How can the effects of the introduction of a new airline on a national airline network be measured? A time series approach for the Ryanair case in Spain

How Can the Effects of the Introduction of a New Airline on a National Airline Network be Measured? A Time Series Approach for the Ryanair Case in Spain Jose ´I. Castillo-Manzano, Lourdes Lo ´pez-Valpuesta, and Diego J. Pedregal Address for correspondence: Jose ´I. Castillo-Manzano, Facultad de Ciencias Econo ´micas y Empresariales, University of Seville, Avda. Ramo ´n y Cajal, 1, 41018 Seville, Spain ([email protected]). Lourdes Lo ´pez-Valpuesta is at Facultad de Ciencias Econo ´micas y Empresariales, University of Seville (Spain) ([email protected]). Diego J. Pedregal is at Ingenium Research Group and IMACI, University of Castilla-La Mancha (Spain) ([email protected]). The authors would like to express their gratitude to the Fundacio ´n Centro de Estudios Andaluces for the financial resources that it provided for this study. The authors are also grateful to Steven A. Morrison, the anonymous reviewer, and Germa `Bel for their very helpful comments. Abstract This paper quantifies the Ryanair Effect on the Spanish airline network. It proposes new methodology based on an advanced time series approach that allows both the direct and indirect effects of the incorporation of a new airline to be measured and that can be easily extrapolated to other airport systems. The findings show the mean indirect effect on other airlines, in absolute value, is 8.6 per cent of the total airport traffic, peaking at a maximum of almost 29 per cent. Also, surprisingly, there is found to be a negative indirect effect at only four of the ten airports analysed. Date of receipt of final manuscript: June 2011 263 Journal of Transport Economics and Policy, Volume 46, Part 2, May 2012, pp. 263–279 1.0 Introduction There is a wealth of literature illustrating the international development of the low-cost model and its expansion into different geographical areas (see Francis et al., 2006, for an overview of this development). To be specific, in Europe, the early mid-1980s’ liberalisation between Ireland and the UK created the conditions that allowed the first low-cost airline to appear in Europe, Ryanair (see Francis et al., 2006, for the reasons that favoured the creation of Low Cost Carriers (LCCs) in these two countries). This airline has developed into the main low-cost European airline and the most successful LCC in the world in terms of profitability (Oliveira, 2008). Ryanair followed in the footsteps of the Southwest Airlines company (see Guillen and Lall, 2004, on the main differences between Ryanair and Southwest), although time has shown that Ryanair has remained more faithful to the features of the original lowcost model than its precursor, Southwest (Alamdari and Fagan, 2005). It has also created a model that can be easily replicated (Guillen and Lall, 2004). One of the salient features of the Ryanair management model for our analysis is the use of underutilised secondary airports, often far from main city airports, and regional airports. This is what differentiates the airline from other major LCCs like easyJet or Southwest (Dobruszkes, 2006; Francis et al., 2006; Graham, 2009). Ryanair usually avoids using primary airports, except when a secondary airport is not available in the region (Graham, 2009), as the latter are less expensive in terms of landing charges; they are less congested than the major hub airports; shorter turnarounds are possible and, therefore, more journeys per day per plane can be achieved (Barrett, 2004). Ryanair plays the dominant role in many of these secondary or regional airports and in some, such as Charleroi and Girona (Barbot, 2006), almost exercises a monopoly. This power influences its price strategy, as Ryanair seems to set lower fares when flights depart from or arrive at dominated airports (Barbot, 2006; Malighetti et al., 2009). This is, in turn, influenced by the fact that operating at airports of this type allows Ryanair always to be in a position to negotiate airport fees (Fro ¨idh, 2008; Oliveira, 2008). In this way Ryanair has shown that secondary airports are not only able to charge less (see Castillo-Manzano, 2010) but also offer subsidies to attract airlines (Papatheodorou and Lei, 2006), often with the aid of local or regional public authorities (see Castillo- Manzano and Lo ´pez-Valpuesta, 2010). Secondary airport managers where LCCs are found should be aware that they are facing high volatility in their traffic, due both to the greater likelihood of bankruptcy or merging of these kinds of airlines and to their dependence on the aid and subsidies that they apply for. There is even higher volatility when the airport has a dominant single LCC (Guillen and Lall, 2004; Barbot, 2006; Bel, 2009), as is the case of Ryanair at many of the secondary airports where it operates. In this context, the objective of this article is an analysis of what has been dubbed, according to Guillen and Lall (2004), the Ryanair Effect on the Spanish airline network. Following Vowles (2001) and Pitfield (2008) on the Southwest Effect, the Ryanair Effect would be the effect on average airfares, traffic volumes, and market shares experienced in the markets into which Ryanair enters. The prior academic literature has studied the impact of the arrival of a new LCC at the airports which accept them, mainly analysing the effect that this arrival has on airfares and the spillover effects on nearby airports Journal of Transport Economics and Policy Volume 46, Part 2 264 (Morrison, 2001; Vowles, 2001; Daraban and Fournier, 2008). With respect to the effects on traffic, most of the studies (Donzelli, 2010; Graham and Dennis, 2010) analyse the impact of the arrival of an LCC by computing the overall growth in passenger numbers without analysing any possible collateral effects on the traffic of other companies. This paper seeks to quantify the overall effect on commercial traffic by responding to the following question: given the evident increase that the arrival of Ryanair means for an airport’s traffic which has been highlighted in the literature (Barrett, 2004; Francis et al., 2004; York Aviation, 2007; Bel, 2009), how much of this growth can be directly attributed to Ryanair’s own traffic, and what proportion can be attributed to the reaction of the other airlines at the airport to the arrival of said low-cost airline? For the other companies, the arrival of this low-cost company can have either a competitive or a catalyst effect — that is, be a stimulus that increases their volume of traffic — or it can have no effect whatsoever, or it can lead to a reduction in their traffic due to the substitution effect. As described previously, the first effect, the competitive or catalyst effect, would be attributable to Ryanair having become a leading company in the European aviation sector. In some countries, like Spain, it is even the leading airline in passenger traffic. It should therefore be anticipated that when a leading company breaks into new markets/ airports, it could raise the competitiveness of the other companies/airlines operating there and, especially, point the way (pull or knock-on effect) for others that were not operating in this market (generally, a regional or previously underutilised secondary airport) to follow them. With respect to increased competitiveness, this has been documented by Southwest. To be specific, according to Vowles (2001), the entry of Southwest can in some cases lead to other carriers lowering their fares to remain competitive, which stimulates more traffic. Gillen and Lall (2004) also state that fares fall prior to the arrival of Southwest at a new destination and this generates tremendous demand which also benefits other carriers. With regard to the possible pull effect, according to York Aviation (2007), Ryanair acted as a catalyst to further growth at Eindhoven airport by proving that low-fare services were sustainable, thus attracting new airlines. Generally, this competitive effect, in any of its forms, could also be supported by the increase in the appeal of the airport from the perspective of transfers on the back of Ryanair’s new destinations. With regard to the above-described substitution effect, this can in turn be one of two types: First, the arrival of Ryanair with connections to the main European cities, like London and Paris, can have an effect on the connections that already exist between the Spanish airports and destinations such as these which had previously been in the hands of the network carriers (NCs). Second, the introduction of new point-to-point destinations, especially in the case of regional airports, will reduce the number of flights to hubs like Madrid and Barcelona, which were previously an obligatory stopover point when travelling to the new destinations. This possible negative point, namely the fall in NCs traffic, has been condemned by Spanish travel agency associations protesting about nonreturnable subsidies and other economic aid being granted to Ryanair and other LCCs by the public authorities (see Castillo-Manzano and Lo ´pez-Valpuesta, 2010). According to these associations, these substitution effects are especially detrimental to conference tourism (see Castillo-Manzano et al., 2011). Both the total direct and indirect effects of Ryanair need to be measured from the point of view of transport policy for local and regional public administrations to assess How Can the Effects of the Introduction of a New Airline be Measured? Castillo-Manzano et al. 265 the increasingly frequent and higher economic compensation that the company demands to operate in regional airports both in Spain (see Castillo-Manzano et al., 2011) and in the rest of Europe. Methodologically, this study uses an econometric model to estimate the net quantitative impact of the arrival of this airline on the main traffic at Spanish airports in line with other studies (Pitfield, 2007, 2008). The major advantage of the proposed methodological approach is that it can be easily extrapolated to other airport systems to measure the effects that the arrival of any new airline, be it an LCC or an NC, would have on the other airlines at the various airports. The article is organised as follows. Section 2 lays out the data and presents the methodological approach. Section 3 presents the empirical results, while Section 4 includes the discussion of these findings and, finally, Section 5 presents the conclusions of the study. 2.0 Data and Methods The Spanish airports analysed in this study were chosen for having achieved Ryanair traffic figures of over 400,000 passengers in 2008. Four of the ten airports in the sample were in the list of the top fifty busiest airports in Europe in 2008, specifically Madrid- Barajas (fourth), Palma de Majorca (thirteenth), Ma ´laga (thirty-second), and Alicante (forty-first). When we add the case of Barcelona (ninth), where Ryanair started operating in the autumn of 2010, to these airports, it can be seen that, unlike in other countries, Ryaniar operates at the major hubs in Spain, not only at secondary or regional airports. This is due both to Spanish airport charges being low and to the fact that there are no great differences between these charges at the various airports, whatever their size.1 The total effect of Ryanair’s arrival on the above-mentioned airports to December 2008 will be split into a direct effect and an indirect effect: 1 The direct effect is the effect that the literature has traditionally measured — that is, the percentage represented by Ryanair traffic out of the total traffic at the airport (see equation (1)). TRi;t¼Ryanair traffic at airport iduring month tand TTi;t¼total traffic at airport iin month t(see Figure 1). Finally, lis the month that Ryanair began to operate at airport iand Nis the number of observations: DEi¼P N t¼l TRi;t P N t¼l TTi;t 100:ð1Þ 1 For example, the airport charge for domestic and European Union flights at Barcelona airport’s spectacular new terminal was €6.12 per tonne (10–100 tonne aircraft), while at Barcelona’s secondary and most distant airports, Reus and Girona, the charge stood at €5.55 — that is, only 10 per cent less. Nevertheless, new charges have come into force in 2011 with greater discrimination according to airport size. Journal of Transport Economics and Policy Volume 46, Part 2 266 2 The indirect effect, often ignored by previous studies, will comprise two entries, which will be aggregated for their estimation. These entries are the competitive or catalyst effect on other airlines, with a positive sign, and the substitution effect on other airlines — NCs in the main — with a negative sign. The substitution effect can be caused both by Ryanair offering connections which had previously been offered by other airlines and also by the company’s new destinations, which reduce traffic to national hubs like Madrid and Barcelona. The data used to measure the indirect effects of Ryanair on airport ican be divided into three groups: (A) The endogenous variables will be the monthly air traffic, without considering Ryanair traffic, at the ten Spanish airports included in the study; this is residual traffic. The available time series spans from January 1996 to December 2008 are taken from AENA yearbooks (source: www.aena.es). (B) The exogenous variables, separated into two groups, are as follows: (B.1) Dummy exogenous variables: a wide range of variables are included in models to estimate a number of intervention variables and outlier effects seen in the data. The most important, with their definitions, are as follows: Figure 1 Millions of Air Passengers (Per Month) at the Ten Spanish Airports Considered 1996 1998 2000 2002 2004 2006 2008 0 0.05 0.1 0.15 0.2 Millions 0 2 4 6 Millions 0 0.2 0.4 0.6 Millions Madrid Palma de Majorca Málaga Alicante Girona Valencia Seville Murcia Reus Santander How Can the Effects of the Introduction of a New Airline be Measured? Castillo-Manzano et al. 267 (B.1.1) Easter: Air traffic around this holiday period is especially intense in Spain. Indeed, it is considered to be high season for tourists, among other reasons due to the numerous celebrations of the passion of Christ. Accordingly, the moveable feast of Easter variable is defined by assigning different weights to the days in question depending on the expected traffic density at Spanish airports (these weights have to add up to one). Maximum weights are assigned to Wednesday, Thursday, Easter Sunday, and Monday. Weights of zero are assigned to the rest of the days. (B.1.2) Business: Monthly time series that are totals of daily activities can be influenced by each calendar month’s weekday and weekend composition. This variable is introduced to take into account the differences among months regarding the proportion of weekdays with respect to weekends. It is constructed as the number of business or trading days with respect to weekend days and holidays in each individual month — that is, the number of business or trading days minus the number of Saturdays and Sundays multiplied by 5/2. Extra holidays in each month are subtracted from the business days. (B.1.3) Leap: Addresses the leap year effect. The value is 1 when February contains 29 days, and 0 otherwise. (B.1.4) 9/11: The negative effect on air traffic that resulted from the 9/11 terrorist attacks which, as found in earlier studies (Inglada and Rey, 2004), also had a significant effect on the Spanish airport system. The duration of these effects in number of months has been determined empirically by estimating successively models in which this effect have increasing durations from one to six years and choosing the model with the best fit. (B.1.5) There are other outliers, often related to bad weather conditions, covert strikes by air-traffic controllers, or even the opening of new air terminals like Terminal T4 at Madrid- Barajas airport (LST4). These have all been detected by statistical tools. The procedure to search for such outliers consists of selecting the residuals outside four times standard deviation and including them as potential candidates in the models under different specifications (see the methodological section below). The outliers are included in final models with the specification that provides the best fit when they are statistically significant. (B.2) Economic activity: The literature argues that economic activity is closely linked to air traffic as a result of which it is generally included as an indicator when modelling air traffic (see Inglada and Rey, 2004). In this article, economic activity is represented using a Spanish Ministry of the Economy and Treasury synthetic economic activity index (source: http://serviciosweb.meh.es/apps/dgpe/default.aspx). (C) The Ryanair indirect effect: This effect has been split into two terms for testing: (i) one constant for the periods when Ryanair was operating at each airport, Journal of Transport Economics and Policy Volume 46, Part 2 268 measuring the shock it produces (called ‘Ryan Shock’ afterwards); and (ii) mean corrected Ryanair traffic, measuring how the dynamics of Ryanair traffic affect the overall dynamics at a given airport and any possible lags for this variable (‘Ryan Dynamic’). There were four cases (Girona, Reus, Murcia, and Santander) where the previous procedure had to be refined in order to find appropriate models. Ryanair has a dominant position at these four airports that would explain the structural changes that have been detected after Ryanair commenced operations, resulting in the need for special treatment in these cases (see details below). The time series models employed in the analysis are in the class of discrete time linear Transfer Function models (see Castillo-Manzano et al., 2010, for an analysis of the advantages of this methodology on transportation research studies). The general formulation may be expressed as in equation (2): yi;t¼X h j¼1 oni;jBðÞ dmi;jBðÞ ui;j;tþNiBðÞei;t;ð2Þ where yi;tare the air passenger total data for the ith airport excluding the Ryanair passengers; ui;j;tare the inputs on which the output data depend (most of them deterministic, with the sole exceptions of the economic cycle and part of the Ryanair indirect effect, see the list above); ei;tis a zero mean and constant variance Gaussian white noise; oni;jðBÞ¼ðoi;0þoi;1Bþþoni;jBni;jÞðj¼1;...;hÞare polynomials in the backshift operator (that is, Bkyt¼ytk) that may have leading zero coefficients when a pure time delay is necessary; and dmi;jðBÞ¼ð1þdi;1Bþþdmi;jBmi;jÞðj¼1;...;hÞare stationary or stable polynomials. It is important to include a specific comment on the Ryanair input variables introduced in equation (2). Broadly speaking, the input variables linked to the Ryanair indirect effect are: (i) ‘Ryan Shock’: one step variables with zeros before Ryanair commenced operation, and ones after the company had commenced operation; and (ii) ‘Ryan Dynamic’: Ryanair traffic minus its mean level during the period of operation. However, although this would be the general way that the airports are treated, there are four airports that behave in a special way given the dominant role played by Ryanair in their traffic. These are the aforementioned cases of Girona, Reus, Murcia, and Santander. As such, the standard method used for handling the Ryanair indirect effect by dividing it into ‘Ryan Shock’ and ‘Ryan Dynamic’, as in the cases shown in Table 1, was inadequate for these four airports. By way of example, Figure 2 shows the special case of Girona and compares variable yi;t(that is, the total number of passengers excluding Ryanair) with Ryanair traffic at Girona airport (where Ryanair commenced operations in December 2002). It is clear that the total effect of Ryanair is enormous in this case, since, to put it in simple terms, Ryanair traffic is greater than all the other flights together and is on the increase. The importance of Ryanair at Girona even affects the dynamics of the traffic there as, after Ryanair started to operate in Girona, the tendency that was seen was for numbers of non-Ryanair passengers to decrease during summer (peaks), while they tended to increase during winter (troughs). This suggests that Ryanair flights have different and opposing effects in winter and in summer (although it will be shown below How Can the Effects of the Introduction of a New Airline be Measured? Castillo-Manzano et al. 269 Table 1 Estimation Results for Univariate Models with Intervention Variables Madrid-Barajas Alicante Valencia Ma ´laga Palma de Majorca Seville Easter Business Leap 9/11 Cycle 0.032 0.021 0.056 (20) 1.845 0.035 0.003 0.044 0.044 (13) 1.732 0.071 (20) 1.966B 0.052 0.007 0.035 0.052 (19) 1.168 0.088 0.005 0.077 (14) 2.109B 0.118 (28) 2.523B 4 Ryan Shock Ryan Dynamic 0.158 0.119 0.0350.079 0.0360.048–0.061B 0.014B 3 0.085–0.087B 0.077B AO97JUN AO97DEC AO98APR AO98JUN AO99NOV AO99DEC AO00MAR LS03JAN AO03MAY LST4 LS08NOV 0.062 0.048 0.058 0.044 0.044 0.041 0.092 0.165 0.063 0.037 0.065 0.135 0.061 0.172 MA1 MA2 MA12 MA24 0.202 0.474 0.666 0.392 0.240 0.312 0.294 0.559 0.568 0.152 0.192 0.463 s20.468 0.797 2.282 0.787 1.855 1.897 Q(12) Q(24) Bera-Jarque 4.432 15.062 5.805 (0.054) 19.142 27.949 0.428 (0.807) 3.775 9.094 1.277 (0.528) 12.281 28.466 0.3077 (0.857) 19.577 33.701 0.0042 (0.997) 10.935 22.001 0.332 (0.847) Note: Significant at 10 per cent, 5 per cent, and 1 per cent levels, respectively. s2stands for the innovations variance; Q(12) are the Ljung–Box Q statistics for 12, respectively; Bera-Jarque is a normality test (P-values in brackets); His a variance ratio homoscedasticity test (P-values in brackets). The number of months that the 9/11 effect lasted is given in brackets after the coefficient for the variable. Journal of Transport Economics and Policy Volume 46, Part 2 270 that the summer effect is in fact insignificant, since it would seem that the summer declines had started before Ryanair commenced operations at the airport — see Table 2). For this reason, winter and summer are estimated as separate effects in the model (see new variables Sum## and Win## in Table 2, where ‘##’ stands for the last two digits of the year). Traffic at Reus airport was seen to follow a similar behaviour, perhaps due to the fact that before Ryanair arrived at these airports, both were only used during the summer season (mainly as operating bases for charter flights to the Catalonian coast). Similarly, the regional airports in Murcia and Santander also have to address Ryanair’s dominant position, as a result of which the Ryanair indirect effect has had to be adapted to the structural changes produced by the company after its arrival. Specifically, the Ryanair indirect effect at both of these airports was divided into several empirically identified steps (see Table 2, fourth column, third row). Specifically, for Murcia airport three different steps were found (February 2005 to February 2007, March 2007 to October 2007, and November 2007 onwards), while at Santander airport, only two steps were needed (March 2005 to February 2007 and March 2007 onwards). To summarise, the Ryanair indirect effect (Hi;t) would be the addition of the Transfer Function terms in (2) that have links with the Ryanair variables. In most cases it will be just two terms, the shock and the dynamic effects (‘Ryan Shock’ and ‘Ryan Dynamic’ as defined above and shown in Table 1), that is: Hi;t¼X n j2Ryanair oni;jBðÞ dmi;jBðÞ ui;j;t:ð3Þ The general representation of the noise model NiBðÞei;tin (2) is an ARIMA pi;di;qi ðÞ Pi;Di;Qi ðÞ 12 formulation shown in equation (4): NiBðÞei;t¼1 1BðÞ di1B12 ðÞ Di #qiBðÞ fpiBðÞ QiB12  PiB12 ðÞ ei;t:ð4Þ Figure 2 Million Passengers (Per Month) at Girona Airport 1996 1998 2000 2002 2004 2006 2008 0 0.1 0.2 0.3 0.4 0.5 Million passengers Passengers at Girona airport Ryanair y i,t How Can the Effects of the Introduction of a New Airline be Measured? Castillo-Manzano et al. 271 charter flights to the Catalonian coast (83.7 per cent of flights at Girona and 92.2 per cent at Reus were charter flights in 2002, before the arrival of Ryanair at Girona in December of that same year). The large negative indirect effect at Reus of almost 29 per cent must be highlighted. 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