THE EFFICIENCY MODEL OF THE AIRPORT INDUSTRY CASE STUDY …euroregioeuram.eu/new/media/Resum-euram-Ane-Ripoll1.pdf · THE EFFICIENCY MODEL OF THE AIRPORT INDUSTRY CASE STUDY OF AENA

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THE EFFICIENCY MODEL OF THE AIRPORT INDUSTRY CASE STUDY OF AENA

Ane Elixabete Ripoll Zarraga

Beques Convocatograveria 2008

Papers EURAM

Institut drsquoeconomia i empresa Ignasi Villalonga

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Abstract This paper focuses on comparing the efficiency of the Spanish airports to the British major airports during a period of seven years Since the deregulation process in Europe started in the middle of 80rsquos (1986-87) European countries have achieved different degree levels of deregulation translated into mixed types of ownership and management Strong evidence through the literature shows that airports with government majority ownership are less efficient than those with a private majority therefore the main question to be answered it is if the way that airports are owned and managed affects their technical efficiency The deregulated model of ownership by excellence is the case of the British major airports (Gatwick Heathrow and Stansted) which have improved their technical efficiency after privatization in 1987 The British example has inspired other countries to start the deregulation process in the airport industry In the first stage this research is focused only in the Spanish market since AENA as full airport system differs considerably from the UK1 The main idea is to determine which airports are the most technically efficient with regards their current capacity (infrastructure and permanent staff) and therefore which airports could be labelled as the leaders in the case of a fully regulated sector during period of seven years (2005-2011) In the second stage a bootstrap truncated regression is applied to explain the main reasons behind the efficiency frontier why a specific airport is technical inefficient and the main competitors In the regression the environmental variable ownership is considered to evaluate the potential impact in the way the airports perform if they are fully government owned instead of privately The efficiency scores are obtained for the Spanish airports as well as the major British airports for six years (2006-2011) The efficiency results are compared to the level of efficiency achieved by the main British airports in order to evaluate the potential effects of different ownership and management forms in the airportsrsquo technical efficiency The methodology to calculate the efficiency frontier which airports are the best from a technical perspective is based on non-parametric models DEA (Data Envelopment Analysis) The fact of analysing more than one year compared to other studies prior done in the AENA case allows the introduction of dynamism and therefore consistency and reliability in order to draw accurate conclusions based on the results obtained In the second stage the fact of applying a truncated regression which does not include the efficient airports is to avoid potential correlation problems between the explanatory variables and the efficiency units and therefore inaccurate estimations of the regression coefficients The results show that ownership forms affect the technical efficiency achieved resulting in private airports being more efficient compared to airports government owned Further research is necessary in order to consolidate these findings by using different combinations of ownership and management forms in order to confirm the effects of privatisation and commercialisation processes in the Spanish airport industry

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 1 An airport system is related to a managerial organization of airports in which more than one airport is managed by the same company or authority

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Key words Airports Data Envelopment Analysis Bootstrap Truncated Regression Address for correspondence aeripoll-zarragalseacuk

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1 Introduction

Structural changes in the airport industry lead the interest on benchmarking Most of the policy debate regarding the efficacy of the airport governance reform is barely focused on the efficiency and airport charges effects of privatisation (Hooper 2002 Truitt amp Esler 1996) The way that privatisation is in practise is sensitive to the nature of ownership and property rights as well as other institutional factors which vary across frontiers (Carney amp Mew 2003) Scenarios with an institutional infrastructure less developed therefore with not well defined property rights without transparent regulation or liquid capital markets may make privatisation to be less effective Privatisation is in general a term used to define different administrative structures to achieve specific objectives but this does not describe how airport governance reform achieves these objectives A social concern which ownership-management model should be used in the airport sector has increased considerably Historically the airport industry has been with others such utilities monopolies owned by governments (natural monopolies) In the Spanish case the pressure of local governments professional bodies and social networks is focused unsuccessfully on obtaining the management of the airports located in their geographical areas The governmentrsquos intention to change the AENA model had an initial step on the 25th March 2009 with the announcement of the creation of a subsidiary named initially EGAESA Empresa de Gestion de Aeropuertos del Estado which it would be in charge of managing the Spanish Airports Although it seemed an initial positive reform process for privatisation since private investors could participate of the equity of EGAESA as well as followed by the creation of commercial companies where local councils and bodies could also hold shares this process would have no impact in focusing on the main needs of the different airports this is clustering the management for large medium and small airports Therefore it becomes an apparently decentralised structure with a little appeal for achieving genuine private sector participation2 The decentralisation of the airport network with the allowance of private management will increase the efficiency in line with the changes applied in other OECD countries with similar size airport network and political structure (Nombela G in Abertis June 2009) The airport management is decentralised in most wealthy European countries such as France Italy Germany and the UK as well as overseas (Canada and the States) Although in some big cities with a high frequency of commuters and air travellers airports may have a unique management form (public sector or private sector) this is focused on defending the competition in the airport market (London Paris Roma and Milan) Again this was a topic publicly discussed by the Spanish Government on January 2010 under the name of lsquosingular airportsrsquo which included the term of lsquobig and complex airportsrsquo (Madrid and Barcelona) The idea was to create subsidiaries in order to manage the big airports separately of the rest 100 government owned but with the chance of including in the General Board local governments and commercial bodies (Cambra de Comerc de Barcelona 2010) The decentralisation allows specialisation (long distance low cost etc) but above all flexibility when it is required a

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 2 The first draft of Egaesa Statute stated that the main aim is lsquothe management and use of the air spaces and airport services are competence of AENA as well as the construction of future infrastructures and the way to finance them use and maintenancersquo related to a 47 airports A second draft stated that lsquoAENA will be able to manage and use other type of airport infrastructuresrsquo refering to handling or concessions of non-aeronautical services as shops restaurants etc

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specific response to the current needs of the geographical area where the airport is located for instance remote areas that require a minimum of routes to be provided by airlines (in regulated airports these routes are obligatory to avoid emigration or collateral negative effects to watch health and wealth of the region) In order to understand the effects of the centralisation of management and ownership in the airport industry the main question for the discussion is firstly if the AENA model affects the Spanish airports competition and secondly which is the impact when comparing this model to the one applied by other countries that have achieved total deregulation The existence of competition allows market orientation Competition starts with rivalry in the industry (Porter M 1979) and the existence of competition depends on managerial decisions Each airport is different to each other since they have different investment needs (number of terminal buildings runways etc) Investment decisions generally may depend on macro variables such as the GPD of the city or the demand of flights but a micro perspective should be also taken into consideration (number and type of airlines operating geographical location amenities like public transport into the airport etc) Even assuming the existence of a positive correlation between investment and productivity will managerial decisions increase the efficiency of the airport The capacity (size) of an airport may or not be used to produce its aeronautical activity if an airport is too big for the current productivity achieved its resources has been underutilised One could also discuss that the manager has made a wrong investment decision based on overcapacity for the amount of activity performed measure for instance by the number of take-offs per year Considering that an airport has a fixed number of resources labelled as lsquoinfrastructurersquo if the airport activity (output) is lower than in a previous period then the airport becomes inefficiency because a higher level of activity is feasible due to the current infrastructure Smaller airports can be more efficient than larger ones by using all their feasible resources The way that airports use their infrastructure and by extension the investment decisions previously made by the management (operator) will affect the airportsrsquo level of efficiency In a regulated industry it is essential to evaluate the impact of investments decisions in the individual performance compared to others non-regulated in order to prove if the management model used is the best possible to help the economic development of the region in a globalise economy Airport privatisation commercialisation and other issues such as airport infrastructure market regulation and growth of traffic determine a dynamic and constantly changing environment Benchmarking has become an important performance management tool in order to improve the operational performance learning from other enterprises (Francis Humphreys and Fry 2002) This study constitutes the first step in order to provide evidence enough regarding if privatisation is required in the Spanish case by comparing the efficiency of the Spanish airports managed and owned by AENA to the British major privatised airports under Margaret Thatcherrsquos policies of privatising government owned assets The main idea is to show the current situation of the Spanish airport industry in terms of infrastructure based on benchmarking a market where competition is not feasible but restricted due to the fully airport system of AENA In further research benchmarking with other European cases that succeed in the privatisation and mixed forms will be considered in order to demonstrate the relation between different forms of ownership and management and the efficiency achieved by the airports In the first stage of the methodology a benchmarking of the Spanish regional airports is performed assuming a hypothetical existence of competition this is by comparing to each other but within a closed market in order to state which airports are the most

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efficient performing the aeronautical activity by using their feasible resources The methodology used is a non-parametric lineal programming model of data envelopment analysis (DEA) Since DEA only provides information regarding the value of the efficiency scores but does not with regards to plausible reasons behind the efficiency levels a second analysis is applied in order to explore the determinants of the inefficiency of airports In the second stage a regression is performed by using variables that defined the environment where airports operate Ownership and management forms are considered to determine if they could be affecting the technical efficiency of the Spanish airports The period of time is seven years from 2005 to 2011 both included for more accuracy and reliability of the results DEA has a lack of dynamism since benchmarks efficiency levels of making decision units in a specific period but without comparing the efficiency scores achieved in others To solve this trend variables are introduced as dummies to evaluate if the change of efficiency between different periods is significant and therefore opening a new discussion regarding of how the political and economic situation of the Spanish market could be affecting the airport industry Previous studies analyse more than one year but only focusing on one sole country others do not apply a second stage after DEA in order to identify the reasons behind the competitive frontier Revision of literature proves that authors differ in their conclusions when using only one stage instead of applying a second methodology3 In section 2 a revision of the literature related to different ownership and management forms as well as governance reforms in the airport industry A discussion if deregulation incentives competition and if the price to pay for having privatisation is the diversification to commercial activities instead of focusing in aeronautical revenues due to the operating costs associated with the airport main activity In section 3 an overview of the methodology used in the first stage based on lineal programming (Data Envelopment Analysis) Section 4 presents the methodology and variables applied in this research and the results In the next section a second stage methodology is used The results obtained from DEA are analysed in order to search for plausible explanations of the current situation of the Spanish airports Section 6 include conclusions and further research lines analysis of the Spanish airport industry applying both returns to scale (variable and constant) and the discussion of future papers with regards management-ownership and extensions of DEA could airports become technically efficient with a decentralisation of the management rather than privatisation of the ownership

2 Literature review deregulation process of the Airport Industry

Airports used to be considered as natural monopolies Deregulation and liberalization of the air transport industry allows airports to compete with each other for both airlines and passengers and as a consequence the airport market becomes more competitive and dynamic (ICAO 2013) Deregulation is considered as a form of privatisation that normally involves tax incentives The main points defending deregulation are related to the improvement of efficiency and economic 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 3 Parker (1999) found that British Airports Authority (BAA) privatization had not impact in airport technical efficiency whereas Yokomi (2005) using Malmquist Index which provides dinamism found that almost all airports managed by BAA have improved their technical efficiency after privatization

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benefit through competition (Gormley 1991)4 Based on this statement privatisation involves both public (social) and private aims because it incentives the entry of different companies to provide a fair relation between quality-price-productservice in order to survive in a rapidly changing environment Although privatisation encourages competition it is necessary to control that private activities meet the public goals of the specific sector deregulated It is important to be aware that tax incentives are not subsidies to the private sector and therefore they cannot be used as the main reason to privatise The deregulation process in the airport sector has a vast literature since traditionally airports have been fully owned by governments and therefore treated as a public utility Deregulation should increase competition but empirical evidence after deregulation in Europe has been limited (Burghouwt amp Hakfoort 2001) Following the successful example in the UK under Margaret Thatcherrsquos legislation other countries have privatised partially or totally their airports Nevertheless it is necessary to analyse and compare each situation (privatisation and deregulation) in order to satisfy not only the private aims such maximise the value of the company but also the social benefits involved when regulation take place like health and safety Since privatisation involves transferring the ownership from government owned industries to private enterprises the privatisation of an industry is appropriate if markets work perfectly well and competition can generate efficiency (Bishop amp Kay 1989) Privatisation does not affect the strength of the market except in the case of airports located closely with similar traffic profiles (Humphries 1999) Regarding airport infrastructures it is possible to privatise economic characteristics to promote development lack of competition externalities and natural monopoly practises (Hooper 2002) Nevertheless it is essential to have an adequate framework to manage the relationship between the airport operator and the government Governments could deny privatising (or even after privatisation the industry could be regulated) if it is perceived that airport operators may exploit their market power to earn monopoly profits or even they may have a negative environmental or social impact In this case is when regulation would be more appropriate the intervention on a regulated industry by the government is usually related to competition price and profit for the period In this sense British Airports Authority (BAA) was forced to sell three of its seven airports by the UK Competition Commission due to a potential monopoly position over London and Scotlandrsquos airports which it could have a negative impact in airlines and passengers5

21The need of Investment and Competition (market efficiency) The review of the literature shows a concern regarding the need of privatisation in the airport industry and if regulation promotes or discourages competition The need to liberalise the airport sector affects not only management and ownerships forms but airline control too The justification relays on the lack of investment as well as the impact in the operating result The main question for the discussion is if the current investment is enough to assist the growing air traffic which affects most of the European airports Gillen (2009) claims the need of reconsidering the role of

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 4 There are different forms of carrying out privatisation process such as selling government owned assets contracting out some activities (handling fuelling catering etc) deregulation and vouchers regarding to some services provided by goverment funding part of population under certain conditions such as low incomes disability etc 5 The Times (August 2008) BAA monopoly heads for break-up as report takes aim at poor service httpwwwthetimescoukttobusinessindustriestransportarticle2195446ece

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governments as suppliers of investment when an expansion of capacity is needed The International Civil Aviation Organization (ICAO) warned about the future investment needed of at least 250 billion dollars for ten years (Hooper 2002) Airports must now compete with each other for both passengers and airlines airports have had to become more commercially focused although they are still seen as monopoly infrastructure providers Policy makers and regulators must realise about the extent of the changes that have taken place during the last twenty years with the progressive liberalization of aviation markets worldwide resulting a more competitive and dynamic market (ICAO 2013) In order to ensure the economic viability such infrastructure different processes have been taken across Europe from local or federal government ownership to airport commercialization through contracts or full privatisation (Adler 2013) Jensen and Meckling (1979) consider ownership forms as part of the production function since they limit the feasible production of companies by deciding among technology and resources6 If the ownership form affects the production level it will also influence the companyrsquos efficiency when the resources are limited or the production is capped Privatisation improves efficiency technically and economically but especially allows governments to provide the necessary investment that airports cannot achieve government investments in infrastructures are required in order to end recessions and to increase the current productivity (Costas-Centivany 1999)7 The aim of privatisation is the result of the inability of governments to supply the infrastructures needed and the lack of capital (Gillen amp Lall 1997) Oum Adler and Yu (2006) demonstrate that the reason behind commercialisation and privatisation processes is the access to financial resources investment as well as the improvement of operational efficiency Their results demonstrate that airports with government majority are considerably less efficient than airports with private majority although partial privatisation seems not to improve the operating efficiency Pestana and Dieke (2008) also prove that fully private managed airports have higher efficiency compared to partially privatised The allowance of private presence in the airport industry is the result of decentralisation of decisions but also efficiency decisions by increasing competition (Hooper 2002) White (1994) defines lsquoprivatisationrsquo from five different points of view one definition is the process based on contracting-out certain activities in order to improve the current efficiency or to reduce inefficiency by retaining public control Therefore it is possible to achieve certain levels of efficiency without being a wholly private owned company Oum (2006) pleads for a sensible view to consider airports owned by governments less efficient than being owned by private companies in the same conditions The reasonable explanation is that when the owner is the government the objectives are not clearly defined and also they change in every legatorial period (Levy 1987 De Alessi 1983 and Backs 2002) Airports as mature firms should be able to survive by their own they should cover the operating costs by generating enough income but since airports are considered as public goods governments cap the level of investment and provide the needed capitals Nevertheless

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 6 The relationship between ownership forms and performance of companies is located in the Agency Theory which does not agree with the Managerial Theory in the sense that different ownership structures does not imply same size-return combinations 7 Costas-Centivany (1999) identifies the infrastructure needs in the Spanish airports as a consequence of the growth of the tourism

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airports have to compete with other areas of public expenditure such as health and education in order to receive the adequate financial resources (Freathy 2004) Evidence suggests that congestion pushes the need of infrastructure renewal In order to enhance private participation it is necessary to demonstrate if owned and managed (operated) government forms are correlated with competition is it possible to have a competitive airport industry when airports are fully managed and owned by the Government Does the Government efficiently supply the investment required by each airport to satisfy the peaks of demand If the government provides the sufficient resources to generate competition within the industry it should not be any need to transfer the ownership to private companies but it could be possible to allow the entry of private management in order to obtain higher efficiency by separating the management of large sized airports from the medium and small ones

22Privatisation and Operational Performance aeronautical versus commercial revenues From a theoretical perspective Galve and Salas (1996) discuss the conflict of interests between owners and managers (decision makers) affecting the return on investment (ROI) 8 The shareholdersrsquo main aim is to increase the market value of their shares whereas managers tend to be empathised with increasing the utility of the company These authors claim the need to demonstrate empirically the way that ownershiprsquos forms affect the companyrsquos performance9 Aeronautical revenues are decreasing since competition is emerging within the airport industry and therefore it is necessary to focus in other ways of obtaining financial resources instead of public ones The necessary operating costs to run an airport show the need to diversify the main aeronautical activity towards commercial ones (Freathy and Orsquo Connell 1998 Humphries 1999) Humphries (1999) defends private participation since it is an efficient and cost effective way for the government to maximise revenue and at the same time to improve customer and quality services The main reason to privatise Australian airports and other type of business owned by governments is the reduction of government debt without raising taxes (Hooper 2000) The success of implementing privatisation is reflected in the increase of revenues more than cost savings as these depend on a certain level of net revenue with a limit to save (Mew 2000) Under private ownership the increase of revenues is due to the deviation of resources to airport activities able to provide cash-flow activities that they do not have impact in the revenues do not receive investments The idea is to incentive those areas that maximise the performance of the airport such as departure and arrival areas10 It is inadequate that AENA being a government owned company is not subsidised but especially because of having managerial independence to exploit the individual earnings of airports (Costas-Centivany 1999) An open market is the efficient way for the government to maximise revenues without losing customer services and quality (Freathy 2004) Different ownership forms regardless of fully ownership government or private increase cost efficiency (Oum Yan amp Yu 2008) Empirical evidence comparing performance before and after privatisation process concludes that private ownership in competitive markets represent the

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 8 Based on classical Managerial Firm Theory 9 The theorical literature states that both management and owned controlled firms under different governance forms have the same chances of profitability and therefore if the investment is equal they will achieve the same performance but only if they are equally efficient This theory is related to size of firm and return possibilities 10 BAA has invested resources only in those activities that contribute to generate income in Heathrow Airport (Pope 1996)

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conditions for improving the current performance (Al Jazzad 1999 Parker and Martin 1996 Pollit 1995) The fact of having a mixed ownership model may provide a chance to achieve the best of both public and private sectors private ownership model does not necessarily improve the company performance (Backx 2002) The abusive potential power of unregulated monopolistic behaviour by privately owned and managed airports can generate conflict between different objectives (Carney and Mew 2003) Despite the interest in analysing the effects of different management and ownership forms in airportsrsquo performance it is not cleared which combination is more effective The relation between ownership and performance cannot be assessed on its own it is necessary to take into consideration policy concerns (Backx Carney and Gedajlovic 2002) The government regulatory policy has implications in financial performance after beginning the privatisation process There are additional difficulties when assessing the relationship between ownership and performance since privatisation enhances revenues (now commercial revenues as important overall as aeronautical revenues ICAO 2013) whereas the government could be more focused on providing financial resources or decisions based on increasing social welfare Agency and Strategic Management Theory suggest that ownership forms have an impact in performance because of focusing on achieving different objectives11 Carney and Mew (2003) are confident with the main benefits of deregulation a relative efficiency of the management translated into lower charges for the demand 12 and productivity enhancements through facilities and externalities to other related industries Again there is a potential risk of market power if the airports are fully sold to independent owners These authors consider that the growth of traffic is pushing the need to reform the airport governance form airports can be differentiated in operational ways but infrastructures cannot be changed constantly to satisfy the traffic demand Starkie (2002) points out that competition is good for the economy as it pushes companies to be cost efficient to drive down prices leading to expanding levels of output Market imperfections are not a sufficient justification for government regulation If regulation takes place in a sector the process should be analysed periodically Airports costs are predominantly fixed due to the investment in infrastructure (through the annual depreciation of the non-current assets) but also due to operational costs related to safety and security which do not reduce even if the airport traffic increases (scale economies) Consequently airports have huge incentives to reduce costs by diversifying the main aeronautical activity towards a commercial revenues (eg car parking) The profitability of airports depend on the traffic volume as aeronautical revenues increase depending on the number of passengers whereas the costs increase slower due to the fixed component of the depreciation of the infrastructure Therefore airports must compete to retain and attract traffic (new airlines) (ICAO 2013) Halpern and Pagliari (2007) find that airports managed independently achieve higher levels of market orientation resulting in higher commercial and aeronautical revenues as well as lower costs In the case of an airport system this is more than one airport managed by the same company the financial trade-offs for an airport include the possibility of receiving professional managerial advise as well as cross-subsidies (Pagliari 2005) Regulated airports may cause prices no competitive and therefore to appear less efficient comparing to airports managed under no-regulated market 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 11 Public Theory stated that policy makers prefer employment over efficiency and productivity Private Rights theorists defend that private ownership is inherently superior to the state ownership (De Alessi 1983) 12 Evidence show that private management form is more customer oriented (Advani 1999)

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conditions (Adler 2013) In the Spanish case the fact of not allowing airports to manage their own revenues to finance their operating costs may end up with airports with deficit and therefore airports which maximise their operational performance will end up subsidising them This is also facilitated due to AENA is self-financed exclusively with aeronautical and commercial incomes Indeed cross subsides may not encourage cost minimisation since losses of less economic efficient airports are financed by the more profitable ones therefore under this airport system is feasible to determine which airports are economical efficient The impact of the AENA model in the airportsrsquo operational margin could be evaluated through the EBITA13 of each airport if individual annual reports and financial statements were published by AENA instated of consolidated ones Gerber (2002) analyses the ideal market conditions for privatisation to be successful The main reasons behind privatisation are the unused potential of the government owned airports that should be used but more important the natural monopoly of terminals and runways that remain after privatisation has been concluded14 If the natural monopoly is misused conflicts may appear regarding the main objectives between the owners and the society The danger of a monopoly misused tends to appear because of the interest of the owners to earn higher results and in a fast way As a consequence privatisation becomes a process against the interest of airlines and customers Furthermore privatisation may then cause less quality and safety instead of an increase of efficiency because investment has not been carried out Another issue is the effective way to assess the airportsrsquo performance when fully government owned Performance can be understood from a pure financial accounting perspective (operational result gross margin or result for the period in the income statement) or from a managerial point of view as the way that airports perform their ordinary course of business (this is the activities which contribute to increase the aeronautical revenues) Since AENA does not publish disaggregated annual reports the analysis of performance can only be done from a managerial perspective by demonstrating how well each specific airport develops its main activity this is based on the resources that the airport has which level of aeronautical activity achieves and therefore how efficient it is compared to others operating in the same market This seems to have a conflict with literature with authors consider that regulation does not facilitate efficiency analysis The process to privatise and commercialise airports is the main factor to assess the efficiency of airports (Doganis Lobbenberg amp Graham 1995) The absence of airport competition with geographical political and regulatory restrictions do not allow measures to measure the efficiency of airports (Nyshadham amp Rao 2000) Since airports are different regarding the services offered in a competitive environment the market will equal profitability and the optimal performance for each airport (Doganis 1992)15 Nevertheless Gormley (1991) states that theories regarding to which one is preferred government or market should be tested It is necessary to take into consideration both premises firstly that privatisation process is a multidisciplinary discipline and secondly a study of privatisation process needs to be analysed in a comparative

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 EBIT is an acronym refer to earnings before deducting interest tax and depreciation expenses Depreciation is not considered since accounting standards across countries may have different regulation regarding the useful life of assets and therefore to avoid conflicts of harmonisation 14 The cash flows generated by natural monopolies come from aeronautical revenues (departures) and other taxes as well as from non-aeronautical revenues (Koll and Stevins 1999) 15 Profitability is defined as the relationship between the result obtained and the investment required in a period In presence of competition therefore privatisation investments made by airports will be fully covered and therefore the ratio will be higher than the unity If the ratio is smaller companies achieve losses equal to (1-ratio)

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analysis (benchmarking) The first statement defends evaluating privatisation in different policy areas levels of government (eg regional councils or departments) and countries The second premise is referred to compare forms of governance across or within frontiers Costas-Centivany (1999) states that privatisation improves the current efficiency as well as cost savings allowing to finance the lack of investment and therefore increasing the potential capacity of airports Privatisation avoids political moves with regard investment and price decisions to boost some regions detrimental to others This author reminds the need to demonstrate the impact of investment in airport infrastructure and in productivity Gillen and Llall (1997) point out that the conditions with airports must survive are not competitive at all due to the constraints such as regulatory political or even social and geographical These authors link the airport conditions with the customer satisfaction as well They consider that variables such as runways and terminal capacity must be taken into consideration in order to attend the peak demands and avoid potential delays They state that the rhythm of the economy affects straight on to airport efficiency because all factors are exogenous and therefore the inputs cannot be changed yearly16 The Air Transportation Association (ATA) estimated the value of inefficiency in the air traffic cost to the airlines companies more than 3 billion of dollars in 1995 (Jenkins 1999) Sarkis (2000) considers that airline companies choose airports depending on their efficiency level tending to select those that are more efficient The revision of literature shows that most studies take into consideration the terms of lsquocompetitionrsquo and lsquocompetitive environmentrsquo as well as the relation between ownership and management forms and their potential effects in airportsrsquo performance Privatisation increases competition but it seems to generate a potential conflict of interests between government and private owner-operators and also a bias from aeronautical activities to commercials in order to finance the operating costs of running the airport Authors agree that governments cannot provide the necessary investment to supply enough airport activity for congested airports (high volume of traffic) Evidence through literature shows that fully government (or private) forms do not necessary will imply better economic and social results Privatisation or commercialisation reforms follow to increase both financial performance and operating efficiency but much deeper is the consequence of the lack of capital when airports are government owned Humphreys (1999) analyses the case of the British airports concluding that privatisation and commercialisation is the answer to their finance expansion since they had to rely on their own profits (until June of 1998) The major impact of privatisation is related to commercial revenues more than an increase of infrastructures unless they have a viable financial return Again there is not enough evidence to prove which ownership and management model is more effective to reduce the interestsrsquo conflict between the government and the market Also which model or combination has a better impact in airportsrsquo performance empirical evidence may show that private ownership is more adequate to improve the company performance nevertheless it is essential to have a competitive market The International Civil Aviation Organisation (ICAO) recommends avoiding regulation of airports in areas where competition is already effective in order to avoid detriment of customers (March 2013)

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 16 Any industrial private company that experiments a reduction of revenues in a specific period can improve its cash-flow by adjusting the production level Governement owned andor managed companies are subject to restictions that may be focused in social benefits instead

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In order to evaluate if fully government owned and managed (operated) is adequate to generate competitive conditions of the market and therefore to supply a good service for customers as well as a positive potential contribution in airportsrsquo performance it is necessary to analyse the impact of the AENA model in the operational efficiency of the Spanish airports this is which airports are the most technically efficient and the comparison to the efficiency achieved by airports subject to a different model of ownership and management such as the British major airports As stated and due to AENA does not publish financial individual reports (financial statements) the airport performance is understood from a managerial perspective based on the current resources of an airport (infrastructure) which is the production activity achieved yearly (number of passengers cargo and movements) Since airports are matured firms and they should be able to generate enough operating resources to finance its ordinary course of business in this paper only operational activity (aeronautical revenues) are considered Again this is supported by literature since privatisation has been proved to change airportsrsquo managers and their investorsrsquo financial interests from aeronautical to commercial revenues The Spanish airport market is fully regulated (is 100 owned and management by the government) and therefore commercial activities are not regarded as contribution to the airport main activity 3 Methodology Data Envelopment Analysis

Efficiency is referred to the relation (ratio) between inputs and outputs when one of these variables is pre-determined or exogenously restricted This definition is based on the assumption that companies have limited resources (inputs such as technology infrastructure etc) and therefore the production frontier has a boundary (feasible output surface) Efficiency has two possible interpretations from an economic and technological point of view Economic efficiency is referred to the use of combinations of feasible resources in order to obtain the lowest production cost per unit of output possible Technological efficiency is the relation between the maximum amount of output subject to a limited amount of resources or the minimum amount of resources used to produce a determined level of production In this paper the technological efficiency concept is evaluated since the unknown purchasing costs of the inputs are relatively difficult to estimate Technological efficiency is related to the current resources that an airport uses to develop its main activity (aeronautical revenues) and therefore it represents purely how airports perform It allows a more accurate benchmarking due to be focused on the number of resources used instead of their value where the cost of inputs could be distorting the reality when comparing two different airports (eg an airport with low production cost may be using a high number of resources than another one but at better price This implies economic efficiency but not to be technical efficient) The overall technical efficiency score can be disaggregated into two sources of efficiency pure technical efficiency and scale efficiency This separation is possible because the technical efficiency depends on the type of returns to scale variables or constant Returns to scale refers to increasing or decreasing efficiency based on size (Adler amp Yazhemsky 2010) Constant returns to scale (CRS) means that the producers are able to linearly scale the inputs (input orientation) or outputs (output orientation) without increasing or decreasing efficiency and therefore any variation

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of resources or production level do not improve the current efficiency level17 The technical efficiency under the assumption of constant returns to scale (CRS) is the overall measure which is used to determine the main causes of inefficiency based on the relation of quantity of inputs and outputs (pure technical efficiency) and also due to the size of operations (scale efficiency) Variable returns to scale (VRS) assume proportional variations of inputs (decreases) or outputs (increases) to improve the level of efficiency achieved in a specific period Therefore by modifying the amount of resources the output will increase more than proportional compared to the amount of inputs used and consequently the efficiency level will also change The pure technical efficiency is obtained under variable returns to scale and reflects the impact of managerial decisions in the production process among the quantity of resources to be used or the level of production to be achieved Therefore it can be used as measure of the managerial performance within the company The ratio between the overall technical efficiency and the pure technical efficiency is the scale efficiency The scale efficiency is the ability of managers to choose the optimum size (amount of resources within the feasible surface) to obtain the pre-determined production level (or to choose the maximum production level achievable based on the current resources available) In Figure 1 the pre-determined production level is the point A (YA) which based on the current technology requires a specific minimum amount of resources (XA) If instead the airport is using more inputs (XB) than the amount required for the expected output (YB = YA) then it becomes scale inefficient under decreasing returns to scale (DRS) airports can achieve the same amount of activity (namely traffic) by using a lower number of resources Point C is the case of airports which are not achieving the expected production (YA) since they are not using enough resources (XC lt XA) These airports are scale inefficient under increasing returns to scale (IRS) since an increase of inputs (XA - XC) will achieve the production target (YA) For instance airports with only one runway or low staffing level will probably have difficulties to get a large number of flights per day compared to others with two runways or more employees Generalising increasing returns to scale (IRS) is related to companies using a small size of inputs for the level of operations pre-determined (output) and therefore in order to become scale efficient they will have to increase the current number of resources decreasing returns to scale (DRS) is referred to a large size of inputs for the amount of operations performed since with lower quantity of resources it is possible to achieve the same production level

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 17 If the inputs for instance number of runways and terminal buildings increase by a factor of 2 the airport activity will also be double and therefore the efficiency score will remain the same that in the previous period

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Y (Output) CRS YA = YB A B

YC lt YA C VRS

IRS DRS

XC XA XB X (Input) Figure 1 Returns to scale in the production process

There are several methodologies to calculate efficiency which are generally classified as parametric and non-parametric Parametric functions are pre-defined before analysing the characteristics of the observations and therefore they assume an established relation between dependent and independent variables Non-parametric functions are not based in a predicted function form this means that the relation between variables is not assumed but will depend on the specific database used The production function is the result of the association between variables based on the information across the observations In this research a non-parametric model of Data Envelopment Analysis (DEA) is used Airports are different from each other due to having different level of resources available and therefore it is not possible to define the same production function for all the Spanish airports DEA has been applied in multiple airport researches over the last 15 years Data Envelopment Analysis (DEA) originally developed by Charnes et al (1978) and subsequently extended by Banker et al (1984) is a non-parametric linear programming-based method to evaluate the relative efficiency of a set of homogeneous decision making units (DMUs) The literature in the airport sector shows the interest of researches in using DEA models to evaluate the way that airports compete within the airport industry by using different variables across countries The Spanish case has been considered in a reduce number of studies probably due to the difficulties to access to disaggregate data per airports Also the studies have been based on one period (normally one year) rather than more This becomes a limitation since by evaluating one year it is not possible to provide enough evidence to explain the plausible reasons behind the competitive frontier (if a second methodology is used) but above all because of the competitive frontier results are no reliable due to a lack of dynamism DEA provides a snap shot of the efficiency level of the units in an specific moment of time but not variations between periods In DEA the technical efficiency can be calculated based on two managerial decisions among inputs and outputs Input orientation models are focused on a proportional reduction of the amount of

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resources used to produce the specific determined production Output orientation will involve decisions of increasing the current level of production to achieve the competitive frontier The efficiency score for each decision making unit ī and for each period θī t is calculated as a relation between the optimum amount of resources (minimum possible quantity of inputs) and the current quantity used if the model is input orientated If it is output orientated then the efficiency level is the relation between the current production levels and the possible to be achieved based on the feasible inputs (maximum outputs) θī t (input orientation) = $amp

$amp $amp θī t (output orientation) = $amp $

$ (1)

The overall technical efficiency score represents the divergence between the actual production and the feasible production (see Figure 2) A unit is technically inefficient overall if the current production is located under the boundary of the production set determined by the competitive frontier under constant return to scale (CRS) The scale efficiency is obtained by comparing the technical efficiency under the assumptions of constant returns to scale (CRS) and variable returns to scale (VRS) The pure technical efficiency level is measure as the distance to the variable returns frontier Pure technical inefficiencies are due to managerial bad performance in making decisions whereas the overall measure is due to both managerial performance and size of operations (too big-DRS or too small-IRS) Figure 2 shows that the constant return to scale frontier (CRS) is achieved only by one decision making unit(DMU1) The units 1 2 3 and 5 are efficient under variable returns to scale assumption (VRS) Projecting the inefficient DMU4 to the VRS frontier to minimize the quantity of inputs used holding the pre-determined production level (input orientation) the unit becomes 100 pure technical efficient(θī t = Xprime₄ X₄) If the model is output orientated the projection is based on variating the level of output and holding the amount of resources in this case the projection is toward the VRS frontier (O) which makes the unit pure technical efficient (θī t = Y₄ Yprime₄) The overall technical efficiency is focused on the CRS frontier by projecting the actual amount of resources used the unit becomes efficient (θī t = Xprimeprime₄ X₄) Again if the model is output orientated the overall technical efficiency is defined as the ratio between the actual production obtained using a pre-determined inputs (Y₄) and the maximum production feasible (θī t = Y₄ Yprimeprime₄) When a unit is efficient its score is equal to the unit and therefore it is posible to establish the equivalence Xprimeprime₄ X₄ = Y₄Yprimeprime₄ which assures that the type of orientation does not change the level of efficiency achieved by a decision making unit In the same way the scale efficiency scores are equal to Xprimeprime₄ Xprime₄ if the model is input orientated and Yprime₄Yprimeprime₄ if it is output orientated

13 13

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Y (Output)

Yrsquorsquo4 CRS VRS D DMU2

C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3

Xrsquorsquo4 Xrsquo4 X4 X (Input)

Figure 2 Competitive frontiers projections of inefficient units 31 Benchmarking in the Airport Industry BCC Model

The aim of benchmarking is to search outside of the enterprise in order to achieve the best practice in the production process to gain competitive advantages Holloway (1999) defines lsquobenchmarkingrsquo as the process where the company enhances its current performance by learning from successful practises of its competitors It is a partnership cooperation process and a mutual benefit over a period of time Camp (1989) defines it as a positive and proactive process changing operations in order to obtain a higher performance Therefore benchmarked best practice becomes the best process by comparing similar activities to produce a specific operational activity This definition shows the insight that benchmarking goes beyond performance measures because it concerns learning and practise process (Francis 2002) In this section the research method used is based on identifying the best practise in order to compare the current level of efficiency of the Spanish airports from a relative perspective The competitive frontier is built with the best practises the airports that achieve their airport activity namely traffic with less amount of resources DEA methodology is considered in the group of non-parametric techniques Parametric methods are based on stochastic cost and production functions which a specific distribution defined prior to the research Non-parametric methods include partial and total factor productivity indexes as well as DEA Partial factor productivity indexes (labour capital etc) do not consider differences in cost of the inputs and also the aggregation of inputs and outputs is unable because they may be measured in different units Total factor productivity indexes allow the aggregation but require costs information and further research if the results will be used

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in management strategies and making decision processes DEA does not require price information variables can be measured in currency or physical units without any need to disaggregate them Therefore this methodology is really useful in sectors where prices and costs are unknown or the outputs are difficult to define such as hospitals councils banks governments etc Furthermore DEA allows the aggregation of multiple inputs and outputs compared to partial productivity indexes and different feasible combinations of inputs-outputs DEA draws the relation between outputs and inputs as an efficiency production surface which can be achieved with the current technology or management strategy applied by the company (Gillen amp Lall 1997) One of its disadvantages is that it is very sensitive towards potential outliers and provides unreliable results when the number of inputs and outputs are much larger compared to the observations (Nyshadham amp Rao 2000) Outliers are identified through a super-efficiency analysis by removing the outliers from the initial sample the rest of observations increase potentially their initial efficiency levels BCC Model (Banker Charnes amp Cooper 1984) The BCC model is based on the CCR radial model (Charnes Cooper and Rhodes 1978)18 The BCC model features variables returns to scale (VRS) which are more flexible and reflect managerial efficiency apart from purely technical limits (Wanke 2012) Variable returns to scale assumes the relation between dependent and independent variables non-proportional producers cannot linearly scale inputs and outputs without increasing or decreasing efficiency Any decision of increasing or decreasing the current amount of inputs used will affect the efficiency score (Adler amp Yazhemsky 2006) Based on airport managers discussions and literature review the variable returns to scale model seems to be more appropriate to measure airport efficiency (Adler 2013) The variable returns to scale provides economies of scale which is important since small airports tend to have increasing return to scale They also allow including variables with negative or zero value that are essential for instance when airports have no cargo (Lovell amp Pastor 1995) BCC model can be applied from two alternatives of orientation The orientation is referred to variables relatively easy to be changed by the management in the short term variables that are not exogenously determine also called discretionary In the input orientation version the production resources are modified whereas in the output model changes in the production amount will be made in order to improve the efficiency level achieved by a decision making unit Input orientated DEA models quantify the input reduction that is necessary to be made to become technically efficient by holding the production constant An output orientated DEA model will quantify the expansion of production necessary to become technically efficient with a fixed amount of inputs A non-orientated measure quantifies improvements when both inputs and outputs can be changed simultaneously

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 18 CCR (Charnes et al 1978) BCC (Banker et al 1984) which improves the Debreu-Farrell efficiency since this ignores the presence of non-zero slack Radial technical inefficiency means that all inputs can be simultaneously reduced by θ without altering the proportions in which they are utilized The other type of DEA models are additive (Charnes et al 1985) which is based on the Pareto-Koopmans (mixed) efficiency a decision making unit is fully efficient if and only if it is not possible to improve any input (or output) without worsening one or more of its other inputs (or outputs) An observation is rated as relatively efficient if and only if there are no output shortfalls or resource wastage at the optimal solution Due to the unitsrsquo invariance properties a normalized weighted additive DEA (Lovell and Pastor (1995)) may be used in place of the simple additive 13

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In the airport sector the inputs seem to be modified relatively easier than the airport outputs which will depend more on external factors beyond the airportsrsquo infrastructures The input orientation in DEA assumes that the main output namely airport traffic is exogenous and therefore it cannot be changed or impacted by the airport management The input-orientated model is focused on minimising the amount of resources used in the ordinary course of business to achieve a target of output level Consequently inputs including runways and terminals are assumed being semi-variable not fixed and therefore relative easier to be changed regarding the amount to be used when updating the airport activity to the current demand Both models imply that only discretionary variables not restricted affect the efficient targets for inefficient units In this case decisions among inputs are made by inefficient airports to achieve the frontier to become efficient but decisions to change the number of passengers cargo or movements are not viable The Bankerrsquos model in its input orientation version assumes that the efficiency score for each airport ī (120579ī) will be determined by the way that airports use their current resources in order to achieve a fixed (expected) production level of airport activity The BBC model in its input orientation version for a specific decision making unit ī (119863119872119880ī) is based on an objective function which minimise the distance of a specific decision making unit to the efficiency frontier where are located the units who are the most efficient

119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905

īrsquo 120582īrsquo = 1

īrsquo 120582īrsquo ge 0

The DEA models are based on the current technology existing in a period 119905 describing the conjunct of internal processes within the production process consequently DEA does not imply dynamism but a snap shot of the current competitive frontier in a specific moment of time Investments made from one period to the next one are known as technological change which represents the potential increase of the current production capacity This can be quantified by the Malmquist Index which also quantifies variations in efficiency due to investments in new technologies In this paper the fact of analysing more than one period responds to the need of providing dynamism to the DEA frontier Changes in technology are understood as investments in the current infrastructure which will be incorporate in the value of the non-current assets across the years Each 119863119872119880ī uses 119898 resources 119883119895 119895 = 1hellip119898 in producing a pre-determined level of production represented by 119899 outputs 119884119894 119894 = 1hellip119899 In each period the objective function minimise the distance between the airport and

the frontier formed by the best airports (technically efficient) The solution to the linear program

13 13

2013

119863ī 119905 119883119895ī 119905119884119894ī 119905 = 120579ī is the technical efficiency level representing the way that a specific airport ī performs this is how it is using its feasible resources to produce a fixed airport activity (119884119894ī) The efficiency score obtained from the optimisation model represents the distance of the decision making unit to the competitive frontier if 119863ī 119905 119883119895ī 119905119884119894ī 119905 = 1 then the unit is allocated in the frontier and therefore technically efficient If the efficiency score is lower than the unity 119863ī 119905 119883119895ī 119905119884119894ī 119905 lt 1 the distance to the frontier(1minus 120579ī) measures its technical inefficiency Since the efficiency score is calculated from the feasible current resources the frontier is potentially achievable by any inefficient unit the inefficiency or distance to the competitive frontier can be reduced with its current technology The reference group of an inefficient unit are similar units but are best practitioners The peers become the guidance of the inefficient 119863119872119880ī to improve its performance to reduce the amount of resources used The restriction of lambdas is referred to the reference group built by 119896 units īrsquo = 1 119896 Each competitor īrsquo has some relative influence when making the airport analysed

technically inefficient the importance is measured with relative weights represented by the lambdas as percentages (120582īrsquo ge 0) being the total sum equals to the unity ( 120582īrsquo = 1) 119948

īrsquo 120783 The main competitors become the reference to the airport evaluated to use the resources in a better way If there is only one main competitor the restriction of sum of lambdas will be equal to the unity a similar situation to a monopolistic power The other two restrictions bound the production volume and consume of inputs The first restriction states that the production level of an airport (119884119894ī) cannot be higher than its references The second restriction determines that the amount of resources used by the unit is higher or equal to its competitors ( 119883119895īrsquo 119905 120582īrsquo) 119948

īrsquo 120783 The potential improvement of efficiency is calculated by the proportional reduction of the current amount of resources used (120579ī 119883119895ī 119905)

32Data airports and variables

Figure 3 Spanish airport network (Source AENA Annual Report 2011) In order to measure the efficiency of the Spanish airports it is necessary to identify the main resources used in the operational airport activity Since privatisation has not occurred in the Spanish

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2113

market and literature has proved that privatisation biases activities from aeronautical towards commercial ones in this study the ordinary course of business is understood from the aeronautical perspective Non-aeronautical revenues are the only discretionary output over which the influence of management can be substantial (Adler 2013) Non-discretionary variables are externally determined and therefore they are beyond the control of management in the short run or exogenously restricted (Banker amp Morey 1986) Consequently the main focus of this analysis is the operational activity based on those inputs that mainly contribute to the gross margin of the airports An overall concept which summarizes the aeronautical inputs used in the airport industry is the airports infrastructure Nevertheless the review of literature shows that the capital variable (infrastructure) is difficult to define or quantify One of the main challenges of airport benchmarking analysis is the inclusion of capital measures (Parker 1999) Different measures of a proxy of capital have been used in airport industry research Parker (1999) estimates rent expenses Murillo and Melchor (1999) amortization and Barros and Sampaio (2004) use the book value of the tangible assets (this is its current cost deducted by the accumulated depreciation) Adler (2013) states that these measures can be problematic if comparing regulatory frameworks and accounting standards across countries but it is possible to avoid the influence of differences in estimation if the historical cost is obtained by using the disclosures of the annual reports The harmonisation of the accounting standards (International Accounting Standards-IASs) has been applied in Spain from January 2008 where the introduction of concepts such as net realisable value reducing balance method value in use recoverable amount fair value revaluation model impairment of assets etc were used for the very first time Generally speaking the amortisation tables tended to be publicly published by governments allowing companies to use the same percentage of depreciation before 2008 With the new international standards the amortisation per year is a more accurate measure since is a reflection of the number of years that companies are going to use the fixed asset to generate revenues (accruals and matching principles) In this study a capital measure of airports has been estimated based on the historical cost of the non-current assets which has been approximate to the airportrsquos physical inputs such runways gates parking spaces terminal and airport area (Gillen amp Lall 1997 Sarkis 2000 Pels et al 2001) Table 2 summarizes the inputs and outputs used in this research to measure the airport operational activity Terminal buildings have not been included since it could be argue being a redundant variable when considering other variables such as boarding gates checking desks and surface of waiting areas The outputs globalise aeronautical activities and exclude commercial revenues due to the AENA corporate governance model On the input side six variables have been used that represent the current infrastructure to be used potentially by each airport when performing its main activity19 depreciation of runways depreciation of non-current assets excluding runways terminal charge wages and number of permanent employees per year and total operational costs The runways have been analysed separately of the rest of the non-current assets since they seem to have a more direct impact in the operational airport activity The runways are defined as the surface area plus all the systems involved in the taking-off and landing operations from the taxiway to the runway head There are three traditional indicators of outputs that measure the main aeronautical activity number of total passengers per year number of operations or movements and goods transported per year including all types of traffic and goods such as in transit 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 19 The fact of using the term lsquopotentiallyrsquo is referred to the competitive frontier If an airport does not use all its capacity (infrastructure) or uses it in an inefficient way it will become inefficient compare to other airports Each airport has a current infrastructure that can be under-used for the size of operations

13 13

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Variables Definition (units per year) Label MPAX Passengers Output OPERATIONS Number of aircraft movements Output GOODS Tones transported Output EMPLOYEES Number of permanent staff Input DEPRECIATION ASSETS

Charge per Depreciation Non-current Tangible Assets (th euro)

Input

DEPRECIATION RUNWAYS

Charge per Depreciation Runways (th euro) Input

TERMINAL CHARGE

Tax of approaching and taking-off per year (th euro)

Input

WAGES Wages permanent staff per year (th euro) Input OPERATING COSTS

Operating Costs per year (th euro) Input

Table 2 Definition of the variables In order to calculate the annual depreciation of the non-current assets the historical cost has been estimated for each airport using the constructions certifications published by AENA from 2000 and the charge per depreciation applied by AENA in 2004 year in which individual accounts of the income statement per airports have been published Also it has been assumed that none revaluation and impairment losses have been incurred in order to apply the same percentage of depreciation in all years of the study The historical cost in each period is equivalent to the accumulated cost of the assets from 2000 since beyond this year AENA has not published disaggregate construction certifications The works have been disclosure into two types works in runways and works in the rest of fixed assets which exclude runways Each year contains certifications regarding lsquoworks finished during the periodrsquo and lsquowork in progress (not finished) during the periodrsquo The harmonised process of the International Accounting Standards state that tests works or improvements made in an asset after the initial recognition increasing its original useful life or its production capacity should be recognised as more cost (IAS 16 IAS 38 and IAS 36) After the works are finished and as soon as the asset is ready to be used it should be depreciated regardless if the asset is used or it is not Consequently any work finished during a specific period has been considered when calculating the charge per depreciation for the same year Due to the lack of information provided by AENA regarding construction certifications before 2000 this period is considered as the reference one (base) being the first year of study 2001 when the airports are assumed to be opened to civil aviation The cost of the fixed assets in 2001 is equal to the historical cost plus any works done and finished in the assets during 2001 For the rest of the periods 2002 to 2011 the cost of the non-current assets is estimated with the opening balance of the costs in each period closing balance of the previous period plus any work finished during the same period The percentage of depreciation used to estimate the historical cost of assets is equal to 19 which is the average of depreciation of the total non-current assets The individual percentage for each different type of fixed asset has been obtained

13 13

2313

from the tables published by the New Zealand government20 under the assumption of qualifying assets and using straight line method (see Table 3)

Assets (Airport and Building Sectors) Useful Life (years)

X Depreciation

Aprons (airports) 360 02778 Runways (for airports) 480 02083 Speed humps (rubber) 1620 00617 Taxiways (airports) 360 02778 Baggage conveyor systems 1020 00980 Baggage imaging machines 2100 00476 Radar navigational traffic control equipment 1620 00617 Radar navigational signalling equipment 1620 00617 Flight simulators (FTD and FNPT certifiable) aircraft specific (full-motion) 1020 00980 Lighting controllers (emergency) 1260 00794 Reservoirs (above ground concrete) 360 02778 Tunnels (block) 180 05556 Buildings with reinforced concrete (including 2011 income year) 200 05000 Structures (default class) 360 02778 Heating systems 840 01190 Air conditioning systems 840 01190 Car-parking buildings (acquired or binding contract before 30 July 2009) 300 03333 Security systems 1620 00617 Monitoring systems 1620 00617 Roadways 360 02778

Table 3 Percentage of depreciation of non-current assets (source New Zealand Government) In this research since the non-current assets have been approximate to an estimation of infrastructure an average of the depreciation coefficients seems to be sensible Future studies in efficiency will consider each asset separately with their specific useful life in order to evaluate the potential individual impact of the different assets in the operational activity of the Spanish airports The assets that are involved in the non-current assets represent an aggregation of all buildings and systems required to perform the aeronautical activity excluding the runways surface as well as surrounding areas such as airfield and access to the airports The historical cost of the assets per airport has been disclosure in the following aeronautical fixed assets terminal buildings which include passengers handling-cargo and aviation buildings security buildings (fire ambulance board inspection and

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 20 httpwwwirdgovtnzcalculatorskeyworddepreciationcalculator-depreciation-rate-finderhtml The fact of using the information provided by New Zealand Government is because it is really detailed compared to other countries For instance British Airports Authority Limited (BAA) publishes as percentage of depreciation for runways taxiways and aprons a value of 1 and for runway lighting and building plant between 5-20 without providing specific and detail information per individual asset

13 13

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flight simulators) control tower beacon systems aprons (fingers bridges headers of platforms aircraft services) runways (taxiway waiting header breaking areas stop-emergency exits) airfield (connexion between terminals upgrade of airfield around the runway works done to accomplish the standards settle by International Civil Aviation Organization-ICAO ATEX regulation NTAC) parking area and other works (water plants fences access area to the airport recycling systems) The historical cost of the total assets estimated in 2000 for each airport (6) has been distributed across each aeronautical fixed asset using weights of depreciation (see Table 4)

Assets (Airport and Building Sectors) X Weight of Depreciation


Table 4 Weights of percentage of depreciation of non-current assets The value of the assets belonging to terminals apron airfield runways beacon systems etc has been estimated using the weighted percentages of depreciation For instance a runway (00104) includes the taxiway (00139) and also the speed humps (00031) and therefore the sum of the weights related to runways is equal to 274 this is how much represents the percentage of depreciation per year of the runways among the average of total assets equal to 1928 The average of depreciation of the non-current assets implies a useful life of 518 years and therefore the assets built in 2001 are fully depreciated in 2006 the improvements finished in 2002 are fully depreciated in 2007 and so on The two airports which were opened to the civil aviation after 2000 are Logrontildeo and Albacete which start performing in 2003 The input terminal charge has been defined as the cost incurred per airport regarding movements operations of landing and taking off The maximum weight authorised of the aircraft in the taking-off

13 13

2513

operation (MTOW) has been used as a proxy measure of the terminal charge The MTOW is a standard unit of the maximum take-off weight authorized of the aircraft this includes the taxi-out fuel the trip and reserve fuel passengers and their hand luggage carried as well as the operating empty weight (OEW) The terminal charge per airport and year has been calculated using the tax per proximity operation charged (R) according to AENA regulation21 119877 119905 = ʈ 119909 (

) (3)

The proximity tax expressed in euros per operation is related to security and air control services provided by AENA when an aircraft is approaching to a specific airport The tax is applied in all the Spanish airports and includes the operations of approaching and taking-off as one unique service Following the European Regulation (17942006 6th of December) AENA applies in 2013 119899 equal to 070 in order to harmonise the tax applied across Europe This coefficient has been used in all the years of the study to avoid potential bias in the efficiency scores if variations exist across the periods Finally the terminal charge (TR) per airport and year 119905 expressed in euros has been estimated following the next expression 119879119877 119905 = 119874119875119864119877119860119879119868119874119873119878 119905 119909 119877 119905 (4) In order to have an accurate measure of the labour factor involved exclusively in the aeronautical activity the number of employees per airport and year are the permanent staff not related to outsourced activities (handling fuel security etc) The information published by AENA regarding the overall number of employees has been used in order to estimate the number of employees and labour costs per airport and year It has not been possible to obtain more disclosure information regarding different units such as corporate commercial areas infrastructures air navigation and control of airports The number of employees per airport and year has been estimated using the disaggregated data for 2004 and the overall information published by AENA in the annual reports The deflator of labour costs has been applied to avoid the effect of the inflation when calculating the individual costs per airport22 The real salaries allow comparing the expenses incurred across years without being biased by changes in prices of labour The rest of inputs have also been adjusted using the deflator of the gross domestic product (GDP) in order to avoid potential biases of the inflation in the price of the inputs The total operating costs per each airport is the sum of the charge per depreciation of runways and the other fixed assets annual labour costs and terminal charge It could be argue that this variable is redundant since globalised the previous inputs used in the same model but the main idea is to evaluate the impact of the operational costs overall in the efficiency scores compared to the individual effect of each input These variables (inputs and outputs) have been estimated for the seven years of the study The main idea is to obtain the efficiency scores for each year and to compare the consistency of the efficiency frontier through time Since DEA is a non-dynamic non-parametric tool the scores reflect a snapshot of the current situation for the period analyse but not the variations between periods Therefore it is necessary to analysed more than one year in order to achieve accurate results and conclusions of the current situation of the airports managed and owned by AENA A good rule-of-thumb when applying DEA is to include a minimum set of data points in the evaluation to discriminate better between efficient and inefficient units Cooper (2001) states the number of decision making units should be at least three times bigger than the inputs and outputs used With a proper number of observations the surface in each period is constructed smoothly avoiding instability in the value of efficiency scores from period to period the units that are technical 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 21 httpwwwaenaescseeccurl513892guiaTarifasNA_2013-ESpdf 22 Source wwwinees Indice de Costes Laborales por trimestre Reference period 2008

13 13

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efficient in one period remain efficient across different years providing and accurate efficient frontier to be evaluated in the second stage In this study a final sample of 39 regional airports with data for seven years seems to be reliable enough to build a competitive frontier based on three outputs and six inputs (annual depreciation of fixed assets annual depreciation of runways terminal charges wages total operating costs and number of permanent employees)23

33Data Envelopment Analysis model (BCC-model) Results After removing the two outliers from the initial sample (Logrono and Melilla) and also the terminal charge as input due to causing collinearity problems the optimization model is applied considering 80 of information retained among the final inputs and outputs used for the seven years of the study This is done to avoid residual potential collinearity between the other variables involved It could be argue that Madrid due to the high level of output compare to the rest of airports could be also a potential outlier but the fact that it does not become main reference for most of the airports makes Madrid compete on its own For instance even being in the reference group for Barcelona Palma de Mallorca shows more consistency becoming Barcelonarsquos main reference (with lambdas higher than 05000) compared to Madrid across all years24 This has been evidenced in the previous section after removing several outliers from the initial sample meaning that Madrid has no other airports which may influence in its operational activity consequently becomes frontier in most of the years of the study Palma de Mallorca shows also consistency across all the periods showing scores between 81 and 100 (exceptionally in 2005 it achieves 7803 with Gironarsquos lambda equal to 07231)25 representing competing on its own After removing Logrontildeo and Melilla as well as the terminal charge as input Girona and Badajoz become the main referees for the inefficient airports Girona becomes reference for airports located in the Mediterranean area close to it or cities with beaches Alicante Valencia Malaga Ibiza and Bilbao Girona and Ibiza become competitors of Tenerife North Menorca has as main competitor Reus (in 2010 and 2011) and in the other years a similar weight of Badajoz and Girona as main references Badajoz achieves really high consistent scores across all the years of the study Badajoz is one of the airports that does not have cargo but with a significant higher number of passengers per year compared to other airports with zero cargo Also Badajoz has a relatively lower operational costs compared to the airports that have no cargo (see Table 7) Airports with no cargo Average MPAX Operational Costs Badajoz 9970 74219 168293 Salamanca 7386 44515 551113 Albacete 5677 15354 327030 Cordoba 3964 16653 543756

Table 7 Average of efficiency scores passengers and operational costs for airports without cargo (2005-2011) 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 23 The airports not analysed in this occasion due to not having enough data across the years are Algeciras Alcala Burgos Castellon Ceuta Getafe Huesca Madrid 4 vientos Madrid Torrejon and Sabadell 24 Barcelona has as major referee Palma de Mallorca when all the airports are included in the sample as well as when Logrontildeo is excluded After removing Melilla from the sample Girona substitutes Palma de Mallorca as main competitor of Barcelona 25 It seems that 2005 shows less consistancy in the efficiency scores overall This could be explained due to being the first year of the study and most of the information regarding inputs is estimated from 2000

13 13

2713

These results show that airports with no cargo are potentially more efficient than others with cargo if they have enough aeronautical output measured through the number of passengers to cover their operational costs Generally it can be stated that airports with no cargo and low number of movements are more efficient than those with a slightly higher number of movements and cargo but significant number of passengers The final frontier results show that the airports with higher scores of efficiency and consistency across the seven years are Madrid Palma de Mallorca Lanzarote and Badajoz Tenerife South has as main competitor Girona in the early years of the study (2005-2007) and then the weights are split up between Girona and Badajoz having more importance Badajoz in the recent years Bilbao has as main reference Girona sharing in some years with Ibiza Ibiza also has as main competitor Girona Vitoria has a huge variation of peers not showing consistency across the years Finally Badajoz seems to be the main reference for airports with a low average of efficiency of 50 (overall between 3280 and 7750) and Girona for airports with higher scores being in average 75 (5971 and 8559) Comparing the units that are at least one year 100 technically efficient the scores that show more consistency across all the seven years are Badajoz Madrid Palma de Mallorca Lanzarote and Barcelona (see Table 8) Airports Average 2011 2010 2009 2008 2007 2006 2005 Badajoz 09970 10000 10000 10000 09789 10000 10000 10000 Madrid 09656 10000 09982 08324 09420 09864 10000 10000 Palma de Mallorca 09412 10000 10000 08137 10000 10000 09948 07803 Lanzarote 09250 10000 10000 08482 09354 09505 08884 08527 Barcelona 08945 10000 08876 07243 07737 08758 10000 10000 Girona 08582 06395 10000 07931 09305 10000 07744 08697 Alicante 08559 07858 08002 06499 08206 10000 09351 10000 Gran Canaria 08122 07680 07409 05811 10000 09076 08872 08008 Sevilla 08121 06058 09755 08600 10000 10000 06713 05720 Reus 07750 08708 08167 06975 06532 06769 07099 10000 Fuerteventura 07613 08407 07409 05032 07727 07995 08026 08697 Valencia 07417 07178 07715 06857 08674 06927 06582 07985 Salamanca 07386 10000 09543 06752 07553 06552 05560 05739 Ibiza 07158 10000 08799 05948 06205 06762 06052 06338 Malaga 07025 06470 06384 04739 05926 06777 10000 08880 A Coruna 06758 04409 04825 03418 10000 09042 10000 05612 Jerez 06376 05843 04953 04540 05930 07279 10000 06084 Leon 06316 03977 09128 04427 10000 06897 05165 04618 La Gomera 06248 05690 10000 08401 05376 05347 04708 04212 Asturias 06161 04662 09416 07696 10000 03951 03681 03720 Albacete 05772 10000 09225 04872 03704 03873 04159 04568 Menorca 05557 09468 09645 02892 03636 04125 04776 04354 El Hierro 03956 10000 04462 02452 02992 02996 02524 02268

Table 8 Technical efficiency scores frontier airports and airports close to the frontier

13 13

2813

Girona Alicante Gran Canaria and Sevilla achieve in average a high level of efficiency but with less consistency during the years analysed Fuerteventura and Valencia although not being frontier in any year both achieve higher levels of efficiency and more consistency compared to other airports Menorca although being close to the frontier in 2010 and 2011 shows a huge variability not providing reliability enough to be labelled as frontier As it is shown Badajoz and Palma de Mallorca are the airports more efficient being frontier for six and four years respectively Madrid is frontier in three years and Lanzarote in two both showing consistency across the efficiency scores achieved Lanzarote also shows consistency even being frontier for only two years It seems that airports such as Barcelona Girona Alicante Gran Canaria and Sevilla may have more variations in their respective scores due to becoming holiday destinations and potential passengers would change their choices across the years of the study Another cluster can be potentially identified for Reus Fuerteventura Valencia Salamanca Ibiza and Malaga offering more youth orientated holiday package or cheaper compared to other destinations All these assumptions will be tested in future research Madrid is only shadow for Barcelona when El Prat becomes inefficient but with low weights compared to Girona that seems clear competitor of Barcelona For the rest of the airports Madrid does not have any influence in their respective technical efficiency level confirming that Madrid competes on its26 Consequently Madrid could be labelled as outlier but since it does not become referee for any of the inefficient airports the fact of removing it from the initial sample will not improve significantly the efficiency scores of the rest of the airports this is a similar situation close to lsquomonopolyrsquo The correlation matrix between the efficiency scores and the inputs and outputs finally included in the DEA model shows really low levels of association

Efficiency

Depreciation Assets

Depreciation Runways

Wages

Operating Costs

MPAX

Operations

Goods

Efficiency 1 Depreciatio

n Assets 01704 1 Depreciatio

n Runways 01124 02386 1 Wages 03658 04443 02671 1

Operating Costs 03237 04684 03388

07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1

Table 9 Correlation between efficiency scores and explanatory variables

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 2613 2009 is the only year that Madrid achieves its lowest score of 8324 and its lsquopeersrsquo have very low lambdas Girona has a weight of 00573 and Barcelona 00301

13 13

2913

These results show that the number of passengers operations and goods as well the depreciation of runways and infrastructure salaries and operating costs of each airport apparently do not have influence in the level of technical efficiency achieved In order to determine the main causes behind this competitive frontier is necessary to apply a second stage regression based on the bootstrapping methodology 4 Second stage methodology bootstrap truncated regression The idea of this second section is to seek additional more plausible causes of the efficiency scores obtained in the first stage by applying the Banker DEA model (1984) BCC The efficiency scores are used as dependent variable to determine if environmental variables can explain the efficiency levels of the Spanish airports owned and managed by AENA The main aim behind a second stage analysis after applying DEA is to confirm if causality exits in this case if different ownership forms could be causing the current efficiency scores This second stage is essential to find out the main reasons behind why some airports are efficient and others are not therefore to explain the efficiency map obtained in the earlier stage to achieve reliable explanations to consolidate the main causes of the efficiency frontier obtained previously (Oum 2006) The environmental variables are defined as not-controllable since they cannot be change depending on managerial decisions In literature they are named as non-discretionary variables since are assumed to be beyond the control of management in the short run or they are exogenously restricted (Banker amp Morey 1986) Non-discretionary variables can be understood as the environment where airports develop their aeronautical activity The most common environmental variable in the airport sector is the type of management and ownership especially when the regulator is the government The methodology used in the second stage is bootstrap regression in order to avoid inconsistent estimators of the standard errors Bootstrap regression is more accurate than the regression in presence of outliers for instance hub airports compared to others which are not (Madrid versus Barcelona) or big airports compared to small ones It also provides further information regarding which variables are better predictors Oum (1999) quotes that results may be affected by the methodology used in efficiency analysis Initially introduced by Simar and Wilson (1998) bootstrapping method allows sensitivity analysis on efficiency scores as well as to test the predictors (explanatory variables) to predict the value of the dependent variable by repeatedly sampling from the original data base (resampling) and recalculating the parameter of interest By applying bootstrapping consistency among the explanatory model is achieved in order to explain the efficiency scores potential reasons behind the inefficiencies of the Spanish airports Simar and Wilson (2007) applied a truncated regression to avoid correlation between the estimators (explanatory variables) and the efficiency units consequently between variability not explained by the model (Ɛ119895) and the estimators27 The regression model with 119896 predictors follows 119884 = 119883120573 + 120576 (5)

119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 27 In multivariate regression the assumption of the errors is that are identically and independently distributed therefore no correlation exists between the residuals and the estimators

13 13

3013

The dependent variable in (18) is the technical level of efficiency obtained in the earlier stage 120579ī to be regressed (explained) by the environmental variables (predictors) The truncated regression is a non-parametric regression due to not making assumptions regarding the structure of the population In this case since the efficiency units 120579ī = 1 are not considered in the analysis the regression is truncated in the upper limit (119884 ge 1) and therefore the efficiency scores must be lower than the unity to be evaluated (119884 lt 1) Regression models are based on the assumption that the errors have a constant variance (homoscedasticity) and therefore they do not depend on the explanatory variables or predictors use to estimate the dependent variable (119881119886119903 Ɛ = 120590) Heteroscedasticity implies that differences between the observed value and the real value of the population are potentially variable as the error depends on the predictorsrsquo conditions used in the model consequently the variance of the errors is not constant 119881119886119903 Ɛ 119883 = 120590 119883 Bootstrap methodology allows estimating a robust standard error by relaxing the assumption of homoscedasticity when there is no assurance regarding how the errors are distributed Bootstrapping is a technique to obtain robust estimator of standard errors and confidence intervals such as mean median proportion odds ratio correlation coefficients and regression coefficients This is the most useful alternative to parametric estimates when the assumptions are in doubt In the regression model it is really useful when heteroscedasticity exists or when parametric inference is really complicated to calculate the standard errors The bootstrap technique consist on estimating any statistic 119879 characteristic of the population by applying the estimation technique chosen to different samples (bootstrap samples) a large number of times or replications (119877) Each bootstrap sample is obtained from the initial sample that contains all the observations drawn from the population From each bootstrap sample a statistic 119879119887 is obtained and the estimated bootstrap statistic 119879 is the average of the bootstrapped statistics obtained in the 119877 replications

119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )

(7) There are different methods to replace the observations in the samples used in the replication In this case a random selection is applied therefore each observation has the same probability to be part of a bootstrap sample (1 119899)

28 The regression model is the estimation technique chosen to estimate the statistics of the population therefore the bootstrap method will obtain replicates of the regression coefficients (bootstrapping regression) On bootstrapping regression each iteration applied to a bootstrap sample will provide the estimated coefficients (120573120485119887) predicted by the explanatory variables (119883119895 119895 = 1hellip 119896) After all the replicates are done the estimated coefficient for any predictor j (120573120485) is the average of the bootstrapped regression coefficients obtained in each replication therefore (19) is equivalent to

120573119895 = 119864 120573119895 =

(8)

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 28 lsquoThe random selection of bootstrap samples is not an essential aspect of the nonparametric bootstraprsquo (Fox J 2002)

13 13

3113

41The UK regulatory airport framework Heathrow Airport Holdings formerly British Airports Authority Limited (BAA) is the United Kingdom based operator for four British airports29 The company resulted from the policy of assets privatisation applied by the British Government from 1986 (The Airports Act 1986) The BAA is a classic example of private transportation company in charge with managing airport infrastructure in the United Kingdom (Barros amp Dieke 2008) The information regarding the major airports owned and managed by the British Airport Authority Ltd Heathrow airport Stansted and before 2009 Gatwick has been used in order to compare the impact in the efficiency scores of airports owned and managed privately The information regarding these airports has been obtained from the annual reports published by BAA from 2006 to 201130 The information contained in the financial statements is referred to depreciation of non-current assets labour costs and total operational costs

MPAX Air traffic movements Operational Costs

Airport Average Minimum Maximum Average Minimum Maximum Average Minimum Maximum

Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549

Palma de Mallorca 22252888 21117414 23228879 185559 174635 197384 785993 700274

868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716

Table 10 Descriptive statistics of the airports with more volume of aeronautical outputs Following the same procedure than in the Spanish airports the efficiency scores have obtained applying the BCC model (2) Nevertheless due to for the British major airports there is no disclosure information regarding the depreciation of runways a new frontier has been also obtained for all the Spanish airports between 2006 and 2011 using the same variables to avoid bias in the efficiency scores previously obtained Also the deflator of the UK GDP has been used to avoid bias in the results due to inflation The initial correlations between the inputs outputs and the efficiency scores show low associations between the type of ownership and the efficiency level achieved by the airports as well as with other inputs and outputs 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 29 The company changed the name to Heathrow Airport Holdings given the reduction in the number of airports owned by the company in 2012 30 Information before 2006 is no longer available

13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods

Efficiency 1 Ownership 02577 1 Depreciation

Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1

Table 11 Correlation between efficiency scores and explanatory variables The results show that when the British airports enter in the same market that the Spanish ones (joint score) some of the Spanish airports experiment major changes (decreases) in their efficiency (see Table 12 for 2011)31 The individual score represents the efficiency achieved by the airports when there are competing in their own country without any other airport managed and owned differently government owned and managed (Spain) or privately owned and managed (UK) The joint score represents the efficiency level when different models of ownership exist for airports competing in the same market The variation in average is a general decrease in the efficiency scores due to having airports under a different model of ownership and management in this case owned and managed privately by the same operator The DEA outputs show that the airports that experiment major decreasing in their efficiency levels overall are Barcelona (3426) Palma de Mallorca (2044) Sevilla (2448) Leon (16) Gran Canaria (1338) Stansted (1277) and Malaga (862) It could be stated that generally the airports that experiment major decreases in their efficiency when competing with other airports owned-managed differently are the ones with higher size of operations number of passengers and air transport movements This will be confirmed in future research when using the CCR model to estimate scale inefficiencies As it is seen Madrid and Heathrow are not influenced by other airports with different ownership-management form both of them are hubs (international connector) The British airports have lower decreases in their respective efficiency level confirming a better performance if are privately owned and managed In 2011 the main competitor for Barcelona is Gatwick (06594) and with less importance Heathrow (02375) and Madrid Barajas (01031) Gatwick also becomes one of the main competitors for Malaga (Badajoz 05981 Gatwick 04020) and for Palma de Mallorca (Girona 04072 Gatwick 03871 and Barcelona 02057)

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 31 Gatwick has an average regarding the period 2005-2008 of an increase in efficiency equal 222

13 13

3313

For the rest of the airports the weight of Gatwick as main peer is really low These weights are consistent across all the years of the study whereas in the previous DEA Palma de Mallorca did not have any main competitor at all The importance of the peer analysis rely on that the airports who seem to compete on their own when only one ownership-management form exists in a market(eg Palma de Mallorca) lose their monopolistic position of main competitors with the entrance of other airports the market is shared with other competitors owned and managed differently

Airports Average∆ (2006-2011)

∆Score 2011

Individual Score

Joint Score

Heathrow -273 0 10000 10000 Stansted -1277 -2033 09777 07744 A Coruntildea -043 053 04553 04606 Albacete -645 -1698 07875 06177 Alicante -537 -2617 06684 04067 Almeria -318 -337 03271 02934 Asturias -471 -586 04672 04087 Barcelona -3426 -5021 09966 04945 Bilbao -314 131 07010 07140 Cordoba 426 133 03710 03843 El Hierro -352 -796 03631 02835 Fuerteventura -842 -052 07969 07918 Girona-Costa Brava -081 088 06516 06604 Gran Canaria -1338 -272 06959 06687 Granada-Jaen -054 086 03164 03250 Ibiza -649 -1515 10000 08485 Jerez -213 564 05766 06331 La Gomera -455 020 05384 05405 La Palma -211 -376 03869 03493 Lanzarote -776 -286 08839 08552 Leon -1600 154 04244 04398 Madrid Barajas 136 000 10000 10000 Malaga -862 -2498 06546 04048 Menorca 398 398 03706 04105 Murcia 053 -071 04968 04897 Palma de Mallorca -2044 -1112 09162 08049 Pamplona -081 097 02348 02445 Reus 165 -185 06942 06758 Salamanca 762 770 06218 06988 San Sebastian -104 -243 03420 03177 Santander 308 385 04257 04642 Santiago -312 -604 02891 02287 Seville -2448 020 06122 06142

13 13

3413

Tenerife North 009 158 06757 06916 Tenerife South -078 -073 05126 05053 Valencia -654 -1214 06383 05169 Valladolid 078 064 03958 04022 Vigo -197 -795 03580 02785 Vitoria -304 -1396 06203 04807 Zaragoza 212 302 05886 06188

Table 12 Efficiency scores Spanish airports and others with different ownership model (2011) The results from the truncated regression using as dependent variable the efficiency scores and the ownership form as predictor shows that the fact that an airport is fully government (or privately) owned-managed affects its level of efficiency (see Table 13) In the truncated regression 43 observations have been dropped (efficiency airports across 2006-2011)

Efficiency Coef Std Err z P gt brvbarzbrvbar [95 Conf Interval]

Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495

Table 13 Truncated regression efficiency-ownership form (upper limit = 1) These results have been tested by applying a bootstrap with 1000 replications among the truncated regression with the same variables (see Table 14)


Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531

Table 14 Bootstrap of truncated regression efficiency-ownership form (upper limit = 1) The bootstrap truncated regression confirms that the ownership form affects significantly to the level of technical efficiency achieved The regression coefficient shows that the fact of changing from government owned and managed form (categorical variable with value 0) to a private one (with value 1) the efficiency improves in 0535218 These results are consistent with the previous findings in literature that airports with government majority ownership are less efficient than those with a private majority In order to evaluate the potential variations in efficiency due to the effect of seasonality a final truncated regression has been estimated including the efficiency scores

13 13

3513

ownership model as well as the years 2006 and 2011 The results show that the difference of years do not influence in the efficiency scores (no significant) 5 Conclusions and further research The fact that AENA government owned and managed company does not publish disaggregated information about the Spanish airports has generated a public debate The deregulation process started in the 80rsquos in Europe in the UK has extended a social claim across countries in order to request partial of fully privatisation in the airport industry This is support by the fact that private airports are more efficient compared to those government owned and managed The case in Europe which is similar to the situation of the Spanish airport industry is the Norway model The Norwegian government corporatized most of Norwegian airports in 2003 which become owned and managed by Avinor In this airport system the four biggest airports cross-subsidize the rest of 42 and the obligate routes (PSO Public Service Obligations) were financed by subsidies receipt form the government Is this model efficient enough to help airports to achieve not only technical efficiency but scale economies It is necessary further research to analyse the effect of different types of ownership forms in the efficiency of airports but especially to confirm the main causes behind the way that airports perform In this study and regarding the AENA case the five DEA models have been used to detect potential outliers as well as collinearity problems with the inputs and outputs The main outliers have been Logrontildeo and Melilla which have been removed from the initial sample The redundant input terminal charge has been also removed from the sample in order to avoid potential unreliable results when explaining the efficiency scores The output of efficiency scores without these two airports and input shows consistency regarding the technical efficiency level for the rest of the airports as well as main competitors (peers or reference) across the years of the study The number of passengers operations and goods as well the depreciation of runways and infrastructure salaries and operating costs of each airport apparently do not have influence in the level of technical efficiency It can be generally stated though that in the scenario where all airports are owned-managed by the government airports with no cargo and low number of movements are more efficient that those with a slightly higher number of movements and cargo but significant number of passengers In order to test if the type of ownership and management is affecting the level of efficiency of airports a truncated regression has been applied not including the units that they are technically efficient When opening markets private ownership affects significantly the level of efficiency achieved by airports This is also demonstrated in literature where private airports are more efficient compared to government owned and managed Nevertheless is necessary to increase the number of airports owned or managed in different ways to provide reliable conclusions regarding the potential effects of privatisation in the Spanish airports Airports that are hubs (Madrid and Heathrow) are not affected by the presence of other airports owned and managed differently since continue being equally efficient whereas non-hub airports with big volume of aeronautical activity tend to suffer important decreases in their efficiency levels Future research will analyse the scale inefficiencies of airports in a market with different ownership and management forms (CCR DEA-model) The market share is distributed from monopolistic airports (no being hubs) to others with different ownership-management forms Limitations One major limitation on this study is the fact that AENA does not provide information regarding most of the inputs used The historical costs of the assets as well as the labour costs have been

13 13

3613

estimating In some cases airports do not have any value of assets in 2000 or even charge per depreciation of runways since the carrying amount of the assets has been built based on the works done and published in the certifications This means that if no works have done in runways during the years of the study the carrying amount of the runways has been considered nil The wages paid by categories is also unknown and the total employees in a specific period used is the number of employees overall More accurate results would be achieved if further information was provided regarding the type of job performed or category they belong to ideally using only the permanent employees who develop the aeronautical activity (controllers handling if not outsourced personnel in the boarding gates baggage belts and cargo etc) Regarding the inputs used in this case future studies in efficiency also will consider each asset representing the airport infrastructure separately with their specific useful life in order to evaluate the potential individual impact of the different assets in the operational activity of the Spanish airports The British airports used in this study are only three privately owned and managed by British Airport Authority Limited (BAA) The fact of not considering more British airports is due to having more combinations between private and public ownership and management which also may be separated that could have additional influences in the DEA estimations Heathrow Stansted and Gatwick are the initial airports who were privatised under Margaret Thatcherrsquos policy from 1986 therefore they are traditional representatives of the impact of the privatisation assets in the British airport market Nevertheless is necessary to increase the number of British airports to assure the initial results obtained regarding how the type of ownership affects the current efficiency of the airports This could be also extended to other European countries that may have similar government forms that in the case of AENA Further Research The main idea of this research is to provide a first step showing the reality of the Spanish airports in terms of efficiency and the main reasons because inefficiencies have arisen Since the deregulation process in Europe started in the early to mid-90rsquos (1993-1997) European countries have achieved different degree levels of deregulation translated as mixed types of ownership and management Strong evidence through the literature shows that airports with government majority ownership are less efficient than those with a private majority This inefficiency affects the productivity results and therefore in their performance The amount of investment is generally decided by those in charge therefore investments (infrastructure) will depend on the type of ownership and management The main objective of deregulation is to improve the financial resources and investment and their operational efficiency The deregulated model of ownership by excellence is the case of the UK major airports (Gatwick Heathrow and Stansted) that after been privatised in 1987 have improved their annual results The English process became the first step to push other countries to privatise their own airports all over the world Future lines of researching will consider other mixed forms for instance comparing European airports that being property of a private company or belonging to the government may be managed by private operators Also in future studies the type of returns to scale will be disclosure between constant (CCR model) and variable (BCC model) with the aim of separating the total efficiency score into pure technical efficiency and scale efficiency The pure technical efficiency level will show how airports develop their activity with the current resources feasible The efficiency scale will determinate if airports are using their whole capacity therefore if the size is adequate to produce the airport activity or they are under-using their current resources One must take into consideration the importance of this

13 13

3713

efficiency as these results could become biased by the existence of larger and smaller airports in the sample The fact that the trend (years) is not significant regarding the efficiency level of the Spanish airports and the British ones opens a new future line of research regarding using environmental variables such as the wealth of the region-city (GDP) per year Other variables such as geographical location of the airports size of the airport capacity used type and number of airlines if the airport is a hub etc will also be considered in order to provide plausible reasons of the competitive frontier Evidence through literature shows that big airports are more efficient compared to small ones Sarkis (2000) demonstrates using DEA that hub airports achieve higher level of efficiency compared to non-hub airports in the United States Similar results for Oum and Yu (2004) by using factor productivity analysis of airports all over the world Assaf (2009) in the UK as well as Yoshida and Fujimoto (2004) by finding that small Japanese airports are relative less efficient This also has been demonstrated when having differences regarding ownership between private and government but what about different management Also BAA was forced to sell Gatwick airport due to potential monopolistic practises in London would the results obtained change if BAA had more airports under its property It is necessary to consider other private companies which may have a large number of airports with a range of size of aeronautical operations It is also required to increase the number of environmental variables in the second stage There is a range of studies regarding the relation between the size of airports and efficiency by using different methodology Due to sparse population densities in mobility remote areas airline services may be difficult to justify from a purely economic perspective (Graham et al 2010) Governments may subsidise then specific routes to discourage people to move from remote areas (small airport) to big cities for different reasons such as health tourism promotion etc Consequently governments will necessary have to provide grants in order for a service to continue (Halpern amp Brathen 2011) Air carriers receive subsidies in order to serve a predetermined route (Williams amp Pagliari 2004) These fixed routes are called Public Service Obligations (PSO)32 The number of population of the city differences in the cost of living measured through the labour costs at real prices when comparing big cities such Barcelona and Madrid to small ones should be also taken into consideration The geographical location of the airport is also affected by weather conditions causing a season effect in the efficiency of airports potential passengers tend to choose destinies with higher temperatures instead of areas with low temperatures or with a high average of rain fail per year Finally the n-year-window DEA method (adjusted DEA) will be used to construct a smoothly frontier avoiding instable efficiency scores across years (Nghiem and Coelli 2001) and therefore obtaining an accurate frontier to be used in the second stage methodology References Adler N amp Golany B (2002) lsquoIncluding Principal Component weights to improve discrimination in Data Envelopment Analysisrsquo Journal of the Operational Research Society 53 1-7 Adler N amp Yazhemsky E (2010) lsquoImproving discrimination in Data Envelopment Analysis PCA-DEA or variable reductionrsquo European Journal of Operational Research 202 273-284

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 32 In The EU there were more than 220 Public Service Obligation routes in 2007 (Graham et al 2010)

13 13

3813

Adler N amp Yazhemsky E (2006) lsquoA Guide to PCA-DEA Version 1 Principal Component Analysis amp Data Envelopment Analysisrsquo Internal Publication School of Business Administration Hebrew University of Jerusalem Adler N Liebert V amp Yazhemsky E (2013) lsquoBenchmarking airports from a managerial perspectiversquo Omega 41 442-458 Asmild M amp Pastor JT (2010) lsquoSlack free MEA and RDM with comprehensive efficiency measuresrsquo Omega 38 475-483 Assaf A (2009) lsquoAccounting for size in efficiency comparisons of airportsrsquo Journal of Air Transport Management 15 256-258 Advani A (1999) ldquoPassenger friendly airports another reason for airport privatisationrdquo The Reason Public Policy Institute LA Policy Study 254 Backx M Carney M amp Gedajlovic E (2002) ldquoPublic private and mixed ownership and the performance of international airlinesrdquo Journal of Air Transport Management 8 213-220

Banker R D amp Morey R C (1986) lsquoEfficiency analysis for exogenously fixed inputs and outputsrsquo Operations Research 34 513-521 Barros C P amp Dieke PUC (2008) lsquoMeasuring the economic efficiency of airports A Simar-Wilson methodology analysisrsquo Transportation Research Part E 44 1039-1051 Barros C P amp Sampaio A (2004) lsquoTechnical and allocative efficiency in airportsrsquo International Journal of Transport Economics 31 355-377 Bazargan M amp Vasigh B (2003) ldquoSize versus efficiency a case study of US commercial airportsrdquo Journal of Air Transport Management 9 187-193

Banker R D amp Morey R C (1986) lsquoEfficiency analysis for exogenously fixed inputs and outputsrsquo Operations Research 34 513-521 Bishop M amp Kay J (1989) ldquoPrivatization in the United Kingdom Lessons from Experiencerdquo World Development Vol 17 (5) 643-657 Brockett PL Rousseau J J Wang Y amp Zhow L (1997) lsquoImplementation of DEA Models using GAMSrsquo University of Texas Austing Research Report 765 Camp R (1989) ldquoBenchmarking the search for best practices that lead to superior performancerdquo Quality Progress 22(1) 61-68

Carney M amp Mew K (2003) lsquoAirport governance reform a strategic management perspectiversquo Air Transport Management 9 221-232

13 13

3913

Charkham J P (1994) lsquoKeeping good company a study of corporate governance in five countriesrsquo Oxford University Press New York Charnes A Cooper W W Rhodes E (1978) lsquoMeasuring the efficiency of decision making unitsrsquo European Journal of Operational Research 2 429-444 Charnes A Cooper W W Golany B Seiford L amp Stutz J (1985) lsquoFoundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functionsrsquo Journal of Econometrics 30 91-107 Coelli TJ Rao DSP OrsquoDonnell CJ amp Battese GE (2005) lsquoAn Introduction to Efficiency and Productivity Analysisrsquo Springer New York Cooper W W Seiford L M amp Thrall R M amp Zhu J (2001) lsquoSensitivity and stability analysis in DEA some recent developmentsrsquo Journal of Productivity Analysis 15 217-246 Cooper W W Kyung SP amp Pastor J T (1999) lsquoRAM a range adjusted measure of inefficiency for use with additive models and relations to other models and measures in DEArsquo Journal of Productivity Analysis 11 5-42 Costas-Centivany CM (1999) ldquoSpainrsquos airport infrastructure adaptations to liberalization and privatisationrdquo Journal of Transport Geography 7 215-223

De Alessi L (1980) ldquoThe economics of property rights a review of the evidencerdquo Research in Law and Economics JAI Press (Greenwich)

De Alessi L (1983) ldquoProperty rights transaction costs and X-efficiency an essay in economic theoryrdquo American Economic Review 73 64-81

Debreu G (1951) lsquoThe coefficient of resource utilizationrsquo Econometrica 19 273-292 Fare R amp Grosskopf S (2000) lsquoTheory and application of directional distance functionsrsquo Journal of Productivity Analysis 13(2) 93-103 Farrell M J (1957) lsquoThe measurement of productive efficiencyrsquo Journal of the Royal Statistical Society (A) 120 253-290 Francis G Humphreys I amp Fry J (2002) ldquoThe benchmarking of airport performancerdquo Air Transport Management 8 239-247

Freathy P (2004) ldquoThe commercialisation of European airports successful strategies in a decade of turbulence ldquo Air Transport Management 10 191-197

13 13

4013

Freathy P amp Orsquo Connell F (1998) European Airport Retailing Macmillan Basingstoke

Galve C amp Salas V (1996) ldquoOwnership Structure and Firm Performance Some Empirical Evidence from Spainrdquo Managerial and Decision Economics 17 575-586 Gerber P (2002) ldquoSuccess factors for the privatisation of airports-an airline perspectiverdquo Air Transport Management 8 29-36

Graham A Papatheodorou A amp Forsyth P (2010) lsquoAviation and Tourism Implications for Leisure Travelrsquo Ashgate Surrey Gorin T amp Belobaba P (2004) ldquoImpacts of entry in airline markets effects of revenue management on traditional measures of airline performancerdquo Air Transport Management 10 259-270

Gormley W T (1991) ldquoPrivatization and Its Alternativesrdquo The University of Wisconsin Press Madison (Wisconsin)

Guillaume B amp Hakfoort J (2001) ldquoThe evolution of the European aviation network 1990-1998rdquo Air Transport Management 7 311-318

Gillen D (2009) ldquoThe evolution of the Airport Business Governance Regulation and Two-Sided Platformsrdquo Paper presented in Hamburg Aviation Conference Hamburg (February 2009)

Gillen D amp Lall A (1997) ldquoDeveloping measures of airport productivity and performance an application of data envelopment analysisrdquo Transportation Research-E 33 Issue 4 261-273

Jenkins D (1999) ldquoSmall changes make big differences the need for improved air traffic control and aviation infrastructurerdquo George Washington University

Jensen M amp Meckling W (1979) ldquoRights and production function an application to labour-managed firms and co-determinationrdquo Journal of Financial Economics 3 305-506

Henser DA amp Waters WG (1993) ldquoUsing Total Factor Productivity and Data Envelopment Analysis for performance comparisons among government enterprises concepts and issuesrdquo Institute of Transportation Studies University of Sidney (Sidney)

Hooper P (2002) ldquo Privatization of airports in Asia ldquo Air Transport Management 8 289-300

Humphries I (1999) ldquo Privatisation and commercialisation Changes in UK airport ownership patterns ldquo Journal of Transport Geography 7 121-134

Koopmans TC (1951) lsquoActivity analysis of production and allocationrsquo Willey New York

13 13

4113

Levy N (1987) ldquoA theory of public enterprise behaviourrdquo Journal of Economic Behaviour and Organization 8 75-96

Lin LC amp Hong CH (2006) ldquoOperational performance evaluation of international major airports An application of data envelopment analysisrdquo Journal of Air Transport Management 12 342-351

Lovell C A K amp Pastor JT (1995) lsquoUnits invariant and translation invariant DEA modelsrsquo Operational Research Letters 18 147-151 Martiacuten JC amp Romaacuten C (2001) ldquoAn Application of DEA to Measure the Efficiency Spanish Airports Prior to Privatizationrdquo Journal of Air Transport Management 7 Issue 3 149 ndash 157

Massaud B amp Vasigh B (2003) ldquoSize versus efficiency a case study of US commercial airportsrdquo Journal of Air Transport Management 9 187-193

Mew K (2000) ldquoThe Privatization of Commercial Airports in the United States What is wrong with the Federal Aviation Administration Privatization Program and what might be more successfulrdquo Public Works Management amp Policy 5(2) 99-105

Murillo-Melchor C (1999) lsquoAn Analysis of Technical Efficiency and Productivity Changes in Spanish Airports by using the Malmquist Indexrsquo Departamento de Economiacutea Universidad de Cantabria

Nyshadham EA amp Rao VK (2000) ldquoAssessing Efficiency of European Airports A Total Factor Productivity Aproachrdquo Public Works Management and Policy 5(2) 106-114

Halpern N amp Brathen S (2011) lsquoImpact of airports on regional accessibility and social developmentrsquo Journal of Transport Geography 19 1145-1154 Holloway JA Hinton C M amp Francis GA (1999) ldquoIdentifying best practice in benchmarkingrdquo CIMA Research Monograph London Hooper P (2002) lsquoPrivatisation of airports in Asiarsquo Journal of Air Transport Management 8 289-300 Hooper P Cain R amp White S (2000) lsquoThe privatisation of Australiarsquos airportsrsquo Transportation Research Part E 36 181-204

Humphreys I (1999) ldquoPrivatisation and commercialisation Changes in UK airport ownerships patternsrdquo Journal of Transport Geography 7 121-134 Oum T H Yu C amp Fu X (2003) lsquoA comparative analysis of productivity performance of the worldrsquos major airports summary report of the ATRS global airport benchmarking research report 2002rsquo Journal of Air Transport Management 9 285-297

13 13

4213

Oum T H amp Yu C (2004) lsquoMeasuring airportsrsquo operating efficiency a summary of the 2003 ATRS global airport benchmarking reportrsquo Transportation Research Part E Logistics and Transportation Review 40 515-546 Oum T H Adler N amp Yu C (2006) Privatization corporatization ownership forms and their effects on the performance of the worldrsquos major airports Air Transport Management 12 109-201

Oum T H Adler N Yan J amp Yu C (2008) lsquoOwnership forms matter for airport efficiency A stochastic frontier investigation of worldwide airportsrsquo Journal of Urban Economics 64 422-435

Parker D (1999) lsquoThe performance of BAA before and after privatisationrsquo Journal of Transport Economics and Policy 33 133-145

Pels E Nijkamp P amp Rietveld P (2001) lsquoRelative efficiency of European airportsrsquo Transport Policy 8 183-192

Pels E Nijkamp P amp Rietveld P (2003) lsquoInefficiencies and scale economies of European airport operationsrsquo Transport Research Part E 39 341-361

Pestana BC (2008) lsquoAirports in Argentina Technical efficiency in the context of an economic crisisrsquo Journal of Air Transport Management 14 315-319

Pestana B C amp Dieke P (2007) lsquoPerformance evaluation of Italian airports A data envelopment analysisrsquo Journal Air Transport Management 13 184-191

Pestana B C amp Dieke P (2008) lsquoMeasuring the economic efficiency of airports A Simar-Wilson methodology analysisrsquo Transport Research-E 44 1039-1051

Pestana B C amp Sampaio A (2004) lsquoTechnical and allocative efficency in airportsrsquo International Journal of Transport Economics 31 355-377

Pope K (1996) lsquoAirport privatisation begins to take off led by Britainrsquos BAArsquo The Wall Street Journal 14 A1-A8 Porter ME (MarchApril 1979) lsquoHow Competitive Forces Shape Strategyrsquo Harvard Business Review

Rendeiro R (2002) lsquoAn approximation to the productive efficiency of the Spanish airports network through a deterministic cost frontierrsquo Air Transport Management 8 233-238

Russell R R (1985) lsquoMeasures of technical efficiencyrsquo Journal of Economic Theory 35 109-126

13 13

4313

Sarkis J (2000) lsquoAn analysis of the operational efficiency of major airports in the United Statesrsquo Journal of Operations Management 18 335-351

Starkie D (2002) lsquoAirport regulation and competitionrsquo Journal of Air Transport Management 8 63-72

Simar L amp Wilson PW (1999 2007) lsquoEstimation and inference in two stage semi-parametric models of productive efficiencyrsquo Journal of Econometrics 136 31-64 Simar L amp Wilson PW (1999) lsquoOf course we can bootstrap DEA scores But does it mean anything Logic trumps wishful thinkingrsquo Journal of Productivity Analysis 11 93-97 Tapiador F J Mateos A amp Martiacute-Henneberg J (2008) lsquoThe geographical efficiency of Spainrsquos regional airports A quantitative analysisrsquo Journal of Air Transport Management 14 205-212

Tone K (2001) lsquoA slack-based measure of efficiency in data envelopment analysisrsquo European Journal of Operational Research 130 498-509 Tovar B amp Martin-Cejas RR (2009) lsquoAre outsourcing and non-aeronautical revenues important drivers in the efficiency of Spanish airportsrsquo Journal of Air Transport Management 15 217-220 Truitt LJ amp Esler M (1996) lsquoAirport privatization full divestiture and its alternativesrsquo Policy Studies Journal 24 100ndash110

Wanke P (2012) lsquoEfficiency of Brazilrsquos airports Evidences from bootstrapped DEA and FDH estimatesrsquo Journal of Air Transport Management 23 47-53 White P (1994) ldquoPublic transport privatisation and investmentrdquo Transport Policy 1 (3) 184-194 Williams G amp Pagliari R (2004) lsquoA comparative analysis of the application and use of public service obligations in air transport within the EUrsquo Transport Policy 11 55-66

Yokomi M (2005) lsquoEvaluation of technical efficiency at privatised airports case of BAA Plcrsquo In a paper presented at the Air Transport Research Society (ATRS) Conference 3rd-6th July 2005 Rio de Janeiro (Brasil) Yoshida Y amp Fujimoto H (2004) lsquoJapanese-airport benchmarking with the DEA and endogenous-weight TFP methods testing the criticism of over investment in Japanese regional airportsrsquo Transportation Research Part E Logistics and Transportation Review 40 533-546 Abertis (2009) lsquoTen years that marked an historic era change in the presidencyrsquo Issue 17 Cambra de Comerc de Barcelona (November 2010) lsquoEl modelo de gestion aeroportuaria en Espana marco institutional y juridico y lineas maestras para una propuesta de cambiorsquo

13 13

4413

La Vanguardia (25th March 2009) International Civil Aviation Organization (ICAO) (March 2013) Worldwide Air Transport Conference (ATCONF) Sixth meeting Airport Competition httpwwwineesjaxitabladotype=pcaxisamppath=t38p604a2000l0ampfile=07050c1px httpwwwheathrowairportcomabout-usinvestor-centredocument-centreannual-accounts

13 13

213

Abstract This paper focuses on comparing the efficiency of the Spanish airports to the British major airports during a period of seven years Since the deregulation process in Europe started in the middle of 80rsquos (1986-87) European countries have achieved different degree levels of deregulation translated into mixed types of ownership and management Strong evidence through the literature shows that airports with government majority ownership are less efficient than those with a private majority therefore the main question to be answered it is if the way that airports are owned and managed affects their technical efficiency The deregulated model of ownership by excellence is the case of the British major airports (Gatwick Heathrow and Stansted) which have improved their technical efficiency after privatization in 1987 The British example has inspired other countries to start the deregulation process in the airport industry In the first stage this research is focused only in the Spanish market since AENA as full airport system differs considerably from the UK1 The main idea is to determine which airports are the most technically efficient with regards their current capacity (infrastructure and permanent staff) and therefore which airports could be labelled as the leaders in the case of a fully regulated sector during period of seven years (2005-2011) In the second stage a bootstrap truncated regression is applied to explain the main reasons behind the efficiency frontier why a specific airport is technical inefficient and the main competitors In the regression the environmental variable ownership is considered to evaluate the potential impact in the way the airports perform if they are fully government owned instead of privately The efficiency scores are obtained for the Spanish airports as well as the major British airports for six years (2006-2011) The efficiency results are compared to the level of efficiency achieved by the main British airports in order to evaluate the potential effects of different ownership and management forms in the airportsrsquo technical efficiency The methodology to calculate the efficiency frontier which airports are the best from a technical perspective is based on non-parametric models DEA (Data Envelopment Analysis) The fact of analysing more than one year compared to other studies prior done in the AENA case allows the introduction of dynamism and therefore consistency and reliability in order to draw accurate conclusions based on the results obtained In the second stage the fact of applying a truncated regression which does not include the efficient airports is to avoid potential correlation problems between the explanatory variables and the efficiency units and therefore inaccurate estimations of the regression coefficients The results show that ownership forms affect the technical efficiency achieved resulting in private airports being more efficient compared to airports government owned Further research is necessary in order to consolidate these findings by using different combinations of ownership and management forms in order to confirm the effects of privatisation and commercialisation processes in the Spanish airport industry

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 1 An airport system is related to a managerial organization of airports in which more than one airport is managed by the same company or authority

13 13

313


13 13

413

1 Introduction



13 13

513


13 13

613




13 13

713




13 13

813



13 13

913




13 13

1013


13 13

1113



13 13

1213



13 13

1313



13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

313


13 13

413

1 Introduction



13 13

513


13 13

613




13 13

713




13 13

813



13 13

913




13 13

1013


13 13

1113



13 13

1213



13 13

1313



13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

413

1 Introduction



13 13

513


13 13

613




13 13

713




13 13

813



13 13

913




13 13

1013


13 13

1113



13 13

1213



13 13

1313



13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

513


13 13

613




13 13

713




13 13

813



13 13

913




13 13

1013


13 13

1113



13 13

1213



13 13

1313



13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

613




13 13

713




13 13

813



13 13

913




13 13

1013


13 13

1113



13 13

1213



13 13

1313



13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

713




13 13

813



13 13

913




13 13

1013


13 13

1113



13 13

1213



13 13

1313



13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

813



13 13

913




13 13

1013


13 13

1113



13 13

1213



13 13

1313



13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

913




13 13

1013


13 13

1113



13 13

1213



13 13

1313



13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

1013


13 13

1113



13 13

1213



13 13

1313



13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

1113



13 13

1213



13 13

1313



13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

1213



13 13

1313



13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

1313



13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

1413



13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

1513


YC lt YA C VRS

IRS DRS



13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

1613



$ (1)


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

1713

Y (Output)


C DMU5 Yrsquo4 O

B DMU1

I Y4 DMU4 (X4 Y4)

A DMU3




13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

1813



13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

1913


119863ī 119905 119883119895ī 119905119884119894ī 119905 = 119872119894119899 120579ī (2) 120579 120582

119904 119905 119884119894īrsquo119905 120582īrsquo 119884119894ī 119905

īrsquo

119883119895īrsquo 119905 120582īrsquo 120579ī 119883119895ī 119905





13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

2013







13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

2113


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

2213



Input



TERMINAL CHARGE


Input




13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

2313



X Depreciation




13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

2413





13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

2513


) (3)


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

2613




13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

2713



13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

2813


Efficiency

Depreciation Assets


Wages

Operating Costs

MPAX

Operations

Goods



n Runways 01124 02386 1 Wages 03658 04443 02671 1


07971 1

MPAX 04383 05234 03555

09317 09228 1

Operations 04425 05376 03911

09209 09226

09892 1

Goods 02843 03572 03042

07322 09813

08660 08650 1



13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

2913


119884119894 = 120573deg+ 120573119895 ∙ 119883119895119894 + 120576119894

119884119894 = 120573deg+ 1205731 ∙ 1198831119894 + 1205732 ∙ 1198832119894 +⋯ 120573119896 ∙ 119883119896119894 + 120576119894


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

3013


119879 = 119864 119879 =

(6)

119881119886119903 119879 =( )



120573119895 = 119864 120573119895 =

(8)


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

3113




Heathrow 67190175 65745250 69390591 467605 449271 476295 961085 910578 990991

Gatwick 34469254 34080345 35165404 256562 254414 258921 292601 238416 326631

Stansted 21056445 18047403 23759250 171556 136899 191522 150004 138975 168683

Madrid 49455299 45799983 52110787 447713 429390 483292 6634639 6184554 7362448

Barcelona 30701347 27421682 34398226 310285 277832 352501 2851197 2435608 3152549


868344

Malaga 12665099 11622429 13590803 115644 103539 129698 324936 138975 366454

Gran Canaria 10005714 9155665 10538829 110246 101557 116252 843118 727774 964716


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

3213

Efficiency

Ownership

Depreciation Assets

Wages

Operating Costs

MPAX

Operations

Goods


Assets 02238 04514 1 Wages 03523 06392 07224 1


05099 1

MPAX 04441 06268 07111

09497 06772 1

Operations 04675 05311 06847

09059 07649

09820 1

Goods 02561 -00633 02326

03778 09745

05563 06510 1



13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

3313



∆Score 2011

Individual Score

Joint Score


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

3413




Ownership 0535218 01522072 352 00000 236897 8335384

Constant 0053833 01535804 035 07260 -2471791 3548451

Sigma 0225864 00133094 1697 00000 1997778 2519495



Ownership 0535218 02367527 226 00240 0711909 9992445

Constant 0053833 02374627 023 08210 -4115852 5192513

Sigma | 0225864 00111683 2022 00000

2039742 2477531


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

3513


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

3613


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

3713



13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

3813





13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

3913






13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

4013














13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

4113










13 13

4213













13 13

4313







13 13

4413


13 13

4213













13 13

4313







13 13

4413


13 13

4313







13 13

4413


13 13

4413


Documents

THE EFFICIENCY MODEL OF THE AIRPORT INDUSTRY CASE STUDY …euroregioeuram.eu/new/media/Resum-euram-Ane-Ripoll1.pdf · THE EFFICIENCY MODEL OF THE AIRPORT INDUSTRY CASE STUDY OF AENA