While there is an extensive literature on benchmarking applied to a diverse range of economic fields, the scarcity of studies regarding European airports bears testimony to the fact that this is a relatively under-researched topic (Humphreys and Francis, 2002 and Humphreys et al., 2002). Research on the technical efficiency and productivity of airports has adopted three alternative methods of measuring efficiency: First, the non-parametric linear programming methods called data envelopment analysis-DEA (Gillen and Lall, 1997, Gillen and Lall, 2001, Murillo-Melchor, 1999, Sarkis, 2000, Adler and Berechman, 2001, Martín and Román, 2001, Pels et al., 2001, Pels et al., 2003, Fernandes and Pacheco, 2002, Sarkis and Talluri, 2004, Yoshida and Fujimoto, 2004, Barros and Sampaio, 2004, Graham, 2005, Barros and Dieke, 2007, Barros and Dieke, 2008 and Fung et al., 2008). Second, are methods which use the parametric stochastic frontier model (Pels et al., 2001, Pels et al., 2003, Martín-Cejas, 2002, Barros, 2008a and Barros, 2008b). Third, are index numbers approaches, such as the Tornquist total factor productivity index (Coelli et al., 2003, p. 27; Douganis et al., 1995 and Hooper and Hensher, 1997) where such indexes are extended and estimated using the endogenous-weight total factor productivity approach (Yoshida, 2004 and Oum et al., 2003). We extend the efficiency studies cited above by estimating total factor productivity for UK airports using a Malmquist index, Malmquist (1953). In addition to measuring efficiency change from period to period, our method allows for biased technological change in the production of airport outputs and in the use of airport inputs.
The motivation for the present research is the following: first, in prior research on UK airports’ technical efficiency, Barros (2008b) estimated a stochastic frontier model and found that the majority of UK airports were not improving their efficiency after 2000. Barros’ results contrast with prior research by Parker (1999) on BAA (British Airports Authority) airports, but these two papers used different data periods and different methods. However, the cause for declining technical efficiency is unclear and therefore an issue justifying more research. Second, recent acquisitions of UK airports by Spanish enterprises have increased competition. In 2004, TBI PLC, the owner of three regional airports in England, Wales, and Northern Ireland was acquired by a Spanish enterprise owned by AENA, the company that manages the Spanish airports, and Abertis, a Spanish construction company. In July 2006, BAA was taken over by a consortium led by the Spanish transportation group, Grupo Ferrovial. These acquisitions introduced competition in the field which is reflected in different efficient performance. Finally, while UK airports’ technical efficiency has been analyzed using DEA and stochastic frontier models, the productivity growth of those airports has not been analyzed, further justifying the present research. Therefore, the aim of this research is to investigate total factor productivity change of the UK airports using a Malmquist index (Färe and Grosskopf, 1996). The Malmquist index decomposes productivity change into gains or losses due to efficiency change and gains or losses due to technological change. Furthermore, our method relaxes the assumption of Hicks’ neutrality in the production of outputs and use of inputs by allowing for biased technological change to occur. Our method identifies the source of the bias in technological change.
The article is structured as follows: Section 2 presents the institutional setting on UK airports. Section 3 presents the productivity models. Section 4 presents the data and the results. Section 5 discusses the results and provides some concluding remarks.
In this paper we used DEA to estimate the Malmquist input-based index of total factor productivity for 27 UK airports operating during 2000/01 to 2004/05. Productivity change was factored into an index of efficiency change and an index of technological change. Throughout the period, UK airports experienced average decreases in productivity, which confirms previous research by Barros (2008b). The decline in productivity occurs because airports on average became less efficient and experienced technological regress during the period. When we broke the index of technological change into separate indexes of output bias, input bias, and an index of the magnitude of technological change we found a clear bias in the use of inputs and the production of outputs. A majority of airports experienced a labor-saving/other cost-using input bias. For capital and other costs, the results were mixed. We also found that a majority of airports experienced a bias in favor of producing aircraft movements relative to passengers. For cargo shipments and aircraft movements the result on biased technological change is mixed, with some airports experiencing a bias in favor of producing cargo shipments and other airports experiencing a bias in favor of aircraft movements. Our estimates of productivity change and technological bias indicate that the traditional growth accounting method, which assumes Hicks neutral technological change, is not appropriate for analyzing changes in productivity for UK airports.
No clear relationship emerges between ownership and productivity improvement nor ownership and regulation. Of the four airports managed by the Manchester airport group, only Manchester airport experienced an increase in productivity. For the three airports operated by TBI PLC, only Luton experienced an increase in productivity. Finally, only three of the seven airports overseen by BAA experienced productivity growth. In addition, for the three regulated airports, only Stansted experienced productivity growth. Further research is needed to confirm the present results.
