficiency analysis through individual effects: Hospital cost frontiers , 1997 .

[30]  Thomas G. Cowing,et al.  Productivity Measurement in Regulated Industries , 1981 .

[31]  Timothy Coelli,et al.  A Bayesian Approach to Imposing Curvature on Distance Functions , 2005 .

[32]  J. Bowen,et al.  Airline hubs in Southeast Asia: national economic development and nodal accessibility , 2000 .

[33]  Luis Orea,et al.  Parametric Decomposition of a Generalized Malmquist Productivity Index , 2002 .

[34]  Carlos Pestana Barros,et al.  An evaluation of European airlines’ operational performance , 2009 .

[35]  W. Eichhorn,et al.  Models and Measurement of Welfare and Inequality , 1994 .

[36]  Robin C. Sickles,et al.  Airline efficiency differences between Europe and the US: Implications for the pace of EC integration and domestic regulation , 1995 .

[37]  M. Steel,et al.  Stochastic frontier models: a bayesian perspective , 1994 .