The rest of the paper is organized as follows. Section 2 presents a brief review of the existing related literature. In Section 3 we present our estimations of a DEA activity frontier for commercial airports around the world and use these results to identify the peers of each of the airports in LAC. Section 4 studies the determinants of airport efficiency by estimating a truncated regression model. Section 5 presents Malmquist quantity indexes of TFPC for LAC airports over the period 1995-2007. Finally, Section 6 presents some concluding remarks.

# **2. Literature Review**

Guillen and Lall (1997) pioneered the use of Data Envelopment Analysis techniques to study efficiency in the airport sector. Their paper uses data from 21 US airports for the period 1989-1993. Using this dataset they define airports as producing two different classes of services – terminal services and movements – and then proceed to compute two different DEA frontiers, one for each of these two services. Finally, using Tobit regressions, they estimate the effect that different variables (like whether or not an airport has rotational runways, preferential runway use or existence of airport operational constraints) have on the efficiency scores of each airport.

Following Guillen and Lall (1997), a literature flourished using DEA methods to study the technical efficiency of the airport sector. In what follows we do not attempt to provide a complete account of this literature. Instead, we review the set of existing papers that is the most relevant for our paper. For a more complete and comprehensive account we refer the reader to Pestana Barros and Dieke (2008)<sup>2</sup> .

Using a Malmquist total factor productivity index and data envelopment analysis, Abbot and Wu (2002) investigate the efficiency and productivity of Australian airports during the 1990s. Their results show that Australian airports recorded strong growth in technological change and total factor productivity during this period. However, this growth was based almost exclusively on a shift of the production frontier, with growth in technical and scale efficiency lagging behind.

Pestana, Barros, and Dieke (2008) compute a single DEA frontier for Italian airports using data from the period 2001-2003. However, instead of using a Tobit regression to find determinants of airport efficiency as in Gillen and Lall (1997), following the suggestions made by Simar and Wilson (2007), they estimate a truncated regression. Among many other results, Pestana, Barros, and Dieke (2008) find that Hub airports tend to be more efficient and that privately operated airports also tend to have higher efficiency scores than their publicly operated counterparts. Following Pestana, Barros, and Dieke (2008) and Simar and Wilson (2007), in this paper we also rely on truncated regressions to study the determinants of the observed differences in airport efficiency.

It is worth highlighting that there are a few papers that study the efficiency of airports in Latin America. For example, Flor and de la Torre (2008) use DEA methods to analyze efficiency and total factor productivity of airports in Peru. Similarly, Fernandes and Pacheco (2002) employ DEA methods to compute a production frontier using data for Brazilian airports and Gomez

> 2 Some other examples not mentioned in the main text are Gillen and Lall (2001), Murillo-Melchor (1999) and Fung _et al_ (2008).

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