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Airline travel is composed of business and nonbusiness travelers, each with different preferences that give rise to differences in demand elasticities and substitution not only across airlines but also airports. In this study, we develop and estimate a model of airline wherein consumers choose which airports and airline to use that allows for unobserved differences between travelers (e.g., business and nonbusiness travelers). The results point to the role that airports themselves play in the ultimate selection of a flight, and that there are strong interactive effects between the airlines’ networks and the consumers’ preferences across airports.

In the last decades, low-cost carriers (LCCs) have generated several changes in the air market for both passengers and airports. Mainly for regional airports, LCCs have represented an important opportunity to improve their connectivity levels and passenger traffic. Furthermore, many regional airports have become key factors to regenerate the local economy by improving accessibility and stimulating several markets, such as tourism. However, the relationship between LCCs and airports is rather complex and the outcomes not always predictable. In order to analyze and understand better such relationship and its outcomes, this chapter discusses the main underlying factors identified in: relation with the regional air market (secondary/primary airports), balance of power (dominated/non-dominated airports), and industrial organization (bases/non-bases). Starting from the proposed Relative Closeness Index, which combines yearly airport passengers and distance between airport pairs, a large sample of European airports is analyzed. Then, a smaller sub-sample – which includes selected, significant case studies referring to mid-sized airports – is discussed in detail. Among the main findings, airports sharing their catchment area with others are in a very risky position, due to the potential mobility of LCCs, while geographically isolated airports in good catchment areas can better counterbalance the power of carriers.

This chapter examines the effects that airports have had on economic development in cities from 1950 to 2010. It uses a novel dataset consisting of previously unexploited data on the origins and history of the aviation system in the United States. Applying the method of synthetic controls to a set of medium and small airports, I examine both the overall impacts and the heterogeneity within the outcomes of various airports. Then, I use regression analysis to determine key factors differentiating successful airports from less successful ones, as it pertains particularly to population and employment growth. I find that, first, on average, cities have benefited from airports over this period. Airports, overall, provided a causal contribution of 0.2– 0.6% per year on population and employment growth over the time period. Second, I show that city-level factors contributing to airport success include: (1) closer proximity to a major research university, (2) a capital city location, and (3) climate factors, particularly higher January mean temperatures and/or hours of sunshine. City size is a consideration as well; cities in larger metropolitan areas, with larger shares of employment in nontradables in the 1950s, were also better positioned to reap the benefits that airports provided on city growth. Significant differences were not found across regions, airport governance structures, or other factors.