european air carrier investment model...scenarios, the impact on (passenger) traffic and the...

EUROPEAN ORGANISATION FOR THE SAFETY OF AIR NAVIGATION

EUROCONTROL EXPERIMENTAL CENTRE

An Economic Model for European Air Transport

EEC Note No. 17/03 Version 1

Project NCD

Issued: September 2003

The information contained in this document is the property of the EUROCONTROL Agency and no part should be

reproduced in any form without the Agency’s permission The views expressed herein do not necessarily reflect the official views or policy of the Agency..

REPORT DOCUMENTATION PAGE

Reference: EEC Note No. 17/03

Security Classification: Unclassified

Originator: EEC - NCD (Network capacity and demand management)

Originator (Corporate Author) Name/Location: EUROCONTROL Experimental Centre Centre de Bois des Bordes B.P.15 F - 91222 Brétigny-sur-Orge CEDEX FRANCE Telephone : +33 (0)1 69 88 75 00

Sponsor: EEC

Sponsor (Contract Authority) Name/Location: EUROCONTROL Experimental Centre Centre de Bois des Bordes B.P.15 F - 91222 Brétigny-sur-Orge CEDEX FRANCE Telephone : +33 (0)1 69 88 75 00

TITLE: AN ECONOMIC MODEL FOR EUROPEAN AIR TRANSPORT

Author I. Laplace, M3 Systems A. Marsden, EEC

Date 09/03

Pages V+38

Figures 19

Tables 8

Appendices 5

References 7

Project NCD-F-CG

Task No. Sponsor Period 2002 to 2003

Distribution Statement: (a) Controlled by: Head of NCD (b) Special Limitations: None (c) Copy to NTIS: YES / NO

Descriptors (keywords): Economic model, European air transport, airlines, elasticities, forecasts Abstract: The “Economic model for European Air Transport (EMEAT)” aspires to provide a global insight into future evolutions of the airline industry by simulating evolutions in parameters such as passenger demand elasticities as well as airline cost and revenue values. Through definition of appropriate scenarios, the impact on (passenger) traffic and the financial “health” of the airline industry can be assessed over a 30 years time horizon. This note presents this model’s functioning as well as its database. In addition, the estimation of the passenger demand elasticities used by EMEAT is also reported and the results of two scenarios run on EMEAT are presented.

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Executive Summary

The “Economic model for European Air Transport (EMEAT)” aspires to provide a global insight into future evolutions of the airline industry by simulating evolutions in parameters such as passenger demand as well as airline cost and revenue values. Through definition of appropriate scenarios, the impact on passenger traffic, fare yield, costs and the resultant financial “health” of the airline industry can be assessed over a 30 years time horizon. This report describes the development of the model, the data used and the essential underlying assumptions. For analysis purposes and as a reflection of the different operating characteristics adopted by airlines, each carrier has been assigned to a specific group, namely Major, National, Regional, Charter, Low-Cost and Extra-European. An additional characteristic of the EMEAT model is that it comprises an integrated passenger demand elasticity function. The development of this aspect of the model has been made possible by analysis of a large amount of economic, demographic and airline traffic data. The elaboration of this functionality is described in detail in the report. In addition, result analysis of two simple scenarios is also reported. The EMEAT simulation of an annual increase of fuel price of 1% shows that airline would react differently according to their airline type. While low-cost airlines would be those the most limiting their employment increase, charter airlines would be those most limiting their fleet increase. Moreover, these two airline types would be those most reducing their traffic level, compared to a baseline situation without fuel price increase. When simulating a 1% decrease in fare yields, passengers would increase their demand level for air transportation. Hence, low-cost and charter airlines would have the highest traffic increase allowing them to incur the lowest profit margin losses. Major airlines would be those most limiting their fleet and level employment increase in order to limit their profit margin decrease.

An Economic Model for European Air Transport iii


Table of Contents

1. INTRODUCTION...................................................................................................................... 1

2. DEMAND SENSITIVITY........................................................................................................... 2 2.1. GLOBAL FACTORS .............................................................................................................. 3 2.2. YIELD FACTORS ................................................................................................................. 4 2.3. DEMAND FUNCTION ESTIMATION.......................................................................................... 6

3. OPERATING COST, REVENUE AND ACTIVITY INDICATORS.......................................... 10 3.1. AIRLINES ......................................................................................................................... 10 3.2. SOURCES OF VARIABLES .................................................................................................. 11

4. THE ECONOMIC AIR TRANSPORTATION MODEL ........................................................... 13 4.1. OBJECTIVES .................................................................................................................... 13 4.2. EMEAT VARIABLES.......................................................................................................... 14 4.3. EMEAT SCENARIOS ........................................................................................................ 14 4.4. DETAILED MODEL............................................................................................................. 15

4.4.1. Key equation ......................................................................................................... 17 4.4.2. Impact of delays.................................................................................................... 18

5. MODEL APPLICATIONS....................................................................................................... 19 4.5.1. Example1: Fuel price increase.............................................................................. 19 4.4.3. Example 2: Fare yield decrease ........................................................................... 23

BIBLIOGRAPHY............................................................................................................................. 27

APPENDIX 1................................................................................................................................... 28

APPENDIX 2................................................................................................................................... 29

APPENDIX 3................................................................................................................................... 31

APPENDIX 4................................................................................................................................... 32

APPENDIX 5................................................................................................................................... 34

An Economic Model for European Air Transport iv


Abbreviations

ASK Available Seat Kilometres

ATC Air Traffic Control

ATFM Air Traffic Flow Management

ATM Air Traffic Management

ANSP Air Navigation Service Provider

CFMU Central Flow Management Unit

CRCO Central Route Charges Office

EATMP European Air Traffic Management Programme

ECAC European Civil Aviation Conference

EEC EUROCONTROL Experimental Centre

EMEAT Economic Model for European Air Transport

EU European Union

IATA International Air Transport Association

LMI Logistics Management Institute

NASA National Aeronautics and Space Administration

RPK Revenue Passenger Kilometres

An Economic Model for European Air Transport v


1. Introduction Although lower ticket prices invariably promote additional demand for air travel, the fare yield and therefore the airline profitability is intrinsically linked to the underlying cost structure. In fact, airlines are well known for the plethora of different ticket prices available on a given flight. These prices taking into account interalia factors such as passenger segregation, point of sale as well as a number of discount schemes that may be introduced as the date of the flight draws nearer in order to improve anticipated load factors. For a “large” airline, these issues (sometimes referred to as Revenue Management), may implicate significant human and computer resources. Bearing in mind that each airline knows well its own cost and revenue structure, it can evaluate impacts of passenger demand changes on its costs and revenues. If airlines have a sufficient knowledge of the passenger demand sensitivity, they are able to forecast their level of traffic. Nevertheless, this forecast has difficulty to take into account competitor airlines’ strategies, all the more difficult given that competitor airlines may have different characteristics, i.e. operating according to a completely different cost management regime. A global insight into the European air transportation market not only requires knowledge of the passenger demand sensitivity by airline type but also the airline cost and revenue structures and particularly the interactions between these variables. This is the aim behind the “Economic model for European Air Transport (EMEAT)” an economic model developed jointly by EUROCONTROL and the Logistic Management Institute (LMI). The NASA has used previous derivatives of this model in the context of their technology assessment programmes. Whilst such a model cannot hope to simulate the intricacies of Revenue Management projections performed by individual airlines, it can aspire to provide a global insight into future evolutions of the airline industry by simulating evolutions in parameters such as passenger demand as well as airline cost and revenue values. Through definition of appropriate scenarios, the impact on (passenger) traffic and the financial “health” of the airline industry can be assessed over a 30 years time horizon. The architecture of EMEAT is such that airlines are divided into specific operational groups1 (derived from their passenger profile and the characteristics of their operation, notably the nature of their route structure and their approach to cost control). In constructing the model, the operations of some 37 “airlines” have been taken into account. The following table gives an example of the segregation that has been applied for a number of the more “well-known” carriers: Airline Group Type Examples Major British Airways, Air France, Lufthansa National Aer Lingus, Alitalia, Austrian Airlines, Finnair, Iberia, KLM, Olympic

Airways, SAS, TAP Air Portugal, Turkish Airlines Regional Braathens Safe, British Midland, Crossair, KLM UK LTD, Meridiana Italia,

Spanair Charter Air 2000 Ltd, Monarch Airlines, Transavia Airlines Low Cost Easyjet, Martinair, Ryanair, Virgin Atlantic Extra-European Air Canada, American Airlines, Continental Airlines, Delta Airlines, El Al,

Emirates Intl, Northwest Airlines, Royal Air Maroc, Singapore Airlines, Tunis Air, United Airlines

1 This attribution reflects our assessment of the current operation but the model is sufficiently flexible to assign any airline to any operational group

1


2. Demand Sensitivity When choosing between the possibility of several different airlines for their travel requirements, passengers take into account a wide range of factors, including (but not limited to) ticket price, flight frequencies, convenience of airport access etc. The sensitivity of passenger demand is therefore defined by the way passengers react to changes in these parameters. This reaction could be benign or more “serious” i.e. passengers may seek an alternative carrier or even mode of transport. This is the aim behind a “demand function” which attempts to “explain” passenger demand through a series of different variables. In general terms, the quantity demanded of a product or service (often referred to as a “good” in economic literature) depends on several factors, the two principal ones being the price of the good and the consumer income. This quantity demanded could be represented by a demand function, which is usually formulated as follows:

),....,,,( 1 nZZRpfD = where D = demand for the product p = unit price of product R = consumer income Z1,…,Zn = other demand factors Measuring the reactivity of the demand level with respect to a variation in a factor X requires an estimation of the “demand elasticity” of this factor, which is defined as following:

XXDD

//

∆∆

=η

This demand elasticity measures the responsiveness of the quantity demanded to a change in the factor X, with all other factors being held constant. For example 2=η , indicates that the demand quantity increases by 2% following a 1% increase in the factor X. According to the absolute value of the derived elasticity, the quantity demanded is called “elastic” or “inelastic”: • If >1η (in absolute value), the quantity demanded is relatively elastic, meaning that the factor X will cause an even larger change in quantity demanded. The case of

∞=η is referred to as perfectly elastic, for instance if a price-elasticity is −∞=η , a change in the price level (even a small change) will cause an infinitely demand decrease. • If 1<η (in absolute value), the quantity demanded is relatively inelastic, meaning that a change in factor X will cause less of a change in quantity demanded. The case of 0=η , is referred to as perfectly inelastic. • If 1=η (in absolute value), the product is said to have unitary elasticity, meaning that a change in factor X will cause the same change in quantity demanded. The most commonly used elasticities of demand are price and revenue.

2


- The price-elasticity ppDD

//

∆p∆

=η represents the variation of demand quantity

due to a change in the level of the price. The price-elasticity is expected to be negative or null, since when the price of the good increases the consumer under normal circumstances consumers will reduce their requirement for the given product

- The revenue-elasticity RRDD

R //

∆∆

=η represents the variation of demand quantity

due to a change in the level of the consumer income. The price-elasticity is expected to be positive or null, since under normal circumstances consumers will increase their demand for a given product in line with their spending power. Within the EMEAT, the passenger demand function assesses changes in passenger demand quantity (RPKs) by several explanatory variables (ticket price, passenger revenue, etc.). By deriving the demand function, we seek to obtain: - Global elasticities for economic and demographic variables including population2, unemployment rate, gross domestic product per capita (representing passenger revenue), etc. - Price-elasticities by airline group (Major, Regional, Charter, etc.). Sufficiently detailed information concerning airline ticket prices is not available to support such analysis. An alternative approach is to use fare yield instead, where fare yield is the airline’s revenue per RPK. Price-elasticities can then be called yield-elasticities. Hence, if the major airlines price-elasticity is -1 and charter airline price-elasticity is -2, then when a major airline fare yield increases by 1%, its passenger demand decreases by 1%, whereas when a charter airline fare yield increases by 1%, its passenger demand decreases by 2%. Data used for the demand function estimation are at the level of “per airline and per city-pair”. We consider 400 airlines operating over 2500 city-pairs in the year 2000. The method of including airline groups in the demand function equation consists of creating a fare yield for each airline group by using dummy variables. These dummies are equal to 1 or 0. For example, if a dummy variable equals to 1 each time the airline is a major and 0 if not, the multiplication of the airline ticket fare variable with this dummy will give a new variable called major fare yield. This method is applied to create a fare yield variable per airline group. Two kinds of variables are used for the demand function estimation: global variables and yield variables.

2.1. Global factors - Passenger demand is represented by Revenue Passenger-Kilometres (RPK). It is hence necessary to have the RPK values per city-pair. Unlike the US, this data is not available for the European market and therefore we are required to derive the relevant parameters in an accurate manner as possible using available data3. The solution adopted is to take two periods, one for Winter and one for Summer. For each flight, we compute the number of controlled kilometres. In addition, knowing the aircraft type, it is possible to deduce relatively accurately the typical number of seats. In multiplying the airline load factor (known for its whole activity) by the aircraft number of seats, we obtain the average number of passengers. The multiplication of the number of passengers by the number of controlled kilometres gives the RPK value for this flight. In summing the RPK values for all of the airline

2 For example, a population elasticity of 2 would means that when population increases by 1%, airline passenger demand increases by 2%. 3 The data used corresponds to the daily filed flight plans held by the Central Flow Management Unit

3


flights on this city-pair for the considered period, we get the total airline RPK value on the city-pair. The extrapolation to the year gives the airline annual RPK value per city-pair. - Gross National Income per Capita (GNI) representing passenger income (source: OCDE, World Bank Group, etc.). This variable is computed for each city pair by weighting the two cities’ incomes, where the weight factor is the population. - Population (POP) representing persons that are susceptible to travel between the two cities (source: OCDE, World Bank Group, etc.). This variable is computed from the average of the two cities’ populations. - Unemployment rate (UnEmpl) giving information on the population features: the higher the unemployment rate, the lower the demand for travel (source OCDE, World Bank Group, etc.). This variable is computed from a weighting the two cities’ unemployment rates, where the weight factor is the population. - Market share (MS) of the airline on the city-pair i.e. the share of airline number of flights in the sum of all the flights operated on the city-pair (source: CFMU) - Herfindhal index (HI) representing the market concentration. It is computed by summing the square of each carrier’s market share for the given city-pair market.

2.2. Yield factors Given that airline ticket prices are not explicitly documented, it is necessary to determine some suitable approximation. The method employed consists of dividing the airline total revenue by its total RPK. The fact that individual airline revenues per city-pair are not well documented necessitates estimating them. We have chosen to use the regression method for expressing airlines’ total operating revenue relative to other factors including the total number of transported passengers, the average population on served city-pairs, etc… Once a suitably accurate « model » is determined we use it for estimating revenues per city-pair. This method is represented by Figure 1 and a more detailed explanation is provided in Appendix 1. We choose to use an exponential functional form for the passenger demand, mainly because the estimated coefficients of the explanatory variables represent directly the demand elasticities. Let’s take an exponential demand function with two explanatory variables: the price p and the consumer income R:

χβα RpD =

By definition, the elasticity of demand with respect to the price p is:

Dp

dpdD

Dp

pD

ppDD

p ×≈×∆∆

=∆∆

=//η

The derivative of the demand function respect to the price factor is:

χββα RpdpdD 1−=

Hence,

βα

βαη χβχβ =×= −

RppRpp

1

4


In a same way, the demand elasticity respect to the income factor is

χη =p In order to make the estimation easy, we linearise the demand function in taking the logarithm of the equation. Once again, estimated coefficients are elasticity values. For the price factor for example,

β=×=∂∂

Dp

dpdD

pD

lnln

Total passenger revenue

Average value on all city-pairs:•population•Unemployment rate•GDP per capita

Summed value on all city-pairs:•Nb of pax if Major•Nb of pax if National•Nb of pax if Extra-European•Nb of pax if Regional•Nb of pax if Charter•Nb of pax if Low-cost

Regression method

Regression expressedaccording to average and summed values

Values per city-pairs:•population•Unemployment rate•GDP per capita•Nb of pax if Major•Nb of pax if National•Nb of pax if Extra-European•Nb of pax if Regional•Nb of pax if Charter•Nb of pax if Low-cost

Estimated revenue per city-pair

Figure 1

Several yield variables are then introduced into the demand function: - Own Yield (Major) (YLDMaj) representing the airline yield if it is one of the three biggest European airlines: Air France, British Airways and Lufthansa. - Own Yield (National) (YLDMaj) representing the airline yield if it is a major airline (i.e. European carriers, principally the flag carriers, who operate a domestic and international network comprising both short-haul and long-haul flights. - Own Yield (Regional) (YLDReg) representing the airline yield if it is a regional airline (i.e. European carriers performing intra-European flights generally linking regional airports, but also linking regional and international airports). - Own Yield (Extra-European) (YLDExtEur) representing the airline yield if it is a non-European carrier - Own Yield (Charter) (YLDCh) representing the airline yield if it is a Charter airline i.e. focused on the leisure market and generally using large aircraft with a high load factor.

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- Own Yield (Low-Cost) (YLDLow) representing the airline yield if it is a “Low-Cost” airline (i.e. operating according to the now well-accepted paradigm of cost minimilisation with generally the lowest available fares to passengers). - Own Yield (Monopoly) (YLDMon) representing the airline yield if it is the only airline operating on the city-pair. - Competitors yield (CompYLD) representing the fare yield of the competitors airlines operating on the city-pair. If the airline is in monopoly on the city-pair it is equals to zero, if not it is the average of the competitor yields (see Figure 2).

Airline’smarket-shareon the city-pair

MonopolyAirline’s competitor yield=0

CompetitionNb of airline’s competitors=n

Airline’s competitoryield=

n

in

i∑=1

competitor of Yield

Figure 2

2.3. Demand function estimation We choose to use an exponential functional form for the passenger demand, mainly because the estimated coefficients of the explanatory variables represent directly the demand elasticities (see Appendix 1). In order to make the estimation easy, we convert the demand function to a linear form by taking the logarithm of the equation. Our passenger demand function is then the following:

YLDMonaYLDLowaYLDChaYLDExtEuragYLDaYLDMaja

CompYLDaHIaMSaUnEmplaPOPaGNIaaRPK

lnlnlnlnRelnln

lnlnlnlnlnlnln

12

1110987

6543210

++++++++++++=

This regression is then estimated using Ordinary Least Squares method (see Appendix 3). The estimated coefficients are presented in Table 1:

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Table 1

Dependant variable: RPK R2=0.95

7512 observations Variables Coefficients Student statistics Constant -4.12 -27.13 Pop 0.97 204.00 UnEmpl -0.30 -21.13 GNI 0.83 63.06 MS 0.19 37.96 HI 0.14 12.32 CompYLD 0.15 24.15 YLDMaj -1.08 -94.80 YLDNat -1.11 -137.84 YLDExtEur -1.22 -113.80 YLDReg -1.23 -133.78 YLDCh -1.28 -145.59 YLDLow -1.36 -63.08 YLDMon 0.002 0.191

R2 is the determination coefficient. It represents the share of demand explained by all variables. Hence the model explains 95% of the passenger demand. Initial examination of Table 1 reveals that there is a 95% probability that all estimated coefficients (excepted the YLDMon one) are significantly different from zero since their Student statistics exceeds (in absolute value) 1.96 i.e. the critical value. This critical value is relative to the number of observations and can be readily extracted from a Student table. Where the Student statistic exceeds or is equal to the critical value, the probability that the coefficient is different from zero is 95%. The coefficient is then considered as significant. In a general way, airline’ yield elasticities can be quantified as “relatively elastic” (since their values exceeds 1 in absolute value) whereas the other elasticities are “relatively inelastic” (since they are less than 1 in absolute values) (see section 2). Moreover the sign (positive/negative) of the values of the coefficients in Table 1 are those which would be intuitively expected from their individual definitions, as explained below: - Population-elasticity is positive since when the city-pair population increases whilst the other variables remain unchanged, the number of person susceptible to travel by air increases. Its value is 0.97 meaning that when the population increases by 1%, the passenger demand (in terms of RPK) increases by 0.97%. - Unemployment-elasticity is negative since when the city-pair unemployment rate increases whilst the other variables remain unchanged, the number of person susceptible to travel by air decreases. Our model shows that if the unemployment rate increases by 1%, the passenger demand decreases by 0.30%. - Revenue-elasticity is positive since when the Gross National Income per capita increases whilst the other variables remain unchanged, passenger have higher purchasing power and tend to increase their demand for transportation. If the passenger revenue increases by 1%, demand raises by 0.83% - Market-share-elasticity is positive. Travelers show a slight preference for carriers with more market share. Probably because greater market share means more connections, greater frequency of flights throughout the day, and better destination available through

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frequent flier programs. Hence if the airline market share increases whilst the other variables remain unchanged, it means that this airline will probably increase its flight frequency relative to its competitors. Passengers tend to choose those airlines on a given city pair offering the most attractive range of frequencies. The model shows that if the market share increases by 1%, the passenger demand raises by 0.19%. - Herfindhal-elasticity is positive. This variable represents the traffic density on the city-pair. It gives information on the market level of competition. This herfindhal index ranges from zero (if there are an infinity of competitors) to one (if there is only one airline in monopoly). An increase of this index means that there are fewer competitors on the city-pair. In this case passenger choice is reduced with a consequential positive impact for those airlines remaining on the city pair. . Our model allows saying that if the Herfindhal index increases by 1%, passenger demand increases by 0.14%. - Competitor price-elasticity is positive since when competitors of an airline X increase their fare yields, a part of the competitor demand is transferred to airline X. If competitor fare yields increase by 1%, the demand of the considered airline rises by 0.15% - Airline price elasticity is negative since when the airline increases its fare yield, passengers reduce their demand for travel. The impact for a given airline of a change in its fare yield is dependant upon the characteristics of its passengers and the “importance” of fare as a parameter in their choice of whether to travel and with which airline. Therefore, the approach to the construction of EMEAT has been to segregate different airlines into specific “types” and to subsequently capture their individual price elasticity (sensitivity to price changes) per airline type. ♦ If a low-cost airline increases its fare yield by 1%, its demand will decrease by 1.36%. Low-cost airlines manage to propose cheap flights using in most cases secondary airports relatively far from the main cities. If a low cost airline increases its prices, passengers can estimate that the gain made in using a low-cost flight is reduced, preferring instead to arriving directly in the main airport of the city and travel with an increased level of in-flight comfort. It is logical that passenger demand of this type of carrier is the most sensitive to an increase of the fare yields. ♦ If a charter airline increases its fare yield by 1%, its demand will decrease by 1.28%. Passengers choose to travel with charter airlines because they propose cheaper flights. On the other hand passengers have no choices between several timetables since flights are non-scheduled and the quality of service is inferior to the major airline ones. Hence if the price gap between a charter airline and its competitors reduces, passengers may tend to transfer their demand to airlines proposing less constrained flights. ♦ If a regional airline increases its fare yield by 1%, its demand will decrease by 1.23%. This can more be explained by the presence of competitor transportation mode as the train than the presence of a lot of competitors (since there are generally few airlines operating on regional city-pairs). ♦ If an extra-European airline increases its fare yield by 1%, its demand will decrease by 1.22%. These extra-European airlines operate regular flights between their original country and Europe. If their prices increase, passenger demand can move towards major European airlines. ♦ If a national airline increases its fare yield by 1%, its demand will decrease by 1.11%. These airlines often ask a higher price to passengers than charter or low-cost airlines, but offer more flight frequencies or highest levels of in-flight comfort. The effect of a price increase on the passenger demand decrease is then less important than for charter or low-cost airlines. ♦ If a major airline increases its fare yield by 1%, its demand will decrease by 1.08%. British Airways, Air France and Lufthansa represent 30% of the total number of RPKs and operate on all the main city-pairs in Europe on which they generally offer considerable

8


passenger flexibility concerning flight schedules. That is why business travellers prefer using their services, a passenger group who under normal economic circumstances will be less sensitive to increases in airline fare yields... The presence of these business travellers allows explaining that passenger demand of this type of carrier is the less sensitive to an increase of the fare yields. Unfortunately, we do not dispose for our regression of the values of fare yields per passenger class (business, economy,…) and we only have to use an average fare yield. Therefore, we cannot evaluate the specific elasticity of business travellers. - Monopoly Yield elasticity is non-significant meaning that an increase of 1% in the fare yield of an airline in monopoly on a city-pair does not change its passenger demand – providing that no alternative mode of transport is available

9


3. Operating cost, revenue and activity indicators Although the passenger demand is a determining element of the transportation offer proposed by airlines, it is not the only element. Indeed, airlines also take into account the production cost and the revenue they forecast that the ticket sale could give. A better understanding of interactions between costs, revenues and activity indicators, requires to firstly decompose each of them. , , and present the decomposition for the airline activity.

Table 2

Table 2

Table 3

Table 3

Table 4

Table 4

Table 5

Table 5

Operating costs

Flight crew labour costNon-flight crew labour costFlight equipment capital costGround equipment capital costMaintenance costFuel costLanding feesEn-route feesOther indirect costsTotal operating cost

Operating revenues

Passenger revenueTotal operating revenue

Fares

Passenger ticket fares

Activity indicators

RPKASKBlock hourNumber of days an aircraft is in useAircraft fuel consumptionAircraft capacityAircraft velocityFlight crew employeesNon-flight crew employeesDelay per flightAverage flight time without delay

Clearly there must be an inherent and strong link between the activity indicators and the operating cost. For instance, if the fuel consumption changes the fuel cost will be affected. In a same manner the operating revenue is a direct result of the level of RPKs and fares. Hence, if a model manages to establish the links between these variables it will simulate the impact on an airline of a given strategy or certain external “events”. Nevertheless, before getting such a model for the European passenger air transportation market, it is essential to collect the cost, revenue and activity indicators values for a significant sample of airlines operating in the European market.

3.1. Airlines When estimating passengers demand elasticities, a certain number of activity indicators relating to some 400 airlines were calculated in order to ensure the quality of the demand function. In order to model in a sufficiently accurate manner the different behaviour of the airline groups, we collected pertinent information for 38 of the most active airlines in Europe in 2000. These airlines are distributed in six groups by the following way: - Major airlines: Air France, British Airways, Lufthansa - National airlines: Aer Lingus, Alitalia, Austrian Airlines, Finnair, Iberia, KLM, Olympic Airways, SAS, TAP Air Portugal, Turkish Airlines - Extra-European airlines: Air Canada, American Airlines, Continental Airlines, Delta Airlines, El Al, Emirates Intl, Northwest Airlines, Royal Air Maroc, Singapore Airlines, Tunis Air, United Airlines

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- Regional airlines: Braathens Safe, British Midland, Crossair, KLM UK LTD, Meridiana Italia, Spanair, - Charter airlines: Air 2000 Ltd, Monarch Airlines, Transavia Airlines - Low-Cost airlines: Easyjet, Martinair, Ryanair, Virgin Atlantic

3.2. Sources of variables The following tables (Table 6, Table 7, Table 8) present the source information concerning the different cost and activity indicators:

Table 6 Category of activity indicator Sources

RPK ICAO, Airline reports, IATA, ATW, AEA

ASK ICAO, Airline reports, IATA, ATW, AEA

Block Hours ICAO, Airline reports, IATA, AEA

Aircraft Days Aircraft Days =fleet*365 IATA, AEA, Airline reports

Litres of Fuel IATA, airline report, AEA, several websites

Employees: Pilots & Copilots IATA, Airline reports

Employees: Other Flight Personnel: Flight Operations

IATA, Airline reports

Employees: Other Flight Personnel: Passenger and General Service and Administration

IATA, Airline reports

Total Weighted Average Employees: IATA, ATW, Airline reports

Table 7

Revenue category Sources

Revenue from Passengers ICAO, Airline reports

Total Operating Revenue ICAO, Airline reports, IATA

11


Table 8

Cost category Sources

Salaries and Wages for Flight Personnel

ICAO

Salaries and Wages for Non-Flight Personnel

Airline reports

Total Staff cost Sum of Salaries and wages for flight and non-flight personnel Airline reports

Maintenance cost ICAO, Airline reports

Aircraft Fuel & Oil ICAO, Airline reports

Passenger commissions Airline reports

Flight equipment capital cost Flight equipment capital cost= aircraft lease cost+flight equipment amortization and depreciation ICAO, Airline reports

Ground equipment capital cost Ground equipment capital cost= ground equipment amortization and depreciation ICAO, Airline reports

Landing and associated airport charges

ICAO, Airline reports

En-Route facility charges outside the EUROCONTROL zone

ICAO, Airline reports

En-Route facility charges in the EUROCONTROL zone

EUROCONTROL

En-Route charges ICAO, Airline reports Sum of en-route charges in and outside the EUROCONTROL zone

Other costs Other costs=Total operating cost-total staff cost-maintenance cost-aircraft fuel and oil-passenger commissions-flight equipment capital cost-ground equipment capital cost-landing charges-en-route charges

Total Operating Cost ICAO, Airline reports, IATA

In those cases where a necessary cost or activity indicator was not reported by a specific airline, we were required to estimate its value using techniques presented in Appendix 4.

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4. The Economic Air Transportation model Whilst the knowledge of passenger demand sensitivity by airline group as well as specific cost, revenue and activity values represents a rich data source, it does not allow us to predict future performance of the industry. For this, it is necessary to simulate the impacts of change of each variable on the others. The EUROCONTROL Experimental Centre, in collaboration with the Logistic Management Institute (LMI) has developed an economic model taking into account the parameters explained in the previous sections (demand, cost, revenue) as well as the means by which the interaction and evolution of these parameters may be analysed. Such an approach will enable the provision of passenger traffic forecasts and studies of the financial health of the airline industry.

4.1. Objectives The Economic Model for European Air Transport (EMEAT) model enables the forecasting of airline activity in terms of traffic, fleet, employees, as well as in terms of costs, revenues and fare yields (representing ticket prices). Assumptions are made on annual changes in input prices, airline productivity, airline load factor, etc., and predicted values of revenue-passenger kilometres, fleet size, workforce requirements, profit margin, etc., are derived. Airlines included in the database are grouped according to their main activity (major, national, extra-European, regional, charter, low-cost). This database includes information on each airline’s revenue and cost (staff cost, maintenance cost, etc.) as well as on airline activity indicators (RPK, ASK, etc.) for the baseline year (see section 3). It also contains values of passenger demand elasticities i.e. the relationship between passenger numbers and a number of economic and demographic explanatory variables, discussed previously in Section 2.3. Once these values are introduced into the model, a range of predictions about the “state” of the industry can be made taking into account each of the assumptions concerning annual variations made by the user.

Airline operating costsAirline operating revenueAirline trafficPassenger demand elasticities

Database

EMEAT

Scenarios

RPK Traffic Financial health

Per airlinePer category of airlineFor the market

Forecasts

Figure 3

In a general way, airline strategy consists in controlling costs and revenues so as to obtain a certain desired level of profit margin. The EMEAT tool is flexible in so far as it allows either: - The definition of a required profit margin for each (or for several) years of the simulation and the model will adjust ticket fares (as well as quantity of production factors) in order to obtain the required profit margin or - The fixing of the ticket fare changes, for each (or for several) years of the simulation and the model will determine the impact on the passenger demand and consequently on airline cost and revenue.

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Once database values are introduced into the model, and the running method chosen, EMEAT executes taking into account all the assumptions on input annual variations made by the user. . The model will provide a solution in the limit of reasonable scenario (i.e. for example the model will not find solutions for non realisable profit or fare yield increase).

4.2. EMEAT variables The database is composed of values of each major, national and extra-European airline. Charter, low-cost and regional groups are only composed by one representative variable whose cost, revenue and activity values are the average of all the values of airlines of this group. For example, the flight crew cost of the charter representative airlines is the average of the flight crew costs of the charter airlines of our sample (i.e. Air 2000 Ltd, Airtours intl., LTU Lufttransport, Monarch Airlines, Premiair, Transavia Airlines). Interactions between variables (simulating the impact on an airline of a given strategy or certain external “events”) directly appear in each value computation. Indeed, variables are computed in the following way: Input cost data are: - Flight Crew Labour Cost per Block Hour - Flight Equipment Capital Cost per Aircraft Day - Ground Equipment Capital Cost per Aircraft Day - Maintenance Cost per Block Hour - Landing fees per ASK - Passenger commission per ASK - Other Indirect Costs per ASK Productivity data are: - Gallons per Block Hour (Fuel consumption) - Seats per Aircraft (Aircraft capacity) - Aircraft Miles per Block Hour (Aircraft Velocity) - Block Hour per Aircraft Day (Aircraft utilisation) - ASK per Block Hour (Aircraft capacity) Employee Productivity data are: - Block Hours per Flight Crew Employees - Block Hour per non-Flight Crew Employees Delay variables are: - Delay per flight - Average Flight Time without delay In addition, the model integrates the passenger behaviour thanks to several demand elasticities (as described in section 2.3). All these data are collected for each airline of the sample for the year 2000. The results are then averaged by airline group (regional, charter, etc.), with the exception that each airline within the “major” and “national” group is treated separately.

4.3. EMEAT Scenarios When creating the scenario, the user fixes annual changes on variables. One of the big advantages of EMEAT is that it is easily possible when making the scenario to apply at the same time changes to all airlines, as well as specific changes by categories of airlines. In addition, the EMEAT model provides a framework for the assessment of the effect on airline

14


operations, of the introduction of new technology. After having fixed the year of technology introduction as well as the year of technology saturation, the user impose the annual changes on several variables influenced by this technology change (as or example ASK/Aircraft, Aircraft Km/Block Hour, Fuel Litres/Block Hour, etc.).

Database variablesFuel cost/litreASK/Block HourFlight crew cost/Block Houretc.

Scenarios

Common changes for all airlines:-Fuel price/litre-Load factor-ATC charges/ASK-Passenger commission/BH-etc.

Specific changes per airline type:-Aircraft capacity-Aircraft velocity-Flight crew cost/BH-Block hour/flight crew employees-etc.

New technology introduction:Effects on:-Aircraft capacity-Aircraft velocity-Fuel consumption-Flight equipment cost/BHetc.

Figure 4

In a general way, the user of input prices, airline productivity, airline load factor, airline block hour, and employees’ productivity, and obtain new levels of RPK, ASK, Fleet number, employees’ number, operating cost and operating revenue.

4.4. Detailed Model RPK values for the baseline year 2000 are available for all airlines. The multiplication of this value with the airline load factor (also available) give the ASK value. This value associated with the changes in variables fixed by the user in the scenario, is then used to compute the 2001 values for the fleet, the number of employees, the block hours, the cost and the revenue. The difference between the revenue and the cost value gives the profit margin. The methodology subsequently differs between the two running methods: - If the user has chosen the running method: “Change fare yield to met target operating margins”, EMEAT compares the computed profit margin (for the whole industry) with the fixed one. If they are equal, the model uses demand elasticities to compute the RPK value for 2001. If not, the model changes the fare yield value and computes a new RPK value for 2000 thanks to demand elasticities’ values. It computes then the new ASK value and all the other variables’ values. It compares the new profit margin value with the fixed one, and if it still does not correspond it changes the fare yield and reiterates until getting the required profit margin. EMEAT uses then demand elasticities values to compute the RPK value for 2001

15


Data values year t Scenarios

New data values

Profit margin=target Forecastst+1

Profit margin≠target

Change in fare yieldChanges in

RPK Figure 5

- If the user has chosen the running method: “Calculate operating profit margins corresponding to target fare yield” the model uses the given value of fare yield and compute all the other values.

Scenarios

Fare yieldset by the user

Data values year t Forecasts

t+1

Figure 6

The following schema (Figure 7) presents the EMEAT model schema.

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RPK

Load Factor

ASK

Total Block Hour

Flight CrewEmployees

Input Costs

Employees

Fleet

Operating Cost

FareYield

Operating Revenue

ProfitMargin

9

1

3

4

5

7

Passenger demand elasticities

Delay per flight

Average flight timewithout delay

2

Entry data

Computed data

Legend:

Aircraft daily time of use

Non Flight CrewEmployees

Aircraft seats

Entry data for 2000 and computed for theother years

European ACIM

6

8

Figure 7

4.4.1. Key equation In a general way, the model uses ratios of variables (Cost/Block hour, ASK/Number of employees, etc.) to run. Computations of all variables made by EMEAT are presented in Appendix 5. For example the fuel cost is computed by the following way:

∆+

×

∆+

∆+

=+ HourBlock

LitreHourBlock

Litre

BlockHourASK

BlockHourASK

LitreCostFuel

LitreCostFuel

CostFuel

t

t

t

t

11

1

1

The model computes the forecasted value for t+1, with values for the year t and with variations of some ratios fixed by the user (for instance the variation in the fuel consumption). In particular, the ratio ASK/Block hour is often used in computations but EMEAT does not compute it in dividing total ASK by total block hour. It uses a key equation:

( ) hour)Km/Block Aircraft (raftSeats/AirchourBlock /ASK ×= Indeed,

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our)Km/Block H(Aircraft craft)(Seats/Airock houraverage bl

age lenghtaverage sttsrcraft seaaverage ai flights/Number ofBlock Hour

s of flightASK/NumberBlock Hour

ASK

×=

×=

=

This equation means that:

elocityaircraft vcapacityaircraft hourBlock /ASK ×=

4.4.2. Impact of delays No cost of delays is computed in the EMEAT model. Considering that only airborne delays are considered in this model, flight block hours are the sum of the average flight time and of the delays per flight. Hence each time the block hours are used for computation, impacts of delays are taken into account. In addition,

t

t

t

t

t

t

hourBlockKmAircraft

hourBlockKmAircraft

hourBlockKmAircraft

hourBlockkmAircraft

−=∆ +

+

1

1

If we assume that the average stage length is held constant, then we have:

11111

−++

=−=∆+++ tt

tt

t

t

delaytimeFlightdelaytimeFlight

hourBlockhourBlock

hourBlockKmAircraft

The implication from a constant stage length is that the minimum flight time required is also held constant:

1

1

+

+

+−

=∆tt

tt

delaytimeFlightdelaydelay

hourBlockKmAircraft

If moreover the aircraft capacity remains constant,

1

1

+

+

+−

×

=∆

tt

tt

delaytimeFlightdelaydelay

aircraftseats

hourBlockASK

Hence the term hourBlock

ASK∆ often used in the EMEAT model is sensitive to changes in

aircraft capacity, as well as changes in delays.

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5. Model applications This section presents two applications of the EMEAT model, both using different execution methods. In the first example, the model changes fare yield to meet target operating margins whereas in the second one, the model calculates operating profit margins corresponding to a target fare yield.

4.5.1. Example1: Fuel price increase For comparative purposes, it is necessary to establish a “baseline” or reference scenario. More precisely, it fixes the following assumptions for providing forecasts for 2015: - The profit margin of the whole market does not change between 2000 and 2015 - The average flight time is 120 mins. - The delay per flight is 10 mins for the whole period - The GDP per capita increases annually by 2.14% (source OECD) - The unemployment rate increases annually by 2.45% (source OECD) - The world population increases annually by 1.1% (source Energy Information Administration) The second scenario adds to the same assumptions than the reference scenario an annual increase of 1% in the fuel price. When computing the forecast for the first year of simulation 2001, the model uses the values of the database and applies the increase in the fuel price. For compensating an increase in their fuel cost (and then in their total operating cost), airlines increase their fare yields leading to a reduction in the demand quantity (compared to the baseline scenario) i.e. in the level of RPKs. This allows airlines to limit the cost increase.

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Comparisons of scenario resultsfor the whole market

Baseline Fuel price increase 1%

Figure 8

The Figure 8 compares the forecasted evolutions between 2000 and 2015 for each scenario for all airlines. The fact that the total operating revenue and cost increase in the same proportion in both models (+19.6%) is due to the constraint on constant profit margin. In order

19


to achieve the same level of profit margin when facing an increase in the fuel price, airlines would have to increase their passenger fare yield by 2.2% between 2000 and 2015 for compensating the 36.1% increase of the fuel cost (due to the 1% annual increase in the fuel price). Compared to the baseline forecasts, passengers reduce their demand for air transportation by 2.5%. This traffic reduction results in the same reduction in airline capacity: compared to the baseline, airlines reduce their fleet and their staff by 2.5%. Of course it does not mean that the traffic and airline capacity decrease over the period, but only that the increase in these variable quantities is smaller for the fuel price increase scenario than for the baseline scenario. This limitation in airlines’ capacity also allows airlines to limit the increase in the ground equipment capital cost: this cost increase would be 6.7% inferior compared to the baseline. The previous scenarios’ comparison shows what would be the general strategy of airlines since it considers the whole market. Knowing that airlines have their own specificity according to their type it is now particularly interesting to compares the model results per airline type.

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Charter Extra-European Low Cost Major National Regional

Variation of RPKs between 2000 and 2015

Baseline Fuel price +1%

-2.83% -2.68% -3.03%-2.34% -2.41% -2.71%

Figure 9

The Figure 9 shows clearly that Low-Cost and Charter airlines would be the most sensitive to a fuel price increase. The model forecasts that their traffic increase between 2000 and 2015 would be respectively 3.03% and 2.83% lower than the baseline. This result is closely related to the yield elasticities introduced in the database. The model applies the same fare yield increase to all airline types and Low-Cost and Charter airlines presenting the highest yield-elasticities values in absolute value, it follows that their passenger demand would be the most affected by a fare yield increase. Major and National airlines presenting the highest level of traffic increase (in the fuel price increase scenario), it is not surprising that they would bear the highest increase in the fuel cost ( ). Figure 10

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Fuel cost variation between 2000 and 2015


+15.92% +16.13% +15.71% +16.52% +16.44% +16.08%

Figure 10

A 1% annual increase in the fuel price would lead to a fuel cost increase of 36.03% for Major, and of 35.95% for National airlines. Nevertheless these types of airlines wouldn’t be those having to take the most drastic decision for compensating the fuel cost increase (Figure 11, Figure 12).

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Fleet variation between 2000 and 2015


-4.11% -2.58% -3.30% -2.41% -2.37% -2.60%

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Total employment variation between 2000 and 2015


-2.83% -2.69%-3.04%

-2.34% -2.41% -2.71%

Figure 11

Figure 12

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Indeed, facing an annual fuel price increase, Charter and Low-Cost airlines would reduce in the highest proportion their fleet and number of employees, compared to the baseline scenario.Both airline types would choose to mainly limit their fleet increase, but this limitation would be particularly marked for Charters since their fleet increase would be 4.11% inferior in the fuel price scenario than in the baseline scenario. Charters aiming to keep using large aircraft with a high load factor would mainly prefer limiting their fleet increase. Low-Cost airlines aiming to operate at the minimum cost would try to limit as much as possible all the cost increases and would choose to limit in a close proportion their fleet and their employment increase. Major and National airlines would seem to be the less sensitive to a fuel price increase since they would reduce in a lesser proportion their fleet and employment level compared to the baseline. These airlines operating on a large network and sometimes in monopoly, they face a lower decrease in demand for air transportation than the other airline types, when increasing their fare yield. That is why they can increase their fleet and employment level in a higher proportion than the other types of airlines. Despite this, the model forecasts that they would not have to bear the highest increase in operating cost (Figure 13).

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Operating cost between evolution with the fuel price increase scenario

+18.99% +19.50% +20.01% +19.86% +19.37%+18.55%

Figure 13

Low-Cost airlines would have to bear the highest operating cost increase while Regional airlines would have to bear the lowest one. This result is closely linked to the mean cost per RPK evolution (Figure 14).

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Figure 14

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Despite the limitation in the other cost increase when facing the large increase in their fuel costs (Figure 15), airlines wouldn’t manage to keep a constant mean cost per RPK. This would increase for all airlines but not in the same proportion. For Low-Cost airlines even a small increase in their cost base could lead to a high growth rate of their mean cost per RPK (+3%). In the same time Regional airlines would have to bear the lowest evolution (+1.5%) because of their high cost per RPK.(These airlines performe short and medium haul flights with a relatively low overall level of RPKs) . Hence some of their costs divided by RPKs, and particularly their staff cost, give higher values than for the other types.

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Evolution of fuel cost and other costs

Fuel costOther costs

Figure 15

When facing an annual increase of 1% in the fuel price, airline would increase their fare yield by 2% between 2000 and 2015 leading to a passenger demand reduction of 2.5% over the period (compared to the baseline scenario forecasts). Airlines would then reduce their capacity increase in buying 2.5% less aircraft and in hiring 2.5% less of employees than the level forecasted in the baseline scenario. Charters would be the airline type the most limiting their fleet increase, while Low-Cost would be those the most limiting their employment increase. In a general way, airlines would adapt their fleet and employment plans so as to ensure that the other cost growth rates over the period do not exceed 50% of the fuel cost growth rate.

4.4.3. Example 2: Fare yield decrease As in the first example, the second example compares two different scenarios. The ‘baseline” fixes the following assumptions for providing forecasts for 2015: - The fare yield does not change between 2000 and 2015 - The average flight time is 120 mins. - The delay per flight is 10 mins for the whole period - The GDP per capita increases annually by 2.14% (source OECD) - The unemployment rate increases annually by 2.45% (source OECD) - The world population increases annually by 1.1% (source Energy Information Administration) The second scenario only changes the constant fare yield assumptions. It assumes that the fare yields decrease by 1% in 2002, and keep the 2002 values for the following years. The decrease in the fare yield would attract more passenger demand so that the air traffic level would increase by 20.7% between 2000 and 2015 as opposed to 19.5% in the baseline scenario (Figure 16). Hence a decrease of 1% in the fare yield would lead to a traffic increase of 1.2% compared to the baseline. Of course this difference of traffic also assumes a difference in operating costs and the total operating cost would increase by 1.2% compared to

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the baseline. In a general way, comparisons of cost evolutions between both scenario show that all costs as well as the fleet and the employment level would increase by 1.2% compared to the baseline. This result is due to the computation formula of costs. As it is shown in

, all cost computations include the level of ASKs. As in this simple scenario other variables (under the ratio form) are not supposed to vary, cost variations are directly proportional to the ASKs variation.

Appendix 5

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Baseline Fare yield decrease 1% Figure 16

In the same time, the increase in the total operating revenue would be the same for both scenarios. This result would tend to show that the decrease in revenue due to the decrease in fare yield would be compensated by the increase in the traffic level. This is due to the fact that fare yield elasticities are quite closed to 1%, so that the demand increase is quite closed to 1% when the fare yield decrease by 1%. As the total revenue is computed by multiplying the fare yield with the level of traffic, it finally is quite the same than in the baseline scenario. A 1% decrease in airlines’ fare yield would lead to an increase in the passenger demand for all airlines, some of whom would see their demand for air transportation increase in higher proportions than the others (depending on their fare-yield elasticity). Low-Cost and Charter airlines would particularly take advantage of this fare decrease, since their RPK traffic would increase by 1.43% and 1.33% respectively (Figure 17) compared to the baseline. In the same time, Major and National airlines would see their traffic increase in a smaller proportion (1.10% and 1.13% respectively).

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variation of RPKs between 2000 and 2015

BaselineYield -1%

+1.33% +1.26% +1.43% +1.10% +1.13% +1.28%

Figure 17

In order to satisfy the additional passenger demand, airlines would have to increase their employment level as well as their fleet size. Knowing that Charter and Low-Cost would have the highest level of traffic increase, it is not surprising that they would be the airline categories most increasing their hiring level (Figure 18).

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Fleet and employment evolution between 2000 and 2015

Aircraft Employment

Figure 18

However, as opposed to Low-Cost airlines, Charters would increase their fleet in higher proportion than their number of employees. Indeed, as it has been shown in the example 1 Charters principally change their number of aircraft when they face events. When increasing their fleet and their employee number, airlines automatically would increase their operating cost, but in the same time they would not be able to increase their operating revenue in the same proportion. Indeed, while the fare yield decrease would attract more passenger demand, and then increase the operating cost, it also would limit the operating revenue increase. This would finally lead to a reduction in airlines’ profit margins (Figure 19).

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-3.00%

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Profit margins in 2000 and 2015

20002015

-0.94%

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Figure 19

It appears that airlines with the highest level of traffic increase as Low-Cost and Charter airlines, would incur the smallest profit margin reductions. On the other hand, National airlines would have to bear the highest profit margin reduction. Indeed, Major airlines would have the smallest level of RPK increase (Figure 17), and we could expect that their profit margin would be the most reduced among the six airline categories. Nevertheless, Majors have for flexibility reasons, additional aircraft and employees in order to quickly react when facing technical problems or lack of staff. This could explain the fact that they could increase their fleet and employment level in a smaller proportion than the other airline types, for satisfying and increasing passenger demand (Figure 18). It follows that they could more easily than the others limit their reduction in profit margin. When decreasing their fare yields by 1%, airlines would attract 1.2% of additional passenger demand (compared to the baseline scenario) between 2000 and 2015 leading to an additional operating cost of 1.2% over the period (since cost computations are proportional to ASKs). In the same time, the additional traffic would allow to compensate the revenue loss due to the fare yield reduction so as to the operating revenue increase remains the same than in the baseline. Airlines would then bear a reduction in their profit margin value between 2000 and 2015, decreasing from 3.89% to 2.92%. Major airlines would be those most limiting their fleet and employment level increase allowing them to limit their profit margin decrease. On the other hand Low-Cost and Charter airlines would have the highest traffic increase allowing them to incur the lowest profit margin losses. .

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BIBLIOGRAPHY 1. Airline annual reports for 2000 on airline web-sites 2. Energy Information Administration (2002) “International energy outlook 2002” 3. IATA (June 2001) “World Air Transport Statisitics”, International Air Transport Association, 45th edition, ISBN: 92-9171-588-3 4. ICAO (2002) “Financial Data. Commercial Air Carriers”, International Civil Aviation Organisation”, Digest of statistics No. 493, Serie F-No. 54 5. Stouffer V. (May 2002) “Derivation of ‘Scale-8’ European Air Carrier Investment Model”, Working document, Logistic Management Institutes, EU201L1 6. Stouffer V. (May 2002) “Design document for European Air Carrier Investment model”, Working document, Logistic Management Institutes, EU201L2 7. Wingrove E.R., Gaier E.M., Santmire T.E. (April 1998) “The ASAC Air Carrier Investment Model (third generation)”, Logistic Management Institutes, NASA/CR-1998-207656

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Appendix 1 We choose using an exponential functional form for the passenger demand, mainly because the estimated coefficients of the explanatory variables represent directly the demand elasticities. Let’s take an exponential demand function with two explanatory variables: the price p and the consumer income R:

χβα RpD =

By definition, the elasticity of demand with respect to the price p is:

Dp

dpdD

Dp

pD

ppDD

p ×≈×∆∆

=∆∆

=//η

The derivative of the demand function respect to the price factor is:

χββα RpdpdD 1−=

Hence,

βα

βαη χβχβ =×= −

RppRpp

1

In a same way, the demand elasticity respect to the income factor is

χη =p In order to make the estimation easy, we linearise the demand function in taking the logarithm of the equation. Once again, estimated coefficients are elasticity values. For the price factor for example,

β=×=∂∂

Dp

dpdD

pD

lnln

28


Appendix 2 The fact that individual airline fare yields are not well documented necessitates an approximation in the computation of fare yields. Yield is computed in dividing airline revenue by RPK. The airline revenue per city-pair not being available we have to estimate it. An approximation of the airline revenue per city pair is possible by regressing on a sample of airlines, their revenue for the passenger activity on several factors (all variables are in log and come from the year 2000):

Dependent variable: Airline Revenue from their passenger activity R2=0.93

43 observations Variables Coefficients Student

statistics Constant -5.11 -1.50

Population 0.81 5.94 Unemployment rate -0.38 -1.53

GDP 0.74 2.64 NB_Pax_Maj 0.40 13.37 NB_Pax_Nat 0.38 12.08 NB_Pax_Ext 0.31 9.28 NB_Pax_Reg 0.35 9.77 NB_Pax_Ch 0.33 8.54

NB_Pax_Low 0.29 8.21 Where: Population is the average population of city-pairs on which the airline operates

Unemployment rate is the average Unemployment rate of city-pairs on which the

airline operates GDP is the average Gross domestic product per capita of city-pairs on which the

airline operates NB_Pax_Maj equals to zero if the airline is not a Major airline. If not, it is the total

number of passenger carried of all city-pairs on our database over the year. NB_Pax_Nat equals to zero if the airline is not a National airline. If not, it is the

total number of passenger carried of all city-pairs on our database over the year. NB_Pax_Ext equals to zero if the airline is not an extra-European airline. If not, it

is the total number of passenger carried of all city-pairs on our database over the year. NB_Pax_Reg equals to zero if the airline is not a Regional airline. If not, it is the

total number of passenger carried of all city-pairs on our database over the year. NB_Pax_Ch equals to zero if the airline is not a Charter airline. If not, it is the

total number of passenger carried of all city-pairs on our database over the year. NB_Pax_Low equals to zero if the airline is not a Low-Cost airline. If not, it is the

total number of passenger carried of all city-pairs on our database over the year. Applying the following equation to all airlines of our studied city-pairs provides revenue per airline per city-pair.

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LowNBPaxChPaxNBExtEurPaxNBgPaxNBNatPaxNB

MajPaxNBUnEmplPOPGNI

_ln29.0__ln33.0__ln31.0Re__ln35.0__ln38.0

__ln40.0ln74.0ln38.0ln81.011.5Revenueln

+++++++−+−=

This revenue divided by the number of RPKs made by the airline on the city-pair, gives the airline yield on this city-pair.

30


Appendix 3 The Ordinary least Square method (OLS) This method allows estimating equations with one or more variables. For example, for the following equation:

niuczbxay iiii ,...,1=+++= (0) where: - y is the dependant (or endogenous) variable i

- , are the independent (or exogenous) variables ix iz- is the error term. This term is added to the equation iu ii bxay += since this equation is never exactly verified for a sample of data. This error term represents the difference between the real and the estimated value of . iy

- , and are the coefficients to estimate. Their estimators will be noted , and .

a b c a bc

Assumptions: The validity of results depends on the assumptions made for the sample data. The following Gauss-Markov assumptions are commonly used: H1: The error terms u have normal distribution with i iuE i ∀= 0)(

H2: The variance of u is constant for each observation: i iuE i ∀= 22 )( σH3: The error terms of different observations are not correlated: jiuuE ji ≠∀= 0)(

H4: and are independent of the error term ix iz iuH5: and are linearly independent, i.e. no independent variable can be expressed as linear combination of the others.

ix iz

Equation (0) can be also written in a matrix form:

UXBY +=

Where Y , , and U

=

ny

yy

...2

1

=

cba

B

=

nn zx

zxzx

X

1.............

11

22

11

=

nu

uu

...2

1

The OLS method consists in choosing a , and so as to minimise the sum of the error

terms square ∑ , where n is the number of observations.

ˆ b c

=

n

iiu

1

2

In the matrix form, it is equivalent to choosing B so as to minimise: S=U (1) )()'(' XBYXBYU −−= The first order condition is:

0)('2'

=−−=∂∂ XBYXBS

⇔ YXXXB ')'(ˆ 1−= (2)

The OLS method applies this formula in order to get estimators of coefficients a , and c . b

31


Appendix 4 Missing data The most complete set of data being available on carriers in the year 2000, this became our baseline. Unfortunately, all European airlines do not provide complete detailed values of their costs and missing values remained in our database for one or more airlines. We have then tried to express the cost values available for the other airlines relatively to several variables in making regressions. These equations have then been used to estimate the missing values. Equations that minimize residual errors best are (where student statistics are in brackets:

Flight equipment capital costs per aircraft day:

seatsofnumberaveraget

capitalFlight ×+−

−=)43.4(

70.45 1.39)(

38.3453cos

with R2=0.99

Ground equipment capital cost per aircraft day:

seatsofnumberaveraget

capitalGround ×+−

−=)37.3(

50.4)64.0(

58.207cos

with R2=0.94

Airport landing costs (in thousands Euros):

RPKsofThousands

tLanding ×=

)97.6(18.2

cos with R2=0.66

Maintenance cost (in thousands Euros):

( )RPKsofThousands

tenanceMa ×=

11.2779.10

cosint

with R2=0.97

Passenger commission costs (in thousands of Euros):

RPKsofThousandst

Commission ×=)06.19(

17.5cos with R2=0.89

Fuel cost (in thousands of Euros):

RPKsofThousandst

Fuel ×=)71.30(

96.12cos with R2=0.96

Flight crew cost:

employeesTotalt

wFlight cre ×=)15.16(

27120cos with R2=0.90

En-route facility charges outside the Eurocontrol zone:

RPKsEuropeanNonzonelEurocontrothe

outsideeschEn-Route ×=)10.4(

75.0

arg with R2=0.53

32


Total staff cost: employeesTotal

t

staffTotal ×=)27.25(

69875

cos

with R2=0.97

In addition, when block hours were missing we have computed them by the following way:

Block Hours: fleetseatsofnumberaverage

HoursBlock ×+×+

−−=

)53.49( 3817

)30.3( 678

)55.3(347144

with R2=0.99

33


Appendix 5 This model can be decomposed in the following way:

)1(1

1 FactorLoadFactorLoadRPK

ASKt

tt ∆+

= ++

( )( )

++

××

=

+

+

++

1

1

11

//

tt

tt

t

t

t

t

tt

delaytimeFlightdelaytimeFlight

aircraftSeatsaircraftSeats

hourBlockASK

ASKhourblockTotal

Total Block HourDelay per

flight

Average flight timewithout delay

2

Aircraft seats

ASK

RPK

Load Factor ASK

1

ASK

Total Block Hour

Flight CrewEmployees

Employees

3

Non Flight CrewEmployees

∆+

= +

+

EmployeesCrewFlightNonASK

EmployeesCrewFlightNonASK

ASKEmployees

PersonnelFlightNon

t

t

t 1

1

1

34


∆+

×

∆+

=

+

+

EmployeesCrewFlightHourBlock

EmployeesCrewFlightHourBlock

ASK

BlockHourASK

BlockHourASKEmployees

PersonnelFlight

t

t

t

t

1

1

1

1

1

3651

1

1

1

1

×

∆+

×

∆+

=

+

+

DayAircraftHourBlock

DayAircraftHourBlock

ASKBlockHour

ASKBlockHour

ASKaircraftofNumber

t

t

t

t

Where Aircraft Day is the total fleet block hour per day

ASK

Total Block Hour

Fleet4

Aircraft daily time of use

ASK

Input Costs

C Operating

Where:

=+

BCostFuel

t 1

Total Block Hour

Operating Cost

5

Cost Indirect Cost RouteEn&Airport Cost CapitalEquipment GroundCostEquipment Flight

Cost eMaintenancCost CrewFlight Cost Fuelost

+−+++

++=

∆+

×

∆+

∆+

HourBlockLitre

HourBlockLitre

BlockHourASK

lockHourASK

LitreCostFuel

LitreCostFuel

t

t

t 11

1

35


11 1

1

++

×

∆+

∆+

= t

t

t

t

ASK

BlockHourASK

BlockHourASK

HourBlockCostLaborCrewFlight

HourBlockCostLaborCrewFlight

CostCrewFligh

11 1

CosteMaintenanc1Cost eMaintenanc

++

×

∆+

∆+

= t

t

t

t

ASK

BlockHourASK

BlockHourASK

HourBlockHourBlockCost

eMaintenanc

1

111

Cost Capital1Cost Capital

+

+

×

∆+

×

∆+

∆+

= t

tt

t

t

ASK

DayAircraftBlockHour

DayAircraftBlockHour

BlockHourASK

BlockHourASK

DayAircraftDayAircraft

CostCapital

EquipmentFlight

11

1 ++

×

∆+

= t

t

t

ASKASK

CostCapitalEquipmentGround

ASKCostCapital

EquipmentGround

CostCapitalEquipmentGround

1

1

1

&

+

+

×

+

∆+

×

+

=−

t

t

t

ASKASK

FeesControlTrafficAir

FeesLandingAirport

ASKFees

ControlTrafficAirFees

LandingAirport

CostrouteEnAirport

11

1 ++

×

∆+

= t

t

t

ASKASKCost

Indirect

ASKCost

Indirect

CostIndirect

(Where indirect costs are passenger commissions and other indirect costs)

36


37

RPK

FareYield

6

Operating Revenue

111tRevenue +++ ×= tt FareYieldRPK In the first run method, [ ]Yield Fare 1Yield FareYield Fare t1t ∆+=+ In the second run method, EMEAT model adjusts the ticket fare value until the fixed profit margin be got. Operating

Cost

Operating Revenue

ProfitMargin

7

1t1t1t CostOperatingRevenueOperatingMarginrofitP +++ −=

european air carrier investment model...scenarios, the impact on (passenger) traffic and the...

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