Abstract
We investigate the competitive effects of exchanges or sales of airport landing slots, using a model where airlines allocate their slot endowments across routes consistent with a Cournot–Nash equilibrium. With symmetric endowments, an increase in the number of slot-holding airlines raises social welfare and consumer surplus. Under asymmetric slot endowments, larger slot holders serve “thin” demand routes that are not served by smaller slot holders. Transfers of slots from larger to smaller slot holders increase social welfare and consumer surplus; however, fewer routes may be served. These results may be reversed if airlines face substantial route-level fixed costs.
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Notes
The term, “landing slot,” is frequently used to refer to slot pairs, which include a landing slot and a take-off slot. Naturally, an airline that wishes to offer a flight between a slot-constrained airport and another city’s airport frequently uses both a landing slot and a take-off slot, so that it can both deliver passengers to that city’s airport and receive passengers (bound for the slot-constrained airport or elsewhere) from the same airport.
Our slot allocation issue also differs from the usual setting of changes in the concentration of a scarce input, where to the extent that increased concentration harms competition, it does so by reducing output of both the input and the associated downstream industry, which is typically characterized by single-product production. Here, economic interest focuses on the allocation of a fixed input quantity across firms, as well as the resulting firm decisions of how to allocate that input across various products, rather than concentration’s impact on the total quantity of input and output that is produced.
The airline slot allocation issue bears some similarity to a situation where there is an import quota and the quota rights can be allocated to different firms in different quantities. However, in that case, the import quota only constrains certain foreign producers, since there is typically a domestic industry that produces a substitute product. Also, in cases where quota rights are allocated, firms may not face issues with regard to the multi-product production of distinct goods (as opposed to different substitutable varieties or qualities of a particular good).
While the extraction of resources from a common non-renewable pool is a situation where there is potentially an aggregate output limitation, that limitation is a dynamic one where output can be shifted between periods. In that setting, an increase in the number of firms can be inefficient as resources are overused from an inter-temporal standpoint.
In the current context of airport slot constraints, there is no inter-temporal substitution. The output limitation is static such that less slot use in one period does not make more slots available in a future period. However, similar to the use of resources from a common pool (such as fish, oil, electromagnetic spectrum, etc.), demand growth over time, coupled with technology improvement, has raised the value of the scarce resource (i.e., slots) and placed more policy focus on how it is allocated.
See Financial Times (2008).
See U.S. Department of Transportation (2011).
Mergers, of course, have competitive effects for reasons other than increased concentration of slot holdings. For example, the US Department of Justice approved the Delta-Northwest merger in part because “[c]onsumers are also likely to benefit from improved service made possible by combining under single ownership the complementary aspects of the airlines’ networks” (U.S. Department of Justice 2008).
Slot concentration at two airports was a relevant issue in the recent US Airways-American Airlines merger. The consent decree that allowed the completion of that merger contained a provision that required the merged entity to divest 104 slot pairs at Washington National (DCA) airport and 34 slot pairs at New York LaGuardia airport (LGA). See U.S. Department of Justice (2013). Of the DCA slots, 54 were awarded to Southwest Airlines, 40 to JetBlue, and 8 to Virgin America. At LaGuardia, Southwest won 22 slots while Virgin America won the remaining 12. See Reuters (2014).
A related body of literature analyzes the implications of other measures that affect airport congestion and concentration. Snider and Williams (2013) find that legislative measures to reduce concentration in airport facilities (e.g., gate availability and other space constrains) were successful in decreasing air fares, with little change in the quality of service. Ciliberto and Williams (2010) find that airport concentration, in the form of control of gates at hub airports, leads to higher fares. Oliveira (2010) finds that increased airport concentration, including slot holdings, facilitated the exercise of market power in Brazil’s airline markets. Finally, Forbes (2008) empirically examines the impact of a legislative change in 2000 with respect to take-off and landing restrictions at LaGuardia airport that has the effect of increasing capacity usage, which allows her to estimate the effects of increased air traffic delays on air fares.
Technically, there are landing slots and take-off slots. For simplicity, we will assume that “slots” refers to “slot pairs.” Thus, one slot consists of a landing slot and a take-off slot on the same route (i.e., take-off slot from slot-constrained airport A to airport B, and landing slot from airport B to slot-constrained airport A). Also, without loss of generality, we do not distinguish demand by the direction of travel, such that our demand function for a route is effectively consolidated across both directions (i.e., round trip demand from A to B, and round trip demand from B to A).
In our model, airlines choose the number of flights to offer on a particular route (i.e., its route capacity), where each flight requires a slot. Since there are no connecting passengers in our model, choosing the number of flights between A and B is essentially equivalent to choosing the number of non-stop passengers that are transported between A and B, as long as flights operate at full capacity. In the absence of stochastic demand conditions, airlines would seek to fill every seat on their planes in order to maximize revenue, unless filling that seat produces negative marginal revenue, in which case the airline would not have offered the flight. In our model, demand on each route is deterministic.
We assume homogeneous costs across routes merely so that we can refer in the exposition to “high-priced routes” as opposed to “high-priced routes net of flight costs” (i.e., high-margin routes). Allowing airlines on the same route to choose different plane sizes, while facing similar costs in providing a flight for a plane of a specified size, adds additional complexity to our analysis but should not alter the general thrust of our results. Moreover, one would expect that, absent significant heterogeneity in cost efficiency across airlines in using different sizes of planes, airlines would tend to use planes of similar size on the same route as profit-maximizing behavior.
The second assumption ensures that an airline’s marginal revenue on a specified route is declining in the number of flights that are offered by rival airlines on that route, which is sufficient to ensure that output choices are strategic substitutes.
See for example, Federal Aviation Administration (2008).
We can interpret \(p_r \left( 0 \right) \) as either the minimum price on the inverse demand curve that is associated with zero quantity demanded, or the limit of \(p_r \left( {X_r } \right) \) as \(X_r\) approaches zero.
Our assumptions allow the slope of the demand function to differ across routes. Thus, the route ordering by “intercept values” may differ from the ordering by equilibrium prices, depending on how different the slope of the demand functions are across routes and how slots are allocated across airlines. For example, a route with a lower intercept value (i.e., \(p_{r}(0)\)) may have a higher equilibrium price than another route if demand on that route is relatively less elastic.
We assume that slots are divisible. In other words, airlines can be allocated a non-integer number of slots, and each airline can allocate its slots across routes in non-integer values.
Similarly, equilibrium selection criteria affect whether the total allocation under Duopoly Case B differs from the monopoly allocation when fixed costs are 0. When the game is played sequentially, the outcome depends in part on the first mover’s risk preferences. When firms move simultaneously, equilibrium selection becomes more challenging. Informally, a risk-dominance criterion might point to equilibrium g in Table 3, which has the same total route allocation as the monopoly case. However, other plausible equilibrium-selection criteria would result in fewer routes being served than the monopoly case. Both outcomes are consistent with our results up to this point: The decrease in concentration that is implied by moving from monopoly to duopoly results in (weakly) fewer routes served and (weakly) increased consumer surplus and social welfare. Payoff matrices of the normal form games for each example are available from the authors upon request.
See for example, Reuters (2011).
See for example, Bloomberg (2011).
The slot transfer was finalized in December 2011; the decrease in Delta’s service and increase in US Airways service was phased in, with the last changes taking effect in mid-July 2012. We have opted to compare August 2011 and August 2012 to minimize the impact of seasonal differences in operations. As far as we are aware, this transfer was the only major change in the slot allocation at DCA during this period.
Large and mid-size markets that are not (and were not) served directly from DCA by any of the carriers involved in the slot swap have been omitted from the table: Chicago, Houston, Cleveland, and Milwaukee.
Between August 2011 and August 2012, the HHI measuring the concentration of departures by carrier (across all routes) increased from 2752 to 3350, as US Airways’ share of departures increased from 44.9 to 54.4 %. During the same period, the HHI measuring the concentration of departures by destination (across all carriers) decreased from 279 to 236. In August 2011, there were non-stop flights to 78 different destinations from DCA, with 66 of those routes served by one or more of the three carriers involved in the slot swap. In August 2012, there were 93 destinations that were served non-stop from DCA, with 76 of those routes served by one or more of the three carriers that were involved in the slot swap. These stylized facts (increased concentration of slot operations among carriers leading to decreased concentration among destinations as well as more destinations being served) are broadly consistent with the results of our model.
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Acknowledgments
The authors would like to thank two anonymous referees, and in particular the editor, for many helpful comments. We also express our thanks to attendees at the International Industrial Organization Conference, Center for Research in Regulated Industries Eastern Conference, Xan Vongsathorn, and Josh Cherry for their comments. Finally, we thank Michael Ball and Yi Liu for providing assistance with data.
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Reitzes, J.D., McVeigh, B., Powers, N. et al. Competitive Effects of Exchanges or Sales of Airport Landing Slots. Rev Ind Organ 46, 95–125 (2015). https://doi.org/10.1007/s11151-014-9438-8
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DOI: https://doi.org/10.1007/s11151-014-9438-8