DEA models with the second-stage analysis

The key determinants causing variations in airport efficiency cannot be clearly understood from looking at the operational and/or financial variables used in the DEA analysis, although DEA studies of airport efficiency evaluations showed the capacity to evaluate airport efficiency (Gillen & Gill, 1997). A clear understanding of the significant factors affecting airport efficiency would provide insight to airport managers and policy makers for improving airport efficiency through benchmarking; it helps airport managers to compare their airports’ performance with those of their peers and improve their own operations.

Only a few studies have combined DEA models and the second-stage analysis. Some researchers have used the operational and/or financial variables as the inputs and outputs

in DEA models, and then conducted further statistical analysis to identify the significant determinants causing the variations in airport efficiency. For example, Gillen and Lall (1997) provided a very influential paper that applied the DEA and Tobit models to assess and rank the performance of 21 US airports between 1989 and 2003, and also to show the advantages of DEA models for evaluating airport efficiency. They also set up an exemplar for airport efficiency evaluation with the two-stage analysis based on two major aspects: air passenger movements (APM) and air traffic movements (ATM). Similarly, Pels, Nijkamp and Rietveld (2001, 2003) utilised DEA models and followed the approach of Gillen & Lall (1997), separating airport activities into two parts. Their research results were compared to SFA to assess the efficiency of 33 European airports between 1995 and 1997. The conclusions of the study showed that an average airport in Europe operates under Constant Returns to Scale (CRS) when handling air transport movements, but operating under increasing return to scale when handling air passengers. In addition, Abbott and Wu (2002) employed the Total Factor Productivity (TFP) and DEA models to investigate the productivity and efficiency of 12 Australian airports for the period of 1990–2000, and also identified the sources of variations in airport efficiency using the Tobit model. The study reported that airports improved their productivity over the study periods and that the Australian’s largest airport was relatively more efficient than overseas airports. Also, six factors (i.e. the rate of return, capital labour ratio, aircraft standing areas, total asset growth rate for each airport, state dummy for airport ownership, and year dummy) jointly affect airport efficiency.

Barros and Sampaio (2004) used the DEA and Tobit models to assess the efficiency of 14 Portuguese airports between 1990 and 2000. The study revealed that smaller airports are less efficient and that the most efficient airports are located in the main cities. The Tobit model also indicated that four significant factors (i.e. the percentage share held by the regional government, airport location, the population size around the airport, and the ratio of operational costs to sales) can explain the dissimilarities in airport efficiency. Furthermore, the efficiency of 34 Italian airports was studied by Malighettiet al. (2007) applying the DEA and Tobit models from 2005 to 2006. The study concluded that larger airports are more efficient than smaller airports. Hub premium (i.e. an airline dominates an airport) and privatisation have positive impacts on airport efficiency, unlike the negative impacts caused by military activities and seasonal effects. Pathomsiri et al.

(2006) also employed DEA models to measure the airport productivity of 14 Multiple Airport Systems (MAS) in the US between 2000 and 2002, where the second-stage Censored Tobit regression analysis was performed to analyse the influences of key factors causing the differences in productivity. Four factors (i.e. the utilisation of runway areas, market dominance, the proportion of international passengers, and management style) were determined to cause the variations in airport productivity.

The DEA, MPI, and Tobit models were employed by Li and Liu (2007) to evaluate the efficiency of 41 Chinese airports form 2001 to 2005. They concluded that Chinese airports operated with low technical efficiency levels over the study periods, and six factors (i.e. runway length, passenger terminal area, cargo volume carried per flight, regional GDP per square kilometre, the airport’s hub status, and airport location) were considered as the significant factors to explain the variations in airport efficiency. Similarly, Yuen and Zhang (2009) used the DEA, OLS and Tobit models to evaluate a panel of 25 Chinese airports between 1995 and 2006. The findings of the study suggested that five significant factors (i.e. ownership of the listed airports, airport competition, airport localisation programme, the ‘open-skies’ agreements, and airline mergers) have positive impacts on the efficiency levels of Chinese airports. However, the study included only two inputs and three outputs for Chinese airports, which it could not capture the different operating characteristics and services provided by the Chinese airports.

For comparing airport efficiency worldwide, Perelman and Serebrisky (2010) used the DEA and Tobit models to compare and analyse the technical efficiency of 22 Latin American airports relative to 23 Asia-Pacific airports, 40 European airports, and 63 Canadian and US airports between 2005 and 2006. The research suggested that Latin American airports are less efficient than Asian and North American airports under the CRS model, but Latin America became the second most efficient region under the VRS model behind Asia. Several factors such as institutional variables (private vs. public operation), the socioeconomic environment (GDP), and airport characteristics (hub and share of commercial revenues) were found to be significant determinants for explaining variations in airport efficiency around the world.

Many other studies have used other kinds of second-stage analysis techniques to assess airport efficiency, combining different airport inputs and outputs. For example, Bazargan and Vasigh (2003) adopted DEA models to analyse the efficiency of 45 US airports (i.e. the top 15 large, medium, and small hub airports) between 1996 and 2000. Statistical tests were performed on the resulting DEA indexes to determine whether the size of airport affected airport efficiency. The conclusion was that small hub airports consistently outperform the larger hubs in the US. Barros and Dieke (2008) also used DEA models to measure the efficiency of 31 Italian airports between 2001 and 2003, and employed Simar & Wilson regression analysis to identify the determinants of airport efficiency rather than the Tobit model. The findings of this study revealed that three factors (i.e. the airport’s regional hub status, the privately-owned airports, and the WLU parameters) increase airport efficiency. Similarly, DEA model and the second-stage Simar & Wilson methodology were adopted by Malighetti et al. (2009) to measure the efficiency of 57 European airports and to identify the efficiency determinants during 2006 focusing on APM and ATM. The results indicated that airport efficiency is positively related to airport’s centrality in the European network and the intensity of competition between the airports. Muller, Ulku and Zivanovic (2009) used the Partial Factor Productivity (PFP), DEA, SFA, and Tobit models to analyse the economic and technical performance of 13 UK and German airports from 1998 to 2005 under the scheme of privatisation. The study results indicated that the fully-privatised British airports are more efficient than their German counterparts.

The ability of DEA models to assess airport efficiency, coupled with the ability of second-stage analysis to identify the key determinants that explain the variations in airport efficiency, prompted this study to adopt a method of two-stage analysis: the first-stage analysis used DEA model to examine the operational efficiency of 30 Asia- Pacific airports, and then the second-stage analysis used the OLS and Tobit models to identify the statistically significant factors causing the differences in airport efficiency levels. The DEA, OLS, and Tobit models will be elaborated on in the following sections.