Abstract
I examine how regulatory preferences in setting a federal price cap on passenger facility charges (PFCs), the variable portion of an access price in the U.S. domestic aviation industry, have evolved over time. PFCs are a per-passenger charge paid by airlines to airports. Despite the fact that the PFC cap has declined in real terms since 2001, I find that regulators have given greater importance to airports since the turn of the century. There are a number of recent proposals to increase the price cap and, at a minimum, restore the cap to 2001 real levels. I find that a price cap decrease would instead be necessary to maintain the preferences of regulators in 2001.
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Notes
Within a large theoretical literature on access pricing regulation, studies that allow for imperfect downstream competition are most relevant to the U.S. aviation industry. Laffont and Tirole (1994) and Armstrong et al. (1996) examine optimal access pricing regulation when an upstream monopolistic provider of access also competes downstream (i.e., the case of a vertically integrated upstream supplier). In Lewis and Sap**ton (1999), the upstream supplier of access competes downstream, but the regulator is uncertain about the production costs of the unregulated competitor. Valletti (1998) examines access pricing in a vertically separated industry with a monopoly upstream supplier. Downstream firms engage in Cournot competition. The U.S. domestic aviation industry is vertically separated, but downstream airlines compete in prices. Armstrong (2001) and Vogelsang (2003) provide more general reviews of the theoretical access pricing literature.
Morrison (1987) examines an access charge, runway prices, in the aviation industry. However, this study treats runway access as a final good purchased by airlines. Unlike the present study, consumer welfare is not considered in the regulator’s surplus function.
In Online Appendix D, I consider other sources of airport revenue such as non-aeronautical revenues (e.g., parking or concessions) or revenues from other aeronautical fees (e.g., landing fees). I find that my key qualitative findings are likely to continue to hold if these alternative sources of airport revenue are included in the airport surplus function.
Even if the regulator did not explicitly act to maximize such a weighted welfare function, its policy choices may be consistent with such behavior (Ross, 1984).
While the price cap increase went into effect in 2001, I use 2000 data for two reasons. First, the legislation that mandated the price cap increase was signed in 2000 so regulators likely used market conditions in 2000 when making the decision. Second, data from quarter 3 of 2001 may not be representative due to the September 11th attacks. Results are robust to instead using data from quarter 2 of 2001. See Online Appendix C.3 for details.
I do not attempt to explain the cause of this shift in regulatory preference. One potential explanation is that a reduction in the price cap, which would be necessary to maintain regulatory preference, would be politically unpopular as it benefits airlines and potentially weakens public infrastructure.
See Online Appendix B.1 for a review of proposed price cap changes.
The price cap would need to be increased from $4.50 to $6.26 in 2018 to return the price cap to 2001 levels (when the cap was last increased).
The Online Appendix can be found at https://douglascturner.com/access-pricing-online-appendix/.
For example, airport improvement grants (AIPs).
Federal Code 14 C.F.R. §158.9 2007.
Federal Register Vol. 79 No. 3. Notices. Monday, January 6, 2014.
Federal Register Vol. 75 No. 53. Notices. Friday, March 19, 2010.
Figure graphic scheme source: Bischof (2017).
See 49 U.S. Code § 40117.
The method of Ahmad and Stern (1984) and Ross (1984) has been applied extensively to analyze regulatory decision making in a variety of industries including electricity (Naughton, 1988; Nelson & Roberts, 1989), milk (Ahn & Sumner, 2009), natural gas (Klein & Sweeney, 1999), telecommunications (Montes, 2013), airport runway pricing (Morrison, 1987) and water utilities (Resende, 1997).
If \(\Pi ^{U}(w)\) is strictly concave, this assumption holds if the cap is less than the monopoly access charge, \({\overline{w}}<w^{M}=\text {argmax}_{w}\Pi ^{U}(w)\).
For simplicity, I assume any relevant participation constraints are satisfied at the solution to the regulator’s problem.
As the price cap is binding by assumption, the observed price cap equals the observed access charge.
As the regulator maximizes the objective over only one variable (the price cap), its not possible to infer regulatory weights for both airlines and airports simultaneously. However, one can examine the robustness of results by inferring \(\alpha \) under a range of values for \(\gamma \).
While the price cap increase went into effect in 2001, I use 2000 data for two reasons. First, the legislation that mandated the price cap increase was signed in 2000 so regulators likely used market conditions in 2000 when making the decision. Second, data from quarter 3 of 2001 may not be representative due to the September 11th attacks. Results are robust to instead using data from quarter 2 of 2001. See Online Appendix C.3 for details.
The most recently available data, when this project began, was from quarter 3 of 2018.
A nested logit demand allows for correlations between choices that are within the same nest but requires independence of choices between nests.
Population data is from the U.S. Census (https://www2.census.gov/programs-surveys/popest/datasets/).
For a connecting product from LGA to MIA connecting through ATL in both directions, \(a_{1}=LGA\), \(a_{2}=ATL\), \(a_{3}=MIA\) and \(a_{4}=ATL\). Note that two PFCs are collected at ATL, one PFC is collected at LGA and one PFC is collected at MIA. See 49 U.S. Code § 40117.
In Online Appendix C.4, I show that the main results are robust to relaxing this assumption and allowing airlines to coordinate pricing decisions.
I use pyblp (Conlon & Gortmaker, 2020) for demand and marginal cost estimation.
The Houston Airport System states “The mission of the Houston Airport System (HAS) is to provide safe, efficient and appealing facilities to satisfy the air transportation needs of the Greater Houston Region at competitive prices while stimulating growth in its economy.” (Source: http://www.houstontx.gov/budget/12budprop/IX_AIR.pdf).
Although increased competition is a stated objective of the PFC program, only approximately \(23\%\) of PFC revenue (https://www.faa.gov/airports/pfc/monthly_reports/media/category-interest-lump-sum.pdf) since the inception of the program has been allocated to the construction of new terminals or terminal expansion. Terminal, or gate access, is the primary barrier to entry in the U.S. airline industry (Ciliberto & Williams, 2010).
In Online Appendix D, I consider the impact of including other sources of airport revenue in the airport surplus function on implied regulatory preference.
In Online Appendix C.2, I relax this assumption and instead assume some airports charging the maximum PFC level would not increase their cap to the new level upon a price cap increase. Results are qualitatively unchanged.
For example, CLT airport charges an access charge of $3. If the cap increases from $4.50 to $5.50, I assume the PFC level at CLT increases to $4. Results are robust to instead assuming a corresponding percentage increase. Results are also robust to instead assuming airports which charge a PFC below the cap do not change their PFC after the cap is raised. See Online Appendix C.2 for additional details.
With regards to airlines, this is potentially a strong assumption. Without a complete model of firm entry, exit and fixed costs, it would not be feasible to consider the participation constraints of airlines. With regards to airports, PFC revenue is not intended to cover operational costs so a lack of PFC revenue should not cause the closure of an airport.
I restrict attention to roundtrip products and products consisting of no more than 1 connection in each direction (see Sect. 4).
This is primarily due to data availability. PFCs have a smaller impact on international markets as they are a smaller percentage of the ticket price and are not collected on flight segments departing foreign airports.
While over 300 U.S. airports charge PFCs, many constitute only a very small fraction of U.S. travel in terms of passengers and make up only a small portion of total PFC revenue.
Among roundtrip itineraries, \(64.2\%\) of connecting products connect through at most one airport.
Allegiant Airlines (G4), Spirit Airlines (NK), Frontier Airlines (F9), JetBlue Airlines (B6), Airtran Airways (FL), National Airlines (N7), Vanguard Airlines (NJ), ATA Airlines (TZ) and Sun Country Airlines (SY)
No 2000 data is available because the survey was not conducted between 1998 and 2014.
Bontemps et al. (2020) find an elasticity of \(-4.69\) in 2011 using a nested logit model. Ciliberto and Williams (2014) find an elasticity of \(-4.320\) in 2006-2008, while Berry and Jia (2010) find a far lower elasticity of \(-2.10\) in their main specification. This difference is likely driven by the way Berry and Jia (2010) define products in terms of fare bins (grou** tickets with similar fares into the same product). The approach of this paper, which does not utilize fare bins, is more comparable to Ciliberto and Williams (2014). Gayle (2013), using a random coefficients logit with continuous heterogeneity, found an elasticity of \(-4.72\) with data from 2006. Using data from 1995 and both nested logit and generalized extreme value demand, Peters (2006) estimated elasticities ranging from \(-3.2\) to \(-4\), depending on the specification. Lastly, using more recent data from 2005 to 2013, Chen and Gayle (2019) found a value of \(-1.67\) using a nested logit demand.
This computation assumes Bertrand Nash competition. The 2000 Lerner index estimate is consistent with estimates of Berry et al. (2006) and Berry and Jia (2010), who report Lerner indexes around .6. Gayle (2013), using data from 2006, reports, for the subset of products he considers, a Lerner index of .39, which is closer to the result of this paper in 2018. Bontemps et al. (2020) find a Lerner index of .24 in 2011.
Figure graphic scheme source: Bischof (2017).
One potential explanation for this shift in preference is a public and political perception that U.S. infrastructure is unsatisfactory. This sentiment could make a decrease in the cap (which is what would maintain regulatory preference) politically unpopular.
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Acknowledgements
I thank Germán Bet, Jeongwoo Lee, David Sap**ton, Steven Slutsky, Daniel Sokol and two anonymous for helpful comments.
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Turner, D.C. Access pricing regulation in the U.S. domestic aviation industry. J Regul Econ 62, 24–46 (2022). https://doi.org/10.1007/s11149-022-09453-8
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DOI: https://doi.org/10.1007/s11149-022-09453-8