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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

Castillo Manzano, José I.; López Valpuesta, Lourdes; Pedregal Tercero, Diego José

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.

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How Can he Effec s o he In oduc ion o a New Ai line on a Na ional Ai line Ne wo k be Measu ed? A Time Se ies App oach o he Ryanai Case in Spain Jose ´I. Cas illo-Manzano, Lou des Lo ´pez-Valpues a, and Diego J. Ped egal Add ess o co espondence: Jose ´I. Cas illo-Manzano, Facul ad de Ciencias Econo ´micas y Emp es- a iales, Uni e si y o Se ille, A da. Ramo ´n y Cajal, 1, 41018 Se ille, Spain ([email p o ec ed]). Lou des Lo ´pez-Valpues a is a Facul ad de Ciencias Econo ´micas y Emp esa iales, Uni e si y o Se ille (Spain) ([email p o ec ed]). Diego J. Ped egal is a Ingenium Resea ch G oup and IMACI, Uni e si y o Cas illa-La Mancha (Spain) ([email p o ec ed]). The au ho s would like o exp ess hei g a i ude o he Fundacio ´n Cen o de Es udios Andaluces o he financial esou ces ha i p o ided o his s udy. The au ho s a e also g a e ul o S e en A. Mo ison, he anonymous e iewe , and Ge ma `Bel o hei e y help ul commen s. Abs ac This pape quan ifies he Ryanai Effec on he Spanish ai line ne wo k. I p oposes new me hodology based on an ad anced ime se ies app oach ha allows bo h he di ec and indi ec effec s o he inco po a ion o a new ai line o be measu ed and ha can be easily ex apola ed o o he ai po sys ems. The findings show he mean indi ec effec on o he ai lines, in absolu e alue, is 8.6 pe cen o he o al ai po affic, peaking a a maximum o almos 29 pe cen . Also, su p isingly, he e is ound o be a nega i e indi ec effec a only ou o he en ai po s analysed. Da e o eceip o final manusc ip : June 2011 263 Jou nal o T anspo Economics and Policy, Volume 46, Pa 2, May 2012, pp. 263–279 1.0 In oduc ion The e is a weal h o li e a u e illus a ing he in e na ional de elopmen o he low-cos model and i s expansion in o diffe en geog aphical a eas (see F ancis e al., 2006, o an o e iew o his de elopmen ). To be specific, in Eu ope, he ea ly mid-1980s’ libe alisa- ion be ween I eland and he UK c ea ed he condi ions ha allowed he fi s low-cos ai line o appea in Eu ope, Ryanai (see F ancis e al., 2006, o he easons ha a ou ed he c ea ion o Low Cos Ca ie s (LCCs) in hese wo coun ies). This ai line has de eloped in o he main low-cos Eu opean ai line and he mos success ul LCC in he wo ld in e ms o p ofi abili y (Oli ei a, 2008). Ryanai ollowed in he oo s eps o he Sou hwes Ai lines company (see Guillen and Lall, 2004, on he main diffe ences be ween Ryanai and Sou hwes ), al hough ime has shown ha Ryanai has emained mo e ai h ul o he ea u es o he o iginal low- cos model han i s p ecu so , Sou hwes (Alamda i and Fagan, 2005). I has also c ea ed a model ha can be easily eplica ed (Guillen and Lall, 2004). One o he salien ea u es o he Ryanai managemen model o ou analysis is he use o unde u ilised seconda y ai po s, o en a om main ci y ai po s, and egional ai po s. This is wha diffe en ia es he ai line om o he majo LCCs like easyJe o Sou hwes (Dob uszkes, 2006; F ancis e al., 2006; G aham, 2009). Ryanai usually a oids using p ima y ai po s, excep when a seconda y ai po is no a ailable in he egion (G aham, 2009), as he la e a e less expensi e in e ms o landing cha ges; hey a e less conges ed han he majo hub ai po s; sho e u na ounds a e possible and, he e o e, mo e jou neys pe day pe plane can be achie ed (Ba e , 2004). Ryanai plays he dominan ole in many o hese seconda y o egional ai po s and in some, such as Cha le oi and Gi ona (Ba bo , 2006), almos exe cises a monopoly. This powe influences i s p ice s a egy, as Ryanai seems o se lowe a es when fligh s depa om o a i e a domina ed ai po s (Ba bo , 2006; Malighe i e al., 2009). This is, in u n, influenced by he ac ha ope a ing a ai po s o his ype allows Ryanai always o be in a posi ion o nego ia e ai po ees (F o ¨idh, 2008; Oli ei a, 2008). In his way Ryanai has shown ha seconda y ai po s a e no only able o cha ge less (see Cas illo-Manzano, 2010) bu also offe subsidies o a ac ai lines (Papa heodo ou and Lei, 2006), o en wi h he aid o local o egional public au ho i ies (see Cas illo- Manzano and Lo ´pez-Valpues a, 2010). Seconda y ai po manage s whe e LCCs a e ound should be awa e ha hey a e acing high ola ili y in hei affic, due bo h o he g ea e likelihood o bank up cy o me ging o hese kinds o ai lines and o hei dependence on he aid and subsidies ha hey apply o . The e is e en highe ola ili y when he ai po has a dominan single LCC (Guillen and Lall, 2004; Ba bo , 2006; Bel, 2009), as is he case o Ryanai a many o he seconda y ai po s whe e i ope a es. In his con ex , he objec i e o his a icle is an analysis o wha has been dubbed, acco ding o Guillen and Lall (2004), he Ryanai Effec on he Spanish ai line ne wo k. Following Vowles (2001) and Pi field (2008) on he Sou hwes Effec , he Ryanai Effec would be he effec on a e age ai a es, affic olumes, and ma ke sha es expe ienced in he ma ke s in o which Ryanai en e s. The p io academic li e a u e has s udied he impac o he a i al o a new LCC a he ai po s which accep hem, mainly analysing he effec ha his a i al has on ai a es and he spillo e effec s on nea by ai po s Jou nal o T anspo Economics and Policy Volume 46, Pa 2 264 (Mo ison, 2001; Vowles, 2001; Da aban and Fou nie , 2008). Wi h espec o he effec s on affic, mos o he s udies (Donzelli, 2010; G aham and Dennis, 2010) analyse he impac o he a i al o an LCC by compu ing he o e all g ow h in passenge numbe s wi hou analysing any possible colla e al effec s on he affic o o he companies. This pape seeks o quan i y he o e all effec on comme cial affic by esponding o he ollowing ques ion: gi en he e iden inc ease ha he a i al o Ryanai means o an ai po ’s affic which has been highligh ed in he li e a u e (Ba e , 2004; F ancis e al., 2004; Yo k A ia ion, 2007; Bel, 2009), how much o his g ow h can be di ec ly a ibu ed o Ryanai ’s own affic, and wha p opo ion can be a ibu ed o he eac ion o he o he ai lines a he ai po o he a i al o said low-cos ai line? Fo he o he companies, he a i al o his low-cos company can ha e ei he a compe i i e o a ca alys effec — ha is, be a s imulus ha inc eases hei olume o affic — o i can ha e no effec wha soe e , o i can lead o a educ ion in hei affic due o he subs i u ion effec . As desc ibed p e iously, he fi s effec , he compe i i e o ca alys effec , would be a ibu able o Ryanai ha ing become a leading company in he Eu opean a ia ion sec o . In some coun ies, like Spain, i is e en he leading ai line in passenge affic. I should he e o e be an icipa ed ha when a leading company b eaks in o new ma ke s/ ai po s, i could aise he compe i i eness o he o he companies/ai lines ope a ing he e and, especially, poin he way (pull o knock-on effec ) o o he s ha we e no ope a ing in his ma ke (gene ally, a egional o p e iously unde u ilised seconda y ai po ) o ollow hem. Wi h espec o inc eased compe i i eness, his has been docu- men ed by Sou hwes . To be specific, acco ding o Vowles (2001), he en y o Sou hwes can in some cases lead o o he ca ie s lowe ing hei a es o emain compe i i e, which s imula es mo e affic. Gillen and Lall (2004) also s a e ha a es all p io o he a i al o Sou hwes a a new des ina ion and his gene a es emendous demand which also benefi s o he ca ie s. Wi h ega d o he possible pull effec , acco ding o Yo k A ia ion (2007), Ryanai ac ed as a ca alys o u he g ow h a Eindho en ai po by p o ing ha low- a e se ices we e sus ainable, hus a ac ing new ai lines. Gene ally, his compe i i e effec , in any o i s o ms, could also be suppo ed by he inc ease in he appeal o he ai po om he pe spec i e o ans e s on he back o Ryanai ’s new des ina ions. Wi h ega d o he abo e-desc ibed subs i u ion effec , his can in u n be one o wo ypes: Fi s , he a i al o Ryanai wi h connec ions o he main Eu opean ci ies, like London and Pa is, can ha e an effec on he connec ions ha al eady exis be ween he Spanish ai po s and des ina ions such as hese which had p e iously been in he hands o he ne wo k ca ie s (NCs). Second, he in oduc ion o new poin - o-poin des ina- ions, especially in he case o egional ai po s, will educe he numbe o fligh s o hubs like Mad id and Ba celona, which we e p e iously an obliga o y s opo e poin when a elling o he new des ina ions. This possible nega i e poin , namely he all in NCs affic, has been condemned by Spanish a el agency associa ions p o es ing abou non- e u nable subsidies and o he economic aid being g an ed o Ryanai and o he LCCs by he public au ho i ies (see Cas illo-Manzano and Lo ´pez-Valpues a, 2010). Acco ding o hese associa ions, hese subs i u ion effec s a e especially de imen al o con e ence ou ism (see Cas illo-Manzano e al., 2011). Bo h he o al di ec and indi ec effec s o Ryanai need o be measu ed om he poin o iew o anspo policy o local and egional public adminis a ions o assess How Can he Effec s o he In oduc ion o a New Ai line be Measu ed? Cas illo-Manzano e al. 265 he inc easingly equen and highe economic compensa ion ha he company demands o ope a e in egional ai po s bo h in Spain (see Cas illo-Manzano e al., 2011) and in he es o Eu ope. Me hodologically, his s udy uses an econome ic model o es ima e he ne quan i a- i e impac o he a i al o his ai line on he main affic a Spanish ai po s in line wi h o he s udies (Pi field, 2007, 2008). The majo ad an age o he p oposed me hodological app oach is ha i can be easily ex apola ed o o he ai po sys ems o measu e he effec s ha he a i al o any new ai line, be i an LCC o an NC, would ha e on he o he ai lines a he a ious ai po s. The a icle is o ganised as ollows. Sec ion 2 lays ou he da a and p esen s he me hodological app oach. Sec ion 3 p esen s he empi ical esul s, while Sec ion 4 includes he discussion o hese findings and, finally, Sec ion 5 p esen s he conclusions o he s udy. 2.0 Da a and Me hods The Spanish ai po s analysed in his s udy we e chosen o ha ing achie ed Ryanai affic figu es o o e 400,000 passenge s in 2008. Fou o he en ai po s in he sample we e in he lis o he op fi y busies ai po s in Eu ope in 2008, specifically Mad id- Ba ajas ( ou h), Palma de Majo ca ( hi een h), Ma ´laga ( hi y-second), and Alican e ( o y-fi s ). When we add he case o Ba celona (nin h), whe e Ryanai s a ed ope a ing in he au umn o 2010, o hese ai po s, i can be seen ha , unlike in o he coun ies, Ryania ope a es a he majo hubs in Spain, no only a seconda y o egional ai po s. This is due bo h o Spanish ai po cha ges being low and o he ac ha he e a e no g ea diffe ences be ween hese cha ges a he a ious ai po s, wha e e hei size.1 The o al effec o Ryanai ’s a i al on he abo e-men ioned ai po s o Decembe 2008 will be spli in o a di ec effec and an indi ec effec : 1 The di ec effec is he effec ha he li e a u e has adi ionally measu ed — ha is, he pe cen age ep esen ed by Ryanai affic ou o he o al affic a he ai po (see equa ion (1)). TRi; ¼Ryanai affic a ai po idu ing mon h and TTi; ¼ o al affic a ai po iin mon h (see Figu e 1). Finally, lis he mon h ha Ryanai began o ope a e a ai po iand Nis he numbe o obse a ions: DEi¼P N ¼l TRi; P N ¼l TTi; 100:ð1Þ 1 Fo example, he ai po cha ge o domes ic and Eu opean Union fligh s a Ba celona ai po ’s spec acula new e minal was €6.12 pe onne (10–100 onne ai c a ), while a Ba celona’s seconda y and mos dis an ai po s, Reus and Gi ona, he cha ge s ood a €5.55 — ha is, only 10 pe cen less. Ne e heless, new cha ges ha e come in o o ce in 2011 wi h g ea e disc imina ion acco ding o ai po size. Jou nal o T anspo Economics and Policy Volume 46, Pa 2 266 2 The indi ec effec , o en igno ed by p e ious s udies, will comp ise wo en ies, which will be agg ega ed o hei es ima ion. These en ies a e he compe i i e o ca alys effec on o he ai lines, wi h a posi i e sign, and he subs i u ion effec on o he ai lines — NCs in he main — wi h a nega i e sign. The subs i u ion effec can be caused bo h by Ryanai offe ing connec ions which had p e iously been offe ed by o he ai lines and also by he company’s new des ina ions, which educe affic o na ional hubs like Mad id and Ba celona. The da a used o measu e he indi ec effec s o Ryanai on ai po ican be di ided in o h ee g oups: (A) The endogenous a iables will be he mon hly ai affic, wi hou conside ing Ryanai affic, a he en Spanish ai po s included in he s udy; his is esidual affic. The a ailable ime se ies spans om Janua y 1996 o Decembe 2008 a e aken om AENA yea books (sou ce: www.aena.es). (B) The exogenous a iables, sepa a ed in o wo g oups, a e as ollows: (B.1) Dummy exogenous a iables: a wide ange o a iables a e included in models o es ima e a numbe o in e en ion a iables and ou lie effec s seen in he da a. The mos impo an , wi h hei defini ions, a e as ollows: Figu e 1 Millions o Ai Passenge s (Pe Mon h) a he Ten Spanish Ai po s Conside ed 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 Mad id Palma de Majo ca Málaga Alican e Gi ona Valencia Se ille Mu cia Reus San ande How Can he Effec s o he In oduc ion o a New Ai line be Measu ed? Cas illo-Manzano e al. 267 (B.1.1) Eas e : Ai affic a ound his holiday pe iod is especially in ense in Spain. Indeed, i is conside ed o be high season o ou is s, among o he easons due o he nume ous celeb a ions o he passion o Ch is . Acco dingly, he mo eable eas o Eas e a iable is defined by assigning diffe en weigh s o he days in ques ion depending on he expec ed affic densi y a Spanish ai po s ( hese weigh s ha e o add up o one). Maximum weigh s a e assigned o Wednesday, Thu sday, Eas e Sunday, and Monday. Weigh s o ze o a e assigned o he es o he days. (B.1.2) Business: Mon hly ime se ies ha a e o als o daily ac i i ies can be influenced by each calenda mon h’s weekday and weekend composi ion. This a iable is in oduced o ake in o accoun he diffe ences among mon hs ega ding he p opo - ion o weekdays wi h espec o weekends. I is cons uc ed as he numbe o business o ading days wi h espec o weekend days and holidays in each indi idual mon h — ha is, he numbe o business o ading days minus he numbe o Sa u days and Sundays mul iplied by 5/2. Ex a holidays in each mon h a e sub ac ed om he business days. (B.1.3) Leap: Add esses he leap yea effec . The alue is 1 when Feb ua y con ains 29 days, and 0 o he wise. (B.1.4) 9/11: The nega i e effec on ai affic ha esul ed om he 9/11 e o is a acks which, as ound in ea lie s udies (Inglada and Rey, 2004), also had a significan effec on he Spanish ai po sys em. The du a ion o hese effec s in numbe o mon hs has been de e mined empi ically by es ima ing succes- si ely models in which his effec ha e inc easing du a ions om one o six yea s and choosing he model wi h he bes fi . (B.1.5) The e a e o he ou lie s, o en ela ed o bad wea he condi- ions, co e s ikes by ai - affic con olle s, o e en he opening o new ai e minals like Te minal T4 a Mad id- Ba ajas ai po (LST4). These ha e all been de ec ed by s a is- ical ools. The p ocedu e o sea ch o such ou lie s consis s o selec ing he esiduals ou side ou imes s anda d de ia ion and including hem as po en ial candida es in he models unde diffe en specifica ions (see he me hodological sec ion below). The ou lie s a e included in final models wi h he specifica ion ha p o ides he bes fi when hey a e s a is ically significan . (B.2) Economic ac i i y: The li e a u e a gues ha economic ac i i y is closely linked o ai affic as a esul o which i is gene ally included as an indica o when modelling ai affic (see Inglada and Rey, 2004). In his a icle, economic ac i i y is ep esen ed using a Spanish Minis y o he Economy and T easu y syn he ic economic ac i i y index (sou ce: h p://se iciosweb.meh.es/apps/dgpe/de aul .aspx). (C) The Ryanai indi ec effec : This effec has been spli in o wo e ms o es ing: (i) one cons an o he pe iods when Ryanai was ope a ing a each ai po , Jou nal o T anspo Economics and Policy Volume 46, Pa 2 268 measu ing he shock i p oduces (called ‘Ryan Shock’ a e wa ds); and (ii) mean co ec ed Ryanai affic, measu ing how he dynamics o Ryanai affic affec he o e all dynamics a a gi en ai po and any possible lags o his a iable (‘Ryan Dynamic’). The e we e ou cases (Gi ona, Reus, Mu cia, and San ande ) whe e he p e ious p ocedu e had o be efined in o de o find app op ia e models. Ryanai has a dominan posi ion a hese ou ai po s ha would explain he s uc u al changes ha ha e been de ec ed a e Ryanai commenced ope a- ions, esul ing in he need o special ea men in hese cases (see de ails below). The ime se ies models employed in he analysis a e in he class o disc e e ime linea T ans e Func ion models (see Cas illo-Manzano e al., 2010, o an analysis o he ad an ages o his me hodology on anspo a ion esea ch s udies). The gene al o mu- la ion may be exp essed as in equa ion (2): yi; ¼X h j¼1 oni;jBðÞ dmi;jBðÞ ui;j; þNiBðÞei; ;ð2Þ whe e yi; a e he ai passenge o al da a o he i h ai po excluding he Ryanai passenge s; ui;j; a e he inpu s on which he ou pu da a depend (mos o hem de e - minis ic, wi h he sole excep ions o he economic cycle and pa o he Ryanai indi ec effec , see he lis abo e); ei; is a ze o mean and cons an a iance Gaussian whi e noise; oni;jðBÞ¼ðoi;0þoi;1Bþþoni;jBni;jÞðj¼1;...;hÞa e polynomials in he backshi ope a o ( ha is, Bky ¼y k) ha may ha e leading ze o coefficien s when a pu e ime delay is necessa y; and dmi;jðBÞ¼ð1þdi;1Bþþdmi;jBmi;jÞðj¼1;...;hÞa e s a iona y o s able polynomials. I is impo an o include a specific commen on he Ryanai inpu a iables in o- duced in equa ion (2). B oadly speaking, he inpu a iables linked o he Ryanai indi ec effec a e: (i) ‘Ryan Shock’: one s ep a iables wi h ze os be o e Ryanai commenced ope a ion, and ones a e he company had commenced ope a ion; and (ii) ‘Ryan Dynamic’: Ryanai affic minus i s mean le el du ing he pe iod o ope a ion. Howe e , al hough his would be he gene al way ha he ai po s a e ea ed, he e a e ou ai po s ha beha e in a special way gi en he dominan ole played by Ryanai in hei affic. These a e he a o emen ioned cases o Gi ona, Reus, Mu cia, and San ande . As such, he s anda d me hod used o handling he Ryanai indi ec effec by di iding i in o ‘Ryan Shock’ and ‘Ryan Dynamic’, as in he cases shown in Table 1, was inadequa e o hese ou ai po s. By way o example, Figu e 2 shows he special case o Gi ona and compa es a iable yi; ( ha is, he o al numbe o passenge s excluding Ryanai ) wi h Ryanai affic a Gi ona ai po (whe e Ryanai commenced ope a ions in Decembe 2002). I is clea ha he o al effec o Ryanai is eno mous in his case, since, o pu i in simple e ms, Ryanai affic is g ea e han all he o he fligh s oge he and is on he inc ease. The impo ance o Ryanai a Gi ona e en affec s he dynamics o he affic he e as, a e Ryanai s a ed o ope a e in Gi ona, he endency ha was seen was o numbe s o non-Ryanai passenge s o dec ease du ing summe (peaks), while hey ended o inc ease du ing win e ( oughs). This sugges s ha Ryanai fligh s ha e diffe en and opposing effec s in win e and in summe (al hough i will be shown below How Can he Effec s o he In oduc ion o a New Ai line be Measu ed? Cas illo-Manzano e al. 269 Table 1 Es ima ion Resul s o Uni a ia e Models wi h In e en ion Va iables Mad id-Ba ajas Alican e Valencia Ma ´laga Palma de Majo ca Se ille Eas e 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) Be a-Ja que 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) No e: Significan a 10 pe cen , 5 pe cen , and 1 pe cen le els, espec i ely. s2s ands o he inno a ions a iance; Q(12) a e he Ljung–Box Q s a is ics o 12, espec i ely; Be a-Ja que is a no mali y es (P- alues in b acke s); His a a iance a io homoscedas ici y es (P- alues in b acke s). The numbe o mon hs ha he 9/11 effec las ed is gi en in b acke s a e he coefficien o he a iable. Jou nal o T anspo Economics and Policy Volume 46, Pa 2 270 ha he summe effec is in ac insignifican , since i would seem ha he summe declines had s a ed be o e Ryanai commenced ope a ions a he ai po — see Table 2). Fo his eason, win e and summe a e es ima ed as sepa a e effec s in he model (see new a iables Sum## and Win## in Table 2, whe e ‘##’ s ands o he las wo digi s o he yea ). T affic a Reus ai po was seen o ollow a simila beha iou , pe haps due o he ac ha be o e Ryanai a i ed a hese ai po s, bo h we e only used du ing he summe season (mainly as ope a ing bases o cha e fligh s o he Ca alonian coas ). Simila ly, he egional ai po s in Mu cia and San ande also ha e o add ess Ryanai ’s dominan posi ion, as a esul o which he Ryanai indi ec effec has had o be adap ed o he s uc u al changes p oduced by he company a e i s a i al. Specifi- cally, he Ryanai indi ec effec a bo h o hese ai po s was di ided in o se e al empi ically iden ified s eps (see Table 2, ou h column, hi d ow). Specifically, o Mu cia ai po h ee diffe en s eps we e ound (Feb ua y 2005 o Feb ua y 2007, Ma ch 2007 o Oc obe 2007, and No embe 2007 onwa ds), while a San ande ai po , only wo s eps we e needed (Ma ch 2005 o Feb ua y 2007 and Ma ch 2007 onwa ds). To summa ise, he Ryanai indi ec effec (Hi; ) would be he addi ion o he T ans e Func ion e ms in (2) ha ha e links wi h he Ryanai a iables. In mos cases i will be jus wo e ms, he shock and he dynamic effec s (‘Ryan Shock’ and ‘Ryan Dynamic’ as defined abo e and shown in Table 1), ha is: Hi; ¼X n j2Ryanai oni;jBðÞ dmi;jBðÞ ui;j; :ð3Þ The gene al ep esen a ion o he noise model NiBðÞei; in (2) is an ARIMA pi;di;qi ðÞ Pi;Di;Qi ðÞ 12 o mula ion shown in equa ion (4): NiBðÞei; ¼1 1BðÞ di1B12 ðÞ Di #qiBðÞ piBðÞ QiB12  PiB12 ðÞ ei; :ð4Þ Figu e 2 Million Passenge s (Pe Mon h) a Gi ona Ai po 1996 1998 2000 2002 2004 2006 2008 0 0.1 0.2 0.3 0.4 0.5 Million passenge s Passenge s a Gi ona ai po Ryanai y i, How Can he Effec s o he In oduc ion o a New Ai line be Measu ed? Cas illo-Manzano e al. 271 cha e fligh s o he Ca alonian coas (83.7 pe cen o fligh s a Gi ona and 92.2 pe cen a Reus we e cha e fligh s in 2002, be o e he a i al o Ryanai a Gi ona in Decembe o ha same yea ). The la ge nega i e indi ec effec a Reus o almos 29 pe cen mus be highligh ed. 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