Lobo and Gonzalez (2008) also employ DEA to compare only one airport in LAC (Benito Merino airport in Santiago de Chile) with a set of airports in other regions. However, these papers focus on the efficiency of the airport sector in one single Latin American country. Indeed, to the best extent of our knowledge, this paper is the first that computes a global efficient frontier for the airport sector including data from a representative set of Latin American countries. In contrast to previous studies, this paper identifies how far away Latin American airports stand from the best practice worldwide.

Given the trend towards the introduction of private sector participation in the airport sector, one of the variables we are interested in testing is the effect that ownership has on airport efficiency. There are other papers that study this issue. For example, using DEA methods Parker (1999) analyses the effect that privatization had on the efficiency level of British airports and finds that privatization had no noticeable impact on technical efficiency. Based on panel data for the major airports in Asia-Pacific, Europe and North America for the years 2001-2003, Oum, Adler and Yu (2006) study the effect that the type of ownership has on productive efficiency and profitability. Their results suggest that airports with government majority ownership and those owned by multi-level government are significantly less efficient than airports operating under private majority ownership.

Lastly, it should be noted that DEA is not the only methodology available that can be used to study the efficiency of the airport sector.<sup>3</sup> Indeed, some authors have studied productivity in this sector through methods different than DEA. For instance, Hooper and Hensher (1997) use index number methods to study the evolution of total factor productivity of Australian airports for the period 1988-1992. Oum, Yan and Yu (2008) study the effects of ownership types on airports' cost efficiency by applying stochastic frontier analysis to a panel data of the world's major airports. Pestana Barros (2008) also uses stochastic frontier analysis to study the technical efficiency of airports in the UK. Finally, analyzing the efficiency of European airports, Pels, Nijkamp and Rietveld (2001) compare the results they get from DEA methods to the results obtained using stochastic frontier analysis. Their analysis shows that the stochastic frontier model they consider reproduces the DEA results in quite a reasonable way.

# **3. Computing a Technical Efficiency Frontier for Airports around the World**

In this section we compute a DEA activity frontier for commercial airports around the world. We use data for the years 2005 and 2006 from 22 LAC airports, in addition to 23 airports from Asia-Pacific, 40 from Europe and 63 from Canada and the US (see Tables A1 and A2 in the Appendix for details of airports included in the sample and the results for non Latin American airports).

DEA is a deterministic non parametric approach used to build a benchmark, best practice frontier, based on available information. The method was first developed by Farrel (1957) and later consolidated by Charnes _et al_ (1978). One of the main advantages of this approach is that it takes into account the multi-output multi-input dimensionality of production. Another advantage is that computations are based exclusively on measures of physical outputs and inputs, without

> 3 For a complete and updated presentation of frontier analysis methods proposed in the literature, see Coelli et al. (2005) and Fried, Lovell and Schmit (2008).

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