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Exploratory analysis on LCC potential to influence airport efficiency
António Sérgio de Azevedo Domingues
Dissertação para obtenção do Grau de Mestre em
Complex Transport Infrastructure Systems
Júri
Presidente: Prof. Dr. Luís Guilherme de Picado Santos
Orientador: Prof. Dr. Maria do Rosário Maurício Ribeiro Macário
Vogal: Prof. Dr. Paulo Manuel da Fonseca Teixeira
July 2011
1
Abstract
The aim of this study is to perform an evaluation of the Portuguese airport infrastructure’s efficiency
performance. It will be undertaken an extensive literature review of methodological approaches in airport
performance research in order to understand which techniques are the most meaningful to be used in this
case study. Then, airport efficiency will be object of study in the different areas that characterize it:
airside, landside and within the airport-airline relationship. Finally, we will choose one of the
methodologies to perform the analysis of the Portuguese airport efficiency performance in order to
withdraw and analyse the results.
Keywords: LCC, Airports, DEA, efficiency
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Acknowledgments
I would like to thank Professor Rosário Macário for supervising my thesis dissertation. Moreover,
the development of this dissertation would not be as valuable without the contributions of Professor
Richard deNeufville’s priceless advices and Professor Carlos Pestana Barros’ support regarding the DEA
methodology. In addition, I would like to thank all CTIS’ faculty, lecturers and colleagues, responsible for
some of most the interesting discussions I had in the last two years.
I am also indebted to ANA’s marketing directors Renata Tavares, Andreia Pavão and Jocelyn
Ferreira for their availability in providing useful data. I want to address a special thank you note to Nuno
Costa for his personal availability and explanations concerning Lisbon airport’s operations.
Last but not least, I want to thank my family and closest friends for their enthusiastic support on this
stage of my academic experience. I am truly grateful to have you all in my life.
3
List of Contents
1 Introduction ................................................................................................................................ 8
1.1 Deregulation of the air transport market ............................................................................... 8
1.2 The background of no-frills airlines ..................................................................................... 10
2 Literature review ...................................................................................................................... 13
2.1 First steps on efficiency research: Farrell’s contribution .................................................... 13
2.1.1 Efficiency measurement: Simple Case ........................................................................ 14
2.1.2 The efficient production: Simple case.......................................................................... 15
2.2 The development of Farrell’s ideas .................................................................................... 17
2.2.1 Estimation processes for parametric frontiers ............................................................. 17
2.2.2 Linear programming methods ...................................................................................... 18
2.3 Methodological framework on airport benchmarking .......................................................... 19
2.3.1 Partial Measures .......................................................................................................... 19
2.3.2 Total factor productivity ............................................................................................... 20
2.3.3 Malmquist index of productivity change....................................................................... 21
2.3.4 Data Envelopment Analysis (DEA) .............................................................................. 22
2.3.5 Stochastic Frontier Analysis (SFA) .............................................................................. 24
2.4 Literature on airport efficiency research ............................................................................. 26
3 Main Drivers of Airport Efficiency ............................................................................................ 30
3.1 Airside ................................................................................................................................. 30
3.1.1 Airfield design .............................................................................................................. 31
3.1.2 Capacity and delays of airfields ................................................................................... 38
3.1.3 Demand management ................................................................................................. 43
3.2 Landside ............................................................................................................................. 44
3.2.1 Passenger buildings .................................................................................................... 45
3.2.2 Security and check-in processes ................................................................................. 51
3.2.3 Low-Cost Airports ........................................................................................................ 53
3.3 Airport-Airline Relationship ................................................................................................. 54
3.3.1 Airport privatization and management ......................................................................... 54
3.3.2 LCC’s implication on airports’ revenues ...................................................................... 60
3.3.3 Regulatory environment .............................................................................................. 61
4 Analysis of Portuguese airports’ efficiency .............................................................................. 67
4.1 Institutional setting .............................................................................................................. 67
4
4.2 Data Collection ................................................................................................................... 68
4.3 Model formulation ............................................................................................................... 72
4.3.1 Selection of Inputs and Outputs .................................................................................. 74
4.4 Discussion of results ........................................................................................................... 77
4.4.1 Approach 1: Monthly analysis ...................................................................................... 77
4.4.2 Approach 2: Each airport in each year as an individual firm ....................................... 79
4.4.3 Approach 3: Five year panel data ................................................................................ 81
4.4.4 Overall results .............................................................................................................. 82
5 Conclusions ............................................................................................................................. 84
Bibliography ....................................................................................................................................... 87
Appendix 1 – Airport efficiency performance research ..................................................................... 95
Appendix 2 – Compilation of Data ................................................................................................... 101
Appendix 3 – DEA scores on Portuguese airports .......................................................................... 109
5
List of Figures
1-1: European Seat Capacity by Service Type between 2002 and 2006 AND annual variation
(Source: RDC, 2007) .................................................................................................................................. 11
1-2: Evolution of passenger traffic structure in portuguese airports (Source: ANA, 2009) .............. 11
2-1: Farrell's Technical Efficiency (left) and piecewise Production Frontier (Right) (Source: (Farrell,
1957)) ......................................................................................................................................................... 15
2-2: Farrell's original diagrams on Increasing and Diminishing Returns to Scale (Source: (Farrell,
1957)) ......................................................................................................................................................... 16
2-3: ATRS TFP for world airports (Source: (Graham A., 2008)) ...................................................... 21
2-4: Scale efficiency in the BCC-dea MODEL (source: (Civil Aviation Authority, 2000)) ................. 23
3-1: New York LaGuardia Airport Layout (Source: (FAA, 2003)) ..................................................... 35
3-2: Denver International Airport layout (Source: (FAA, 2003)) ....................................................... 36
3-3: Left-Conventional exit taxiway (source: (FAA, 2007) ) Right - high-speed exit (source: (ICAO,
2004) ) ........................................................................................................................................................ 37
3-4: Potential congestion points at boston Logan (Adapted from (de Neufville & Odoni, 2003) and
(FAA, 2003)) ............................................................................................................................................... 41
3-5: 2009'S MONTHLY PAX DISTRIBUTION ON THE TOP5 BUSIEST AIRPORTS (SOURCE:
Adadpted from (ANA, 2009)) ...................................................................................................................... 46
3-6: Finger pier at Laguardia airport (left) and satellites at Tampa airport (right) (source: (FAA,
2003)) ......................................................................................................................................................... 48
3-7: Midfield linear at Denver airport (left) and X-shaped at Pittsburgh airport (source: (FAA, 2003) )
.................................................................................................................................................................... 49
3-8: Linear (Left) and Hybrid configuration (right) at Kansas City and Seattle Tacoma airports
(source: (FAA, 2003)) ................................................................................................................................. 50
3-9: World distribution of airports using CUSS kiosks and percentage with BTC (source: (IATA,
2010)) ......................................................................................................................................................... 52
3-10: Portuguese airport operator’s net profit between 2004 and 2009 (source: (ANA, 2009b)) .... 58
4-1: Structure of ANA’s group of companies (Source: (ANA, 2009B)) ............................................ 68
4-2: Annual variations of PAX and ATM between 2005 and 2009 (SOURCE: ANA) ...................... 69
4-3: VRS-DEA OUTPUT ORIENTED: MODELS 1 (LEFT) AND 2 (RIGHT) ................................... 78
6
4-4: VRS-DEA OUTPUT ORIENTED: MODEL 3 ............................................................................ 79
4-5: VRS-DEA OUTPUT ORIENTED: MODEL 4 ............................................................................ 80
4-6: VRS-DEA OUTPUT ORIENTED: MODELS 5 (LEFT) and 6 (RIGHT) .................................... 81
List of Tables
Table 2-1: Major methodologies in airport performance analysis (adapted from (Lai, Potter, &
Beynon, 2010)) ........................................................................................................................................... 25
Table 2-2: Airport efficiency papers (adapted from (Graham A. , 2008) and (Lai, Potter, & Beynon,
2010)) ......................................................................................................................................................... 28
Table 3-1: Traffic at worlds' 25 busiest airports (source: (ICAO, 2009), ACI web site) ................... 33
Table 3-2: Utilization ratio of Portuguese busiest airports in 2009 (Source: ANA) .......................... 40
Table 3-3: Airport regulatory method in EU countries in 2006 (Source: (Marques & Brochado,
2008)) ......................................................................................................................................................... 63
Table 3-4: Single or dual-till approach in EU countries in 2006 (adapted from (Marques &
Brochado, 2008) ) ....................................................................................................................................... 64
Table 3-5: Characteristics of the new Portuguese economic regulation on airports' charges ......... 65
Table 4-1: LCC operating in Portuguese airports between 2005 and 2009 ..................................... 72
Table 4-2: Yearly disaggregated data used in the VRS-DEA Output-Oriented models .................. 76
Table 4-3: Model characterization on the VRS-DEA efficiency benchmark..................................... 77
Table 4-4: Global statistics for models 1 (disaggregated) and 2 (aggregated) ............................... 78
Table 4-5: Global statistics for models 3 (disaggregated) and 4 (aggregated) ................................ 81
Table 4-6: Global statistcs for models 5 (disaggregated) and 6 (aggregated) ................................ 82
7
List of Abbreviations
ACI Airport Council International
ANA Aeroportos de Portugal, SA
ANAM Aeroportos e Navegação Aérea da Madeira, S.A
ATM Air Transport Movement
BSC Baggage System Capacity
BTC Bag tag capability
CAA British Civil Aviation Authority
CID Check-In Desks
CRS Constant Returns to Scale
CUSS Common-use Self-service (Check-in)
DBOT Design-Build-Operate-Transfer
DEA Data Envelopment Analysis
ELFAA European Low Fares Airlines Association
FAA Federal Aviation Administration
IACTA International Air Transportation Competition Act
ICAO International Civil Aviation Organization
LCC Low Cost Carrier
LoS Level of Service
LP Linear programming
MI Malmquist Index
MOPTC Ministry of Public Works, Transports and Telecommunications
NIRS Non-Increasing Returns to Scale
NLA New Lisbon Airport
OLS Ordinary least squares
PCR Price Cap Regulation
RDC Runway Declared Capacity
RoR Rate of Return Regulation
SFA Stochastic Frontier Analysis
SMC Social Marginal Cost
STATFOR Eurocontrol’s Air Traffic Statistics and Forecasts
TBG Total Boarding Gates
TE Technical Efficiency
TFP Total Factor Productivity
TPS Total parking stands
TTA Total Terminal Area
VRS Variable Returns to Scale
WLU Workload unit
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1 INTRODUCTION
This dissertation aims to understand the influence of Low-Cost carriers’ traffic in Portuguese
airports’ efficiency. Air travelling is now accessible to more people that could not afford to do so twenty
years ago, and low-fares airlines are largely responsible for this democratization process.
In one hand, the low-fares segment in air transportation has been registering a steady growth in the
past decade. On the other hand, as a public monopoly, the Portuguese airport operator registers yearly
profits that are becoming more attractive to private operators that coupled to State’s need of budgetary
balance, may result in privatization process to perform additional income in order to reduce national debt.
The structure of this dissertation is as follows. Chapter 1 will introduce the historical framework of
no-frills airlines. In chapter 2, we will conduct a literature review on efficiency research, from the first steps
taken on this field to the research conducted on airport efficiency. Afterwards, chapter 3 will try to
describe which are the drivers the influence the most airport operations, and thus their efficiency. Such
drivers will be approached under three perspectives – airside, landside and the airport-airline relationship.
We then hope to have gathered all necessary information to conduct an analysis on Portuguese airports’
efficiency on chapter 4. This chapter will focus on the institutional setting, model formulation and
ultimately discussion of results. Finally, conclusions will be drawn in chapter 5.
1.1 DEREGULATION OF THE AIR TRANSPORT MARKET
The deregulation and liberalization of air transport, firstly in the United States (US) in the late 70’s
and more recently in Europe and Asia in the late 90’s, transformed entirely the industry and travel
patterns worldwide. This is the primary reason why we face today a paradigm shift in the management of
airport infrastructures and the need for in-depth research of airport’s performance.
Although formal deregulation of air transport in the US only started in 1978 when President Carter
signed the Airline Deregulation Act, there was already an history of continuous efforts to adopt
competitive bilateral agreements that goes back to the 1940’s. This process culminated in the
International Air Transportation Competition Act of 1979 (IATCA) that stated clearly the main purpose to
allow designation of multiple carriers, liberal access to charter carriers, the elimination of capacity and
fare restrictions, and common treatment of domestic and foreign carriers for airport facilities. (H. Good,
Röller, & Sickles, 1995)
Boosted by the enthusiasm of US deregulation, one of the first IACTA’s implementations was the
US-Netherlands bilateral, signed in March 1978, which allowed individual carriers to determine their own
capacity, frequency and tariffs with reduced government intervention. It also provided multiple carrier
designation and virtually unlimited access by charter operators. This was to become the trendsetter for
future US bilateral. By the end of the year, Germany and Belgium, mainly due to geographic reasons, had
9
also reviewed their bilateral agreements with the US, as they could not be less liberal on either scheduled
or charter rights than the Dutch had been, otherwise transatlantic air traffic would be diverted via
Amsterdam. (Doganis R. , 2001)
In 1982, the US also succeeded in negotiating an agreement with the European Civil Aviation
Conference for North Atlantic routes. This agreement stipulated that governments would automatically
approve any fare that was in a 'zone of reasonableness’ that was as low as 50% of current fares. (H.
Good, Röller, & Sickles, 1995). Deregulation through bilateral renegotiation was also being pursued by
the US in other international markets. Following the same pattern as of the agreements established in
Europe, the North and mid-Pacific markets were target of several key bilateral agreements between 1978
and 1980, with Singapore, Thailand and Korea, and other states later (Doganis R. , 2001). These bilateral
agreements were undoubtedly more favourable to the United Stated rather than to Europe or Asia, since
the number of departing points from the US was restricted.
On the other hand, European air and sea transport was exempt from rules regarding competition
policy by the article 84 of the 1957’s Treaty of Rome. This resulted in a majority of stated-owned airline
companies, with three main sets of implications according to Good, Röller, & Sickles (1995). Firstly,
managerial incentives for productive efficiency are compromised because ownership is not concentrated
in the hands of individuals who have the lowest monitoring costs. Secondly, government ownership does
not focus on simple profit maximization due to other sets of political agendas (usage of national airlines
for employment policy, power limitation to avoid competition with other state-owned transportation
companies such as railroads, etc). Finally, government ownership has brought with it access to subsidy
which tends to restrain the pursuit of productive efficiency.
Until the “French Seamen” case (European Court of Justice, 1974), European Member-States
strongly opposed EU’s interference in the air and maritime sectors. This case was crucial for the long-
term application of completion rules to transportation sectors. However, the historical “Nouvelles
Frontiéres” case (European Court of Justice, 1986) was the turning point in the Commission’s attempts to
introduce liberality into the air sector. In this case, the European Court of Justice definitively confirmed
that EU Treaty’s competition rules applied to the air transport sector.
Although from 1984 onwards, Europe too moved away from the traditional bilateral agreements into
more liberal and free-market policies pushed by the United Kingdom, the jurisprudence of the “French
Seaman” case was of great importance as the driving force of the Commission’s efforts to liberalize
European air transport.
Nevertheless, this process still took five years and three legislation packages (respectively
applicable from January 1st 1988, November 1st 1990 and January 1st 1993) to be finalized, and it only
concerned Member-states. These gradual steps can only be understood in a wider political perspective of
European integration. Signed in February 7th 1992, Maastricht Treaty had as goal the creation of a single
internal market, covering (by then) the twelve member-states, to come into existence in the beginning of
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1993. This meant that by the end of 1992, the twelve member-states’ immigration and custom controls
were to be abolished, so that the European Union would truly be single “domestic” market, open to the
free movement of goods, services and people (Doganis R. , 2001).
In April 2004, a new package was enforced. Single European Sky (SES) is an example of EU’s
reregulation of aviation policy as it aims to rationalise the costs and emissions along with the
improvement of air safety and it involves currently 38 countries. (Kawagoe, 2008)
As Good, Röller, & Sickles (1995) had foreseen, “full liberalization can hardly be expected within
the next few years, air carriers are currently feeling the impact of a more competitive environment.
Several carriers are going through strategic evaluations of their competitive position, not only vis-à-vis
other European competitors, but also vis-a-vis the US carriers”. Indeed, air transportation market in
Europe has become very competitive with constant entry and exit of players, mergers and managerial
revolutions.
1.2 THE BACKGROUND OF NO-FRILLS AIRLINES
One would think that the impact of deregulation on air transport market and consequent evolution
of airlines’ business models such as alliances or e-commerce, would also be the natural cause of the low-
cost revolution. On the contrary, Southwest Airlines first introduced the low-cost, non-frills business model
in 1967 in the United States. By 1978, when the US deregulation act came, Southwest was well placed to
expand beyond its home base in Texas.
Southwest, as many European low-cost airlines did later, concentrated its strategy on operating
short-haul distances at low and unrestricted fares, with high point-to-point frequencies and excellent
departure punctuality. It achieved so by offering high frequency, scheduled, point-to-point, short-haul
services at low simple fares. In order to be able to offer low fares, it operated a single aircraft type with
high-density seating and aim at high daily utilisation by reducing turnaround times to thirty minutes or
less. Using less congested and secondary airports, reduced airport related costs were possible to
achieve, and facilitated short turnarounds and higher punctuality. All traditional scheduled frills such as
free in-flight meals, pre-assigned seats and connecting flights were cut back. Some airlines went even
further by completely cutting out travel agents’ commissions and only selling directly to their customers.
Furthermore, Southwest betted on an intensive marketing strategy where “flying is fun”, and to do so, the
key factor was flexible and highly motivated staff. (Doganis R. , 2001).
Launched in 1985, Ryanair was the first low-cost, no frills European airline. However, it was not
profitable and by 1991, it had accumulated big losses. In 1992, following the “Southwest Model”, Michael
O’Leary’s Ryanair recorded a small pre-tax profit and was to become responsible for the radical change
in European air transport.
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1-1: EUROPEAN SEAT CAPACITY BY SERVICE TYPE BETWEEN 2002 AND 2006 AND ANNUAL VARIATION (SOURCE:
RDC, 2007)
Low Cost Carriers (LCC) have come a long way since Ryanair broke the mould of conventional
European airlines in the early 1990s. Between 1994 and 2002, the European low-cost market has grown
from around 3 million passengers to over 20 million annual passengers (RDC, 2002). According to the
last RDC Low Cost Monitor report available at this time, by 2006 the European low-cost seat capacity
was already over 178 million passengers. (RDC, 2007)
Portugal is not an exception and it has been registering a continuous and steady growth of no-frills
airlines’ traffic. The change in passenger traffic structure at Portuguese airports between 2004 and 2009
highlights the exponential growth of low-cost traffic as depicted in figure 1-2. Growing on average 35.1%
per annum, the low cost segment has conquered 21,6 % of market share in 5 years to traditional
companies and charter companies. (ANA, 2009b)
1-2: EVOLUTION OF PASSENGER TRAFFIC STRUCTURE IN PORTUGUESE AIRPORTS (SOURCE: ANA, 2009)
16,9%8,0%
8,6% 30,2%
74,6%61,9%
0%
20%
40%
60%
80%
100%
2004 2009
Traditional
LCC
Charter
12
The increase of LCC operating in the European market has lead to complaints demanding lower
airport fees. Nonetheless, the monopoly position of the Portuguese airports and the centralized
management by the Ministry of transports, make them not open to lower the fees and attracting more
traffic. Moreover, the government has a national airline, which is protected with preferential fees, by the
main Portuguese airports. A complaint from a private airline at European Union resulted in the
condemnation of the Portuguese airports (Barros C. P., 2008). Moreover, rigid masterplans with lifespan
of 30 years or more are usually the cornerstone of Portuguese airport infrastructure’s building and
expansion policies, which frequently did not forecast the current trends in civil aviation. Therefore, the
bigger question aimed to be answered is to which extent are Portuguese airports efficient in leading with
the expansion of LCC.
As the air transport market continues to develop, the efficiency evaluation of European airports is
subject of many studies, and it is our intention to contribute to that research as thoroughly as possible by
trying to understand how LCC are influencing Portuguese airports’ efficiency.
13
2 LITERATURE REVIEW
The present chapter consists mostly in the literature review of the different methodologies used on
efficiency research. Although some of the techniques used go back to the 50’s, the amount of literature in
this field has grown enormously in the past years, mostly due to the severe changes that the worldwide
aeronautical market has experienced in the last quarter of the 20th century as mentioned previously.
To perform the intended efficiency analysis, it is crucial to start by understanding what does
efficiency mean and which are the possible resources to use.
Therefore, this literature review will start by the first steps given towards efficiency measurement
and followed by the contributions made by the scientific community. Afterwards, the methodological
framework for airport efficiency benchmarking is presented, and finally, the literature on airport efficiency
analysis is given.
2.1 FIRST STEPS ON EFFICIENCY RESEARCH: FARRELL’S CONTRIBUTION
Firstly, it is important to distinguish efficiency from productivity. This is important in the sense that
the terms efficiency and productivity are often used interchangeably, even though the underlying
meanings of these two terms are not identical.
According to (Fried, Lovell, & Schmidt, 2008) producer’s productivity is defined as the ratio of its
output to its input. This ratio is easy to calculate if the producer uses a single input to produce a single
output. In the more likely event that the producer uses several inputs to produce several outputs, the
outputs in the numerator must be aggregated in some economically sensible fashion, as must the inputs
in the denominator, so that productivity remains the ratio of two scalars. On the other hand, by the
efficiency of a producer, we have in mind a comparison between observed and optimal values of its
output and input. The exercise can involve comparing observed output to maximum potential output
obtainable from the input, or comparing observed input to minimum potential input required to produce
the output, or some combination of the two. In these two comparisons, the optimum is defined in terms of
production possibilities, and efficiency is technical. It is also possible to define the optimum in terms of the
behavioural goal of the producer. In this event, efficiency is measured by comparing observed and
optimum cost, revenue, profit, or whatever goal the producer is assumed to pursue, subject, of course, to
any appropriate constraints on quantities and prices. In these comparisons, the optimum is expressed in
value terms, and efficiency is economic.
Immediately after World War II, for obvious reasons, there was a general interest in growth and
productivity. One of the most influential papers on these issues was by Robert M. Solow (Solow, 1957)
within a macroeconomic setting. At the same time, Farrell laid the foundation for new approaches to
efficiency and productivity studies at the micro level, involving new insights on two issues: how to define
14
efficiency and productivity, and how to calculate the benchmark technology and the efficiency measures.
(Førsund & Sarafoglou, 2002)
According to Farrell, “the problem of measuring the productive efficiency of an industry is important
to both the economic theorist and the economic policy maker. If the theoretical arguments as to the
relative efficiency of different economic systems are to be subjected to empirical testing, it is essential to
be able to make some actual measurements of efficiency. Equally, if economic planning is to concern
itself with particular industries, it is important to know how far a given industry can be expected to
increase its output by simply increasing its efficiency, without absorbing further resources”. (Farrell, 1957)
2.1.1 EFFICIENCY MEASUREMENT: SIMPLE CASE
The fundamental assumption present in his paper was the possibility of inefficient operations.
Opposing the mainstream economic literature of the time that suggested the average performance as the
production function, Farrell’s concept of frontier production function as benchmark was pioneer. To that
respect, the new aspect was to decompose efficiency at the micro level of a firm in three levels: technical,
price and overall efficiency.
Even though Farrell himself refers to the existence of similarities between Debreu’s coefficient of
resource utilization (Debreu, 1951) and his own measure of technical efficiency (TE), the former was able
to create a much more relevant with the creation of a benchmarking tool in respect to firms operating in
the same market whereas the latter worked mostly on the resource cost side, defining his coefficient as
the ratio between minimized resource costs of obtaining a given consumption bundle and actual costs, for
given prices and a proportional contraction of resources. (Førsund & Sarafoglou, 2002).
Farrell’s original diagrams, depicted in figure 2-1, illustrate the case of a firm producing a single
output by employing two factors of production (x and y), while, for simplicity reasons, assuming constant
returns to scale (CRS). The diagram on the left assumes to know the efficient production function, by
which a perfectly efficient firm obtains the maximum output from any given combination of inputs. P
represents the inputs of the two factor of production and the assumption of CRS allows representing the
isoquant SS’, where the firm might produce unit output efficiently with the various combinations of the two
production factors.
Understandably, Q represents an efficient firm using production factors in the same ratio as P
(since OP and OQ have the same slope). Given so, one can say that the efficient firm produces OP/OQ
times as much output from the same inputs, or in other words, Q produces the same output as P using
only a fraction OQ/OP as much of each factor. Farrell defines OQ/OP as the technical efficiency of the
firm P.
However, one also needs a measure of the extent to which a firm uses the various factors of
production in the best proportions, in view of their prices. (Farrell, 1957). By defining AA’s slope equal to
the ratio of the two production factors’ prices, we are able to find Q’ as the optimal method for production,
15
since that the production costs at Q’ will only be a fraction of OR/OQ of those at Q. Following the same
line of reasoning above mentioned, the ratio OR/OQ is the price efficiency of the firm Q. Furthermore, if
the firm P were to change the inputs’ proportions until the same defined in Q’, while keeping the same
technical efficiency and assuming that factor prices did not change, its costs would also be reduced by
the same factor as Q, hence, OR/OQ is the price efficiency of the firm P too. Nevertheless, Farrell does
call out attention to the impossibility to foresee whether TE remains constant with changes in proportions
of inputs or not.
Finally, if P were to be truly efficient, both technically and in respect of prices, its costs would be a
fraction OR/OP of what in fact they are to produce the same output. Farrell calls this ratio the overall
efficiency of the firm, and notes its equivalence to the product of technical and price efficiencies.
2-1: FARRELL'S TECHNICAL EFFICIENCY (LEFT) AND PIECEWISE PRODUCTION FRONTIER (RIGHT) (SOURCE:
(FARRELL, 1957))
2.1.2 THE EFFICIENT PRODUCTION: SIMPLE CASE
Farrell’s efficiency measures, as mentioned above, were defined based on the assumption that the
efficient production function is known, that is, each firm’s performance observed is compared to some
standard of perfect efficiency. Hence, the definition of the efficient production function is in need in order
to understand the significance of the efficiency measures.
Farrell starts by refusing the postulate standard of perfect efficiency that represents what is
theoretically attainable in despite of an empirical function based on the best results observed in practice.
Although the former is “a reasonable and perhaps the best concept for the efficiency of a single
production process, there are considerable objections to its application to anything so complex as a
typical manufacturing firm, let alone an industry. If the measures are to be used as some sort of yardstick
16
for judging the success of individual firms or industries, it is far better to compare performances with the
best actually achieved than with some unattainable ideal.” (Farrell, 1957)
For instance, as we shall see later in this discussion, the variety of ways to address airport’s inputs
(outsourcing of labour, services, etc), is such that it would be a process too complex, and consequently,
less likely to obtain an accurate theoretical production function. Furthermore, being the airport industry an
economy both of scale and scope, it is also more exposed to the inherent human imperfection. Hence,
the theoretical function is likely to be widely optimistic. The problem then becomes how to estimate an
efficient production function from the observed firms.
Based on the same assumptions above mentioned, Farrell suggested that each firm could be
represented by a point on an isoquant diagram, therefore obtaining the scatter of points in left diagram of
figure 2-1. His contribution was to introduce a piecewise linear envelopment of the data as the most
pessimistic specification of the frontier, in the sense of the function being as close to the observations “as
possible”. (Førsund & Sarafoglou, 2002).
Moreover, Farrell has had also the foresight to address the generalization to the case of multiple
inputs and outputs, and analyzed the issue of increasing and diminishing returns to scale. Although his
method was perfectly valid for the case of diseconomies of scale, his primary assumption of convexity
would not hold for the opposite situation. Meaning that the method will give an optimistic instead of a
conservative estimate of S (some straight line like OR in figure 2-2) and, of course a pessimistic estimate
of the efficiency of any point. (Farrell, 1957). Finally, he also approached, although very roughly, the
parametric method, by approximating his methodology to the Cobb-Douglas’ production function.
2-2: FARRELL'S ORIGINAL DIAGRAMS ON INCREASING AND DIMINISHING RETURNS TO SCALE (SOURCE: (FARRELL,
1957))
17
Nonetheless, the first step has been given, and it was indubitably a very important one. As Farrell
himself suggested, this paper would be “of interest to a wide range of economic statisticians, business
men and civil servants”, and therefore the object of research for many years to come.
2.2 THE DEVELOPMENT OF FARRELL’S IDEAS
Farrell’s efficiency concepts are still the basic definitions in use today. The estimation methods for
both the non-parametric and parametric frontier introduced by Farrell are the foundation for later
contributions. (Førsund & Sarafoglou, 2005).
Both importantly, the broad range of applications and limitations of the piecewise efficiency frontier
proposed by Farrell, gave rise to different research fields to contribute with further investigation. In this
section, estimation processes and linear programming (LP) developments will be addressed, given that,
they turned out to be today’s most influential efficiency measurement tools.
2.2.1 ESTIMATION PROCESSES FOR PARAMETRIC FRONTIERS
Regarding the developments in the estimation processes for parametric frontiers, Førsund &
Sarafoglou (2005) proposes three main methodologies.
Chronologically, C. B. Winsten was the first when, in the 1957’s Discussion on Mr. Farrell,
wondered if “the efficient production function turned out to be parallel to the average production function,
and whether it might not be possible to fit a line to the averages, and then to shift it parallel to itself to
estimate the efficient production function.” Nevertheless, only seventeen years later, J. Richmond’s
“Estimating the Efficiency of Production” defines this approach as “Corrected Ordinary Least Squares”
with any references neither to Farrell nor to Farrell’s Discussion.
In 1968, the Cobb-Douglas production function was used by (Aigner & Chu, 1968) as a benchmark
for the efficiency frontier estimation, using linear and quadratic programming to calculate the frontier, as a
deterministic parametric approach.
(Afriat, 1972) was the next milestone. He elaborated further ideas from Farrell and the later
discussion of Farrell’s paper. A statistical framework was formulated for finding maximum likelihood
estimators for the parameters of frontier functions, leaving the pure programming format. Nonetheless,
according to (Førsund & Sarafoglou, 2005), “ (Afriat, 1972) also contributed within the non-parametric
framework of piecewise linear frontier functions by formulating the model with variable returns to scale (in
the single output case). This was later referred to as the BCC model (Banker, Charnes, & Cooper, 1984)
by the group of the operations research and management science community.”
Finally, and simultaneously in 1977, (Aigner, Lovell, & Schmidt, 1977) and (Meeusen & van den
Broeck, 1977) opened the door for more rigorous econometric analysis of frontier functions by introducing
the composed error term in parametric models. This methodology, named by the former as stochastic
18
frontier function, suggested that the composed error consisted of two parts, a stochastic component
symmetrically distributed, catching “white noise” and a stochastic component with a one-sided
distribution, representing inefficiency. Moreover, within the single-output model the connection between
the one-sided term and the Farrell measure of (output-oriented) technical efficiency was direct. (Førsund
& Sarafoglou, 2002). This methodology is today known as Stochastic Frontier Analysis (SFA).
Once again, the tribute to Farrell is paid. “It has only been since the pioneering work of Farrell
(1957) that serious consideration has been given to the possibility of estimating so-called frontier
production functions.” (Aigner, Lovell, & Schmidt, 1977)
2.2.2 LINEAR PROGRAMMING METHODS
In 1967, J.N. Boles developed an explicit linear programming model used by Berkeley agricultural
economists to improve Farrell estimation method. Their efforts, however, failed to become
acknowledgeable. Nevertheless, this methodology was to become a centrepiece of later important work.
It was only one decade later, when in 1978, Abraham Charnes, William. W. Cooper and Edwardo
Rhodes published the highly influential paper “Measuring the efficiency of decision making units”, based
on Farrell’s concept of efficiency measurement. According to (Førsund & Sarafoglou, 2002), “the model
was readily computable, either using standard linear programming codes on mainframes or developing
more efficient tailor-made software. However, the linear programming model is identical to one of the
models in Boles (1971).”
A very important contribution by (Charnes, Cooper, & Rhodes, 1978) was the explicit connection
made between a productivity index (in the form of weighted sum of outputs on a weighted sum of inputs)
and Farrell’s technical efficiency measure. Although some may dispute that Farrell’s ingenious concept of
efficiency measure was put in simple terms for better comprehension, and therefore omitted “heavier”
economic terminology, CCR offered the bridged between the engineering concepts of micro productivity
ratios and economists’ concept of efficiency. (Førsund & Sarafoglou, 2002)
This methodology would become later known as Data Envelopment Analysis (DEA), and, it is
nowadays, the most used methodology in airports’ efficiency measurement.
The 70’s witnessed major developments in estimation methods, all based on Farrell’s ideas,
spreading worldwide his ideas. However, all methodological developments were related with statistical
inference, hence parametric methods, and apart from Berkeley agricultural economists, economic
statisticians did not follow the non-parametric method. Nonetheless, Charnes, Cooper & Rhodes’s paper
was the one that allowed contributions from operations research and management sciences for further
developments.
Whereas the parametric approach is usually associated with statistical inference, the non-
parametric has been labelled of deterministic. On the turn to the 21st century new research has been
19
conducted, trying to merge both methodologies, by making statistical inference possible for the later. See,
(Banker R. D., 1993), (Simar & Wilson, 2000a) and (Simar & Wilson, 1998)
2.3 METHODOLOGICAL FRAMEWORK ON AIRPORT BENCHMARKING
In this section, a description of the most relevant methodologies used in airport’s performance
benchmarking is undertaken, followed by an inter-comparison between them.
Regarding the methodological framework classification of performance benchmarking, (Tovar &
Martin-Cejas, 2010) for example, suggests the division of the different approaches into two groups. On
one hand, a traditional group that ignores inefficiency, hence considering that the observed output is
always the best output. On the other hand, when inefficiency exists, a frontier approach where a best
practice frontier, by which each firm is to be compared, has to be estimated. In both groups, parametric
and non-parametric approaches exist. In the frontier group, SFA and DEA are, respectively, examples of
parametric and non-parametric approaches, whereas in the traditional group, index numbers and ordinary
least squares (OLS) are, respectively, examples of non-parametric and parametric approaches.
Historically, it is also worthwhile mentioning a third group, which involves the use performance indicators
in a partial approach.
Currently, numerous methodologies allow the comparison of different firms’ performance in order to
depict the best practice of that industry. Nonetheless, each one of the available methodologies on
performance benchmarking has its pros and cons, and much is still to be done in order to overcome those
drawbacks.
2.3.1 PARTIAL MEASURES
This method uses partial ratio data to carry out performance comparison of target sample in single
dimension, such as on financial and cost performance. (Lai, Potter, & Beynon, 2010).
For instance, (Francis, Humphreys, & Fry, 2002) reveals that, comparative performance of airports
amounted to the collection and comparison of financial and output measures by Governments, who
typically owned and operated the majority of airports. Profiting from the airline’s definition of workload unit
(WLU) as one passenger processed or 100 kg of freight handled, airports adopted this measure during
the 1980’s to provide a single measure of output for passenger or freight service. Typical partial
measures used included: total cost per WLU; operating cost per WLU; labour cost per WLU; WLU per
employee; total revenue per WLU and aeronautical revenue per WLU.
According to the (Civil Aviation Authority, 2000), this method can provide useful insights into
particular areas of inefficiency. However, when taken alone, it can also provide a distorted picture of
performance by ignoring the interaction between inputs used and outputs produced. These partial
productivity measures have been criticized on the ground that they:
20
• do not reflect differences in factor prices;
• do not take into account of possible factor substitution in production;
• fail to take account of the differences in operating environments between firms;
• are unable to handle multiple outputs.
Alternatively, to obtain an overall picture of performance it may be used index number techniques,
as discussed hereafter.
2.3.2 TOTAL FACTOR PRODUCTIVITY
In essence, the total factor productivity (TFP) method compares an index of outputs to an index of
inputs, allowing comparisons of the same airport in different periods or between different airports. The
weights in constructing the indexes can be the revenue shares and cost shares as indicators of the
importance of outputs and inputs in the production process. (Hooper & Hensher, 1997). TFP is then
calculated as the ratio of aggregated output to aggregated input. Although this measure does not suffer
from the shortcomings of partial measures, as an aggregate measure it has no sufficient information for
evaluating management strategies when taken alone. Furthermore, it requires information on prices,
which are used to aggregate inputs and outputs, which may not be readily available. (Civil Aviation
Authority, 2000).
It is important to emphasize that there are a number of different ways in which TFP can be
assessed. The above mentioned, is also called of non-parametric as it requires only the aggregation of all
outputs into a weighted outputs index and all inputs into a weighted input index with no assumptions or
estimates of the parameters of the underlying production or cost function having to be made. According to
(Graham A. , 2008), one of the most comprehensive studies of TFP in the airport sector is contained in
the Global Airport Benchmarking Report, produced annually by the Air Transport Research Society. The
methodology uses revenue shares as weights for the outputs (aircraft movements, passengers, cargo)
and cost shares as weights for the inputs (labour, runways, terminals, gates) where capital input was
excluded due the difficulties in accurate and comparable data.
Conversely, in the parametric approach, a production or cost function is estimated by using either
regression analysis or a stochastic frontier method. These models can be used to investigate factor
substitution, the impact of variations of input and output prices and to test for economies of scale.
However, because this approach has detailed data requirements, it has not been used very often for
airports.
A final approach is the endogenous-weight TFP method, where detailed cost and revenue data is
deferred to a multi-input/multi-output production function instead. Then, physical and financial input and
output data is used to estimate the TFP. Yoshida Y. (2004) applied this method to Japanese airports.
(Graham A. , 2008)
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2-3: ATRS TFP FOR WORLD AIRPORTS (SOURCE: (GRAHAM A., 2008))
2.3.3 MALMQUIST INDEX OF PRODUCTIVITY CHANGE
The Malmquist Index (MI) is a bilateral index that can be used to compare the production
technology of two economies. To calculate the MI of economy A with respect to economy B, we must
substitute the labour and capital inputs of economy A into the production function of B, and vice versa.
(Wikipedia, 2010)
The CAA (Civil Aviation Authority, 2000) recognizes some advantages of MI over TFP in that:
• price data are not required;
• assumption of cost minimization or revenue maximization is not needed;
• the MI obtained may be further decomposed into:
o technical efficiency change (firms getting closer to the frontier);
o technical change (shifts in the frontier itself).
Hence, MI uses distance functions to describe a multi-input, multi-output production technology
without the need to specify a behavioural objective. It measures the productivity change between two
data points by calculating the ratio of the distances of each data point relative to a common technology. In
fact, MI is the geometric mean of two TFP indices: one is evaluated with respect to base period s
technology and the other one with respect to next period t technology. A value of MI bigger than one
implies a positive TFP growth from period s to period t. In practice, four input-oriented distance measures
must be calculated for each firm in each pair of adjacent periods by solving four (CCR) DEA-like linear
programming problems. Similar to DEA approach, this can be further extended by decomposing the
22
technical efficiency change into scale efficiency and pure technical efficiency components by adding the
convexity restriction.
2.3.4 DATA ENVELOPMENT ANALYSIS (DEA)
Non-parametric frontier methodologies use the panel data to establish best-practice frontiers. They
are parameter-free because no assumptions on production or cost functions are made, and the efficiency
measurements are based on the comparison among the different firms.
Based on (Farrell, 1957), and coined by (Charnes, Cooper, & Rhodes, 1978), the DEA
methodology is a non-parametric approach that uses linear programming to construct a piecewise linear
efficient frontier that envelops the data based on information of inputs and outputs only. Efficiency
measures are then calculated relative to this frontier. DEA measures relative efficiency by comparing the
efficiency of a decision-making unit (DMU) with the efficiency of other DMUs (or peer groups) that have a
similar mix of inputs. This ensures that like-with-like comparisons are made. The peer group is defined
such that a linear combination of these DMUs can be shown to have at least as great an output as the
target DMU given the similar input mix and operating environment. (Civil Aviation Authority, 2000).
Farrell’s efficiency frontier, as depicted previously in figure 2-2, provides a more easily
understanding example of an input minimisation DEA model under the CRS assumption.
This methodology can consider a variety of models, such as:
• Constant returns to scale (CRS), also known as the CCR-DEA model, after (Charnes,
Cooper, & Rhodes, 1978), ;
• Variable returns to scale (VRS), also known as BCC-DEA model, after (Banker, Charnes,
& Cooper, 1984);
• Application of Malmquist DEA method to panel data to calculate indices of total factors of
productivity change.
There is some resemblance with the non-parametric TFP as this method also produces a weighted
output index relative to a weighted input index. The key advantage is that not only DEA does not involve
the estimation of underlying production or cost functions, but also the weights for inputs and outputs are
not predetermined, but instead the result of the programming procedure.
According to (Graham A. , 2008), the DEA is therefore a more attractive technique for dealing with
multiple input and output activities than the index number TFP because it has less demanding data
requirements. Furthermore, this method measures the efficiency of DMUs engaged in performing the
same function, therefore with regard to the sample instead of absolute terms. The most efficient DMUs or
firms, will be located on the efficiency frontier with relative index of 1,0. Although DEA produces relative
rankings, it does not explain by itself the observations. This can be partially overcome with the application
of the MI, which when used with DEA is a useful way of identifying the sources of productivity differences
23
over a certain period. This happens because this index allows productivity change to be decomposed into
technical changes gained from adopting new technologies and efficiency changes (which in turn can be
decomposed into gains from utilizing scale and reducing inefficiency).
It is also worth mentioning the fact the DEA methodology can be either input or output oriented. In
other words, the former is desired if the objective is to produce the existing level of output with at least
one less unit of input, whilst in the later more output must be produced for the same existing inputs, so
that efficiency gains are attained.
As mentioned above, as DEA allows for both CRS and VRS, it can also be used for measuring
scale effects on airports. In the BCC-DEA model, technical efficiency can be further decomposed into
scale efficiency and pure technical efficiency. Scale efficiency being defined relatively to the ratio of TE in
the CCR-DEA model for TE in the BCC-DEA model. Figure 2-4 depicts clearly this fact.
2-4: SCALE EFFICIENCY IN THE BCC-DEA MODEL (SOURCE: (CIVIL AVIATION AUTHORITY, 2000))
However, since it is not statistically based, any deviation from the frontier is entirely attributable to
inefficiency, making DEA more prone to data measurement error and outlying data problem than
parametric approaches. (Civil Aviation Authority, 2000). On the other hand, the paper recently published
by (Simar & Wilson, 2007), result of continuous work on the last decade on methods for bootstrapping
non-parametric approaches, has lead to a two-stage DEA, which allows for statistical inference.
24
Other criticisms pointed to DEA are related with the tendency to overstate performance when the
combined number of outputs and inputs is large relative to the number of DMUs, the difficulty to analyse
productive efficiency changes over time and any deviation, which is sensitive to the presence of outliers,
is labelled as inefficient.
An alternative to DEA, is the free disposable hull methodology (FDH). It is a special case of the
DEA model, where the points on lines connecting the DEA vertices are not included in the frontier.
Because the FDH is interior to the DEA frontier, the estimates of average efficiency are larger than in
DEA. FDH, like in DEA, assumes no assumption regarding the function form and do not require random
error.
However, to the best of my knowledge, there is no literature in airport performance benchmarking
using this last methodology, thus, should not be considered as a relevant methodology for our case study.
2.3.5 STOCHASTIC FRONTIER ANALYSIS (SFA)
Contrarily to the non-parametric methodologies, parametric approaches not only specify functional
form, but also take account of the residual term in the analysis. In other words, it provides not only a
measurement of efficiency, but can also be used as an “explanation” for inefficiency.
SFA originated with two papers, published nearly simultaneously by two teams on two continents.
(Meeusen & van den Broeck, 1977) appeared in June, and (Aigner, Lovell, & Schmidt, 1977) appeared
one month later. The two papers are themselves very similar, since both were three years in the making
and appeared shortly before a third SFA paper. (Kumbhakar & Lovell, 2000)
Stochastic frontiers differ from the parametric approach in that they can accommodate data noise
and statistical tests.
The SFA methodology specifies a function form of the cost, profit or production relationship among
inputs, outputs and environmental factors and allows for random errors. Hence, it is also called
econometric approach. It gives a composed error model where inefficiencies are assumed to follow an
asymmetric distribution, while random errors follow a symmetric distribution. The logic behind is that the
inefficiencies must have a truncated distribution because inefficiencies cannot be negative. If inefficiency
is bigger than zero, than the composed error term is negatively skewed, and there is evidence of
technical (in)efficiency (TE). In order to obtain an estimate of the TE of each producer, this requires
strong distributional assumptions in order to decompose the two error components.
However, according to (Civil Aviation Authority, 2000), in many practical cases, there may not be
sufficient data available to support these assumptions. In despite of the strong distributional assumptions,
the success of SFA also depends critically on the functional form used to describe the relationship
between the inputs and outputs, the variables included in or excluded from the model, and potential
problems of correlation between two or more DMUs (multicollinearity) or correlation between DMUs and
25
the error term (endogeneity). However, being a statistically based approach, some statistical diagnostics
could be used to gauge the appropriateness of the SFA model specification and assumptions if sufficient
data is available.
Therefore, the major criticisms made to SFA methodology are related with the fact that it requires
behavioural assumptions (such as cost minimisation or revenue maximisation), has the need for clear
definition of inputs and outputs to avoid endogeneity problem, is prone to misspecification of the
functional forms (of both technology and inefficiency) and is subject to aggregation and multicollinearity
problems.
TABLE 2-1: MAJOR METHODOLOGIES IN AIRPORT PERFORMANCE ANALYSIS (ADAPTED FROM (LAI, POTTER, &
BEYNON, 2010))
Methodology Description Weakness
Partial measures
This method uses partial ratio data to carry out performance comparison of target sample in single dimension such as on financial and cost performance of an airport.
This method only focuses on certain fields of airport performance. The evaluation result of this method would not be able to provide a more comprehensive evaluation of an airport’s performance.
Index Numbers
Total Factor Productivity (TFP) allows for measuring cost efficiency and effectiveness and for distinguishing productivity differences in airport performance. This technique can also be used for investigating the impact of variations of input and output price on an airport’s performance.
TFP requires an aggregation of all outputs into a weighted output index and all inputs into a weighted input index using pre-defined weights, which can be biased.
Fro
ntie
r an
alys
is Parametric
approach
Stochastic Frontier Analysis (SFA) Sometimes referred to as econometric frontier approach, is one of the main parametric approaches used by researchers to evaluate efficiency.
Although the parametric approaches take into account the effect error, the parametric methods still faces challenges on separating random error from efficiency.
Non-Parametric approach
Data Envelopment Analysis (DEA) Is a nonparametric approach, which requires no assumptions about the functional form and calculates a maximal performance measure for each firm relative to all other firms.
The key drawback of the technique is that it does not allow for random error in the data, assuming away measurement error and luck as factors affecting outcome, which implies that the measured inefficiency is likely to be overstated.
Additional parametric approaches are the distribution free analysis and the thick frontier approach.
Such as the free disposable hull methodology in the non-parametric approach, neither one of these
parametric approaches are seldom used in airport performance analysis, and for such reason, they will be
briefly addressed.
The former specifies a function form for the frontier approach, but separates the inefficiencies from
the random error in a different way. It assumes that the efficiency of each firm is stable over time,
whereas random error tends to average out to zero over time. The estimate of efficiency for each firm is
determined as the difference between its average residual and the average residual of the firm on the
frontier.
26
The later provides efficiency measure for the overall firms and not for individual firms. It specifies a
functional form and assumes that deviations from predicted performance values within the highest and
the lowest quartiles of observations represent random error, while deviations in predicted performance
between the highest and the lowest quartiles represent inefficiencies. This approach does not intrude
assumptions regarding distributions on either inefficiencies or random error.
Above in table 2-1, we can see the description of major methodologies used in airport performance
analysis and their main weaknesses. Each one of the previously presented has their own advantages and
drawbacks, covers various aspects of performance and requires different data and different assumptions.
Hence, the chosen methodology must be subject to a careful analysis so that one takes the best option
possible within the methodology framework.
2.4 LITERATURE ON AIRPORT EFFICIENCY RESEARCH
It is consensual that management sciences and operational research have provided several useful
methodologies necessary for the efficiency analysis of industries. In this section, it will be addressed the
motivation of literature on airport efficiency research in order to understand the existing methodological
framework.
Airport performance measures are important for day-to-day business and operational management,
regulatory bodies, Government and other stakeholders such as passengers and airlines. According to
(Humphreys & Francis, 2002), complex and dynamic organisations such as international airports provide
a challenge in establishing an appropriate performance measurement system. The many interacting parts
of airports; passengers, airlines, handling agents and surface transport service providers, in addition to
the interests of the regional and national economy, complicate the development of performance
measurement systems. Performance measurement is a critical management activity, both at the
operational level of the individual airport and at the wider system level.
Doganis (1992) proposes that there are several reasons why airport managers and governments
measure airport performance: to measure efficiency from a financial and an operational perspective, to
evaluate alternative investment strategies, to monitor airport activity from a safety perspective and to
monitor environmental impact.
Hence, given the present presence of various stakeholders in the airport infrastructure,
performance analysis serves necessarily many purposes. Thus, each stakeholder will measure airport
performance for different reasons. Whereas governments may use this data for environmental and
economic regulation, airport managers along with owners and/or shareholders will privilege data related
with business performance and return on the investment. Also very importantly, airlines will use this data
so they can compare cost performance across airports.
27
Humphreys & Francis (2002) emphasizes that it is also important to recognise that airlines are the
key customers of airports and that the airlines act as an intermediary between the airport and passengers
or freight shippers. In addition to this fact, if we underline the increasing role of LCCs in today’s air
transportation market, we can easily infer that in a very near future, they will inevitably be responsible for
a large share of transported passengers. Thus, airports must prepare themselves to be also efficient in a
low-cost perspective, in order to attract those same companies.
Most studies made in airport efficiency research have often used the methodologies described in
the previous section. For instance, (Gillen & Lall, 1997) examined the performance of 23 US airports
using DEA, whereas (Hooper & Hensher, 1997) have used TFP to assess the economic performance of
six Australian airports. Econometric approaches have also been used in airport performance, as did
(Oum, Yan, & Yu, 2008) using SFA for assessing the influence of the ownership forms in airport’s
efficiency. Similarly, but using variable factor productivity indexes (Oum, Adler, & Yu, 2006) conducted
research in that area.
However, according to (Lai, Potter, & Beynon, 2010), most airport efficiency measurement
research fails to consider other important factors that can influence an airport’s performance evaluation,
such as the characteristics of airport authorities and airport users (airline companies or passengers). To
the best extent of my knowledge, no studies on airport performance have been carried out regarding the
LCCs’ perspective. That is to say that most benchmarking studies made have traditionally introduced
input and output variables that account for the overall airport performance, not discriminating the
contribution of traditional airline operators from that of LCCs. Separating for instance, traditional
passengers from LCC’s passengers, check-in desks from automatic check-in kiosks, and finger-bridged
gates from walk-by or bus driven boarding gates.
Airport’s economic performance has thus become an important task for those involved, directly or
indirectly, with the airport industry. The liberalization of air transport market, and consequently, airport
commercialization and privatization, has marked interest in performance comparisons. Furthermore,
consumer satisfaction levels and increasing concern on environmental impacts requires benchmarking
techniques that are important to assess the state of the art in airports’ management. Nonetheless,
according to (Graham A. , 2005), there are still a wide range of operational activities which need to be
monitored by looking at measures related to airside delays, baggage delivery, terminal processing times,
equipment availability and so on.
A summary of airport performance studies was compiled below, in table 2-2. It is further detailed
with the respective input and output variables in Appendix 1. It gathers most of the work conducted on
airport performance, involving several aspects in respect to what may influence that same performance.
Despite most of the existing work is based in the comparison of airports within the same country,
international benchmarking has also been subject to research studies.
28
TABLE 2-2: AIRPORT EFFICIENCY PAPERS (ADAPTED FROM (GRAHAM A. , 2008) AND (LAI, POTTER, & BEYNON, 2010))
Authors Year Methodology Coverage
Tololari 1989 Parametric TFP BAA UK airports
Prices Surveillance Authority 1993 Index number TFP 6 Australian airports
Gillen and Lall 1997 DEA 23 US airports
Hooper and Hensher 1997 Index number TFP 6 Australian airports
Graham and Holvad 1997 DEA 25 European + 12 Australian airports
Parker 1999 DEA BAA and 16 other UK airports
Murillo-Melchor 1999 DEA/Malmquist index 33 Spanish airports
Salazar de la Gruz 1999 DEA 16 Spanish airports
Jessop 1999 DEA/Multi-attribute assessment 32 major international airports
Nyshadham and Rao 2000 Index number TFP 25 European airports
Sarkis 2000 DEA 44 US airports
Pels et al. 2001 DEA/Parametric TFP 34 European airports
Gillen and Lall 2001 DEA/Malmquist index 22 US airports
Martin and Roman 2001 DEA 37 Spanish airports
Abbott and Wu 2002 DEA/Malmquist index 12 Australian airports
Martin-Gejas 2002 Parametric TFP 40 Spanish airports
Pacheco and Fernandes 2002 DEA 33 Brazilian airports
Bazargan and Vasigh 2003 DEA 45 US airports
Holvad and Graham 2003 DEA 21 UK airports
Oum et al. 2003 DEA 50 major airports around the world
Oum and Yu 2004 VFP 76 major airports around the world
Barros and Sampaio 2004 DEA 13 Portuguese airports
Sarkis and Talluri 2004 DEA 44 US airports
Yoshida 2004 Endogenous weight TFP 30 Japanese airports
Yoshida 2004 DEA/Endogenous weight TFP 67 Japanese airports
Yu 2004 DEA 14 Taiwan airports
Hanaoka and Phomma 2004 DEA 12 Thai airports
Kamp and Niemeier 2005 DEA/Malmquist index 17 European airports
Vogel 2006 DEA 35 European airports
Lin and Hong 2006 DEA 20 major airports around the world
Martin and Roman 2006 DEA 34 Spanish airports
Vasigh and Gorjidooz 2006 Index number TFP 22 US and European airports
Barros and Dieke 2007 DEA 31 Italian airports
Fung et al. 2007 DEA/Malmquist index 25 Chinese airports
Barros 2008 DEA 31 Argentina airports
Barros 2008 SFA 27 UK airports
Barros and Dieke 2008 Two-Stages DEA 31 Italian airports
Yu et al. 2008 DEA 4 Thai airports
Oum et al. 2008 SFA 109 major airports around the world
Barros 2009 Random SPA model 27 UK airports
Chi-Lok and Zhang 2009 DEA 25 Chinese airports
Martin et al. 2009 MC Monte Carlo Simulation/SFA 37 Spanish airports
Lan et al. 2009 DEA 11 major airports in Asia Pacific
29
Tovar et al. 2010 SFA/Malmquist TFP index 26 Spanish airports
Nonetheless, producing meaningful inter-airport performance indicators is fraught with difficulties of
serious problems of comparability, particularly due to the varying range of activities undertaken by airport
operators themselves. According to (Graham A. , 2005), comparing indicators from raw data can give
misleading impressions as airports involved with more activities would inevitable have higher cost and
revenues levels and poorer labour productivity. The fundamental difficulties associated with inter-airport
comparisons (particularly from different countries) and with dealing with problems of comparability, arising
largely from the diversity of inputs and outputs, still remain and have yet to be resolved effectively.
Relatively few benchmarking studies have made a truly international comparison of performance. This
seems to be out of line with the fact that both the airport and airline industry are becoming increasingly
international or global in nature. Further research is needed. Interest in this area will undoubtedly
increase with more of the industry being expected to go through the commercialisation and privatisation
stages in the evolutionary cycle of the airport industry. Other organisations, such as regulatory authorities,
may also help to improve the current practices in this area.
Similarly, comparability difficulties are also expected to be found in our case study. Scale
differences between Portuguese airports are evident. For instance, Lisbon airport registered over 13
million passengers in 2009, Porto and Faro had 4,5 and 5 million passengers respectively, and Funchal a
bit more than 2 million (ANA, 2009b). Additionally, Aeroportos de Portugal, SA (hereafter ANA), makes
mainland airports and Azores islands airports financial reports whereas Aeroportos e Navegação Aérea
da Madeira, S.A (ANAM) is the responsible public entity for the accounting of Madeira’s airports, which
may implicate different methodologies in the financial reports. Discussed in more detail later, ANA has a
participation of 70% on ANAM, but accounting differences may still happen. Taking for instance, in terms
of outsourcing labour instead of direct labour, or hypothetical revenues from concessions put in
commercial revenues instead of aviation revenues. Although performance evaluations may be
undermined, to some extent, by data availability, the performance assessment of a country’s internal
market will always be easier than international benchmarking.
Thus, difficulties in data interpretation are expected. Nonetheless, we will also rely on the
considerable amount of literature to help us perform as unbiased as possible the evaluation on how LCCs
affect Portuguese airports’ efficiency.
30
3 MAIN DRIVERS OF AIRPORT EFFICIENCY
Airport production of outputs (passengers and freight) relates with several inputs that are not as
concrete as in other industries. Moreover, the production process in airports is segmented by different
operations that further complicate the assessment of airport’s efficiency. From the air transport
movements (ATM) in the airside of the airport, passing by the processing stage of passengers, freight and
luggage on the landside, to the intrinsic relationship between airports and airlines, there are many details
that influence the overall performance of an airport.
Furthermore, the technical design of any airport must be aligned with the political objective drawn
by decision-makers regarding the role of that same airport. In other words, a regional airport aimed to
boost local economy is not expected to have the same infrastructures as a major international hub. In
addition, technological innovation and new business models tend to shape or alter travel patterns that
were not initially foreseen. These factors combined can result in the misuse of resources and
consequently the inoperability of an infrastructure so expensive such an airport.
Despite the several and unpredictable difficulties that arise within the air transport market, it is the
airport manager responsibility to explore its assets in the most efficient way. In this chapter, it will be
addressed the factors that have most impact on airports’ efficiency, namely on the airside, landside, and
within the airport-airline relationship.
Moreover, LCCs are becoming significant factors in airport planning. As their requirements differ
from those of traditional carriers, they drive the development of secondary airports and cheaper
passenger buildings (de Neufville R. , 2008). Hence, the recognition of the different stakeholders in airport
performance is of vital importance in order to achieve higher efficiency.
3.1 AIRSIDE
The airside of an airport is constituted by all areas accessible to aircraft, including runways,
taxiways, aprons and parking places. Airfield efficiency relates to the core business of airports that is the
transport of people and goods.
Airfield design follows international standard practices, namely the Annex 14 of the International
Civil Aviation Organization (ICAO) (ICAO, 2004) or the Advisory Circular 150/5300-13 of the US Federal
Aviation Administration (FAA) (FAA, 1989). Nevertheless, designers often tend to be misinformed about
the economic implications of extra minutes in an aircraft operation for each hundreds of thousands of
movements per year. For instance, an efficient airport nowadays must be aware of how crucial quick
turnaround times are for LCCs and how delays affect the airport’s economic performance by reducing the
number of aircrafts allowed per day. The importance of efficient airside operations is thus motive of study
in the following sections.
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3.1.1 AIRFIELD DESIGN
According to (de Neufville & Odoni, 2003), airfields typically account for 80 to 95% of the total land
area occupied by an airport and affect in critical ways every facet of airport operations. Main determinants
of airfield size are number and orientation of runways, geometric configuration of the runway system, the
most demanding type of aircraft the airport can serve (also defined as “critical airplane”), land area for
possible future expansion and or environmental mitigation. Moreover, airfield configuration also affects
landside facilities and services, as the layout of runways has enormous influence in the geographical
distribution of passenger and cargo terminals, hangars and other buildings. Finally, technical planners are
also subject to environmental, political and economical constraints at each site.
As mentioned above, there are international sets of standards regarding airfield design that explore
thoroughly the wide range of characteristics in order to assure desired levels of safety, and should be
consulted if curiosity arises. Nevertheless, factors such as wind coverage, runway orientation and/or
geometry, or physical obstacles are not crucial in the assessment of airport’s efficiency performance
when using the methodologies presented in the previous chapter. In appendix A, it is stated clearly that
most authors make use of inputs such as the number of runways, gates and parking spots, apron area
among others to perform efficiency analysis of airport. More rarely, runway length is also used.
Therefore, it is not the ambition of this section to describe exhaustibly the characteristics of the
airfield design process. Instead, we aim to enhance those that contribute to the overall efficient
performance of airside operations, namely by reviewing airport layouts and the influence of taxiways and
apron areas. This review is preceded by the identification of major constraints of current practices in
airfield design.
3.1.1.1 CURRENT APPROACH AND ALTERNATIVES
The traditional approach of airport design bases on the mould of masterplans as defined by FAA’s
Advisory Circular 150/5070 or ICAO’s Airport Planning Manual-Part 1, for instance. And despite the
advice of these documents regarding the fact that “planners should tailor an individual master plan to the
unique conditions at the study airport” (FAA, 2007), they are also quite rigid in the sense that one single
forecast becomes the cornerstone of the entire master plan development. As all alternatives of airport
development are based in one single forecast (typically a linear regression from historical data regarding
the number of passengers), and forecasts are most likely to be wrong, a major infrastructure master plan
is started without examination of further alternative scenarios.
The problem with this approach lies on the failure to anticipate the risk of possible changes in
market conditions and to provide insurance against those same risks. This is specially truth when we take
for example the growth of LCCs and how they changed the modus-operandi in terms of passenger
transport between aircrafts and the passenger building. Whereas traditional airlines use expensive
bridges, LCCs prefer buses or having their costumers walking in order to cut costs. Master planning is
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thus inflexible and unresponsive to risks and has potential serious consequences such as extra costs or
losses of opportunities.
Although there are alternatives to traditional master planning, old habits die hard. According to (de
Neufville & Odoni, 2003), strategic planning refers to a disciplined process for analyzing the current
situation of a business activity, and identifying the vision of how that entity should position itself with
respect to its customers and competitors. SWOT (acronym for strength, weakness, opportunity and
threats) analysis is a generic version of strategic planning. By properly positioning their organizations
according to this analysis, airport operators become more flexible to respond to possible events.
An analogy to chess is convenient, in the sense that airport operators should look ahead several
moves, but deciding only one at a time. Airport strategic planning can be divided in three phases. Firstly,
one should recognize risk and complexity. It is wiser to foresee a wider range of futures since forecasts
tend to be always wrong. Furthermore, as the number of possible choices is so big, it is advisable to
adopt hybrid (mixing single configurations) solutions that are able to adapt to different outcomes.
Secondly, airport operators should analyse possible futures. Using SWOT analysis is easier to identify
risks and possibilities of response to actual events. Thirdly, a dynamic strategic planning is in order.
Different choices differ in their likely benefits and performance over a range of possible futures. Any
choice is thus a portfolio of risk and there is need for a dynamic approach to real events. Moreover,
strategic planning buys insurance by flexible building and committing only to immediate. At the same
time, it maintains present the understanding of need for flexibility. Although strategic planning may
sometimes sacrifice maximum performance, it achieves overall best performance since it prepares the
infrastructure to adjust to actual situations in later periods, which tends to be very costly.
Airfield designers should therefore be able to provide flexibility to the infrastructure. According to
(de Neufville & Odoni, 2003) they should respond correctly to the following questions at any period:
• How much land should be acquired or reserved for a new airport?
• What should be the overall geometric layout of the airfield?
• What size of aircraft should the airfield be designed for?
• How should the construction of airside facilities be phased?
The current trend is towards airport expansion or remodelling, since that there is a relatively small
number of major new airports. This does not mean however that the planning task is simpler. On the
contrary, it becomes even harder when it comes to conciliate scheduled construction activities with the
regular operations that take place in an airport.
3.1.1.2 AIRPORT LAYOUTS
There is a variety of solutions concerning the layout of airfields. Naturally, each solution has its
own pros and cons, and must be tailored to the specificity of the site in study. As mentioned above,
airfields occupies most of the land area of an airport (between 80 and 95%), with larger percentages
33
applying to airport with smaller land areas such as the 2,6 million m2 of New York/LaGuardia (see figure
3-1) that contrast with the 136 million m2 of Denver/International Airport (see figure 3-2).
It is important to emphasize that the need for intersecting runways relates to the need for wind
coverage. Both FAA and ICAO define a minimum threshold of 95% of airport usability with regard to
crosswind coverage. Nevertheless, the number of runways is largely responsible for airport’s capacity,
and for how much land area is needed. In respect to traffic demand capacity, there are singular
differences among different regions of the world. When analysing world’s busiest airports, we realize little
differences between 2000 and 2009.
North America remains the region of the world where airports have the largest number of ATMs.
Inversely, it has the smallest number of passengers per movement. Whereas in 2009, Charlotte-Douglas
airport reported 68 passengers per movement and New York’s JFK had 110, in 2000 values ranged
between 56 to 96 passengers per movement. Asian1 airports also maintained the largest number of
passengers per movement, although decreasing on average from 178 in 2000 to 151 in 2009. European
airports slightly increased the number of passengers per movement between 2000 and 2009, going on
average from 105 to 116.
The explanation for these discrepancies appears to remain constant, though. As (de Neufville &
Odoni, 2003) explains, the differences are mostly due to the fact that Asia’s busiest airports prefer wide-
body aircrafts in favour of the aircraft mix of narrow-body and regional jets as happens in North America
and Europe. Furthermore, this fact reflects the importance for runway requirements at each region’s
principal airports. North America’s busy air market, and to a lesser extent, European airports, need more
runways than the Asian airports given the higher number of operations. Ranking by ATMs, within the first
eighteen busiest airports, we observe that the top six is constituted by North American airports, and
Beijing International is the only Asian airport in 10th place. The most accentuated trend appears to be the
increase of the Asian market, gaining two airports (Tokyo/Haneda and Beijing Capital International) in the
top-5 and five airports in the top-25 busiest airports in terms of passengers.
TABLE 3-1: TRAFFIC AT WORLDS' 25 BUSIEST AIRPORTS (SOURCE: (ICAO, 2009), ACI WEB SITE)
Airport Passengers
(millions) Movements (thousands)
Passengers/Movement
Atlanta/Hartsfield-Jakson 88 970 91
London/Heathrow 66 466 142
Beijing Capital International 65 489 134
Chicago/ O'Hare 64 828 77
Tokyo/Haneda 62 321 193
1 Including Dubai International airport, not present in 2000’s top-30 busiest airports.
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Paris/Charles de Gaulle 58 525 110
Los Angeles/International 57 634 89
Dallas/Fort Worth 56 639 88
Frankfurt 51 463 110
Denver/International 50 607 83
Madrid Barajas 48 435 111
New York/John F. Kennedy 46 417 110
Hong Kong/International 46 288 158
Amsterdam-Schiphol 44 407 107
Dubai International 41 281 146
Bangkok/Suvarnabhumi 41 258 157
Las Vegas Maccarran 40 511 79
Houston/George Bush 40 538 74
Phoenix Sky Harbor 38 457 83
San Francisco/International 37 380 98
Singapore Changi 37 245 152
Guangzhou/Baiyun 37 269 138
Jakarta/Soekarno Hatta 37 309 120
Charlotte-Douglas 35 509 68
Miami/International 34 351 96
Above, in table 3-1, it was summarized data obtained from Airport Council International’s (ACI) web
site (ACI, 2010) and (ICAO, 2009), similarly to (de Neufville & Odoni, 2003) for comparison purposes. It is
also worthwhile mentioning that 2009 had all around negative variations with respect to 2008, whereas
2000 had shown much better performance in the overall airports.
Without entering deeply into technical details, it is important to refer to some geometric
characteristics of the runway systems, as they are also important when referring to land area
requirements. The range of runways’ length may vary from 2000 to 4000m in major airports, depending
on the site elevation among other factors. If parallel runways exist, the distance between them also
critically affects total land requirements. Last, but not least, apron areas and taxiways affect greatly the
size and cost of the infrastructure, being normally dependent of the reference code for design purposes.
In one hand, urban expansion in the last century has limited largely the expansion of several major
and secondary airports. Land unavailability is therefore a factor that makes unlikely for those airports to
add new runways. On the other hand, major international airports have two or more runways that may be
parallel or not. Such is the case of the above mentioned New York LaGuardia and Denver International
airports, which are also good examples for this purpose.
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3-1: NEW YORK LAGUARDIA AIRPORT LAYOUT (SOURCE: (FAA, 2003))
New York LaGuardia (figure 3-1) has high land restrictions, and its intersecting runways present
bigger managing difficulties from the air traffic point of view. The intersection of runways implies the
existence of conflict points. Thus, aircraft movements in one runway must be carefully coordinated with
those happening in the other. In addition, the airport capacity may be strongly affected if strong winds in
one direction force the shutdown of the correspondent runway. Hence, operational challenges in this kind
of airports are enormous.
On the other hand, airfields such as Denver International airport (figure 3-2), consist in two (or
more) parallel runways. According to the distance of their centrelines, they are said to be “close”,
“medium-spaced” or “independent”. In the first case, the second runway is typically used as taxiway
whereas independent parallel runways allow for any pair of ATMs to happen simultaneously. The
intermediary case allows for arrivals on one runway and departures on the other. Furthermore, close and
medium-spaced runways tend to be associated to limited land availability, whereas independent runways
allow for landside facilities development in between.
36
3-2: DENVER INTERNATIONAL AIRPORT LAYOUT (SOURCE: (FAA, 2003))
Independent runways, as in Denver International, offer a set of advantages in comparison to close
and medium-spaced in the measure that they promote better airfield traffic circulation and bigger
proximity to passenger and cargo buildings, thus improving the efficiency of aircraft operations.
Furthermore, they also allow for a better control of landside’s development as well as ground access to
the airport. However, there are disadvantages as well. Independent runways not only require more
extensive taxiways systems but also larger transportation links such as highways of railways to the
airport. Additionally, it may also affect the flexibility of expansion on central placed landside facilities. (de
Neufville & Odoni, 2003)
The geometry of runways also influences the shape and geography of landside buildings. For this
reason, further airport diagrams are presented later in the text.
3.1.1.3 TAXIWAYS AND APRONS
Taxiway systems are of huge importance for efficient air traffic operations. By allowing quick entries
and exits of the runway systems, it reduces delays and thus increasing the number of ATMs, which is the
fundamental core business of airports. Nonetheless, these systems tend to be seen as complementary
infrastructure. The positioning and configuration of taxiway systems often occurs after runway and
landside facilities are set in place.
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Taking back the example of Denver International airport, we can easily identify that taxiway
systems can be of complex configuration, of extensive dimensions and involve high maintenance and
operational costs. Such airports, with landside buildings positioned in the central area of the airfield are
especially prone to problems of accessibility. For instance, they require a large number of expensive
bridges so that road traffic arrives to its destiny. Furthermore, as the extension of these systems
increases, so does the operating costs of airlines, that spend more time in tortuous paths between the
runway and the apron stands.
Also in this matter, FAA and ICAO’s design standards explore thoroughly the safety
recommendations, throughout geometric characteristics such as longitudinal and transversal slope, sight
distance, width, taxiway curves, separation distances and other factors. For further details, see for
instance chapter 3 of ICAO’s Annex 14 or chapter 4 of FAA’s AC 150/5300-13.
However, it is worthwhile the discussion of exit taxiways. These segments provide the way out of
the runway. The cheapest solution is a segment perpendicular both to the taxiway and runway.
Additionally, due to the importance of fast clearance of the runway, 30-degree high-speed exit are also
feasible solutions, despite the natural higher costs. Figure 4-3, depicts FAA’s and ICAO’s figures of both
solutions.
According to (de Neufville & Odoni, 2003), the location of taxiways plays a significant role in
determining runway occupancy times, and to some extent, runway capacity. Although high-speed exits
contribute to reduce runway occupancy, returns diminish as their number increases, due their higher cost.
Thus, they are advisable for runways with more than 30 peak ATM per hour, and should rarely surpass
three exits for each direction of operation when other conventional exits exist. Naturally, the introduction
of high-speed exits in the direction of runways used only for departures has no increase of capacity of the
runway system. It has significant capacity benefits on mixed operations mode and to a smaller extent, in
runways used for arrivals only.
3-3: LEFT-CONVENTIONAL EXIT TAXIWAY (SOURCE: (FAA, 2007) ) RIGHT - HIGH-SPEED EXIT (SOURCE: (ICAO, 2004) )
Aprons, on the other hand, provide the necessary interface between airside and landside facilities,
namely long-term parking, refuelling, boarding passengers or loading/unloading goods.
38
Passenger building stands can be further divided into two groups. They may be “contact” or
“remote” depending on their location regarding the landside facility. Several configurations are possible,
depending on the concept of the passenger building. Nonetheless, this subject will be explored further
ahead. However, (de Neufville & Odoni, 2003) emphasizes the fact that different apron designs involve
complex questions regarding the operations’ efficiency. Thus, trade-offs must be met between easiness
of aircraft movement and passenger comfort.
In the LCCs point of view, remote stands suit the best with lower fixed costs, since expensive
bridges are not used. On the other hand, it implies high variable costs if road transport is necessary and
the acceptance of passengers for earlier boarding.
3.1.2 CAPACITY AND DELAYS OF AIRFIELDS
Ultimately, the runway system capacity defines the capacity of the airfield and of the airport. As
seen previously, land limitations and environmental factor deny the increase of runway capacity on
several airports. Furthermore, it is on the runway that flows of air traffic from various origins meet together
in “toll plazas”, originating delays. Oppositely, taxiway systems, apron areas and landside facilities can be
increased, in order to match the capacity of the runway.
3.1.2.1 RUNWAY SYSTEMS
Being the production of aircraft movements the core business of airports, capacity of airfields is
thus an important driver of airport efficiency. When demand approximates limit capacity, delays can
represent huge capital costs for both airlines and airports.
Traditionally, capacity of runways is defined as the maximum throughput capacity, which
represents the expected number of movements that can performe on a runway system in one hour,
without violating air traffic management rules, and assuming continuous aircraft demand (de Neufville &
Odoni, 2003). This definition requires information regarding the conditions under which the runway is
operating, namely, type or aircrafts, type of movements (departures or arrivals), allocation of movements
if there is more than one runway, among other factors. On the other hand, it does not refer to any level of
service. Whether delays occur or not, this measure only cares for the number of movements operated in
peak hours, or under conditions of continuous aircraft demand.
Other definitions considering levels of service exist, although rather ambiguous. Such is the case of
practical hourly capacity, which is defined as the expected number of movements that can be performed
in one hour with an average delay of four minutes per movement. Being the delay of four minutes
considered the threshold for acceptable levels of service, the practical hourly capacity roughly represents
80 to 90% of the maximum throughput capacity, depending on specific conditions. Declared capacity is
another measure based on the notion of sustained capacity, or in other words, the number of aircraft
movements per hour that can be accommodated at a reasonable LOS. According to (de Neufville &
Odoni, 2003), declared capacity seems to be roughly 85-90 percent of the maximum throughput capacity.
39
Measures considering levels of service are important in delay measurement and more recently, in
airport benchmarking2. Furthermore, the data available on Portuguese airports uses different capacity
designations, although declared capacity seems to be used more often.
There is a very wide range of factors that affect the capacity of the runway system. The complexity
of the relationships between factors such as those presented below is enormous: (de Neufville & Odoni,
2003)
• Number and geometric layout of the runway system;
• Separation requirements between aircrafts;
• Visibility and overall weather conditions (wind, precipitation, snow, etc);
• Mix of aircrafts;
• Mix and sequencing of movements on runways (departures only, arrivals only or mixed)
• Type and location of taxiway exits from runways;
• Performance of the air traffic management system;
• Environmental constraints (noise, land availability, etc)
Although mathematical models exist and can obtain good results, the dynamic characteristics of
airport delays are quite difficult to predict accurately. As optimum conditions for maximum throughput
capacity occurs less often than desirably, queues of arrival and departing aircraft will most certainly
happen. The reason for delays is rather obvious for peak traffic periods of the day, but less clear to
periods when the demand rate is reasonably smaller than the capacity offered. The later are essentially
due to the variability of time intervals between the continuous flows of requests for runway usage, as well
as to the variability of the time necessary to process each movement of landing or departure. (de
Neufville & Odoni, 2003)
Regarding airport efficiency analysis, many studies used variables such as the number of runways
and runway length. In the analysis of European airports, (Pels, Nijkamp, & Rietveld, 2003) found that the
number of runways is not significantly relevant. Additionally, second order effects indicate that airports
with a number of runways larger than the average do not lead to an increase of ATM, all things remaining
equal. Similarly, (Gillen & Lall, 1997) found that the number of runways have no significant impact on the
airside efficiency. Whereas the number of runways is widely used in the field literature, such does not
happens with declared capacity, with no model having used this variable, to the best of my knowledge.
Lisbon airport is the only Portuguese airport that has two operating runway. This fact may have
strong influence on its performance, when taking into account the influence of LCCs in Portuguese
airports. Additionally, most LCCs operating in Portugal use similar aircrafts such as Ryanairs’ Boeing 737-
800 or easyjets’ Airbus A319, and have similar runway length requirements in the order of magnitude of
2 See (Forsyth & Niemeier, 2010) for further details.
40
2100 meters. Thus, runway length can be a significant input variable if the type of aircraft traffic is taken
into account, otherwise it may be negligible. Since all Portuguese airports operating LCC services have
runways larger than 2400 meters, this aspect does not seem to be of high significance with respect to
inter-aiport performance.
TABLE 3-2: UTILIZATION RATIO OF PORTUGUESE BUSIEST AIRPORTS IN 2009 (SOURCE: ANA)
IATA Code
ATM PAX Runway Length
Night Time Operations
1st Peak Hour ATM
Declared Capacity
Utilization Ratio
LIS 132.381 13.241.636 2400/3850 10,8% 46 37 61,3%OPO 52.194 4.473.455 3.480 10,2% 29 20 44,7%FAO 37.328 5.013.207 2.610 8,5% 37 22 29,1%FUN 21.955 2.335.811 2.781 11,8% 41 14 26,9%PDL 12.349 875.088 2.525 9,8% 18 12 17,6%
In table 3-2, some characteristics of the five busiest Portuguese airports were compiled. Firstly, it is
notorious the discrepancy between Ponta Delgada (the main airport in Azores archipelago) and Lisbon
airports in terms of scale of operations. Secondly, according to ANA’s annual reports, night time
operations (between 21h and 05 h) represent in average 10,2% of total operations. Whereas in Lisbon
and Porto airports there is a considerable amount of aircraft movements until midnight, in other airports
often happens of no traffic movements at all in certain hours. Nonetheless, this percentage is similar in all
five airports, indicating that not even Lisbon airport has the characteristics of a major international hub.
Thirdly, it is also notorious the difference between declared capacity and the number of operations
registered in the first peak hour at each airport, where more touristic or seasonal airports such as Faro,
Funchal and Ponta Delgada suffer of more coincident arrivals and/or departures not only throughout the
day but also all year around.
Hence, if we consider that nearly 90% of air traffic movements in Portugal occur within a 16 hours
period, we can say that potential annual capacity of airports will be in the range of 5840 (16x365) hours of
declared capacity. The utilization ratio will then be the quotient of total ATM’s and the potential annual
capacity of a given airport, and here we find high variability too. Lisbon and Porto have the higher
utilization ratios whereas Ponta Delgada airport has the lowest value. This index is particularly relevant in
terms of operations delays. It tends to be higher in airports with hub characteristics such as Lisbon or to a
smaller extent Porto, where the 30th and 40th peak hour are relatively close to the 1st. On the other hand,
delays themselves tend to be longer in airports with more touristic vocation such as Faro and Funchal
where aircrafts tend to operate at similar times of the day, causing severe capacity constraints in small
periods during the day.
Although is commonly found in the existing research the use of length and number of runways as
inputs as opposed to declared capacity, the previous exercise reveals how Porto and Faro airports with
similar characteristics in terms of passengers and number of runways present very different utilization
ratios, that will mostly certainly influence the airports’ overall performance.
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3.1.2.2 TAXIWAYS AND APRONS
Full-length taxiway is the designation for taxiways running parallel to runways, representing the
principal element of taxiway systems. Aircraft traffic between runways and aprons (and vice-versa) use
full-length taxiways as one-way lanes. By having smaller restrictions with regard to separation of
successive aircrafts, flow capacity of taxiways usually exceeds that of runways.
There are however, elements of the taxiway system that may form local bottlenecks. As mentioned
above, runway exits to taxiways may impose limitations on the airfield capacity. Moreover, short taxiways
segments, intersection points of taxi lanes with different speed characteristics, or runways intersecting
taxiways may form potential critical points to the normal flow of aircraft movements.
3-4: POTENTIAL CONGESTION POINTS AT BOSTON LOGAN (ADAPTED FROM (DE NEUFVILLE & ODONI, 2003) AND (FAA,
2003))
Let us take the example of Boston/Logan Airport as depicted in figure 3-4. When runways 22L and
27 are used for arrivals (represented by the blue arrow), arriving aircrafts must cross runway 22R used for
departures (represented by green arrow). The movement of aircrafts is represent by red arrows, and
42
when several are closely, it represents tendency for queues formation. Finally, yellow bolts represent
potential collision points.
Queuing happens more frequently in short taxiway segments and locations where high-speed exits
merge with taxiways. When this configuration is used, air traffic controllers may be obliged to interrupt the
flow of departures of runway 22R in order to arriving aircrafts from runways 22L and 27 have the
opportunity to cross that runway and reach apron areas. According to (de Neufville & Odoni, 2003), such
flow-constraining points typically exist in the taxiway systems of older, space-constrained airports. Hence,
the majority of fully developed taxiways systems do not compromise the airfield capacity, and delays
sustained at critical points are typically much smaller than delays originated by capacity limitations of the
runway system.
On the other hand, aprons may constrain airfields’ overall capacity. Aprons are defined as areas
reserved for aircraft stands and the taxilanes that pass through those areas. As mentioned in section
3.1.1.3, these areas may be remote or contact, if they are adjacent or not to passenger buildings,
respectively.
Aprons’ capacity can be classified of static or dynamic. Whereas the former is defined by the
maximum number of aircrafts that can occupy the apron at one moment in time, the later is more
consistent with notion of runway capacity in the measure that specifies the number of aircrafts that is
possible to accommodate within an hour.
Additionally, aircraft stands may be of exclusive use of an airline or of shared use by members of
an airline alliance. Airport operators and airlines normally prefer fixed locations instead of last minute
changes because it is disruptive and costly. Furthermore, since delays happen frequently, in addition to
the scheduled period, a tolerance time is given to that stand occupation. Periods of stand occupation by
aircrafts may vary from 20 minutes to over one hour, depending if it is a LCC or an intercontinental flight.
(de Neufville & Odoni, 2003)
As mentioned earlier, LCCs prefer remote stands, thus avoiding the costs of costly bridges
necessary in contact stands. Additionally, it reduces manoeuvring times of the aircraft to enter or exit the
taxiway system, diminishing its turnaround time.
From the airport efficiency point of view, researchers frequently use aprons area and the number of
parking spots as input variables, but only (Pels, Nijkamp, & Rietveld, 2001) and (Pels, Nijkamp, &
Rietveld, 2003) use the number of remote stands. Nevertheless, the efficiency analysis of 34 European
airports using DEA/TFP methodology in (Pels, Nijkamp, & Rietveld, 2001) revealed that the number of
contact parking and remote parking stands is clearly significant. Also (Gillen & Lall, 1997) found that the
increase of boarding gates (and implicitly, the number of parking stands) has significant importance in the
increase of airside efficiency. Since the efficiency analysis will be performed taking into account LCCs,
43
the ratio between the number of remote and total parking stands seems to be an important variable to
consider.
3.1.3 DEMAND MANAGEMENT
In the business world, the term demand management is used to describe the proactive
management of increasing demand with business constraints (supply).
According to (de Neufville & Odoni, 2003), airports’ demand management refers to any set of
administrative or economic measures and regulations aimed at constraining the demand for access to a
busy airfield and /or modifying the temporal characteristics of such demand. The main goal is to assist in
maintaining efficient operations at airports threatened by congestion. Being demand the subject in case,
no capital investments on capacity expansion (supply) are considered. On the contrary, the aim is to
change demand patterns through regulation or economic measures that will either reduce overall demand
for airfield operations or shift demand from certain critical time periods of the day to less critical ones.
Furthermore, the author divides demand management in three sub-categories: purely administrative,
purely economic and hybrids (the combination of the former two), depending on which measures are
used to reach the desired objective.
Regarding the administrative approach, “slot”, i.e., the reserved interval of time for the arrival or
departure of a flight is a critical concept. In this sense, a set of criteria is defined in order to allow a proper
allocation of slots among different users. The most widely administrative approach is IATA’s schedule
coordination. It considers criteria such as flight length and regularity (e.g. charter flights may be deferred
to regular flights operated on daily or weekly basis), origin and destination of the flight (e.g. new markets
or existing locations may be seen as particularly important) or even the characteristics of the airline
requesting the slot (e.g. promotion of more competitive environment or management of other
infrastructures such as terminal gates and parking stands).
The main criticism to this approach however, is that whenever significant excess of demand over
capacity exists, the lack of economic penalties or incentives may well lead to market distortions by, for
instance, preventing the entrance of new players or by protecting any sort of perceived public services
such as flag carriers. The European Commission has responded to such complaints through the third
deregulation package in 1993, allowing a more market based distribution of slots. In practice however,
some member States still protect their flag carriers.
On the other hand, pure economic approaches use various sorts of congestion pricing in order to
control airport access. By applying higher access fees on peak periods (hour, day, week and/or year) and
lower and the rest of the time, airport managers aim to redistribute demand to less congested periods.
In economic terms, congestion pricing relates with users’ willingness to pay for the infrastructure
access. This congestion toll is equal to the marginal external cost of the airfield in peak periods, or in
other words, the cost of delay that the last aircraft will impose to the rest of the users. When the supply
44
curve is left-shifted due to the internalization of congestion toll, there is a decrease in the number of
airline carriers willing to pay for this additional cost. Hence, airlines with lower cost of delay time will be
more willing to persist using airports at congested times, whilst high value of time operations such as LCC
with short turnaround times will be more sensitive to worsening congestion.
By adding the marginal private cost, i.e., the cost supported by user due to the delay that will incur,
we obtain the social marginal cost (SMC). As in other transportation infrastructures, SMC pricing
schemes with its welfare enhancing characteristics are increasingly reason of studies and debate. And
although SMC may occur naturally in perfect markets, this is far from happening in transport
infrastructures, where there still exists information asymmetries, the existence of public or semi-public
goods (the airport itself) or increasing returns to scale. Despite its relevance on airside efficiency, SMC
pricing analysis goes far beyond the above mentioned, and should therefore be object of study in more
detail.
In practice however, bigger difficulties arise in the implementation of marginal external costs. In one
hand, it is rather difficult to estimate accurately marginal external costs for any given level of demand as it
is to predict the effects on demand for the proposed congestion pricing scheme due to lack of information
on demand elasticity. In the other hand, and probably to larger extent, the problem is political, where
conflict of interests among different stakeholders is prone to slow down implementation of such pricing
policies. For instance, whereas regional and general carriers may see it as discriminatory as they are the
users that can least afford to compensate others for external costs, environmentalists or airport
neighbours may favour this form of access restriction as a possible way of controlling airport expansion
(de Neufville & Odoni, 2003).
Finally, hybrid approaches to demand management are such that combine administrative and
economic mechanisms. Hence, in addition to slot coordination, hybrid approaches will include economic
measures such as congestion pricing, slot market or slot auctions to achieve the final allocation among
users.
Demand management is hence a critical factor that influences airports’ operational efficiency and
has an important role from the LCC viewpoint. However, the difficulties associated with both data
unavailability regarding managerial aspects and the introduction of such variables on the methodological
frameworks for efficiency analysis, are enormous. To the best of our knowledge, demand management
has never been used as input or output on airport’s efficiency literature despite its huge importance. Best
practices in demand management are of the interest of any airport manager and such is widely accepted.
Thus, the issue is not whether to apply demand management, but how to apply it the best.
3.2 LANDSIDE
According to (de Neufville R. , 2008), airport planners and investors need to recognize the effect of
low cost airlines. It implies a downward shift in standards and acceptance of these carriers volatility.
45
Moreover, although many airport operators find this reality difficult to accept, the trend runs against the
practice of massive, multi-billion Euro investments of extravagant buildings designed by signature
architects. Summarizing, policy makers and investors should focus more attention on the development of
airport facilities serving LCC, both at legacy and low-cost airports.
The discussion on landside operational efficiency however, goes far beyond the passenger
buildings. For instance, with regard to the detailed design of passenger buildings, one could also bear in
mind the existence of “hot spots” that undermine airport’s level of performance or even ground access
and parking in the airport and the necessary distribution of baggage, goods and services. Although such
operations are remarkably important for the efficient economic performance of this complex transport
infrastructure, we will not inter in deeper considerations due its natural complexity and, at the same time,
because it does not fit the econometric model we intend to use.
In the following section, the influence of passenger buildings’ configuration and the introduction of
low-cost airports will be discussed as important factors in the efficiency of airports’ landside operations.
Its importance, if in one hand is highly important, not only in the consideration of new investments but
also in the management of existing buildings, in the other hand does not serve the benchmarking model
we will use. Hence, the subsequent analysis will be shortened as it is reasonably possible to do.
3.2.1 PASSENGER BUILDINGS
Historically, national governments tended to view airports as national entry gates and thus symbols
of the nation grandiosity, leading into multi-billion euro projects that may not be economically viable in the
long run. The industry deregulation has been contributing to a paradigm shift, where increasing efficiency
is a must. Hence, the usual designation of terminals has shifted to passenger buildings in the sense that
they are not the terminus of a journey, but instead the beginning of a new one.
The configuration of passenger buildings has great importance on airports’ operational efficiency as
it provides to the different users of the airport the necessary infrastructures for the several purposes
happening in that space. From passengers check-in process to waiting lounges, passing by all the
necessary security check-points and baggage handling systems, retail opportunities and the cost
associated with this usually pharaonic buildings, interests among different stakeholders tend to collide
and the need of conflict mediation is needed. Designers thus face critical issues when putting different
stakeholders’ interests in a single building. Passengers, owners, managers, airlines, commercial retailers
among others have a word to say in the financial efficiency of such complex infrastructure.
The efficient performance of passenger buildings thus relates with the traffic it handles, and more
precisely, with three derived characteristics. Firstly, the overall level of traffic. Secondly, traffic’s
seasonality and finally the percentage of transfer passengers (de Neufville & Odoni, 2003).
46
3-5: 2009'S MONTHLY PAX DISTRIBUTION ON THE TOP5 BUSIEST AIRPORTS (SOURCE: ADADPTED FROM (ANA, 2009))
Lisbon airport accounts for more than 50% of total traffic in Portuguese airports, with over 13
million passengers in 2009. Porto and Faro airports operated roughly 5 million passengers in the same
year whereas Funchal has had over 2 million and Ponta Delgada less than 900 thousand. It is curious to
note though, that in 2009 (a particular sensitive year for the industry), Lisbon airport registered an
homologous drop of nearly 73% on transfers to 19.342 passengers, far beyond Porto and Faro airports
with 34.875 and 48.594 transfer passengers respectively, and even Ponta Delgada airport has had more
transfer passengers with 21.725 passengers (ANA, 2009). Regarding yearly seasonality (see figure 3-5),
Faro airport is the infrastructure more sensitive to total passenger variation along the year, with almost
five times more passengers in the busiest month in comparison with the less busiest. On the other hand,
Lisbon, Porto and Funchal have similar monthly distributions, more “flatten” but also with the summer
peak in August representing, in average, 50% more passengers than the traditionally less busy month of
February.
Such analysis is particularly important when new infrastructure investments are to be done, as is
the case of the New Lisbon Airport (NLA). The proper characterization of travel patterns may prevent
erroneous conceptions of what buildings suit the best each airport.
3.2.1.1 PASSENGER BUILDING REQUIREMENTS
As stated above, among airports’ different users, it is interesting to understand four primary
perspectives, namely passengers, airlines, owners and retailers.
2,0%
4,0%
6,0%
8,0%
10,0%
12,0%
14,0%
16,0%
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
LIS OPO FAO FUN PDL
47
Passengers present an enormous range of needs to be met, and depending on the type of
passenger, the costs for operating an airport may differ a lot. Depending on whether domestic or
international passengers dominate traffic, it influences the number of border and customs control points.
Furthermore, the needs of business or leisure passengers will also impose determined requirements, as
typically the former travels with less baggage and requires special amenities (e.g. lounges, duty-free
shops, restaurants) whereas the later normally has much luggage and prefers inexpensive facilities. As
mentioned above, transfer passengers may also influence severely airport’s operational efficiency,
especially at major international hubs, since they are potential non-aeronautical revenue sources. In our
case study however, transfer passengers have a marginal effect as they represent less than 0,8% of total
commercial passengers (ANA, 2009).
From the airlines perspective, poor passenger buildings’ configuration may impose heavy burden
on the operational level, given that even small savings in time, when cumulated over a day can have a
major impact by allowing, for instance, an extra departure or arrival with the same crew and airplane.
Airlines give particular attention to costs associated with ground operations. In fact, when considering
peak delays and the way they spread throughout the day, small savings in operations time may represent
enormous savings and airlines’ willingness to invest in new passenger buildings. (de Neufville & Odoni,
2003)
The owner’s perspective depends largely on the shareholders structure. Depending on whether it is
state-owned (as it is the case of Portuguese airports) or fully privatized, the infrastructure can be seen in
one hand as monumental entry gates in the country or political milestones by the former, or in the other
hand as a financially efficient company by the later.
Finally, the viewpoint of retail operators is based on persons wanting to spend their money and
time at their shops. Visibility, access and a coherent environment are crucial factor for these
stakeholders. Hence, any arrangement featuring central areas is more attractive to stores than buildings
with many entries and exits. (de Neufville & Odoni, 2003)
3.2.1.2 BUILDINGS CONFIGURATION AND EVALUATION
The requirements above-mentioned present a critical problem for passenger buildings designers:
they need both to concentrate and spread them out. (de Neufville & Odoni, 2003). In this section, a short
description of passenger buildings configurations will be given from a functional perspective and its
evaluation made according the stakeholders’ needs.
It is important to note that some configurations are evolutions or variations of existing concepts of
passenger buildings, and therefore, some difficulties may arise while theoretically distinguishing them. In
practice however, the name given has little importance. To the stakeholders, the infrastructure’s
operational efficiency matters the most.
48
Furthermore, the following configurations are best suited for larger airports. For instance, Ponta
Delgada airport’s passenger building, in order to handle its 800.000 annual passengers, does not have
the need of a heavily elaborated building. On the other hand, other configurations may arise from the
“tailoring” of basic configurations, resulting in hybrid configurations that may suit the best each case
study.
Hence, and according to (de Neufville & Odoni, 2003), the basic passenger building configurations
are:
• Finger piers;
• Satellites, with or without finger piers;
• Midfield, either linear or X-shaped;
• Linear, with one side devoted to aircraft;
• Transporters;
Airfield designers first introduced finger pier configuration in the 1950’s. Similar to the plan view of
the palm of a hand, the fingers can “hold” aircrafts on both sides of the building, from the central core to
the end of the pier. An alternative configuration to finger piers consists in creating “hammerhead” fingers,
which allows serving a higher number of aircrafts and reduces the space needed for lounges as they are
placed in a shared area (the crosspiece of the T).
By providing steady flows of passengers to the central building, this configuration is likely to
increase retail opportunities with the cost of longer walking distances from passengers that use gates
more distant from the main building. The inconvenient hike that passengers suffer in such configuration
has lead airport designers to shorten the finger piers extension or, alternatively, to replace them with
people movers that serve independent satellites and/or midfield concourses.
3-6: FINGER PIER AT LAGUARDIA AIRPORT (LEFT) AND SATELLITES AT TAMPA AIRPORT (RIGHT) (SOURCE: (FAA,
2003))
49
The satellite configuration, as depicted in figure 3-6, is the natural extension of T-shaped finger
piers, by eliminating the gates along the fingers and concentrating them at the end. As in other
configurations, some variations occur. In one hand, the connection between satellites and central
passenger building may happen underground or at surface level. Whereas underground connections
have higher sunk costs it also allows aircrafts to maneuver more easily around the satellite, which may
represent savings both for airlines in the short run as for airport operators in the long run as it increases
the possible number of operations. In the other hand, the use or not of people movers can also influence
greatly airport’s operational costs.
The main difference between satellite and midfield concourses configurations lies on the size and
distance of the passenger building to the groundside, despite this distinction is not firm. Typically, midfield
concourses are defined as major independent passenger buildings located far from the groundside.
Although often located between parallel runways, it also occurs to find midfield concourses of complex
passenger buildings positioned at the edge of runways, as it happens at Chicago O’Hare airport. (de
Neufville & Odoni, 2003).
3-7: MIDFIELD LINEAR AT DENVER AIRPORT (LEFT) AND X-SHAPED AT PITTSBURGH AIRPORT (SOURCE: (FAA, 2003) )
Figure 3-7 depicts the two traditional shapes of midfield concourses: linear and X-shaped. Although
both configurations support aircraft stands on both sides of the buildings and have central shopping
areas, linear midfield concourses provide better services for transfer passengers. The main argument to
choose the former configuration relates mostly with the limited available area for airport expansion. An
alternative variation to the X-shaped configuration is the cross-shaped passenger building. The former
has appeared as a solution to reduce passengers’ walking distances in opposition to the best use of the
scarce land area. One way or the other, X-shaped (or cross-shaped) concourses imply more complicated
aircraft operations due to buildings’ geometry in comparison to linear midfield concourses.
50
Linear buildings, devoting one side to aircrafts and the other to the groundside, are long and
relatively thin structures. The original idea was that passengers arriving to the airport would be able to
get to their flight gate just by walking though the thin building, hence minimizing walking distances.
However, such concept is counterproductive to the extent that requires additional security and check-in
facilities, does not provide steady passenger flows for retail opportunities, and passengers do not
traditionally arrive at the exact point where their flight gate is located. Hence, designers now tend to give
fewer access points, which increases passengers’ walking distances, as opposed to the initial concept.
Although some airports have chosen curved layouts (as Kansas City airport depicted in figure 3-8)
which provides more frontage on the airside, such layout complicates both the initial construction and
landside traffic flows. (de Neufville & Odoni, 2003)
3-8: LINEAR (LEFT) AND HYBRID CONFIGURATION (RIGHT) AT KANSAS CITY AND SEATTLE TACOMA AIRPORTS
(SOURCE: (FAA, 2003))
Finally, transporters consist in the transport of passengers between buildings and aircrafts, typically
done by low-platform buses, which also usually require passengers to use stairways while carrying their
bags. Alternatively, lift lounges are used to move passengers between the building and the remote aircraft
stand, thus preventing passengers from suffering climate exposure and speeding up the (un)loading
process, with the natural side effect of being particularly expensive infrastructures.
Transporters allow dealing with strong seasonal variations in passenger traffic. Since the cost of
transporters can be minimized when they are not needed, it provides a cost-effective solution to aircraft
gates in buildings that require maintenance, cleaning and considerable depreciation costs relentless their
use. (de Neufville & Odoni, 2003)
Seattle Tacoma airport’s diagram, as depicted in figure 3-8, uses an underground people mover to
the satellite (or midfield, depending on the interpretation) concourses, has two linear one-side devoted
51
aircraft buildings and two finger piers. Such is an example of an hybrid solution, with the intent of more
capably satisfy the airport’s different needs.
In our case study, transfer passengers represent a residual sample of total commercial traffic. As
mentioned previously, in 2009, Ponta Delgada airport has registered more transfer passengers than
Lisbon, when the later has 15 times more commercial passengers. Although it should be noted that Ponta
Delgada airport operates as a regional hub, diverting traffic to the other 3 airports of the Azores
archipelago, and that 2009 was a particular sensate year in the aviation industry, the percentage of
transfer of transfer passengers to total commercial passengers on the top-5 busiest Portuguese airport
varies between 0,15% and 2,4%. In one hand, Faro airport confirms its touristic and thus, seasonal
vocation with 0,9% of transfers. The seasonality previously identified at Ponta Delgada airport balances
with its hub characteristics at the regional level. On the other hand, Lisbon’s airport image of international
hub is demystified with its mere 0,15% transfers of total commercial traffic. (ANA, 2009)
The high seasonal variation of Faro and Ponta Delgada airports comply with the use of transporters
in such peak traffic periods, with particular emphasis at Faro airport given the volume of low-cost
passenger traffic reaching nearly 70% of total commercial traffic and the low-cost ATM representing 64%
of total commercial movements in 2009. (ANA, 2009)
From the stated above, it becomes clear that issues such as passenger walking distances, average
taxiing times around passenger buildings, transporter economics and flexibility in airport design are
central concerns from the managerial point of view, and must be dealt taking into consideration the
various stakeholders interests. Yet, there are no magical recipes for passenger buildings’ design. Instead,
it is the detailed knowledge of the airport characteristics and needs that will allow the airport designer
preparing it for a wide range of possible circumstances that will enable to best adapt when paradigm
shifts occur.
3.2.2 SECURITY AND CHECK-IN PROCESSES
Security is a major issue regarding landside operational efficiency. According to (Graham A. ,
2008), airport security relates with the prevention of illegal activities such as terrorism. In this respect, the
9/11 events have lead to a higher scrutiny of airport operations, with many additional security measures
being introduced and, more importantly, international binding legislation was introduced.
The introduction of such measures has overburden airport’s operational costs, with particular
incidence in the airlines, which saw alternative check-in processes as a possible solution to overcome
airport’s scarce space and the delays inflicted by the additional security measures.
Self-service check-in desks became a complementary tool to process passenger and baggage
flows in airports. In the first decade of the 21st century, airlines first introduced for their own use
proprietary kiosks, copying other industries cost-effective self-use technologies. Later, and following the
logic of economies of scale of airlines alliances, common-use self-service (CUSS) check-in kiosks were
52
implemented, allowing costs to be shared between different airlines and less airport counter staff was
required. The main disadvantage with most self-service check-in kiosks arises in the difficulty to cope with
hold baggage. In order to solve this problem, some airports already incorporate a bag tag printing
capability (BTC) in CUSS kiosks, along with common bag drop locations.
The use of CUSS kiosks is one of the aims of IATA’s “Simplifying the Business” initiative launched
in 2004. Since then, the number of airports with CUSS kiosks has raisin from only 10 to 134. Within the
same initiative, it was also given focus in other areas such as electronic ticketing, bar-coded boarding
passes, radio-frequency baggage identification and paperless cargo movements. (Graham A. , 2008)
Figure 3-9 below, shows (on the left axis) the world distribution of airports with CUSS kiosks and
(on the right axis) the percentage of airports that have incorporated BTC system. (IATA, 2010)
3-9: WORLD DISTRIBUTION OF AIRPORTS USING CUSS KIOSKS AND PERCENTAGE WITH BTC (SOURCE: (IATA, 2010))
More recently, personal computer and mobile check-in has increased the number of possible
alternatives for passengers. Although such technologies are not always at the dispose of travellers, they
are even more attractive to the airlines as they do not need to install CUSS kiosks and passengers print
boarding passes with their own ink and paper.
Nowadays, the debate around CUSS kiosks has evolved from discrete check-in services to a
concept of a more efficient integrated model that shares the information with different stakeholders.
According to (Graham A. , 2005), the ideal process flow concept combines CUSS, new generation
passports, biometrics, secure databases and other technological advances to facilitate automatic
authentication of a passenger’s identity whenever this is required at the different stages in the travel
0%
20%
0%
30%
67%
3%
43%
25%
0%
10%
20%
30%
40%
50%
60%
70%
80%
0
10
20
30
40
50
60
AFRICA ASIA PACIFIC CIS EUROPE MENA NORTH ASIA THE AMERICAS
US
Airports with CUSS Kioks % of Airports with BTC on CUSS
53
process. Although concerns may arise from the individual data protection point of view, the integration of
this kind of technologies in airport operations most certainly involves benefits for all stakeholders,
passengers included.
3.2.3 LOW-COST AIRPORTS
Low-cost airlines are becoming significant factors in airport planning. Their requirements differ from
those of “legacy” carriers. They drive the development of secondary airports and cheaper airport
terminals. They catalyze low-cost airports around the “legacy airports” built for the “legacy airlines”. (de
Neufville R. , 2008)
In “Low-cost airports for low-cost airlines”, Professor de Neufville presents a particular interesting
paper for our case study. By challenging the traditional airport masterplan design with a flexible design
strategy that copes with the uncertainty of the aeronautical industry, he presents a comprehensive
economic analysis through a simple cash-flow exercise that reveals the advantages of flexible design
regarding the construction of the NLA, taking into account the important growth of LCCs.
The increasing role of LCCs relates with the expansion of low-cost airports, either secondary
airports in multi-airport systems or former military bases. Whilst LCC’s market- share sky-rockets, the
provision of more accessible facilities capable to attract these companies starts to grow.
To this respect, a new form of competition not experienced before now threatens traditional
airports. From the geostrategic point of view, such competition is especially strong at multi-airport
systems, where low-cost airports offer interesting alternatives to major international hubs. To a smaller
extent, these airports also allow to bypass those same hubs, as it happens at Faro airport, where
European touristic traffic avoids Lisbon’s busy airport. Furthermore, LCCs and low-cost airports also
provide parallel routes against established legacy networks, as it happens at Porto airport, with Ryanair
flights to Girona (Barcelona), Stansted (London), Charleroi (Brussels) or Beauvais (Paris) among others.
Additionally, there are also big differences between traditional and low-cost business models.
Firstly, just like LCCs, low-cost airports avoid expensive infrastructures. Secondly, passenger’s level of
service in terms of space is lowered, and when combined with faster turnaround times, it directly relates
to smaller infrastructure costs per high annual passenger volumes. Finally, and in clear contrast with
airport’s trendy bet on commercial lounges (that increase airports’ non-aeronautical revenue), low-cost
airports will avoid this spaces, as it implicates further construction, security and other operational costs,
diverging from the no-frills concept. The combination of these factors results in lower charges for the
airlines, which are one of LCC’s most relevant fixed costs. On the other hand, being relatively
uncongested, low-cost airports avoid ground and air delays and when combined with quick turnaround
times, are the cornerstone for smaller variable costs.
These are the main arguments for the paradigm shift, whereby airport planners and investors
should recognize the volatility of LCCs and accept the necessary downgrade in service standards.
54
Demand has thus become more uncertain than ever. Additionally, traffic growth rate changing according
to different economic cycles, industry’s volatile regulation and the uncertainties of an industry passing
through an era of change, important consequences for airport planning must be taken in consideration, as
(de Neufville R. , 2008) enumerates:
• Major airport users may disappear. Following the Unites States, extinction and mergers of
established legacy carriers in Europe has already started with the bankruptcy of Sabena
and Swissair, and merge of KLM with air France. The Portuguese flag carrier TAP is soon
expected to be privatized, mostly to remove an important expense from the national
payroll.
• Change of distribution and patterns of traffic. LCCs may divert traffic to secondary
metropolitan airports, or nationwide, as it happened at Faro and Porto, with high volumes
of LCC passengers.
• Different design standards may apply. The airline clientele may reject facilities provided,
leaving them underutilized and possibly unprofitable, has Ryanair has done in Porto.
Ultimately, airport developers and decision makers face great risks regarding new infrastructure
investments. It is thus needed a design process that allows to recognize explicitly the uncertainties that
threaten planned investments and developments. As mentioned previously in the airfield design process,
flexibility is the key to successful development strategies, by preparing airport managers to adapt more
efficiently to reality changes.
3.3 AIRPORT-AIRLINE RELATIONSHIP
The relationship between the airport operator and the airline is clearly fundamental to the success
of any airport business. (Graham A. , 2008)
As primary users of airports, airlines throughout the recent decades have forced airports to become
increasingly more efficient. Their entrepreneurial spirit and competitive industry promotes a rigid cost
control in a deregulated environment. Increasing oil prices in recent years and scarce capacity for
increasing demand obliges airlines to seek cost reductions and efficiency gains at their primary supplier.
Meanwhile, airports with traditional governmental management have started to pursuit more
business-oriented strategies that affect inevitably the airlines.
In order to understand this important relationship, it is crucial to understand how airports are
organized and managed, which are the economic ties that connect both partners in this business and
finally, to comprehend the regulatory environment that dictates how is the game played. Such topics will
be briefly addressed in the subsequent sections.
3.3.1 AIRPORT PRIVATIZATION AND MANAGEMENT
55
Powered by the airlines’ industry deregulation, airport management began to change in the last
quarter of the 20th century, moving towards to a more commercial perspective. This shift was done in
many different ways, but mostly through the establishment of independent authorities that, in some cases,
opened the door to private cooperation and investment. In Europe for instance, the Polish Airport State
Enterprise was created in 1987, Spain launched in 1991 Aeropuertos Espanoles y Navegacion Aerea,
and in the Portuguese case, ANA was created in 1998 (Wikipedia, 2010).
The innovatory commercial aspects of airport management included financial management
benchmarking, non-aeronautical revenue generation and airport marketing were important contributes to
the traditionally operational perspective. A business like approach to airport management, coupled with a
more commercially driven and competitive airline industry, encouraged airports to take a much more
active and proactive role. (Graham A. , 2008)
In the end of the 20th century, airports’ privatization was subject of intensive debate. In one hand,
privation would reduce the need for public sector investment (meeting the European Member-States’ goal
of finding financing sources outside the public budget) and provide access to the commercial markets. On
the other hand, private monopolies along with its intrinsic overcharging schemes, poor standards of
service, inadequate investments or lack of consideration towards social equity could appear as fearful
arguments regarding the privatization of public services.
Hence, for many countries, transferring airports, which are considered national or regional assets,
to the private sector remains a politically sensitive policy. The fear is that social welfare will not deserve
the same attention as shareholders’ interests. As in other transportation modes such as railways or
highways, airports have the monopolistic position of the infrastructure manager that does not suffer the
same kind of competition of airlines. It is therefore understandable why views of privatization vary in
different regions around the world and even between local and central government bodies in individual
countries. (Graham A. , 2008). One interesting example is given by (Hooper P. , 2002) regarding
privatization of Asian airports. The author shows that despite the fact that some governments say that
efficiency is important to them, the most common and important motive in ‘‘privatization’’ in Asia is to
mobilize a new source of finance and while governments are concerned about abuse of monopoly powers
they want to cross-subsidize regional airports, but they lack the institutional strengths to regulate
effectively. The crucial risk by retaining majority control however, is losing the efficiency benefits of
privatization.
3.3.1.1 MODELS FOR AIRPORT PRIVATIZATION
Although seldom used, airports’ privatization may be a misleading term for airports’ changes in
ownership. Airport privatization does not actually involve the sale of property but, quite differently, the
transfer of ownership rights such as profits and management control on short and long-term development
issues. (de Neufville & Odoni, 2003)
56
Due its monopolistic position, airports are generally seen as attractive organizations to investors.
Moreover, the industry has shown steady growth in the recent decades, and has very high barriers to
entry, either as of large capital investments or as the lack of convenient locations where airport
development is allowed. (Graham A. , 2008) Notwithstanding, there are considerable risks. Airport
regulation and development can be changed through political legislation and along with airlines’ industry
volatility, poses significant risks to shareholders. The privatization model adopted by political decision-
makers is therefore a complex process depending on government’s objectives.
According to (Graham A. , 2008), airport “privatization” can occur in different ways:
• Share floatation;
• Trade sale;
• Concession;
• Project finance privatization;
• Management contract;
Share flotation is the mean by which airport’s share capital is issued and subsequently traded on
the stock market. British Airport Authority is a rare example of an 100% share floatation undertaken in
1987, whereas in many other airports, less than half of the airport’s capital is put on the market, or in
other words, partial share floatation. One way or another, this kind of privatization will reduce the need
for state involvement in the financing of airport investment while transferring partial or effective control
and the inherent economic risks to the new shareholders. Nevertheless, governments can maintain a
certain degree of influence in case of preservation of national interests through “golden shares”.
Trade sales usually involve a public tender whereby the airport is sold to a strategic partner or
consortium of investors, rather than just passive investors. Often, the strategic partner is an established
airport operator or the purchasing consortium will contain a member with airport management experience.
Same cases have occurred of participating airports in the privatization process were not actually
privatized themselves, which leads to further complications in the definition of “private” airport. (Graham
A. , 2008)
Concession is the type of arrangement whereby public tendering, consortiums or airport
management companies purchase a lease to operate the airport during a defined period. Concession
agreements are most popular in developing countries, with particular incidence in South America as it is
the case of ANA that, in a consortium lead by Ferrovial, was awarded with a 25-year concession of 12
regional airports in Peru (MOPTC, 2008). Such is typically related with the involvement of initial
payments, guaranteed levels of investments and/or payment of annual fees, factors of great importance
for nations with less access to high volumes of financing. (Graham A. , 2008) Although more politically
acceptable since the state will not actually sell the airport and still receive a regular income, this process
tends to be more complex with high transaction costs and requires particular attention in the design and
implementation in order to fully achieve the governmental political objectives.
57
Project finance privatization is the option preferred for relatively large investments in greenfield
projects or even redevelopments. After public tendering, the awarded private consortium or private-public
partnerships (PPP) will bear the initial investment costs and all operational costs for a predetermined
period of time, after which ownership reverts to government. Many methods exist in this privatization
model, with the most popular being build-operate-transfer. In the Portuguese case, the government has
chosen to build and finance the NLA, under a Design-Build-Operate-Transfer (DBOT) in conjunction with
the partial privatization of the concessionaire ANA. In the context of an international public tender, the
selected private partner while acquiring a majority stake of its share capital will be entrusted with the
obligation to build and operate the NLA for a concession period of 40 years. (MOPTC, 2008)
Finally, management contracts are the least radical privatization model. This option consists in
maintaining airport ownership under governmental control while the winning consortium takes
responsibility for daily operational management of the airport, usually for periods of 10-15 years (Graham
A. , 2008). Such model has more political acceptance although investments are done at expense of the
public budget. On the other hand, the private partner may find this alternative more attractive in countries
with higher financial exposure.
Some issues arise regarding airport privatization. One regards how governmental control depends
on the chosen privatization model. Project finance or concessions are more politically acceptable and
governments maintain a considerable degree of control that can influence the efficient performance of the
airport. On the other hand, trade sales or share floatation usually carries along fears of private
monopolies and have thus lead to the introduction of economic regulation. Another concern arises when
the privatization of a group of airports happens, the question being whether airports should be sold
together or split up into different companies. If sold as a group and successful financial records exist,
higher prices can be achieved due to the lack of perceived competition. However, if the operator’s income
is largely affected by loss-making airports, the sale price may suffer accordingly. Hence, unprofitable
airports are removed from the government’s payroll at expense of a smaller sale. Then again, if only
profitable airports are privatized, one option would be to use concession fees (in a concession
arrangement) to cross-subsidize smaller airports. Furthermore, airlines tend to suspect of airport groups,
fearing that the fees paid will be used as cross-subsidization for loss-making airports they do not use.
Airport operators normally respond to this criticism arguing that through economies of scale, higher
efficiency can be achieved. (Graham A. , 2008)
58
3-10: PORTUGUESE AIRPORT OPERATOR’S NET PROFIT BETWEEN 2004 AND 2009 (SOURCE: (ANA, 2009B))
In the Portuguese case, the partial privatization of ANA relates to Azores and mainland airports.
Notwithstanding, ANA’s group of companies still comprises one of the two Portuguese state-owned
handling companies, 70% of the ANAM and around 84% of the special purpose company established for
the planning and development of the NLA. From figure 3-10 above, we can observe both the increasing
net profits of the group along the recent years and an average 10,6 million € difference between ANA and
the rest of the group companies. Furthermore, peripheral Azores Islands’ airports have typically negative
results and are hence cross-subsidized by mainland airports. We can thus infer that the concessionaire
will finance loss-making airports with mainland airports revenues, while the government will use its share
of revenues to pay for the losses of the remaining companies of the group. The question to be answered
some years from now is whether efficiency gains expected from the private operator will pay off or not
today’s profits derived from the state-owned monopoly.
3.3.1.2 OWNERSHIP EFFICIENCY STUDIES
Privatization not only affects competition between airports but also raises doubts on whether it will
lead to efficiency gains or not. To date, there has been only limited and in some cases contradictory
research in this area. (Graham A. , 2008)
One of the first studies by (Parker D. , 1999) used DEA methodology to assess to what extent
technical efficiency changed following BAA’s privatisation. The study found that privatisation had no
noticeable impact on efficiency. On the other hand (Vogel, 2006) revealed economically meaningful and
statistically significant differences between publicly owned and privatised airports. Using DEA on a panel
data of 35 European airports, the author found that major differences lie in operating efficiency, capital
productivity and capital structure. Although partially and fully privatised airports operate more efficiently, it
does not translate into significantly higher returns on shareholders’ funds. Furthermore, due to their at
0
10
20
30
40
50
60
2004 2005 2006 2007 2008 2009
Net
Pro
fit in
Mill
ion
€
ANA Group ANA
59
least indirectly government-backed credit standing, publicly owned airport companies can assume more
debt relative to their respective equity.
Within an international context of airport ownership benchmarking, (Oum, Adler, & Yu, 2006)
conducted a cross-sectional, time series of major European, North American and Asia-Pacific airports.
The authors found strong evidences that airports with government majority ownership and those owned
by multi-level of government are significantly less efficient than airports with a private majority ownership.
In addition, no statistically significant evidence to suggest that airports owned and operated by US
government branches, independent airport authorities in North America, or airports elsewhere operated
by 100% government corporations have lower operating efficiency than airports with a private majority
ownership. The commercialization of airports with a private majority ownership becomes evident with a
much higher proportion of their total revenue from non-aviation services than any other category of
airports while offering significantly lower aeronautical charges than airports in other ownership categories
excluding US airports. It is also found that airports with private majority ownership achieve significantly
higher operating profit margins than other airports; whereas airports with government majority ownership
or multi-level government ownership have the lowest operating profit margin. Particularly relevant to our
case study, the authors suggest that private–public–partnership with minority private sector participation
and multi-level governments’ ownership should be avoided, supporting the majority private sector
ownership and operation of airports.
Using a different methodology but with a similar sample, (Oum, Yan, & Yu, 2008) report four key
findings. Firstly, countries considering privatization of airports should transfer majority shares to the
private sector. Secondly, mixed ownership of airport with a government majority should be avoided in
favour of even 100% government owned public firm. Thirdly, US airports operated by port authorities
should consider transferring ownership/management to independent airport authorities. Finally,
privatization of one or more airports in cities with multiple airports would improve the efficiency of all
airports.
Regarding Latin American airports, (Vasigh, Erfani, & Miner, 2009) suggest that privatized airports
outperform government owned airports. However, there is no conclusive evidence between privatized
airports and selected North American airports.
There are thus strong arguments for and against privatization. For our case study, it is not a
relevant issue in the sense that the same operator manages the airports in question. To analyze such
influence, other benchmarking study approaching similar size European airports should be considered.
The reality however is that he airport sector has moved from and industry characterized by public sector
ownership and national requirements into a changed era of airport management, which is beginning to be
dominated by the private sector and global players. (Graham A. , 2008) In the Portuguese case, as
mentioned previously, only some years from now will we be able to assess to what extent was the
privatization of the public monopoly successful. Notwithstanding, it is crucial for political decision makers
60
to follow international guidelines and make best use existing literature on airport benchmarking in order to
potentially maximize social welfare.
3.3.2 LCC’S IMPLICATION ON AIRPORTS’ REVENUES
Traditionally, airports were dependent on a combination of governmental funding and revenue from
airlines by charging them so-called aeronautical charges for the use of their services. Although there is a
variety of practice worldwide, typically, airports will charge airlines a weight related fee to land their
aircraft, a fee per passenger that passes through the terminal, aircraft parking charges and charges for
office space. Additional charges relate to ground handling and fuel and these may be provided by the
airline itself or by a third party company (or companies). (Humphreys, Ison, & Francis, 2006).
Nonetheless, as airports become increasingly more commercially oriented, typical aeronautical revenues
are now complemented with charging schemes more complex and market based.
In recent years, airport charges have been of growing concern to airlines especially due to the
increased competitiveness in the industry and falling yields, which have resulted in cost cutting activities
of internal activities (staff, wages etc.) they can best control. However, airlines have also been looking at
their external cost such as airport charges and demanding that airports adopt cost cutting and efficiency
saving measures themselves, rather than raising their charges. To this respect, LCCs and charter airlines
have particular strong reasons to worry then traditional carriers. Whereas the later typically operates long-
haul services, the former operates short sectors, representing payments of airport charges more
frequently. And although airport costs represent on average 4% of traditional airlines’ operating costs, it
accounts up to 17% for LCCs, being the third most important cost for LCCs right after fuel and aircraft
leasing cost. (Graham A. , 2008)
Furthermore, the existence of airport incentive schemes or discounts for the exploration of new
routes makes the airport charging policy having its greatest impact. Such discounts have, in many cases,
been critical factor when LCCs are selecting suitable airports for their operations in addition to sufficient
demand and fast turnaround facilities. Nevertheless, airport managers also tend to fear LCCs’ flexibility
to enter and exit routes. Although it gives LCCs bargaining power, such lack of commitment poses the
threat of unsuccessful investments to national or regional governments.
LCCs have thus reshaped the airport-airline relationship. Airports have responded to the potential
opportunities that have arisen from LCCs’ growth. The low cost model has implications for the airline-
airport relationship, forcing airports to negotiate contracts which significantly reduce aeronautical
revenues whilst seeking to address this short fall by commercial revenues via increased passenger
numbers. According to (Humphreys, Ison, & Francis, 2006), airports have sometimes found it difficult to
turn increased passenger volume into additional revenue and traditional airports are challenged in terms
of if and how they should accommodate LCCs. The author further enunciates eight issues airport
managers need to consider when accommodating LCCs:
61
i. Continual market monitoring: keeping the low-cost airline market under continual review and
reassessing whether to accommodate LCCs or not;
ii. Volatile nature of the low-cost sector: consider volatility in both revenue streams, networks
available, high number of route entries, operators going out of business or transferring
operations to another airport, especially when the airport management should be considering
investing into new capacity;
iii. Significance of the non-aeronautical revenue: having sufficient retail and car parking capacity
to create commercial revenue streams, carefully calculate the break-even, monitor revenue
streams;
iv. Capacity to cope with the LCCs: in terms of airport capacity (both terminal and runway),
need for new low-cost facilities (devoid of air bridges etc.)
v. Tensions between incoming and incumbent airlines: the threat of pressure to reduce charges
to existing operators when accepting LCCs;
vi. Need for transparency: especially applies to publicly owned airports where the incentives
might be seen as a not permissible subsidy;
vii. Benefits to local economy: again, especially applies to public owned airports with interest in
bringing benefits to the local economy, management should beware that such effects can be
difficult to predict and quantify;
viii. Innovative/risk sharing contracts: some airports use contracts that contain clauses that relate
charges to the number of passengers carried or the number of services operated and along
with investments into software that enables passenger monitoring by flight number. With
such detailed data, airport managers can build up a picture of what passengers spend, on
what routes, at what times of day. As finding shows, particular routes at particular times can
pay for themselves just by passenger spending.
With increasing passenger traffic, LCCs are thus a new powerful player in the airport-airline
relationship, and key shareholders in the development of new or existing airports.
3.3.3 REGULATORY ENVIRONMENT
According to (Graham A. , 2008), airports, are subject to a number of different regulations at both
international and national level. Many of these regulations relates to the operational, safety and security
aspects of airport management. Also increasingly important, airports are now facing more strict
environmental regulations, restricting for instance, the number of hours of operation or the available
landside for expansion. However, the regulation that influences the most the airport-airline relationship is
the economical, with particular focus being on charge control. In some areas of the world, other economic
aspects of operational activities such as handling and slot allocation are also regulated.
International guidelines have also been issued both by ICAO (ICAO, 2004) and the EU (European
Comission, 2009). In fact, they do not differ much in the main essence that is cost related charges.
62
Furthermore, these guidelines encourage principles of non-discrimination, users’ consultation and
transparency. Along with the recommendation of setting up of an independent national regulator, it is
suggested that airports supply of financial information to airlines regarding the charges calculation
method whereas airlines would supply their traffic forecasts and airport requirements. One meaningful
change of EU’s regulation was the scope of airports obliged to comply with this regulation. The threshold
increase from one to five million passengers would reduce the number of regulated airports from 180 to
70 in 2011. (Graham A. , 2008),
The airlines response to the EU directive was rather of disappointment, in the sense that pre-
financing was not prohibited and the general lack of provisions to make airports more cost efficient.
Similarly, LCCs said that the directive marked a missed opportunity to introduce targeted, robust and
effective regulation of those relatively few dominant airports in Europe that actually need to be regulated.
Furthermore, LCCs see no justification for allowing airports to pre-finance future facilities from charges on
existing users and were disappointed that airports are allowed to choose a dual-till business model.
(ELFAA, 2008). As (Graham A. , 2008) points out, also the CAA feels that regulation can be burdensome,
and should only apply to airports with substantial market power. In a nutshell, competition is preferable to
regulation, and even where competition acts only as a weak discipline on behaviour, regulation should
only be preferred if it can deliver a clear net benefit. (CAA, 2007)
According to (Marques & Brochado, 2008), there are several economic regulation methods at work
worldwide. It is not easy to find consensus in their classification, but they can be sorted into two main
groups, according to the incentives they offer the regulated industries towards costs minimization.
The first group, with a very low degree of incentive, includes the rate of return regulation (RoR),
whereas the second, with a high degree, corresponds to the incentive regulation. The remaining
economic regulation methods are variations or interactions between these two classes, such as the well-
known sliding scale approach, in which the costs and revenues (profits) are shared among stakeholders.
Although the RoR is widely used, and ensures that prices are cost related, this system is highly criticized
since it provides no incentives to reduce costs, and therefore, the operator will be guaranteed a certain
RoR irrespective of efficiency.
On the other group, incentive regulation promotes efficiency and innovation. Created on Tatcher’s
utilities privatization legislature, this kind of regulation comprises different methods such as price cap
regulation (PCR), revenue cap regulation, hybrid and yardstick competition methods. As (Marques &
Brochado, 2008) refers, PCR consists in the imposition of an average maximum threshold for the charges
of the services provided. With the prices ceilings defined at the beginning of each regulatory period, the
regulated services hold the earnings corresponding to the cost reduction which happens during that
period. As PCR is not based on individual costs, it fosters appropriate price structures, maximizing the
welfare. The price cap formula is composed of two parts (CPI–X), one corresponding to the consumer
price index (CPI), and the other (X) to the operator’s productivity change expected.
63
TABLE 3-3: AIRPORT REGULATORY METHOD IN EU COUNTRIES IN 2006 (SOURCE: (MARQUES & BROCHADO, 2008))
Country Regulatory method
Country Regulatory method
Country Regulatory method
Austria Non-pure price cap Greece No regulation Poland No regulation
Belgium Yardstick competition
Hungary Pure price cap Portugal Rate of return
Czech R. No regulation Ireland Revenue cap Slovak R. No regulation
Cyprus No regulation Italy No regulation Slovenia No regulation
Estonia Rate of return Latvia No regulation Spain Rate of return
Denmark Pure price cap Lithuania No regulation Sweden Pure price cap
Finland No regulation Luxembourg Rate of return UK Pure price cap
France Revenue cap Malta Pure price cap
Germany Non-pure price and revenue cap and RoR
The Netherlands
Rate of return
Table 3-3 above, summarizes the airport economic regulatory models in European countries in
2006. In this table, ‘‘no regulation’’ means that the charges of airports are determined directly and
opaquely by the Government. However, it is probable that most of them employ less incentivizing RoR
methods.
Curiously, (Graham A. , 2008) remarks that while the airline industry is passing through a
deregulation phase, the airport industry is to a certain extent burdensome with regulation.
Notwithstanding, economic regulation can have enormous impact in the relationship between airlines and
airport operators. Such impact is even greater when the debate around the sources of revenue of airports
takes place, as briefly discussed in the next section.
3.3.3.1 SINGLE TILL VS. DUAL TILL
The airport facilities and services that are considered when the airport prices are being set is a
major concern of all airlines. Two alternative approaches exist.
The first is the single till, whereby all revenues generated by both aeronautical and commercial
activities are taken into account. In the great majority of cases, non-aeronautical services and facilities,
which are becoming increasingly important sources of revenue for airports, will help reduce charges of
aeronautical services. Airlines are thus strong supporters of the single till approach. (de Neufville &
Odoni, 2003) Probably, the most important drawback to this approach relates to traffic growth and
increasing congestion, where bringing down charges in an environment of scarce supply of resources
makes no economic sense. (Graham A. , 2008)
The other approach is the dual till. It treats aeronautical and non-aeronautical areas as separate
financial entities and focuses on the monopoly aeronautical airport services. It is however, a difficult task
due to the allocation of many fixed and joint costs between both areas. Airport managers often argue that
64
commercial revenues reflect the premium location as opposed to monopolistic pricing, and hence, should
not be subject to economic regulation.
Such debate has leaded the British Civil Aviation Authority (CAA) in late 2000 to conduct a
consultation paper on which approach to follow regarding price regulation in British airports. In this paper,
five arguments for and against the dual till approach implementation are discussed and present an
enlightening summary of this debate. It is first argued that designated airports might earn high profits due
to market power in relation to commercial activities. If in one hand, the possibility of market power may
exist, the CAA is not convinced it is such that economic regulation is warranted. Moreover, the single till
would not be the answer, as it merely transfers the profits from this to lower the regulated charges. A
second argument relates to the fact that commercial revenues are derived from the airlines’ passengers,
and should therefore benefit from profits generated by passengers buying. The counter argument here is
that single till has the effect of an additional tax on profits which serves to dampen the airports incentives
to develop it efficiently. The third argument often is that the single till ensures that the price of the airport
total bundle of prices is kept to competitive levels, permitting only normal returns on capital. Again, this
does not protect passengers from market abuse by airports in commercial activities. Ultimately, it would
only serve airlines, by transferring those rents to reduce aeronautical charges. Fourthly, it is argued that
the removing the single till would generate windfall gains for airport operators. Here, the CAA shows
some caution, admitting that a regulatory framework needs to be sustained over time, so that both an
increase of airport charges and the overall level does not happens. The final typical argument is that the
single till is simple to administer and therefore reduces regulatory intervention, rather than increasing it.
CAA recognizes such advantage of the single till being relatively straightforward. Nevertheless, CAA’s
view is that the high level arguments in favour of the single till are not compelling. The basic presumption
is that economic regulation should apply only to the core monopoly functions that an airport provides and
should not be extended to cover other activities.
TABLE 3-4: SINGLE OR DUAL-TILL APPROACH IN EU COUNTRIES IN 2006 (ADAPTED FROM (MARQUES & BROCHADO,
2008) )
Country Single/dual till Country Single/dual till
Austria Single till Italy Dual till
Belgium Single till Malta Dual till
Denmark Dual till The Netherlands Dual till
France Single till Portugal Single till
Germany Dual/single till Spain Single till
Greece Dual till Sweden Single till
Hungary Single till UK Single till
Ireland Single till
According to (Marques & Brochado, 2008), the dual-till approach has recently gained prominence
in Europe. Indeed, despite producing higher airport charges for users and stand up cost allocation issues,
65
dual-till regulation makes charges reflect costs more closely and maximizes the airport value. The authors
further point out several studies defending the dual till to the detriment of the single-till regulation. Such is
the case of (Beesley, 1999) arguing that regulation should focus on activities characterized by a natural
monopoly and (Starkie, 2001) that goes farther by neglecting the need for economic regulation for the
non-congested airports. Starkie defends, however, that for congested airports the application of a dual-till
scheme would lead to higher aeronautical charges which would have positive effects on the allocation of
scarce slot capacity and on the investment incentives. On the other hand, (Lu & Pagliari, 2004) stand for
the single-till approach as welfare maximizer when compared with the dual-till method at non-congested
airports. Table 3-4 above summarizes the approaches followed by European countries in 2006.
The approval of economical regulation (DL 16/2009) in late 2009 in Portugal was done in order to
prepare the eminent privatization of ANA. According to the Ministry of Public Works, Transports and
Telecommunications (MOPTC) this model will follow international guidelines by ensuring transparent,
predictable and stable rules. Another goal is to warrant aeronautical charges compatible with passenger
rights and that enhance airports’ efficiency, thus fostering the levels of service from the airlines and
passengers’ point of view. (MOPTC, 2009). Table 3-5 below indicates the main changes for the
intervening actors with the implementation of the economical regulation.
TABLE 3-5: CHARACTERISTICS OF THE NEW PORTUGUESE ECONOMIC REGULATION ON AIRPORTS' CHARGES
Characteristics New model on Airports’ Economic Regulation Before
Incentives Management efficiency; Refusal to accept proved inefficient investments by the regulatory authority;
None;
Single till Commercial revenues will be used to reduce aeronautical charges;
Reduction of aeronautical charges is partial and casuistic;
Stability
When large investment occur the model enforces tariff stability through: - Profit transfer over time; - Decomposition of revenues in fixed and variable compounds;
No stability. When large investments are done, charges sky rocket and fall abruptly after the investment amortization;
Flexibility
Regulator defines an average maximum revenue per passenger in quinquennial periods; Charges adjustment according to demand in different airports and/or different times of the year; Some charges are eliminated;
Aeronautical charges are defined on an annual basis without any sort of flexibility
Level of Service
For each regulatory period, LoS are defined for several services (check-in, passport control, baggage claim, etc); Settlement of objectives to be met by the airport operator for each service;
No regulation on the LoS; No systematic measure of LoS;
The economic regulation model used is undoubtedly important for any airport’s overall performance
and the current discussion around which model suits the best each airport says it all. Albeit the
66
importance of this variable, it is not so relevant for our case study, given that all Portuguese airports
operate within the same economic framework. In a near future however, with the eventual privatization of
mainland and Azores islands’ airports, an efficacy performance comparison between them and the
Madeira Islands’ airports maybe relevant as the later will remain under total state ownership and will not
be under the scope of the new economic regulation.
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4 ANALYSIS OF PORTUGUESE AIRPORTS’ EFFICIENCY
4.1 INSTITUTIONAL SETTING
There are 37 Portuguese airports, most of which are small regional airports operating in a
discontinuous fashion. The main airports are run by public enterprises, regulated by a public body,
Instituto Nacional de Aviação Civil (National Institute of Civil Aviation), and this same public organization
comes under the direct control of the MOPTC. This public authority manages the airports, which are
situated throughout mainland Portugal and the archipelagos of Madeira and the Azores. The airports are
owned by public enterprises: ANA owns the airports of mainland Portugal and the Azores, whilst ANAM
owns the Madeira airports. (Barros C. P., 2008)
One of the major shareholders of ANA is Parpública, a state holding. Parpública has particular
relevance in the Portuguese aeronautical services since it also detains 100% of the flag-carrier TAP. In
one hand, Parpública calls out attention to the need of recapitalization of the Portuguese airline group of
companies and to the excessive personnel costs that represented 25% of operational costs in 2009. On
the other hand, ANA’s group registered a 43% profit increase in 2009 to 43 million € (affected negatively
by ANAM and Portway’s 5 million € losses each), mainly through control of operational costs. Parpública
states how the low-cost segment was critical on national airports with an average 35% increase per year
and having inaugurated 15 new routes in Lisbon, 26 in Porto, and 14 in Faro between 2007 and 2009.
Furthermore, it points out the need of increasing efficiency in order to retain this segment of companies
and passengers. (PARPÚBLICA, SA, 2009)
The financial background of both companies is of great importance when considering the
construction of the NAL. Whereas the privatization of a solid asset like ANA is sure to attract many
investors, the uncertainty regarding the future of TAP along with the planning of a new airport that is
TAP’s main hub, responsible for 39% and 44% of total Lisbon’s passengers and movements respectively,
brings many doubts to this project.
ANA – Aeroportos de Portugal S.A is the concessionaire of the 3 airports in mainland Portugal
(Porto, Lisbon and Faro) and 4 airports in the Azores Islands. Additionally, ANA holds equity stakes of
70% in ANAM (the airport operator of Madeira Islands) 49% in ADA (the Macao airport operator) and
100% in the company Portway (one of the two ground handling companies operating in Portuguese
airports). As part of a consortium, ANA is also directly involved in the management of 12 regional airports
in Peru. The company also holds a share of approximately 84% in NAER, S.A., a special purpose
organization responsible for the studies and procedures inherent to the launching of the tender for the
privatization of ANA and the construction of the new airport. (MOPTC, 2008).
ANA’s shares are divided between the Ministry of Finance (31,44%) and a Parpública (68,56%) – a
state holding focused on managing financial assets and real estate, support to public investment and to
68
the program of non strategic assets’ alienation. (ANA, 2009b). ANAM was first created in 1991 as a
subsidiary of ANA’s group of companies. The remaining 30% shares are divided between the Madeira
Regional Government (20%) and the Portuguese State (10%). Below, figure 4-1 represents the structure
of the main Portuguese airport operator.
4-1: STRUCTURE OF ANA’S GROUP OF COMPANIES (SOURCE: (ANA, 2009B))
4.2 DATA COLLECTION
Regarding the collection of data for our case study, some considerations are in order.
Firstly, the numbers of ATMs and passengers used in our model relate to commercial flights. All
information was drawn from ANA’s yearly statistics report. In the five busiest airports, commercial ATMs
represent in average 92,3% of total flights. The significance of commercial flights is higher in Lisbon and
Porto airports with nearly 96% whereas in touristic oriented airports such as Faro and Funchal the ratio
drops to 88%. Such drop is explained by the increase of private aircrafts, related with the aeronautical
services provided to private clients. Ponta Delgada airport sticks to the average with slightly over 93%.
On the other hand, commercial passengers represent in average 99,9% of total passengers.
In Figure 4-2 we can observe the annual variations between 2005 and 2009 on both passenger
and aircraft commercial movements. Porto airport registers the highest increases both on passenger and
movements annual variations. Lisbon airport has similar trends to Porto despite being rather inferior. Faro
airport also shows high annual increases but remains the only airport with two years in the third quadrant.
Curiously, 2008 was the only year that Funchal airport registered positive variations both on passengers
and movements, when the liberalization of routes to Funchal occurred. Moreover, Faro is the only airport
that registered two annual decreases in passenger traffic between 2005 and 2009. Ponta Delgada airport
is the only infrastructure with constant positive annual variations of aircraft movements despite two years
with decrease in the number of passengers. The overall picture reflects the increasing effect on the
demand side with an average increase of 1,7% on ATM and 3,4% on passengers traffic. The other side of
coin is how economical downturns play a major role in the industry, as 2009 proved to be.
69
4-2: ANNUAL VARIATIONS OF PAX AND ATM BETWEEN 2005 AND 2009 (SOURCE: ANA)
Secondly, data regarding LCC’s operations was obtained from ANA and ANAM, but with some
nuances. The initial concept was to gather data on a monthly basis in order to obtain a wider data panel
that could define a more accurate efficient frontier. Evidently, this would lead to peak summer months
being more efficient, with seasonality distorting the analysis. Nonetheless, data collection followed the
principle of gathering information with the highest level of disaggregation possible in order to reduce the
number of eventual assumptions.
All airports with exception to Porto collaborated by providing the monthly distribution of LCC’s
traffic. Notwithstanding, the yearly number of LCC’s passengers and movements was found as well as
2009’s LCCs monthly traffic at Porto (ANA, 2009a). For Lisbon airport, we also had to estimate LCC’s
monthly traffic distribution between 2005 and 2006 (ANA, 2006). The procedure followed was the same
as for Porto airport in the period between 2005 and 2008.
Such forced to introduce an assumption regarding the monthly distribution of the remaining years.
For this purpose, three options were considered. The first was to use data gathered from STATFOR
(Eurocontrol’s air traffic statistics) which identifies the different segments of all Instrument Flight Rules
(IFR) flights departing from any European country. This option required further assumptions, such as
doubling the traffic to obtain total traffic and the number of passengers per movement to obtain passenger
traffic. While assuming 50-50 distribution between departures and arrivals could be consensual, the same
09/08
08/07
07/0606/05
09/08
08/07
07/06
06/05
09/08
08/07
07/06 06/05
09/08
08/0706/07 06/05
09/0808/07
07/0606/05
-10,0%
-5,0%
0,0%
5,0%
10,0%
15,0%
20,0%
-10% -8% -6% -4% -2% 0% 2% 4% 6% 8% 10% 12%
Annu
al V
aria
tion
of C
omm
erci
al P
AX
Annual Variation of Commercial ATM
LIS OPO FAO FUN PDL
70
did not hold for the second assumption. For instance, differences up to 12% were found in the annual
average ATM when comparing Porto to Faro given that STATFOR takes into account typical charter
companies (Monarch, Thomas Cook) for LCC traffic, that do not operate at Porto, resulting in lower
numbers of passenger per movement. The other two options consisted in using LCC’s yearly traffic
between 2005 and 2008 from ANA. LCC’s monthly distribution at Porto airport could then assumed to be
identical either to the distribution of total commercial traffic at Porto or to Faro’s monthly distribution of
low-cost traffic. It was found that similarities between LCC and total traffic distribution along the year are
stronger in Faro than in Porto. This is mostly due to the continuous increase of LCC relevance at Faro
airport in comparison to other airports. In December 2009 at Faro airport, 72% of total movements and
83% of total passengers were derived from the LCC segment, whereas for the same period in Porto the
shares were only 31% and 49%, respectively. In addition, Faro’s high seasonality would also compromise
such assumption. The ratio between the busiest and slowest month in terms of monthly passenger traffic
in Faro is nearly five, whilst in Porto is two. Hence, between 2005 and 2008, the monthly distribution of
LCC traffic in Porto airport followed the same monthly distribution of total traffic in that period. The same
line of reasoning was applied for Lisbon’s monthly traffic distribution in the years of 2005 and 2006.
The third aspect relates to the availability of infrastructures between 2005 and 2009. For Faro,
Ponta Delgada and Funchal airports no changes were registered, hence data is constant over time.
Lisbon and Porto, on the other hand, have had several renewal and expansion works in that period, which
obliged to a more detailed scrutiny. As in traffic data, infrastructure data was gathered on a monthly basis,
being considered an average yearly value for our analysis.
Lisbon airport’s second passenger terminal was finished in July 2007, and therefore the landside
increase of capacity counts thereafter, namely with the addition of 22 check-in desks and 12 boarding
gates. Baggage belts capacity also suffered improvements in June 2008, by converting 3 out of 7 linear
belts to carousel-shaped type belts, increasing capacity in the former by 75% from 400 to 700 baggage
per hour. The extensive usage of the number of baggage belts as input in efficiency literature appears to
be inaccurate to the extent that Lisbon remained with 7 belts while increasing its overall handling capacity
from 2800 to 3700 baggage per hour. On the airside, the increase in runways’ declared capacity was
done gradually, passing from 32 to 36 ATM/h, with an intercalary period in 2006 with 34 ATM/h.
Furthermore, an increase of 12 remote parking stands occurred in mid 2008, from 44 to 56 remote
stands.
Porto airport also suffered structural works during the period in study. Unfortunately, there was not
the same availability from airport managers to explain when the changes occurred as it happened in
Lisbon. To that respect, contradictory information was found. According to (Tribunal de Contas, 2009),
while the passenger capacity increase in the passenger building was achieved before the study period for
purposes of the 2004 European Football Cup, works in the passenger building continued until January
2007 with the conclusion of the escalators. ANA replied to this report alleging that regardless the
71
conclusion date of all physical works, the spaces that suffered interventions were made available as soon
they were ready to use. Nevertheless, the only infrastructures relevant for our case study that appear to
have suffered interventions during our period are the air bridges (increasing from 9 to 11 in November
2005) and the rehabilitation of the runway (resulting in increased declared capacity from 16 to 20 ATM/h
by May 2006). Finally, we have assumed no change regarding available areas in the terminal for the
period in study due to lack of information.
The final aspect relates to the operational characteristics of each airport, namely how LCC’s traffic
is counted. The marketing department of Funchal airport informed that the LCC segment is registered
according to the ELFAA’s member list. On the other hand, Faro’s marketing department made available
an incomplete list of the main LCC operating in ANA’s airports by 2010, further explaining that some
carriers (airberlin, Brussels airlines) recently asked to be classified as full-service carrier. That is the case
of airberlin, which counts for LCC traffic in Ponta Delgada in 2009, but requested to change its
classification in February 2011. The segmentation appears to be similar to the one used by STATFOR3,
counting typical charter companies that operate under scheduled flights as well as typically low-fares
airlines, that despite having low price tickets, still include some services traditionally paid in LCCs.
This point arises some issues when we intent to perform a benchmarking analysis. If we take for
instance the year of 2009, the airlines Thomsonfly, Air Berlin and Thomas Cook had a combined ATMs
market share of 9% in Funchal, whilst ELFAA’s members easyJet and Transavia combined 12,4% of
market share (ANAM, 2009). According to Faro’s criteria, this leads to a reduction of Funchal’s LCC
market share to nearly half. On the other hand, in 2009 airlines non-members of ELFAA (Germanwings,
NIKI, Aer Lingus, SkyEurope, bmibaby, Blue Air) operated regularly in Lisbon and were counted as nearly
21% of the low-cost traffic (or 3,15% of total traffic). Moreover, whereas airberlin was by the time the only
LCC in Ponta Delgada airport, in Faro, that claims to have 80% of low-cost traffic, more than 30% of the
movements was generated by non-ELFAA members (ANA, 2009d). One cherry on the top of this problem
is for instance Porto considering Tuifly as a LCC in 2009 whereas Faro did not.
In practice, this means that Funchal airport’s low-cost traffic is diminished in comparison to Azores
and mainland airports. If in one hand, we could have considered all regular low-fares flights in Funchal as
low-cost flights, on the hand, other airports do not even make available what criteria is used in the
segmentation of markets. In addition, the disaggregation to monthly traffic would impose more
assumptions regarding each airline’s traffic distribution in each airport. Hence, for simplicity reasons, data
used in our efficiency model will follow the same criteria as obtained from the original source.
Table 4-1, summarizes the airlines operating in Portuguese airports counted in the low-cost
segment. Companies are presented in a cumulative perspective. In other words, companies involved in
3 http://www.eurocontrol.int/statfor/gallery/content/public/documents/Market_Segments_Rules.xls
72
mergers (LTU bought by air berlin in 2007), bankruptcies (Sky Europe in 2009), or punctual routes are all
present in the table.
TABLE 4-1: LCC OPERATING IN PORTUGUESE AIRPORTS BETWEEN 2005 AND 2009
IATA CODE
LCCs
LIS easyJet, Vueling, Brussels Airlines, Clickair, Germanwings, Air Berlin/Flyniki, Aer Lingus, Monarch Scheduled, LTU, Thomsonfly, bmibaby, Centralwings1, Blue Air, Skyeurope4
OPO Air Berlin, easyJet, Ryanair, Tuifly, Transavia France
FAO Ryanair, Aer Lingus, easyJet, easyJet Switzerland, bmibaby, flybe, Jet2.com, Norwegian, Jet air, Blue air, Monarch, Niki, Jetairfly, Wizz air (Hungary and Ukraine), SmartWings, Cimber Sterling, Transavia, Transavia.com (France and Denmark), Germanwings, Arkefly, Vueling
FUN easyJet, flybe, Jet2.com, Norwegian Air Shuttle, Ryanair, Sverige Flyg, transavia.com, Vueling, Wizz Air
PDL Air Berlin
To conclude the considerations regarding data collection, there are two aspects discussed both
with Lisbon’s and Funchal’s marketing responsible worth mentioning. The first concerns easyJet’s
operations in Lisbon. With an overall ATM’s market share of 9,5% and around 64% in its segment in 2009
(ANA, 2009c), easyJet only uses the low-cost-purposed Terminal 2 built in 2007 for flights to Funchal, all
other flights being operated in Terminal 1 with necessary use of air bridges. This is because remote
parking stands in Terminal 2 are too far away, and as the boarding process takes longer thus increases
the turnaround time. All added up, it becomes cheaper to pay for the “expensive” air bridges that allow
shorter turnaround times, than to use the low-cost terminal. The second aspect relates to transit numbers
in Funchal airport. Adverse weather conditions often oblige traffic intended to land in Funchal to divert to
Porto Santo, also operated by ANAM. This solution is obviously preferred in comparison to diverting
aircrafts to Canarias Islands, as passenger and landing fees “stay at home”. Nonetheless, it creates
bogus traffic to and from Porto Santo airport. Albeit the small individual importance of each of these
issues, these examples show how only with the proper understanding of each firm’s reality it is possible to
perform a detailed benchmark of an industry.
4.3 MODEL FORMULATION
The efficiency analysis will follow the DEA methodology described in section 2.3.4. For this
purpose, we will use DEAP, a MS-DOS freeware. This software allows applying the CRS, VRS and
Malmquist DEA models4. In our assessment of Portuguese airports’ efficiency the VRS model will be used
in order to examine the existence of scale efficiency. The goal is to understand which airports fully
4 For more details see (Charnes, Cooper, & Rhodes, 1978), (Banker, Charnes, & Cooper, 1984) and (Coelli, 1999)
73
explore their fixed quantity of resources (airside and landside infrastructures) while increasing their
production (number of low-cost passengers and aircraft movements). Hence, the above-mentioned model
will be output oriented.
In order to explain the VRS output oriented approach, we will start by addressing Farrell’s input-
based measure of technical efficiency under constant returns to scale.
In our data, we have inputs and outputs on each airports (DMUs). For the i-th DMU, these
are represented by the vectors and , respectively. The input matrix and the output matrix thus
represent all airports. For each DMU we would like to obtain a measure of the ratio of all outputs over
all inputs, such as ′ ′⁄ , where is an × 1 vector of output weights and is an × 1 vector of input
weights (Coelli, 1999). The mathematical LP problem that selects optimal weights is as follows:
One particular problem with this ratio formulation is that it has an infinite number of solutions since
if ∗, ∗ is a solution, then ∗, ∗ is another solution. The multiplier form of the LP solves this
problem by imposing the constraint ′ = 1, transforming (1) in:
The equivalent envelopment form can be obtained using the duality property of LP, converting the
maximization of optimal weights through the ratio of inputs and outputs into the following model:
Where is the efficiency score for the i-th DMU and is a × 1 vector of constants. As mentioned
in section 3.3.4., the VRS model proposed by (Banker, Charnes, & Cooper, 1984) will permit us the
, ,:
st − + ≥ 0, − ≥ 0, ≥ 0,
, ′ ,:
st ′ = 1, − ′ ≤ 0, = 1,2, . . , , ≥ 0,
, ′ ′⁄ ,:
st ′ ′⁄ ≤ 1, = 1,2, . . , , ≥ 0, (1)
(2)
(3)
74
calculation of technical efficiency devoid of scale efficiency effects such as imperfect competition or
finance constraints. The CRS model can be easily modified to account for VRS by adding the convexity
constraint 1 ≤ 1 to (3) where 1 is an × 1 vector of ones. The inequality in this constraint provides
technical efficiency scores that are greater than or equal to those obtained using the CRS model.
Furthermore, it plots the non-increasing returns to scale (NIRS) frontier (defined by OCDE in figure 3-4)
that allows the identification of the DMUs’ nature of scale inefficiencies. In other words, if a DMU has a
NIRS technical efficiency score equal to the VRS score, then decreasing returns to scale apply. If they
are unequal (as will be the case for the point F in figure 3-4) then increasing returns to scale exist for that
DMU (Coelli, 1999).
The output oriented model is very similar to the input oriented. In order to measure technical
inefficiency as a proportional increase in output with input quantities held constant, we must consider the
following LP model:
where 1 ≤ ≤ ∞, and − 1 is the proportional increase in outputs that could be achieved by the ith
DMU. Note that 1 defines a technical efficiency score which varies between zero and one. The VRS
and CRS models estimate exactly the same efficiency frontier and only measures associated with
inefficient DMUs may differ between the two models.
By calculating both the CRS and VRS efficiency scores, DEAP further computes the ratio between
the two scores that measures scale efficiency. Literature typically separates the VRS-DEA technical
efficiency into two components – scale efficiency and “pure” technical efficiency. (Coelli, 1999)
4.3.1 SELECTION OF INPUTS AND OUTPUTS
In chapter 3, several drivers for airport efficiency were identified. One problem persists though.
According to (Lin & Hong, 2006), a good rule of thumb to the necessary number of DMUs points to no
less than the double of the sum of inputs and outputs. Since our assessment comprises five airports only,
it is likely to verify efficiency overstatements. This was the main reason to disaggregate annual traffic into
monthly traffic. Instead of having five airports in five years (25 observations) we would have 25x12
observations. Since we have no information regarding whether infrastructures are shut down when not
used or not, airports are expected to be more efficient on summer time, typical peak load periods with
, ,:
st − + ≥ 0, − ≥ 0, 1 ≤ 1 ≥ 0,
(4)
75
more emphasis in Faro and Ponta Delgada. In theory, we will be assessing the efficiency of five airports
between 2005 and 2009. In practice, either we can treat each airport in each year (25 DMUs) as an
individual firm or as a panel data of five airports (5 DMUs) in five years or even in sixty months. Whereas
the former will create a more detailed efficiency frontier, the later will compute the average efficiency
score of each airport for the period in study in comparison to the average values of other airports.
To work out this pitfall in our study, a restriction of the number of outputs and inputs is in order.
From the output perspective, it is inevitable to consider the number of low-cost ATMs and passengers in
addition to the complementary non-low-cost traffic. Alternately, we will also consider only total commercial
traffic, thus reducing outputs by half. From the input viewpoint, the selection of which variables to use in
our model was more difficult. For once, important aspects such as capital cost or labour cost were not
considered due to lack of financial information on Portuguese airports, thus focusing our analysis on the
operational efficiency.
In the airside, remote parking stands were deferred to total parking stands (TPS) for two reasons.
Whereas the first relates to the fact that all stands in Funchal and Ponta Delgada airports are remote, the
second has to do with easyjet’s operations in Lisbon happening in the same framework as full service
carriers. The same line of reasoning was applied to the total number of boarding gates (TBG), since it
was initialled intended to discriminate whether boarding gates were bridged or not. Furthermore, to
consider the number of runways seemed rather doubtful. Only Lisbon has two operational runways, but
the second has little use and more prone to military flights, all other airports having one runway. In order
to better characterize airfield capacity we chose to use the runway declared capacity (RDC) of each
airport. It is particularly relevant in Porto and Lisbon, where expansion works that did not comprise the
construction of new runways have contributed to the airfield capacity.
On the landside, and similarly to airfield capacity, the overall capacity of baggage collection
systems (BSC) was considered as opposed to the number of baggage belts. In this matter, great
discrepancies were found to the extent that Faro has more arrival baggage capacity than Lisbon airport.
Moreover, it was also difficult to obtain precise data regarding the use of self-service check-in kiosks. To
this respect, only Lisbon airport cared to inform that the common-use kiosks belong to the main airline
alliances, namely Star Alliance and SkyTeam and that no LCC has implement CUSS kiosks by this time.
Hence, only traditional check-in desks (CID) were considered. Finally, and despite the availability of data
concerning the different areas of passenger terminals (check-in, boarding and total), only total terminal
area (TTA) was considered as a probable key factor in the production of outputs.
Table 4-2 below summarizes the yearly aggregated data for the five busiest airports in Portugal
between 2005 and 2009. It includes all above mentioned inputs and outputs, albeit some variations in the
will be done in order to better characterize the industry reality. For instance, the outputs’ column has the
total commercial number of aircraft movements and passengers disaggregated in LCC and non-LCC
traffic and our model will also run total commercial and passenger values as outputs. Furthermore, data is
76
presented in a yearly basis, but we will also assess airports’ efficiency based on monthly evolution. All
collected data is presented in Appendix 2.
TABLE 4-2: YEARLY DISAGGREGATED DATA USED IN THE VRS-DEA OUTPUT-ORIENTED MODELS
DMU #
DMU NAME
OUTPUTS INPUTS
ATM PAX TPS RDC BSC CID TBG TTA
NON-LCC LCC NON-LCC LCC # ATM/h bag/h # # m2
1 LIS_05 122.829 1.296 10.936.809 297.842 51 33 2.800 106 28 65.943
2 LIS_06 123.929 8.529 11.197.402 1.116.912 51 35 2.800 106 28 66.646
3 LIS_07 122.865 16.651 11.321.465 2.070.594 51 36 2.800 115 34 69.266
4 LIS_08 121.781 18.235 11.386.875 2.216.745 57 36 3.250 128 42 72.231
5 LIS_09 116.968 15.413 11.273.466 1.987.512 65 36 3.700 128 44 76.676
6 OPO_05 42.744 1.977 2.817.745 290.441 35 16 3.200 60 23 69.112
7 OPO_06 42.378 4.689 2.729.211 673.605 35 19 3.200 60 23 69.112
8 OPO_07 41.730 9.015 2.739.634 1.247.114 35 20 3.200 60 23 69.112
9 OPO_08 43.035 13.059 2.713.080 1.821.749 35 20 3.200 60 23 69.112
10 OPO_09 38.485 13.709 2.535.757 1.972.573 35 20 3.200 60 23 69.112
11 FAO_05 20.325 13.830 2.892.757 1.861.222 22 22 4.500 60 36 68.500
12 FAO_06 19.102 18.329 2.612.690 2.476.927 22 22 4.500 60 36 68.500
13 FAO_07 18.977 21.276 2.546.250 2.924.222 22 22 4.500 60 36 68.500
14 FAO_08 16.871 22.918 2.164.058 3.283.142 22 22 4.500 60 36 68.500
15 FAO_09 14.605 22.723 1.755.691 3.306.110 22 22 4.500 60 36 68.500
16 FUN_05 24.204 0 2.319.753 0 15 14 3.600 40 16 43.315
17 FUN_06 23.687 0 2.360.857 0 15 14 3.600 40 16 43.315
18 FUN_07 21.532 422 2.365.511 52.978 15 14 3.600 40 16 43.315
19 FUN_08 20.542 2.257 2.134.290 312.634 15 14 3.600 40 16 43.315
20 FUN_09 19.343 2.612 2.000.943 345.706 15 14 3.600 40 16 43.315
21 PDL_05 11.192 0 873.533 0 10 12 900 12 11 10.796
22 PDL_06 11.384 0 909.609 0 10 12 900 12 12 10.796
23 PDL_07 11.850 0 940.772 0 10 12 900 12 12 10.796
24 PDL_08 12.105 18 919.852 1.733 10 12 900 12 12 10.796
25 PDL_09 12.247 102 885.609 11.204 10 12 900 12 12 10.796
77
4.4 DISCUSSION OF RESULTS
Our analysis of Portuguese airport’s efficiency was conducted bearing in mind all inherent
difficulties with the application of this model to such small data panel. Nevertheless, our intent to analyse
the influence of LCCs in Portuguese airports lead to the reformulation of our assessment according to the
difficulties found along the way.
For this purpose, only output data was disaggregated in order to understand whether LCCs have
influence or not on Portuguese airports, by assessing either total commercial traffic or LCC plus non-LCC
traffic. In practice, this means that models using disaggregated output data will have the four outputs
(ATMLCC; ATMNon-LCC; PAXLCC, PAXNon-LCC) whereas the others will have only two (ATMTOTAL, PAXTOTAL),
whilst all six models will use the same inputs.
Furthermore, we considered three approaches that are only distinct in a temporal perspective, that
were tested under the two hypotheses of output data (dis)aggregation, resulting in a total of six models.
Table 4-3 resumes the characterization of the results obtained from the above mentioned models.
Hence, in this section we will analyze the results of each of such models. The disaggregation of
output data corresponds to models with an odd number (1, 3 and 5), whilst models 2, 4 and 6 correspond
to each one of three approaches under the assumption of aggregated output data. It is important to notice
that we have conducted our analysis maintaining the same six inputs in all models – total-parking stands,
declared capacity; baggage belts capacity, total boarding gates, check-in desks and total terminal area.
All results are compiled in Annex 3.
TABLE 4-3: MODEL CHARACTERIZATION ON THE VRS-DEA EFFICIENCY BENCHMARK
Approach description Output Data
Disaggregated (LCC + Non-LCC)
Aggregated (Total)
1) Analysis on seasonality influence (5 DMUs in 60 months)
Model 1 Model 2
2) Each airport in each year as an individual firm (25 DMUs)
Model 3 Model 4
3) Panel data (5 DMUs in 5 years) Model 5 Model 6
4.4.1 APPROACH 1: MONTHLY ANALYSIS
Firstly, models 1 and 2 consider the influence of seasonality on the analysis of airport’s efficiency.
In order to perform the evaluation of the five airports’ efficiency we considered the 60 periods that
correspond to the total number of months between 2005 and 2009. The results are presented below in
figure 4-3. Albeit our large sample of 300 observations, DEAP presents only the average efficiency score
of each airport.
78
If we take for sake of comparison figure -5, that illustrates 2009’s monthly passenger distribution in
all five airports, one may cross relate high efficiency scores obtained at Faro and Ponta Delgada airports
with summer peak traffic. For this reason, airports operating under the optimum efficiency level are
located in the region of decreasing returns to scale. That is the case of Lisbon, Porto and Funchal in
model 1 and all airports except for Ponta Delgada in model 2. Moreover, airports that more than quintuple
the passenger’s flow in summer time are strongly advised to implement the share use of facilities or even
shut down facilities that are not used in low-demand periods, thus increasing its efficiency.
Scale efficiency scores are found to decrease in the busiest mainland airports when aggregated
output data is considered, meaning that LCCs have a positive effect in the scale of airports’ production.
The biggest drop in scale efficiency happens in Faro (from 100% to 62,7%) and is most likely related with
the high share of LCC traffic in this airport. Porto airport registers the worst efficiency scores albeit its high
annual increases in commercial traffic. The misuse of landside infrastructures at Porto airport from the
LCC point of view can be an important explanation for such inefficiency. Overall, the average VRS TE
score of Portuguese airports drops only 0,2% from to 94,6% in model 1 to 94,4% in model 2. On the other
hand, it is found a great increase in global standard deviation of scale efficiency scores, passing from
12,1% in model 1 to 23,7% in model 2, which may be an important indicator of LCCs’ influence in airport’s
efficiency. Global statistics are for models 1 and 2 are presented below.
4-3: VRS-DEA OUTPUT ORIENTED: MODELS 1 (LEFT) AND 2 (RIGHT)
TABLE 4-4: GLOBAL STATISTICS FOR MODELS 1 (DISAGGREGATED) AND 2 (AGGREGATED)
MODEL 1 MODEL 2
CRS TE VRS TE SCALE E. CRS TE VRS TE SCALE E.
Average 86,0% 94,6% 90,8% 68,5% 94,4% 72,2%
Standard Deviation 14,2% 6,5% 12,1% 24,4% 6,6% 23,7%
0%
20%
40%
60%
80%
100%
LIS OPO FAO FUN PDL
CRS TE VRS TE SCALE E.
0%
20%
40%
60%
80%
100%
LIS OPO FAO FUN PDL
CRS TE VRS TE SCALE E.
79
Ultimately, disaggregation of output data has shown to have an important influence on airport’s
scale efficiency without major disruptions on each airport’s technical efficiency score.
4.4.2 APPROACH 2: EACH AIRPORT IN EACH YEAR AS AN INDIVIDUAL FIRM
The second approach results of the consideration of 25 DMUs, or in other words, the treatment of
each one of the five airports in all five years as an individual firm. The obtained efficiency scores are
represented graphically in figures 4-4 and 4-5.
Under this approach, Lisbon airport stands out as the most efficient airport in the national
framework on both variations of ouput aggregation of data. Moreover, Lisbon airport in 2009 is the only
airport operating under decreasing returns to scale in model 3, while all others are operating under
increasing returns to scale. As Faro airport between 2007 and 2009 operates fully efficiently in model 3 it
is operating under CRS. Model 4 however, indicates that all airtports apart from Lisbon between 2006 and
2009 are operating under increasing returns to scale.
4-4: VRS-DEA OUTPUT ORIENTED: MODEL 3
Consistent with models 1 and 2, Porto maintains the worst efficiency scores with an average VRS
TE value of 94,8% in model 3 and 90,2% in model 4, while the remaining Portuguese airports attain up to
99,2% and 98,1% respectively. 2005 is for Porto airport the best performing year, when LCCs
represented merely 4,4% of commercial passengers and 9,3% of aircraft movements. The continuous
growth of LCC traffic between 2005 and 2009 moving from 290.000 passengers and nearly 2.000 ATMs
to almost 2 million passengers and around 13.700 ATMs has surely helped to counterweight the loss of
market power of non-LCCs, that have only registered an increase of aircraft movements in 2008. This is
parcially explained in model 3 (that uses disaggregated output data) where we can observe the impact of
0%
20%
40%
60%
80%
100%
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
LIS OPO FAO FUN PDLCRS TE VRS TE SCALE E.
80
LCC’s growth in the steady increase of VRS TE, whereas in model 4 (using aggregated ouput data) VRS
TE decreases from 100% in 2005 to around 85% in the following years, rising to 94% with growth of non-
LCC traffic in 2008. The high VRS TE attained in the year 2005 is also explained by the existing declared
capacity of 16 ATM/h in 2005, expanded to 19 and 20 ATM/h in the following years, all other inputs
remaining equal.
Another relevant aspect has to do with consistent efficiency growth at Faro and Porto in model 3,
likely related to the increase of output production of the LCC segment as oposed to managerial practices.
On the other hand, Funchal airport presents slightly unstable VRS TE results in model 4, appearing to be
more sensible to output variation with 2009 being a particular bad year, while all inputs remained equal.
Regarding scale efficiency one can point out the continous increase in all airports of model 4, where
Funchal airport presents the highest inefficiency, followed by Porto and Ponta Delgada airports
4-5: VRS-DEA OUTPUT ORIENTED: MODEL 4
. An interesting case study in this approach is Funchal airport. In one hand the decrease of non-
LCC ATMs from 24.204 in 2005 to 19.343 in 2009 while loosing nearly 320.000 passengers to 2 million
passengers, appears to suffer little influence from the liberalization of routes to Funchal in October 2007.
On the other hand, LCC’s passenger traffic rose from 52.978 in late 2007 to almost 345.000 in 2009 while
the number of aircraft movements moved from 422 to 2.612, clearly supporting the low growth of Funchal
airport in terms of passengers but not in in terms of ATM. Furthermore, as mentioned previously, the
assumptions regarding the number of LCC’s operations at Funchal airport are different as of other
airports with the natural prejudice of counting only for ELFAA members. It is thus curious to note that
scale efficiency in model 3 shifts down from 70% to 60%, whilst in model 4 raises from 59% to 62%.
Since inputs remain constant between 2005 and 2009, scale efficiency in model 3 appears to be
negatively affected by the decrease of the non-low-cost segment along with the inability of the low-cost
0%
20%
40%
60%
80%
100%
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
2005
2006
2007
2008
2009
LIS OPO FAO FUN PDLCRS TE VRS TE SCALE E.
81
segment to compensate for the reduction of ouput production of aircraft movements. Such is in line with
the scale efficiency results obtained in model 4 that are in average 61%, and all outputs considered, have
small variations accros the period of study.
TABLE 4-5: GLOBAL STATISTICS FOR MODELS 3 (DISAGGREGATED) AND 4 (AGGREGATED)
MODEL 3 MODEL 4
CRS TE VRS TE SCALE E. CRS TE VRS TE SCALE E.
Average 85,9% 98,3% 87,3% 79,4% 96,5% 82,4%
Standard Deviation 13,7% 3,8% 13,2% 14,4% 5,3% 14,2%
Hence, two main similarities are found in comparison to the first approach, namely that
disaggregation of outputs appears to manifest influence on airports’ efficiency and Porto airport poorest
performance in comparison to the other Portuguese aiports operated either by ANA or by ANAM. On the
other hand, in clear contrast to models 1 and 2, statistics of efficiency scores in model 3 and 4 revealed
higher average efficiency scores and lower standard deviations, due to the fact that yearly analysis of the
firms is subject to less variation then in the monthly analysis of models 1 and 2
4.4.3 APPROACH 3: FIVE YEAR PANEL DATA
Finally, the third approach is the mix of the previous two, to the extent that we will have 5 firms
(each one of the airports) organized in a panel data of 5 years.
4-6: VRS-DEA OUTPUT ORIENTED: MODELS 5 (LEFT) AND 6 (RIGHT)
The main common characteristic between models 5 and 6 is that all airports register maximum
VRS TE scores as depicted in figure 4-6. Moreover, similarly to models 3 and 4 , Lisbon and Faro airports
outperform the other airports on the ouput disaggregated model and Lisbon outperforms all other airports
0%
20%
40%
60%
80%
100%
LIS OPO FAO FUN PDL
CRS TE VRS TE SCALE E.
0%
20%
40%
60%
80%
100%
LIS OPO FAO FUN PDL
CRS TE VRS TE SCALE E.
82
on the output aggregated model. Furthermore, with the exception of best-practitioners Lisbon and Faro
(Faro in model 5 only), all other airports are operating under increasing returns to scale. On the other
hand, Funchal airport registers the worst performance relegating Porto to the third and fourth positions in
models 5 and 6 respectively.
Under the perspective of scale efficiency, models 5 and 6 have also lead to similar results obtained
in the second approach, with a slight increase of the average score in the aggregated output model in
comparison to model 4. Moreover, the aggregation of outputs under this approach reveals a small
decrease of the average scale efficiency, thus limiting conclusions to withdraw. On the other hand, the
fact that all airports obtain maximum pure technical efficiency emphasises the existence of scale
economies in Portuguese airports, with Porto airport performing slightly better in the disaggregated output
model and registering the second worst score in the aggregated one which further strengths the
hypothesis of LCC’s influence on Portuguese airports’ efficiency.
TABLE 4-6: GLOBAL STATISTCS FOR MODELS 5 (DISAGGREGATED) AND 6 (AGGREGATED)
MODEL 5 MODEL 6
CRS TE VRS TE SCALE E. CRS TE VRS TE SCALE E.
Average 87,2% 100,0% 87,2% 84,4% 100,0% 84,4%
Standard Deviation 12,4% 0,0% 12,4% 13,8% 0,0% 13,8%
4.4.4 OVERALL RESULTS
All three approaches show that Portuguese airports operate efficiently obtaining an average pure
technical efficiency score of 97,3% and standard deviation of 3,7%. Scale efficiency drops to an average
score of 84,1% whilst standard deviation reaches 14,9%. The VRS-DEA model has proven to be
consistent with influence of airports’ seasonality and LCC’s traffic.
Since each one of the three approaches used data in a different manner, different best practitioners
are found. On a monthly basis, touristic airports of Faro and Ponta Delgada register higher efficiency
scores which are most likely related to great increase of passenger and aircraft increase in summer time.
Approaches 2 and 3 both reveal Faro and Lisbon airports as best practitioners. Theoretically, such was
expected, since panel data in approach 3 should result in average efficiency scores of each airport in the
considered study period. The advantage of approach 2 is that has allowed us to depict more
comprehensively the evolution of Porto and Ponta Delgada airports’ technical efficiency between 2005
and 2009.
Regarding data disaggregation, it is consistent in all three approaches the influence of LCC traffic
in scale efficiency scores. On the other hand, however, the share of LCC traffic in each does not appear
to have strong influence on airports’ technical efficiency. To this respect, Lisbon that has a similar volume
of LCC traffic than Porto, but that is quite smaller in terms of percentage of total traffic, is the most
efficient airport in 4 out of 6 models. This is likely to relate to the fact that DEA uses ratios of outputs over
83
inputs, or in other words, the ratio of infrastructures used to accommodate both LCC and non-LCC traffic.
Such relegates the analysis of LCC traffic share in each airport.
Our small sample is thus a strong limitation to the DEA methodology, preventing a more
meaningful analysis on the potential influence of LCC on airport efficiency. For instance, it would be
desirable a more detailed characterization of which infrastructures are devoted to each type of airline
carrier. Notwithstanding, and probably related to the growth of LCC traffic in Portuguese airports, our
results reveal strong evidence of LCC influence on Portuguese airports’ efficiency.
84
5 CONCLUSIONS
The main goal of this master dissertation is to try to shed a light on airport efficiency, by explaining
it in detail, presenting the methodologies used in the field literature and stating the influencing factors.
Ultimately, we wanted to understand how the growth of LCC in Europe and Portugal in the beginning of
the 21st century has influenced managerial practices and airports’ operational efficiency. A widespread
literature review was conducted in order to understand the general framework in terms of methodologies,
focus of interest and the data structure, as commonly done by researches of the field. Moreover, an
extensive investigation is done concerning the main drivers of airport efficiency in three key topics –
airside, landside and the airport-airline relationship.
Data envelopment analysis is used to assess the relative efficiency of Portuguese airports and
conclusions for the efficiency levels are drawn. Although this analysis tried to capture as much as
information on airport efficiency, it was not possible to account for every single variable due to the fact
that airports are complex production units. In addition, the number of airports in our sample has proven to
be a strong limitation in the use of this benchmarking tool. And although topics related to economics and
finance were not as explored as operational issues, key factors such as airports’ privatization, the
economic regulatory environment and the implication of LCCs in airports’ revenues were tried to be
covered as thoroughly as possible.
The merits of DEA are several and outweigh its pitfalls. While it is considered the best methodology
to deal with multiple input/output firms and with the issue of biased weighs, a relative large number of
inputs and outputs in comparison to the number of DMUs are likely to conduct to performance
overstatement, as verified in the third approach used for our output-oriented VRS-DEA model. Moreover,
while most literature assessing airports’ efficiency suggests the output-oriented approach, passengers
and aircraft movements are not airports only source of revenue. Airports’ commercialization has lead to
increasing non-aeronautical revenues, and such has not been considered in our model, which has surely
weakened the model. A more comprehensive study would have to comprise all five Portuguese airports
and similar size European airports in order to draw results that are more reliable.
Data is therefore an obstacle. The development of this efficiency benchmarking analysis has
shown major difficulties in both data collection and criteria standardization. The simple fact that ANAM is
70% owned by ANA does not imply that the same managerial and accountancy practices occur. This was
particularly evident with the criteria used for classification of traffic in the low-cost segment, even within
ANA’s own airports. Here, Funchal airport efficiency scores were most likely prejudiced since it output
data considered a smaller group of LCC. Hence, one must conclude that it is impossible to make
omelettes without eggs and demystify that DEA is such a powerful benchmarking tool, that no matter
what we put in it, we will obtain reliable results all the time.
85
Nevertheless, interesting results were achieved. The answer to the initial question on whether LCC
have or not influence on Portuguese airports’ efficiency appears to be affirmative. Albeit all limitations
found in data and the methodology used for our assessment, the exponential growth of LCCs in Portugal
as well in Europe has an undeniable effect on managerial practices of airport operators. Such seems to
be sustained in our efficiency models that considered disaggregation of commercial traffic into low-cost
and non-low-cost segments.
The answer under the perspective of touristic destinations such as Faro, Ponta Delgada or Funchal
with summer peak traffic and/or high shares of charter airlines is affirmative. A new breed of tourists has
emerged with the low-cost revolution, and airports with these characteristics that typically do not suffer
from capacity constraints and operate with cheaper infrastructures as LCCs prefer, now recognize to
have a market with strong possibilities for expansion. To this respect, the introduction of flexible solutions
such as shared used of facilities may well derive in efficiency gains.
Hub airports that have not reached capacity such as Lisbon also appear to be positively affected by
the LCC growth. Low-cost airlines manage to negotiate discounted fares to operate on the shoulders of
arrival and departure waves of aircrafts. Nonetheless, Lisbon’s high efficiency scores can also be due to
the scale of its operations, maximizing its resources. Finally, in the case of large regional airports as
Porto, which has revealed to be particularly inefficient in comparison with other Portuguese airports,
appears to have failed coping with a segment that rose its market share in terms of passengers from 9%
in 2005 to and 40% in 2009. Even so, Porto airport has been continuously improving its performance
probably boosted by the LCC segment growth, and its efficiency drop in 2005 is likely to be related to the
investments made in several infrastructures.
Ultimately, three major conclusions are drawn. Firstly, different temporal approaches had lead to
different best practitioners. On a monthly basis, seasonal airports are favoured, whilst on a yearly basis,
large-scale operations attain higher efficiency scores, with particular emphasis on Lisbon and Faro
airports. Secondly, disaggregation of output data into LCC and non-LCC segments appears to have
strong influence on scale efficiency scores, and to a much smaller extent, on technical efficiency scores.
Thirdly, it is most likely that our small sample of airports is a strong limitation to our analysis. To this
respect, a more comprehensive study should embrace similar size European airports, and then proceed
to a more realistic benchmark study.
The increasing importance of LCC traffic in airport’s efficiency is explained by the fact that airports
have very high fixed costs, because marginal average cost of any additional traffic is lower, so that an
efficient operation is stimulated. Capacity expansion is another factor, which has major effects on airport
efficiency. On the one hand, capacity expansion requires a large amount of funds, which bring very high
lump-sum fixed costs to the airport. These fixed costs are amortized during time, which are reflected in
the financial situation of airport with different amounts. On the other hand, despite its all-at-once
provision, new capacity needs a long period to bring its demand to the airport. Traffic increases only
86
gradually, year-by-year, achieving its efficient level during a long period. (Ülkü, 2009) This was also found
true in our model, particularly the influence of the airfield capacity of Porto airport in models 3 and 4,
which expansion in 2005 affected negatively its efficiency score and afterwards progressively returned to
increase.
The results achieved from our benchmarking research along with the extensive research on the
aspects that influence airports’ efficiency not only allow us to recognize the importance of the emergence
of this segment in the last decade, but also further poses new challenges and therefore questions. In light
of the importance that LCCs now have and will increase to represent to airport operators, the million-
dollar question is how to achieve efficiency gains that allow for profit maximization.
Would then those efficiency gains be achievable through the privatization of Portuguese airports?
It is doubtful that the privatization model proposed by the Portuguese Government that consists in the
concession of public monopoly of mainland and Azores airports to a private operator in order to finance
the NLA will promote the competition that will overcome those inefficiencies. Moreover, by the time these
conclusions were written, the Portuguese Government has resigned and is likely to impose severe
austerity measures and approve a privatization plan of several public companies in order to “merit” a 78
billion € bailout, thus increasing the risks of creating a poor privatization scheme. Nonetheless, one may
also expect that once the future of the Portuguese flag-carrier TAP is decided and a privatization scheme
for ANA that promotes both internal and external competition is created, social and cultural causes of
inefficiency may be overcome. At the same time, with public investment comes political decision-makers
and public entities, that require monumental buildings to satisfy their monumental egos, leaving little
space for flexible approaches such as real options in the creation of green-field projects (as in the NLA) or
solutions such as the shared used of facilities, particularly useful on airports as Faro and Ponta Delgada.
Taking all this in consideration, explained by means of empirical analysis or presented with the help
of political science and economic theory, airport managers should set their priorities according to the
economic, political, operational and financial conditions of airports. Furthermore, airport operators are
expected to adopt benchmark management procedures to catch up with best practitioners. (Barros C. P.,
2008) Finally, in order to develop strategies for reaching short and long-term goals, airport managers
should pursue market-oriented strategies that increase outputs and decrease inputs, combining them
with strategies that allow achieving efficient levels of operation for factors they do not hold control over.
87
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APPENDIX 1 – AIRPORT EFFICIENCY PERFORMANCE RESEARCH
Authors, Year Methodology Coverage Inputs Outputs
(Tolofari, 1989) Parametric TFP
BAA UK airports
(Prices Surveillance Authority, 1993)
Index number TFP
6 Australian airports
(Gillen & Lall, 1997) DEA 23 US airports
Number of runways Number of passengers
Number of gates Pounds of cargo
Number of employees
Number of collection belts
Length of runway
Airport and terminal areas
Number of parking spots
(Hooper & Hensher, 1997)
Index number TFP
6 Australian airports
Labour cost Non-aeronautical revenue
Capital cost Aeronautical revenue
Other cost
(Graham & Holvad, 1997) DEA
25 European and 12 Australian airports
(Parker D. , 1999) DEA BAA and 16 other UK airports
(Murillo-Melchor, 1999)
DEA/ Malmquist index
33 Spanish airports
Number of workers Number of passengers
Accumulated capital stock proxied by amortization
Intermediate expenses
(Salazar de la Cruz, 1999) DEA 16 Spanish
airports
(Jessop, 1999) DEA/ Multi-attribute assessment
32 major international airports
(Nyshadham & Rao, 2000)
Index number TFP
25 European airports
(Sarkis J. , 2000) DEA 44 US airports
Operational cost Number of passengers
Number of employees Aircraft movements
Number of gates Amounts of operational revenue
Number of runways Amount of cargo
(Pels, Nijkamp, & DEA/SFA 34 European Terminal size in square i) Terminal model
96
Authors, Year Methodology Coverage Inputs Outputs
Rietveld, 2001) airports meters
Number of aircraft parking positions at the terminal Number of passengers
Number of remote aircraft parking positions (ii) Movement model
Number of collection belts Aircraft transport movements
Number of check-in desks
(Gillen & Lall, 2001) DEA / Malmquist index
22 US airports
Number of gates Number of passengers
Number of runways Pounds of cargo
Number of employees
Number of collection belts
Number of parking spots
(Martin & Roman, 2001) DEA 37 Spanish
airports
Labour cost Aircraft movements
Capital cost Number of passengers
Materials cost Amount of cargo
(Abbott & Wu, 2002) DEA / Malmquist index
12 Australian airports
Number of employees Number of passengers
Amount of capital stock Amount of cargo
Length of runway
(Martin-Cejas, 2002) Parametric TFP
40 Spanish airports
(Fernandes & Pacheco, 2002) DEA 35 Brazilian
airports
Areas of apron Number of domestic passengers
Area of departure lounges
Number of check-in desks
Number of vehicle parking spots
Area of baggage claim
Length of curb frontage
(Pels, Nijkamp, & Rietveld, 2003) DEA/ SFA 34 European
airports
Terminal size in square meters i) Terminal model
Number of aircraft parking positions at the terminal Number of passengers
Number of remote aircraft parking positions (ii) Movement model
Number of collection belts Aircraft transport movements
Number of check-in desks
(Pacheco & Fernandes, 2003) DEA 34 Brazilian
airports Areas of apron Number of passengers
Area of departure lounges
97
Authors, Year Methodology Coverage Inputs Outputs
Number of check-in desks
Number of vehicle parking spots
Area of baggage claim
Length of curb frontage
(Bazargan & Vasigh, 2003) DEA 45 US
airports
Operational cost Aeronautical revenue
Non- Operating expense Non- Aeronautical revenue
Number of runways Percentage of on time operations
Number of gates
number of passenger
Number of air carrier operations
Number of other operations
(Holvad & Graham, 2003) DEA 21 UK
airports
(Oum, Yu, & Fu, 2003) TFP
50 major airports around the world
Number of employees Number of passengers
Terminal size Number of aircraft movements
Number of runways Amount of non-aeronautical revenue
Number of gates
(Oum & Yu, 2004) VFP
76 major airports around the world
Number of workers Number of passengers
Soft Cost Input Number of aircraft movements
Amount of non-aeronautical revenue
(Barros & Sampaio, 2004) DEA
13 Portuguese airports
Number of labour Number of planes
Capital cost Number of passengers
Amount of general cargo
Amount of mail cargo
Sales to planes
Sales to passengers
(Sarkis & Talluri, 2004) DEA 44 US
airports
Operational cost; Number of passengers
Number of employees Number of aircraft movements
Number of runways Amounts of operational revenue
Number of gates Amount of cargo
98
Authors, Year Methodology Coverage Inputs Outputs
(Yoshida Y. , 2004) Endogenous weight TFP
30 Japanese airports
Size of terminal Aircraft movement
Total length of runways Number of passengers
Amount of cargo
(Yoshida & Fujimoto, 2004)
DEA/ Endogenous weight TFP
67 Japanese airports
Size of terminal Number of aircraft movements
Total length of runways Number of passengers
Access cost Amount of cargo
Number of employees
(Yu M.-M. , 2004) DEA 14 Taiwan airports
Area of runway Number of aircraft movements
Area of apron Number of passengers
Area of terminal Aircraft noise
Active route
Population
(Hanaoka & Phomma, 2004) DEA 12 Thai
airports
(Kamp & H.-M., 2005)
DEA/ Malmquist index
17 European airports
(Vogel, 2006) DEA 35 European airports
(Lin & Hong, 2006) DEA
20 major airports around the world
Number of employees Number of aircraft movements
Number of check-in desks Number of passengers
Number of runways Amount of cargo
Number of parking spots
Number of boarding gates
Size of terminal
Number of baggage claims
Number of aprons
(Martin & Roman, 2006) DEA 34 Spanish
airports
Labour cost Aircraft movements
Capital cost Number of passengers
Materials cost Amount of general cargo
Amount of mail cargo
Sales to planes
Sales to passengers
(Oum, Adler, & Yu, 2006) VFP
76 major airports around the
Number of workers Number of passengers
Soft Cost Input Number of aircraft movements
99
Authors, Year Methodology Coverage Inputs Outputs
world Amount of non-aeronautical revenue
(Vasigh & Gorjidooz, 2006)
Index number TFP
22 US and European airports
Operation cost Operational revenue
Net total assets Non-operational revenue
Runway area Total terminal passengers
Total airport movements aircraft
Landing fee
(Barros & Dieke, 2007) DEA 31 Italian
airports
Labour cost Number of passengers
Capital cost Number of planes
Operational cost excluding labour General cargo
Handling receipt
Aeronautical sales
Commercial sales
(Fung, Wan, Hui, & Law, 2008)
DEA/ Malmquist index
25 Chinese airports
Length of runway Number of passengers
Terminal area Number of aircraft movements
Amount of cargo
(Barros C. P., 2008a) DEA 31 Argentina airports
Number of labour Number of passengers
Area of aprons Number of planes
Number of runways General cargo
Terminal area
(Barros C. P., 2008b) SFA 27 UK airports
Operational cost Price of capital-investment
Price of workers Number of passengers
Price of capital-premises Number of aircraft movements
(Barros & Dieke, 2008)
Two-Stages DEA
31 Italian airports
Labour cost Number of passengers
Capital cost Number of planes
Operational cost excluding labour General cargo
Handling receipt
Aeronautical sales
(Yu, Hsu, Chang, & Lee, 2008) DEA 4 Thai
airports
Number of employees Number of passengers
Accumulated capital stock Commercial sales
Intermediate expenses
100
Authors, Year Methodology Coverage Inputs Outputs
(Oum, Yan, & Yu, 2008) SFA
109 major airports around the world
Terminal area Number of passengers
Length of runway Amount of cargo
Number of aircraft movements
(Barros C. P., 2009) Random SPA model
27 UK airports
Operational cost Price of capital-investment
Price of workers Number of passengers
Price of capital-premises Number of aircraft movements
(Chi-Lok & Zhang, 2009) DEA 25 Chinese
airports
Terminal area Amount of cargo
Length of runway Number of passengers
Number of aircraft movements
(Martin, Roman, & Voltes-Dorta, 2009)
Markov Chain Monte Carlo Simulation/ SFA
37 Spanish airports
Number of labour The air traffic movement (ATM)
Capital costs The work load units (WLU)
Material
(Lam & Tang, 2009) DEA 11 major airports in Asia Pacific
Number of labour Number of aeronautic movements
Value of capital Number of passengers
Soft Input Number of tonnes of cargo.
Trade value
(Tovar & Martin-Cejas, 2010)
SFA/ Malmquist TFP index
26 Spanish airports
Number of labour The air traffic movement (ATM)
Number of gates Average size of aircraft
Airport area
The share of non-aeronautical revenues in total airport revenue
101
APPENDIX 2 – COMPILATION OF DATA
CODE
Y
M
OUTPUTS INPUTS
ATM PAX TPS RDC BSC CID TBG TTA
NON-LCC LCC NON-LCC LCC # ATM/h bag/h # # m2
LIS 2005 1 9.473 100 714.431 19.456 51 32 2.800 106 28 65.943 LIS 2005 2 8.598 91 656.247 17.872 51 32 2.800 106 28 65.943 LIS 2005 3 9.845 104 882.454 24.032 51 32 2.800 106 28 65.943 LIS 2005 4 9.964 105 889.974 24.237 51 32 2.800 106 28 65.943 LIS 2005 5 10.390 110 947.444 25.802 51 32 2.800 106 28 65.943 LIS 2005 6 10.482 111 933.955 25.434 51 32 2.800 106 28 65.943 LIS 2005 7 11.449 121 1.122.618 30.572 51 32 2.800 106 28 65.943 LIS 2005 8 11.694 123 1.260.379 34.324 51 32 2.800 106 28 65.943 LIS 2005 9 10.700 113 1.056.221 28.764 51 32 2.800 106 28 65.943 LIS 2005 10 10.264 108 930.767 25.348 51 34 2.800 106 28 65.943 LIS 2005 11 9.896 104 752.199 20.485 51 34 2.800 106 28 65.943 LIS 2005 12 10.074 106 790.119 21.517 51 34 2.800 106 28 65.943
LIS 2006 1 9.531 656 700.588 69.882 51 34 2.800 106 28 65.943 LIS 2006 2 8.527 587 650.667 64.902 51 34 2.800 106 28 65.943 LIS 2006 3 9.814 675 807.936 80.590 51 34 2.800 106 28 65.943 LIS 2006 4 10.252 706 1.038.192 103.557 51 34 2.800 106 28 65.943 LIS 2006 5 10.721 738 966.491 96.405 51 34 2.800 106 28 65.943 LIS 2006 6 10.639 732 952.994 95.059 51 34 2.800 106 28 67.148 LIS 2006 7 11.526 793 1.149.209 114.631 51 34 2.800 106 28 67.148 LIS 2006 8 11.516 793 1.249.472 124.632 51 34 2.800 106 28 67.148 LIS 2006 9 10.693 736 1.076.665 107.395 51 34 2.800 106 28 67.148 LIS 2006 10 10.688 736 999.448 99.692 51 36 2.800 106 28 67.148 LIS 2006 11 9.987 687 782.302 78.033 51 36 2.800 106 28 67.148 LIS 2006 12 10.034 691 823.436 82.136 51 36 2.800 106 28 67.148
LIS 2007 1 9.614 1.303 714.062 130.595 51 36 2.800 106 28 67.148 LIS 2007 2 8.755 1.187 671.446 122.801 51 36 2.800 106 28 67.148 LIS 2007 3 9.974 1.352 855.473 156.458 51 36 2.800 106 28 67.148 LIS 2007 4 10.204 1.383 983.299 179.836 51 36 2.800 106 28 67.148 LIS 2007 5 10.710 1.452 960.339 175.637 51 36 2.800 106 28 67.148 LIS 2007 6 10.533 1.427 968.238 177.082 51 36 2.800 106 28 67.148 LIS 2007 7 11.374 1.542 1.178.201 215.482 51 36 2.800 106 28 67.148 LIS 2007 8 11.438 1.550 1.258.508 230.170 51 36 2.800 128 42 72.231 LIS 2007 9 10.470 1.419 1.106.971 202.455 51 36 2.800 128 42 72.231 LIS 2007 10 10.359 1.404 1.004.733 183.757 51 36 2.800 128 42 72.231 LIS 2007 11 9.607 1.302 810.257 148.189 51 36 2.800 128 42 72.231 LIS 2007 12 9.825 1.332 809.939 148.131 51 36 2.800 128 42 72.231
LIS 2008 1 9.741 1.459 761.917 148.327 51 36 2.800 128 42 72.231 LIS 2008 2 9.118 1.365 729.910 142.095 51 36 2.800 128 42 72.231 LIS 2008 3 9.974 1.493 955.101 185.935 51 36 2.800 128 42 72.231
102
CODE
Y
M
OUTPUTS INPUTS
ATM PAX TPS RDC BSC CID TBG TTA
NON-LCC LCC NON-LCC LCC # ATM/h bag/h # # m2
LIS 2008 4 9.815 1.470 903.234 175.838 51 36 2.800 128 42 72.231 LIS 2008 5 10.305 1.543 999.608 194.599 51 36 2.800 128 42 72.231 LIS 2008 6 10.548 1.579 1.000.578 194.788 51 36 2.800 128 42 72.231 LIS 2008 7 11.396 1.706 1.176.576 229.050 63 36 3.700 128 42 72.231 LIS 2008 8 11.569 1.732 1.267.150 246.683 63 36 3.700 128 42 72.231 LIS 2008 9 10.506 1.573 1.077.031 209.671 63 36 3.700 128 42 72.231 LIS 2008 10 10.279 1.539 957.393 186.381 63 36 3.700 128 42 72.231 LIS 2008 11 8.998 1.347 760.258 148.004 63 36 3.700 128 42 72.231 LIS 2008 12 9.533 1.428 798.119 155.374 63 36 3.700 128 42 72.231
LIS 2009 1 9.312 1.227 743.296 131.043 63 36 3.700 128 42 72.231 LIS 2009 2 8.370 1.103 680.185 119.917 63 36 3.700 128 42 72.231 LIS 2009 3 9.241 1.218 803.715 141.695 63 36 3.700 128 42 72.231 LIS 2009 4 9.801 1.291 1.012.151 178.442 63 36 3.700 128 42 72.231 LIS 2009 5 9.823 1.294 952.924 168.001 63 36 3.700 128 42 72.231 LIS 2009 6 9.606 1.266 968.636 170.771 63 36 3.700 128 42 72.231 LIS 2009 7 11.266 1.485 1.182.408 208.459 66 36 3.700 128 45 81.120 LIS 2009 8 11.469 1.511 1.270.720 224.028 66 36 3.700 128 45 81.120 LIS 2009 9 9.930 1.309 1.043.314 183.936 66 36 3.700 128 45 81.120 LIS 2009 10 9.808 1.292 985.023 173.660 66 36 3.700 128 45 81.120 LIS 2009 11 9.005 1.187 788.069 138.936 66 36 3.700 128 45 81.120 LIS 2009 12 9.337 1.230 843.024 148.625 66 36 3.700 128 45 81.120
OPO 2005 1 3.275 151 191.895 19.780 35 16 3.200 60 23 69.112 OPO 2005 2 2.927 135 161.452 16.642 35 16 3.200 60 23 69.112 OPO 2005 3 3.324 154 217.187 22.387 35 16 3.200 60 23 69.112 OPO 2005 4 3.508 162 219.465 22.621 35 16 3.200 60 23 69.112 OPO 2005 5 3.650 169 234.521 24.173 35 16 3.200 60 23 69.112 OPO 2005 6 3.626 168 236.884 24.417 35 16 3.200 60 23 69.112 OPO 2005 7 4.077 189 307.410 31.686 35 16 3.200 60 23 69.112 OPO 2005 8 4.162 192 354.813 36.573 35 16 3.200 60 23 69.112 OPO 2005 9 3.802 176 280.904 28.954 35 16 3.200 60 23 69.112 OPO 2005 10 3.541 164 218.368 22.508 35 16 3.200 60 23 69.112 OPO 2005 11 3.426 158 184.116 18.978 35 16 3.200 60 23 69.112 OPO 2005 12 3.427 158 210.731 21.721 35 16 3.200 60 23 69.112
OPO 2006 1 3.257 360 176.918 43.666 35 16 3.200 60 23 69.112 OPO 2006 2 2.915 322 146.085 36.056 35 16 3.200 60 23 69.112 OPO 2006 3 3.374 373 181.789 44.868 35 16 3.200 60 23 69.112 OPO 2006 4 3.386 375 238.866 58.955 35 16 3.200 60 23 69.112 OPO 2006 5 3.597 398 231.051 57.026 35 20 3.200 60 23 69.112 OPO 2006 6 3.612 400 231.525 57.143 35 20 3.200 60 23 69.112 OPO 2006 7 3.984 441 297.381 73.397 35 20 3.200 60 23 69.112 OPO 2006 8 4.039 447 336.971 83.169 35 20 3.200 60 23 69.112 OPO 2006 9 3.748 415 270.537 66.772 35 20 3.200 60 23 69.112
103
CODE
Y
M
OUTPUTS INPUTS
ATM PAX TPS RDC BSC CID TBG TTA
NON-LCC LCC NON-LCC LCC # ATM/h bag/h # # m2
OPO 2006 10 3.593 398 220.983 54.541 35 20 3.200 60 23 69.112 OPO 2006 11 3.454 382 179.008 44.182 35 20 3.200 60 23 69.112 OPO 2006 12 3.419 378 218.098 53.829 35 20 3.200 60 23 69.112
OPO 2007 1 3.310 715 177.883 80.974 35 20 3.200 60 23 69.112 OPO 2007 2 2.975 643 157.818 71.840 35 20 3.200 60 23 69.112 OPO 2007 3 3.357 725 197.454 89.884 35 20 3.200 60 23 69.112 OPO 2007 4 3.253 703 226.102 102.924 35 20 3.200 60 23 69.112 OPO 2007 5 3.442 743 225.406 102.607 35 20 3.200 60 23 69.112 OPO 2007 6 3.459 747 227.082 103.371 35 20 3.200 60 23 69.112 OPO 2007 7 3.877 837 291.907 132.880 35 20 3.200 60 23 69.112 OPO 2007 8 3.949 853 327.170 148.931 35 20 3.200 60 23 69.112 OPO 2007 9 3.607 779 271.826 123.739 35 20 3.200 60 23 69.112 OPO 2007 10 3.465 749 222.914 101.473 35 20 3.200 60 23 69.112 OPO 2007 11 3.473 750 193.558 88.110 35 20 3.200 60 23 69.112 OPO 2007 12 3.564 770 220.514 100.381 35 20 3.200 60 23 69.112
OPO 2008 1 3.363 1.020 181.993 122.202 35 20 3.200 60 23 69.112 OPO 2008 2 3.151 956 169.058 113.517 35 20 3.200 60 23 69.112 OPO 2008 3 3.481 1.056 226.361 151.994 35 20 3.200 60 23 69.112 OPO 2008 4 3.539 1.074 217.420 145.991 35 20 3.200 60 23 69.112 OPO 2008 5 3.694 1.121 239.121 160.562 35 20 3.200 60 23 69.112 OPO 2008 6 3.753 1.139 237.677 159.592 35 20 3.200 60 23 69.112 OPO 2008 7 3.969 1.205 280.919 188.629 35 20 3.200 60 23 69.112 OPO 2008 8 4.096 1.243 315.958 212.156 35 20 3.200 60 23 69.112 OPO 2008 9 3.782 1.148 255.521 171.574 35 20 3.200 60 23 69.112 OPO 2008 10 3.620 1.099 218.658 146.822 35 20 3.200 60 23 69.112 OPO 2008 11 3.205 972 170.728 114.639 35 20 3.200 60 23 69.112 OPO 2008 12 3.382 1.026 199.667 134.070 35 20 3.200 60 23 69.112
OPO 2009 1 3.305 954 178.203 124.050 35 20 3.200 60 23 69.112 OPO 2009 2 2.957 866 146.801 120.672 35 20 3.200 60 23 69.112 OPO 2009 3 3.239 1.000 175.744 139.710 35 20 3.200 60 23 69.112 OPO 2009 4 3.289 1.131 227.809 166.734 35 20 3.200 60 23 69.112 OPO 2009 5 3.223 1.139 209.262 164.785 35 20 3.200 60 23 69.112 OPO 2009 6 3.043 1.093 216.048 162.974 35 20 3.200 60 23 69.112 OPO 2009 7 3.717 1.198 276.811 177.029 35 20 3.200 60 23 69.112 OPO 2009 8 3.776 1.192 314.614 186.747 35 20 3.200 60 23 69.112 OPO 2009 9 3.166 1.287 235.026 188.681 35 20 3.200 60 23 69.112 OPO 2009 10 2.981 1.306 198.002 190.345 35 20 3.200 60 23 69.112 OPO 2009 11 2.813 1.239 161.716 164.929 35 20 3.200 60 23 69.112 OPO 2009 12 2.976 1.304 195.721 185.917 35 20 3.200 60 23 69.112
FAO 2005 1 822 584 87.736 68.035 22 22 4.500 60 36 68.500 FAO 2005 2 936 540 114.637 70.985 22 22 4.500 60 36 68.500 FAO 2005 3 1.235 728 170.693 100.846 22 22 4.500 60 36 68.500
104
CODE
Y
M
OUTPUTS INPUTS
ATM PAX TPS RDC BSC CID TBG TTA
NON-LCC LCC NON-LCC LCC # ATM/h bag/h # # m2
FAO 2005 4 1.641 1.063 217.148 137.842 22 22 4.500 60 36 68.500 FAO 2005 5 2.211 1.344 327.484 175.015 22 22 4.500 60 36 68.500 FAO 2005 6 2.362 1.440 360.754 202.171 22 22 4.500 60 36 68.500 FAO 2005 7 2.625 1.644 399.079 235.261 22 22 4.500 60 36 68.500 FAO 2005 8 2.497 1.606 384.443 238.581 22 22 4.500 60 36 68.500 FAO 2005 9 2.348 1.511 368.603 224.517 22 22 4.500 60 36 68.500 FAO 2005 10 2.129 1.548 306.269 214.310 22 22 4.500 60 36 68.500 FAO 2005 11 882 924 98.650 105.713 22 22 4.500 60 36 68.500 FAO 2005 12 637 898 57.261 87.946 22 22 4.500 60 36 68.500
FAO 2006 1 682 880 64.334 93.235 22 22 4.500 60 36 68.500 FAO 2006 2 819 866 90.052 105.529 22 22 4.500 60 36 68.500 FAO 2006 3 1.096 1.050 135.495 138.391 22 22 4.500 60 36 68.500 FAO 2006 4 1.610 1.444 212.412 202.433 22 22 4.500 60 36 68.500 FAO 2006 5 1.983 1.883 285.530 255.225 22 22 4.500 60 36 68.500 FAO 2006 6 2.158 1.951 328.489 277.803 22 22 4.500 60 36 68.500 FAO 2006 7 2.461 2.111 370.526 305.604 22 22 4.500 60 36 68.500 FAO 2006 8 2.516 2.074 371.746 304.387 22 22 4.500 60 36 68.500 FAO 2006 9 2.246 1.972 343.411 290.958 22 22 4.500 60 36 68.500 FAO 2006 10 1.878 1.901 261.064 258.955 22 22 4.500 60 36 68.500 FAO 2006 11 938 1.111 93.252 132.635 22 22 4.500 60 36 68.500 FAO 2006 12 715 1.086 56.379 111.772 22 22 4.500 60 36 68.500
FAO 2007 1 751 1.037 64.251 115.429 22 22 4.500 60 36 68.500 FAO 2007 2 800 1.021 82.827 130.677 22 22 4.500 60 36 68.500 FAO 2007 3 1.112 1.380 137.870 186.942 22 22 4.500 60 36 68.500 FAO 2007 4 1.440 1.885 183.339 249.684 22 22 4.500 60 36 68.500 FAO 2007 5 2.039 2.213 288.358 295.230 22 22 4.500 60 36 68.500 FAO 2007 6 2.225 2.303 322.500 322.722 22 22 4.500 60 36 68.500 FAO 2007 7 2.555 2.406 371.230 352.705 22 22 4.500 60 36 68.500 FAO 2007 8 2.618 2.450 375.707 362.163 22 22 4.500 60 36 68.500 FAO 2007 9 2.215 2.318 339.766 351.379 22 22 4.500 60 36 68.500 FAO 2007 10 1.821 2.165 245.663 302.541 22 22 4.500 60 36 68.500 FAO 2007 11 784 1.041 85.291 139.625 22 22 4.500 60 36 68.500 FAO 2007 12 617 1.057 49.448 115.125 22 22 4.500 60 36 68.500
FAO 2008 1 607 948 52.736 112.836 22 22 4.500 60 36 68.500 FAO 2008 2 714 1.019 75.058 137.868 22 22 4.500 60 36 68.500 FAO 2008 3 1.066 1.366 128.411 196.116 22 22 4.500 60 36 68.500 FAO 2008 4 1.277 2.125 145.332 277.239 22 22 4.500 60 36 68.500 FAO 2008 5 1.947 2.521 266.558 347.894 22 22 4.500 60 36 68.500 FAO 2008 6 1.891 2.533 268.701 368.693 22 22 4.500 60 36 68.500 FAO 2008 7 2.263 2.731 318.960 414.960 22 22 4.500 60 36 68.500 FAO 2008 8 2.294 2.760 321.378 427.180 22 22 4.500 60 36 68.500 FAO 2008 9 1.789 2.553 258.619 398.333 22 22 4.500 60 36 68.500
105
CODE
Y
M
OUTPUTS INPUTS
ATM PAX TPS RDC BSC CID TBG TTA
NON-LCC LCC NON-LCC LCC # ATM/h bag/h # # m2
FAO 2008 10 1.602 2.376 205.243 343.302 22 22 4.500 60 36 68.500 FAO 2008 11 856 1.075 82.369 146.560 22 22 4.500 60 36 68.500 FAO 2008 12 565 911 40.693 112.161 22 22 4.500 60 36 68.500
FAO 2009 1 602 900 43.787 113.912 22 22 4.500 60 36 68.500 FAO 2009 2 642 826 55.181 115.248 22 22 4.500 60 36 68.500 FAO 2009 3 868 1.062 92.684 157.345 22 22 4.500 60 36 68.500 FAO 2009 4 1.254 1.989 137.936 277.938 22 22 4.500 60 36 68.500 FAO 2009 5 1.666 2.445 210.647 338.402 22 22 4.500 60 36 68.500 FAO 2009 6 1.665 2.496 218.838 368.377 22 22 4.500 60 36 68.500 FAO 2009 7 1.930 2.797 259.565 430.463 22 22 4.500 60 36 68.500 FAO 2009 8 1.938 2.920 262.766 459.895 22 22 4.500 60 36 68.500 FAO 2009 9 1.619 2.567 224.175 402.829 22 22 4.500 60 36 68.500 FAO 2009 10 1.386 2.406 171.211 349.572 22 22 4.500 60 36 68.500 FAO 2009 11 605 1.192 50.995 158.009 22 22 4.500 60 36 68.500 FAO 2009 12 430 1.123 27.906 134.120 22 22 4.500 60 36 68.500
FUN 2005 1 1.880 0 176.664 0 15 14 3.600 40 16 43.315 FUN 2005 2 1.599 0 154.779 0 15 14 3.600 40 16 43.315 FUN 2005 3 1.897 0 201.843 0 15 14 3.600 40 16 43.315 FUN 2005 4 2.059 0 201.342 0 15 14 3.600 40 16 43.315 FUN 2005 5 2.085 0 195.385 0 15 14 3.600 40 16 43.315 FUN 2005 6 2.028 0 180.548 0 15 14 3.600 40 16 43.315 FUN 2005 7 2.115 0 207.210 0 15 14 3.600 40 16 43.315 FUN 2005 8 2.286 0 262.292 0 15 14 3.600 40 16 43.315 FUN 2005 9 2.194 0 216.626 0 15 14 3.600 40 16 43.315 FUN 2005 10 2.174 0 196.090 0 15 14 3.600 40 16 43.315 FUN 2005 11 1.881 0 160.909 0 15 14 3.600 40 16 43.315 FUN 2005 12 2.006 0 166.065 0 15 14 3.600 40 43.315
FUN 2006 1 1.934 0 174.186 0 15 14 3.600 40 16 43.315 FUN 2006 2 1.663 0 153.552 0 15 14 3.600 40 16 43.315 FUN 2006 3 1.907 0 188.082 0 15 14 3.600 40 16 43.315 FUN 2006 4 2.111 0 240.451 0 15 14 3.600 40 16 43.315 FUN 2006 5 2.107 0 211.466 0 15 14 3.600 40 16 43.315 FUN 2006 6 2.000 0 185.501 0 15 14 3.600 40 16 43.315 FUN 2006 7 2.142 0 217.752 0 15 14 3.600 40 16 43.315 FUN 2006 8 2.243 0 259.769 0 15 14 3.600 40 16 43.315 FUN 2006 9 2.002 0 214.086 0 15 14 3.600 40 16 43.315 FUN 2006 10 1.993 0 200.224 0 15 14 3.600 40 16 43.315 FUN 2006 11 1.741 0 156.327 0 15 14 3.600 40 16 43.315 FUN 2006 12 1.844 0 159.461 0 15 14 3.600 40 16 43.315
FUN 2007 1 1.798 0 169.957 0 15 14 3.600 40 16 43.315 FUN 2007 2 1.578 0 154.537 0 15 14 3.600 40 16 43.315 FUN 2007 3 1.788 0 196.939 0 15 14 3.600 40 16 43.315
106
CODE
Y
M
OUTPUTS INPUTS
ATM PAX TPS RDC BSC CID TBG TTA
NON-LCC LCC NON-LCC LCC # ATM/h bag/h # # m2
FUN 2007 4 2.015 0 243.207 0 15 14 3.600 40 16 43.315 FUN 2007 5 1.873 0 208.978 0 15 14 3.600 40 16 43.315 FUN 2007 6 1.769 0 191.690 0 15 14 3.600 40 16 43.315 FUN 2007 7 1.942 0 228.797 0 15 14 3.600 40 16 43.315 FUN 2007 8 1.940 0 249.368 0 15 14 3.600 40 16 43.315 FUN 2007 9 1.847 0 222.267 0 15 14 3.600 40 16 43.315 FUN 2007 10 1.747 69 193.077 9.503 15 14 3.600 40 16 43.315 FUN 2007 11 1.562 173 150.650 22.989 15 14 3.600 40 16 43.315 FUN 2007 12 1.673 180 156.044 20.486 15 14 3.600 40 16 43.315
FUN 2008 1 1.648 147 157.192 17.616 15 14 3.600 40 16 43.315 FUN 2008 2 1.546 148 149.185 20.645 15 14 3.600 40 16 43.315 FUN 2008 3 1.830 162 211.844 23.540 15 14 3.600 40 16 43.315 FUN 2008 4 1.791 176 193.222 23.964 15 14 3.600 40 16 43.315 FUN 2008 5 1.773 179 195.812 23.886 15 14 3.600 40 16 43.315 FUN 2008 6 1.748 178 183.107 24.698 15 14 3.600 40 16 43.315 FUN 2008 7 1.869 182 194.598 27.100 15 14 3.600 40 16 43.315 FUN 2008 8 1.916 182 220.161 28.492 15 14 3.600 40 16 43.315 FUN 2008 9 1.815 182 200.218 28.183 15 14 3.600 40 16 43.315 FUN 2008 10 1.650 186 164.240 26.903 15 14 3.600 40 16 43.315 FUN 2008 11 1.454 262 129.974 33.310 15 14 3.600 40 16 43.315 FUN 2008 12 1.502 273 134.737 34.297 15 14 3.600 40 16 43.315
FUN 2009 1 1.512 228 137.466 23.685 15 14 3.600 40 16 43.315 FUN 2009 2 1.345 200 127.180 24.814 15 14 3.600 40 16 43.315 FUN 2009 3 1.623 238 169.913 28.766 15 14 3.600 40 16 43.315 FUN 2009 4 1.766 220 199.867 31.042 15 14 3.600 40 16 43.315 FUN 2009 5 1.672 216 176.264 29.524 15 14 3.600 40 16 43.315 FUN 2009 6 1.645 219 173.612 30.494 15 14 3.600 40 16 43.315 FUN 2009 7 1.776 214 184.037 30.895 15 14 3.600 40 16 43.315 FUN 2009 8 1.888 228 220.456 33.537 15 14 3.600 40 16 43.315 FUN 2009 9 1.649 208 178.398 29.643 15 14 3.600 40 16 43.315 FUN 2009 10 1.569 200 163.903 28.643 15 14 3.600 40 16 43.315 FUN 2009 11 1.439 234 133.366 29.471 15 14 3.600 40 16 43.315 FUN 2009 12 1.459 207 136.481 25.192 15 14 3.600 40 16 43.315
PDL 2005 1 690 0 50.726 0 10 12 900 12 6 10.796 PDL 2005 2 637 0 44.864 0 10 12 900 12 6 10.796 PDL 2005 3 807 0 62.889 0 10 12 900 12 12 10.796 PDL 2005 4 953 0 68.387 0 10 12 900 12 12 10.796 PDL 2005 5 1.015 0 76.739 0 10 12 900 12 12 10.796 PDL 2005 6 1.028 0 75.496 0 10 12 900 12 12 10.796 PDL 2005 7 1.248 0 105.129 0 10 12 900 12 12 10.796 PDL 2005 8 1.435 0 131.219 0 10 12 900 12 12 10.796 PDL 2005 9 1.098 0 88.506 0 10 12 900 12 12 10.796
107
CODE
Y
M
OUTPUTS INPUTS
ATM PAX TPS RDC BSC CID TBG TTA
NON-LCC LCC NON-LCC LCC # ATM/h bag/h # # m2
PDL 2005 10 907 0 68.003 0 10 12 900 12 12 10.796 PDL 2005 11 680 0 49.803 0 10 12 900 12 12 10.796 PDL 2005 12 694 0 51.772 0 10 12 900 12 12 10.796
PDL 2006 1 713 0 48.738 0 10 12 900 12 12 10.796 PDL 2006 2 608 0 42.442 0 10 12 900 12 12 10.796 PDL 2006 3 728 0 55.777 0 10 12 900 12 12 10.796 PDL 2006 4 964 0 81.635 0 10 12 900 12 12 10.796 PDL 2006 5 1.056 0 82.194 0 10 12 900 12 12 10.796 PDL 2006 6 1.023 0 80.864 0 10 12 900 12 12 10.796 PDL 2006 7 1.385 0 115.341 0 10 12 900 12 12 10.796 PDL 2006 8 1.468 0 135.533 0 10 12 900 12 12 10.796 PDL 2006 9 1.042 0 89.805 0 10 12 900 12 12 10.796 PDL 2006 10 961 0 72.874 0 10 12 900 12 12 10.796 PDL 2006 11 714 0 50.897 0 10 12 900 12 12 10.796 PDL 2006 12 722 0 53.509 0 10 12 900 12 12 10.796
PDL 2007 1 740 0 52.471 0 10 12 900 12 12 10.796 PDL 2007 2 640 0 45.793 0 10 12 900 12 12 10.796 PDL 2007 3 794 0 63.393 0 10 12 900 12 12 10.796 PDL 2007 4 1.024 0 80.747 0 10 12 900 12 12 10.796 PDL 2007 5 1.073 0 82.373 0 10 12 900 12 12 10.796 PDL 2007 6 1.110 0 86.342 0 10 12 900 12 12 10.796 PDL 2007 7 1.430 0 115.067 0 10 12 900 12 12 10.796 PDL 2007 8 1.495 0 138.771 0 10 12 900 12 12 10.796 PDL 2007 9 1.135 0 93.947 0 10 12 900 12 12 10.796 PDL 2007 10 982 0 76.661 0 10 12 900 12 12 10.796 PDL 2007 11 706 0 53.394 0 10 12 900 12 12 10.796 PDL 2007 12 721 0 51.813 0 10 12 900 12 12 10.796
PDL 2008 1 746 0 51.522 0 10 12 900 12 12 10.796 PDL 2008 2 683 0 46.457 0 10 12 900 12 12 10.796 PDL 2008 3 830 0 73.915 0 10 12 900 12 12 10.796 PDL 2008 4 1.011 0 71.051 0 10 12 900 12 12 10.796 PDL 2008 5 1.136 0 85.068 0 10 12 900 12 12 10.796 PDL 2008 6 1.147 0 82.640 0 10 12 900 12 12 10.796 PDL 2008 7 1.462 0 114.513 0 10 12 900 12 12 10.796 PDL 2008 8 1.528 0 136.644 0 10 12 900 12 12 10.796 PDL 2008 9 1.130 0 88.874 0 10 12 900 12 12 10.796 PDL 2008 10 959 0 65.697 0 10 12 900 12 12 10.796 PDL 2008 11 724 8 50.485 844 10 12 900 12 12 10.796 PDL 2008 12 749 10 52.986 889 10 12 900 12 12 10.796
PDL 2009 1 747 8 49.737 848 10 12 900 12 12 10.796 PDL 2009 2 654 8 43.170 655 10 12 900 12 12 10.796 PDL 2009 3 761 10 52.437 1.369 10 12 900 12 12 10.796
108
CODE
Y
M
OUTPUTS INPUTS
ATM PAX TPS RDC BSC CID TBG TTA
NON-LCC LCC NON-LCC LCC # ATM/h bag/h # # m2
PDL 2009 4 1.059 8 80.641 1.238 10 12 900 12 12 10.796 PDL 2009 5 1.152 8 82.797 752 10 12 900 12 12 10.796 PDL 2009 6 1.093 8 85.309 887 10 12 900 12 12 10.796 PDL 2009 7 1.392 10 109.103 1.159 10 12 900 12 12 10.796 PDL 2009 8 1.559 8 129.072 931 10 12 900 12 12 10.796 PDL 2009 9 1.183 10 83.018 1.272 10 12 900 12 12 10.796 PDL 2009 10 1.003 6 65.194 532 10 12 900 12 12 10.796 PDL 2009 11 787 10 49.783 964 10 12 900 12 12 10.796 PDL 2009 12 857 8 55.348 597 10 12 900 12 12 10.796
109
APPENDIX 3 – DEA SCORES ON PORTUGUESE AIRPORTS
CODE MODEL 1 MODEL 2
CRS TE VRS TE SCALE E. CRS TE VRS TE SCALE E.
LIS 66,6% 92,7% 71,8% drs 34,6% 92,0% 37,6% drs
OPO 81,4% 84,3% 96,6% drs 63,2% 84,1% 75,1% drs
FAO 100,0% 100,0% 100,0% - 62,7% 100,0% 62,7% drs
FUN 82,2% 95,9% 85,7% drs 82,2% 95,8% 85,8% drs
PDL 100,0% 100,0% 100,0% - 100,0% 100,0% 100,0% -
Average 86,0% 94,6% 90,8% 68,5% 94,4% 72,2%
Standard Deviation 14,2% 6,5% 12,1% 24,4% 6,6% 23,7%
CODE YEAR MODEL 3 MODEL 4
CRS TE VRS TE SCALE E. CRS TE VRS TE SCALE E.
LIS
2005 100,0% 100,0% 100,0% - 98,6% 100,0% 98,6% irs
2006 100,0% 100,0% 100,0% - 100,0% 100,0% 100,0% -
2007 100,0% 100,0% 100,0% - 100,0% 100,0% 100,0% -
2008 100,0% 100,0% 100,0% - 100,0% 100,0% 100,0% -
2009 98,5% 99,0% 99,5% drs 97,5% 97,5% 100,0% -
OPO
2005 73,7% 100,0% 73,7% irs 71,9% 100,0% 71,9% irs
2006 65,4% 85,7% 76,3% irs 64,3% 84,1% 76,4% irs
2007 78,5% 88,5% 88,6% irs 67,7% 85,0% 79,6% irs
2008 98,2% 100,0% 98,2% irs 74,8% 94,0% 79,6% irs
2009 100,0% 100,0% 100,0% - 69,6% 87,8% 79,3% irs
FAO
2005 85,4% 93,0% 91,8% irs 82,3% 86,9% 94,7% irs
2006 93,0% 95,6% 97,3% irs 88,1% 93,0% 94,7% irs
2007 100,0% 100,0% 100,0% - 94,7% 100,0% 94,7% irs
2008 100,0% 100,0% 100,0% - 94,3% 99,6% 94,7% irs
2009 100,0% 100,0% 100,0% - 87,6% 92,6% 94,6% irs
FUN
2005 69,7% 100,0% 69,7% irs 59,0% 100,0% 59,0% irs
2006 70,9% 100,0% 70,9% irs 59,9% 99,9% 60,0% irs
2007 71,0% 100,0% 71,0% irs 61,4% 98,8% 62,1% irs
2008 64,1% 100,0% 64,1% irs 62,1% 100,0% 62,1% irs
2009 60,1% 100,0% 60,1% irs 59,6% 96,1% 62,0% irs
PDL
2005 79,8% 100,0% 79,8% irs 74,6% 100,0% 74,6% irs
2006 81,1% 96,7% 83,9% irs 75,9% 96,7% 78,5% irs
2007 84,5% 100,0% 84,5% irs 79,0% 100,0% 79,0% irs
2008 86,3% 100,0% 86,3% irs 80,8% 100,0% 80,8% irs
2009 87,3% 100,0% 87,3% irs 82,4% 100,0% 82,4% irs
Average 85,9% 98,3% 87,3% 79,4% 96,5% 82,4%
Standard Deviation 13,7% 3,8% 13,2% 14,4% 5,3% 14,2%
110
CODE MODEL 5 MODEL 6
CRS TE VRS TE SCALE E. CRS TE VRS TE SCALE E.
LIS 100,0% 100,0% 100,0% - 100,0% 100,0% 100,0% -
OPO 83,4% 100,0% 83,4% irs 74,3% 100,0% 74,3% irs
FAO 100,0% 100,0% 100,0% - 98,1% 100,0% 98,1% irs
FUN 72,1% 100,0% 72,1% irs 70,2% 100,0% 70,2% irs
PDL 80,5% 100,0% 80,5% irs 79,6% 100,0% 79,6% irs
Average 87,2% 100,0% 87,2% 84,4% 100,0% 84,4%
Standard Deviation 12,4% 0,0% 12,4% 13,8% 0,0% 13,8%
