scores of the previous section as dependent variables and these factors as explanatory variables. The choice of a truncated model is dictated by the nature of the technical efficiency measure (which is by definition truncated at 1.0) and by the findings of the recent academic literature (Simar and Wilson (2007)).<sup>9</sup> Before presenting our results we stress that service quality is likely to be another potential factor behind the observed differences in airport efficiency. It is likely that, other things equal, airports operating with a large staff and/or a large number of boarding bridges provide better service quality to passengers. Unfortunately, survey data on users’ satisfaction is not yet available at an international scale for us to be able to include quality indicators in our regression analysis.

Table 5 presents average values by region for the candidate variables to account for observed differences in technical efficiency. Starting with the institutional setting, Table 5 shows that on average LAC airports operate under a more liberalized framework. Indeed, more than half of LAC airports (54.5%) in our sample operate as private concessions, and 31.8% are regulated by an independent regulatory agency. In contrast, only 25.6% of Asian airports and 37.9% of European airports are under private management, while 10.3% and 16.7% of Asian and European airports respectively are regulated by an independent regulatory agency. Finally, all airports in Canada and the United States are operated by state-owned enterprises, and regulatory agencies in these two countries still depend directly from a political authority (a ministry).

Another potential factor that could have a role in the explanation of airport performance is the socioeconomic environment in which they operate. We incorporate this effect with two indicators: GDP per capita (measured in current dollars) and tourism expenditures (also measured in current dollars). However, it is worth stressing that these variables are only available at the country level and don’t correspond necessarily to the area of influence of the airports.<sup>10</sup> The demographic environment is represented by the concentration of population in the area served by the airport. On average, LAC airports appear to serve very large urban agglomerations, like their Asian counterparts. Compared to European and North-American airports, which are on average located in cities with 3 to 4 million inhabitants, LAC airports are on average located in cities with 8 million people. In the regression analysis this information will be incorporated with a binary (dummy) variable that takes a value 1 for airports located in cities with more than 5 million people and 0 otherwise<sup>11</sup> .

Finally, we introduce a set of variables that represent characteristics that are particular to each airport. One of them is their specialization as a hub, represented by the percentage of connecting passengers. LAC airports have the lowest percentage of connecting passengers (and also have the lowest percentage of hubs), followed by Asian airports. The highest percentage is observed among European airports, where nearly one-third of passengers are connecting. Another variable that is particular to each airport is the share of aeronautical revenues in total revenues. In Table 5 below we see that aeronautical revenues are on average rather more

> 9 We estimate truncated regressions using the “truncreg” procedure of SATA 9.0.

> 10 Given that our dataset contains several airports in the United States and given the availability of data, for these airports we used GDP per capita of the state in which each airport is located instead of GDP per capita for the country as a whole.

> 11 The value of 5 million corresponds to the mean of the population of the cities where airports are located.

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