the operational performance of uk airlines 2002-2007

8/7/2019 The operational performance of UK airlines 2002-2007

1/12

The operational performance ofUK airlines: 2002-2007

A. George AssafUniversity of Massachusetts-Amherst, Amherst, Massachusetts, USA, and

Alexander JosiassenThe Centre for Tourism and Services Research (CTSR), Victoria University,

Melbourne, Australia

Abstract

Purpose The purpose of this paper is to measure the efficiency of UK airlines in light of all therecent industry challenges.

Design/methodology/approach The study measured the technical efficiency of airlines throughthe innovative data envelopment analysis (DEA) bootstrap methodology.

Findings Results based on a sample of recent input/output data indicated that the efficiency of UKairlines has continuously declined since 2004 to reach a value of 73.39 per cent in 2007. Factors whichwere found to be significantly and positively related to technical efficiency variations include airlinesize and load factor. The paper also highlights that factors such as increase in oil price and fiercemarket competition were also potential inefficiency determinants.

Practical implications The findings of this paper provide a fresh link between airlineperformance and the current industry characteristics. UK airlines also have a major role in theEuropean and international aviation sector, and thus a reflection on their efficiency could be of interestto private and public policy makers.

Originality/value The paper focuses on a recent period and thus provide a fresh efficiencyassessment of the airline industry. The study also extends the limited literature available on UK

airlines.Keywords Airlines, Performance management, Data analysis, United Kingdom

Paper type Research paper

1. IntroductionRecent industry reports from the UK and international airline associations havewarned that airlines currently face a threatening period of major financial lossesfollowing the last four years increase in oil prices. The predicted global loss in 2008was around $2.3 billion and latest figures from the International Air TransportAssociation (IATA) also indicated that the financial losses could even exceed the $6billion dollars in the near future if oil prices stay high (Robertson, 2008a). The president

of IATA described the global situation as desperate and indicated that in early 2008around 24 airlines have gone out business. Figures from the US, for example, indicatedthat in the first quarter of 2008, United Airlines lost around $537 million, American lost$328 million, and Northwest lost $191 million (Wilber, 2008; Robertson, 2008b). Otherairlines such as Continental and JetBlue have also suffered major losses. In the UK,airline analysts expected that airlines might face considerable challenges in the nearfuture. British Airways for example predicted a profit reduction of 250 million in 2008(Robertson, 2008b).

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0144-3585.htm

Operationalperformance of

UK airlines

5

Received 13 January 2009Accepted 2 November 2009

Journal of Economic StudiesVol. 38 No. 1, 2011

pp. 5-16q Emerald Group Publishing Limited

0144-3585DOI 10.1108/01443581111096114


2/12

In the literature, the link between performance and profit was well established. It ishypothesised that with a sharp drop in profits only the high performing airlines willsurvive. Airlines are currently adopting cost-reduction strategies and IATA predictedthat airlines will be forced to increase their prices in the near future (Fleming, 2008).

Thus, the need for high performance takes an additional importance within the currentmarket challenges.

In this study, we were driven by all the above-mentioned factors and the major aimwas to reflect the efficiency standing of UK airlines in light of all the recent marketchallenges. Another aim of this study was to determine those factors that explain thesources of efficiency variations between airlines. We focused on UK airlines because oftheir international market power; however the findings could also be generalised toreflect the link between performance and current industry characteristics. Themethodology used in this study also aimed to improve the accuracy in modeling theperformance of airlines. We introduced the innovative data envelopment analysis(DEA) bootstrap methodology. Traditional methods used in the airline and airportefficiency research were the traditional DEA or stochastic frontier analysis (SFA). Oneof the major limitations of DEA is that it is a deterministic technique and thus does notaccount for measurement error in deriving the efficiency measures. SFA was againsubject to criticism, as it requires a pre-specification of the functional form in theestimation of cost or production frontier technologies. SFA also requires larger samplesize than DEA. On the other hand, it is possible with the DEA bootstrap to keep theflexibility of DEA and also obtain statistical properties of the efficiency scores. Dataused in the analysis range from 2002 to 2007 and covered 15 airlines. More details onthe literature, methodology, variable used, results and discussions are provided in thenext four sections of the paper.

2. Literature review

Studies on airline frontier models are generally limited and outdated and clearlyindicate the need for additional evidences and implications on the efficiencycharacteristics of the airline industry. None of the available studies has focused purelyon UK airlines, but many studies have analysed the performance North Americanairlines and in some cases compared the performance of European, and Asia Pacificairlines. North American airlines usually appeared to be the most efficient (Oum andYu, 1995). In a recent study, it was also confirmed that US and European airlines weremore efficient than Asia Pacific airlines (Barbott et al., 2008).

In general, most studies have also followed traditional approaches in the analysis ofefficiency. However, in most cases these studies have addressed the limitations ofsimple partial productivity measurements by incorporating multiple inputs/output aswell as environmental variables in the analysis of efficiency. Some of the most common

input/output variables are illustrated in Table I. The more common environmentalvariables are the average load factor and airline size, which in most cases have beenfound to play a positive role in improving airlines performance (Caves et al., 1984;Coelli et al., 1999). Other variables in the same category are float characteristics like theaverage speed and size of aircraft.

Some of the methodologies used in these studies include the traditional SFA model(e.g. Caves et al., 1984; Baltagi et al., 1995), the DEA-based Malmquist approach(Barbott et al., 2008), and the traditional DEA models (Good et al., 1995). As mentioned

JES38,1

6


3/12

before, all these methodologies suffer from common limitations. The main limitation ofDEA is that it is a non-statistical technique and thus treats all measurement error assources of inefficiency, while the main limitation of SFA is that it requires apre-specification of the functional form in the estimation of cost or production frontiertechnologies. In this context, the use of the bootstrap approach in this study is thereforeconsidered an innovation as it takes into account the limitations of both the DEA andSFA methods. The findings are consequently expected to be more accurate and as aresult provide a more solid ground for policy implications. More details about theadvantages of the bootstrap approach are provided in later sections.

3. DataThe data selection process started with a detailed review of the existing studies in theliterature. Guidance from experts in the area was also helpful in confirming the list ofvariables selected. Data were collected directly from the Civil Aviation Authority andconsisted of 15 major UK airline companies during the period 2002-2007 (90

observations). The authority collects data using the same criteria of the InternationalCivil Aviation Organisation (ICAO).

On the output side, two variables are used namely, TKA or tonne kilometresavailable and total operational revenues. TKA is a reflection on two airline outputs:passenger service and cargo operation and is calculated by multiplying the number oftonnes available for the carriage of revenue load (passengers, freight and mail) on eachflight by the flight distance. The total operational revenues (sum of aeronautical andnon-aeronautical revenues) was also used in previous studies (Ahn et al., 1997; Goodet al., 1995), and is strong indicator of overall performance. On the inputs side threeinputs were selected namely labour expenses (total expenditure for the salaries andallowances of all employees), aircraft fuel and oil expenses (includes fuel,de-mineralised water and water methanol consumed), and aircraft value[1] (total

financial value of aircraft minus depreciation).In order to account for the sources of efficiency changes, we have also regressed

DEA on two environmental variables, namely load factor and airline size. Load factor(defined as the ratio of performed tonne-kilometres to available tonne kilometres) wasused to account for the environment in which the airlines operate. As mentioned in theliterature review, this variable was used in several related studies (Coelli et al., 1999;Ahn et al., 1997), and it is considered as a measure of market demand. Airlinesoperating with a high load factor coefficient would expect to have a stronger demand,

Study Inputs Outputs Other variables

Coelli et al. (1999) Labour, capital TKA Load factor, aircraft capacityAhn et al. (1997) Labour, materials, fuel Revenue Load factor, aircraft size

Good et al. (1995) Labour, materials, planes Revenue Load factor, network sizeBaltagi et al. (1995) Capital, labour TKA Load factor, aircraft size,

hubs, mergersCornwell et al. (1990) Labour index, materials,

energy, capital expensesTKA Stage length, service quality,

seasonalitySchmidt and Sickles (1984) Labour index, materials,

energy, capital expensesTKA Size, load factor

Table I.Overview of existing

studies


UK airlines

7


4/12

and thus consequently a higher production/efficiency. The identification of airline sizewas based on the percentage share of each airline in the total kilometres performed byall UK airlines[2]. It is hypothesised that larger are more productive, as these airlinesusually possess stronger economies of scale and more flexible access to technology and

innovation. The descriptive statistics of all variables are listed in Table II. In the nextsection we describe in detail the methodology used in the paper.

4. DEA and the bootstrap4.1 Data envelopment analysisDEA is one of the most popular methods to estimate technical efficiency (for someapplications refer to Barros, 2004; and Goncharuk, 2007). It aims to define a frontierenvelopment surface for all sample observations. The frontier is a representation of allthe efficient firms. Firms that do not lie on that surface can be considered as inefficientand an individual efficiency score will be calculated for each one of them. The outputoriented DEA efficiency estimator di can be simply derived by solving the followinglinear programming:

di di;l

max d. 0 diyi#Xni1

yil;xi$Xni1

xil;Xni1

l 1;l $ 0

( )

;

i 1 . . . n airlines

1

where yi is vector of airline outputs, xi is s vector of airline inputs, l is a I 1 vector ofconstants. The value of di obtained is the technical efficiency score for the i-th firm. Ameasure of di 1 indicates that the airline is technically efficient, and inefficient ifdi. 1. This linear programming problem must be solved n times, once for each airline

in the sample.Note that the DEA model can also be estimated using either the constant return toscale (CRS)[3] or variable return to scale (VRS) assumptions and the shape of thefrontier will differ depending on the scale assumptions that underline the model. In thispaper we mainly rely on the VRS assumption, as the CRS[4] is only correct as long as itis appropriate to assume that airlines are operating at an optimal level of scale.However, given the recent market condition and the high level of competition in thesector, it is safer to expect that airlines might not be operating at an optimal level ofscale. Note that the VRS model has also the advantage of ensuring that an inefficientairline is only compared against those airlines of similar size. This is achieved throughthe convexity constraint

Pni1l 1, which is not imposed in the CRS case.

Variable n Mean SD Min Max

TKA 90 2809542867 5197060693 19946000 12516000000Total operational revenues () 90 914045511 166422193 16538000 7609755000Labour cost () 90 72574533 136431496 1384000 722509000Fuel cost () 90 155359444 301960425 353000 1856853000Aircraft value () 90 667228033 205300722 492000 1004300000Load factor (%) 90 60.89 16.95 33.40 90.90

Table II.Descriptive statistics ofthe data

JES38,1

8


5/12

4.2 The bootstrapWhile the above DEA model is relatively simple to estimate, it has long been criticisedfor being a non-statistical or deterministic technique, given that it does not allow forrandom error in the estimation of efficiency. Some researchers try to avoid this problem

by using parametric techniques that have the advantage of allowing for random error;however, these techniques impose a particular functional form that predetermines theshape of the efficient technology or frontier. DEA, on the other hand, tends to envelopthe data more closely. Simar and Wilson (1998, 1999, 2000) have recently discussed thatit is possible to maintain the interesting features of DEA, and also obtain statisticalproperties via the use of the bootstrap approach. When applied to DEA, the bootstrapallows the construction of confidences intervals, thus making it possible to obtainstatistical properties of the efficiency estimates and also perform some hypothesestesting.

The basic idea of bootstrapping is to approximate the distribution of the estimatorvia re-sampling and recalculation of the parameter of interest, which in our case is theDEA efficiency score. The bootstrap procedure can also be extended to account for the

impact of environmental variables[5] on efficiency. For example, if we take thefollowing model:

di zib 1i 2

where zi is a vector of management related variables which is expected to affect theefficiency of firms under consideration and b refers to a vector of parameters withsome statistical noise 1i. A popular procedure in the literature is to use the ordinarilyleast square (OLS) regression to estimate this relationship. However, as described inSimar and Wilson (2007), this might lead to two main problems. First, the of efficiencyscores estimated by DEA are expected to be correlated with each other, as thecalculation of efficiency of one firm incorporates observation of all other firms in the

same data set. Therefore, direct regression analysis is invalid because of thedependency of the efficiency scores. Similarly, in small samples, a strong correlation isexpected between the input/output variables and environmental variables, therefore,violating the regression assumption that 1i are independent of zi.

To overcome these problems we use in this paper the double bootstrappingprocedure, proposed by Simar and Wilson (2007), in which the bootstrap estimators aresubstituted from the estimators in the regression stage to calculate the standard errorof the estimates. The procedure produces, with bias corrected estimates of di validestimates for the parameters in the second stage regression. For more details on thebootstrap procedure used in this stage refer to Simar and Wilson (2007).

5. Results and discussions

The VRS technical efficiency estimates of the different UK airlines, obtained from2000[6] bootstrap iterations are reported in Table III. As the results indicate, theaverage technical efficiency follows two different patterns. From 2002 to 2004 therewas a strong increase in the average technical efficiency from 78.36 to 85.20 per cent,while from 2005, the average technical efficiency started to decline to reach its lowestlevel of 73.39 per cent in 2007. Some of the lowest performing airlines in that yearinclude First Choice, Monarch, and Jet 2, while some of the highest performing airlinesinclude British Airways, Thomas Cook, and Thomson fly. The last two columns of


UK airlines

9


6/12

Year Bootstrapped DEA RTS BIAS s LB UB

British Airways 2002 0.8806 DRS 0.1203 0.0135 0.7610 0.9968Virgin Atlantic 2002 0.8785 DRS 0.0455 0.0009 0.8214 0.9193

EasyJet 2002 0.7490 DRS 0.0267 0.0001 0.7264 0.7728ThomsonFly 2002 0.8880 DRS 0.0307 0.0003 0.8558 0.9163First Choice 2002 0.8890 DRS 0.0396 0.0010 0.8319 0.9256Monarch 2002 0.5624 IRS 0.0247 0.0002 0.5362 0.5840Thomas Cook 2002 0.9204 DRS 0.0522 0.0013 0.8638 0.9690BMI Group 2002 0.8831 DRS 0.1177 0.0078 0.8087 0.9960MyTravel 2002 0.7772 DRS 0.2235 0.0688 0.6740 0.9963 Jet2.com 2002 0.7769 IRS 0.2289 0.0704 0.6802 0.9963GB Airways 2002 0.7110 IRS 0.0234 0.0001 0.6888 0.7319Flybe Ltd 2002 0.8725 DRS 0.0318 0.0002 0.8428 0.9003Titan 2002 0.6348 IRS 0.0363 0.0007 0.5880 0.6694Flightline 2002 0.7799 IRS 0.2315 0.0753 0.6629 0.9965European 2002 0.5514 IRS 0.0362 0.0008 0.5071 0.5869Average 0.7836

British Airways 2003 0.8918 DRS 0.1098 0.0080 0.8074 0.9965Virgin Atlantic 2003 0.9232 DRS 0.0767 0.0026 0.8542 0.9955EasyJet 2003 0.8843 DRS 0.0470 0.0010 0.8325 0.9263ThomsonFly 2003 0.9499 DRS 0.0447 0.0007 0.9087 0.9918First Choice 2003 0.8890 DRS 0.0396 0.0010 0.8319 0.9256Monarch 2003 0.6801 DRS 0.0236 0.0002 0.6536 0.7024Thomas Cook 2003 0.9060 DRS 0.0638 0.0027 0.8240 0.9635BMI Group 2003 0.9022 DRS 0.0531 0.0012 0.8451 0.9514MyTravel 2003 0.9157 DRS 0.0472 0.0016 0.8511 0.9588 Jet2.com 2003 0.8263 DRS 0.0885 0.0070 0.7196 0.9093GB Airways 2003 0.7346 IRS 0.0238 0.0002 0.7084 0.7561Flybe Ltd 2003 0.7814 DRS 0.2173 0.0651 0.6833 0.9965Titan 2003 0.6927 IRS 0.0552 0.0029 0.6108 0.7444

Flightline 2003 0.7863 IRS 0.2219 0.0671 0.6803 0.9953European 2003 0.6055 IRS 0.0566 0.0049 0.5120 0.6609Average 0.8246

British Airways 2004 0.9093 DRS 0.0943 0.0055 0.8246 0.9963Virgin Atlantic 2004 0.9104 DRS 0.0907 0.0043 0.8315 0.9962EasyJet 2004 0.9323 DRS 0.0285 0.0001 0.9089 0.9569ThomsonFly 2004 0.9375 DRS 0.0606 0.0024 0.8718 0.9981First Choice 2004 0.9537 DRS 0.0463 0.0009 0.9008 0.9964Monarch 2004 0.6801 IRS 0.0236 0.0002 0.6536 0.7024Thomas Cook 2004 0.8787 DRS 0.1259 0.0100 0.7970 0.9973BMI Group 2004 0.9333 DRS 0.0658 0.0013 0.8896 0.9966MyTravel 2004 0.9289 DRS 0.0713 0.0022 0.8744 0.9963 Jet2.com 2004 0.8255 DRS 0.1710 0.0329 0.7137 0.9954GB Airways 2004 0.7551 IRS 0.0256 0.0001 0.7298 0.7772

Flybe Ltd 2004 0.9061 DRS 0.0285 0.0002 0.8771 0.9311Titan 2004 0.6340 IRS 0.0330 0.0004 0.6002 0.6640Flightline 2004 0.7734 IRS 0.2200 0.0707 0.6695 0.9968European 2004 0.8214 IRS 0.1804 0.0332 0.7149 0.9958Average 0.8520

British Airways 2005 0.8410 DRS 0.1583 0.0218 0.7516 0.9962Virgin Atlantic 2005 0.9174 DRS 0.0836 0.0030 0.8442 0.9950

(continued)

Table III.Bootstrapped efficiencyresults

JES38,1

10


7/12

Year Bootstrapped DEA RTS BIAS s LB UB

EasyJet 2005 0.9520 DRS 0.0480 0.0004 0.9260 0.9962ThomsonFly 2005 0.9104 DRS 0.0889 0.0029 0.8656 0.9971

First Choice 2005 0.8520 DRS 0.0352 0.0005 0.8131 0.8836Monarch 2005 0.6229 IRS 0.0354 0.0007 0.5744 0.6545Thomas Cook 2005 0.9193 DRS 0.0835 0.0034 0.8446 0.9961BMI Group 2005 0.9371 DRS 0.0394 0.0003 0.9069 0.9717MyTravel 2005 0.9439 DRS 0.0374 0.0008 0.8926 0.9797 Jet2.com 2005 0.8012 DRS 0.1995 0.0466 0.7072 0.9956GB Airways 2005 0.6295 IRS 0.0245 0.0001 0.6079 0.6515Flybe Ltd 2005 0.8916 DRS 0.0285 0.0002 0.8659 0.9162Titan 2005 0.6616 IRS 0.0430 0.0008 0.6209 0.7024Flightline 2005 0.7789 IRS 0.2217 0.0687 0.6843 0.9959European 2005 0.6791 IRS 0.0606 0.0033 0.5965 0.7369Average 0.8225

British Airways 2006 0.9192 DRS 0.0791 0.0045 0.8247 0.9971

Virgin Atlantic 2006 0.9172 DRS 0.0735 0.0061 0.8015 0.9882EasyJet 2006 0.9409 DRS 0.0312 0.0002 0.9085 0.9685ThomsonFly 2006 0.8203 DRS 0.0518 0.0016 0.7574 0.8684First Choice 2006 0.8054 DRS 0.0421 0.0006 0.7634 0.8431Monarch 2006 0.5334 IRS 0.0224 0.0001 0.5142 0.5534Thomas Cook 2006 0.9305 DRS 0.0539 0.0013 0.8754 0.9810BMI Group 2006 0.8724 DRS 0.0352 0.0003 0.8410 0.9034MyTravel 2006 0.6258 IRS 0.0349 0.0006 0.5888 0.6587 Jet2.com 2006 0.7274 IRS 0.0345 0.0005 0.6885 0.7584GB Airways 2006 0.4554 IRS 0.0209 0.0001 0.4376 0.4740Flybe Ltd 2006 0.6333 DRS 0.0209 0.0001 0.6130 0.6511Titan 2006 0.5670 IRS 0.0328 0.0005 0.5306 0.5977Flightline 2006 0.9305 IRS 0.0664 0.0024 0.8553 0.9926European 2006 0.5631 IRS 0.0381 0.0007 0.5218 0.5985

Average 0.7495British Airways 2007 0.8911 DRS 0.1050 0.0082 0.7927 0.9968Virgin Atlantic 2007 0.8186 DRS 0.1784 0.0325 0.7233 0.9954EasyJet 2007 0.8659 DRS 0.0396 0.0008 0.8127 0.9016ThomsonFly 2007 0.9439 DRS 0.0564 0.0009 0.9031 0.9958First Choice 2007 0.6613 IRS 0.0462 0.0012 0.6083 0.7022Monarch 2007 0.5253 IRS 0.0225 0.0001 0.5097 0.5456Thomas Cook 2007 0.9013 DRS 0.0971 0.0044 0.8424 0.9953BMI Group 2007 0.8487 DRS 0.0420 0.0005 0.8137 0.8871MyTravel 2007 0.5296 IRS 0.0350 0.0009 0.4839 0.5615 Jet2.com 2007 0.5509 IRS 0.0478 0.0015 0.4919 0.5935GB Airways 2007 0.4329 IRS 0.0236 0.0001 0.4145 0.4537Flybe Ltd 2007 0.7073 IRS 0.0256 0.0001 0.6837 0.7293Titan 2007 0.7776 IRS 0.2262 0.0678 0.6827 0.9962

Flightline 2007 0.7803 IRS 0.2265 0.0689 0.6786 0.9959European 2007 0.7734 IRS 0.2183 0.0693 0.6640 0.9959Average 0.7339

Notes: LB and UB represent the lower and upper bound for the 95 per cent confidence intervals ofDEA efficiency scores; RTS: Returns to Scale; DRS: Decreasing Returns to Scale; IRS: IncreasingReturns to Scale; Note that in this table we took the inverse of DEA efficiency scores to simplify theinterpretation of the results; The scores are now between 0 and 1; An airline which has an efficiencyscore of 1 is considered to be fully efficient Table III.


UK airlines

11


8/12

Table III show the 95 per cent confidence intervals of the bootstrap estimates. As foundbefore by Simar and Wilson (1998), the bias corrected estimates lied for everyobservation inside the confidence interval.

The estimates of returns to scale were also in line with the efficiency results. As it

was shown, most airlines in 2007 were operating under increasing returns to scale thatis an indicator that these airlines were motivated to increase the scale of theiroperations, but there were certain external factors that have stopped these airlines fromachieving an optimum level of scale. Some of these airlines include Flybe, Titan,Flightline, and GB Airways, which are mainly described as small airlines.

As mentioned before, we also employed in this paper a second stage estimation todetermine those factors that explain the variations in technical efficiency scoresbetween the different airlines. The two variables included in the truncated regressionmodel are the load factor and airline size. Specifically, the model can be expressed asfollows:

dit b0 b1LFit b4Sizeit 1it 3

where dit is the technical efficiency scores; LFit is the load factor; Sizeit is a dummyvariable reflecting the size of different airlines, with 1 representing large UK airlines;and 1it is random error representing statistical noise. The model in equation (3) wasalso bootstrapped along with the double bootstrap procedure presented in section 4.2.The results are presented in Table IV. We verified that both variables have theexpected signs with each of load factor and airline size had a significant positiveimpact on technical efficiency. These findings are also in line with previous studies inthe literature (Coelli et al., 1999; Cornwell et al., 1990).

Thus, what do all the results indicate? It is possible to relate the results from thefirst and second stage estimation to the recent and current industry trends in theinternational aviation industry. The results, from the first stage, for example, clearly

indicated that there was a decreasing trend in the efficiency of UK airlines post 2004,while before 2004 the efficiency was strongly improving. Several external factorsmight have contributed to this finding. The sharp increase in oil price[7] post 2004, forexample, was an important factor. The oil price started to rise from 2004 to reach itshighest value on June 16, 2008 with a record price of $139.89 per gallon (Goldman,2008). Prior to 2004, the inflation adjusted the oil price and it was generally under$25/barrel. Following the increase in oil price, many UK airlines have recorded a sharpdecline in profit and high increase in costs, with some examples include Britishairways and Virgin. The trend was also international. In the US, American Airlineshas expressed intention to stop 75 of its planes and cut the number of seats. Fuel costshave also contributed to several airlines going bankrupt. Some recent examples include

Variable Coefficient Standard Error T-ratio

Constant 1.358 * * 0.200 6.781 *

LF 0.340 * 0.115 2.956 *

Size 0.014 * 0.003 0.666 *

Notes: Log-Likelihood 25.066; *Significant at the 5 per cent confidence level; * *significant at the1 per cent confidence level

Table IV.Bootstrapped truncatedregression of technicalefficiency

JES38,1

12


9/12

business carries MaxJet, Eos, Hawaiis Aloha Airlines and the US carrier ATA (Clark,2008). A European example is Alitalia Airline which has been funded from the Italiangovernment to keep flying (Clark, 2008).

The increase in oil price had also a direct impact of the load factor of certain airlines.

Some examples of airlines which recorded drop in the load factor include Britishairways (Davies, 2007) and Easy Jet (fall by 3 per cent in 2007). Those operating inregional areas were also directly affected. In this study, it was shown that the loadfactor has a significant positive impact on technical efficiency, so it is possible that byaffecting the load factor, oil price has also indirectly affected technical efficiency. Onemore factor worth mentioning is the link between oil price and increase in competition.This is because for some airlines the number of international passengers and thedomestic market share was decreasing. For example British Airways, together withVirgin have in the past two years announced capacity cut. Recently, ContinentalAirlines also announced cut by 11 percent and Air Canada by 7 percent. Small airlineswere also directly affected as they generally fly short and domestic routes that are lessprofitable than international long routes. Several recent reports have also discussedthat small airlines might not be able to sustain the future competitive environment, andas a result might consider merging with larger airlines.

To sum up, all the above factors might have had a direct or indirect impact on theefficiency variations of UK airlines. It is also worth noting that sometimes technicalinefficiency could also be attributed to many internal factors, such as managementmistakes or staff training. These factors are not part of the analysis, but future studiesmight consider investigating the impact of these factors through detailed case studieson some airlines. Future studies might collect data on future years of observations toassess the impact of the current economic situation or the drop in oil price, on theperformance of UK airlines. The inclusion of other international airlines might also addnew dimensions to the study and provide more validations to the results.

6. ConclusionsThe study has introduced the DEA double bootstrap methodology to measure thetechnical efficiency of UK airlines. The results could generate a starting point for policymakers at the different airlines by providing them with a comprehensive figure on theirlevel of scale and efficiency standings. The results can be used as an incentive to targetoperational deficiencies and seek new areas of efficiency improvements. However, themanagement of different airlines is also strongly encouraged to adopt a benchmarkingmanagement procedure in order to perform a continuous evaluation of theirperformance against operational strategies and to make the necessarily correctiveactions. It is also possible to validate the results of this study with some qualitativecase investigations of the major airlines involved, so future strategies taken by these

airlines in response to the current industry trends can be identified. The results canalso be used for international benchmarking purpose. For example, several studieshave looked at the efficiency of US and other European airlines, and each of studiesprovides an efficiency score on key airlines in these countries. Thus, it is possible forUK airlines to compare their average efficiency score with those international airlinesthat share similar characteristics.

The efficiency estimates indicated that the performance of UK airlines experiencedstrong increase between 2002 and 2004, while from 2005, the average technical


UK airlines

13


10/12

efficiency started to decline to reach its lowest level in 2007. In this paper we have alsoemployed a second stage estimation to explain the variation in technical efficiencyscores. Factors such as load factor and airline size were found to be significantly andpositively related to technical efficiency. The paper discussed that the decrease in

efficiency of UK airlines might be due to factors such as increase in oil price, intensemarket competition and drop in load factor. For small airlines the impact of thesefactors might have also been stronger as they usually suffer from weak economies ofscale.

Notes

1. Some other studies have also used the number of planes as a proxy for capital input. In thisstudy we the aircraft value as data on the number of planes are not consistently available forall airlines. Ahn et al. (1997) have previously used aircraft value in assessing the efficiency ofUS airlines.

2. Note that we followed the Federal Aviation Administration, and we defined large airlines

as those having 1 per cent or more of national enplaned passengers.3. A production function is said to exhibit constant return to scale (CRS) if a proportionate

increase in inputs results in the same proportionate increase in outputs. The variable returnto scale (VRS), on the other hand, does not assume full proportionality between the inputsand outputs.

4. The results reported in this paper are the VRS results; however we have also estimated theCRS model in order to determine the return to scale (RTS) properties of the different airlines.Fore more details on how to calculate RTS refer to Coelli et al. (1998). An airline is said to beoperating either at decreasing returns to scale (DRS) if a proportional increase of all inputlevels produces a less-than-proportional increase in output levels or increasing return toscale (IRS) at the converse case.

5. These are variables that are neither inputs nor outputs but are used to mainly explain the

variation in the efficiency scores.6. Simar and Wilson (1998) recommended the use of 2,000 bootstrap iterations to obtain reliable

bootstrap estimates.

7. It is possible to include oil price in equation (3) as a potential determinant on technicalefficiency. However, it is theoretically known that an increase in oil price affect the coststructure of airlines and this is more related to the concept of allovative or cost efficiency.The relationship between technical, cost, and allovative efficiency is expressed as follows:cost efficiency technical efficiency allocative efficiency. So, it possible that oil priceaffected indirectly technical efficiency through its direct impact on cost and allocativeefficiency. For more details on each type of efficiency refer to Coelli et al. (1998).

References

Ahn, S.C., Good, D. and Sickles, R.C. (1997), A dynamic frontier approach to assessing therelative efficiency of Asian and North American airline firms, paper presented at theTaipei International Conference on Efficiency and Productivity Growth.

Baltagi, B.H., Griffin, J.M. and Rich, D.P. (1995), Airline deregulation: the cost pieces of thepuzzle, International Economic Review, Vol. 36, pp. 245-59.

Barbott, C., Costa, A. and Sochirca, E. (2008), Airlines performance in the new market context:a comparative productive and efficiency analysis, Journal of Air Transport Management,Vol. 14, pp. 270-4.

JES38,1

14


11/12

Barros, C.P. (2004), Measuring performance in defense-sector companies in a small NATOmember country, Journal of Economic Studies, Vol. 31 No. 2, pp. 112-28.

Caves, D.W., Christensen, L.R. and Tretheway, M.W. (1984), Economies of density versuseconomies of scale: why trunk and local service airline costs differ, Rand Journal of

Economics, Vol. 15, pp. 471-89.Clark, A. (2008), American Airlines to cut planes and jobs, available at: www.guardian.co.uk/

business/2008/may/22/theairlineindustry (accessed May 2008).

Coelli, T., Prasada Rao, D.S. and Battese, G.E. (1998), An Introduction to Efficiency andProductivity Analysis, Kluwer Academic Publishers, Boston, MA.

Coelli, T., Perelman, S. and Romano, E. (1999), Accounting for environmental influences instochastic frontier models: with application to international airlines, Journal of

Productivity Analysis, Vol. 11, pp. 251-73.

Cornwell, C., Schmidt, P. and Sickles, R.C. (1990), Production frontiers with cross-sectional andtime-series variation in efficiency levels, Journal of Econometrics, Vol. 46 Nos 1/2,pp. 185-200.

Davies, P. (2007), BA and Ryanair see load factors decline, available at: www.travelmole.com(accessed October 2008).

Fleming, A. (2008), High oil prices mean higher airfares, available at: http://airtravel.about.com/od/traveltools/a/RisingOil.htm (accessed March 2008).

Goldman, D. (2008), Oil crosses $110, despite supply rise, available at: http://money.cnn.com/2008/03/12/markets/oil_eia/index.htm (accessed March 2008).

Goncharuk, A. (2007), Impact of political changes on industrial efficiency: a case of Ukraine, Journal of Economic Studies, Vol. 34, pp. 324-40.

Good, D., Roller, L.H. and Sickles, R.C. (1995), Airline efficiency differences between Europe andthe US: implications for the pace of EC integration and domestic regulation, European

Journal of Operational Research, Vol. 80, pp. 508-18.

Oum, T.H. and Yu, C. (1995), A productivity comparison of the worlds major airlines, Journalof Air Transport Management, Vol. 2, pp. 181-95.

Robertson, D. (2008a), Worlds airlines say they face $2.3 bn loss this year as oil price stayshigh, available at: http://business.timesonline.co.uk/tol/business/industry_sectors/transport/article4053861.ece (accessed June 2008).

Robertson, D. (2008b), European airlines face squeeze on profit, available at: http://business.timesonline.co.uk/tol/business/industry_sectors/transport/article3810477.ece

Schmidt, P. and Sickles, R. (1984), Production frontiers and panel data, Journal of Business andEconomic Statistics, Vol. 2 No. 4, pp. 367-74.

Simar, L. and Wilson, P.W. (1998), Sensitivity analysis of efficiency scores: how to bootstrap innonparametric frontier models, Management Science, Vol. 44, pp. 49-61.

Simar, L. and Wilson, P.W. (1999), Estimating and bootstrapping Malmquist indices, EuropeanJournal of Operational Research, Vol. 115, pp. 459-71.

Simar, L. and Wilson, P.W. (2000), A general methodology for bootstrapping in nonparametricfrontier models, Journal of Applied Statistics, Vol. 27, pp. 779-802.

Simar, L. and Wilson, P.W. (2007), Estimation and inference in two-stage, semi-parametricmodels of production processes, Journal of Econometrics, Vol. 136, pp. 31-64.

Wilber, D. (2008), Fuel costs squeeze US airlines, available at: www.washingtonpost.com/wp-dyn/content/article/2008/04/22/AR2008042202638.html (accessed May 2008).


UK airlines

15


12/12

Further reading

(The) Australian (2008), available at: www.theaustralian.news.com.au/story/ (accessed June,2008).

Blake, A. and Sinclair, M. (2003), Tourism crisis management: US response to September 11,

Annals of Tourism Research, Vol. 90 No. 4, pp. 813-32.

Giokas, D. (2008), Assessing the efficiency in operations of a large Greek bank branch networkadopting different economic behaviours, Economic Modelling, Vol. 25, pp. 559-74.

Good, D., Nadiri, M., Roller, L.H. and Sickles, R.C. (1996), Efficiency and productivity growthcomparisons of European and US air carriers: a first look at the data, Journal of

Productivity Analysis, Vol. 4, pp. 115-25.

Lall, S. (1980), Monopolistic advantages and foreign involvement by US manufacturingindustry, Oxford Economic Papers, Vol. 32, pp. 102-22.

Li, Y. and Hu, J. (2002), Technical efficiency and location choice of small and medium sizedenterprises, Small Business Economics, Vol. 19, pp. 1-12.

Morley, C. (2003), Impact of international airline alliances on tourism, Tourism Economics,

Vol. 9 No. 1, pp. 31-51.Wolf, B. (1977), Industrial diversification and internationalization: some empirical evidence,

Journal of Industrial Economics, Vol. 26, pp. 177-91.

Corresponding authorA. George Asaf can be contacted at: [email protected]

JES38,1

16

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints

the operational performance of uk airlines 2002-2007

Documents