Abstract
In this paper, we study whether competition in the airport market and the vertical interactions between upstream airports and downstream airlines influence the airport pricing decisions. Using a panel of the 24 largest UK airports, as well as a refined definition of airports’ market structure, we find that lower concentration in an airport’s catchment area and higher airlines countervailing power are associated to lower aeronautical charges.
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Notes
A few empirical works study the impact of market structure on airport efficiency and productivity. For example, Bottasso et al. (2013), Pels et al. (2009), Choo and Oum (2013) investigate the importance of the presence of low cost carriers for airports efficiency and productivity; in turn, Scotti et al. (2012) explore the relationship between airports competition and efficiency. There is also a vast empirical literature that has sought to analyze the cost structure of the airport industry as well as the determinants of airport efficiency and productivity; for recent examples of this strand of the literature, see Bottasso and Conti (2012), Yan and Oum (2014). More in general, there is a literature that seeks to evaluate the economic impacts of airports (see Albalate and Fageda (2016) and Blonigen and Cristea (2015), among the others).
The same authors underline the role played by the complementarity between the demand of aviation and commercial services in reducing the incentives for airports to increase aeronautical charges.
“Local authority (LA) is a generic term for any level of local government in the UK. In geographic terms LAs include English counties, non-metropolitan districts, metropolitan districts, unitary authorities and London boroughs; Welsh unitary authorities; Scottish council areas; and Northern Irish district council areas.” Office for National Statistics, available at: http://www.ons.gov.uk/ons/guide-method/geography/beginner-s-guide/glossary/glossary-l.html.
“The Nomenclature of Units for Territorial Statistics (NUTS) is a hierarchical classification of spatial units that provides a breakdown of the European Union’s territory for the purposes of producing comparable regional statistics.” Office for National Statistics, available at: http://www.ons.gov.uk/ons/guide-method/geography/beginner-s-guide/glossary/glossary-n.html.
Pavlyuk (2009) states that an airport’s catchment area should be built taking into account the nearest airports but also their flights availability and that those areas can vary for different destinations.
If an airport in the catchment area of airport i does not serve superoute r, we impute a market share of zero.
In doing this, we follow Scotti et al. (2012), which, however, used the number of available seats instead of the number of passengers
ELFAA is the European Low Fare Airline Association; information on its members are available at http://www.elfaa.com/members.htm.
We have computed these variables at the catchment area level by summing over local authorities included in the catchment area.
The airports were only required to report charges to the economic regulator, the CAA.
The HH index of concentration in the 1996–1999 period was, on average, 0.89 for regulated airports, ranging from 0.85 in the case of Manchester to 0.91 for BAA-regulated airports. Instead, the HH index was only 0.7 for non-regulated airports. It is important to recall that an HH index of one corresponds to a fully monopolized market.
The price cap regulation in place in the UK over the sample period considered in this study is of the single-till type, whereby the revenues included in the price cap are both those stemming from aeronautical charges (as in the dual-till approach) and those arising from commercial revenues. For a comparison of the dual versus single-till approaches and in particular the relative incentives to raise or cut aeronautical charges and their relationship with congestion, see Czerny and Zang (2015).
Starkie (2008) reports data for most airports in our sample for the period 2005–2006 and shows that the net return on their assets was comparable to that of UK private non-financial corporations. Moreover, his data do not suggest that publicly-owned airports had significantly lower rates of return than their private counterparts.
In other words, by weighting observations for a measure that captures the size of an airport we should get an estimate of the effect of concentration on charges that takes into account the fact that Heathrow serves more passengers and is more relevant in the UK than, say, Norwich. We use atm in 1994 to avoid endogeneity concerns. Our results are not affected if we experiment using the square root of atm as a measure of size, thereby attenuating the importance of large airports with respect to a weighting scheme based on atm levels. We also obtain similar results if we weight by the log of atm.
The bias might be large in magnitude because it depends on how HHI varies with respect to the correlation between HHI and the omitted variable. We do not know the latter, but we now that HHI varies little within airport, which is the variation we use to identify its effect on prices.
Wlu can be considered a poor proxy of an airport’s output. Unlike the case of a regressor affected by measurement error, an instrument that is badly measured is still in principle a valid instrument. Nevertheless, for robustness check, we also consider IV regressions where we use instruments based on airport passengers and cargo. See below in Sect. 5.2.
See end of Table 6. Full results of first stage available upon request.
According to Stock et al. (2002), one possible definition of the weak instrument problem is that instruments are weak if the \(\alpha \) level Wald test based on IV statistics has an actual size that exceeds a certain threshold, such as 10 or 20%. The tabulated maximum critical values in the case of one endogenous regressor and one instrument, for an actual size of 10% (instead of the nominal 5%) is 16.38, which is generally lower with respect to the Kleibergen–Paap rk Wald F statistics of 29.1 as shown in column 6. This suggests that the maximum size distortion in this application is no larger than 5%: we interpret this result as evidence that our IV estimates are probably not affected by serious weak instrument problems.
In order to evaluate the possible existence of a weak instrument problem, Angrist and Pischke (2009) also suggest to consider the reduced form regression: indeed, the coefficient of the excluded instrument in the reduced form regression (i.e. the coefficient of WLUC in our case) is proportional to the causal effect of interest. Thus, if the excluded instrument in the reduced form is not statistical significant, Angrist and Pischke (2009) argue that a possible significant effect in the IV procedure is likely to be biased for the existence of a weak instrument problem. Reassuringly, our instrument is positively and significantly correlated with aeronautical charges, as expected. Results are available from the authors upon request.
It is important to note that, for our measure to be a valid proxy for aircraft size, one needs to assume that there is 100% load factors, that all aircrafts are configured for their maximum number of passengers and that there is no cargo movement without passengers. While our control for the share of cargo makes the validity of the third assumption more defendable, our measure is indeed an imperfect proxy for aircraft. In fact, it might be picking up also the effect of lower loads factors in smaller airports (which is however, at least to some extent, controlled for by airport fixed effects) and the higher costs of operating with smaller aircrafts.
We have checked that our results are robust to measuring airport unit costs as operating costs divided by wlu.
We define an airport as capacity constrained when it has a level of atm per km of runways below the sample average.
In our regression specification we cannot include a regulation dummy, because it would be absorbed by airport fixed effects. However, we have experimented by including an interaction term between a linear time trend and a dummy for regulated airports: this control allows us to check whether there is any differential trend in the dynamics of aeronautical charges between regulated and unregulated airports. Empirical results, not shown but available from the authors, do not confirm the existence of such differential trend. As a final robustness check, we have included the share of commercial revenues over total revenue (lagged on period in order to alleviate obvious endogeneity concerns) as an additional regressor, in order to check whether airports relying more on commercial activities tend to cross-subsidize aeronautical charges. The sign was negative, as expected, although it was not very precisely estimated: reassuringly, the impact of our main variables of interest was barely changed.
ICF (2016), in a report produced for the UK Competition Market Authority, undertook some regression analysis and was not able to find a negative impact of the divestiture of BAA airports on aeronautical charges. However, they also argue that some airlines reported a reduction in charges in both the case of Edinburgh and Stanstead, while an airport argued that airports in the south east of England might have been forced to cut charges, suggesting that the increase competition between airports brought about by the BAA divesture might have indeed lowered aeronautical charges. Of course, more research is needed on this issue.
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We thank seminar participants at the University of Salento and SIEPI Conference in Milan. A special thanks to Dr. Alessio Tei for his help with the GIS software. The usual disclaimer applies. The views here expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.
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Bottasso, A., Bruno, M., Conti, M. et al. Competition, vertical relationship and countervailing power in the UK airport industry. J Regul Econ 52, 37–62 (2017). https://doi.org/10.1007/s11149-017-9332-z
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DOI: https://doi.org/10.1007/s11149-017-9332-z