Benchmarking Selected European Airports by Their Profitability Frontier - Branko Bubalo - PUBLIC -...

7/27/2019 Benchmarking Selected European Airports by Their Profitability Frontier - Branko Bubalo - PUBLIC - 2012

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Benchmarking selected European Airports by their ProfitabilityEnvelope a Break-Even Analysis

Branko Bubalo1German Airport Performance (GAP) Research Project

(Berlin, June 13th 2012)

Abstract:In this article a simplified benchmarking methodology is presented. This new approach is

based on the creation of a stepwise discrete maximum profitability envelope over distributeddata points. Financial and operational data of 210 European airports from 17 countries have

been analyzed over a period of nine years (2002 to 2010). For reasons of comparability thecross-country time-series data are deflated to a reference location, currency and time. The datarequirements have been reduced to the core variables of the airport production process - level of

passengers and profits or deficits (before interests and taxes) in a particular year. These data areused to isolate industrybest practices, to estimate its benchmarks and to aid decision-makingabout feasible maximum profits, given the volumes of passengers, and the expected break-even

point. The benchmarks can be used to calculate potential efficiency gains for underperformingairports.

Keywords: Airport benchmarking, profit maximization, break-even analysis

I. Introduction

To this day large-scale comparisons of airports, either financial or operational, acrosscountries are still rare and far from sufficient in guiding decision makers in a simplified manner.Given the importance of airports in a globalized economy regarding the linkage of national andinternational destinations and the magnitude of daily travelers and cargo, I feel that there existsan overemphasis on theoretical modeling in favor of careful empirical and numericaldescriptions, which would aid our understanding of the real processes.

Therefore, I am developing a new partial factor productivity (PFP) measurement approach inairport benchmarking (see Vogel and Graham, 2010, p. 22) driven by extensive amounts of

collected data

2

by strictly focusing on the core variables of the production process and bypinpointing the best-practices.In the discussion about maximizing social welfare, I take the view that airports, independent

of type of ownership - public, partially private or fully privatized -, should strive for (at least) afinancial break-even by maximizing profitability and performance, given the managerialfeasibility limited by existing demand. Similar to most (private) businesses economic lossesneed to be minimized to keep subsidies or other compensations, e.g. debt from credits, down. It

1 Doctoral student at Hamburg University under supervision of Prof. Dr. Stefan Vo;[email protected] data for this paper was gathered while working on a benchmarking study commissioned by the NorwegianMinistry of Transport and Communications. I appreciate the participation in our data collection survey by airport

staff, for example by the main airport operator in Norway, Avinor, and by the Highland and Island airport operator inthe United Kingdom, HIAL. The help in the data collection by our GAP project members Ivana Strycekova, KeithLukwago, Sascha Michalski, Eric Njoya and Tolga lk is greatly acknowledged.
mailto:[email protected]:[email protected]:[email protected]


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should be a common goal for airports to maximize profits and to reach break-even, so taxes,benefitting the whole society, may be paid. There is no clear reason why the society shouldconstantly pay for subsidies benefitting few passengers travelling to or from loss-makingairports frequently in remote locations.

Hence, in this article I take a general view which leads to reducing the data requirements toan absolute minimum, where demandas a given input (and output) is measured in number ofarriving (and departing) passengers and unit profits (or losses) as an output is measured inearnings before interests and taxes (EBIT) per passenger (Figure 1). In many cases3 these datacan be directly extracted from income statements or other profit-and-loss accounts and airportoperational statistics (Doganis and Thompson, 1975, chapter II; Koopmans, 2008, p. 23; Vogeland Graham, 2010, p. 23).

It is obvious that loss-making airports would not be able to survive in a competitive and fullyprivate market (Gillen and Lall 1997, p.6). Airports rarely or never achieving a break-even musttherefore receive subsidies to some degree4, often times by cross-subsidization inside a pool of amulti-airport operatorbetween profitable and non-profitable airports, or by direct subsidies inform of public funds (taxpayers money), (low interest) grants, national capital expenditure

programs or other non-operating sources of income. Adler, Liebert and Yazhemsky (2012)define an airport as a private [or public] production system in which society maximizes socialwelfare by encouraging airport management to maximise profits.

I will look at the trend and shift of the frontier of maximum profitability the profitabilityenvelope at 210 European airports over a time-frame of nine years, answering generalquestions like: Which benchmark or range of profits could a priori be expected for any airportgiven the local level of demand and, how does my airport perform relative to the best-

practice? Where is the break-even point for the airport-industry located in a particular year, andin which direction does the break-even point and the profitability envelope shift over the years?

The paper is structured in five parts, starting with this introduction. I continue in part twowith the theoretical background including the review of literature, overview of differentmethodologies and description of data. The third part will show the application of the

profitability envelope over the collected dataset. The fourth part will look at some possible gainsin profitability, if the identified benchmarks would be reached. The final part will draw someconclusions and will give an outlook on further research.

II. Theoretical Background, Methodology and Data Description

In this short discussion on measuring the profitability and cost efficiency of airports, I amaiming to draw parallel conclusions to more sophisticated multiple input and output linear

programming5 and optimization techniques, such as Data Envelopment Analysis (DEA), butwith a straightforward and simpler approach, where the analyst preserves full comprehension

and control over his or her data

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. It was found by Adler et al. (2012) and in supplementregression analyses for a recent benchmarking study7 that there exists a sufficient correlationbetween DEA relative efficiency scores8 and the PFP measure of unit profits, measured in

3 There existRessentiments against the publication of confidential financial data in the airport industry, whichrequires personal appeasements, non-disclosure agreements or the usage of carefully coordinated questionnaires.4 Subsidies lower social welfare by requiring public funds, which could be used elsewhere on e.g. schools, hospitalsor universities.5 Linear Programming is an optimization technique invented by George Dantzig in 1947. See an application of themethod in Dorfman (1951).6 For example by reducing the analysis to two dimensions, x and f(x), for explanatory convenience compared to n-dimensions used in the DEA.7 The second-stage regressions were conducted by Tolga lk of the GAP research project for the benchmarking

study of Avinor airports (which is currently under review) in comparison to other European airports.8 The DEA model in Adler et al. (2012) makes use of four inputs (staff costs, other operating costs, runway capacityand terminal capacity [largely avoiding the complications involved in the definition of capacity, especially in


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Earnings before Interests and Taxes (EBIT) per passenger (PAX). However, it is evident thatairports need the critical mass (Heymann, 2005, p. 2) in number of PAX and consequentialrevenues in order to operate self-sufficient. Therefore, deeming loss-making airports to beinefficient and vice versa profit-making airports to be efficient would not allow a fair

comparison among different sized entities. Similar to the DEA we aim at revealing andcomputing the relative efficiencycompared to other similar sized peers (Adler et al., 2012).Accordingly, I relate ratios of outputs over inputs to the size of airports, thus relative tocomparable peers. An airport is deemed the more inefficient the further the calculated ratio islocated from the according profitability benchmark.

One reason of not using EBIT or other profit measures in the DEA is the inability of themethod to deal with negative numbers, although this drawback could be circumvented in alllinear programs by adding a sufficiently large constant

9 to the data in order to make them allpositive, thereby not changing the optimum strategies (Dorfman, 1951, p. 349). A DEA studyusing EBIT or another measure of profitability has yet to be conducted. Most existing studiesfocus on the input-output balance of costs and revenues and make use of additional traffic orcapacity data.

Figure 1: Revenues, Costs and EBIT for sample airports in the years 2002 to 2010 (PPP-adjusted base Norway in 2010 prices) (Source: Own survey data)

I observed, when plotting the adjusted data10 of the 210 European airports11 that especiallysmall and regional airports lie below some threshold of (unit) profits (the break-even line) and

connection to level-of-service]), four intermediate goods (international and domestic PAX, tons of cargo, and numberof flights) and two outputs (non-aeronautical and aeronautical revenues).9 Similar to the conversion from Celsius to Kelvin temperature scales.10 I made the required financial data adjustments for price-level differences and inflation to the German AirportPerformance (GAP) database. I used for Purchasing Power Parity (PPP) adjustments and currency conversion, basedon Norwegian Kroners, the following table from OECD:

http://stats.oecd.org/Index.aspx?datasetcode=SNA_TABLE4. Furthermore, I used, for Consumer Price Index (CPI)adjustments, based on base year 2010, from another table from OECD:http://stats.oecd.org/Index.aspx?DataSetCode=MEI_PRICES.

Revenues = 108.7 * PAX1.03142

R = 0.96

Costs = 7841.136*PAX0.72307

R = 0.92

EBIT = 0.0000006*PAX2 + 34.0532*PAX - 25,972,498

R = 0.84

-100

0

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EBITinNOK(2-010prices)

Millions

RevenueandCostsinNOK(2010prices)(log.Scale)

Millions

Passengers (log Scale)

Total Revenues

Total Costs

EBIT

Power (Total Revenues)

Power (Total Costs)

Poly. (EBIT)

Break-Even Line
http://stats.oecd.org/Index.aspx?datasetcode=SNA_TABLE4http://stats.oecd.org/Index.aspx?datasetcode=SNA_TABLE4http://stats.oecd.org/Index.aspx?DataSetCode=MEI_PRICEShttp://stats.oecd.org/Index.aspx?DataSetCode=MEI_PRICEShttp://stats.oecd.org/Index.aspx?datasetcode=SNA_TABLE4http://stats.oecd.org/Index.aspx?DataSetCode=MEI_PRICEShttp://stats.oecd.org/Index.aspx?datasetcode=SNA_TABLE4


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usually do not report any profits in subsequent years (Figure 1). There are very few airportswhich are volatile with regard to their profitability, showing deficits as well as profits infollowing years. Another main observation are decreasing returns-to-scale on a unit profit basis(i.e. EBIT per PAX), but constant (or even increasing) returns-to-scale on absolute profits (i.e.

EBIT). With increasing output, which can be observed in figure 1 and subsequent figure 4 and 5in Section 3, these returns-to-scale pick up below the break-even line at volumes of 300.000 to400.000 PAX per year. With the approximation functions in figure 1 the average industry break-even point is calculated at 1.06 million PAX when total operating costs equal total operatingrevenues, and is about 750.000 PAX, when EBIT is equal to zero12.

In the literature one can find different assumptions regarding the airport industry break-evenpoint. For example in the 1970s Doganis and Thompson (1975, p. 338) have calculated thebreak-even point for British airports, where costs are fully covered by revenues, to be around 3million passengers. Heymann (2005, p. 3) (citing a study by the European Commission)estimates the break-even point for European airports to be in the broad range of 500.000 to 2million passengers13.

Koopmans (2009) has empirically tested the correlations between the closely relatedprofitability measure earnings before interests, taxes, depreciation and amortization (EBITDA)per workload unit (WLU)14 and found it a good descriptor of operational performance(Koopmans, 2009, p.60). Hence, this strongly implies that EBIT per PAX could be asufficiently good descriptor of airport managerial efficiency. This is a similar result as shown byVogel and Graham (2010, p. 37) who found that profit per WLU correlates significantly andhighly significantly to ten of 15 of the studied airport performance measures, such as return-on-investments, EBITDA margin and asset turnover. Doganis and Thompson (1975, p. 334-337)use absolute surplus and deficits (including interests15), costs and revenues, relative to theoutput level as a descriptor of managerial effectiveness and for determining the break-even

point. Gritta, Adams and Adrangi (2006, p.1) have chosen EBIT and its variation over time as ameasure to quantify the business risk of a firm and to construct financial performancemeasures in their study of the effects of operating and financial leverage on [] Major U.S.Air carriers rate of return.

Therefore, I am convinced that annual EBIT per passenger (PAX) serves as an adequateapproximation of airport operational productivity and relative efficiency.

However, Gillen and Lall (1997, p. 6) dispute the usefulness of profitability measures andstate that these are totally misleading, given the unique position of airports. In light of ourgraphical and numerical analysis of data from 213 airports over a period of nine years this

judgement may not be accurate, since we observe strong industry-wide (Doganis andThompson, 1975, p. 336) trends in profit generating ability with regard to the output level,largely independent of type of ownership and/or capacity16. It should be noted that our resultsonly account for European airports. The situation may look differently at North-American

airports and other continents.

11 The set consists mainly of 49 French, 49 Norwegian, 39 British, 32 Italian and 18 German airports. See full samplelist in the Appendix A.12 There are exceptional airports in France and the United Kingdom (and in the United States), which make profits atlower throughput levels than the break-even point. At some cases these data have been removed from the analysis,since data inconsistencies have been suspected. These exceptional cases will be looked at in preceding analyses andpublications.13Certainly these figures vary with our definition of profits and which costs and/or revenues are included in it. Ourdata is cleaned from outside revenue sources such as state grants, government transfers and so on. 14 One workload unit (WLU) equals one passenger or 100 kg of cargo. In the literature the usage of this combinedmeasure and the equivalence of passengers to cargo is disputed, especially in terms of different handling costs,revenues and infrastructure requirements.15The measure in Doganis and Thompson (1975) is in financial terms called earnings before taxes (EBT). 16 An independence of capacity and/or ownership has not been proven, but these influences may be rather smallacross the range, especially for airports unable to breaking-even. Logical reasoning may relate operationalefficiency closer to capacity utilization and private ownership to profit making entities.


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One could argue, that first, using EBIT as a single aggregate output measure has theadvantage that this figure accounts for the net amount and balance of all required operatingresources (costs), all generated operating income (revenues), and all investments in airportdevelopment and capacity (depreciation). In a simplified sense annual (positive or negative)

EBIT are defined as operating revenues minus operating costs minus depreciation of assets(capital). For many (privatized) airports EBIT is generally published in annual reports, financialstatements or other statistics, as it represents a common figure in finance. For reasons ofcomparability the collected nominal financial data are converted to Norwegian Kroners (NOK),then PPP-adjusted relative to Norway17 to account for different price levels and wages betweencountries, and furthermore inflation adjusted to 2010 prices to account for loss of currency valueover time18.

Figure 2: Core Inputs and Output of the Production Process of Airports (Source: Ownillustration)

Second, I limit the operational variables only to the input number of passengers (PAX), as

this figure represents the original (origin, transfer and destination) demand, and is usually easilyavailable. Divisions by passenger characteristics, such as international, domestic, business orleisure, have not been made, since these ultimately reflect groups of people with differentspending behaviour, service requirements and associated costs, which need to be targetedcommercially by the airport business and charges model.

In Doganis and Thompson (1975, p. 331) it was emphasised that airport management hasonly limited control over externally defined factors, such as demand, i.e. level of passengersdeparting from or arriving at a particular location, and fixed costs. Therefore, the airportmanagements prime function is to balance (expected) revenues and cost, and to maximizeoutput (EBIT) given the level of input (PAX). To generate demand for certain destinations is amajor function of airline marketing and route development.

Figure 2 exemplarily shows the main inputs and outputs of the airport production process,yet, it is not so clear which side needs to be maximized or minimized in an optimization

program. Typically, the inputs are minimized and the outputs are maximized, for example in anoutput-oriented DEA. In figure 2 one may define all costs, including depreciation, as a financialinput, and all revenues as a financial output. On the operational side of the production processarriving or originating PAX, who request to be served by the airport, may be viewed as anoperational input, and all departing and terminating PAX, who have been served by the airportsmay be viewed as an operational output. Therefore, total PAX show input and outputcharacteristics, in our case these are defined as inputs. Externalities, such as noise and delay,

17 Norwegian Kroners have been chosen, since this study is partly based on the results of a Benchmarking study forthe Norwegian Ministry of Transport and Communications.18 See footnote 4, which tables have been used to derive the real monetary values.

Production Process

Depreciation

of Assets

Costs

arriving PAX

originating PAX

EBIT

Revenues

departing PAX

terminating PAX

Facility Capacity

financial

operational

Inputs Outputs

externalitiesenvironmental


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maybe viewed as undesirable output (Adler et. al., 2012) and its reciprocal (i.e. inverse) maybe maximized19.

The data points can have five characteristic locations in relation to the profitability envelope,which displays the benchmark profits. Figure 3 shows theoretically the different stages of

development of an airport with regard to increase in size and profitability20

. The first groupwould represent loss-making airport, which data points lay below the reference profitabilitybenchmark(s). The second group would include loss-making airports which define theprofitability envelope and represent a benchmark. The third group consists of one (or more)airports, which break-even exactly. More realistically it is this airport in the data set, which isfirst making profits in EBIT per PAX, with minimum demand in terms of number of PAX.

The fourth group consist of profitable airports, which are relatively inefficient with regard totheir position below the (maximum) profitability envelope. Similar to the first group, it is

possible to calculateproductivity gains, if the benchmarks would be reached (please see Chapter4). The fifth group represents superior airports, which are profitable and define the profitabilityenvelope. These airports show maximum profitability with regard to their output level andcomparable peers, therefore these truly represent best-practices. It is this group of airport, where

one could expect the largest interest from private investors, as these promise the highestdividends and return-on-investments (ROI).

Figure 3: Data points in relation to Break-Even line, (Industry) Profitability Envelope andBreak-Even Point (Source: Own illustration)

Frequencies of flights and available seats per route are under the control of carriers which try

to match capacity to demand on each flight on a particular route by maximizing load factorswith the exception of subsidized PSO traffic. Certainly, the aircraft sizes in each carriers fleet

put a significant limitation to matching airport demand exactly, since the ability of a flightbreaking even and economies of scale must be considered by airline management. Therefore thestrategies behind the required number of total flights measured in annual air transportmovements (ATM) have not been analysed. We find that the applicability of ATM as a holisticoperational variable is not given, since this measure is typically only related to airside input andoutput.

19 I appreciate this suggestion by Nicole Adler.20 See for example Reinhard (1973, p. 830) for an application of a similar function to the break-even analysis of theTri-star aircraft development program of Lockheed.

Group 1 Group 2 Group 3 Group 4 Group 5

Break-Even line

EBIT

per PAX

PAX

Losses -

Profits +

not profitable,

benchmark not reached

not profitable,

benchmark

break-even

point

profitable,

benchmark not reached

profitable,

benchmark

(maximum) Profitablity Envelope

: Data Point

: Benchmark


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Airport IATA % Cargo of Total WLU

Leipzig LEJ >60%

Cologne CGN 41%

East-Midlands EMA 38%

Bergamo BGY 30%Benbecula BEB 28%

Brussels BRU 27%

Rennes RNS 23%

Billund BLL 21%

Table 1: Airports with relevant average share of cargo >20% of total Workload Units (WLU)across the years 2002 to 2010 (Own survey data)

In order to account for airports which make a majority of their turnover with the handling ofcargo the use of the measure Workload Unit(WLU = 1 PAX or 1/10 tonnes of cargo) may beconsidered as an alternative operational measure, i.e.EBIT per WLU. Sensitivity analyses acrossthe sample only showed minor differences between the trends of average EBIT per PAX andaverage EBIT per WLU. For the full data sample the average amount of cargo accounts forabout 5% of the total WLU, with eight airports having shares of more than 20% of the totalWLU (Table 1). The Norwegian airports Hasvik (HAA), Rost (RET) and Svalbard (LYR) arethe only ones having significant shares of cargo of up to 10% cargo of total WLU. For reasonsof clarity we do not discuss the alternative measure WLU any further in this article.

III. Application of the Profitability Envelope on the Dataset

In our heterogeneous sample of airports across different countries, years and sizes we relatethe surplus or losses in EBIT to the number of passengers, which gives us the profitability ratio

of average unit of output per average unit of input - EBIT per PAX. It is state-of-the-art incurrent research to divide the airport sample by classes or clusters (see Adler et al. 2012), suchas above 2 million or below 2 million passengers, and present averages for each class. Since thiskind of division is always arbitrary we chose to relate the ratio to the according continuousvolume of passengers, ordered from the lowest to the highest observation in the sample. Thismade the choice of displaying a logarithmic scale for passenger numbers in a majority of ourfigures necessary.

We observe in Figure 4 that growth in number of passengers is a strong driver of more thanproportional increases in EBIT per passenger. However, the chosen profitability ratio stagnatesat a certain level of saturation, after which it appears the average EBIT per passenger can onlymarginally be increased any further. The maximum benchmark has been observed at London-City (LCY) airport with 144 NOK per passenger at a level of 3.3 million passengers in 2008.

LCY defined the maximum benchmark for the years 2007 and 2009 as well, with passengerlevels of 2.9 or 2.8 million PAX and an according EBIT per PAX of 133 and 99 NOK,respectively. LCY airport is operating a short (STOL) runway and has very limited terminaland/or aircraft parking stand capacity, which makes this achievement of high average

profitability even more special. This example shows that the airport management at LCY airporthas reached a high value-added productivity by fully exploiting its available resources.


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Figure 4: Profitability and Profitability Envelope by Airport Size for the Years 2002 to 2010(All Airports in the DEA Sample, PPP-adjusted base Norway in 2010 prices; Italy and Franceonly until 2009) (Source: Own survey data)

It can be concluded that airports with a critical level around the break-even point of morethan, say 300.000 to 1 million passengers, are becoming increasingly interesting for investors,but also request less regulation for achieving maximum profitability. A marginal increase inannual number of passengers directly leads to increasing absolute profits and returns to scale atthis high level of output (see Figure 1). However, from a welfare state point of view, theapplication of price-cap regulation to profitable airports (group four and five) may be anadvantageous instrument. This regulation limits monopoly power and decelerates growth.Therefore it could balance airport revenues from charges and from non-aeronautical sourcesagainst true (societal) costs.

For loss-making airports (in groups one to three) a laissez-faire approach may beappropriate, if social obligations for transportation services are not of public interest. Otherwise,there is a lack of incentives and degrees of freedom for airport management to change the

revenue and cost structure in order to become profitable. Similar to private entrepreneurs, thestate may provide start-up funds or low-interest grants in initial stages of airport development.Even airport closures should remain as a last option, if an airport has no financially sustainablestrategy.

To derive the profitability envelope exemplarily shown in figure 4, I made use of a fairlysimple algorithm, which plots the envelope over the data points. The result of the algorithm

provides profitability benchmarks for each level of passengers, thus gives a feasible strategictarget for the airport management.

The algorithm and required steps to create the profitability envelope are outlined asfollows:

1. i = 1 to n # n = Number of airports in the sample.2. PAXi < PAXi+1< < PAXn # Sort PAX column in ascending order.

-3,000

-2,500

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EBIT

perPassengerinNOK(2010prices)

Passengers (log. Scale)

Break-Even LineBreak-Even LineBreak-Even Line


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3. Envelopei = EBIT per PAXi # First data in EBIT per PAX column is equal to the# initial starting point for the Envelope; i = 1.

4. For i to n 2, # From the second entry onwards, the new entry isEnvelopei+1 = # compared, if larger than last Benchmark. If yes, it

# is set as new EBIT per PAX Benchmark.If EBIT per PAXi+1 > EnvelopeiThen EBIT per PAXi+1Else Envelopei .

IV. Break-Even Analysis

We want to get clearer answers on where the best-practice break-even point lies in each year,particularly at which level of demand observations surpass the break-even line. Thus, at whichcritical level of demand full coverage of operating cost could possibly be managed. We have

attempted no effort in calculating the break-even points exactly by interpolation between the lastobservation below break-even and first observation above break-even, because we recognizewhen working with real data, the quality is commonly mixed (also because of transmissionissues) and certain limitations of accuracy exist. Therefore, all presented figures and thresholdsare real and accurate, given the resources. For a more mathematical and theoretical treatmentin the future, such as the derivation of the profitability envelope or an approximation of an exactfunction, a smoother construction of the curve by interpolation seems appropriate.

Figure 5: Sample Airports Profitability Trend by selected years (PPP-adjusted base Norway in2010 prices; 2010 without Italy and France) (Source: Own survey data)

By dividing the sample under study into years we are able to derive more precise

benchmarks for particular years which can be related to the observations. Figure 5 gives anoverview of the profitability envelope change for even years between 2002 and 2010. As it can

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Profitabil ity Envelope 2002 2002

Profitabi li ty Envelope 2004 2004




Break-Even Line


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be seen the part of the envelope below the break even line shifts downwards, which could beinterpreted, that, in general, the profitability worsened for the low-demand airports. The break-even point moves right suggesting that a higher level of passengers is required to increase thechances of breaking even on the EBIT level, while the top part of the envelope stagnates at

average EBIT levels of 60 to 100 NOK per PAX, with London-City (LCY) airport with 144NOK per passenger being the exception.In 2002 airports on the lower tail were more profitable than in the following years. The

break-even point is located between such low passenger volumes as 17,680 and 63,000 definedby French airports AUR and EGC. Already in 2003 the break-even point significantly shifts tothe right towards larger passenger volumes and is then situated somewhere between theobservations Pescara (PSR) airport with about 300,000 passengers and an average EBIT of -3

NOK per passenger and Forli (FRL) airport (both in Italy) with about 350,000 passengers andan average EBIT of 4 NOK per passenger.

In the years 2004 to 2009 the break even point lies between top performing French airportsin the range of between 180,000 and 290,000 passengers. In 2010, where Italian and Frenchairport data is missing, the break even point is shifted even more significantly to the right and

lies approximately between German airport Friedrichshafen (FDH) with 590,000 passengers andan average EBIT of -23 NOK per PAX and British airport Exeter (EXT) with 737,000

passengers and an average EBIT of 12 NOK per PAX. This leads to the conclusion that Franceis managing its low-demand airports quite well, while achieving break even with around200,000 passengers.

Benchmark

Airports

IATACode

Country

2002

2003

2004

2005

2006

2007

2008

2009

2010

NumberofTimes

definingthe

Envelope

Tiree TRE HIAL 1 1 1 1 1 1 1 1 1 9Aurillac AUR France 1 1 1 1 1 1 1 1 8

Barra BRR HIAL 1 1 1 1 1 1 1 1 8Dinard-Pleurtuit-Saint-Malo DNR France 1 1 1 1 1 1 1 7

London City LCY UK 1 1 1 1 1 1 6Bournemouth BOH UK 1 1 1 1 1 5

Bergerac-Roumaniere EGC France 1 1 1 1 1 5Southampton SOU UK 1 1 1 1 1 5

Graz GRZ Austria 1 1 1 1 1 5London Heathrow LHR UK 1 1 1 1 4

Stavanger SVG Norway 1 1 1 1 4Fagernes VDB Norway 1 1 1 1 4

Caen-Carpiquet CFR France 1 1 1 3Berlevg BVG Norway 1 1 1 3

Svolvr SVJ Norway 1 1 1 3Rennes RNS France 1 1 1 3Exeter EXT UK 1 1 1 3Hasvik HAA Norway 1 1 1 3Bristol BRS UK 1 1 2

Lorient-Lann-Bihoue LRT France 1 1 2Stokmarknes SKN Norway 1 1 2

Rst RET Norway 1 1 2rsta-Volda HOV Norway 1 1 2

Calvi-Sainte-Catherine CLY France 1 1 2Vads VDS Norway 1 1 2

La-Rochelle-Ile De Re LRH France 1 1 2

Table 3: Airports frequently defining the profitability envelope (2010 without Italy and France)


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The profitability benchmarks back the argument, that volatile airports, which achieveprofitability in certain years, should receive special attention from the airport operator towardsincreasing profitability and/or demand (for example by pushing route development) in order forthe airport to reach or remain profitable in the long-term. The observed trend of the profitability

envelope is clearly downwards for low demand airports below one million passengers andupwards for the profitable airports above the break-even point. Obviously, profitability on theEBIT level could mainly be achieved either by reducing operating costs, such as staff costs,and/or by increasing charges in cases of high elasticity (> than 1) of demand (see Ramsey-Pricing). Furthermore, profitability may be increased by a higher degree of commercialization,such as revenues from non-aeronautical sources, and/or by reducing investments, hencedepreciation. The ideal balance of these factors should ideally be under the responsibility of theairport management, in order to set motivating incentives.

In such cases where the airports lie below the profitability benchmarks (groups two andfour), we do not expect the airports to have positive EBIT per se, but rather to achieve theaccording benchmark by analyzing the business model of its best-practice peers.

Furthermore, I calculated the potential relative efficiency gains for each airport, if theaccording profitability benchmarks of other European airports would be reached. Reachingthese gains means that increasingly less profit must be paid in cross-subsidies to loss-makingairports in the system and increasingly more profits could be paid in dividends (or would besubject to taxes, hence would increase social welfare). We highly recommend a long-termsustainable efficiency policy, 1) by continuously benchmarking with comparable peer airports,2) by analyzing business models of national and international best-practices and 3) by trying toadjust profits (hence, finding the ideal balance between revenues and costs) to the best-

practices.

Figure 6: Cumulative EBIT (in NOK, 2010 prices) with and without potential efficiency gainsfor 140 European Airports 2005, 2007, 2009 (Source: Own survey data)

Cum EBIT 2005 = 0.145x6 -68.289x5 + 12,802.876x4 -1,219,443.284x3 + 62,794,660.032x2 - 1,700,218,782.785x + 17,423,475,065.827

R = 0.985

Cum EBIT incl. eff. Gain 2005 = 0.183x 6 -87.620x5 + 16,760.816x4 - 1,654,500.889x3 + 91,136,901.084x2 -2,803,133,373.909x + 40,023,314,477.013

R = 0.996

Cum EBIT 2007 = 0.150x6 -71.996x5 + 13,831.015x4 - 1,361,985.164x3 + 73,299,220.107x2 - 2,091,814,403.661x + 23,326,803,710.582

R = 0.997

Cum EBIT incl. eff. Gain 2007 = 0.232x6 -113.289x5 + 22,391.200x4 -2,319,496.593x3 + 136,198,580.808x2 - 4,474,370,714.490x +

66,612,887,900.123

R = 0.997

Cum EBIT 2009 = 0.117x6 -55.216x5 + 10,383.057x4 - 998,314.971x3 + 52,302,272.173x2 - 1,445,033,037.298x + 13,612,692,500.415

R = 0.996

Cum EBIT incl. eff. Gain 2009 = 0.183x 6 -88.170x5 + 17,043.047x4 -1,713,834.180x3 + 97,505,968.200x2 - 3,152,336,965.074x + 47,980,934,444.730

R = 0.998

-10,000

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0%10%20%30%40%50%60%70%80%90%100%

EarningsbeforeInterestsand

Taxes(EBIT)inNorwegianKroners(2010prices)

Millions

Percentage Rank of Sample of 140 European Airports ordered by EBIT

Cumulative EBIT in 2005 Cumulative EBIT incl. efficiency gains in 2005




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As a consequence, cross-subsidizations will be minimized from profitable to loss-makingairports, particularly in a national system or in a multi-airport organization. This effect is easiestshown by a Lorenz-like cumulative curve for a sample of 140 European airports (of the 210airports in appendix A), showing cumulative EBIT (ordered from smallest to largest).

In Figure 6 it can be observed that 80% to 90% of the European airports are not profitable. In2009 only the top 13% of the airports made a profit after depreciation. Overall the 140 airportsmade a substantial system profit of 12 to 22 billion Kroners (1.5 to 2.75 billion Euro 21). This

picture changes significantly, if we account for the potential efficiency gains, which amount toabout 28 to 38 billion Kroners (3.5 to 4.75 billion Euro) in absolute terms, or increases of

between 233 to 317%. The order of magnitude of these calculations shows that efforts toincrease efficiency are a worthwhile endeavor, especially for larger systems of airports.

When including the feasible profitability gains in the cumulative distribution curves inFigure 6, the immediate relief on the loss-making airports underneath the break-even line can beseen. With the gains included in the 2009 figures, significantly less overall cross-subsidieswould be required for the loss-making airports. In this case the proportions are much differentand about 50% of the 140 airports report profits.

In more detail the results for the 140 European airports are as follows: In 2005, 54% of theairports report losses, which are compensated by the profits of the next 36% higher rankedairports. The break-even point lies at the rank of the top 18% of the airports. With efficiencygains in place the proportions would change significantly and 33% of the airports would reportlosses. These losses are compensated by the next 22% higher ranked airports, therefore the

break-even lies at 55%. The top 45% airports in this case contribute to the system profits of 41.3billion Kroners.

In the strong financial year of 2007 the efficiency gains have also the most significant impacton the feasible cumulative profits. In this year, 55% of the airports report losses, which areoffset by 25% higher ranked airports which are reporting profits. We find the break-even pointat around the top 20% of the sample, which generate the accounted profits of around 23.2 billionKroners. If the efficiency gains are included in the 2007 data, we could expect the largestincreases in system profitability. Again, 33% (100%67%) of the lowest ranked airports wouldreport just losses, which are compensated by profits of the next 13% of airports to reach thesystem equilibrium. In 2007, 50% of all airports, if managed efficiently, could contribute to thetotal system profit of 67.4 billion Kroners. The system profits would almost triple by these

profitability improvements.

In this sense, large airport operators could a priori postulate clear strategic managementtargets, whereas its achievements may a posteriori be evaluated, for example in cost-benefitanalyses regarding policy changes such as changes in the structure of airport charges schemes.These efforts should be continuously conducted and closely monitored.

V. Conclusions and Outlook

It is the aim of this article to present a method of comparison and to spot best-practiceairports with regard to profitability. We wanted to give some numerical evidence of managerialeffectiveness (Doganis and Thompson, 1975) and airport performance. It was recognized that a

benchmarking approach needs the right choice of peers to give meaningful results, therefore acontinuous scale rather than an arbitrary classification has been chosen, where we find the right

peers along the scale. Thereby particular justice is done towards small airports, especially in theregion below 100.000 PAX, which have very different characteristics compared to airports with,say, 100.000 to 1 million PAX and above. As it was shown with empirical data, airports withmore than 1 million PAX can be expected to operate above break-even, thus making profits.

21 Average nominal exchange rate in 2010: 1 Norwegian Kroner 0.124921205 Euro (Source: OECD; see footnote10)


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These profit making airports show sharply increasing returns-to-scale on the absolute EBITlevel, although the marginal returns-to-scale are decreasing. This means that the effort to exploiteach additional passenger is much higher at large airports, than it is at small airports, where itmay be much easier to increase the revenues per passenger. However, any small increase in

revenues per PAX leads directly to increasing profits at large airports, because fixed costs arealready covered. With the construction of a maximum possibility frontier, which here is namedprofitability envelope, it is relatively straightforward to generate appropriate benchmarks andto recognize trends and shifts of the curve over time. In this way, airport management canformulate realistic strategic targets towards profitability.

To be able to replicate this analysis, large amounts of data are needed, which mostly do notlie in the public domain. This is a major drawback of the presented method, because theviewpoint may be only one-dimensional. However, from the airport management perspective

just peers with comparable PAX figures are required, which lowers the data collecting effortdrastically. Therefore, our method is more suitable for large airport system operators, regulatorsor other large scale decision-makers.

If the fundamental effects, which are presented in this paper, are understood, it may seem

reasonable for future research to aggregate the numerical evidence further (as was exemplarilyshown with the cumulative curves in Figure 6) towards even fewer control variables, such as theGini-Coefficient, to measure the changes in the distribution of airport profits in a multi-airportenvironment.


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Literature

Adler, N., Liebert, V., and Yazhemsky, E. (2012),Benchmarking airports from a managerialperspective, Omega, ISSN 0305-0483, doi: 10.1016/j.omega.2012.02.004 [accessed on June 4 th

2012 at:http://userpage.fu-berlin.de/~jmueller/gapprojekt/downloads/gap_papers/bmarking.pdf]

Doganis, R.S., and Thompson, G.F. (1975), Airport Profitability and ManagerialEffectiveness, The Manchester School, 43: 331352. doi: 10.1111/j.1467-9957.1975.tb01236.x.

Dorfman, R. (1951), Application of the Simplex Method to a Game Theory Problem, in:Activity Analysis of Production and Allocation - Proceedings of a Conference, Edited by T.C.Koopmans, John Wiley & Sons, Inc., New York, Chapman & Hall, Limited, London, pp. 348-358 [accessed on June 3rd 2012 at:http://cowles.econ.yale.edu/P/cm/m13/m13-22.pdf]

Gillen, D., and Lall, A. (1997), Developing measures of airport productivity andperformance: an application of data envelopment analysis, Transportation Research Part E:

Logistics and Transportation Review, Volume 33, Issue 4, December 1997, pp. 261-273, ISSN1366-5545, doi: 10.1016/S1366-5545(97)00028-8 [accessed on June 3rd 2012 at:http://www.wilfridlaurier.ca/documents/4296/dea_airport_paper.pdf]

Gritta, R., Adams, B., and Adrangi, B. (2006), An Analysis of the Effects of Operating andFinancial Leverage on the Major U.S. Air Carriers' Rates of Return: 1990-2000, The 47thAnnual Transportation Research Forum (TRF) on Mar. 23-25, 2006, New York University,

New York, NY [accessed on June 3rd 2012 at:http://www.trforum.org/forum/downloads/2006_8A_FinLeverage_paper.pdf]

Heymann, E., and Vollenkemper, J. (2005),Expansion of regional airports: Misallocation ofresources, Deutsche Bank Research, Frankfurt am Main [accessed on June 4 th 2012 at:http://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000193311.PDF]

Hyndman, L. D. (1992),Directions: The Final Report of the Royal Commission on NationalPassenger Transportation, Volume 2, pp. 92-108, Ottawa, Canada, ISBN 0-660-14545-6[accessed on June 4th 2012 at:http://www.e-ajd.net/source-pdf/Directions-VOL2-eng.pdf]

Koopmans, J.-P. (2008), The Impact of Commercial Activities on Airport FinancialPerformance (An Analysis of 35 Airports), Master Thesis submitted to the Faculty of Economicsand Business Administration, Maastricht University, Netherlands [accessed on May 19 th 2012at:http://arno.unimaas.nl/show.cgi?fid=15656]

Reinhardt, U. E. (1973), Break-Even Analysis for Lockheed's Tri Star: An Application ofFinancial Theory. The Journal of Finance, 28: 821838. doi: 10.1111/j.1540-6261.1973.tb01408.x [accessed on June 3rd 2012 at:http://www.estg.ipleiria.pt/files/350738_BREAKEVE_4628e59997e2a.pdf]

Vogel, H.-A., and Graham, A. (2010), Airport valuation: an alternative driver-basedapproach. Journal of Air Transport Studies, 1 (1), pp. 20-47. ISSN 1791-6771 [accessed onJune 4th 2012 at:http://www.airtransportnews.aero/content/jats/jats11.pdf]
http://userpage.fu-berlin.de/~jmueller/gapprojekt/downloads/gap_papers/bmarking.pdfhttp://userpage.fu-berlin.de/~jmueller/gapprojekt/downloads/gap_papers/bmarking.pdfhttp://userpage.fu-berlin.de/~jmueller/gapprojekt/downloads/gap_papers/bmarking.pdfhttp://cowles.econ.yale.edu/P/cm/m13/m13-22.pdfhttp://cowles.econ.yale.edu/P/cm/m13/m13-22.pdfhttp://cowles.econ.yale.edu/P/cm/m13/m13-22.pdfhttp://www.wilfridlaurier.ca/documents/4296/dea_airport_paper.pdfhttp://www.wilfridlaurier.ca/documents/4296/dea_airport_paper.pdfhttp://www.trforum.org/forum/downloads/2006_8A_FinLeverage_paper.pdfhttp://www.trforum.org/forum/downloads/2006_8A_FinLeverage_paper.pdfhttp://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000193311.PDFhttp://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000193311.PDFhttp://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000193311.PDFhttp://www.e-ajd.net/source-pdf/Directions-VOL2-eng.pdfhttp://www.e-ajd.net/source-pdf/Directions-VOL2-eng.pdfhttp://www.e-ajd.net/source-pdf/Directions-VOL2-eng.pdfhttp://arno.unimaas.nl/show.cgi?fid=15656http://arno.unimaas.nl/show.cgi?fid=15656http://arno.unimaas.nl/show.cgi?fid=15656http://www.estg.ipleiria.pt/files/350738_BREAKEVE_4628e59997e2a.pdfhttp://www.estg.ipleiria.pt/files/350738_BREAKEVE_4628e59997e2a.pdfhttp://www.airtransportnews.aero/content/jats/jats11.pdfhttp://www.airtransportnews.aero/content/jats/jats11.pdfhttp://www.airtransportnews.aero/content/jats/jats11.pdfhttp://www.airtransportnews.aero/content/jats/jats11.pdfhttp://www.estg.ipleiria.pt/files/350738_BREAKEVE_4628e59997e2a.pdfhttp://arno.unimaas.nl/show.cgi?fid=15656http://www.e-ajd.net/source-pdf/Directions-VOL2-eng.pdfhttp://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000193311.PDFhttp://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000193311.PDFhttp://www.trforum.org/forum/downloads/2006_8A_FinLeverage_paper.pdfhttp://www.wilfridlaurier.ca/documents/4296/dea_airport_paper.pdfhttp://cowles.econ.yale.edu/P/cm/m13/m13-22.pdfhttp://userpage.fu-berlin.de/~jmueller/gapprojekt/downloads/gap_papers/bmarking.pdf


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Appendix A:

IATA Airport Name Country IATA Airport Name Country IATA Airport Name CountryGRZ Thalerhof Austria HAJ Hanover Germany MOL Aro NorwayINN Kranebitten Austria HAM Fuhlsbuettel Germany MQN Mo I Rana NorwayLNZ Hoersching Austria HHN Frankfurt - Hahn Germany NVK Framnes Norway

SZG W. A. Mozart Austria LEJ Leipzig/Halle Germany OSL Oslo NorwayVIE Vienna International Austria MUC Franz Josef Strauss Germany OSY Namsos Norway

PRG Prague-RuzyneCzechRepublic NUE Metropolitan Area Germany RET Stolport Norway

AAL Aalborg Denmark PAD Paderborn Germany RRS Roros NorwayAAR Tirstrup Denmark SCN Ensheim Germany RVK Ryumsjoen NorwayBLL Billund Denmark STR Echterdingen Germany RYG Moss-Rygge Norway

CPH Copenhagen Denmark ATHEleftherios VenizelosInternational Greece SDN Sandane Norway

FAE Vagar Denmark BUDLiszt FerencInternational Hungary SKN Skagen Norway

TLL Ulemiste Estonia KIR Kerry County Ireland SOG Haukasen NorwayAGF La Garenne France AHO Fertilia Italy SOJ Sorkjosen NorwayAJA Campo Dell Oro France AOI Falconara Italy SSJ Stokka NorwayAUR Aurillac France BGY Orio Al Serio Italy SVG Sola NorwayAVN Avignon-Caum France BLQ Guglielmo Marconi Italy SVJ Helle NorwayBES Guipavas France BZO Bolzano Italy TOS Tromso/Langnes NorwayBIA Poretta France CAG Elmas Italy TRD Vaernes NorwayBIQ Biarritz Parme France CRV Crotone Italy TRF Sandefjord NorwayBOD Bordeaux France CTA Fontanarossa Italy VAW Vardoe NorwayBVA Beauvais-Tille France CUF Levaldigi Italy VDB Valdres NorwayBVE Laroche France FLR Peretola Italy VDS Vadso NorwayBZR Beziers Vias France FRL Luigi Ridolfi Italy VRY Stolport NorwayCCF Salvaza France GOA Cristoforo Colombo Italy BTS Ivanka SlovakiaCFE Aulnat France GRS Baccarini Italy LJU Brnik SloveniaCFR Carpiquet France LCV Lucca Italy THN Trollhattan SwedenCHR Chateauroux France LIDE Reggio Emilia Italy BLP Bellavista SwitzerlandCLY Sainte Catherine France NAP Naples Italy BSL EuroAirport Swiss SwitzerlandDCM Mazamet France OLB Costa Smeralda Italy GVA Geneve-Cointrin SwitzerlandDNR Pleurtuit France PEG Sant Egidio Italy LUG Lugano SwitzerlandDOL Saint Gatien France PMF Parma Italy ZRH Zurich Switzerland

EBU Boutheon France PMO Punta Raisi Italy ABZ Dyce United KingdomEGC Roumanieres France PSA Gal Galilei Italy BEB Benbecula United KingdomFNI Garons France PSR Liberi Italy BFS Belfast International United Kingdom

FSC Sud Corse France QSR Salerno Costa d'Amalfi Italy BHXBirminghamInternational United Kingdom

GNB Grenoble-Isere France REG Tito Menniti Italy BLK Blackpool United KingdomLDE Tarbes Ossun Lourdes France RMI Miramare Italy BOH Bournemouth United KingdomLEH Octeville France SAY Siena Italy BQH Biggin Hill United KingdomLIG Bellegarde France SUF S Eufemia Italy BRR North Bay United KingdomLIL Lesquin France TPS Birgi Italy BRS Bristol United KingdomLRH Laleu France TRN Citta Di Torino Italy CAL Machrihanish United KingdomLRT Lann Bihoue France TRS Dei Legionari Italy CVT Baginton United Kingdom

LYSLyon Saint-ExuperyInternational France VCE Marco Polo Italy CWL Cardiff-Wales United Kingdom

MLH EuroAirport French France VRN Verona Italy DND Dundee United Kingdom

MPL Mediterranee France MLA Luqa Malta DSA Doncaster Sheffield United KingdomMRS Marseille France AES Vigra Norway EDI Turnhouse United KingdomNCE Cote D'Azur France ALF Alta Norway EMA East Midlands United KingdomNCY Annecy-Meythet France ANX Andenes Norway EXT Exeter United Kingdom

NTE Nantes Atlantique France BDU Bardufoss Norway GLAGlasgowInternational United Kingdom

PGF Llabanere France BGO Flesland Norway HUY Humberside United KingdomPIS Biard France BJF Batsfjord Norway ILY Glenegedale United KingdomPTP Le Raizet France BNN Bronnoy Norway INV Inverness United KingdomPUF Uzein France BOO Bodo Norway KOI Kirkwall United KingdomRDZ Marcillac France BVG Berlevag Norway LBA Leeds/Bradford United KingdomRNS St Jacques France EVE Evenes Norway LCY London City United KingdomSXB Entzheim France FDE Bringeland Norway LGW Gatwick United KingdomTLN Hyeres France FRO Flora Norway LHR Heathrow United Kingdom

TLS Blagnac France HAA Hasvik Norway LPLLiverpool JohnLennon United Kingdom

TUF St Symphorien France HAU Haugesund Norway LSI Sumburgh United KingdomUIP Pluguffan France HFT Hammerfest Norway LTN Luton United Kingdom


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Appendix A: Airport sample list

URO Boos France HOV Hovden Norway MANManchesterInternational United Kingdom

BRE Bremen Germany HVG Valan Norway MME Durham Tees Valley United KingdomCGN Koeln/Bonn Germany KKN Hoeybuktmoen Norway NCL Newcastle United KingdomDRS Dresden Germany KRS Kjevik Norway NWI Norwich United KingdomDTM Dortmund Germany KSU Kvernberget Norway SEN Southend Municipal United KingdomDUS Dusseldorf Germany LKL Banak Norway SOU Southampton United KingdomERF Erfurt Germany LKN Leknes Norway STN Stansted United KingdomFDH Friedrichshafen Germany LYR Svalbard Norway SYY Stornoway United KingdomFMO Muenster Germany MEH Mehamn Norway TRE Tiree United KingdomFRA Frankfurt International Germany MJF Kjaerstad Norway WIC Wick United Kingdom

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