Productivity changes in Chinese airports 1995–2004

Fung, Michael Ka Yiu; Wan, Ken Kai Hong; Hui, Yi; Law, Japhet S.

doi:10.1016/j.tre.2007.01.003

Cited by 115 publications

(54 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…show that Australian airports recorded strong growth in technological change and total factor 5 Some other examples not mentioned in the main text are Gillen and Lall (2001), Murillo-Melchor (1999) and Fung et al (2008 The use of DEA has certain limitations being the most important the fact that the inefficient units and their corresponding benchmarks may not be inherently similar which may result in inappropriate targets for improvement. In addition each unit may have a multiplicity of benchmark units and DEA can not inform which of the benchmark unit on the efficiency frontier is the first best target to follow (Sarkis and Talluri, 2004).…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring the technical efficiency of airports in Latin America

Perelman

Serebrisky

2012

Utilities Policy

View full text Add to dashboard Cite

Relying on a unique dataset this paper uses Data Envelopment Analysis methods to compute an efficient production frontier for a representative sample of Latin American airports. Latin America has implemented a wide variety of private sector participation schemes in the airport sector since the late 90s. To assess whether privately operated airports had higher rates of total factor productivity growth than public airports we compute Malmquist indexes for the period [2000][2001][2002][2003][2004][2005][2006][2007]. Results indicate that privately operated airports enjoyed higher rates of total factor productivity growth.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Measuring the technical efficiency of airports in Latin America

Perelman

Serebrisky

2012

Utilities Policy

View full text Add to dashboard Cite

show abstract

“…The significant differentiation in efficiencies between hub and non-hub SMA is supported by the coincident findings of Gillen and Lall (1997) and Sarkis (2000) for U.S. airports, Fung et al (2008) in their evaluation of Chinese airports, and Tsui et al (2014) in the AsiaPacific airports. This result may be due to the economies of scale operating in airport hubs also at the regional level.…”

Section: Regression Analysismentioning

confidence: 74%

Efficiency assessment and output maximization possibilities of European small and medium sized airports

Gutiérrez

Lozano

2016

Research in Transportation Economics

View full text Add to dashboard Cite

The aim of this research is to investigate the operational efficiency and potential output increase scenarios of 21 small and medium sized airports (SMA) located in 10 different European countries. A key feature of the proposed Data Envelopment Analysis (DEA) models is the consideration of two novel inputs related to the number of airlines and scheduled routes operating at each airport, in addition to the more common capital nondiscretionary inputs (runways, boarding gates and apron). These novel inputs are a way of considering the effects of the air transport market on airports' performance and growth prospects. In particular, a model to estimate the growth in passenger numbers, aircraft movements and cargo that can be achieved by an airport through increasing its number of airlines and routes, and assuming an efficient operation, is presented. Also, a multi-objective DEA model is proposed to explore the possible trade-offs in the output Pareto efficient frontier of a given airport. In addition, a second stage fractional regression model specific for explaining DEA efficiency scores is conducted and the results suggest that ownership structure and hub airport status are significant factors for explaining variations in the operational efficiency of European SMA. § Corresponding author.

show abstract

“…While SFA and DEA are the two sides of the same coin (7): they estimate the same thing with and without the error term. Finally, EW-TFP method is the one which unites the most from the different methods seeing that its roots are in the simple indices (8), it is a relative of the decomposed TFP (9), it is based on the function forms similar to those of SFA (10), and creates an efficiency index as based on distance functions, just as DEA (11). Thus with Figure 2 and the relations enumerated, the author would like to stress the finding that the efficiency analysis techniques in the transport sector are not distinctly standing methods, but rather they create a network of solutions from which the researchers have to select a procedure or a combination best suiting their needs.…”

Section: Discussion On the Correlation And Links Between The Methodsmentioning

confidence: 99%