i

Airport Benchmarking: An Efficiency Analysis of

European Airports from an Economic and

Managerial Perspective

by

Dipl. Volksw. (FH) Vanessa Philippa Liebert

A thesis submitted in partial fulfilment of the

requirements for the degree of

Doctor of Philosophy

in Economics

School of Humanities and Social Sciences

Approved, Dissertation Committee

Prof. Dr. Gert Brunekreeft, Jacobs University Bremen (Chair)

Prof. Dr. Adalbert FX Wilhelm, Jacobs University Bremen

Prof. Dr. Hans-Martin Niemeier, University of Applied Sciences Bremen

Date of Defense: 08.03.2011

ii

iii

So eine Arbeit wird eigentlich nie fertig, man muss sie für fertig

erklären, wenn man nach der Zeit und den Umständen das

Möglichste getan hat.

Johann Wolfgang von Goethe (1749-1832)

Acknowledgements

iv

ACKNOWLEDGEMENTS

The expenditure of time to build a new airport on a green field may take three to four years.

Extensive academic research on the efficiency of existing airports with the objective to improve the

airports performance may require twice as long but is certainly more cost efficient… By now, nearly

six years have passed since I began with my research and I would like to use the opportunity and

express my gratefulness to people who have (technically and mentally) supported me during the whole

process of writing this doctoral dissertation.

First and foremost, I owe my gratitude to my supervisors Prof. Gert Brunekreeft and Prof. Hans-

Martin Niemeier for the opportunity to write a dissertation. Both made available their encouragement

and support in a number of ways. I would also like to thank my co-supervisor Prof. Adalbert Wilhelm

for valuable feedback. Furthermore, I would like to acknowledge the Federal Ministry of Education

and Research for financial support of the research project German Airport Performance (GAP) on

which this dissertation is based.

I am grateful to Dr. Nicole Adler for helpful discussion and methodological support. She taught

me to Keep It Simple and Stupid (KISS). I wish to express my gratitude to Adél Németh for her

encouragement and unlimited supply of coffee and chocolate and Nathalie-Chantal McCaughey for

proofreading. Moreover, I would like to thank my PhD fellows Nele Friedrichsen, Karsten Fröhlich,

Roland Meyer, Tolga Ülkü, Volker Wannack and Katya Yazhemsky. The student workers from the

GAP project as well as Prof. Jürgen Müller and Prof. Hansjochen Ehmer are gratefully acknowledged

for assisting me with collecting the data. Furthermore, I thank Prof. David Gillen, Prof. Peter Forsyth

and Dr. Mike Tretheway for helpful remarks on earlier drafts of this thesis.

Without the unlimited patience of my friends ‘outside’ and their repeated gamesmanship I might

have gone mad (academics are strange people…) and I would like to thank them all.

Finally, I owe my deepest gratitude to my family for their unrestricted support. My parents were

always there when I needed them most; they deserve far more credit than I can ever give them. This

thesis would not have been possible without my husband and his continuous encouragement. I am

indebted for his support during his countless days as grass widower and full-time daddy. Last but not

least, I thank my children who shared their mother with her ‘other baby’. This thesis, though not

appropriate for bedtime stories, is dedicated to them.

Hamburg in May 2011,

Vanessa Liebert

Abstract

v

ABSTRACT Subsequent to airline deregulation, an increasing commercialization, privatization and

restructuring gradually changed a sovereign operated airport industry to modern business enterprises.

Where market power was likely to be exploited, airports may now face competition with nearby

airports or other transport modes. Consequently airport benchmarking became popular for

comparisons with competitors and to assess efficiency changes resulting from the structural change.

Within academic benchmarking a number of studies emerged utilizing parametric and non-parametric

approaches to estimate the productivity and efficiency of airports. Building on the limitations and

discussions from previous research the general objective of this thesis is to further the understanding

of the airport industry and to improve airport benchmarking in order to enhance its usefulness for

managerial, political and regulatory purposes. Particular emphasis is given on the consideration of the

heterogeneous character of airports and how to explain efficiency difference across airports. The

cumulative thesis presents the results of three research articles. The first article provides a survey on

the methods, data and findings of empirical research from the current literature in airport

benchmarking. The survey indicates substantial progress in the methodological application however

many issues still remain unresolved such as the appropriate measurement of capital. The second article

assesses the combined impact of ownership form, economic regulation and competition on airport

performance and pricing in order to search for the most desirable combination. Australian and

European are analyzed using non-parametric data envelopment analysis (DEA) in a first stage

efficiency measurement and regression analysis in a second stage environmental study. The results

reveal that airports not facing competition should be regulated to increase cost efficiency and prevent

exploitation of market power. However, in a competitive setting, regulation inhibits airports of any

ownership from operating efficiently. Nevertheless, unregulated private airports appear to remain

profit-maximizer within competition. The third article aims to improve the airport benchmarking

process. Most previous studies either treat the airport production technology as a black box or separate

terminal and airside activities, assessing them individually. This research analyzes European airports

as a single unit due to the direct complementarities but opening the black box through network DEA.

Combined with dynamic clustering appropriate benchmarks are identified based on pre-defined

characteristics. Compared to basic DEA models, the results of the network DEA structure provide

more meaningful benchmarks with comparable peer units and target values that are achievable in the

medium term.

Keywords: benchmarking, airport efficiency, data envelopment analysis (DEA), second-stage

regression, external heterogeneity

List of Abbreviations

vi

LIST OF ABBREVIATIONS

ADP Aéroports de Paris (Paris airports) AENA Aeropuertos Españoles y Navegación Aérea (Spanish Airports and Air Navigation)ATM Air Transport Movements a/c Aircraft ACI Airports Council International ANOVA Analysis of Variance ADV Arbeitsgemeinschaft Deutscher Verkehrsflughäfen (German Airports Association)ACCC Australia Competition & Consumer Commission BAA British Airports Authority capex Capital Expenditures CCD Caves, Christensen and Diewert CAA Civil Aviation Authority CRS Constant Returns-To-Scale CPI Consumer Price Index DHL Dalsey, Hillblom and Lynn (Parcel service) DEA Data Envelopment Analysis DMU Decision Making Unit dom. Domestic DAA Dublin Airport Authority EW-TFP Endogenous-Weight Total Factor Productivity EU European Union FDH Free Disposal Hull FTE Full-Time Equivalents GA General Aviation GAP German Airport Performance GDP Gross Domestic Product int. International IATA International Air Transport Association ICAO International Civil Aviation Organization km kilometres max. Maximum MLE Maximum Likelihood Estimation min. Minimum ND Non-Discretionary NIRS Non-Increasing Returns-To-Scale no. Number obs. Observations op. Operating OLS Ordinary Least Squares PAX Passengers PIM Perpetual Inventory Method

List of Abbreviations

vii

pos. Positions PIN Price-Index Number PC Principal Component PCA Principal Component Analysis reg. Regional RPI Revenue Price Index rev. Revenues RWY Runway SH&E Simat, Helliesen & Eichner (Aviation consultancy) SBM Slack-Based Measure SFA Stochastic Frontier Analysis SMOP Surface Measure of Overall Performance totex Total Expenditures TFP Total Factor Productivity UPS United Parcel Service VFP Variable Factor Productivity VRS Variable Returns-To-Scale WLU Work Load Units

List of Airports and Country Codes

viii

LIST OF AIRPORTS AND COUNTRY

CODES ABZ Aberdeen LEJ Leipzig AMS Amsterdam LGW London Gatwick ATH Athens LHR London Heathrow BFS Belfast LJU Ljubljana BHX Birmingham LTN London Luton BLQ Bologna LYS Lyon BRE Bremen MAN Manchester BRU Brussels MEL Melbourne BTS Bratislava MLA Malta BUD Budapest MLH Basel Mulhouse CGN Cologne Bonn MME Durham Tees Valley CPH Copenhagen MRS Marseille DRS Dresden MUC Munich DTM Dortmund NCE Nice DUB Dublin NUE Nuremberg DUS Dusseldorf OSL Oslo EDI Edinburgh PER Perth EMA East Midlands (Nottingham) RIX Riga FLR Florence SOU Southampton FRA Frankfurt STN London Stansted GLA Glasgow STR Stuttgart GOA Genoa SYD Sydney GVA Geneva SZG Salzburg HAJ Hanover TLL Tallinn HAM Hamburg VCE Venice LBA Leeds Bradford VIE Vienna LCY London City ZRH Zurich

AT Austria IE Ireland AU Australia IT Italy BE Belgium LV Latvia CH Switzerland MT Malta DE Germany NL The Netherlands DK Denmark NO Norway EE Estonia SI Slovenia FR France SK Slovakia GR Greece UK United Kingdom HU Hungary US United States

List of Figures

ix

LIST OF FIGURES

Fig. 1: Quantitative benchmarking approaches ..................................................................20

Fig. 2: Stochastic frontier analysis .....................................................................................22

Fig. 3: Data envelopment analysis......................................................................................25

Fig. 4: Structure of this dissertation....................................................................................29

Fig. 5: Airport production function in DEA.......................................................................34

Fig. 6: Technical, allocative and economic efficiency .......................................................41

Fig. 7: Quantitative methods in productivity and efficiency analysis ................................42

Fig. 8: Models in data envelopment analysis .....................................................................45

Fig. 9: Models in stochastic frontier analysis .....................................................................48

Fig. 10: Inputs and outputs in previous airport benchmarking studies...............................53

Fig. 11: Airport network technology ................................................................................107

Fig. 12: Benchmark clustering..........................................................................................110

Fig. 13: Two-stage airport network technology ...............................................................114

Fig. 14: Kruskal-Wallis ANOVA for outsourcing ...........................................................122

Fig. 15: Co-Plot graphic display of Vienna’s input-oriented strategy..............................127

Fig. 16: Co-plot of input minimization results with emphasis on Hanover .....................129

Fig. 17: Catchment area of Hanover airport (2 hour drive)..............................................130

Fig. 18: Current and target output values for Lyon ..........................................................132

Fig. 19: Co-plot of output maximization results with emphasis on Lyon ........................133

List of Figures

x

LIST OF TABLES

Tab. 1: Comparison of DEA, SFA and PIN properties...................................................... 50

Tab. 2: Studies using non-parametric approaches ............................................................. 62

Tab. 3: Studies using parametric approaches..................................................................... 68

Tab. 4: Studies using price-based index approaches.......................................................... 71

Tab. 5: Regression analysis................................................................................................ 83

Tab. 6: Variables in analysis (DEA) .................................................................................. 84

Tab. 7: Combination of environmental variables analyzed ............................................... 89

Tab. 8: Second-stage regression results from the individual cost efficiency model.......... 94

Tab. 9: Second-stage regression results from the combined cost efficiency model .......... 96

Tab. 10: Second-stage regression results from the combined revenue model ................... 98

Tab. 11: List of airports.................................................................................................... 102

Tab. 12: DEA efficiency scores ....................................................................................... 103

Tab. 13: Variables in airport efficiency analysis ............................................................. 120

Tab. 14: Banker F-test for outsourcing ............................................................................ 123

Tab. 15: Benchmarking Hanover airport ......................................................................... 128

Tab. 16: Output benchmarks for Lyon airport ................................................................. 131

Tab. 17: Airport dataset ................................................................................................... 135

List of Figures

xi

TABLE OF CONTENTS

ACKNOWLEDGEMENTS .........................................................................................................iii

ACKNOWLEDGEMENTS .........................................................................................................iv

ABSTRACT ...............................................................................................................................v

LIST OF ABBREVIATIONS.......................................................................................................vi

LIST OF AIRPORTS AND COUNTRY CODES .........................................................................viii

LIST OF FIGURES ...................................................................................................................ix

1 INTRODUCTION .............................................................................................................14

1.1 Theoretical background ..........................................................................................15

1.1.1 The beginning of a liberalization process in the aviation industry................15

1.1.2 The changing nature of European airports - a motivation for

benchmarking .............................................................................................16

1.1.3 Users of airport benchmarking ...................................................................18

1.1.4 Quantitative approaches of benchmarking ..................................................20

1.2 Contribution of this PhD research ..........................................................................27

1.2.1 Discussions arising from previous research.................................................27

1.2.2 Motivation for research...............................................................................28

1.2.3 Article 1: A survey of empirical research on the productivity and

efficiency measurement of airports .............................................................31

1.2.4 Article 2: Joint impact of competition, ownership form and economic

Regulation on airport performance .............................................................32

1.2.5 Article 3: Benchmarking airports from a managerial perspective .................33

1.3 Concluding remarks ................................................................................................35

List of Figures

xii

2 A SURVEY OF EMPIRICAL RESEARCH ON THE PRODUCTIVITY AND EFFICIENCY

MEASUREMENT OF AIRPORTS....................................................................................... 37

2.1 Introduction ........................................................................................................... 38

2.2 Productivity and efficiency measurement concepts................................................. 40

2.2.1 Productivity and efficiency ......................................................................... 41

2.2.2 Price-based index number approaches........................................................ 42

2.2.3 Data envelopment analysis ......................................................................... 44

2.2.4 Stochastic frontier analysis ......................................................................... 47

2.2.5 Comparison of techniques.......................................................................... 50

2.3 Variable selection in airport studies ........................................................................ 51

2.3.1 Outputs...................................................................................................... 51

2.3.2 Inputs......................................................................................................... 52

2.4 Empirical results of productivity and efficiency studies .......................................... 55

2.4.1 Productivity and efficiency changes over time ............................................ 56

2.4.2 Empirical effects of ownership................................................................... 57

2.4.3 Scale effects................................................................................................ 59

2.5 Conclusion ............................................................................................................. 60

2.A Appendix ............................................................................................................... 62

3 JOINT IMPACT OF COMPETITION, OWNERSHIP FORM AND ECONOMIC

REGULATION ON AIRPORT PERFORMANCE ................................................................. 72

3.1 Introduction ........................................................................................................... 73

3.2 Literature on competition, regulation and ownership ............................................. 76

3.3 Methodology and model specification .................................................................... 80

3.3.1 Data envelopment analysis ......................................................................... 81

3.3.2 Second-stage regression.............................................................................. 82

3.4 Dataset ................................................................................................................... 83

3.4.1 Variables in the first-stage efficiency analysis .............................................. 84

3.4.2 Variables in the second-stage regression ..................................................... 86

3.5 Empirical results..................................................................................................... 89

3.5.1 Efficiency scores from data envelopment analysis ...................................... 89

3.5.2 Regression results explaining cost efficiency ............................................... 92

3.5.3 Regression results explaining airport charges .............................................. 97

3.6 Conclusions............................................................................................................ 99

List of Figures

xiii

3.A Appendix ..............................................................................................................102

4 AIRPORT BENCHMARKING FROM A MANAGERIAL PERSPECTIVE ..............................104

4.1 Introduction .........................................................................................................105

4.2 Methodology ........................................................................................................109

4.2.1 Dynamic clustering ...................................................................................110

4.2.2 Network DEA ..........................................................................................111

4.2.3 Principal component analysis integrated with DEA ..................................111

4.2.4 Visualizing multiple dimensions ................................................................112

4.2.5 Measuring efficiency variation across groups ............................................113

4.3 Model formulations ..............................................................................................113

4.4 Dataset .................................................................................................................118

4.5 Empirical results ...................................................................................................120

4.5.1 Efficiency variation across groups.............................................................121

4.5.2 Comparison of basic and network DEA ...................................................124

4.5.3 Benchmarking airports..............................................................................126

4.6 Conclusion............................................................................................................133

4.A Appendix ..............................................................................................................135

5 REFERENCES................................................................................................................136

14

1 INTRODUCTION

Since I was little, airports have been a fascinating place to me. My dad often took me to

the airport and let me watch the airplanes departing and arriving. However, at that time I have

certainly neither expected to examine the airports’ efficiency nor to submit a doctoral

dissertation 20 years later. The application of benchmarking (or efficiency analysis) aroused

my interest while I was studying one year abroad in England where Data Envelopment

Analysis (DEA) and other efficiency measurement techniques were already widely applied.

Hence what may be more interesting than airport benchmarking? I combined both issues and

together with my professor (Dr. Hans-Martin Niemeier, University of Applied Sciences

Bremen) we applied for a research grant aiming to assess the efficiency of German airports

which were hardly considered at that time. At the beginning of my position as researcher in

the German Airport Performance (GAP) project1 and as PhD candidate at Jacobs University I

have not expected how challenging but also exciting it may be to assess an industry that has

been under continuous structural changes over the last two decades…

1 The GAP project on “Efficiency Measurements of German Airports in Comparison to Europe, Australia and North America” is a joint research project of the University of Applied Sciences Bremen, the Berlin School of Economics and Law (HWR) and the International University of Applied Sciences Bad Honnef, which has been sponsored by the Federal Ministry of Education and Research (BMBF) from 2005 until 2009. Its aim is to investigate the changing nature and performance of European airports, their commercialization and competitive environment, as well as the need for further financial and environmental regulation. For details see http://www.gap-projekt.de.

Introduction

15

1.1 Theoretical background

During the last four decades, an upward trend in international tourism and globalization

substantially increased traffic rates in the aviation sector. Although several external shocks

(e.g. Gulf war, economic downturn, terror attacks in 2001) temporarily interrupted this trend

overall growth was not substantially impacted. One major influence on this growth has been

the deregulation of the airline industry, which began in the late nineteen-seventies and

resulted in lower airfares. This was the starting point of a gradual liberalization process in the

aviation industry. The opening of the aviation market increased competition of a previously

heavily restricted industry. As a result from the airline deregulation, many airports have felt

and still feel increasingly exposed to the cost pressure and are obliged to operate efficiently.

Increasing commercialization, privatization activities, changes in economic regulation or

restructuring lead to substantial changes in an initially sovereign operated industry.

Consequently, performance measurement of airports became increasingly important for

comparisons with competitors and to assess efficiency changes resulted from the structural

change.

The following section provides an introduction to the changing nature of the aviation

industry and highlights the usefulness of airport benchmarking. Efficiency measurement

techniques that are widely adopted in the academic benchmarking literature will be presented

thereafter.

1.1.1 The beginning of a liberalization process in the aviation industry

Historically, the worldwide civil aviation industry has been a fully regulated environment

under the ownership of public authorities. Political restrictions on market entries, ticket fares,

capacities and frequencies protected national carriers in a contestable market characterized by

low economies of scale (Doganis 2002). In the nineteen-seventies, economists began to

debate if there was indeed a need for a regulated airline industry in which passenger growth

and technological progress are likely to be constrained by political restrictions. Considering

the downsides of heavy handed regulation the US removed restrictions on domestic routes,

fares and schedules in 1978. Although many European governments were initially reluctant to

resign control of their flag carriers, they nevertheless followed the liberalization process in

1987. European liberalisation took place in stages. Initially tariff flexibility was increased and

market entry was made easier. Then in 1993 restrictions on routes, capacities and market

Introduction

16

entries were removed within the European Union (EU). Since 1997, cabotage is allowed,

which means that EU airlines can operate city-pairs in foreign countries within the EU. This

move turned the EU into the largest open aviation market in the world (Maurer 2003). A

major step in the liberalization process was reached with the EU-US open sky agreement.

This agreement became effective in March 2008 and displaced numerous bilateral

agreements. Nevertheless the liberalization process is an ongoing one. The next target of the

EU is an open sky with neighbour states and other countries outside Europe in order to

enlarge the single aviation market worldwide (European Commission 2007).

Exposed to increased competitive pressures several airlines in Europe began to complain

about markets of air transport service suppliers such as ground handling2 which remained

regulated. For example in Spain the state-owned carrier Iberia possessed market power in

ground handling operations at hub airports and other airlines were forced to purchase their

overcharged service. In Germany, Italy or Austria the airport provided ground handling

services in a fully regulated market where independent providers had little chance to enter. As

a further stage in the liberalization process of the aviation industry, ground handling services

within the EU were deregulated in 1996. In a gradual implementation schedule, the directive

allowed self handler (airlines) and independent third party providers to enter the market

(Templin 2007).

Following the liberalization process of the airline and ground handling market, a gradual

change in the nature of the airport industry may be expected.

1.1.2 The changing nature of European airports - a motivation for

benchmarking

Similar to the airline market prior liberalization, European airports were mostly deemed

state-owned entities with the objective to provide and operate the infrastructure for airlines.

As with other infrastructure based services and utilities airports were viewed as natural

monopolies enjoying both economies of scale and market power (Czerny 2006).

Consequently, in order to encourage efficiency and avoid market power exploitation, the

majority of commercial airports were subject to economic regulation. In Europe, passenger

and landing fees charged to airlines have traditionally been regulated according to a rate-of-

return or cost-plus principle. Such regulation permits airports to generate sufficient revenue to

2 Ground handling activities include the handling of passengers, baggage, freight and mail, ramp handling, fuel and oil handling, aircraft services and maintenance (Graham 2004).

Introduction

17

cover total expenditures, including the depreciation of capital and an expected rate of return

on capital (Reinhold et al. 2010).

With airline liberalization many airports moved away from being public utilities towards

operating as modern enterprises pursuing commercial objectives. After the successful

privatization of the British Airports Authority (BAA) in the late Eighties, a number of

privatization processes have been actively promoted by governments with the proclaimed

intention of reducing government involvement and increasing airport productivity and

innovation.

Given the assumed behaviour to maximize profits in a monopolistic environment, the

majority of privatized airports in Europe remained subject to economic regulation (Gillen

2010). However, as initial cost-based regulation procedures were assumed to engender

overcapitalization rather than productive efficiency (Averch and Johnson 1962) a number of

privatized airports adopted incentive regulation. Price-cap regulation as proposed by

Littlechild (1983) was introduced at the regulated BAA airports along with privatization.

Price-caps are generally set over a regulatory period of five years according to the RPI-X

formula where RPI represents the retail price index and X is the efficiency improvement that

the regulators consider reasonable within the timeframe. If the airport management achieves

greater cost reductions over the five year period, the gains are enjoyed by the company.

Having started to operate as modern businesses many airports changed their management

style towards increasing commercialization. Substantial investments in non-aeronautical

activities such as shopping malls were undertaken to augment their revenues from non-

aeronautical3 sources in order to cross-subsidize aviation charges and attract additional

airlines and passengers to the airport (Zhang and Zhang 2010). Furthermore vertical

boundaries have changed over time. A number of airports in Germany restructured their

labour-intensive ground handling segment in order to set flexible tariffs and compete with

independent providers.

Airline deregulation induced competition for airport services covering a multiplicity of

markets. The liberalization of bilateral air service agreements offered the opportunity to

attract international traffic to gateway airports (e.g. Amsterdam and Frankfurt) and secondary

hubs. Underutilized secondary airports (e.g. Lübeck) and former military airports started to

serve low cost carriers sharing local catchment areas with primary airports. Alternative modes

3 Throughout the thesis the terms non-aeronautical, non-aviation and commercial activities are used interchangeably.

Introduction

18

of transport such as high speed rail compete with aviation in the medium distance markets

(Tretheway and Kincaid 2010).

Furthermore, subsequent to airline deregulation lower airfares substantially increased the

traffic volume with growing tourism and globalization trends. In order to meet future

demands, congested airports needed to expand their capacity and to introduce new

technologies to increase runway and terminal system capacities. However at many major

airports the excess demand was rationed rather inefficiently through queuing and slot

allocation mechanisms having increased the number of non-weather related delays.

In summary the airport industry evolved into a dynamic market environment. Increasing

commercialization, privatization and restructuring processes, a shift towards incentive

regulation and advanced technologies changed the nature of the airport industry and may have

contributed to productivity and efficiency changes. For these reasons airports offer a rich field

for performance comparisons commonly defined as benchmarking4.

1.1.3 Users of airport benchmarking

Although benchmarking was already applied in other transport sectors and regulated

utilities in the nineteen seventies, it only became important in the airport industry twenty

years later. Graham (2005) argues that the increasing interest in airport benchmarking is a

result of the changes in ownership and the liberalization, commercialization and globalization

trends which have influenced airport business growth, complexity and competitiveness.

However, the comparison of airports generally appears to be challenging given their

unique character. Airports offer a heterogeneous mix of services. Some airports mainly serve

passengers while others fill empty capacities with cargo operations for parcel services (e.g.

Leipzig as the European hub of DHL). Furthermore, airports may be highly vertical integrated

and offer handling services and commercial products whereas others outsource this activity to

independent providers and concessionaires. Generally, the importance of commercial

activities which generate extra revenues may also vary among the airports. Lumpy

investments which are typical for airports further complicate financial comparisons when

airports are in different stages of their life cycles. Moreover, airports are often heavily

affected by multiple factors that are beyond the control of an airport manager. For example

4 Initially, the term of benchmarking was introduced by Xerox in 1979 and was referred to reverse engineering. Due to a decrease in market shares Xerox systematically compared their copy machine with a product by Canon who offer a similar product for a lower price (Dence 1995).

Introduction

19

the location may influence the airport’s operation. Night curfews at city airports reduce the

operating hours and airports near the coast such as Amsterdam require additional runways and

a special configuration to handle operations consistently irrespective of weather conditions. In

short, no airport is a smaller version of a large counterpart (Forsyth 2000). Nevertheless,

Adler et al. (2009) cited Peter Drucker who argues ’what you cannot measure, you cannot

manage’. For a multiple reasons as outlined below airport benchmarking has received

increasing interest by various airport stakeholders.

Airport benchmarking may for instance be utilized for managerial purposes. Airport

managers compare overall or partial processes (e.g. ground handling activities) with potential

competitors or best-practice airports to develop new strategies. In order to avoid improper

comparisons, Frankfurt may include other European hubs such as Amsterdam, London-

Heathrow, Paris or best-practice examples such as Hong Kong, Singapore and Dubai rather

than nearby airports (Kincaid and Tretheway 2006).

Customers, shareholders and investors are interested in benchmarking as decision-making

instrument. Airlines, as the intermediate between airports and passengers, prefer efficient

airports with low costs, high service standards and no delays. Moreover, passengers prefer

airports with low queue lengths that are located close to the city centre and are equipped with

shopping and entertainment facilities. Private shareholders and investors expect high and fast

returns on investments.

National and regional governments mostly assess airport performance from an economic

perspective. They may examine effects of policy changes in before and after comparisons or

with other countries. As an example privatization activities or changes in economic regulation

are assessed that may have lead to improving efficiency, pricing and investments.

Furthermore, benchmarking is applied to inform policy. In Australia, major infrastructure

service industries such as electricity, gas supply or airports were benchmarked in an

international comparison in 1995 in order to identify performance gaps between Australian

providers and the best-in-class worldwide (Kincaid and Tretheway 2006).

Proposed by Shleifer (1985) benchmarking may serve for regulatory purposes, widely

known as yardstick competition. This form of regulation implies virtual competition amongst

regulated firms by comparing their cost levels and determining the permitted price based on

an average level. The intention is to stimulate an airport to operate efficiently. Whereas

yardstick competition evolved into a standard approach in the British water and railway

industries, it has to-date rarely been applied to airports. To the best of our knowledge, the

Introduction

20

Dublin Airport Authority (DAA) is the only European example that attempted to implement

yardstick competition in 2001. However, it was highly criticized by airport management for

identifying inappropriate peer airports and was discontinued (Reinhold et al. 2010). The

British Civil Aviation Authority (CAA) argues that the heterogeneous character of airports

and the challenge to obtain appropriate data contribute to their reluctance to apply this type of

economic regulation (CAA 2000).

In order to improve the use of benchmarking and provide a valuable instrument for

managers, governments, regulators and other stakeholders, academic research continuously

aims to refine quantitative methods to assess the productivity and efficiency of airports. The

methods that are mostly applied are outlined below.

1.1.4 Quantitative approaches of benchmarking

Generally speaking, airports may be defined as a network consisting of multi-production

processes. Aeronautical activities include the handling of passengers, aircrafts and cargo. The

non-aeronautical side may operate car parking facilities, restaurants and retail. A number of

quantitative techniques have emerged that assess the productivity and efficiency of decision

making units (DMU) as can be taken from Figure 1.

Fig. 1: Quantitative benchmarking approaches

Productivity and Efficiency Analysis

One-dimensional

Multi-dimensional

Partial Performance

Average Approaches

Frontier Approaches

Non-Parametric(index numbers)

Parametric (Deterministic)

Parametric (Stochastic)

Non-Parametric (Deterministic)

Total Factor Productivity

(TFP)

Ordinary Least Squares (OLS)

Stochastic Frontier Analysis

(SFA)

Data Envelopment

Analysis (DEA)

Source: adapted from von Hirschhausen and Cullmann (2005)

Introduction

21

One-dimensional approaches are the simplest form to assess the productivity by dividing

one output (y) by one input (x). Being skeptical towards sophisticated overall quantitative

techniques, airport managers mostly prefer partial productivity measures. However this

measure should be treated with caution. As discussed by Forsyth et al. (1986) partial measures

should only be applied if data for overall measures is not available. Results obtained from

partial measures can mislead as they fail to capture substitution effects between different input

factors. In order to receive an overall picture of the airport’s performance multi-dimensional

approaches should be applied instead. Three well-documented quantitative methods are often

applied to analyze the productivity and efficiency of government and private enterprises

which are highlighted in grey in Figure 1.

(i) Total Factor Productivity (TFP)

A non-parametric, index number approach is used to measure the total factor productivity

(TFP). The application of index-number approaches is most common in measuring price and

quantity changes over time; the retail price index (RPI) is the most popular economic

indicator. An advantage of index-number approaches is the provision of meaningful results

with only two observations because the productivity is not assessed relative to other units.

The Törnqvist index is widely used in economic studies however the index is restricted to

time-series analyses. Caves, Christensen and Diewert (1982a) proposed a multilateral translog

index known as the CCD index to compare the TFP of a set of units over different years:

∑ ∑

∑ ∑

−+−−+−

−+−−+=

−−−=

i iiijiijiikiik

i iiijiijiikiik

jkjkkj

XXWWXXWW

YYRRYYRR

XXYYTFP

)ln)(ln(21)ln)(ln(

21

)ln)(ln(21)ln)(ln(

21

)ln(ln)ln(lnln

(1.01)

where Yik and Rik are the output quantity and the revenue share for output i of DMU k; iR

is the arithmetic mean of the revenue share and iY is the geometric mean of output i over the

entire sample. Xik is the input quantity and Wik is the input cost share for input i of DMU k; iW

is the arithmetic mean of cost shares and iX the geometric mean of input i over the entire

sample. However, in order to aggregate multiple inputs and outputs to an index, market prices

are required as weights. Furthermore, this measurement assumes that all units operate

efficiently, which is unlikely to be true for airports that are heavily influenced by external

Introduction

22

factors. Instead, frontier approaches as SFA and DEA are more appropriate to estimate an

efficient production or cost frontier.

(ii) Stochastic Frontier Analysis (SFA)

Parametric stochastic frontier analysis (SFA) assesses the efficiency of DMUs utilizing

econometric analysis. The parameters of a production or cost function are estimated with

regression analysis or maximum likelihood estimation. The model of the stochastic

production frontier was first introduced by Aigner, Lovell and Schmidt (1977) and Meeusen

and van den Broeck (1977):

iiii uvxy −+= β')ln( (1.02)

where the scalar ln(yi) is the observed output; xi represents a vector of inputs; β is a vector

of technology parameters to be estimated; vi is the stochastic random error and ui is a non-

negative term for managerial inefficiency.

SFA allows for a separation of the unobservable random error from technical inefficiency

and is based on assumptions regarding to the distributional forms of the efficiency function

and error term. However, prior assumptions need to be carefully made as they may heavily

affect the results as discussed by Stone (2002).

Fig. 2: Stochastic frontier analysis

Source: adapted from Coelli et al. (2005)

Input (x)

Deterministic Frontier (xi’β)

Frontier Output

Output (y)

Inefficiency effect (ui)

Observed Output

Noise effect (vi)

a

b

Introduction

23

Figure 2 illustrates the concept of SFA. The deterministic frontier production function is

described by the function ln(yi)=xi’β. If the DMU is technically efficient (ui=0), the frontier

output is given by the point a, which lies above the deterministic frontier due to a positive

noise effect (vi>0)5. The observed output b includes both the existence of technical efficiency

(ui>0) and a noise effect where a position below the deterministic frontier is given because vi

<ui (Coelli et al. 2005).

Based on the initial cross-sectional6 model by Aigner, Lovell and Schmidt, panel data

models are proposed by Battese and Coelli (1992) that allow for time varying inefficiencies.

To further capture unobserved cross-firm heterogeneity unrelated to technical inefficiency,

Greene (2005) introduces an additional model to shift time-invariant effects to unobserved

heterogeneity whereas the inefficiency term varies over time.

Observed heterogeneity either within or beyond managerial control affects the production

technology or the inefficiency. Early models by Battese and Coelli (1995) assume all firms to

operate under same conditions. To overcome this limitation, Coelli, Perelman and Romano

(1999) propose a model where the heterogeneity affects the shape of the production

technology. Hence each firm will be compared with the most favorable production frontier.

This application might be of relevance in the transportation sector as network characteristics

are likely to affect the production technology.  

(iii) Data Envelopment Analysis (DEA)

Non-parametric data envelopment analysis (DEA) measures the relative efficiency of

DMUs utilizing multiple inputs and outputs and will be the major approach applied in this

PhD research. The principle idea of DEA is based on a common ratio measure for assessing

the performance of a DMU, namely dividing the output by an input. In order to consider

multiple inputs and outputs, weights are required to aggregate the variables to virtual inputs

(v1x1o+…+vmxmo) and virtual outputs (u1y1o+…+usyso). The weights of this non-parametric

approach are unknown and calculated from the data using linear programming being solved

for each DMU. The weights are chosen in order to show the specific DMU in as positive a

light as possible, under the restriction that no other DMU, analyzed under the same weights, is

more than 100% efficient (Cooper et al. 2007).

5 A negative noise effect (vi<0) results in a frontier output below the deterministic frontier. 6 Cross-sectional data is a one-dimensional set of variables including N firms in one year whereas panel data contains information on a set of firms over more than one time period.

Introduction

24

By aiming to maximize the weighted combination of outputs to inputs under the constraint

that no DMU exceed the efficiency score, θ, of one, fractional programming can be applied in

order to calculate the input weights, vi, and output weights, ur.

0,

n1,...,j , 1

..

1

1

1

1

≥

=∀≤

=

∑

∑

∑

∑

=

=

=

=

ri

m

iiji

s

rrjr

m

iioi

s

rror

uv

xv

yu

ts

xv

yuMax θ

(1.03)

When maximizing the virtual output and setting the virtual input equal to one (in order to

avoid infinite solutions) fractional programming can be transformed to a linear programming

problem. The second constraint assures that the efficiency score, θ, can not exceed a value

greater then one. The optimal weights calculated in this so-called multiplier form can be

interpreted as shadow prices, hence this form is appropriate when trade-offs between inputs

and outputs are analysed.

0

0

1

..

11

10

1

≥

≤−

=

=

∑∑

∑

∑

==

=

=

ri

m

iiji

s

rrjr

m

iii

s

rror

,uv

xvyu

xv

ts

yuMax θ

(1.04)

The dual of the multiplier form is the envelopment form and is most often applied in

empirical research because it identifies benchmarks for inefficient units.

0,1≥

=

≥

≤

∑θ λ

λy Yλθxs.t. Xλ

θMin

a

a

λ,θ

(1.05)

Introduction

25

where θ is a scalar that estimates the radial contraction of all inputs, i.e. the efficiency

score. λ is a non-negative vector of weights that are determined by the optimization process

and xa and ya are the input and output quantities of DMUo, the airport under investigation. X

and Y represent input and output matrices respectively. Adding the constraint Σλ=1 changes

the assumption of CRS to VRS (Cooper et al. 2007).

DEA was first published in Charnes et al. (1978) under the assumption of constant

returns-to-scale and was extended by Banker et al. (1984) to include variable returns-to-scale.

In contrast to parametric approaches, DEA assumes neither a specific functional form for the

production function nor the inefficiency distribution. Historically, DEA has been primarily

applied to not-for-profit organizations because they could not develop rankings with

profitability indicators. To measure the overall performance, a variety of input and output

quantities have to be covered (Ramanathan 2003). Nowadays, also profit-making

organizations have introduced performance techniques such as DEA to measure the

productivity and efficiency as they concluded that "profit per se is not a good indication of the

potential for improvement within an organization, and because other factors are necessary

for a holistic assessment of performance" (Ramanathan 2003, p.26).

A graphical illustration of DEA is given in Figure 3. With linear programming a Pareto

frontier is attained, bounded by specific DMUs on the envelope of input-output variable

space. The inefficient DMU G is compared to the Pareto frontier with C and D as

benchmarks. In order to become relative efficient and move to G’ on the frontier, G should

radially decrease its inputs.

Fig. 3: Data envelopment analysis

Source: adapted from Coelli et al. (2005)

X2/Y

FE

D

G

G’

X1/Y

C

B

A

Introduction

26

The piece-wise linear frontier which runs parallel to the axes can however cause some

problems to capture all sources of inefficiency. Consider for example DMU A in Figure 3

which was identified to be relative efficient. With the same amount of inputs the DMU could

however produce more outputs in order to operate on the level of DMU B and is known as a

slack problem. These slacks will not be identified in so-called radial models as stated above.

Non-radial models such as the additive model proposed by Charnes et al. (1985) account for

both the desired equi-proportional reductions (expansion) in all inputs (outputs) and any

remaining slacks (Coelli et al. 2005).

Over the years, the basic model is continuously developed. In order to rank efficient

airports and improve the discriminatory power of efficiency estimates, Andersen and Petersen

(1993) introduce the super-efficiency model where airports with rather unique input-output

combinations receive excessively high rankings. A sophisticated approach to reduce the curse

of dimensionality is the principal component analysis (PCA) combined with DEA. PCA-DEA

is applied to replace the original inputs and/or outputs with a smaller group of principle

components (PCs), which explain the variance structure of a matrix of data through linear

combinations of variables with minimal information loss (Adler and Golany 2001, 2002).

Panel data models assess productivity and efficiency changes over time. The most popular

tool is the Malmquist index introduced by Caves, Christensen and Diewert (1982b). Utilizing

DEA with distance functions the approach compares two adjacent time periods with each

other. Different to econometric techniques, non-parametric approaches do not allow for

statistical inference. In order to examine the sensitivity of the estimated frontier,

bootstrapping, a re-sampling technique developed by Efron (1979), is introduced to DEA by

Simar and Wilson (1998, 2000).

Unsurprisingly, numerous studies are concerned to explain efficiency differences across

airports. Amongst other factors ownership forms, hub or size effects and the location are

mostly assumed to substantially impact the airports’ efficiency. Whereas parametric

techniques integrate environmental variables in the production or cost function, DEA may

utilize a two-stage approach where the first-stage efficiency estimates are regressed against a

set of environmental variables in order to evaluate their impact. The advantage of second-

stage approaches is that environmental variables are not included in the DEA model, hence

not affecting the discriminatory power of the first stage.

Introduction

27

1.2 Contribution of this PhD research

Within academic benchmarking a number of studies emerged since the late nineteen-

nineties assessing the productivity and efficiency of airports with DEA, SFA and index

number TFP. To-date DEA proves to be the dominant application requiring neither prior

assumptions on the functional form nor price information to aggregate multiple inputs and

outputs. Common objectives of empirical studies are the examination of efficiency changes

over time or aiming to explain efficiency differences with exogenous factors. Nevertheless,

previous research indicates inconsistencies among the results thereby encouraging future

research. The following section will point out the motivation for this PhD research from the

economic and managerial perspective based on the current literature on airport benchmarking.

Furthermore, the three research articles of this cumulative thesis are summarized thereafter.

1.2.1 Discussions arising from previous research

The wave of airport privatizations in the past two decades motivates the assessment of its

empirical effects however, as in other industries the results are so far rather inconclusive

(Megginson and Netter 2001). Parker (1999) utilizes DEA on the British airports owned by

the BAA covering the periods of pre and post privatization. No evidence is found that full

privatization improves technical efficiency. In contrast, Yokomi (2005) reviews six BAA

airports from 1975 to 2001 utilizing Malmquist DEA. As opposed to Parker, Yokomi finds

that the BAA airports exhibit positive changes in efficiency and technology as a result of the

privatization. The effects of ownership on efficiency are further analyzed by comparing

different ownership forms. Barros and Dieke (2007) analyze 31 Italian airports using DEA in

the first stage and Mann-Whitney hypothesis testing in the second stage, revealing that private

airports operate more efficiently than their partially private counterparts. Lin and Hong (2006)

find no connection between ownership form and efficiency after analyzing a dataset of

worldwide airports utilizing DEA and hypothesis testing. Oum et al. (2006) assess a sample of

100 airports worldwide utilizing variable factor productivity and reach the conclusion that the

productivity of a public corporation is not statistically different from that of a major private

airport. However, airports with major public shares or multiple government involvement

appear to operate significantly less efficiently than other ownership forms. Very often,

changes in ownership form are accompanied by changes towards incentive economic

regulation as for example in Hamburg. Consequently, changes in efficiency may be

attributable to multiple explanations rather than a change in ownership per se. Furthermore

Introduction

28

the competitive environment has changed since airline deregulation thereby putting the

general usefulness of economic regulation into question. Following Vickers and Yarrow

(1991) privatization is not a universal solution and should not be separated from the

economics of competition and regulation which are all determinants of corporate incentives.

While the number of academic benchmarking studies is increasing there is also some

rising resistance especially among airport managers who criticize benchmarking as being of

little use for their business. They are often skeptical towards sophisticated overall quantitative

techniques and prefer the analysis of partial processes. Sarkis and Talluri (2004) propose a

second-stage clustering to identify benchmarks for relatively poor performing airports after

applying DEA. However, not conducting a priori clustering may lead to inappropriate

benchmarks with substantially different resource levels. Referring to Section 1.1.3

geographical constraints, product diversification or the degree of vertical integration are likely

to differ across airports.

1.2.2 Motivation for research

Building on the inconsistencies and discussions from previous research the general

objective of this thesis is twofold. Firstly, the aim is to further the understanding of the airport

industry by exploring the current literature on airport benchmarking and conducting empirical

research. Major emphasis is given on how to explain efficiency differences across airports.

Secondly, the application of airport benchmarking will be improved in order to enhance its

usefulness for managerial, political and regulatory purposes.

This cumulative doctoral thesis is divided into three research articles. The first article

provides a comprehensive survey of the empirical literature of airport benchmarking. It aims

at providing an overview for future research. The quantitative techniques and variables

selected are summarized and discussed, and empirical findings are critically compared.

Thematically, this article heads the empirical research of this thesis as depicted in Figure 4.

Introduction

29

Fig. 4: Structure of this dissertation

The second article applies airport benchmarking from an economic perspective and aims

to explain efficiency differences across Australian and European airports7. Amongst other

exogenous factors, particular emphasis is given on the unresolved questions of the role of

ownership and the necessity of regulation in competitive environments. A semi-parametric

approach is utilized with DEA in the first stage and regression analysis in the second stage.

The third article proposes improvements on the use of benchmarking to identify best-practices

in order to alleviate some of the scepticism of airport managers hold with respect to overall

measurement approaches. An a priori dynamic clustering approach integrated in DEA

7 The list of airports in the sample can be taken from Table 11 in Appendix 3.A, p.98.

Outcome of this dissertation: additional information on efficiency differences across airports and the improvement of benchmarking for managerial and regulatory purposes

Overall research objective of this dissertation: To explore the airports’ heterogeneity with benchmarking and to improve the application of benchmarking to airports

Article 1: Literature Review

Research Question: What can be learnt from existing studies? Methodology: Reviewing the literature with regard to benchmarking methods, data

and results Sample: 58 airport benchmarking studies conducting DEA, SFA and TFP Findings: Steady progress but future research needed to improve comparability

Article 2: Benchmarking from Economic Perspective

Research Question: Does competition matter more than ownership and regulation? What else explains inefficiency?

Methodology: 1st stage: additive DEA 2nd stage: robust cluster regression (OLS, censored and truncated regression)

Sample: 48 European and 3 Australian airports (1998-2007)

Findings: New insights on the role of ownership and regulation

Article 3: Benchmarking from Managerial Perspective

Research Question: Can we really benchmark airports?

Methodology: Network DEA integrating dynamic clustering and PCA

Sample: 43 European airports (1998-2007)

Findings: Improved benchmarking instrument for airport managers and regulators to ensure comparability across airports

Introduction

30

categorizes the airports into homogeneous groups thereby providing appropriate best-practice

airports for inefficient units. This model is applied to a set of European airports8.

Both empirical research articles include a large number of German airports, which have

rarely been under review to-date but offer a rich field for examinations in a European context.

German airports are highly vertically integrated including labour-intensive ground handling

operation whereas airports in most European countries leave this operation to their national

carrier or third party providers. Furthermore, whereas public ownership dominates the

German industry, the airports in Dusseldorf, Frankfurt and Hamburg belong to the first

airports in Europe, which became partly privatized, with equal shares in Dusseldorf and major

public shares otherwise. While cost-plus regulation has been kept at public airports, incentive

regulation was introduced in Hamburg and temporarily in Frankfurt and Dusseldorf with the

intention to increase cost efficiency. However, contrary to the UK where both aeronautical

and non-aeronautical revenues are constrained by the single till approach, the dual till

principle is applied and only aeronautical activities are assumed to possess market power.

Compared to other European countries, Germany is highly populated and airports may face

local competition with nearby airports and other transport modes. Consequently, German

airports appear to differ from other European examples such as non-ex-ante regulated and

fully privatized airports in British monopolistic regions and therefore encourage empirical

research in this respect.

Although this thesis is divided in three independent articles they are connected by the

general purpose to improve the application and usefulness of airport benchmarking. The third

article “Airport Benchmarking from the Managerial Perspective” has been submitted to

Omega and is currently under review. The remaining articles are intended for submission to

leading journals in the fields of regulatory economics such as the Journal of Regulatory

Economics and transportation research such as the Journal of Transport Economics and

Policy. An outline of the articles is presented in the following subsection including main

findings.

8 The list of airports in the sample can be taken from Table 17 in Appendix 4.A, p.131.

Introduction

31

1.2.3 Article 1: A survey of empirical research on the productivity and

efficiency measurement of airports

According to Kincaid and Tretheway (2006) as well as Morrison (2009) the past and

current practice of airport performance analysis lacks in consistency. They state that the clear

definition of an airport model is crucial to understand the industry. Therefore the collection of

essential inputs and outputs describing the airport technology is very important. While

Forsyth (2000) and Graham (2005) provide an overview of a number of airport benchmarking

studies and focus on methodological issues, no comprehensive literature research has been

conducted to-date.

With the publication of a substantial number of benchmarking studies, the aim of this

paper is to provide an overview of previous research that utilizes DEA, SFA and index-

number TFP to assess the productivity and efficiency of airports. In order to further our

understanding of the airport industry this paper reviews how the studies define the airport

model and compares empirical findings with respect to consistency. Further a discussion on

the use of quantitative approaches is provided to examine the progress in airport

benchmarking. A contribution to the literature is a tabular summary covering 60 academic

airport benchmarking studies and a synthesis of inputs and outputs considered in previous

research9.

The survey reveals a number of issues that remain unresolved to-date. Although the

studies aimed to capture the overall performance of an airport, difficulties in gathering

sufficient data on non-aeronautical activities often restrict the model to aeronautical outputs

(passengers, cargo and air transport movements). However, staff employed in commercial

activities is not removed from the sample accordingly. This however may bias the efficiency

results for airports that are highly involved in commercial activities. Generally, data

availability proves to be the most difficult issue in all studies as sufficient data is often not

available to the public. Especially including capital and undesirable outputs such as delay

proves to be difficult however both are crucial for consistent estimations. A comparison of

research findings indicates that whereas increasing commercialization and restructuring lead

to efficiency increases in all studies the findings on ownership and scale effects prove to be

rather inconclusive.

9 see Table 2 to 4 in Appendix 2.A, p. 57 ff. and Figure 10, p. 46.

Introduction

32

Methodologically, the studies indicate substantial progress in utilizing frontier approaches

by considering the heterogeneous character of airports which was found to be essential. DEA

remains the dominant methodology but where large datasets are available an increasing trend

in the utilization of econometric techniques is observed. Overall future research is needed to

further improve airport benchmarking for economic and managerial issues.

1.2.4 Article 2: Joint impact of competition, ownership form and economic

Regulation on airport performance

Whereas previous studies analyze the effects of ownership, regulation and competition

individually, the argument of Button and Weyman-Jones (1992) and Vickers and Yarrow

(1991) is supported in this research that all three factors should be accounted for

simultaneously as their combined impact is likely to affect airport productivity.

The second article aims to discuss whether the deregulation of the airline industry and

changes in airport ownership and management has affected the competitive situation, airport

productivity and pricing behaviour to the extent that the benefits of economic regulation are

potentially unnecessary. Furthermore, such an analysis contributes to the search for the most

desirable combinations.

The dataset covers 48 European airports between 1998 and 2007 and three Australian

airports in a bid to include a sufficiently heterogeneous sample with respect to the ownership

structure, regulatory mechanism and competitive environment. The two-stage analysis

combines DEA in the first stage and regression analysis in the second stage. A semi-

parametric approach is chosen to assess the statistical significance of the exogenous effects.

Banker and Natarajan (2008) demonstrate that two-stage procedures in which DEA is applied

in the first stage and regression analysis in the second stage provide consistent estimators and

outperform parametric one- or two-stage applications. The non-radial additive input-oriented

DEA model is chosen to identify all relative inefficiencies of the inputs. A recent debate in the

literature discusses the most appropriate second stage regression model to be applied when

investigating DEA efficiency estimates. Simar and Wilson (2007) argue that truncated

regression, combined with bootstrapping as a re-sampling technique, best overcomes the

unknown serial correlation complicating the two-stage analysis. Banker and Natarajan (2008)

conclude that simple ordinary least squares, maximum likelihood estimation or Tobit

regression dominate other alternatives. Combining the arguments of Simar and Wilson (2007)

and Banker and Natarajan (2008), robust cluster regression is applied based on ordinary least

Introduction

33

squares in order to account for the correlation across observations. Furthermore, in order to

ensure the robustness of the results, robust cluster Tobit and truncated regressions are also

applied.

The empirical results reveal that under monopolistic conditions, airports should be

regulated to encourage cost efficiency. Dual till price-cap regulation appears to be the most

effective form. Furthermore, airports are likely to exploit market power and set higher

passenger and landing charges. However, gateway or regional competition replaces the need

for economic regulation, thereby supporting the argument that competition rather than

privatization is the key driver of cost efficiency. Nevertheless, unregulated major and fully

private airports within a competitive setting remain profit-maximizers and in this regard may

still require ex-ante regulation.

The regression results prove robust since the outcomes of the robust cluster ordinary least

squares, censored and truncated regressions are very close and the general directions are clear

across all three modelling approaches.

1.2.5 Article 3: Benchmarking airports from a managerial perspective

The majority of studies to date treat the airport technology as a single production process

avoiding the complexity inherent in airport systems as depicted in Figure 5(a). Gillen and Lall

(1997) and Pels et al. (2003) are the first to argue that the airport could be analyzed as two

separate decision-making processes, one serving airside activities and the other serving

landside production (see Figure 5(b)), assuming different returns-to-scale for both sides. Since

the liberalization of the aviation industry however, many airports attempt to increase revenues

from non-aeronautical sources which are not directly related to aviation activities in order to

cross-subsidize aviation charges in turn attracting more airlines and passengers to their airport

(Zhang and Zhang 2010). It is therefore arguable to analyze airports as a single unit due to the

direct complementarities. In order to open the black box of non-parametric approaches

network DEA has been proposed by Färe (1991) and is chosen for this research in order to

separate the complexity of airports into partial production stages.

This research develops a network DEA modelling approach in order to measure the

relative cost and revenue efficiencies of 43 European airports over a ten-year period (1998-

2007) with respect to aeronautical and commercial activities simultaneously, whereby

activities are connected via passengers as the common intermediate product. As illustrated in

Figure 5(c) network DEA recognises the fact that generalized and fixed costs connected to the

Introduction

34

two sets of activities can only be split in an artificial manner and that whilst aeronautical

revenues draw from passengers, cargo and air traffic movements, the non-aeronautical

revenue is more closely tied to passenger throughput. Although airports may have limited

control over traffic volume, non-aeronautical revenues drawn from non-airport related

activities, such as airport cities, are indeed within the purview of airport management.

Fig. 5: Airport production function in DEA

In order to improve the comparability of airports, a dynamic clustering approach (Golany

and Thore 1997) is applied using integer linear programming which forms the reference sets

based on similar mixes of inputs or outputs and intermediate products. Furthermore, the

provision of ground handling is shown to severely affect efficiency estimates leading to a

separation in the comparison of those airports that undertake the process in-house compared

to those that outsource.

Finally, principal component analysis (PCA) combined with DEA (Adler and Golany

2001; Adler and Yazhemsky 2010) is applied in the input-oriented model in order to reduce

the curse of dimensionality and any resulting bias, reducing the set of cost efficient airports

from 53 to 38% in the current application.

A comparison with basic DEA results demonstrates that the additional restrictions in the

network PCA-DEA dynamic clustering formulation lead to more reasonable peer

comparisons, permitting an analysis of strategies which could potentially be adopted over

short and medium term planning horizons. By identifying each airport’s individual reference

set, unique airport outliers influence relative efficiency less severely than occurs under basic

DEA.

(a) Aggregated model

INPUTS All activities (staff, capital,

materials, outsourcing)

OUTPUTS All activities (passengers,

cargo, movements,

non-aeronautical revenues)

(b) Separate model

INPUTS Terminal side (staff, capital,

materials, outsourcing)

INPUTS Airside

(staff, capital, materials,

outsourcing)

OUTPUTS

passengers OUTPUTS

cargo, movements

(c) Network model

INPUTS Both activities

(staff, capital, materials, outsourcing)

INTER-MEDIATE passengers

INTER-MEDIATE

cargo, movements

OUTPUTS Aeronautical

revenues

OUTPUTS Non-aeronautical

revenues

Introduction

35

The model in this research allows airport managers to include their industry knowledge in

the form of limitations on airport size, operating conditions and restricted variability of

capacity encapsulated in the dynamic clustering approach. For example, the results of the

under-utilized airport in Hanover indicate that in the medium-term the airport could either

reduce operations to two of their three existing runways, instead of closing two runways as

obtained with basic DEA. Furthermore, a sufficient number of airports in the data set enable

the application of benchmarking for regulatory purposes. By using an a priori clustering

approach, airports operating under similar conditions may be clustered for analysis thereby

improving the comparison of the airports’ cost level and determining airport charges.

In summary, the methodology provides a number of tools for both exploratory data

analysis and inefficiency estimation, removing the need for additional tests of homogeneity.

1.3 Concluding remarks

With the deregulation of the aviation industry, airport benchmarking became an

important instrument for airports, customers and political institutions. In order to improve its

application a number of academic studies emerged during the last two decades. However,

airports are rather unique in its product diversification and the industry proves to be highly

affected by external heterogeneities that are, at least in the short-term, beyond managerial

control. Hence, meaningful comparison among airports proves to be a difficult task.

The aim of this dissertation was to explore the airports’ heterogeneity with benchmarking

and to improve its application to airports. The comprehensive overview of previous studies

suggests a rather unclear definition of the inputs and outputs that define the production

process to-date and encourages airport stakeholders and academics to undertake further

research. A comparison of empirical findings may give recommendations to airport managers

regarding commercialization and restructuring (in particular ground handling); both proving

to increase the airports’ efficiency.

Empirical research was conducted in this research on European and Australian airports

presenting various DEA models to assess the airports’ efficiency from an economic and a

managerial perspective, including network DEA combined with PCA and dynamic clustering

and the assessment of radial and non-radial models. Regression analyses are utilized to assess

the statistical impact of environmental factors on the DEA efficiency estimates.

Introduction

36

Following previous airport benchmarking studies data availability appears to be most

difficult. It would be extremely helpful if government organizations make data available that

they already collect. Data collection also appears to be a serious issue in this research. After

defining salient variables, the model is substantially reduced in the light of data availability

issues. Nevertheless, the results provide additional information to previous research. The

results of the model combining the impact of ownership, regulation and competition provide

additional information compared to the assessment of individual effects. The empirical results

from this research help to understand the operating behavior of airports under different

institutional settings related to cost efficiency and airport pricing. It therefore provides advice

for policy institutions on the role of ownership and the usefulness of regulation under

different competitive settings. For example the results reveal that for public airports effective

competition provides incentives to operate cost efficient and prevent market power abuse.

Hence administration costs that incur from regulatory procedures may be economized.

Furthermore this dissertation aims to improve the utilization of airport benchmarking from the

managerial perspective. Compared with basic DEA, network DEA formulations combined

with a priori dynamic clustering provide more appropriate benchmarks which enable airport

managers to improve performance in the short and medium-term. In addition, the dynamic

clustering approach might be useful for regulatory purposes in order to determine aeronautical

charges with benchmarking. Capturing external heterogeneities across the airports a priori

ensures a comparison of airports under similar operating environments.

Future research will require substantially more data. For improved managerial

benchmarking, disaggregated data with regard to non-aeronautical activities will help to

identify successful strategies on the commercial side. Further, larger number of airports may

improve the quality of results. Including additional factors, such as scheduling practices, and

political and geographical constraints improves the homogeneity in the clustering approach

and the assessment of efficiency differences across airports. Additional undesirable outputs,

including noise, airport-related delay and air pollution, will enable the development of a social

welfare analysis of airports and the trade-off across the different stakeholders.

Since the liberalization of the aviation industry airport benchmarking has become

increasingly important and will remain a key instrument for managers, political decision

makers and may improve its usefulness for regulatory purposes. Therefore, communication

between management, research and policy in the future is crucial to further improve the

application of airport benchmarking.

37

2 A SURVEY OF EMPIRICAL RESEARCH

ON THE PRODUCTIVITY AND

EFFICIENCY MEASUREMENT OF

AIRPORTS10

Subsequent to the changing nature of the airport industry, benchmarking became popular

for economic and managerial purposes. Within academic benchmarking a number of studies

emerged utilizing parametric and non-parametric approaches. This paper provides a literature

survey on the methods, data and findings of empirical research in order to gain further

understanding of the airport industry. The survey indicates substantial progress in capturing

the heterogeneous character of airports however many issues still remain unresolved. Whereas

increasing commercialization and restructuring contribute to efficiency increases findings on

ownership and scale effects are rather inconsistent. Data availability generally proved to be a

serious issue, in particular the collection of capital inputs and undesirable outputs. To improve

the use of benchmarking airport managers and academics should therefore cooperate and

share their expert knowledge.

10 The author is grateful to Prof. Dr. Hans-Martin Niemeier for helpful discussions and support. The paper is not yet submitted for publication.

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

38

2.1 Introduction

For more than three decades benchmarking serves as an important instrument for

managerial and economic purposes. Benchmarking can be utilized to assess the performance

within and across companies and to estimate productivity and efficiency changes over time.

Hensher and Waters (1993) propose three techniques that are often applied in the overall

productivity and efficiency analysis. Non-parametric index number approaches measure the

total factor productivity (TFP) by aggregating inputs and outputs with market prices.

Parametric approaches such as stochastic frontier analysis (SFA) estimate the efficiency with

regression and separate the error term into random noise and managerial inefficiency. The

non-parametric data envelopment analysis (DEA) utilizes linear programming and divides the

set into relatively efficient and inefficient units without prior knowledge of the functional

relationship between inputs and outputs.

The changing nature of the airport industry during the last decades offers an equally

challenging and interesting objective for applying performance and benchmarking techniques.

Traditionally, airports were managed and regulated as public utilities, which is still present in

many countries. However, in the late eighties a worldwide process of privatization emerged

and was often accompanied by regulatory reforms from rate of return towards incentive and

light-handed regulation. A change in the management style towards commercialization led to

substantial investments in non-aeronautical activities. Furthermore vertical and horizontal

boundaries have changed over time. While some airports outsourced their labour-intensive

activities such as ground handling others have remained highly integrated either as a separate

airport or within an airport system organized as a civil aviation authority. Moreover,

competition between airports began to arise. Some airports like Manchester or London-

Stansted face effective competition from airports in their catchment area. With the

deregulation of the airline industry the airports in Pittsburgh or Brussels lost their hub carrier

and experienced competition with other hub airports. The liberalization of bilateral air service

agreements offered the opportunity to attract international traffic to gateway airports and

secondary hubs. In order to meet future demands, congested airports needed to expand their

capacity and introduce new technologies to increase runway and terminal capacities. However

at many major airports the excess demand was rationed rather inefficiently through queuing

and slot allocation mechanisms increasing the number of non-weather related delays. For

these reasons, airports offer a rich field for performance and benchmarking analyses.

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

39

The application of airport benchmarking is manifold (Kincaid and Tretheway 2006).

Airport managers utilize benchmarking to identify best-practice standards and to develop new

concepts for performance improvements. Customers, shareholders and investors are interested

in using benchmarking as decision-making instrument. National and regional governments

aim to promote their region or assess the effects of political decisions such as privatization.

As proposed by Shleifer (1985) benchmarking may serve for regulatory purposes, widely

known as yardstick competition. This form of regulation implies virtual competition amongst

regulated firms by comparing their cost levels and determining the permitted price based on

an average level. Whereas yardstick competition evolved to a standardized approach in the

British water and railway industry it has rarely been applied to airports to-date11. The Civil

Aviation Authority (CAA) in the UK explains this reluctance with the heterogeneous

character of airports and the challenge to find appropriate data (CAA 2000).

In order to improve benchmarking for practical use academic research continuously aims

to refine quantitative techniques. Since the late Nineties, a number of academic research

studies emerged utilizing quantitative approaches to assess the productivity and efficiency of

airports. Kincaid and Tretheway (2006) as well as Morrison (2009) however challenge the

past and current practice of airport performance analysis for inconsistencies and its limited

value to managers. They state that the clear definition of an airport model is crucial to

understand the industry. Therefore the collection of consistent inputs and outputs is very

important. In addition, different techniques and the airports’ heterogeneous character may

lead to different results across the studies. The last argument is discussed in a response to

Morrison (2009) by Adler et al. (2009) as especially econometric approaches have

substantially been developed to account for observed heterogeneity across decision making

units (DMU). From our point of view, the debate has not resolved all issues and will continue;

also because airport managers still prefer to use simple partial measures while academics

utilize sophisticated overall productivity and efficiency approaches.

In other transport sectors that are traditionally under public ownership we find

comprehensive surveys by Oum et al. (1999) on the rail sector, de Borger et al. (2002) on

public transport and Gonzalez and Trujillo (2009) on seaports. While Forsyth (2000) provides

an overview on a few airport benchmarking studies and Graham (2005) focuses on

11 To the best of our knowledge, the Dublin Airport Authority (DAA) is the only European example that has been subject to regulation with yardstick competition by the Commission Aviation for Regulation in 2001. However, it was highly criticized by the airport for inappropriate peer airports that have been identified (Reinhold et al. 2010).

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

40

methodological issues, comprehensive research has not been undertaken to-date that

summarizes the empirical studies and discusses the findings. With the publication of a

substantial number of academic benchmarking studies, the aim of this paper is therefore to

provide an overview of the current literature that utilizes DEA, SFA and TFP to assess the

productivity and efficiency of airports. In order to further our understanding of the airport

industry this paper reviews the variable selection in previous studies and compares empirical

findings. Furthermore, a discussion on the use of quantitative approaches is provided to

examine the progress in airport benchmarking. A contribution to the literature is a tabular

summary covering 60 academic airport benchmarking studies and a synthesis of inputs and

outputs considered in empirical research. The survey reveals a number of issues that remain

unresolved to-date. Whereas increasing commercialization and restructuring consistently lead

to efficiency increases, findings on ownership and scale effects prove to be rather

inconclusive. Furthermore, consistent capital measures and undesirable outputs are crucial for

analyses to prevent an overestimation of the efficiency results but are often problematic to

obtain. Although the studies indicate substantial progress in utilizing frontier approaches by

considering the heterogeneous character of airports, more research is needed to improve

airport benchmarking for regulatory purposes and to alleviate some of the scepticism of

airport managers.

The paper is organized as follows; Section 2.2 introduces the methodology of performance

measurement and reviews their application in airport benchmarking studies. Section 2.3

discusses the inputs and outputs that have been used. Section 2.4 continues with a comparison

of findings from research studies. Special attention will be drawn on the results of

productivity and efficiency changes over time, ownership and scale effects. Concluding

remarks are given in Section 2.5 on the issues that still remained unresolved.

2.2 Productivity and efficiency measurement concepts

In the media productivity and efficiency are often used interchangeably. However, this is

somewhat misleading as both terminologies are defined differently. The following section

introduces the meaning of productivity and efficiency and provides an overview of

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

41

measurement techniques. We then continue with the concepts of price index-based

approaches, DEA and SFA as the dominant approaches in the field of airport benchmarking12.

2.2.1 Productivity and efficiency

The productivity of an airport may be calculated as the ratio of output(s) per input(s).

Partial productivity measures divide for example the number of passengers by the number of

employees. In order to include the capital productivity and assessing total factor productivity,

multiple inputs and outputs are aggregated to an index. Technical efficiency on the other hand

defines the comparison of the observed outputs (inputs) to its optimal values while holding

the inputs (outputs) constant. The seminal paper on efficiency by Farrell (1957) defines three

different forms of efficiency. A firm is technically efficient if it operates on the production

frontier. According to Figure 6, airport P is technically inefficient and ought to change its

input/output combination in order to reach the frontier at Q. An airport that chooses the input

mix which produces a given output quantity at minimum costs is said to be allocative efficient

and operates at R. Technical and allocative efficiency in combination define the economic

efficiency where the firm is perfectly efficient and operates at Q’ (Coelli et al. 2005).

Fig. 6: Technical, allocative and economic efficiency

  Source: adapted from Coelli et al. (1998)

Over the years a number of quantitative techniques emerged which assess the productivity

and efficiency of decision making units (DMU) such as airports (see Figure 7). The one-

dimensional approach is the simplest form to assess the productivity by dividing one output

by one input. However this measure should be treated with caution. As discussed by Forsyth

12 This paper does not explain the methodological approaches in detail. For further information we suggest the following literature: Coelli et al. (2005) and Fried et al. (2008) for an overview of productivity and efficiency analysis and Kumbhakar and Lovell (2000) and Cooper et al. (2007) for an advanced application of SFA and DEA respectively.

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

42

et al. (1986) partial measures should only be applied if data for overall measures are not

available. Results obtained from partial measures can mislead as they fail to capture

substitution effects between different inputs. For this reason academic research (Graham and

Holvad 2000; Oum et al. 2003, 2004) use partial productivity measures only in addition to

overall approaches.

Fig. 7: Quantitative methods in productivity and efficiency analysis

Productivity and Efficiency Analysis

One-dimensional

Multi-dimensional

Partial Performance

Average Approaches

Frontier Approaches

Non-Parametric(index numbers)

Parametric (Deterministic)

Parametric (Stochastic)

Non-Parametric (Deterministic)

Total Factor Productivity

(TFP)

Ordinary Least Squares (OLS)

Stochastic Frontier Analysis

(SFA)

Data Envelopment

Analysis (DEA)

Source: adapted from von Hirschhausen and Cullmann (2005)

In order to gain an overall measure of the airport’s performance multi-dimensional

approaches should be applied instead which can be distinguished in frontier and average

approaches. The most popular average approach is linear regression with ordinary least square

(OLS). The availability of price information enables the utilization of price index-based

number (PIN) approaches to measure the total factor productivity. Anyhow average

techniques assume that all DMUs operate efficiently. Frontier approaches in contrast estimate

the efficient production or cost function where an airport that deviates from the frontier

appears to be inefficient. The pioneering work on efficiency by Farrell (1957) was taken up

by Charnes, Cooper and Rhodes (1978) who developed data envelopment analysis based on

linear programming and inspired the econometricians Aigner, Lovell and Schmidt (1977) to

assess the technical efficiency with stochastic frontier analysis.

2.2.2 Price-based index number approaches

The application of index-number approaches is most common in measuring price and

quantity changes over time. A popular economic indicator is the consumer price index (CPI),

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

43

measuring price changes of goods and services over time. In order to measure total factor

productivity with index numbers, market prices are required to weight and aggregate multiple

input and output quantities. However, information on market prices is often not available, and

instead cost and revenue shares are used. An advantage of index-number approaches is the

provision of meaningful results with only two observations because the productivity is not

assessed relative to other units. The Törnqvist index is widely used in economic studies

however the index is restricted to time-series analyses. Caves, Christensen and Diewert

(1982a) proposed a multilateral translog index known as the CCD index to compare the TFP

of a set of airports over different years:

∑ ∑

∑ ∑

−+−−+−

−+−−+=

−−−=

i iiijiijiikiik

i iiijiijiikiik

jkjkkj

XXWWXXWW

YYRRYYRR

XXYYTFP

)ln)(ln(21)ln)(ln(

21

)ln)(ln(21)ln)(ln(

21

)ln(ln)ln(lnln

(2.01)

where Yik and Rik are the output quantity and the revenue share for output i of DMU k; iR

is the arithmetic mean of the revenue share and iY is the geometric mean of output i over the

entire sample. Xik is the input quantity and Wik is the input cost share for input i of DMU k; iW

is the arithmetic mean of cost shares and iX the geometric mean of input i over the entire

sample.

To-date a limited number of airport benchmarking studies apply index number

approaches. Nyshadham and Rao (2000), Hooper and Hensher (1997) and Vasigh and

Gorjidooz (2006) utilize the CCD index to measure the airport’s TFP, while Oum and Yu

(2004) and Oum et al. (2006) assessed the variable factor productivity (VFP) with the same

index but without capital input. Instead of market prices all studies included cost and revenues

shares as weighting factors.

As aforesaid, index numbers assume technical efficiency for all observations which is

unlikely to be true for airports. Instead environmental constraints, ownership forms, the

regulatory procedure or the market structure which are all exogenous to the airport

management are expected to affect the efficiency. Alternatively non-parametric frontier

approaches may be applied to assess TFP changes. The most popular model in airport

benchmarking is the DEA-based Malmquist index decomposing TFP changes into pure

technical efficiency, scale efficiency and the technological changes. Airport studies utilizing

Malmquist DEA will be introduced in the following section.

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

44

2.2.3 Data envelopment analysis

The majority of airport benchmarking studies apply data envelopment analysis. An

advantage of this approach is that multiple inputs and outputs are aggregated without price

information. Furthermore, DEA neither require the specification of a production or cost

frontier nor assumptions of the distribution of the error term. The mathematical framework of

DEA has first been proposed by Charnes, Cooper and Rhodes (1978) to assume constant

returns-to-scale (CRS) and has been extended by Banker, Charnes and Cooper (1984) to

include variable returns-to-scale (VRS). With linear programming, DEA compares each DMU

to the efficient set of observations, with similar input and output ratios. This non-parametric

approach solves the linear programming formulation for each DMU and the weights assigned

to each linear aggregation are the results of the corresponding linear program. The weights are

chosen in order to show the specific DMU in as positive a light as possible, under the

restriction that no other DMU, analyzed under the same weights, is more than 100% efficient.

Variable returns-to-scale have mostly been assumed as the size of an airport may substantially

differ within the sample set (Abbott and Wu 2002; Barros 2008a; Bazargan and Vasigh 2003;

Fernandes and Pacheco 2002). Gillen and Lall (1997) who divided the airport into terminal

and airside activities suggest VRS for passengers and CRS for movements. Furthermore, a

number of studies compared the results of CRS and VRS assumptions in order to obtain the

level of scale efficiency (Abbott and Wu 2002; Barros and Dieke 2007; de la Cruz 1999; Lam

et al. 2009; Murillo-Melchor 1999). Formulation (2.02) presents an input-oriented model

assuming variable returns-to-scale.

0,1≥

=

≥

≤

∑θ λ

λy Yλθxs.t. Xλ

θMin

a

a

λ,θ

(2.02)

where θ is a scalar that estimates the radial contraction of all inputs. λ is a non-negative

vector of weights that are determined by the optimization process and xa and ya are the input

and output quantities of DMUo, the airport under investigation. X and Y represent input and

output matrices respectively. Adding the constraint Σλ=1 changes the assumption of CRS to

VRS (Cooper et al. 2007).

Over the years, the basic model has been continuously refined. It includes the formulation

of non-radial models which reflect all inefficiencies (including slacks) identified in the inputs

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

45

and outputs, the estimation of efficiency changes over time, the improvement of the

discriminatory power of efficiency estimates, the formulation of weight restrictions, the

introduction of network DEA to open up the black box of DEA, the identification of outliers

and the introduction of statistical inference to DEA (Cooper et al. 2007). Figure 8 summarizes

DEA applications which are utilized in airport efficiency studies.

Fig. 8: Models in data envelopment analysis

Data EnvelopmentAnalysis

Cross-section,pooled

PanelData

Additionalapplications

Basic DEA(CCR, BCC)

Other models(e.g. SBM, FDH,

additive, network DEA)

Malmquistindex

WindowAnalysis

Statistical inference

(e.g. Bootstrapping)

Ranking methods(super-, cross-

efficiency)

Improve Discrmination(e.g. PCA-DEA)

Abbott and Wu (2002), Barros and Sampaio (2004),

Bazargan and Vasigh (2003), de la Cruz (1999), Fernandes

and Pacheco (2002), Gillen and Lall

(1997), Martín and Román (2001), Pacheco and

Fernandes (2003), Pacheco et al. (2006), Parker

(1999), Pels et al. (2003), Pels et al.

(2001), Vogel (2006), Yoshida and

Fujimoto (2004)

Holvad and Graham (2000), Lam et

al. (2009), Adler and Liebert (2010), Adler

et al. (2010)

Abbott and Wu (2002), Barros and

Assaf (2009), Barros and Weber (2009), Chi-Lok and Zhang (2008), Fung et al. (2008), Gillen and

Lall (2001), Murillo-Melchor (1999),

Yokomi (2005)

Yu (2004) Assaf (2010a), Barros (2008a),

Barros and Dieke (2008),

Barros and Dieke (2007), Lin and Hong

(2006), Martín and Román (2008),

Martín and Román (2006), Sarkis

(2000), Sarkis and Talluri (2004)

Adler et al. (2010)

From the beginning, data collection appeared to be a serious issue and the sample size was

mostly rather small. However, a low ratio of observations to the number of inputs and outputs

weakens the discriminatory power of DEA. For example, Parker (1999) assesses the technical

efficiency of the BAA as a single unit over a period of seventeen years with three inputs and

two outputs. He reveals an average efficiency score of 96%, where nine years ought to be

efficient. In order to rank efficient airports, Andersen and Petersen (1993) introduce the super-

efficiency model where airports with unique input-output combinations receive excessively

high rankings and are identified as outliers. Several airport studies apply models to rank

efficient airports in order to improve the discrimination (Adler and Berechman 2001; Sarkis

and Talluri 2004; Barros and Dieke 2007). In order to avoid too many efficient airports

Bazargan and Vasigh (2003), Lam et al. (2009) and Martín and Román (2006) include a

virtual efficient airport which possesses the lowest input and highest output values of the

sample. A sophisticated approach is principal component analysis (PCA) integrated in DEA.

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

46

PCA-DEA is applied to replace the original inputs and/or outputs with a smaller group of

principle components (PCs), which explain the variance structure of a matrix of data through

linear combinations of variables with minimal information loss (Adler and Golany 2001,

2002). Adler et al. (2010) utilizes PCA-DEA to reduce four inputs to two PCs which explain

more than 85% of the variance in the original data.

Panel data models allow the assessment of productivity and efficiency changes over time.

The most popular tool within DEA is the Malmquist index which was introduced by Caves,

Christensen and Diewert (1982b). Distance functions compare two adjacent time periods and

TFP changes are decomposed into pure technical efficiency change, scale efficiency change

and technological change. Malmquist DEA is applied in a number of studies in order to

disentangle technical and efficiency changes (see Figure 8). An alternative to account for

efficiency changes over time is window analysis where different sets of pooled data with

overlapping time periods are assessed to observe a trend in efficiency changes. This model is

a trade-off between solving one aggregate model with pooled data and estimating each time

period separately. Window analysis is utilized by Yu (2004) with a two-year window.

A number of studies are concerned with explaining efficiency differences across airports.

Amongst others ownership forms, hub or size effects and the location are assumed to

substantially impact the relative efficiency results. DEA offers various models to assess the

impact of exogenous effects on the efficiency estimates. The majority of DEA studies utilize a

second-stage regression where the first-stage DEA efficiency estimates are regressed against a

set of environmental variables in order to evaluate its significance. The advantage of second-

stage approaches is that environmental variables are not included in the DEA model, hence

not affecting the discriminatory power of the first-stage. A number of studies adopted

(censored) Tobit regression where the DEA estimate is censored at the value of one (Abbott

and Wu 2002; Barros and Sampaio 2004; Chi-Lok and Zhang 2008; Gillen and Lall 1997).

Arguing that DMUs only appear to be relative efficient due to biased estimations Simar and

Wilson (2007) proposed truncated regression which drops the efficient units from the sample.

This recent approach has been adopted by Barros (2008a) and Barros and Dieke (2008).

Non-parametric Mann-Whitney and Kruskal-Wallis tests assess the significance of

efficiency differences among various groups. Bazarghan and Vasigh (2003) apply both tests

to assess efficiency differences between public and private airports and Graham and Holvad

(2000) conduct Mann-Whitney tests on Australian and European airports. Adler et al. (2010)

utilize the four-step program evaluation procedure by Brockett and Golany (1996) and

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

47

Sueyoshi and Aoki (2001) which incorporates a Kruskal-Walis test in the last stage to assess

efficiency differences between ground handling providing and non-providing airports.

DEA normally treats the production technology as a black box where no functional

relationship is assumed between input and output quantities. To overcome this shortcoming,

Färe (1991) and Färe and Grosskopf (1996; 2000) propose network-DEA which allows an

analysis of the optimal production structure of DMUs, to determine both efficient subsystems

and overall efficiency. Adler et al. (2010) extended the idea of Gillen and Lall (1997) to

separate both operational sides for the assumption of different scale effects. The efficiency of

aeronautical and commercial activities is estimated separately in a single model and both

sides are connected via intermediate products.

Furthermore, DEA does not allow for statistical tests. The estimated technical efficiency

only depends on the observed sample. It may be a matter of concern to examine the sensitivity

of the estimated frontier and how the efficiency estimates correspond to changes in the

sample. The introduction of bootstrapping, a re-sampling technique developed by Efron

(1979), offers statistical inference and hypothesis testing into DEA as proposed by Simar and

Wilson (1998, 2000). In airport studies bootstrapping is still in its early stages. Assaf (2010a)

applied DEA combined with bootstrapping to evaluate and test scale efficiency among UK

airports and found differences between original and bootstrapped results.

2.2.4 Stochastic frontier analysis

An advantage of SFA over deterministic approaches is that it does not purely explain

inefficiency as mismanagement rather than considering a stochastic random error which

accounts for a not observable relation between the inputs and the output. The parametric

frontier approach was first independently proposed by Aigner, Lovell and Schmidt (1977) and

Meeusen and van den Broeck (1977):

iiii uvxy −+= β')ln( (2.03)

where the scalar ln(yi) is the observed output; xi represents a vector of inputs; β is a vector

of technology parameters to be estimated; vi is the stochastic random error and ui is the term

for managerial inefficiency. Over the years the basic model has been continuously refined. It

includes the introduction of panel data models that in addition may capture unobserved and

observed heterogeneity. Furthermore, distance functions rather than traditional Cobb-Douglas

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

48

or translog production functions allow multiple outputs in a single production model. Figure 9

illustrates the most important models that are applied to airports.

Fig. 9: Models in stochastic frontier analysis

Stochastic FrontierAnalysis

Cross-section,pooled

PanelData

Time-invariant

Time-varying

Chow and Fung 2009 (M;T;P) Pels et al. (2001)

Homogeneous function

Homogeneous function,

unobserved heterogeneity

Heterogeneous function

Heterogeneous function,

unobserved heterogeneity

Barros (2008c), Oum et al. (2008)

Assaf (2009b), Barros (2008b),

Martín et al. (2009), Martín and Voltes-

Dorta (2009), Pels et al.

(2003),Tovar and Martín-

Cejas (2010), Tovar and Martín-

Cejas (2009)

Assaf (2008) Barros (2009), Barros and Marques

(2008)

In early stages of airport benchmarking SFA has rarely been utilized due to its sensitivity

to small sample sizes and the requirement to specify a functional relationship (Assaf 2010a).

Today, datasets become larger and its application has been established. Initial studies in SFA

estimated a production function with physical input data of the airport infrastructure such as

the number of gates or the terminal size in order to assess the efficient use of an airport (Pels

et al. 2001, 2003). Nowadays, it is often emphasized to collect operating and capital cost data

in order to assess the cost efficiency (Assaf 2010b; Martín et al. 2009; Martín and Voltes-

Dorta 2007). Allowing for more flexibility, translog functions are preferred over Cobb-

Douglas functions (Barros 2009; Chow et al. 2009; Martín et al. 2009; Oum et al. 2008; Tovar

and Martín-Cejas 2009). Input distance functions are utilized by Tovar and Martín-Cejas

(2009, 2010) however output distance functions are not considered to-date which we find an

important issue for an industry producing multiple aviation and commercial outputs. Instead

the outputs are aggregated to operational income (Assaf 2008) or separated into different

output models (Gillen and Lall 1997; Pels et al. 2003).

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

49

Whereas Chow et al. (2009) examine cross-sectional data with 46 observations the

remaining studies utilize panel data models in order to obtain an increasing number of

observations. Panel data models are initially developed by Pitt and Lee (1981) and Schmidt

and Sickles (1984) where the inefficiency is assumed to be time-invariant. To relax this

restriction, Cornwell, Schmidt and Sickles (1990) and Battese and Coelli (1992) introduce

time-varying inefficiency models where the latter model is dominantly applied in airport

benchmarking (Assaf 2010b; Chow et al. 2009; Pels et al. 2003; Tovar and Martín-Cejas

2009, 2010). To capture cross-firm heterogeneity which is not related to technical inefficiency

Greene (2005) further refines the model of Schmidt and Sickles (1984) and Battese and Coelli

(1992). Time-invariant firm-specific effects are allocated to a parameter explaining

unobserved heterogeneity whereas the inefficiency term allows a variation over time.

Unobserved heterogeneity has been considered in airport studies by Barros (2008c) and Oum

et al. (2008). Based on the formulations of Battese and Coelli (1992) the latent class model by

Orea and Kumbhakar (2004) conducts a clustering of the data set into different classes where

the class membership remains unknown to the analyst. Barros (2009) applies this formulation

on UK airports by defining his classes according to the market share based on passenger

volume.

In contrast to unobservable heterogeneity, observed heterogeneity is reflected in

exogenous effects that contribute to inefficiency. An important issue in parametric research is

whether exogenous factors impact the production technology or the inefficiency term. Early

studies in this area assume a homogenous production technology for all airports as proposed

by Battese and Coelli (1995) where environmental variables affect the inefficiency (Chow et

al. 2009; Pels et al. 2003; Tovar and Martín-Cejas 2009). Heterogeneous models that account

for different production technologies across airports were for instance applied by Oum et al.

(2008). They assume ownership to affect the technical efficiency whereas the shares of

international traffic and cargo are likely to shift the cost function. Assaf (2008) utilizes a

meta-frontier approach by O’Donnell et al. (2007) accounting for technological differences

between small and large airport in UK. The airport’s efficiency is estimated against its own

technology and a meta-frontier which envelopes the technology of both small and large

airports.

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

50

2.2.5 Comparison of techniques

Reviewing the different methodologies introduced in Section 2.2.2 to 2.2.4 it appears that

the decision for a technique remains difficult (see Table 1). DEA and SFA offer various

formulations and indicate substantial progress since their introduction in the late Seventies. In

general, frontier approaches appear to be appropriate for airport benchmarking because they

do not assume that all airports operate efficiently. Further, they do not necessarily require

market prices to aggregate multiple inputs and outputs. If utilizing benchmarking as a

management instrument in order to identify best practice standards, DEA provides

information on composite benchmarks which help to reach the target inputs or outputs. A

major drawback of DEA and SFA however is the requirement of large datasets in order to

obtain fairly robust efficiency estimates. In addition, DEA is very sensitive to outliers as the

efficient frontier is constructed by the data points. Although SFA benefits from the separation

of random noise from managerial inefficiency it requires prior information how to disentangle

the stochastic error and how to specify the functional relationship between inputs and outputs.

Both assumptions may heavily affect and bias the results as argued by Stone (2002).

Tab. 1: Comparison of DEA, SFA and PIN properties

Category DEA SFA PIN Method and specifications

Parametric approach No Yes No Assumes that all airports are efficient No No Yes Accounts for random noise No Yes No Types of measurement:

- Technical efficiency Yes Yes No - Allocative efficiency Yes Yes No - Technical change Yes Yes No - Scale effects Yes Yes No - TFP change Yes Yes Yes

Data requirements Type of data:

- Cross-sectional Yes Yes Yes - Time series No No Yes - Panel Yes Yes Yes

Robust estimates with small number of observations No No Yes Variable requirement:

- Input and output quantities Yes Yes Yes - Input and output prices No No Yes

Can include exogenous variables Yes Yes No Results

Provides full ranking No Yes Yes Provides composite benchmarks Yes No No Conventional hypothesis testing No Yes No

Source: own compilation adapted from Coelli et al. (1998; 2003; 2005)

In price index number approaches in contrast two observations already provide

meaningful productivity measures. However, it requires information on market prices to form

an input and output index which are often not available for airports. To-date DEA has proven

A Survey of Empirical Research on the Productivity and Efficiency Measurement of Airports

51

to be mostly been applied in airport benchmarking which may be explained with the

requirement of fewer assumptions than SFA and the use of multiple inputs and outputs

without price information.

In order to ensure the reliability and verifiability of efficiency estimations the German

regulator for the electricity sector Bundesnetzagentur applies both DEA and SFA (Reinhold et

al. 2008). However, based on empirical studies that compare the efficiency estimates of DEA

and SFA, the results are rather inconsistent (see Ferrier and Lovell 1990 and Bauer et al. 1998

for the banking sector).

In short, the choice of the technique heavily depends on various criteria including the

availability of data, the object of research and prior knowledge of the airport technology that

may be included in the estimations. The following section will continue with a review on the

choice of inputs and outputs in airport benchmarking studies.

2.3 Variable selection in airport studies

Following Coelli et al. (2003, p.83) “Irrespective of which methodology […] -PIN, SFA or

DEA- it cannot avoid the first rule for empirical economics: garbage in = garbage out”. The

data included in a formulation should clearly explain the underlying airport technology. We

find a broad consensus that capital, labour, materials and other external services are

theoretically necessary to handle traffic volume and selling non-aeronautical products. The

following section discusses the inputs and outputs that are included in academic airport

benchmarking studies so far (see Figure 10).

2.3.1 Outputs

Prior airline deregulation airports were often considered as public utilities focussing on

the provision and operation of the infrastructure that handles passengers, cargo and aircrafts

as common outputs13. Hence the objective of an airport was to offer a good level of service

irrespective of commercial and financial purposes. Early studies therefore often limited their

evaluation to an efficient use of the airside infrastructure (Abbott and Wu 2002; Fernandes

and Pacheco 2002; Gillen and Lall 1997; Martín and Román 2001; Murillo-Melchor 1999;

13 Martín et al. (2009) and Martín-Cejas (1999) for example further combine passengers and cargo to work load units (WLU) however, given that both are different in their charging, use of resources and yield in revenues they should not be seen as an equal output to the airport.

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Pels et al. 2001) thereby ignoring non-aviation activities. The deregulation affected the airport

industry by increasing airport competition, changes in ownership forms and the shift towards

incentive and light-handed regulation. Airports began to operate as commercial enterprises

where financial targets became more important. A regulated non-congested airport in a

competitive environment may seek to maximize commercial revenues in order to cross-

subsidize aeronautical charges and thereby attract passengers and airlines to their airport

(Zhang and Zhang 2010). Monopolistic and unregulated private airports may be interested in

optimizing overall income. Unless a quantity index is designed, non-aeronautical products are

expressed in revenues (Barros 2008b; Bazargan and Vasigh 2003; de la Cruz 1999; Hooper

and Hensher 1997; Oum et al. 2006; Pacheco et al. 2006; Yokomi 2005) and are an inherent

part of an airport to-date. Therefore it is crucial to consider this activity in overall airport

analyses unless the input side can be clearly separated into aeronautical and non-aeronautical

activities.

Similar to manufacturing companies airport generate undesirable by-products. Noise and

pollution affect the environment and delays decrease the service quality. Consequently, they

contribute to a decrease in the airport’s performance. Pathomsiri et al. (2008) analyse US

airports and include delays as a negative (undesirable) output. Their results clearly indicate

that ignoring the quality of airport services would otherwise overestimate technical efficiency

gains of airports with higher utilization. To the best of our knowledge information on delays

such as the average delay per movement are not publicly available for a large sample of

European airports. However, the costs of delays to an airline have been estimated by Cook et

al. (2004) and may be considered as a negative output to airports that cause heavy delays.

Furthermore, Yu (2004) integrates aircraft noise as an undesirable output and again concluded

that undesirable outputs severely affect the technical efficiency of airports.

2.3.2 Inputs

Input quantity measures on the airport infrastructure were already available in early stages

of airport benchmarking where financial targets remained secondary. The number of

employees is collected as an operating input, and the numbers of gates, runways or the

terminal size are collected to measure capital. Materials and other outsourced services such as

ground handling are usually expressed as other operating expenditures unless designing a

quantity index (Assaf 2008; Bazargan and Vasigh 2003; Hooper and Hensher 1997; Lam et

al. 2009; Martín et al. 2009; Oum et al. 2003; Pacheco and Fernandes 2003; Sarkis 2000).

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Fig. 10: Inputs and outputs in previous airport benchmarking studies

The consideration of staff varies among the studies. Arguing that the airport industry is

rather heterogeneous, Pels et al. (2001, 2003), Yoshida (2004) and Fung et al. (2008) ignore

labour in their model to avoid an inappropriate comparison between vertically integrated

airports and airports that outsource labour-intensive operations. The number of employees is

mostly reported in annual reports or other public sources and therefore used by a number of

Inputs

Labour • No. of employees

o Heads o Ordered by qualification

• No of. full time equivalents • Staff costs

Capital • Physical infrastructure

o Airport area o Car parking area, no. of car

parking spots o Passenger terminal area, no. of

passenger terminals o No. of check-in desks, area

departure lounge, no. of gates o Baggage claim area, no. of

baggage collection belts o Cargo handling facility area o Runway area, runway length, no.

of runways o Apron area, no. of terminal

bridges, no. of remote stands • Monetary measure

o Capital stock (PIM) o Capital value (book value) o Capital invested o Interests on net assets o Amortization o Rate-of-return on net capital stock

• Capacity measure o Terminal capacity (no. of

passengers per hour) o Declared runway capacity (no. of

movements per hour)

Other • Average access costs • Distance to city centre

Outputs

Desirable outputs Aeronautical side

• Physical measure o No. of passengers

Domestic International

o No. of air transport movements (ATM) Commercial Commuter, general aviation,

military o Work load units (WLU)

• Monetary measure o Aeronautical revenues

Landing revenues Passenger revenues Parking revenues Handling revenues

• % of on-time operations Non-aeronautical side

• Non-aeronautical revenues

Undesirable outputs Delays:

• Delayed air transport movements • Time delays

Noise:

• Aircraft noise (surcharge)

Materials and outsourcing • Other operating costs

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studies (Abbott and Wu 2002; Lin and Hong 2006; Murillo-Melchor 1999; Sarkis 2000;

Tovar and Martín-Cejas 2009; Yokomi 2005; Yoshida and Fujimoto 2004). This figure

however does not distinguish between full-time and part-time employees and may bias results

among airports with different vertical structures. Other studies collected information on full-

time equivalents but this figure is often publicly not available (Assaf 2010a; Martín and

Voltes-Dorta 2007; Oum et al. 2008). Information on staff costs were utilized either in

combination with staff quantities to design an index (Assaf 2010b; Barros 2008b; Martín et al.

2009; Oum et al. 2008) or to differentiate between unskilled, skilled and management staff

(Hooper and Hensher 1997; Martín and Román 2001). Cross-country comparisons however

require adjustments for different staff costs levels.

Airports are a typical example for an industry with lumpy investments such as the runway

system (Golaszewski 2003). Consequently, capital plays a major role in airport

benchmarking. For example, capital requires consideration in the examination of economies

of scale, in order to assess an efficient use of airport infrastructure, for regulatory purposes

and to decide investment projects. The measurement of capital input however appears to be a

serious issue in airport benchmarking. Due to lack of data Oum and Yu (2004) and Oum et al.

(2006) ignore capital and assess the variable factor productivity instead. A number of studies

collect physical information of the infrastructure such as the number of runways, gates and

check-in counters (Barros 2008a; Gillen and Lall 1997; Pels et al. 2003; Oum et al. 2003).

This capital measure seems to be appropriate in order to assess the technical efficiency and for

cross-border studies thereby avoiding adjustments of financial data by different national

accounting procedures. However a simple linear aggregation of these variables is problematic,

for example because the number of runways does not include information on the

configuration, weather impacts or environmental restrictions. As an example the runway

system substantially varies in their configuration thereby making a comparison of the number

of runways problematic. To overcome this problem the terminal and declared runway

capacity can be used as a proxy of capital for estimating the technical efficiency (Adler et al.

2010). The advantage is that a capacity measure considers the entire infrastructure

configuration in one measure and improves comparability. Studies aiming to examine

financial issues measure the monetary value of physical capital. Various studies included

amortization and interests or the book value of fixed assets as a measure of capital costs

(Barros and Weber 2009; Martín and Voltes-Dorta 2007) however different depreciation

procedures and expected useful lives across the countries may affect the results. For example,

the airports of the British Airports Authority depreciate their runways over 100 years whereas

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the airports operated by the Aéroports de Paris apply a 10 to 20 year depreciation rule

(Graham 2005). To improve comparability among the monetary value of physical capital,

Abbott and Wu (2002) and Hooper and Hensher (1997) apply the perpetual inventory method

(PIM) proposed by Christensen and Jorgenson (1969) for Australian airports to estimate the

gross fixed capital stock. The approach estimates the costs of historical capital investments

which are not fully depreciated yet and adjusts the value by inflation. Yet collecting data on

past capital investments and information on expected useful life and depreciation methods are

very time-consuming, especially for cross-border studies.

In summary, supporting Kincaid and Tretheway (2006) and Morrison (2009) the selection

of inputs and outputs needs to be carefully considered. It should be attempted to include

inputs that are connected to all outputs defined in the model. An imbalance would otherwise

distort the results. Ground-handling for example is a labour intensive operation where the

output is measured in revenues. DEA-studies that restrict the output side to airside activities

(i.e. passengers, cargo and movements) will automatically obtain substantially lower

efficiency estimates for airports providing ground handling unless staff employed in this

activity is removed from the input side. According to Adler et al. (2010) ground-handling

providing airports appear 10% less cost efficient than its outsourced counterparts if revenues

generated from ground handling are not included but staff is not adjusted accordingly.

So far, we discover that the empirical studies on airport benchmarking apply various

parametric and non-parametric approaches with different underlying assumptions of the

airport production technology. Furthermore the variables to describe the airport substantially

vary and are often subject to data availability. Consequently, we are concerned that different

approaches and variables may reveal inconclusive results. The following section continues

with a comparison of empirical findings from airport studies.

2.4 Empirical results of productivity and efficiency studies

The object of research varies among the research studies. In order to evaluate an efficient

use of the airport infrastructure the technical efficiency is assessed with physical input and

output quantities. Productivity and efficiency changes over time are estimated in order to

capture technological progress. Furthermore, it is often aimed to explain efficiency

differences across airports with factors that are at least in the short-term beyond managerial

control (e.g. geographical and environmental constraints or political restrictions). Consistent

findings are revealed on the impact of increasing commercialization and outsourcing both

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affecting the airport in a positive direction (Adler et al. 2010; Oum et al. 2006; Tovar and

Martín-Cejas 2009). Other scopes of analysis appeared to be inconclusive as we illustrate in

the following. This section compares the findings on productivity and efficiency changes and

the effects of ownership and size, all appearing to be frequently considered in efficiency

analyses.

2.4.1 Productivity and efficiency changes over time

Productivity and efficiency increases may be explained with an efficient use of the airport

infrastructure, innovations or changes in political decisions which aim to encourage

performance increases such as privatization and incentive regulation. The majority of studies

find positive changes mostly explained with technological improvements due to airport

investment programmes.

Assaf (2010b) utilizing SFA discovers cost efficiency increases in Australia since their

complete privatization in 2002 which he explains with the introduction of a light-handed price

monitoring that encourages investments and innovations at airports. Abbott and Wu (2002)

reveal technical progress prior privatization (1990-2000) due to advanced computer and air

traffic systems. The results from the Malmquist-DEA model point out that capacity expansion

is unneeded in the near future. Inconsistent outcomes were received for the British airport

market. Whereas Yokomi (2005) reveals an improvement post BAA privatization (1975-

2001), Barros and Weber (2009) find an average efficiency decrease between 2000 and 2004;

both utilizing Malmquist-DEA. However, given their period under review the declining trend

is not surprising where staff and other operating costs may have increased disproportionately

high to passengers, cargo and movements in the aftermath of the terror attacks in New York.

Furthermore, non-aeronautical activities have been ignored by Barros and Weber. Positive

changes are assessed for Chinese airports by Chi-Lok and Zhang (2008) and Fung et al.

(2008) conducting similar estimations between 1995 and 2004 with DEA. Fung et al. report

an annual productivity growth of 3% which is mainly explained with technical progress.

Gillen and Lall (2001) utilizing Malmquist-DEA find high productivity growth for terminal

side operations at US airports which however do not necessarily imply high growth rates for

the airside production and vice versa. On the terminal side Gillen and Lall identify hub and

gateway hubs as innovative airports by introducing fully automated baggage handling

systems. Technological progress is further revealed in the SFA estimation by Tovar and

Martín-Cejas (2009) for Spanish airports between 1993 and 1999. They explain

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improvements as an outcome of the massive investment programme by the airport operator

AENA. Murillo-Melchor (1999) in turn concludes negative changes in Spain from 1992 to

1994 utilizing Malmquist-DEA. However positive technical changes coincide with efficiency

decrease in the first year comparison and vice versa in the year after.

2.4.2 Empirical effects of ownership

With the intention to reduce government involvement, to minimize costs and to maximize

productivity, a wave of airport privatizations began in the late Eighties in UK. Most European

countries followed to partially privatize their airports in the mid Nineties (Gillen and

Niemeier 2008). Reviewing the theoretical literature on privatization, its effects seem to be

somewhat controversial. Sappington and Stiglitz (1987) and Shapiro and Willig (1990)

support public ownership due to lower transaction costs and less asymmetric information.

Opponents of this point-of-view sought evidence to demonstrate that state intervention leads

to inefficiency as discussed by Shleifer and Vishny (1994) arguing with incomplete contracts.

Empirical studies attempting to assess the effects of ownership on the efficiency of

airports are so far rather inconclusive. Two different opportunities occur to consider

ownership as efficiency driver. The first study in this field is an analysis of privatization

effects of BAA airports. Parker (1999) estimates the technical efficiency prior and after

privatization (1979-1996) with basic DEA. He finds no evidence that privatization has

improved the airport’s technical efficiency and concludes that the golden share which is kept

by the government does not induce enough capital market pressures. Further he argues that

BAA is still subject to economic regulation and it may be argued whether incentives to

operate more efficiently can be distorted by government regulation. In contrast, Yokomi

(2005) reviews the technical and efficiency change of 6 BAA airports from 1975 to 2001

utilizing Malmquist-DEA. Different to Parker he finds that BAA airports have improved after

their privatization exhibiting positive changes in technical efficiency and technology. In

particular on the non-aeronautical side, the growth after privatization is substantial; this

activity is not considered in Parker’s analysis. However, before and after comparisons are

often problematic as privatizations are often accompanied with changes in the regulation or

restructuring processes such as outsourcing.

Other studies assess the effects of ownership by comparing the efficiency of public and

private airports. Again the results do not reach a clear conclusion. Lin and Hong (2006), Oum

et al. (2003) and Vasigh and Gorjidooz (2006) measure the effects of ownership on a

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worldwide set and reveal no significant relationship for financial and operational efficiencies.

Oum et al. (2003) argue that the extent of managerial autonomy dominates the effect of

ownership. Furthermore, Vasigh and Haririan (2003) argue that privatized airports intend to

maximize their revenues whereas public airports aim to optimize traffic. Barros and Marques

(2009) find in their SFA estimation that private airports operate more cost efficiently than its

partial private counterparts. Furthermore, Oum et al. (2006) and Oum et al. (2008) are in

favour of privatization conducting index-number VFP and SFA respectively. In contrast to

previous studies they separate airports owned by one public shareholder from airports with

multilevel government involvement. Referring to Charkham (1995) they argue that different

ownership and governance structures can affect the quality of managerial performance. Oum

et al. (2006) reach the conclusion that public corporation are not statistically different from

major private airports. However, airports that are major publicly owned or have multiple

government involvement seem to operate significantly less efficient from the other ownership

forms. Oum et al. (2008) conclude that airports with major private shareholders are more

efficient than public airports or airports with major public influence. The results by Vogel

(2006) on a European set of airports reveal that privatized airports operate more cost efficient

and receive higher returns on total assets and revenues. Public airports in turn enjoy the

advantage of higher gearing and financial leverage.

According to Vickers and Yarrow (1991), privatization can not be seen as a universal

solution and should not be separated from the economics of competition and regulation which

are all determinants of corporate incentives. Airport benchmarking studies on the effects of

regulation and local competition are rare to-date. Barros and Marques (2008) utilizing SFA

reveal that regulatory procedures contribute to cost savings worldwide. Oum et al. (2004)

study alternative forms of regulation to assess the TFP including differences between single

till and dual till approaches and are in favour of a dual till price-cap regulation. Chi-Lok and

Zhang (2009) consider the impact of local competition in China which is likely to contribute

to efficiency improvements. In order to search for the most efficient ownership and regulation

form given the level of local and hub competition Liebert and Adler (2010) combine all

factors in an Australian-European semi-parametric two-stage research. The study concludes

that under monopolistic conditions, airports of any ownership form should be subject to

economic regulation. However, regulation can be replaced by effective competition in order

to ensure cost efficiency. Furthermore, public and major or fully private airports appear to

operate equally cost efficient. Hence, no clear answer reveals on the ownership form in

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competitive situations, thereby indirectly supporting the inconsistent impact of ownership in

studies assessing ownership individually.

2.4.3 Scale effects

Benchmarking airports of different sizes can raise the question of how to eliminate the

effects of size for a multi- product ‘airport’ firm. According to Graham (2004) after the

airport reaches the size of about 3 to 5 million passengers, economies of scale effects flatten

out, so that for benchmarking of medium and large sized airports the size does not matter.

However, various benchmarking studies lead to different conclusions on the scale effects at

airports14.

The British market has been assessed numerous times. Doganis and Thompson (1973)

include UK airports in a regression and find decreasing average costs up to three million

WLU. Tolofari et al. (1990) estimate a long-run translog cost function of seven UK airports

owned by the BAA and find that scale economies exhaust at a level of 20.3 WLUs. However,

this result needs to be treated carefully as London-Heathrow is the only large airport in their

sample. Main et al. (2003) utilizing OLS conclude sharp decreasing costs up to 4 million

passengers and 5 million WLU and weak decreasing costs up to 64 million and 80 million

passengers and WLU respectively. On a worldwide sample they reveal that economies of

scale exhaust at a level of 90 million WLU. Jeong (2005) finds economies of scales at US

airports to exhaust at three million passengers. Different to all other studies under review,

capital is not considered due to lack of data. However non-aeronautical activities were

included thereby capturing a more complete picture of an airport. Keeler (1970) further

assesses US airports with OLS and concluded that scale is not the main source of inefficiency.

Furthermore, Doganis et al. (1995) conduct regression analysis on European airports and find

decreasing average costs up to five million WLU.

Martín and Voltes-Dorta (2007) applied SFA on a worldwide sample and conclude that

even Atlanta and Chicago as the two largest airports in the world operate under increasing

returns-to-scale. Pels et al. (2003) research the European market and reveal in their SFA

estimation for an average airport decreasing returns-to-scale from 12.5 million passengers for

the airside (i.e. movements) but increasing returns on the terminal side (i.e. passengers).

Similar conclusions are reached by de la Cruz (1999) utilizing DEA on Spanish airports in an

14 The studies under review in this section assessed both economies of scale and returns to scale.

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aggregated model considering aeronautical and non-aeronautical activities. He finds constant

returns-to-scale between 3.5 and 12.5 million passengers and decreasing returns-to-scale from

then. In contrast, Martín et al. (2009), utilizing SFA find that all Spanish airports operate at

increasing returns-to-scale. However, different to de la Cruz, they restrict the airport model to

airside activities.

In summary, reviewing the rather inconsistent empirical findings from previous studies, it

becomes clear that different underlying assumptions from the methodology as well as

different inputs and outputs may lead to mixed results. Public and private airports may pursue

different targets and the object of research may influence the results of ownership impacts.

Furthermore, ownership should not be separated from the impact of economic regulation or

competition. In addition, commercial activities may contribute to efficiency improvements

and should implicitly be included. In order to assess the level of economies of scale and

returns-to-scale, regression analyses only permit single outputs unless combining passengers

and cargo to work load units (WLU). Hence the majority of studies failed to capture a

complete picture of an airport. In addition, non-aeronautical activities are mostly ignored

thereby inducing an imbalance on the input and output side where staff is not adjusted

accordingly.

2.5 Conclusion

Since the late Nineties a number of empirical research studies emerged on the productivity

and efficiency analysis of airports with overall quantitative methods such as DEA, SFA or

PIN. Studies proved to mostly assess economic rather than managerial issues. With the

publication of a meanwhile substantial number of benchmarking studies in the academic

literature, this paper aimed to provide an overview of the methodology applied, the variables

selected and the findings on productivity and efficiency changes, scale and ownership effects

in order to learn from previous research.

Frontier approaches have been preferred over productivity measures with index numbers

in order to account for efficiency differences across airports. Both efficiency techniques, DEA

and SFA, have substantially improved over the years and its usefulness for the airport

industry has steadily increased. This regards in particular the consideration of the airports’

heterogeneous character. DEA as a non-parametric technique proved to be mostly utilized

requiring fewer assumptions than SFA however its application has increased since recently.

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Comparing the findings of different studies proved to be difficult. The application of

different methods and data are likely to affect the results on the relative efficiency.

Nevertheless, efficiency improvements from increasing commercialization, restructuring and

technological progress appeared to be fairly consistent. Ownership and scale effects proved to

be inconclusive showing that further research is required.

The collection of appropriate data proved to be a serious issue in airport benchmarking.

Especially early studies ignored non-aeronautical activities in order to assess the airside’s

efficiency thereby obtaining biased efficiency estimates where the input side was not adjusted

accordingly. Another aspect is the measurement of capital which appears to be crucial.

Although the airport industry is typical for being capital intensive with lumpy investments

various studies failed to include capital appropriately or ignored this figure due to lack of

data. Undesirable outputs such as delay or noise proved to be important in efficiency analysis

to prevent an overestimation of efficiency results.

In order to conduct managerial benchmarking we suggest to assess airports with similar

input/output combinations in order to ensure comparability. Management strategies as the

degree of vertical integration or airport characteristics like the traffic structure and size need

to be carefully considered. Furthermore, airports operating under different scheduling

practices may have different declared capacities. Hence a comparison of slot-coordinated

airports with uncoordinated counterparts needs to be treated with caution. Due to lack of

detailed information, all studies considered the overall airport system rather than focussing on

partial processes which airport managers often find more informative. In order to improve the

modelling and its application for political and managerial purposes we argue that airport

managers should contribute with their industry knowledge. In summary, the area of

benchmarking appears to be a valuable instrument for airport managers, governments and

regulators but future research is needed to improve its utilization.

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2.A Appendix15

Tab. 2: Studies using non-parametric approaches

15 For abbreviations see legend at end of the tables.

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Tab. 3: Studies using parametric approaches

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Tab. 4: Studies using price-based index approaches

72

3 JOINT IMPACT OF COMPETITION,

OWNERSHIP FORM AND ECONOMIC

REGULATION ON AIRPORT

PERFORMANCE16

The combined impact of ownership form, economic regulation and local and gateway

competition on airport performance is analyzed using data envelopment analysis in a first

stage efficiency measurement and regression analysis in a second stage environmental study.

The results of an analysis of European and Australian airports over a ten-year period prove to

be stable across different robust cluster regression models and show that airports not facing

regional or hub competition should be regulated to increase cost efficiency. However, in a

competitive setting, economic regulation inhibits airports of any ownership form from

operating efficiently. On the other hand, unregulated major and fully private airports act as

profit-maximizers even within a competitive setting by charging higher aeronautical revenues

than those that are regulated.

16 The author is grateful to Dr. Nicole Adler for helpful discussions and support. Paper is not yet submitted for publication.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

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3.1 Introduction

Historically, airports were mostly deemed state-owned entities with the objective to

provide and operate infrastructure for airlines. Being often viewed as natural monopolies with

large economies of scale airports were subject to economic regulation in order to prevent

abuse of market power. However, the nature of the airport industry has changed over the last

two decades. Moving away from viewing the airport as a public utility, airports have begun to

operate as modern enterprises pursuing commercial objectives. A number of privatization

processes have been actively promoted by governments with the proclaimed intention of

reducing government involvement and increasing airport productivity and innovation.

However, given the assumed profit-maximizing behaviour of private companies working in a

natural monopolistic environment, the majority of privatized airports in Europe remain

subject to economic regulation (Gillen 2010).

Whilst some studies have analyzed the impact of ownership form, regulatory regime and

level of competition from nearby airports on efficiency and airport pricing, none have

examined their joint impact. In other words, the literature has yet to discuss whether the

deregulation of the airline industry and changes in airport ownership and management has

affected the competitive situation, airport pricing and efficiency to the extent that the benefits

of economic regulation are potentially unnecessary. For example, deregulation has led to

increased competition between gateway hubs (e.g. Frankfurt and Amsterdam) and former

military airports have opened to serve low cost carriers within the catchment area of existing

airports (e.g. Hahn in Germany), in turn substantially changing the downstream airline market

and potentially impacting the airport market too. Furthermore, as a result of increasing

commercialization, many airports have augmented their revenues from non-aeronautical

sources in order to cross-subsidize aviation charges and attract additional airlines and

passengers to their airport (Zhang and Zhang 2010). The aim of this research is therefore to

analyze the impact of the structural changes in the aviation markets on airport efficiency and

pricing in order to further our understanding of the most appropriate ownership form and

regulatory regime given the level of regional and hub competition at a specific airport.

Performance measurement may serve multiple purposes, as outlined by Oum et al. (1992).

It may assess the productivity or efficiency of units within or across companies or industries

and identify best-practice standards. Furthermore, the availability of panel data permits the

measurement of changing levels of productivity over time. Although the instrument of

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

74

performance measurement was applied in other transport sectors and regulated utilities in the

nineteen seventies, it only became of primary importance in the airport industry twenty years

later. Graham (2005) argues that the increasing interest in airport benchmarking is a result of

the changes in ownership that began in 1987 with the privatization of BAA and the

liberalization, commercialization and globalization trends which have influenced airport

business growth, complexity and competitiveness.

Three well-documented quantitative methods have been applied to analyze the

productivity and efficiency of government and private enterprises. A non-parametric, index

number approach has been used to measure total factor productivity (Caves, Christensen and

Diewert 1982a), however this approach requires input and output prices and quantities which

are not always available. Parametric stochastic frontier analysis (SFA) assesses efficiency

utilizing regression analysis and disentangles unobservable random error from technical

inefficiency (Aigner, Lovell and Schmidt 1977; Meeusen and van den Broeck 1977) based on

assumptions as to the distributional forms of the efficiency function and error term. Non-

parametric data envelopment analysis (DEA), based on linear programming, categorizes data

into efficient and inefficient groups hence produces weaker results than those of SFA, but

does not require assumptions with respect to a functional form therefore is chosen for the

purposes of this study. Airport studies of efficiency utilizing all three approaches are reviewed

in Liebert (2010).

Various environmental variables that, at least in the short-term, are beyond managerial

control may affect the DEA efficiency estimates. Previous research argues that airport

characteristics such as hub status or traffic structure, outsourcing policies, regulatory

procedures and ownership structure all may contribute to airport efficiency (Gillen and Lall

1997; Oum et al. 2006). Assessing the importance of the environment on the efficiency

estimates may be undertaken utilizing either non-parametric Mann-Whitney and Kruskal-

Wallis tests or parametric regression. Banker and Natarajan (2008) demonstrate that two-stage

procedures in which DEA is applied in the first stage and regression analysis in the second

stage provide consistent estimators and outperform parametric one- or two-stage applications.

Published airport studies apply simple ordinary least squares (Chi-Lok and Zhang 2009),

Tobit regression (e.g. Gillen and Lall 1997; Abbott and Wu 2002) and truncated regression

(Barros 2008a) for this purpose. A recent debate in the literature discusses the most

appropriate second stage regression model to be applied when investigating DEA efficiency

estimates. Simar and Wilson (2007) argue that truncated regression, combined with

bootstrapping as a re-sampling technique, best overcomes the unknown serial correlation

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

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complicating the two-stage analysis. Banker and Natarajan (2008) conclude that simple

ordinary least squares, maximum likelihood estimation or Tobit regression dominate other

alternatives. Combining the arguments of Simar and Wilson (2007) and Banker and Natarajan

(2008), we apply robust cluster regression based on ordinary least squares in order to account

for the correlation across observations. Furthermore, in order to ensure the robustness of the

results, we also apply robust cluster truncated and censored regressions.

The second stage analysis of this research considers the impact of ownership form,

economic regulation and levels of local and hub competition amongst other factors. Several

empirical studies to date have assessed the effects of privatization on airport efficiency.

Parker (1999), utilizing data envelopment analysis, argues that the privatization of BAA had

no effect on subsequent efficiency. Oum et al. (2006), applying variable factor productivity

argues that private majority ownership and pure government ownership are equally efficient

and both are strictly preferable to government majority ownership or multi-tiered government

ownership. Oum et al. (2004) analyze different regulatory regimes and conclude that total

factor productivity is maximized under dual till price-caps rather than single till price-caps or

rate of return regulation. Chi-Lok and Zhang (2009), utilizing data envelopment analysis on a

Chinese airport dataset, reach the conclusion that the intensity of airport competition at the

level of the local catchment area likely encourages greater productivity.

Whereas previous studies analyze the effects of ownership, regulation and competition

individually, we support the argument of Button and Weyman-Jones (1992) that all three

factors should be accounted for simultaneously as their combined impact is likely to affect

airport efficiency. Such an analysis may contribute to the search for the more desirable

combinations and may indicate whether effective competition from nearby airports or

gateway competition replaces the need for economic regulation. In addition, we examine the

combined impact on aeronautical revenues generated from passengers and movements in

order to understand the pricing behaviour of airports under different institutional settings. Bel

and Fageda (2010), who assessed 100 large airports in Europe, argue that local competition

decreases the abuse of market power whereas private unregulated airports tend to charge

higher prices than public and regulated airports.

The dataset in this research consists of European and Australian airports in order to

include a sufficiently heterogeneous sample with respect to the ownership structure,

regulatory mechanism and competitive environment. The empirical results reveal that under

rather monopolistic conditions, airports should be regulated to encourage cost efficiency and

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

76

dual till price-cap regulation appears to be the most effective regulatory form. Furthermore,

airports of any ownership form under monopolistic conditions are likely to abuse market

power and set higher aeronautical charges. However, gateway or regional competition

replaces the need for any form of economic regulation, thereby supporting the argument of

Vickers and Yarrow (1991) that competition rather than privatization is the key driver of

efficiency. Nevertheless, unregulated major and fully private airports within a competitive

setting remain profit-maximizers and in this regard may still require ex-ante regulation.

The paper is organized as follows: the theoretical and empirical literature discussing

ownership form, economic regulation and competition is presented in Section 3.2; Section 3.3

introduces the methodology and model specifications, Section 3.4 discusses the dataset for the

two stages of analysis, the results are presented in Section 3.5 and conclusions and directions

for future research are suggested in Section 3.6.

3.2 Literature on competition, regulation and ownership

The neoclassical theory of the firm states that competition leads to increased productive

and allocative efficiency as a result of lower prices and higher outputs. In the case of

indivisibilities, as typically occurs in the provision of infrastructure based services and

utilities, one large firm might be able to produce at lower costs leading to monopolistic

conditions. In this case, in order to encourage efficiency and avoid abuse of market power, the

natural monopolist should be subject to economic regulation (Lipczynski et al. 2009).

In Europe17, airport charges have traditionally been regulated according to a rate of return

or cost-plus principle (Reinhold et al. 2010). Such regulation permits airports to generate

sufficient revenue to cover total expenditures, including the depreciation of capital and an

expected rate of return on capital. However, according to Averch and Johnson (1962), this

form of regulation may lead to overcapitalization which does not engender productive

efficiency. To solve the problem of overinvestment, Littlechild (1983) proposes an incentive

based price-cap regulation. Price-caps are generally set over a regulatory period of five years

according to the RPI-X formula where RPI represents the retail price index and X is the

efficiency improvement that the regulators consider reasonable within the timeframe. If the

airport management achieves greater cost reductions over the five year period, the gains are

enjoyed by the company. In the case of airports, the single till principle is applied in the UK,

17 Gillen and Niemeier (2008) provide a comprehensive overview of the current economic regulation at European airports.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

77

in which case both aeronautical and non-aeronautical revenues are constrained. Over the

years, price-cap regulation has been emulated by other European authorities however, unlike

the UK model, a dual till approach is applied whereby aeronautical revenues alone are subject

to regulation (Gillen and Niemeier 2008). Compared to traditional rate of return regulation, a

price-cap creates incentives for cost savings hence encourages efficiency, however it equally

may lead to underinvestment on the part of firms with heavy infrastructure sunk costs.

Consequently, it may be necessary to regulate in order to ensure a reasonable level of quality

with respect to the products or services offered. Another approach to stimulate efficiency is

yardstick competition originally proposed by Shleifer (1985). This form of regulation implies

virtual competition amongst regulated firms by comparing their cost levels and determining

the permitted price based on an average level. Common approaches utilized to assess

appropriate cost levels for regulated firms include frontier techniques such as DEA and SFA.

In addition, the cost function should be corrected to take into account external heterogeneities.

Factors, such as geographical constraints, may affect airport costs but are considered to be

beyond the control of the airport management. Whereas yardstick competition evolved to a

standardized approach in the British water and railway industries, it has rarely been applied to

airports so far. To the best of our knowledge, the Dublin Airport Authority (DAA) is the only

European example that attempted to implement yardstick competition in 2001. However, it

was highly criticized by airport management for identifying inappropriate peer airports

(Reinhold et al. 2010) and was discontinued. The British CAA argues that the heterogeneous

character of airports and the challenge to obtain appropriate data contribute to their reluctance

to apply this type of economic regulation (CAA 2000).

In the theoretical literature, the debate as to the necessity for and type of airport regulation

seems to be rather controversial. Gillen and Niemeier (2008) argue in favour of price-cap

regulation but also that commercial and ground handling activities may be disciplined to some

extent by potential competition, hence the dual till price-cap approach is preferable. Czerny

(2006) argues that market power exists in both the aeronautical and commercial spheres of

activity. For non-congested airports, he suggests that the single till outperforms dual till price-

cap regulation in maximizing social welfare. For large, congested airports, Beesley (1999)

argues that the single till is inappropriate because increasing concession profits would lead to

lower airport charges over time. In addition, Starkie (2002) finds no evidence of economies of

scale for airports with large throughput and argues that demand complementarities across

aeronautical and terminal activities will prevent airports from abusing market power,

obviating the need for any regulation. In particular, airports generating additional revenues

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

78

from non-aeronautical activities are likely to lower their charges and cross-subsidize using

commercial revenues in order to attract both passengers and airlines (Zhang and Zhang 2010).

To the best of our knowledge, the impact of regulation on efficiency and airport pricing

has only been empirically assessed in few papers. Barros and Marques (2008) incorporate a

dummy variable defining cost-plus or price-cap regulation in order to assess a worldwide set

of airports from 2003 to 2004, estimating a heterogeneous cost frontier utilizing stochastic

frontier analysis. They conclude that regulatory procedures contribute to cost savings. The

study by Oum et al. (2004) collected data on worldwide airports for the years 1999 and 2000

and applying gross endogenous-weight total factor productivity. They carefully study various

forms of regulation including differences between single till and dual till concepts. The results

indicate that airports under dual till price-cap regulation tend to have higher levels of gross

total factor productivity than those with a single till price-cap or those that operate under the

single till rate of return regulation. Furthermore, dual till approaches together with rate of

return regulation appear to provide incentives to improve efficiency but are very complex to

estimate. Bel and Fageda (2010) examine the impact of privatization, regulation and regional

and intermodal competition on airport charges at European airports in 2007. Utilizing

regression analysis, they reveal that competition with nearby airports and other transport

modes is likely to decrease the potential to abuse market power. Furthermore, private

unregulated airports charge higher prices than public and regulated airports thereby

supporting the analytical findings of Oum et al. (2004). Van Dender (2007) assessed the US

market between 1998 and 2002 utilizing an econometric approach and similarly concluded

that airports under regional competition charge lower fees. He also argued that slot-

constrained airports are likely to charge higher aeronautical fees which are explained by the

airport management’s ability to capture scarcity rents.

With the stated aim of reducing government involvement, minimizing costs and

maximizing productivity, a wave of airport privatizations began in the late Eighties in the UK.

Due to successful initial public offerings and increasing share prices, many European

countries began to partially privatize their airports in the mid Nineties (Gillen and Niemeier

2008). Reviewing the theoretical literature on privatization, its effects seem to be somewhat

controversial. Sappington and Stiglitz (1987) argue that the transaction costs of government

intervention are lower under public ownership. In a similar vein, Shapiro and Willig (1990)

argue that the government is better informed and more capable of regulating state-owned

firms. Opponents of this point-of-view sought evidence to demonstrate that state intervention

leads to inefficiency. Shleifer and Vishny (1994), for example, argue that the relationship

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

79

between politicians and managers is governed by incomplete contracts leading to inefficient

incentives. In addition, the emergence of partially privatized models complicates the debate as

to the effects of ownership on productivity. Boardman and Vining (1989) review the effects of

mixed ownership structures based on theoretical arguments and empirical studies. They

conclude that large, industrial, partly privatized and state-owned companies perform in a less

productive and profitable manner than their fully private counterparts, which may be caused

by the public and private shareholders’ differing objectives. Considering the issue to be more

complex, Vickers and Yarrow (1991) argue that privatization is not a universal solution to the

agency problem in the public sector and should not be separated from the economics of

competition and regulation which are all determinants of corporate incentives.

Empirical studies that attempt to assess the effects of ownership on the efficiency of

airports are so far rather inconclusive. Parker (1999) utilizes DEA to estimate the technical

efficiency of the BAA airports between 1979 and 1996 covering the period pre and post

privatization. No evidence is found that complete privatization leads to improved technical

efficiency and he concludes that the UK government’s golden share limits the impact of

capital market pressures. Furthermore, he argues that BAA remained subject to economic

regulation hence incentives to operate more efficiently are distorted as a result of government

intervention. In contrast, Yokomi (2005) reviews the technical and efficiency change of six

BAA airports from 1975 to 2001 utilizing Malmquist DEA. As opposed to Parker, Yokomi

finds that the BAA airports exhibit positive changes in efficiency and technology as a result of

the privatization. It should be noted that commercial growth after privatization was

substantial; however this activity is not considered in Parker’s analysis.

The effects of different ownership forms on efficiency were also analyzed but again the

results have not reached clear conclusions. Barros and Dieke (2007) analyze 31 Italian

airports from 2001 to 2003 using DEA in the first stage and Mann-Whitney hypothesis testing

in the second stage, to reveal that private airports operate more efficiently than their partially

private counterparts. However, Lin and Hong (2006) find no connection between ownership

form and efficiency after analyzing a dataset of worldwide airports for the years 2001 and

2002 utilizing DEA and hypothesis testing. Oum et al. (2006, 2008) distinguish between

public airports owned by public corporations and those owned by more than one public

shareholder (multilevel). Referring to Charkham (1995), they argue that different ownership

and governance structures affect the quality of managerial performance. Oum et al. (2006)

assess a sample of 100 airports worldwide covering the years 2001 to 2003 utilizing variable

factor productivity. They reach the conclusion that the productivity of a public corporation is

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

80

not statistically different from that of a major private airport. However, airports with major

public shares or multiple government involvement operate significantly less efficiently than

other ownership forms. Oum et al. (2008) estimate a heterogeneous translog cost function

with stochastic frontier analysis on a similar set of airports as that of Oum et al. (2006),

measuring cost efficiency between the years 2001 and 2004. The authors conclude that

airports with major private shareholders are more efficient than public airports, particularly

those with a major public ownership structure.

The traditional perspective of airports behaving as monopolists has changed as a result of

the deregulation of the downstream aviation industry according to Tretheway and Kincaid

(2010). Today, competition for airport services covers a multiplicity of markets including (1)

a shared local catchment area, (2) connecting traffic through regional hubs and international

gateways, (3) cargo traffic, (4) destination competition, (5) non-aeronautical services, (6)

competing ground handling companies and off-site car parks and (7) alternative modes of

transport such as high speed rail in the medium distance markets. Amongst the empirical

literature, only Chi-Lok and Zhang (2009) examine the effects of regional competition

utilizing a Chinese airport dataset for the years 1995 to 2006. After applying DEA in the first

stage and ordinary least squares in the second-stage, they conclude that airports operating in a

locally competitive environment tend towards efficiency. However, the outcome of a Tobit

regression found competition intensity to be insignificant.

In summary, whereas research to date has analyzed the individual effects of ownership,

regulation and competition on efficiency, the joint impacts may be of great interest as argued

in Button and Weyman-Jones (1992, p.440) that “[t]he degree of competitiveness in a firm's

market, the extent to which it is incorporated as part of a public-sector bureaucracy, and the

nature of the regulatory regime under which a firm operates are all primary sources of

possible X-inefficiency”. Consequently, our intention is to assess the combined impact of

ownership structure and economic regulation (or lack thereof) given relevant levels of local

and hub competition. We argue that the choice of ownership form and the regulatory

procedures instituted are clearly within the bounds of public policy initiatives over the

medium term, whereas the competitive environment remains more costly to change.

3.3 Methodology and model specification

The following section presents the weighted additive DEA model (Lovell and Pastor

1995) which we apply in the first-stage analysis in order to account for both the desired equi-

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

81

proportional reductions in all inputs and any remaining slacks. We then discuss the second-

stage regression specifications in which the DEA efficiency estimates are regressed against

environmental factors.

3.3.1 Data envelopment analysis

DEA is a non-parametric method of frontier estimation that measures the relative

efficiency of decision-making units (DMUs) utilizing multiple inputs and outputs. DEA

accounts for multiple objectives simultaneously without attaching ex-ante weights to each

indicator and compares each DMU to the efficient set of observations, with similar input and

output ratios, and assumes neither a specific functional form for the production function nor

the inefficiency distribution. DEA was first published in Charnes et al. (1978) under the

assumption of constant returns-to-scale and was extended by Banker et al. (1984) to include

variable returns-to-scale. This non-parametric approach solves a linear programming

formulation per DMU and the weights assigned to each linear aggregation are the results of

the corresponding linear program. The weights are chosen in order to show the specific DMU

in as positive a light as possible, under the restriction that no other DMU, analyzed under the

same weights, is more than 100% efficient. Consequently, a Pareto frontier is attained,

marked by specific DMUs on the boundary envelope of input-output variable space. Charnes

et al. (1981, p.668) described DEA as a “mathematical programming model applied to

observational data [which] provides a new way of obtaining empirical estimates of extremal

relations – such as the production functions and/or efficient production possibility surfaces

that are a cornerstone of modern economics”.

The weighted additive model (Charnes et al. 1985; Lovell and Pastor 1995), chosen for its

units and translation invariance properties, reflects all inefficiencies identified in the inputs.

The input oriented model is chosen because we assume that airport managers control

operational costs and to a lesser extent airport capacities, but have less control over traffic

volume. By comparing n units with q outputs denoted by Y and r inputs denoted by X, the

efficiency measure for airport a is expressed as in model (3.01).

0,, 1

-- ..

,

≥=

−=

=−

σλλ

σλ

λσ

s e

XXYsYts

swMax

a

a

t

s

(3.01)

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

82

In (3.01), λ represents a vector of DMU weights chosen by the linear program, wt a

transposed vector of the reciprocals of the sample standard deviations, e a vector of ones, σ

and s vectors of input and output slacks respectively and Xa and Ya the input and output

column vectors for DMUa respectively. Hence DMUa, the airport under investigation, is

efficient if and only if all input slacks equal zero. Variable returns-to-scale is assumed (eλ=1)

because the sample dataset consists of airports of substantially different sizes, ranging from

0.5 million passengers at Southampton to more than 50 million per annum at London-

Heathrow and Frankfurt. It should be noted that the objective function value of equation

(3.01) lies between zero and infinity with a DMU deemed efficient when the sum of slacks

equal zero. In order to interpret the coefficients obtained from the second-stage regression as

percentages, the efficiency scores were normalized to a range from zero to one, where one

depicts a relatively efficient airport.

3.3.2 Second-stage regression

The inefficiency scores estimated in the first stage may be explained by factors beyond

managerial control. In order to conduct hypothesis testing, regression analyses is often applied

in a second stage in which the DEA efficiency estimate is regressed against a set of potential

environmental variables. Banker and Natarajan (2008) and Simar and Wilson (2007)

independently review appropriate forms to conduct second-stage regressions of DEA

estimates which led them to different conclusions. Based on Monte Carlo simulations, Banker

and Natarajan (2008) argue that ordinary least squares, Tobit (censored) regression and

maximum likelihood estimation in the second-stage outperform one-stage and two-stage

parametric methods. Simar and Wilson (2007) argue that the majority of empirical two-stage

studies do not properly define the data generating process. The efficiency estimates are likely

to be serially correlated via the efficiency frontier hence the error term, εi, will also be serially

correlated thereby violating the common assumption that the errors are identically and

independently distributed. They also state that any bias in the efficiency estimate is ignored

and will be automatically included in the error term. Consequently, Simar and Wilson

advocate truncated regression in the second stage, which removes the efficient units from the

sample. The problem with this approach is that we would then ignore all airports deemed to

be lying on the efficient frontier, yet we are searching for the most appropriate form of

ownership and regulation given the competitive environment. Drawing from both papers, we

apply ordinary least squares thereby following the Banker and Natarajan (2008) approach. To

handle the issues identified in Simar and Wilson (2007), we utilize a robust cluster approach.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

83

Accounting for heteroscedastic robust standard errors as proposed by White (1980), we

construct unbiased t-tests and confidence intervals hence attempt to solve the limitation that

the error term is not identically distributed. Furthermore, the sample will be clustered at the

airport level in order to overcome the limitation that the error term is not independently

distributed.

For purposes of sensitivity analysis, we present the results of three models; the truncated

regression proposed by Simar and Wilson (2007) and ordinary least squares (OLS) and

(censored) Tobit regression as proposed by Banker and Natarajan (2008). The Tobit

regression is censored at one whereas the truncated regression will remove all observations

whose dependent variables equals one, as presented in Table 5.

Tab. 5: Regression analysis

Ordinary Least Squares Regression Tobit Regression Truncated Regression

εβ += Xy y presents a vector of DEA efficiency estimates; X a matrix of environmental variables, β the parameters to be estimated and ε the error term.

otherwise y

1y if 1y

ˆ

⎩⎨⎧ ≥

=

+= εβXy

ŷ is the true but unobservable efficiency.

εβ += Xy ŷ is truncated if y= ŷ for all ŷ ≥1

In addition to examining the joint impact of ownership form, economic regulation and

regional and gateway competition on airport efficiency, we assess their effects on pricing

behaviour utilizing robust cluster OLS. Unfortunately, we could not obtain sufficient

disaggregated data with regard to departing passenger or landing charges for our sample.

Consequently, we approximate the price via the total revenues obtained from aeronautical

activities18. In two separate regressions, revenues per passenger and revenues per movement19

are regressed over the environmental variables amongst other factors. Although we are aware

of different pricing strategies across airports, for simplicity we assume that passengers and

landing charges represent an equal share of aeronautical revenues.

3.4 Dataset

In this section we present the airports to be analyzed, the variables collected for the

efficiency analysis and then the environmental variables included in the second stage

regression. Table 11 in Appendix 3.A lists the complete set of airports in the sample, which

18 Revenues from ground handling services are not considered. 19 For simplicity the role of cargo has been ignored.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

84

include 48 European airports of which half are located in Germany and the United Kingdom.

To ensure a heterogeneous dataset with respect to the form of ownership, economic regulation

and level of local and hub competition, we further include three fully privatized Australian

airports, Melbourne, Perth and Sydney, which are neither ex-ante regulated20 nor exist in a

competitive environment due to the great distances across this continent. The pooled data

consists of an unbalanced set of 398 observations covering the time period between 1998 and

2007. The size of the airports under review varies considerably between an annual passenger

volume of half a million passengers at regional airports such as Southampton to more than 50

million passengers at international gateways such as London-Heathrow and Frankfurt.

3.4.1 Variables in the first-stage efficiency analysis

For the first-stage efficiency analysis, three inputs and four outputs are collected as

summarized in Table 6. The operating inputs consist of staff costs and other operating costs,

including materials and outsourcing. Despite being a smaller airport than London-Heathrow

in terms of air traffic movements, Frankfurt spends the most on staff costs because it is a

highly integrated airport that operates most airport services in-house or through wholly-

owned subsidiaries. Consequently, Heathrow spends the most in the other operating costs

category, reflecting the high levels of outsourcing undertaken.

Tab. 6: Variables in analysis (DEA)

Variable Description Average Standard Deviation Maximum Minimum Source

Staff costs Wages and salaries, other staff costs (2000=1 and US$=1)

63,654,765 120,554,070 1,080,756,267 3,655,825 Annual Reports

Other operating costs

Costs of materials, outsourcing and other (2000=1 and US$=1)

84,811,284 117,603,464 725,987,196 3,631,353 Annual Reports

Declared runway capacity

Number of movements per hour 46 20 110 15 IATA (2003)

Airport Coordinator

Passengers Annual passenger volume (only terminal passengers) 11,091,246 12,761,170 67,673,000 480,011 Annual Reports

Cargo Metric tons (trucking excluded) 172,922 366,881 2,190,461 0 Annual Reports

Air transport movements

Number of commercial movements 132,482 109,190 492,569 19,397 Annual Reports

Non-aeronautical revenues

Revenues from concessions own retail and restaurants, rents, utilities and ground handling activities (2000=1 and US$=1)

124,647,578 186,748,031 1,167,377,411 6,194,408 Annual Reports

20 According to the Trade Practices Act 1974, the Australian Competition & Consumer Commission (ACCC) is responsible for the monitoring of prices, costs and profits related to aeronautical and airport car parking services and facilities in Adelaide, Brisbane, Melbourne, Perth and Sydney. Hence the airports experience some form of ex-post regulation (Forsyth 2004).

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

85

It is also necessary to consider capital however this variable is extremely problematic

since it is often unreported. If the dataset covers more than one country, the monetary

measurement of physical capital creates difficulties due to different national accounting

standards and depreciation methods or periods across countries. For example, the airports of

the British Airports Authority depreciate their runways over 100 years whereas the airports

operated by the Aéroports de Paris apply a 10 to 20 year depreciation rule (Graham 2005).

Consequently, physical data such as the number of runways, gates or check-in-counters and

terminal size are often collected for cross-border studies as a proxy for capital (Gillen and Lall

1997; Pels et al. 2003). However a simple linear aggregation of these variables is problematic,

for example because the number of runways does not include information on the

configuration, weather impacts or environmental restrictions. In this study, we include

declared runway capacity as a proxy for capital, which is defined as the capacity constraint on

the number of departure and arrival movements per hour. Declared runway capacity is

negotiated twice a year in agreement with airport stakeholders and is primarily used to avoid

congestion at schedule facilitated airports and to allocate slots at coordinated airports.

Compared to the theoretical capacity, declared runway capacity is not only limited by

physical runway constraints but also by the air traffic control system, weather impacts and

noise and emissions restrictions (IATA 2010). Compared to pure physical information, the

capacity measure allows for greater variability since it accounts for bottlenecks that may be

solvable in the short to medium term. In the dataset, Amsterdam possesses the highest runway

capacity at 110 movements per hour, due in part to the geographical location near the coast

which requires additional runways and a special configuration to handle operations

consistently irrespective of weather conditions. The smallest airport with respect to runway

capacity is Ljubljana with a maximum hourly rate of fifteen movements. Consistent terminal

data proved very difficult to collect hence has been excluded in this study, however runway

capacity is highly correlated to terminal capacity therefore this omission should not greatly

impact the results.

On the output side, the annual traffic volume is represented by the number of passengers,

commercial air transport movements and tons of cargo (trucking was excluded). Freight

handling is of differing importance across the airports in the sample set since Dortmund and

London-City do not serve cargo operations whereas Leipzig and Cologne-Bonn are the

European hubs for DHL and UPS respectively. The fourth output variable captures revenues

from the non-aeronautical activities, including concessions, car parking and rent. In addition

to the traditional income sources, non-aeronautical revenues also include revenues from

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

86

labour-intensive ground handling activities. Ignoring this operation on the output side would

otherwise bias the results since the input data could not be adjusted to exclude this service. At

least theoretically we should be able to compare all three airport models, namely airports who

produce ground handling services in-house and have relatively higher employee costs and

requisite revenues, those who outsource which appear in the other costs category and their

respective revenues and the third case in which airports do not provide the service nor earn

revenue beyond perhaps a nominal fee from third party contractors. All financial data is

deflated to the year 2000 and adjusted by the purchasing power parity according to the United

States dollar in order to ensure comparability across countries. In addition, the data has been

normalized by the standard deviation to ensure that all inputs are considered equally within

the additive model.

3.4.2 Variables in the second-stage regression

Variables describing ownership structure, economic regulation and the level of hub and

local competition have been collected for this study in addition to specific airport

characteristics and information on managerial strategies. All factors are at least in the short-

term beyond managerial control yet may contribute to the inefficiency measurement process.

All data is expressed in the form of categorical variables. To further assess efficiency changes

over the review period and remove time-related effects, categorical variables on the financial

years21 have been included.

Airports frequently attempt to increase revenues from non-aeronautical sources that are

not directly related to aviation activities in order to cross-subsidize aviation charges in turn

attracting more airlines and passengers to their airport (Zhang and Zhang 2010).

Consequently, revenue source diversification that exploits demand complementarities across

aeronautical and non-aeronautical services may improve airport efficiency. Oum et al. (2006)

find a positive and highly significant relationship between the share of non-aeronautical

revenues and the level of efficiency. To compare our results with that of Oum et al. (2006),

we compute the percentage share of non-aeronautical revenue ignoring ground handling

activities. Airports are split between those that earn less than 50% of their revenues from non-

aeronautical activities and those that exceed this share. The threshold of 50% was chosen such

that a rich set of airports exist in the two categories and sensitivity analysis show no change in

the results when reducing the threshold to 40% or increasing it to 60%.

21 Note that financial data has been adjusted by different reporting periods.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

87

Oum and Yu (2004) conclude that a higher share of intercontinental traffic leads to

efficiency decreases due to additional service requirements that generate higher costs

compared to domestic traffic. We define a threshold of 15% or more of intercontinental traffic

to separate the set between hubs such as Frankfurt, Amsterdam and London-Heathrow and

airports with predominantly domestic and European destinations.

Pathomsiri et al. (2008) consider the impact of delay on productivity by incorporating the

number of delayed flights and time delays in minutes as a negative output in their non-

parametric model analyzing a sample of US airports. Perhaps unsurprisingly, they conclude

that ignoring delays leads to an overestimation of efficiency at congested airports. For the

European market we were not able to collect airport related delays per movement for the first

stage analysis hence have collected a categorical variable based on the ranking of the most

delayed airports (departure and arrival) as reported by the European air traffic control22. Over

the review period, Amsterdam, the London airports Heathrow, Gatwick and Luton and

Manchester are consistently ranked as the European airports with the greatest levels of delay,

with Zurich listed up until 2005 (Eurocontrol 1999-2008). An airport appearing on the list of

the Top 50 delayed airports in Europe was categorized accordingly23. In addition, we consider

runway utilization in order to assess the effects of congestion which are expected to positively

impact efficiency. The variable was calculated based on annual air transport movements

divided by estimated annual declared capacity24. We define three categories including (1)

airports with less than 50% runway utilization indicating under-utilization, (2) airports

between 51% and 90% runway utilization and (3) airports achieving more than 90%25 runway

utilization indicating congestion.

According to Kamp et al. (2007), analyzing airports without considering the degree of

outsourcing is likely to bias the efficiency results particularly with regard to labour-intensive

ground handling services. In our dataset, airports located in Austria, Germany and Italy

traditionally operate ground handling activities in-house whereas in the UK and Switzerland

these operations are provided by the airlines themselves or via independent third party

providers. Munich, a major German hub, announced in 2009 that their ground handling

services department has suffered losses ever since this activity underwent liberalization in

22 However, the measure reported by Eurocontrol does not capture airport-related delays rather than delays caused by airlines, the air traffic control system and weather, which are beyond control of the airport manager.

23 Australian airports are included in the group of non-delayed airports for lack of further information. 24 The annual capacity has been estimated from the declared hourly capacity obtained from the airport coordinator. 25 Note that the theoretical runway capacity normally exceeds the declared runway capacity hence the runway utilization with

respect to the theoretical value is somewhat lower than 90%.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

88

1996. Munich airport management argue that salaries are paid based on public tariffs which

are on average 20% higher than the private sector and strong labour unions in Germany make

it difficult to either adjust the compensation or to outsource this segment (Hutter 2009). It

should be noted, however, that it may be in the interests of the airport to cross-subsidize the

ground handling operations with alternative revenues in order to survive in the competitive

market (Barbot 2010). In order to test whether ground handling operations in-house affects

the efficiency estimates of airports despite considering all salient variables, we include a

categorical variable with regard to the provision of this activity. To consider competition with

independent ground handling providers since the liberalization in 1996 we further separated

the group of ground handling providing airports into (1) ground handling providing airports

without competing independent providers and (2) ground handling providing airports with at

least one competitor26. Unfortunately, ground handling provision must be analyzed separately

because of the high correlation with regulation (0.65), ownership (0.55) and the share of non-

aeronautical revenues (0.40).

Ownership form is defined according to (1) fully public airports, (2) public-private

airports with minor private shares (less than 50%), (3) public-private airports with major

private shares (above 50%) and (4) fully private airports.

The form of economic regulation has been categorized according to (1) no ex-ante

regulation27, (2) single till cost-plus regulated, (3) dual till cost-plus regulated, (4) single till

price-cap regulated and (5) dual till price-cap regulated airports. Unfortunately, this level of

refinement is not possible in the combined model due to an insufficient level of data hence we

aggregated the classification to ex-ante unregulated and regulated airports in the combined

environmental modelling approach.

Regional competition has been defined as the number of operating commercial airports

with at least 150,000 passengers per annum within a catchment area of 100 km around the

airport. The radius of the catchment area has been defined in line with Bel and Fageda (2010).

In addition, competition between gateway airports is considered. Consequently, weak

competition is defined as competition at the regional level of no more than a single airport and

strong competition as a location with at least two nearby competitors or possessing hub status.

Due to lack of information for the whole sample, we were not able to capture different

product diversification strategies, such as low cost carrier traffic, which may limit the level of

26 We refer to baggage and ramp handling which have been ground handling activities protected from competition prior to liberalization.

27 For simplicity we refer to airports subject to ex-post standard anti-trust regulation as unregulated airports.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

89

competition amongst nearby airports. Hence, our conservative measure indicates the

maximum level of competition between airports. Table 7 presents the combinations and the

number of airports and observations that belong to the different groups in the combined

model.

Tab. 7: Combination of environmental variables analyzed

Weak local competition

Heavy local and hub

competition no. of

airports no. of obs.

no. of airports

no. of obs.

No ex-ante regulation 6 37 5 36 Public Ex-ante regulation 9 59 7 60 No ex-ante regulation 0 0 2 8 Minor private Ex-ante regulation 2 13 3 24 No ex-ante regulation 1 2 2 19 Major private Ex-ante regulation 2 9 2 15 No ex-ante regulation 5 33 6 53 Fully private Ex-ante regulation 2 6 3 24

3.5 Empirical results

In the following section we first discuss the DEA efficiency results. The complete set of

normalized DEA efficiency scores is listed in Table 12 in Appendix 3.A. The second part of

this section discusses the results of the regression analyses on the DEA efficiency estimates,

initially discussing the individual impacts and followed by the combined environmental

regression approach. The third part examines the combined impact as a function of revenues

per passengers and aircraft movements respectively in order to approximate the pricing

behaviour of airports under different institutional and market conditions.

3.5.1 Efficiency scores from data envelopment analysis

The average efficiency score of the dataset obtained from the input-oriented additive DEA

model is 0.62 (after normalization) with 14% of all airports categorized as relatively efficient.

The majority of airports exhibit an efficiency decrease over time which proved significant

according to the paired sample Wilcoxon sign rank test (p-value = 0.000). As a result of the

general economic downturn and the attacks on the world trade centre in New York in 2001,

the majority of airports experienced stable or declining traffic rates with disproportional

increases in staff and other operating costs. Increased security measures for baggage screening

require additional training and the recruitment of specialized workers, expenses which have

been covered at least partially by the airports (Vienna International Airport 2004). Hence one

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

90

could argue that the additional costs provide an increased quality with respect to safety related

to passenger traffic.

Consistently efficient airports include Ljubljana and Malta that represent the smaller

airports in the dataset, many of the Australian airports, as well as the largest operators,

Frankfurt and London-Heathrow. In Australia, domestic terminals are often operated by

incumbent airlines under long-term leases, thereby lowering maintenance and staff costs

(Hooper et al. 2000). In 2002, both Melbourne and Perth experienced efficiency drops of 20%

and 30% respectively. After the collapse of the Australian airline Ansett in 2001, their

dedicated domestic terminals were sold back to the airport owner in 2002 thereby increasing

staff and other operating costs (ACCC 2003). Costs remained relatively consistent thereafter

enabling Melbourne to achieve relative efficiency by 2004 and Perth two years later, as a

result of both traffic and revenue increases. Frankfurt and London-Heathrow obtain

reasonably high cost efficiency estimates over time. It should be noted that both airports are

severely congested and require airside capacity expansions. Whereas Frankfurt has long been

fighting for the construction of a fourth runway which is now expected to open in 2011,

Heathrow was denied the right to construct a third runway in May 2010 by the new UK

government (The Guardian 2010; Fraport 2009). Both airports place great emphasis on cost

efficiency with Heathrow attempting to minimize staff costs and Frankfurt tending to reduce

other operating costs. The diverse strategies are not surprising given the different levels of

outsourcing including ground handling provision. Whereas Frankfurt provides ground

handling services in-house, this operation has long been operated by airlines and third-party

providers at Heathrow.

The airports in Amsterdam, Brussels, Copenhagen, Dortmund, Dusseldorf, Leeds-

Bradford, London-Gatwick and Nice operated on the Pareto frontier at the beginning of their

respective review periods but all experience substantial decreases over time. Basel-Mulhouse,

Bratislava, Marseille, Tallinn and Zurich were inefficient throughout the timeframe and show

further efficiency declines over time. Many of these airports both increased their costs and

served lower traffic throughput which explains the decreasing efficiency scores. In addition,

Basel-Mulhouse and Bratislava suffer from heavy reductions in their cargo operations which

are not fully compensated by passenger growth rates. An increase in declared runway capacity

at Zurich decreased their relative efficiency score from 2005 by 14%. On the other hand, the

average delay per movement dropped from 10.35 to 5.75 minutes between 2005 and 200728,

28 Eurocontrol (2006-2008).

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

91

mainly due to a reduction in the length of the departure queues, which we have been unable to

consider in this analysis due to a lack of comparable data.

At Brussels and Dortmund, efficiency estimates dropped from 1.00 to 0.42 over time.

Brussels suffered heavily from the bankruptcy of the home carrier Sabena in 2001, with a

substantial decrease in traffic whilst concurrently increasing staff and other operating costs.

Compared to their benchmark in Nice, Brussels ought to lower costs in order to achieve

relative efficiency. Dortmund completed large and expensive capacity expansions on the

terminal and airside yet is located in a highly competitive corridor with Dusseldorf, Munster-

Osnabrueck and Paderborn airports within a 90 km radius as well as alternative transport

modes, hence may find it difficult to fill excess capacity even in the medium term.

Furthermore, Dortmund airport report operating losses in all years under review.

Except for Sydney, no airport consistently improved their relative efficiency scores over

time. Between 2003 and 2007, Sydney increased its score from 0.56 to 1.00 which is mainly

attributable to a large increase in non-aeronautical revenues with fairly constant cost inputs. In

contrast to Melbourne and Perth, a cost increase from the sale of the Ansett terminal back to

Sydney’s airport management is not reported as the review period begins in 2003. However,

the ACCC responsible for the price monitoring of aeronautical charges and car parking fees at

the top five Australian airports accused Sydney airport of abusing market power. In March

2010, the ACCC reported that the airport had substantially increased passenger charges and

that the car parking fees had almost doubled from 2008 to 2009 (ACCC 2010). Southampton

airport reached an efficiency peak in 2003 after a substantial increase in cargo operations.

However, a reduction in non-aeronautical revenues in 2004 decreased their efficiency

estimates compared to that of Leeds-Bradford and Ljubljana, their reference peers.

Average efficiency scores are achieved by many of the small to medium sized airports

with less than 10 million passengers per year and this proved to be reasonably consistent over

the review period. The airports of Budapest, Cologne-Bonn, Hanover, Leipzig, Lyon,

Manchester, Munich and Vienna appear to be the least relatively cost efficient airports in the

sample. Vienna, for example, has higher staff and other operating costs compared to its

benchmarks, including London-Gatwick, Nice and Sydney. Manchester airport is also more

expensive than its benchmarks, including Gatwick, Ljubljana and Melbourne. Athens

underwent substantial capacity expansions and a new green-field location hence capacity

utilization is low in comparison to reference airports such as Gatwick and Nice. Furthermore,

the German airports of Cologne-Bonn and Leipzig suffer from excess airside capacities

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

92

despite the extensive cargo operations resulting from their positions as the European hubs for

UPS and DHL respectively. Hanover also suffers from excessively low capacity utilization

and exhibits relatively high operating costs compared to its benchmarks. However, as service

quality indicators such as congestion and delay are not included in the first stage analysis, the

inefficiency may be somewhat overestimated.

3.5.2 Regression results explaining cost efficiency

In this section we analyze the impact of environmental variables on the DEA cost

efficiency scores and also include time indicators as identified in Section 3.5.1. Tables 8 and 9

present the results obtained from the three regression models introduced in Section 3.3 with

the former presenting the results of the modelling approach in which the environmental

impacts are analyzed individually and the latter presenting the joint effects. All three models,

although based on substantially different underlying assumptions, clearly highlight general

trends, despite the fact that the truncated regression removes all efficient observations from

the analysis and Tobit regression censors the score of efficient units at one. The base case for

Table 8 is defined as a monopolistic unregulated public airport with less than 50% non-

aeronautical revenues, no heavy delays, intercontinental traffic of less than 15% and capacity

utilization below 50%. Due to the high correlation between the ground handling dummy and

ownership form, it was not possible to include both sets of variables in a single model hence

we report both sets of results per regression.

All time trend data prove increasingly negative and statistically significant; hence

coefficient estimates of the explanatory variables are adjusted by time-related effects. The

dummy variable defining airports that earn more than 50% of their revenues from non-

aeronautical sources29 prove weakly positively significant, supporting the results of Oum et al.

(2006) and indicating a marginal contribution to cost efficiency of approximately 8% as

occurred at Sydney airport after privatization. A substantial relative share of intercontinental

traffic proves statistically insignificant across all regressions. On the other hand, delay and

congestion have a statistically significant impact on airport cost efficiency, as discussed in

Pathomsiri et al. (2008). Delay impacts cost efficiency negatively in the region of 10 to 20%.

In contrast, efficiency significantly increases with runway capacity utilization. Congestion

(proxied by capacity utilization above 90% which includes gateway airports Frankfurt and

29 Note that revenues from ground handling services were excluded from the figure to compare the results with previous studies.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

93

London-Heathrow) has a large positive impact ranging from 37% to 64% increases in airport

efficiency compared to underutilized airports. Given that the positive impact of congestion is

higher than the negative effect of delays, this would indicate the need for service quality

indicators written into contracts between airlines and airports or internalization through

compensation to airlines and passengers for airport related delays.

Across the board, airports with less than 50% of shares traded privately prove to be

significantly less efficient than other ownership forms, a group which includes Athens,

Hanover and Vienna. Fully private airports do not prove to be statistically significantly

different in terms of cost efficiency than their fully public counterparts, in line with Oum et al.

(2006). Unregulated airports generally dominate their regulated counterparts, such as

Ljubljana and a number of Australian and British airports. Cost-plus regulation appears to be

the least appropriate form of economic regulation whether single or dual till, reducing

efficiency by 14% to 19%. Single till price-caps also appear to be dominated by dual till

price-caps and standard ex-post anti-trust monitoring. The importance of local and gateway

competition is not statistically significant in the models, in line with Chi-Lok and Zhang

(2009).

Our empirical results on ground handling reveal that airports operating this service under

monopolistic conditions are not significantly less cost efficient than non-ground handling

providing airports since only the truncated regression model indicates weakly negative

significance. According to Barbot (2010), airports operating as the sole provider may charge

higher prices for ground handling services if passenger and landing charges are capped. Given

that airports within this category are mostly subject to cost-plus regulation (Bremen,

Dortmund, Dresden, Leipzig, Nuremberg and Salzburg) it would appear that airports

generally charge higher ground handling fees given their level of market power. If airports are

in competition with at least one independent ground handling provider, it appears that they

operate on average 15% less cost efficiently. In line with the analytical result of Barbot

(2010), it would appear that aeronautical charges are not sufficiently capped at the airports in

Cologne-Bonn, Dusseldorf, Hanover, Munich and Stuttgart, which are mostly cost-plus

regulated, thereby permitting cross-subsidization leading to lower ground handling revenues

in order to remain ‘competitive’ with independent providers.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

94

Tab. 8: Second-stage regression results from the individual cost efficiency model

Robust Cluster OLS Regression

Robust Cluster Tobit Regression

Robust Cluster Truncated Regression

Dependent Variable DEA Efficiency Scores DEA Efficiency Scores DEA Efficiency Scores

a) Airport Characteristics and Management Strategies Monopolistic ground handling operation - -0.089 (0.059) - -0.097 (0.067) - -0.081 (0.044)*

Competitive ground handling operation - -0.149 (0.047)*** - -0.162 (0.047)*** - -0.134 (0.047)***

Share of non-aviation revenues >50% 0.080 (0.040)** - 0.094 (0.043)*** - 0.045 (0.035) -

Share of intercontinental traffic > 15% 0.021 (0.040) - 0.032 (0.042) - -0.035 (0.039) -

Heavy delays -0.164 (0.039)*** -0.190 (0.045)*** -0.184 (0.045)*** -0.211 (0.052)*** -0.109 (0.037)*** -0.116 (0.038)***

Runway capacity utilization 50-90% 0.088 (0.034)*** 0.058 (0.039) 0.095 (0.036)*** 0.069 (0.042) 0.059 (0.031)** 0.008 (0.034)

Runway capacity utilization > 90% 0.452 (0.063)*** 0.450 (0.070)*** 0.591 (0.068)*** 0.642 (0.087)*** 0.407 (0.050)*** 0.368 (0.033)***

b) Ownership, Regulation and Competition Minor private airport -0.124 (0.040)*** - -0.127 (0.043)*** - -0.116 (0.036)*** -

Major private airport 0.138 (0.062)*** - 0.173 (0.082)*** - 0.024 (0.041) -

Fully private airport 0.004 (0.047) - 0.001 (0.051) - 0.028 (0.045) -

Heavy competition 0.029 (0.035) 0.052 (0.044) 0.030 (0.037) 0.056 (0.049) 0.015 (0.034) 0.039 (0.037)

Cost-plus regulation, single till -0.171 (0.050)*** - -0.187 (0.059)*** - -0.145 (0.042)*** -

Cost-plus regulation, dual till -0.149 (0.039)*** - -0.155 (0.041)*** - -0.140 (0.039)*** -

Price-cap regulation, single till -0.129 (0.049)*** - -0.133 (0.052)*** - -0.084 (0.047)* -

Price-cap regulation, dual till 0.032 (0.062) - 0.045 (0.079) - -0.047 (0.043) -

C) Time Trend Year 1999 -0.015 (0.020) 0.001 (0.024) -0.022 (0.026) -0.005 (0.031) 0.011 (0.018) 0.019 (0.018)

Year 2000 -0.033 (0.028) -0.002 (0.034) -0.036 (0.032) -0.003 (0.041) -0.011 (0.023) 0.014 (0.028)

Year 2001 -0.089 (0.033)*** -0.054 (0.040) -0.113 (0.040)*** -0.080 (0.049) -0.008 (0.027) 0.007 (0.032)

Year 2002 -0.106 (0.037)*** -0.071 (0.044) -0.127 (0.044)*** -0.092 (0.052) -0.027 (0.027) -0.009 (0.034)

Year 2003 -0.142 (0.039)*** -0.107 (0.045)*** -0.168 (0.046)*** -0.135 (0.054)*** -0.062 (0.026)*** -0.040 (0.035)

Year 2004 -0.122 (0.039)*** -0.088 (0.046)** -0.143 (0.046)*** -0.109 (0.054)*** -0.051 (0.026)** -0.028 (0.035)

Year 2005 -0.147 (0.039)*** -0.112 (0.045)*** -0.167 (0.046)*** -0.132 (0.053)*** -0.085 (0.025)*** -0.064 (0.033)**

Year 2006 -0.168 (0.041)*** -0.134 (0.046)*** -0.195 (0.049)*** -0.160 (0.054)*** -0.090 (0.028)*** -0.080 (0.036)***

Year 2007 -0.173 (0.049)*** -0.138 (0.051)*** -0.197 (0.057)*** -0.159 (0.059)*** -0.109 (0.035)*** -0.099 (0.040)***

Intercept 0.794 (0.052)*** 0.786 (0.068)*** 0.824 (0.061)*** 0.824 (0.080)*** 0.706 (0.045)*** 0.678 (0.053)

R2 0.5678 0.3823 0.561 0.374 0.4859 0.3572

Observations (n) 398 398 398 (342 uncensored)

398 (342 uncensored) 342 342

Note: *, **, *** indicate the level of significance at 10%, 5% and 1% respectively. Robust standard errors in parentheses; clustered at airport level. R2 in Tobit and truncated regression calculated as a rough estimate of the degree of association by correlating the dependent variable with the predicted value and squaring the result.

Table 9 presents the results of the combined model in which the monopolistic, minor

private, regulated airport defines the base case. The time trend dummies prove to be

statistically significant across all regressions from 2002. Hence, after accounting for the

efficiency decreases over time, we conclude that ownership form, competition and regulation

play an important role in explaining efficiency differences across airports both individually

and in combination.

Under weak competitive conditions, defined as at most one airport within the catchment

area, privatized airports with at least 50% of the shares in private hands are the most efficient

ownership form. In comparison to minor private airports (the base case), the major or fully

privatized counterparts are on average 30% more efficient when regulated and 15% more

efficient when unregulated suggesting that economic regulation is desirable. Referring back to

the individual regression model results presented in Table 8, dual till price-cap regulation

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

95

would appear to be the most preferable instrument. The unregulated major and fully private

airports Aberdeen, Bratislava, Melbourne (from 2002), Perth (from 2002) and Sydney

perform on average 25% less efficiently than their regulated counterparts Copenhagen,

Melbourne (until 2002), Malta and Perth (until 2002), after accounting for the time trend.

Purely public airports are also strictly preferable to their minor private counterparts.

Furthermore, publicly owned and regulated airports (Dresden, Dublin, Leipzig, Nuremberg,

Oslo, Salzburg, Stuttgart and Tallinn) perform on average 10% less efficiently than their

unregulated counterparts (Basel-Mulhouse, Bratislava, Budapest, Geneva, Lyon, Riga). This

indicates that managers of public airports behave as welfare maximizers and additional

economic regulation decreases relative cost efficiency. However, it should be noted that the

group of regulated public airports are predominantly regulated according to a cost-plus regime

which according to the individual regression model is significantly less efficient than no ex-

ante regulation. Hence this may explain why the unregulated public airports in a non-

competitive setting appear to be more cost efficient that their regulated counterparts.

In a competitive environment, unregulated purely public airports (Leeds-Bradford,

Marseille and Nice) and major or fully privatised airports (Edinburgh, Glasgow and London-

Stansted) are equally cost efficient. It is clear that such airports do not require economic

regulation to maintain cost efficiency as compared to airports operating in a weakly

competitive environment. The public regulated airports, namely Amsterdam, Bremen,

Cologne-Bonn, Dortmund, Munich and Manchester, perform on average 30% less cost

efficiently than their public unregulated counterparts. Major and fully private unregulated

airports perform to the order of 10% to 15% more cost efficiently than their regulated

counterparts. Apart from minor private ownership, unregulated airports located in a

competitive environment generally operate more efficiently than those operating in weakly

competitive surroundings. For example, the unregulated fully private airports, Aberdeen and

Belfast, are located in uncompetitive environments and operate significantly less efficiently

than the more competitive Edinburgh, Glasgow, London-City and Southampton examples.

Among the regulated public airports, Nuremberg, Stuttgart and Salzburg located in weakly

competitive environments are substantially less cost efficient than the competitive Cologne-

Bonn, Dortmund and Munich examples. Consequently, government intervention would

appear to incur high transaction costs and is required to emulate the competitive environment

when missing but is very expensive when such conditions already exist in the market. Hence,

the results clearly show that competition replaces the need for economic regulation

irrespective of ownership form in order to encourage cost efficiency.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

96

Tab. 9: Second-stage regression results from the combined cost efficiency model

Robust ClusterOLS

Regression

Robust Cluster Tobit Regression

Robust Cluster Truncated Regression

Dependent Variable DEA Efficiency Scores

a) Airport Characteristics and Management Strategies Share of non-aviation revenues >50% 0.068 (0.037)* 0.078 (0.040)** 0.029 (0.032)

Heavy delays -0.182 (0.042)*** -0.200 (0.047)*** -0.122 (0.036)***

Runway capacity utilization 50-90% 0.098 (0.033)*** 0.106 (0.036)*** 0.053 (0.028)*

Runway capacity utilization > 90% 0.502 (0.087)*** 0.660 (0.102)*** 0.361 (0.056)***

b) Ownership, Regulation and Competition No regulation 0.188 (0.048)*** 0,183 (0,047)*** 0.205 (0.055)***

Public Regulation 0.093 (0.057)* 0,083 (0,058) 0.130 (0.053)***

No regulation 0.127 (0.045)*** 0,116 (0,047)*** 0.168 (0.046)*** Major private Regulation 0.438 (0.091)*** 0,501 (0,138)*** 0.228 (0.041)***

No regulation 0.147 (0.084)* 0,139 (0,089) 0.138 (0.065)** Low

com

petit

ion

Fully private Regulation 0.313 (0.091)*** 0,379 (0,105)*** 0.445 (0.146)***

No regulation 0.330 (0.033)*** 0,335 (0,032)*** 0.332 (0.041)*** Public

Regulation 0.028 (0.040) 0,021 (0,040) 0.027 (0.045)

No regulation 0.110 (0.044)*** 0,112 (0,043)*** 0.118 (0.045)*** Minor private Regulation 0.048 (0.050) 0,029 (0,044) 0.025 (0.039)

No regulation 0.351 (0.107)*** 0,386 (0,144)*** 0.213 (0.032)*** Major private Regulation 0.247 (0.037)*** 0,259 (0,036)*** 0.169 (0.051)***

No regulation 0.273 (0.052)*** 0,269 (0,053)*** 0.284 (0.051)***

Hea

vy c

ompe

titio

n

Fully private Regulation 0.158 (0.066)*** 0,165 (0,065)*** 0.257 (0.050)***

C) Time Trend Year 1999 -0.018 (0.021) -0.026 (0.026) 0.005 (0.019)

Year 2000 -0.039 (0.028) -0.042 (0.032) -0.017 (0.023)

Year 2001 -0.094 (0.033)*** -0.120 (0.040)*** -0.030 (0.025)

Year 2002 -0.106 (0.037)*** -0.127 (0.043)*** -0.039 (0.025)

Year 2003 -0.139 (0.039)*** -0.166 (0.046)*** -0.070 (0.025)***

Year 2004 -0.120 (0.039)*** -0.141 (0.045)*** -0.059 (0.025)***

Year 2005 -0.139 (0.038)*** -0.158 (0.044)*** -0.091 (0.024)***

Year 2006 -0.155 (0.039)*** -0.178 (0.046)*** -0.096 (0.026)***

Year 2007 -0.143 (0.044)*** -0.161 (0.050)*** -0.100 (0.032)***

Intercept 0.591 (0.055)*** 0.624 (0.063)*** 0.500 (0.053)***

R2 0.6254 0.6165 0.5421

Observations (n) 398 398 (342 uncensored) 342

Note: *, **, *** indicate the level of significance at 10%, 5% and 1% respectively. Robust standard errors in parentheses; clustered at airport level. R2 in Tobit and truncated regression calculated as a rough estimate of the degree of association by correlating the dependent variable with the predicted value and squaring the result

In summary, minor private airports appear to be the least efficient ownership form. Under

weakly competitive conditions, dual till price caps appears to be the most appropriate form of

economic regulation. However, under competitive market conditions with respect to

catchment area and hub status, regulation is not effective irrespective of ownership form.

According to this empirical analysis, there would not appear to be a most efficient ownership

structure, supporting the theoretical arguments of Vickers and Yarrow (1991) that competition

is more important than ownership form with respect to efficiency. Finally, since the level of

competition is an exogenous factor at least in the short term, economic regulation is an

effective tool to engender cost efficiency when market conditions are poor.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

97

3.5.3 Regression results explaining airport charges

In this section we analyze the same set of variables as described in Table 9 against the

estimated (logged) aeronautical revenues per passenger and aircraft movement in order to

approximate the pricing behaviour under different institutional settings. Additionally, we have

included the (logged) average aircraft size in order to capture the substantial differences in

fleet mix that exist within the sample. Table 10 presents the results obtained from the robust

cluster OLS regression introduced in Section 3.3. From our sample, the Australian airports in

Melbourne and Perth earned the lowest revenues per passenger and aircraft movement whilst

subject to price-cap regulation (until 2002) hence the base case for Table 10 is defined as a

monopolistic, regulated fully private airport with less than 50% non-aeronautical revenues, no

heavy delays and capacity utilization below 50%. Similar to Section 3.5.2, time dummies

were include in the analysis and show a consistent increase in revenue over time except for a

drop in 2002 as a result of the traffic decline following the terror attacks in New York in

2001.

The categorical variable representing airports that generate more than 50% of their

revenues from non-aeronautical activities indicates that such airports earn 19% less on

average from the aeronautical activities than otherwise. In line with the analytical findings of

Zhang and Zhang (2010), these airports may cross-subsidize their aeronautical costs from

additional sources in order to further attract both airlines and passengers. Airports suffering

from heavy delays appear to charge significantly lower aeronautical revenues (9%). We

observe that the airports in our sample that are not listed as heavily delayed are most

frequently either unregulated or subject to cost-plus regulation. We assume that these airports

are charging higher aeronautical fees than the average hence the negative sign in the OLS

regression. Finally, aeronautical revenues at congested airports are significantly higher (23%),

thereby supporting the empirical outcome of van Dender (2007) that congested airports are in

a position to exploit scarcity rents. The average aircraft size was found to have a positive

impact on the revenues per movement due to the fact that such charges are weight-based. An

increase in the average aircraft size of 10% leads to an 8% increase in revenues per

movement. Revenues per passenger have a significantly negative impact because transit

passenger charges are generally significantly lower than that of originating passengers; hence

a 10% increase in the average aircraft size leads to a 2% decrease in revenues per passenger.

The results for the weakly competitive environment reveal that unregulated airports

irrespective of ownership form earn higher aeronautical revenues per passenger and

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

98

movement than their regulated counterparts, suggesting that regulation is necessary from the

revenue perspective too in order to prevent exploitation of market power. The unregulated

fully private airports, Aberdeen, Belfast, Melbourne (from 2002), Perth (from 2002) and

Sydney are paid on average 27% more per passenger and movement than Melbourne and

Perth prior to the introduction of a monitoring process. The difference between public

regulated and unregulated airports, in contrast, is relatively small (5%) suggesting that public

airports are less likely to abuse market power. However, it is worth noting that the majority of

public regulated airports in this category are subject to cost-plus regulation hence it may be

true that the regulated charges are higher than would be expected under price-caps.

Tab. 10: Second-stage regression results from the combined revenue model

Robust Cluster OLS Regression

Dependent Variable revenues per passenger (log)

revenues per movement (log)

a) Airport Characteristics and Management Strategies Share of non-aviation revenues >50% -0.185 (0.058)*** -0.185 (0.058)***

Heavy delays -0.090 (0.039)*** -0.090 (0.039)***

Runway capacity utilization between 50-90% 0.028 (0.035)*** 0.028 (0.036)***

Runway capacity utilization > 90% 0.226 (0.050)*** 0.227 (0.050)***

Average aircraft size (log) -0.190 (0.150)*** 0.809 (0.151)***

b) Ownership. Regulation and Competition No regulation 0.450 (0.103)*** 0.452 (0.103)***

Public Regulation 0.397 (0.056)*** 0.397 (0.056)***

Minor private Regulation 0.474 (0.110)*** 0.473 (0.112)***

No regulation 0.656 (0.051)*** 0.656 (0.051)*** Major private Regulation 0.459 (0.110)*** 0.460 (0.109)*** Lo

w c

ompe

titio

n

Fully private No regulation 0.274 (0.035)*** 0.276 (0.035)***

No regulation 0.298 (0.055)*** 0.299 (0.055)*** Public

Regulation 0.387 (0.095)*** 0.388 (0.095)***

No regulation 0.414 (0.072)*** 0.416 (0.072)*** Minor private Regulation 0.470 (0.079)*** 0.471 (0.079)***

No regulation 0.489 (0.053)*** 0.491 (0.054)*** Major private Regulation 0.374 (0.081)*** 0.375 (0.082)***

No regulation 0.437 (0.051)*** 0.439 (0.051)***

Com

petit

ion

Fully private Regulation 0.419 (0.054)*** 0.419 (0.055)***

C) Time Trend Year 1999 0.033 (0.010) 0.032 (0.010)

Year 2000 0.052 (0.020) 0.052 (0.020)

Year 2001 0.093 (0.021)*** 0.092 (0.021)***

Year 2002 0.083 (0.020)*** 0.082 (0.020)***

Year 2003 0.106 (0.023)*** 0.104 (0.023)***

Year 2004 0.12 (0.030)*** 0.118 (0.030)***

Year 2005 0.159 (0.029)*** 0.157 (0.029)***

Year 2006 0.147 (0.036)*** 0.144 (0.037)***

Year 2007 0.173 (0.039)*** 0.171 (0.039)***

Intercept 0.628 (0.256)*** 0.629 (0.257)***

R2 0.5838 0.6697

Observations (n) 398 398

Note: *, **, *** indicate the level of significance at 10%, 5% and 1% respectively. Standard errors in parentheses. Robust standard errors in parentheses; clustered at airport level.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

99

When operating under local or hub competition, unregulated public and minor private

airports obtain on average 8% lower aeronautical revenues per passenger and movement,

indicating that they are effectively disciplined by the market structure. Unregulated major

and fully private airports on the other hand charge higher fees than their regulated

counterparts, indicating that irrespective of the competitive setting revenues are maximized.

However, the difference in revenues between unregulated and regulated airports is smaller

than under monopolistic conditions. The results support the findings of Bel and Fageda (2010)

and Oum et al. (2004) that unregulated private airports charge higher aeronautical prices.

Furthermore, the results indicate that in contrast to cost efficiency, ownership plays a major

role with respect to the pricing behaviour of airports.

In summary, without local or hub competition, airports of any ownership form ought to be

regulated in order to encourage cost efficiency and to prevent the abuse of market power. In

competitive environments, ex-ante regulation appears to engender cost inefficiency. However,

unregulated major and fully private airports charge higher prices than their regulated

counterparts suggesting that dual till price-cap regulation or possibly yardstick competition

may still need to play a role. Purely public airports in a competitive setting, on the other hand,

could dispense with regulation since they appear to be relatively cost efficient without

charging excessively.

3.6 Conclusions

The inefficiency of airports may be explained not only by input excess and output

shortfalls but also by exogenous factors over which management have little to no control. A

number of empirical studies have assessed the impact of ownership structure, economic

regulation and levels of competition on efficiency however the effects were always

considered separately hence significance was sometimes an issue. The aim of this research

has been to assess the combined impact of the environmental variables in order to gain

understanding as to the most efficient ownership form and regulatory framework whilst

accounting for levels of regional and hub competition as well as other managerial choices.

The two-stage analysis combined DEA in the first stage and regression analysis in the

second stage. The non-radial additive input-oriented DEA model has been chosen to identify

all relative inefficiencies of the input. Following the recent debate on the most appropriate

second-stage regression model, Banker and Natarajan (2008) and Simar and Wilson (2007)

propose standard OLS and truncated regression respectively. Due to issues of

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

100

heteroscedasticity in the error terms, we applied robust clustering to each of these forms and

also included a Tobit regression to ensure that the results are robust. The regression results

proved robust since the outcomes were similar and the general directions were clear across all

three modelling approaches.

Data availability remains a serious issue and the attempt to include all combinations of

institutional settings proved difficult. However, the results of the joint analysis provide

additional information beyond that of the individual regression models. The results suggest

that ex-ante regulation at all airports located in a competitive environment is unnecessary and

generates x-inefficiency of the order of 15%, which rises substantially at purely public

airports. However, unregulated major and fully private airports located in a competitive

setting still pursue profit maximization and charge higher aeronautical fees than regulated

airports of the same ownership structure. Herein lies the trade-off between the x-inefficiency

generated as a result of regulation and the reduction in the abuse of market power as a result

of price caps. On the other hand, non-hub airports with weak local competition generally

require economic regulation in order to prevent an exploitation of market power and to

encourage cost efficiency. Dual till price-cap regulation would appear to be the most efficient

form. Combining the results from the efficiency and revenue model reveals that public

ownership appeared to be the preferable welfare maximizing combination, whereby regulation

is only necessary under monopolistic conditions.

Additional results reveal that ground handling providing airports operating in competition

with independent providers tend to charge lower ground handling fees in order to be

competitive. Public airports particularly appear to cross-subsidize from aeronautical revenues

in order to cover their higher operating costs. Whilst heavy delays impact cost efficiency

negatively, high runway utilization increases efficiency by more than double the negative

impact of delay. This would suggest that airlines may require contracts with service quality

specifications or penalties in order to encourage congested airports to internalize the delay

externalities.

Having defined competition on the regional and hub level in a relatively simple manner,

we may have assumed excessive rates of competition having ignored product diversification

strategies such as low cost carrier traffic or market destination separation. In other words, we

categorized airports as competitive that may be avoiding direct competition. Nevertheless, the

rather conservative measure achieves consistent results and would only prove more significant

had the data been more refined.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

101

Future research would require substantially more data to permit an improved analysis of

all the categories described here. Finer distinctions with respect to ownership form and

regulation might better highlight the most efficient institutional setting given alternative levels

of competition. Additional environmental variables, including airport-related delays, noise

and air pollution, would enable the development of a social welfare analysis of airports and

the trade-off across the different stakeholders. Finally in order to benchmark, a more accurate

measure of the capital required to build, maintain and expand or reduce airports would only

be possible if the ACI, ICAO or equivalent organization were to develop standardized data

collection procedures that airports globally reported annually.

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

102

3.A Appendix

Tab. 11: List of airports

Code Airport Country Time Period Ownership Regulation Competition Ground

Handling ABZ Aberdeen UK 99-05 fully private no ex-ante regulation weak not provided

98-06 cost-plus AMS Amsterdam NL 2007

public incentive

heavy not provided

ATH Athens GR 05-07 minor private cost-plus weak not provided BFS Belfast UK 99-07 fully private no ex-ante regulation weak not provided BHX Birmingham UK 98-07 major private no ex-ante regulation heavy not provided BLQ Bologna IT 00-05 public no ex-ante regulation heavy provided BRE Bremen DE 98-07 public cost-plus heavy provided BRU Brussels BE 99-04 major private cost-plus heavy not provided

03-05 public BTS Bratislava SK 06-07 major private

no ex-ante regulation weak provided

BUD Budapest HU 00-01 public no ex-ante regulation weak not provided CGN Cologne Bonn DE 98-07 public cost-plus heavy provided CPH Copenhagen DK 01-04 major private incentive weak not provided DRS Dresden DE 98-06 public cost-plus weak provided DTM Dortmund DE 98-07 public cost-plus heavy provided DUB Dublin IE 06-07 public incentive weak not provided DUS Dusseldorf DE 99-07 major private cost-plus heavy provided EDI Edinburgh UK 98-07 fully private no ex-ante regulation heavy not provided EMA East Midlands UK 98-06 fully private no ex-ante regulation heavy not provided FRA Frankfurt DE 02-07 minor private incentive heavy provided GLA Glasgow UK 98-06 fully private no ex-ante regulation heavy not provided GVA Geneva CH 98-07 public no ex-ante regulation weak not provided HAJ Hanover DE 98-07 minor private cost-plus weak provided

98-99 public cost-plus HAM Hamburg DE 00-07 minor private incentive

heavy provided

LBA Leeds Bradford UK 98-02, 06-07 public no ex-ante regulation heavy not provided

LCY London-City UK 99-07 fully private no ex-ante regulation heavy not provided LEJ Leipzig DE 98-06 public cost-plus weak provided LGW London-Gatwick UK 98-05 fully private incentive heavy not provided LHR London-Heathrow UK 98-05 fully private incentive heavy not provided LJU Ljubljana SI 98-06 major private no ex-ante regulation heavy provided LTN London-Luton UK 00-07 fully private no ex-ante regulation heavy not provided LYS Lyon FR 98-06 public no ex-ante regulation weak not provided MAN Manchester UK 98-07 public incentive heavy not provided

99-01 incentive MEL Melbourne AU 02-07

fully private no ex-ante regulation

weak not provided

MLA Malta MT 02-06 major private incentive weak not provided MLH Basel Mulhouse FR 98-07 public no ex-ante regulation weak not provided MRS Marseille FR 98-06 public no ex-ante regulation heavy not provided MUC Munich DE 98-05 public cost-plus heavy provided NCE Nice FR 98-06 public no ex-ante regulation heavy not provided NUE Nuremberg DE 98-07 public cost-plus weak provided

99-03 cost-plus OSL Oslo NO 04-07

public incentive

weak not provided

99-01 incentive PER Perth AU 02-07

fully private no ex-ante regulation

weak not provided

RIX Riga LV 04-06 public no ex-ante regulation weak provided SOU Southampton UK 99-05 fully private no ex-ante regulation heavy not provided STN London-Stansted UK 98-06 fully private incentive heavy not provided STR Stuttgart DE 98-07 public cost-plus weak provided SYD Sydney AU 03-07 fully private no ex-ante regulation weak not provided

2004 cost-plus SZG Salzburg AT 05-07

public incentive

weak provided

TLL Tallinn EE 02-07 public cost-plus weak provided 00-04 public VCE Venice IT 2005 minor private

no ex-ante regulation heavy not provided

VIE Vienna AT 98-07 minor private incentive heavy provided ZRH Zurich CH 01-07 minor private no ex-ante regulation heavy not provided

Joint Impact of Competition, Ownership Form and Economic Regulation on Airport Performance

103

Tab. 12: DEA efficiency scores

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 ABZ - 0,579 0,599 0,546 0,522 0,505 0,515 0,536 - - AMS 1,000 1,000 1,000 0,496 0,309 0,249 0,253 0,297 0,259 0,280 ATH - - - - - - - 0,421 0,432 0,450 BFS - 0,512 0,489 0,487 0,480 0,485 0,575 0,502 0,500 0,502 BHX 0,549 0,577 0,598 0,582 0,568 0,583 0,535 0,530 0,514 0,481 BLQ - - 0,755 0,715 0,710 0,733 0,698 0,740 - - BRE 0,587 0,648 0,602 0,589 0,585 0,568 0,575 0,568 0,562 0,562 BRU - 1,000 1,000 0,653 0,455 0,417 0,431 - - - BTS - - - - - 0,854 0,732 0,575 0,579 0,563 BUD - - 0,357 0,336 - - - - - - CGN 0,390 0,410 0,390 0,417 0,409 0,388 0,361 0,350 0,337 0,323 CPH - - - 1,000 0,617 0,562 0,635 - - - DRS 0,639 0,588 0,575 0,547 0,559 0,574 0,548 0,538 0,532 - DTM 1,000 0,843 0,732 0,472 0,433 0,418 0,408 0,410 0,410 0,408 DUB - - - - - - - - 0,504 0,475 DUS - 1,000 1,000 0,605 0,682 0,472 0,588 0,531 0,508 0,574 EDI 0,664 0,712 0,714 0,702 0,713 0,718 0,738 0,711 0,620 0,661 EMA 0,637 0,628 0,629 0,606 0,629 0,640 0,637 0,584 0,558 - FRA - - - - 1,000 1,000 1,000 1,000 0,732 1,000 GLA 0,702 0,712 0,714 0,705 0,693 0,685 0,697 0,703 0,692 - GVA 0,635 0,660 0,791 0,720 0,709 0,700 0,676 0,638 0,645 0,687 HAJ 0,313 0,319 0,331 0,316 0,314 0,310 0,308 0,301 0,302 0,297 HAM 0,535 0,524 0,426 0,394 0,399 0,381 0,420 0,404 0,406 0,414 LBA 1,000 1,000 0,901 0,830 0,842 0,881 0,888 - - - LCY - 1,000 1,000 0,933 0,887 0,867 0,799 0,744 0,888 1,000 LEJ 0,571 0,570 0,392 0,382 0,374 0,369 0,367 0,337 0,359 - LGW 1,000 0,861 1,000 0,744 0,682 0,619 0,658 0,537 - - LHR 1,000 1,000 1,000 0,907 1,000 0,852 1,000 1,000 - - LJU 1,000 1,000 1,000 1,000 1,000 1,000 1,000 1,000 1,000 - LTN - 0,584 0,518 0,525 0,513 0,520 0,564 0,511 0,529 0,473 LYS 0,487 0,504 0,525 0,509 0,459 0,443 0,448 0,414 0,417 - MAN 0,477 0,425 0,407 0,334 0,352 0,434 0,504 0,442 0,434 0,397 MEL - 0,862 1,000 1,000 0,815 0,760 0,903 1,000 1,000 1,000 MLA - - - - 1,000 1,000 1,000 1,000 1,000 - MLH 0,887 0,821 0,670 0,542 0,502 0,439 0,432 0,438 0,414 0,413 MRS 0,832 0,844 0,932 0,808 0,621 0,586 0,597 0,600 0,588 - MUC 0,335 0,338 0,355 0,353 0,332 0,263 0,264 0,234 - - NCE 1,000 1,000 1,000 0,857 0,684 0,612 0,543 0,526 0,538 - NUE 0,628 0,613 0,636 0,619 0,596 0,574 0,578 0,570 0,568 0,564 OSL - 0,555 0,517 0,515 0,440 0,479 0,441 0,458 0,494 0,543 PER - 1,000 1,000 0,972 0,790 0,689 0,678 0,673 0,703 1,000 RIX - - - - - - 0,547 0,558 0,563 - SOU 0,847 0,919 0,943 0,720 0,617 1,000 0,751 - - - STN 0,670 0,770 0,651 0,595 0,723 0,707 0,661 0,635 - - STR 0,561 0,564 0,559 0,534 0,513 0,505 0,462 0,516 0,513 0,519 SYD - - - - - 0,563 0,748 1,000 1,000 1,000 SZG - - - - - - 0,733 0,716 0,722 0,723 TLL - - - - 0,904 0,690 0,628 0,522 0,516 0,513 VCE - - 0,625 0,599 0,627 0,671 0,682 0,545 - - VIE 0,428 0,456 0,471 0,396 0,365 0,338 0,338 0,325 0,320 0,310 ZRH - - - 0,656 0,532 0,516 0,481 0,342 0,315 0,306

104

4 AIRPORT BENCHMARKING FROM A

MANAGERIAL PERSPECTIVE30

Benchmarking airports is currently popular both in the academic literature and in practice

but has proved rather problematic due to the heterogeneity inherent in any reasonably sized

dataset. Most studies either treat the airport production technology as a black box or separate

terminal and airside activities, assessing them individually. In this paper we analyze airports

as a single unit due to the direct complementarities, avoiding the artificial separation of inputs

between the terminal and airside but opening the black box through network data

envelopment analysis (DEA). To further improve the benchmarking process, we identify

appropriate peers for 43 European airports over 10 years through a dynamic clustering

mechanism according to pre-defined characteristics and restrict the integer linear program

with respect to potential reductions in capital inputs. Compared to basic DEA models, the

results of the network DEA structure provide more meaningful benchmarks with comparable

peer units and target values that are achievable in the medium term. By identifying each

airport’s individual reference set, unique airport outliers influence relative efficiency less

severely than occurs under basic DEA. In addition, the formulation is shown to be suitable in

assessing different strategies with respect to aeronautical and commercial activities not only

separately but also in combination.

30 The research of this chapter is obtained in collaboration with Ekaterina Yazhemsky, Hebrew University of Jerusalem. We are grateful to Dr. Nicole Adler for helpful discussions and support. The paper has been submitted to the Journal of Productivity Analysis in June 2010 and is currently under review.

Airport Benchmarking from a Managerial Perspective

105

4.1 Introduction

According to the Princeton dictionary, an airport is defined as “an airfield equipped with

control tower and hangars as well as accommodations for passengers and cargo”. Airports

can be defined as an important basic infrastructure to a society in which aviation is one of the

drivers of a modern economy. An alternative approach defines an airport as a private

production system in which society maximizes social welfare by encouraging airport

management to maximise profits whilst at the same time, considering consumer surplus via

some form of airport regulation if deemed necessary. Consequently, it is unclear whether

airports should be considered as a not-for-profit, public good, the general approach in the

United States, or as a private enterprise maximizing shareholder value. Since it would appear

to be true that large regions of the world are gradually adopting the privatized form (Zhang

and Zhang 2003) and that independent authorities running public airports in the United States

do not behave differently to their private counterparts with respect to productivity (Oum et al.

2006), in this paper we develop an airport benchmarking methodology from an airport

manager’s perspective in which we assume that the airport intends to maximize revenues or

minimize costs.

Oum et al. (2006) and Barros and Dieke (2007) review airport benchmarking studies

applied to a diverse range of activities using various methodologies. The most popular

methods include price index total factor productivity (Hooper and Hensher 1997; Oum and

Yu 2004; Vasigh and Gorjidooz 2006), parametric stochastic frontier analysis (Pels et al.

2003; Oum et al. 2008) and non-parametric data envelopment analysis (DEA). DEA has been

used to compare the performance of airports within national boundaries, U.S. (Gillen and Lall

1997; Sarkis 2000), U.K. (Parker 1999), Spain (Martín and Román 2008; Murillo-Melchor

1999), Australia (Abbott and Wu 2002), Taiwan (Yu 2004), Portugal (Barros and Sampaio

2004) as well as airports around the world (Adler and Berechman 2001; Lin and Hong 2006).

It is rather difficult to draw general inferences since many of these papers arrive at directly

opposing conclusions. For example, Murillo-Melchor (1999) show that Spanish airports in

their dataset suffer from decreasing returns-to-scale whereas Martín et al. (2009) concluded

increasing returns-to-scale for the same set of airports. Abbott and Wu (2002) found most

Australian airports enjoy increasing returns-to-scale, Pels et al. (2003) argue that European

airports operate under constant returns-to-scale in air traffic movements and increasing

returns-to-scale on the terminal side and Lin and Hong (2006) argue that most airports are not

operating at an optimal scale. Graham and Holvad (2000) and Abbott and Wu (2002) argue

Airport Benchmarking from a Managerial Perspective

106

that Australian airports are more efficient than their European counterparts, Lin and Hong

(2006) argue that the US and European airports are more efficient than their Asian and

Australian counterparts and Pels et al. (2003) conclude that widespread European airport

inefficiency is not specific to a country or region. Consequently, Morrison (2009) has called

for a balanced approach and dialogue between airport managers and researchers.

The majority of studies to date treat airport technology as a single production process

avoiding the complexity inherent in airport systems. Gillen and Lall (1997) and Pels et al.

(2003) were the first to argue that the airport could be analyzed as two separate decision-

making processes, one serving airside activities and the other serving landside production.

The approach developed in this research connects the two sides of the production function

whilst opening the black box via network DEA (Färe 1991). We argue that a single black box

approach would be insufficient to capture the rich picture underlying this approximation, as

demonstrated in Figure 11. Since the liberalization of the aviation industry in Europe in the

late eighties, airports have focused on both aeronautical and commercial landside activities.

The network DEA approach recognises the fact that generalized and fixed costs connected to

the two sets of activities can only be split in an artificial manner and that whilst aeronautical

revenues draw from passengers, cargo and air traffic movements, the non-aeronautical

revenue is more closely tied to passenger throughput. Although airports may have limited

control over traffic volume, non-aeronautical revenues drawn from non-airport related

activities, such as airport cities, are indeed within the purview of airport management. As

argued in Oum et al. (2003), the omission of outputs such as commercial services is likely to

bias efficiency results as it underestimates the productivity of airports whose managers focus

on generating additional revenue sources. Many airports attempt to increase revenues from

non-aeronautical sources which are not directly related to aviation activities in order to cross-

subsidize aviation charges in turn attracting more airlines and passengers to their airport

(Zhang and Zhang 2010). Revenue source diversification that exploits demand

complementarities across aeronautical and non-aeronautical services appears to improve

airport productive efficiency (Oum et al. 2006). We would argue that it is more reasonable to

analyze the airports as a single unit due to the direct complementarities yet avoid the need to

separate inputs between the terminal and airside. In general, the airport technology may be

defined as a network that consists of multi-production processes and stages as described in

Figure 11. Consequently, in this paper we develop a network DEA modelling approach in

order to measure the relative cost and revenue efficiencies of airports with respect to

Airport Benchmarking from a Managerial Perspective

107

aeronautical and commercial activities simultaneously, whereby activities are connected via

passengers as the common intermediate product.

Fig. 11: Airport network technology

Another issue that appears in the airport benchmarking literature is the problem of

comparability. A base assumption within the DEA context that has been questioned is the

homogeneity of the decision-making unit under analysis and the appropriateness of this

assumption with respect to airports (Morrison 2009). The aim of the formulation presented

here is to broach the direct question of airport benchmarking in light of the reasonable level of

heterogeneity found in a multiple airport study, necessary to generate sufficient data points for

purposes of analysis. In order to ensure comparability, we apply a dynamic clustering

approach (Golany and Thore 1997) using integer linear programming which forms reference

sets based on similar mixes of inputs or outputs and intermediate products. Certain inputs may

be beyond managerial control in the short to medium term yet affect airport efficiency (Adler

and Berechman 2001). In general, capital is frequently treated as a non-discretionary variable

over which management has little to no control (Banker and Morey 1986). In this research,

capital has been defined in terms of declared runway and terminal capacity. Declared runway

capacity is agreed upon within a multiple stakeholder setting and result in a number that

accounts for the airport system configuration. For example, some airports consist of a

Labour: Full-time equivalent employees Capital: Terminal capacity Runway capacity Apron capacity (terminal/remote) Security capacity Baggage handling capacity Airport area Gates Public parking spots Materials and supplies: Outsourcing costs Snow removal equipment Fire truck & stations Hangers Maintenance costs

Business & leisure passengers: International-transfer International non-transfer Domestic-transfer Domestic non-transfer

Air Transport Movements Cargo (tons)

Non-traveling customers

Undesirable outputs: Non-weather related delays Aircraft noise Air pollution

Concession revenues: Duty free and retail Catering Car parking Rental Banking Entertainment Departing passenger services Airport cities

Aeronautical revenues: Aircraft landing fees Passenger charges & fees Aircraft parking fees Ground handling fees Cargo fees Centralized infrastructure fees Noise surcharges Security charges

Airport Benchmarking from a Managerial Perspective

108

reasonably large number of runways however for reasons of weather and/or geographical

layout, only a smaller portion may be in use in a given timeframe which declared capacity

takes into account as compared to simply counting the number of runways. On the terminal

side, check-in counters, security and passport control and gates together produce a throughput

level per hour that is otherwise assumed to be linear within a standard DEA framework. We

argue that the capacity of an airport, as a proxy for capital, may be adjusted to a certain extent

in the medium term, hence the model restrictions permit terminal and declared runway

capacity to change up to a pre-determined level. Pure capital investment is not an appropriate

measure even within a specific country because the accounting processes differ, rendering the

information incompatible. Finally, principal component analysis (PCA) combined with DEA

(Adler and Golany 2001; Adler and Yazhemsky 2010) is applied in the input-oriented model

in order to reduce the curse of dimensionality and resulting bias, reducing the set of cost

efficient airports from 53% to 38% in the current application.

The aim of this research is to develop a comprehensive methodology tailored to airport

benchmarking from the managerial perspective. A comparison with basic DEA results

demonstrates that the additional restrictions in the network PCA-DEA dynamic clustering

formulation lead to more reasonable peer comparisons, permitting an analysis of strategies

which could potentially be adopted over short and medium term planning horizons. The

model in this research allows airport managers to include their industry knowledge in the

form of limitations on airport size, operating conditions and restricted variability of capacity

encapsulated in the dynamic clustering approach. For example, the results of the under-

utilized airport in Hanover indicate that in the medium-term the airport could either reduce

operations to two of their three existing runways, instead of closing two runways as obtained

with basic DEA, or alternatively attempt to increase cargo throughput as occurred at their two

medium-term benchmark airports located in Venice and Hamburg. The formulations

developed are suitable for assessing appropriate strategies with respect to aeronautical and

commercial activities not only separately but also in combination, assuming cross-

subsidization is an acceptable policy. According to the combined network DEA dynamic

cluster revenue maximization approach, Lyon airport has achieved a sustainable level of

aeronautical revenues and ought to search for appropriate commercial revenue opportunities

as opposed to the basic DEA results which suggest a further increase in aeronautical revenues

of 40%. The methodology provides an airport manager with the tools for both exploratory

data analysis and inefficiency estimation, removing the need for additional tests of

homogeneity. Furthermore, utilizing an hourly capacity measure as both a terminal and airside

Airport Benchmarking from a Managerial Perspective

109

proxy of physical capital appears to be new in airport benchmarking studies. Compared to the

standard quantity measures such as the number of runways or gates, this proxy provides an

improved managerial measure of the airport infrastructure as a system and allows us to

consider bottlenecks at an airport.

The paper is organized as follows: Section 4.2 presents individual modelling formulations

that have been combined in Section 4.3 in order to produce airport benchmarks based on a

network PCA-DEA dynamic clustering approach. Section 4.4 provides a description of the

public data available for analysis and Section 4.5 compares the results of the combined

formulations to those of basic DEA models and benchmarks a select subset of airports in

order to demonstrate the utility of the approach developed in this research. Finally, Section

4.6 concludes and presents recommendations for further research.

4.2 Methodology

DEA is a non-parametric method of frontier estimation that measures the relative

efficiency of decision-making units utilizing multiple inputs and outputs. DEA was first

published in Charnes et al. (1978) under the assumption of constant returns-to-scale31 and was

extended by Banker et al. (1984) to include variable returns-to-scale. The DEA model

categorizes decision-making units into two groups, those that are deemed efficient and define

the Pareto frontier and those that lie within the envelope and are deemed inefficient, for which

benchmarks are clearly defined.

This section discusses the dynamic clustering mechanism that ensures comparable

benchmarks are chosen from a dataset given exogenous parameter values and the network

DEA model first designed to disaggregate the process of decision-making within a unit.

Subsequently we discuss the combination of principal component analysis and data

envelopment analysis, which reduces efficiency over-estimation bias and a multi-dimensional

scaling approach that produces a graphical representation of the data. Finally, we discuss a

non-parametric statistical procedure that measures efficiency variation across different groups

within the dataset in order to estimate the potential impact of environmental variables on the

relative Pareto efficient frontier.

31 Constant returns-to-scale means that the producers are able to linearly scale the inputs and outputs without increasing or decreasing efficiency.

Airport Benchmarking from a Managerial Perspective

110

4.2.1 Dynamic clustering

Basic DEA benchmarking may lead to inappropriate targets for improvement in a dataset

in which there are substantial differences in size among the decision-making units (DMUs)

under analysis. Sarkis and Talluri (2004) propose second-stage clustering to identify

benchmarks for poor performers after applying DEA to determine the relative efficiencies of

airports. This study applies a dynamic clustering approach first proposed by Golany and

Thore (1997) that restricts the selection of best practice DMUs according to predefined

boundaries within the DEA framework in a single stage process. The boundaries of the cluster

are defined in relative terms, limiting the efficient reference set32 to those DMUs whose input-

output values are within the distance defined by the proportions.

Fig. 12: Benchmark clustering

In Figure 12 we demonstrate the impact of the cluster restrictions for a simplified model

with two outputs and a single input. DMUa is compared to the Pareto frontier (blue line

defined by DMUs 3 to 6) in a standard DEA formulation, with DMUs 4 and 5 acting as

benchmarks. In our proposed approach, each inefficient airport may refer to a set of

benchmarks that do not lie directly on the Pareto frontier rather within the dotted radius. If

DMUa lies far enough away from the Pareto frontier as shown in Figure 12, all potential

benchmarks will lie in the interior of the envelope, resulting in DMUs 1 and 2 acting as

benchmarks for DMUa. The assumptions of this approach lead to the conclusion that DMUa’’,

the hypothetical observation lying on the interior frontier, represents a relevant target which is

32 An efficient reference set, or peer group, is defined by a subset of efficient units "closest" to the unit under evaluation i.e. with similar mixes of inputs and outputs.

y1/x

DMU3

DMU2

DMU1

DMUa

DMU4

DMU5

DMU6

DMUa’

DMUa’’

y2/x

Airport Benchmarking from a Managerial Perspective

111

more accessible than DMUa’ in the short to medium term. Dynamic clustering improves on

the Sarkis and Talluri two-stage procedure since additional information, such as the

importance of each target DMU, can be drawn from the one step procedure.

4.2.2 Network DEA

Network DEA models were first introduced by Färe (1991) and Färe and Grosskopf

(1996, 2000) and subsequently extended by Lewis and Sexton (2004), Emrouznejad and

Thanassoulis (2005), Chen (2009), Kao (2009) and Tone and Tsutsui (2009). Opening the

black box permits an analysis of the optimal production structure of DMUs and their

priorities, to determine both efficient subsystems and overall efficiency. In transportation,

network DEA has been applied by Yu and Lin (2008) in order to simultaneously estimate

passenger and freight technical efficiency, service effectiveness and technical effectiveness

for 20 selected railways.

This research develops a network model that defines a multi-product airport in which

capital, labour, materials and outsourcing produce traffic volume, in the form of aircraft

movements, passenger and cargo. This throughput then generates revenues from aeronautical

charges paid mostly by airlines and from commercial terminal-side services serving

passengers. The overall profits of this system are driven by services provided by outside

parties including airlines and third party contractors as well as the airport processes

themselves. Airport management retain reasonable control over labour, materials and levels of

outsourcing but limited control over capital investments. In addition, management control the

variety and the pricing policies offered on the non-aeronautical side and partially control

aeronautical tariffs, dependent on the regulatory regime of the relevant country. Network

DEA lends itself to a more accurate description of this process than standard performance

analyses.

4.2.3 Principal component analysis integrated with DEA

Dependent on the nature of the dataset, the results of the DEA model may not sufficiently

distinguish between the efficient and inefficient DMUs due to an overestimation bias caused

by the curse of dimensionality (Adler and Yazhemsky 2010). PCA-DEA is one of the

methodologies that has been developed to reduce the number of inefficient DMUs incorrectly

classified as efficient (Adler and Golany 2001, 2002). The original variables are replaced with

a smaller group of principle components (PCs), which explain the variance structure of a

Airport Benchmarking from a Managerial Perspective

112

matrix of data through linear combinations of variables. The principal components are

uncorrelated linear combinations ranked by their variances in descending order and those that

explain little of the variance of the original data may be removed thus reducing the

dimensions in the DEA linear program. In order to use principal components instead of the

original data, the DEA model needs to be transformed to take into account the linear

aggregation.

A rule-of-thumb computed in Adler and Yazhemsky (2010) suggests that at least 76-80%

of the information should be retained in the model in order to minimize the overestimation

bias33. Clearly, if we use less than full information, we will lose some of the explanatory

powers of the data but we will improve the discriminatory power of the model. It should be

noted that as a result of the free sign in principal component analysis and the transformed

constraints in the PCA-DEA model, the targets and efficient peers obtained could reflect a

change in the current mix of input-output levels of the inefficient DMUs, along the lines of

weight constrained DEA.

4.2.4 Visualizing multiple dimensions

Co-Plot, a variant of multi-dimensional scaling, aids both in exploring the raw data and in

visualizing the results of DEA (Adler and Raveh 2008). Co-Plot positions each decision-

making unit in a two-dimensional space in which the location of each observation is

determined by all variables simultaneously according to a correlation analysis. The graphical

display technique plots observations as points and variables as arrows, relative to the same

arbitrary center-of-gravity. Observations are mapped such that similar DMUs are closely

located on the plot, signifying that they belong to a group possessing comparable

characteristics and behavior. A general rule-of-thumb states that the picture is statistically

significant if the coefficient of alienation is less than 0.15 and the average of correlations is at

least 0.7534. We apply Co-Plot to the set of variable ratios (each output divided by each input),

in order to align the technique to the idea of efficiency as defined in DEA, such that Co-Plot

graphically displays the DEA results in two dimensions. In general, the efficient DMUs

33 The rule-of-thumb defines the percentage of retained information required to balance the trade-off between the two incorrect definitions of (in)efficiency, namely efficient decision-making units defined as inefficient (under-estimation) and inefficient DMUs defined as efficient (over-estimation).

34 The coefficient of alienation is a single measure of goodness-of-fit for the configuration of n observations obtained from a smallest space analysis (Guttman 1968). The higher the correlation, the better the common direction and order of the projections of the n points along the arrow. The length of the arrow is proportional to the correlation.

Airport Benchmarking from a Managerial Perspective

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appear in the outer circle of the plot signifying their relative achievements and we

exogenously determine the color of the DMUs in order to clarify the results of the DEA.

4.2.5 Measuring efficiency variation across groups

In order to determine whether there are distinct efficiency differences between groups of

airports, we apply the program evaluation procedure outlined in Brockett and Golany (1996)

and Sueyoshi and Aoki (2001). Four steps are required to implement the procedure. In the

first step, the complete set of DMUs (j=1,…,n) are split into k sub-groups and the model is

run separately over each of the k groups. Then, for each of the k individual groups, the

inefficient DMUs are moved to their hypothetical efficient level by projecting them onto the

efficient frontier of their relevant group. In the third step, a pooled DEA is run with all n

DMUs based on their adjusted variables. Finally, a Kruskal-Wallis test is applied to determine

if the k groups possess the same distribution of efficiency values within the pooled set. If the

null hypothesis is correct, we expect to see most of the DMUs rated as efficient in step three.

Note that in order to avoid inaccuracy in the nonparametric rank test, the number of

observations in each of the k subgroups should be of similar size. If this is not the case, the

size of the smallest subgroup is calculated and simple random sampling without replacement

is applied to form subgroups of equally small sized samples. In order to test whether the

findings are robust, Banker’s F-test (1993) may be applied in the last stage of the procedure.

4.3 Model formulations

In this section we describe three network PCA-DEA approaches with dynamic clustering

that are then applied in Section 4.5. The application of network DEA to airports is new and to

the best of our knowledge, we are aware of one working paper in the field, Lozano et al.

(2009), in which capital utilization rather than managerial efficiency is analyzed based on

network-DEA. Given the public data available for the study, Figure 13 presents the airport

network technology that we analyze based on a subset of variables described in Figure 11. X

represent inputs, Y outputs and I intermediate products. The number in brackets represents a

node index in the network.

Airport Benchmarking from a Managerial Perspective

114

Fig. 13: Two-stage airport network technology

Model (4.01) assumes that airport management is interested in maximizing revenues,

drawing from aeronautical activities and concessions given airport throughput on the terminal

and airsides, which are in turn limited by the physical infrastructure and associated costs

available to support the system. Drawing on discussions with airport managers and Pels et al.

(2003), we assume constant returns-to-scale with respect to revenues in that a doubling of the

intermediate inputs, namely passengers, air traffic movements (ATM) and cargo, should

increase revenues at an equivalent rate. The network DEA formulation for the radial, output-

oriented, constant returns-to-scale, mixed integer linear program applied in this research is

presented in model (4.01), where superscript a is the index of DMUa, the unit under

investigation; Xa represents the input values of DMUa; Ya and Ia are the output and

intermediate values of DMUa respectively and the subscript of intensities for DMUa, nijλ ,

denotes the link leading from node i to node j in the network presented in Figure 13. θ1 and θ2

represent the relative efficiency scores for the commercial and airside activities respectively,

where a value of 1 indicates efficiency in generating revenues given the airport’s resources

and a value greater than 1 indicates by how much the relevant revenues ought to be increased

in order for DMUa to be deemed relatively efficient. It should be noted that the first four rows

of model (4.01) are not summed over n, in order to restrict envelopment intensities nλ24 and

nλ235 , thus comparing airports that possess input levels that lie within a boundary of 10% to

300% of DMUa inputs and between 20% and 200% of DMUa intermediate outputs. Parameter

values, αl=0.1, αu=3, βl=0.2, βu=2, were chosen such that a sufficiently rich set of airports

exist in the cluster. Sensitivity analyses of our current dataset suggest that smaller bounds

Inputs (1):

Staff costs X1

Other operating costs X2

Runway capacity X3

Intermediate goods (2): Number of passengers:

International I1

Domestic I2

Intermediate goods (3):

Tons of cargo I3

ATM I4

Output (4):

Non-aeronautical revenues Y1

Output (5):

Aeronautical revenues Y2

Airport Benchmarking from a Managerial Perspective

115

result in excessive limitations and pure self-comparisons over time whereas a wider set lead to

unreasonable benchmarks whereby London-Heathrow and Tallinn, representing the largest

and smallest airports in the dataset, are considered directly comparable.

{ } { }0

1010

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1 ,4321

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n

ak

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n

nk

namu

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naml

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nnj

najl

naku

nnk

nakl

naju

nnj

najl

λ,θ

,λ, λ, θ θ

binary , λ, λ

λ λ λ λ λ λ

YθλY

,,, j IλI

YθλY

, k IλI

,...,Nn , m λIβλIλI β

,...,Nn ,,, j λXαλXλX α

,...,Nn , k λIβλIλI β

,...,Nn ,,, j λXαλXλXs.t. α

θ θMax

(4.01)

In order to connect Figure 13 and the clustering approach, nλ12 and nλ13 are binary

variables and nλ24 and nλ235 are non-negative continuous variables. If nλ12 =1 then DMUn could

be included in the peer group for DMUa on the non-aeronautical side and if nλ12 = nλ13 =1 then

DMUn could be included in the peer group for DMUa on the aeronautical side. nλ12

consequently connects costs to the number of passengers produced and nλ13 connects costs to

cargo and ATM production such that DMUs of similar size and cost structure represent

potential benchmarks35. nλ24 connects the number of passengers to non-aeronautical revenue

derived and nλ235 connects passengers, cargo and ATM to aeronautical revenue. Since no

trade-off between aeronautical and non-aeronautical activities is introduced in the model,

benchmarks on each side of the airport activity are determined independently.

Alternatively, whilst it may be assumed that a private, unregulated airport pursues profit

maximization, airports that are subject to economic regulation may behave as social welfare

maximizers. Hence, maximizing aeronautical revenues may not be the target of airport

management due to regulatory constraints. Furthermore, even profit maximizers may consider

35 The effect of this approach is depicted in Figure 12 resulting in DMUs 1 and 2 acting as benchmarks for DMUa: 2,1

12λ and 2,113λ are binary variables equal to 1 since they lie within the boundaries of the first four equations

in model (4.01) whereas 5,412λ and 5,4

13λ equal 0.

Airport Benchmarking from a Managerial Perspective

116

lower aeronautical charges as an opportunity to expand non-aeronautical activities and

generate additional revenues by attracting airlines through lower airport charges.

Consequently, the network DEA formulation for the radial, output-oriented, constant returns-

to-scale, mixed integer linear program combining both aeronautical and concession activities

is presented in (4.02).

{ }0

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1 ,4,3,21

1 ,4321

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λ,θ

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,,, j IλI

YλY

YθλY

,...,Nn , k λIβλIλI β

,...,Nn ,,, j λXαλXλXs.t. α

θMax

(4.02)

The goal is to maximize non-aeronautical revenue (Y1) given international and domestic

passengers, cargo and ATM. Physical infrastructure (terminal and runway movements), costs

(labour and materials) and intermediate outputs define the reference set for each DMU as in

model (4.01). Aeronautical revenue (Y2) is included in the analysis as a non-discretionary

variable (Banker and Morey 1986). According to this model, benchmarks consist of airports

achieving higher non-aeronautical revenues, given similar levels of aeronautical revenue

whilst comparing airports of similar size and demand levels. In the following we will refer to

(4.01) as the independent model where the clusters were independently defined and the

efficiency of the aeronautical and non-aeronautical side estimated separately. Formulation

(4.02) presents the combined model since a common set of benchmarks are considered but

only non-aeronautical revenues are maximized.

The network PCA-DEA formulation for the radial, input-oriented, variable returns-to-

scale, mixed integer linear program proposed in this research is presented in formulation

(4.03). The cost minimization assumes variable returns-to-scale, since a doubling of output

should not necessarily result in a doubling of staff, materials and outsourcing costs (Gillen

and Lall 1997; Pels et al. 2003). As opposed to the output-oriented model, we have combined

domestic and international passengers into one intermediate variable Ipax in order to reduce the

number of variables. Furthermore, we have applied principal component analysis (PCA) to

reduce the over-estimation bias and improve the level of discrimination in the results. The

first principal component (PCcost) combines staff costs and other operating costs, explaining

Airport Benchmarking from a Managerial Perspective

117

89% of the variance in the original data. PCcap combines terminal and runways capacities,

explaining 85% of the original information. Including all PCs would provide precisely the

same solution as that achieved under the original DEA formulation.

{ }0,,,

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1,1

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1 1

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1231221

352524

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1123

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cos22cos

2

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1cos

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1

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1112

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353535

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it

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nt

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it

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namu

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λ,θ

λ λ θ θ binary, λ λ λ

λ λλ λλ λλ λ

λ λ

, q IλI

IλI

δPCSlλPC

PCθSlλPC

IλI

δPCSlλPC

PCθSlλPC

,...,N n , m λIβλIλI β,...,N n λYαλYλY α

,...,N n λIβλIλI β,...,N n λYαλYλY α

,...,N n λIβλIλI β,...,N n λYαλYλYs.t. α

θ θMin

(4.03)

Model (4.03) clusters airports according to revenue and traffic mix, whereby the total

number of passengers is included in the commercial side and all intermediate activities are

included in the aeronautical side. Parameter values were set at αl=0.1, αu=3, βl=0.2, βu=2. nλ24 , nλ25 and nλ35 are binary variables and nλ12 and nλ123 are non-negative continuous variables.

Scost and Scap are slack variables and lcost and lcap are normalized eigenvectors based on costs

and capacities respectively. θ1 and θ2 represent relative efficiency scores on the terminal side

and airside respectively, where a score of 1 means that the airport is relatively cost efficient

and less that one indicates the level of input retraction required to achieve relative efficiency

in comparison to the benchmarks identified. θ1=1 indicates a cost minimization approach with

respect to the non-aeronautical activities of the airport and θ2=1 indicates cost minimization

with respect to all activities of the airport (passengers, cargo, ATM) whereby the source of

revenues draws from both the non-aeronautical and the aeronautical sides. To restrict the

variability of physical infrastructure, we assume that terminal and runway capacities may be

adjusted up to 30% in the medium term (δ=0.7).

Airport Benchmarking from a Managerial Perspective

118

4.4 Dataset

In this section we describe the variables collected, additional environmental variables that

may be required to adapt the model to ensure homogeneity of the production process and the

complete set of observations together with an initial exploratory data analysis. The dataset

consists of 43 European airports located in 13 different countries. We have pooled the data to

an unbalanced set of 294 observations covering the time period from 1998 to 2007 (Table 17

in Appendix 4.A lists the set of airports under study, the specific timeframe for which the data

was available and whether ground handling processes are undertaken in-house or outsourced).

All airports offer domestic and international routes, however airports located in smaller

countries such as the Netherlands generally have very few domestic destinations. The

passenger volume varies considerably from less than a million passengers at Tallinn and

Durham Tees Valley airports up to more than 50 million at London-Heathrow, the largest

European airport in terms of passenger throughput and number three in the world (ACI 2009).

Ten variables were collected in total for purposes of analysis based on publicly available

data. The variables are categorized into three groups; four inputs (X), four intermediate

products (I) and two outputs (Y). Table 13 presents summary information and specifies the

data sources. The operating inputs consist of staff costs and all other non-labour related

operating costs, which include materials and outsourcing. Although a smaller airport than

Heathrow in terms of air traffic movements, Frankfurt’s staff costs are highest due to the level

of vertical integration whereby the airport operates most of the services by itself or through

wholly-owned subsidiaries. As an example, the airport manages the ground handling

operations which represent one of the most labour intensive activities at an airport, a process

traditionally organized by airlines or independent third party providers at Heathrow.

Consequently, Heathrow spends the most on other operating costs, reflecting the high levels

of outsourcing undertaken.

Generally, as a proxy for capital, physical data such as the number of runways, gates,

check-in counters and overall terminal size is collected. However, such data is often

problematic because the number of runways does not include information on the

configuration or the impact of weather and on the number of runways open within a given

timeframe. Furthermore, the terminal area in square metres is somewhat subjective since

some airports report gross terminal area including sections of an airport that are not open to

the public. If the dataset covers more than one country, the monetary measurement of physical

capital also creates difficulties due to different national accounting standards and depreciation

Airport Benchmarking from a Managerial Perspective

119

methods or periods across countries. For example, the airports of the British Airports

Authority (BAA) depreciate their runways over 100 years whereas the airports operated by

the Aéroports de Paris (ADP) depreciate over a period of 10 to 20 years (Graham 2005). In

this research, terminal capacity is defined in terms of passengers handled within an hour, thus

combining the capacities of all terminal facilities including check-in counters, security

controls, baggage delivery and retail area into one common capacity figure. The airside is

defined by the declared runway capacity, specified as the number of departing and arrival

movements specified per hour. Airport stakeholders negotiate this parameter biannually which

is primarily used to avoid congestion at schedule facilitated airports and aid in the allocation

of slots at coordinated airports (IATA 2010). The advantage of using declared capacity is that

the parameters account for bottlenecks across the terminal and runway systems, providing two

individual capacity measures. Amsterdam possesses the highest agreed terminal and runway

capacities in our sample with 26,000 passengers and 110 movements per hour. Due to their

geographical location near the coast, they require a special runway configuration to operate as

a hub airport. The smallest airport with respect to runway capacity is Florence in the Tuscany

region, with a maximum hourly rate of twelve movements. Due to its short, single runway

system (1,688 m), the airport can handle aircrafts up to the size of a Boeing 737 or an Airbus

A319 (Aeroporto di Firenze 2010).

The annual traffic volume is represented by the number of passengers, commercial ATM

and tons of cargo (trucking is excluded). The passengers are divided according to domestic

and international destinations. Unfortunately, we could not collect enough data to separate the

passengers between intercontinental and European flights or account for transfer passengers,

which would be preferable since these groups probably generate different revenue streams.

Non-aviation revenues include revenues from retail activities and restaurants, concessions and

income from rents and utilities. Aviation revenues are generated from (often regulated)

landing and passenger charges, ground handling undertaken in-house and cargo activities. The

largest non-aeronautical revenues were generated at Heathrow, whereas Frankfurt earned the

highest aviation revenues. Commercial revenues equal 67% of total airport revenues on

average in the dataset, clearly supporting the argument that non-aeronautical activities should

not be ignored in a productivity analysis of airports from a managerial perspective,

particularly when considering the possibility of cross-subsidization.

Airport Benchmarking from a Managerial Perspective

120

Tab. 13: Variables in airport efficiency analysis

Variable Description Name Average Maximum Minimum Source

Staff costs Wages and salaries, other staff costs X1 81,704,359 1,080,756,267 5,962,213 Annual Reports

Other operating costs

Costs of materials, outsourcing and other X2 103,364,471 725,987,196 5,010,381 Annual Reports

Declared runway capacity Total movements per hour X3 49 110 12

IATA (2003), Airport and Coordinator Websites

Terminal capacity Total passenger throughput per hour X4 6,768 26,000 450 IATA (2003), Airport

Websites

International passengers Annual passenger volume I1 10,300,571 61,517,733 355,579 IATA (2003), Airport

Websites

Domestic passengers Annual passenger volume I2 2,433,287 9,932,208 48 IATA (2003), Airport

Websites

Cargo Metric tons (trucking excluded) I3 214,076 2,190,461 37 IATA (2003), Airport Websites

Air transport movements Total commercial movements I4 152,133 492,569 16,000 IATA (2003), Airport

Websites

Non-aeronautical revenues

Revenues from concessions own retail and restaurants, rents, utilities and other Y1 117,906,043 1,107,046,057 4,629,813 Annual Reports

Aeronautical revenues

Landing, passenger and aircraft parking charges; revenues from ground handling, cargo revenues and other

Y2 175,507,645 1,739,331,693 7,199,668 Annual Reports

All financial data is deflated to the year 2000 and adjusted by the purchasing power parity

according to the United States dollar in order to ensure comparability across countries. In

addition, the data has been normalized by the standard deviation to limit the influence of

outliers in the dataset.

4.5 Empirical results

In the following section we identify the impact of vertical integration and subsequently

include the information in the dynamic clusters. In section 4.5.2 we compare and contrast the

results of a basic DEA model with the network PCA-DEA formulation. Section 4.5.3

discusses the benchmarking results for a subset of airports, specifically Vienna as an example

of both an output- and input-oriented efficient airport, Hanover as an example of an input-

oriented inefficient case and Lyon as an example of the differences between the independent

output model (equations 4.01) which assesses the efficiency estimates of both revenue

generating activities separately and the combined output model (equations 4.02) in which only

commercial revenues are maximized.

Airport Benchmarking from a Managerial Perspective

121

4.5.1 Efficiency variation across groups

When estimating the relative efficiencies, it would appear that airports offering in-house

ground handling services operate on a different production frontier to airports that outsource

this activity. This is not immediately obvious since airports providing ground handling

services in-house have higher labour costs, outsourced have higher ‘other’ costs and both

have higher revenues than airports who permit third parties to provide the service hence no

costs appear on the books and only minor concessional fees from the suppliers on the output

side because the contracts themselves do not appear on the airport’s accounting books. To

evaluate the potential for different productivity levels, the non-parametric program evaluation

procedure was applied to basic DEA which contains the last stage of formulations (4.01) and

(4.03), combining both sources of revenues into one efficiency estimate. Based on Figure 13,

the output orientation assumes constant returns-to-scale and includes nodes {2345}, while the

input orientation includes the inputs and outputs from nodes {123} and assumes variable

returns-to-scale. In our sample, 21 airports offer ground handling and 22 outsourced or never

offered this service which translates into 156 DMUs in the ground handling group and 138

DMUs otherwise (see Table 17 in Appendix 4.A). The results are clear and significant that

airport operators providing ground handling appear to be revenue maximisers but were highly

inefficient in cost minimization relative to airports from the non-ground handling group.

Graph (a) in Figure 14 shows the DEA efficiency scores on the vertical axis for the two

groups from a revenue maximization perspective and (b) shows the DEA efficiency scores

from the cost minimization perspective across the two groups. Airports with ground handling

activities perform on average 10% better in maximizing their outputs as their aeronautical

revenues per passenger are naturally higher whereas in the input-oriented model, airports that

do not provide ground handling achieve on average 10% higher efficiencies since no costs are

associated with this service.

Airport Benchmarking from a Managerial Perspective

122

Fig. 14: Kruskal-Wallis ANOVA for outsourcing36

(a) Output-orientation

Source Sum of squares

Degrees of freedom

Mean Squares

Chi-sq

Prob > Chi-sq

Groups 243,606 1 243,606 33.7 6.4e-009

Error 1,873,769 292 6,417

Total 2,117,376 293

(b) Input-orientation

Source Sum of squares

Degrees of freedom

Mean Squares

Chi-sq

Prob > Chi-sq

Groups 357,302 1 357,302 49.4 2.0e-012 Error 1,760,162 292 6,028

Total 2,117,465 293

In order to test the robustness of our findings, the Banker F-test (1993) is also applied both in

the third stage of the program evaluation procedure and on basic DEA scores when two sub-

36 The vertical axis of the boxplot represents the efficiency scores computed in the third step of the program evaluation procedure (a score of one implies relative efficiency).

Airport Benchmarking from a Managerial Perspective

123

groups of DMUs face the same frontier as suggested in Banker (1993), assuming

exponential37 and half-normal38 inefficiency distributions (Table 14).

Tab. 14: Banker F-test for outsourcing

(a) Output-orientation

Inefficiency distribution Test applied on Test statistic Prob>F Entire dataset 2.5726 5.62797E-16 Exponential Program evaluation procedure 3.9766 4.69994E-31 Entire dataset 5.5646 9.70871E-24 Half-normal Program evaluation procedure 9.1030 3.84569E-36

(b) Input-orientation

Inefficiency distribution Test applied on Test statistic Prob>F Entire dataset 2.8054 1.33497E-18

Exponential Program evaluation procedure 9.1584 1.27351E-70 Entire dataset 7.8721 2.68437E-32 Half-normal Program evaluation procedure 22.7297 1.3301E-123

The results also proved to be consistent for a basic DEA model in which air traffic

movements, passengers, cargo and commercial income including ground-handling revenues

were selected as output in order to adjust for the effect of outsourcing. Staff and other

operating costs and runway and terminal capacities were defined as inputs. Having now

considered both costs and revenues in the efficiency estimation, the radial variable returns-to-

scale, input-oriented model still indicated significant efficiency differences across both groups

based on the results of a Kruskal-Wallis test. In summary, both the non-parametric Kruskal-

Wallis and parametric F-test reach the same significant result supporting a rejection of the

null hypothesis (Figure 14 and Table 14). After liberalization in 1996, airports that provided

ground-handling were required to permit competitors’ access. Munich and Frankfurt have

claimed substantial losses in this segment on a regular basis (Dietz 2009; Hutter 2009)39.

However, the strong labour unions in Germany have prevented airport management from

either cutting wages or outsourcing this service to third-party providers without guarantees

37 Test statistic= 1

2

1 11

2 21

/

/

Njj

Njj

u N

u N=

=

∑∑

%

%

and is distributed as F with (2N1, 2N2) degrees of freedom, where 1j ju θ= −% in the

output oriented and 1 1jj

uθ

= −%in the input oriented model and belong to the range [0, ∞).

38 Test Statistic= 1

2

21 11

22 21

/

/

Njj

Njj

u N

u N=

=

∑∑

%

%

and is distributed as F with (N1, N2) degrees of freedom.

39 Most German airports are fully or at least major publicly owned and if ground handling is operated in-house by the parent company, the airport pays salaries based on public tariffs, which are on average 20% higher compared to private ground handling companies. Some German airports, such as the minor-private airport Hamburg, outsourced the ground handling segment to a 100% subsidiary in order to set flexible tariffs however this was not deemed acceptable by the public shareholders of Munich for example.

Airport Benchmarking from a Managerial Perspective

124

that workers would continue under the same conditions. Thus, at least in the short-term, the

degree of outsourcing can be regarded as a political factor that is beyond managerial control

and ground handling is included in the network DEA formulation as an environmental

variable which will further limit the potential benchmark set via clustering.

4.5.2 Comparison of basic and network DEA

In contrast to our formulations (4.01 to 4.03), basic DEA does not restrict potential

benchmarks nor does it permit a limited deviation in one or more variables. In order to assist

the comparison between basic and network DEA results, we have exogenously divided the

dataset according to in-house or outsourced ground handling provision and applied DEA

individually to each category. For the output orientation, the technology of Figure 13 reduces

to nodes {235} and {24} with respect to aeronautical and commercial activities respectively,

while the input orientation case collapses to nodes {12} when assessing the non-aeronautical

side and {123} with respect to both activities.

The basic DEA results generate consistently efficient airports that belong either to the set

of smallest airports e.g. Bremen, Florence and Ljubljana, which provide ground handling in-

house and Malta, Durham Tees Valley and Leeds/Bradford which outsource, or the largest

airports in the sample such as Frankfurt. In neither output-oriented formulations do airports

achieve 100% efficiency over the entire review period, although Salzburg, Ljubljana and

Malta appear consistently close to the frontier. A notable exception is Cologne-Bonn, which

remains cost efficient with respect to both activities but operates very inefficiently (between

44% and 77% over time) with respect to the commercial side. Cologne-Bonn is the European

hub for the parcel service provider UPS, which rents office space and warehouses from the

airport, suggesting a behaviour different to others in the sample (Cologne-Bonn Airport

2010).

Under basic DEA, all airports are compared against a single Pareto frontier and Salzburg

represents an important benchmark for Vienna, Dusseldorf, Frankfurt, Hamburg and Munich.

However, it is doubtful that the management of a primary or secondary hub airport would

adopt the strategies of an airport that handles less than 2 million passengers per year with very

low cargo throughput too. Durham Tees Valley, a small airport in East England with less than

700,000 passengers per year was defined as a benchmark for Lyon, Geneva, Oslo and the

secondary hub airport in Zurich, which would not occur in the formulations we present due to

the dynamic clustering approach.

Airport Benchmarking from a Managerial Perspective

125

Airports in very small clusters are unique in character and in the extreme case tend to

form their own reference set. In the current dataset, these mostly included the smaller and less

congested airports such as Tallinn, Leeds-Bradford and Durham Tees Valley. These airports

can be identified as outliers according to the Andersen and Petersen (1993) super efficiency

procedure. Such observations frequently influence the basic DEA Pareto frontier, for example

Durham Tees Valley appears within the reference sets of Oslo and Zurich airports. In

comparison, the results of the cost minimization formulation (4.03) categorizes Copenhagen

and London Stansted as peer airports for Oslo and Zurich hence the modelling approach

indicates benchmarks that are more homogeneous in character. Another unique example

includes Dortmund, which acts a self benchmark in the cost minimization approach from

2003 to 2007, namely after their capacity expansion and severe reduction in cargo operations.

Dortmund is the only airport that exhibits operational losses over the entire timeframe. The

airport is partly owned by the local electricity distributor and losses are covered by their major

shareholder (Dortmund Airport 2007). Dortmund is located in the Ruhr area (Ruhrgebiet)

with a population of more than five million, representing the largest agglomeration in

Germany. Airport competition includes Dusseldorf, Cologne-Bonn and Paderborn which are

located in their catchment area (defined as 100 km around the airport) and intermodal

competition includes high speed rail and the motorway, especially on domestic routes and

traffic originating in Benelux. Hence despite their high capital investment, it may be

necessary for the airport to further decrease their aviation charges in order to attract airlines

and new destinations thereby generating additional commercial revenues.

The network DEA formulations provide the user with an exploratory data analysis that

does not exist in the basic DEA results. The results of formulations (4.01) and (4.02)

demonstrate that the average cluster size for each inefficient airport was reasonably small

because the capacity of airports varies considerably across the sample and some airports

suffer low utilization rates whereas other are highly congested. The operating costs at highly

congested airports were large mostly due to employee costs hence airports with similar

capacities did not necessarily belong to the same cluster. In general, large clusters indicate

that various airports in the sample possess similar characteristics which in our dataset

included Dusseldorf, Hamburg, Strasbourg, Venice and London-Gatwick.

Airport Benchmarking from a Managerial Perspective

126

4.5.3 Benchmarking airports

In this sub-section, we describe the type of results and analysis that are achievable by

collecting data and applying the formulations described in Section 4.3. We focus on relatively

efficient Vienna, relatively inefficient Hanover and finally Lyon, to describe the potential

balance between the two revenue streams. Vienna is an example of an airport that has

gradually improved in both input and output efficiency over time, achieving Pareto relative

efficiency by 2007. Vienna appears in the reference set of Cologne-Bonn and Dusseldorf in

the input-oriented case. Between 1998 and 2007 Vienna’s costs and revenues increased on

average by similar proportions (99% and 94% respectively)40, while traffic volume grew by

76% for total passengers, 54% for ATM and 37% for cargo. The input-oriented case in Figure

15 shows that from 1998 to 2003, Vienna lies close to the arrows that display the ratio of

intermediate outputs to costs. In 2004, both staff costs and other operating costs substantially

increased partly due to the introduction of a 100% hold baggage screening policy and the

founding of a subsidiary for infrastructure maintenance (Vienna International Airport 2004).

After 2004 greater emphasis has been allocated to the issue of runway utilization, viewed in

Figure 15 by the proximity of the later years to the capital asset related ratios. Vienna airport

moves in a positive direction towards an improved utilization of the runways which increased

from 48% to 66% between 2000 and 2007. Hence, despite substantial cost increases, the

airport still managed to increase its relative efficiency as their costs per ATM decreased over

time. On the aeronautical output side, the airport management changed their regulated tariff

structure by increasing passenger charges from an average price of 3.90€ in 1999 to 5.90€41 in

2007 and decreasing overall landing charges by 3%, whilst reducing them for larger aircraft

by up to 20%. In summation, Vienna airport increased passenger charges out of the total

aviation revenues collected from 33% in 2001 to 46% in 2007 and decreased the share of

landing fees from 44% to 28% over the same period (Vienna International Airport 2001,

2007). These policies appear to have aided Vienna to achieve revenue productivity.

40 Staff costs increased by 111% and other operating costs by 88%. Non-aeronautical revenues increased by 78% and aviation revenues by 109%.

41 These values are an average passenger price and were computed by dividing total passenger revenue charges by passenger throughput as obtained from the annual reports. They could therefore deviate from the passenger charges specified in the charges manual. Both prices were given in nominal values.

Airport Benchmarking from a Managerial Perspective

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Fig. 15: Co-Plot graphic display of Vienna’s input-oriented strategy42

Hanover airport is the ninth largest airport in Germany handling 5.6 million passengers in

2008 (ACI 2009). It is partly owned by Fraport AG which owns and operates Germany’s

gateway hub in Frankfurt. Whilst Hanover’s non-aeronautical revenues from rents and

utilities increased over the decade analyzed due to the development of a large airport city, the

relative cost efficiency score θ1 consistently dropped from 72% in 1998 to 60% in 200643.

Over the same time period, passenger volume increased by 17%, ATM by 8% and cargo

dropped by 37% however staff costs and other operating costs increased by 80% and 64%

respectively, as shown in Table 15.

42 Coefficient of alienation is 0.06 and average of correlations is 0.89. 43 If δ=1 is assumed, terminal and runway capacities may decrease to a lower limit of zero. Hanover’s cluster of 100 DMUs is stable over time, a common set of benchmarks exists between 1998 and 2006 (Florence, Hamburg and Venice) and a comparison of θ1 over time is possible. Over the same period θ2 is close to 1 due to overestimation bias caused by the relatively limited cluster size of 30-40 DMUs.

Airport Benchmarking from a Managerial Perspective

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Tab. 15: Benchmarking Hanover airport

Airport Declared runway capacity

Terminal capacity

Staff costs (US$)

Other operating

costs (US$)

Domestic passengers

International passengers

Air transport movements

Cargo (tons)

DMUs under review

HAJ1998 50 4,000 39,137,748 41,838,277 1,014,723 3,814,405 70,815 10,954

HAJ1999 50 4,000 44,100,404 40,270,806 1,080,384 4,017,528 76,914 7,724

HAJ2000 60 4,000 49,858,032 35,123,344 1,246,083 4,284,201 83,687 9,027

HAJ2001 60 4,000 49,344,453 39,876,433 1,067,834 4,089,724 75,368 6,712

HAJ2002 60 4,000 48,501,264 37,857,069 1,018,412 3,733,509 73,278 6,058

HAJ2003 60 4,000 51,602,584 46,057,783 1,010,975 4,033,895 74,960 6,338

HAJ2004 60 4,000 54,282,812 48,081,380 1,060,005 4,189,164 74,251 6,091

HAJ2005 60 4,000 61,893,620 58,038,723 1,137,940 4,499,445 76,585 6,551

HAJ2006 60 4,000 66,510,634 58,753,898 1,222,533 4,476,766 76,255 5,954

HAJ2007 60 4,000 70,453,727 68,607,888 1,215,036 4,429,546 76,263 6,912

Changing benchmarks over time according to formulation 4.03 and dual values (λ12) for δ=0.7 DMUs under review

Florence 2000

Hamburg 1998/9

Venice 2004/5

Genoa 2000

Nuremberg 2001/2/3/7

Vienna 1999

HAJ1998 0.46 0.29 0.25 HAJ1999 0.43 0.34 0.23 HAJ2000 0.28 0.28 0.43 HAJ2001 0.15 0.29 0.33 0.23 HAJ2002 0.28 0.17 0.37 0.18 HAJ2003 0.13 0.22 0.27 0.38 HAJ2004 0.35 0.21 0.35 0.09 HAJ2005 0.37 0.32 0.03 0.28 HAJ2006 0.19 0.14 0.55 0.13 HAJ2007 0.18 0.57 0.25

Figure 16 displays the gradual decline in productivity (see the red arrow) and the change

in benchmarks over time from Venice to Nuremberg (see white dots), the latter representing a

relatively more expensive airport to operate44. From the technical perspective, Hanover shows

potential to expand airport activities due to a declared runway capacity of 60 movements per

hour. Capacity utilization at Hanover remained at a stable 23%, whereas Florence and Venice

achieve 40% utilization and Hamburg slightly more than 50%. Nuremberg, Hanover’s

benchmark, achieves a capacity utilization of less than 40% which is still higher than that of

Hanover. Bremen and Dresden are the long term benchmarks according to basic DEA, which

appear as black dots on the left edge in Figure 1645. When comparing the results for Hanover

in 2007 with respect to network PCA-DEA (θ1=0.7) and basic DEA (θ1=0.49) it becomes

clear that the long term goal for Hanover, ceteris paribus, would be to close two of the three

runways. The medium-term network DEA results suggest that it would be sufficient to close

the equivalent of a single runway.

44 If δ=0.7 is assumed, terminal and runway capacities may be adjusted up to 30% in the medium term. As a result θ1=0.7 since 2001, although the dynamic clustering shows the change in productivity over time through changes in the set of benchmarks (see Table 15 and Figure 16). 45 According to basic DEA, θ1 decreased slightly from 54% in 1998 to 50% in 2006 and the benchmarks include Bremen, Dresden, Hamburg and Florence over the entire period.

Airport Benchmarking from a Managerial Perspective

129

Fig. 16: Co-plot of input minimization results with emphasis on Hanover46

Hanover’s management may find it rather difficult to improve capacity utilization due to

the airport’s highly competitive location. The airport faces direct competition from Hamburg

and Bremen that are in close proximity, as well as Dortmund, Paderborn and Münster-

Osnabrück, which primarily serve charter and low cost carriers and are less than two hours

drive by car, as shown in Fig. 8. Potential competition includes regional airports located in

Braunschweig-Wolfsburg, Kassel-Calden and Magdeburg-Cochstedt, none of which currently

operate commercially although plans exist to offer commercial flights from Kassel-Calden in

2012 (Flughafen Kassel-Calden). Additional intermodal competition includes the ICE high

speed rail alternative and a highly connected motorway network. In conclusion, Hanover

faces direct, potential and intermodal competition hence the airport needs to cut costs by as

much as 40%, further develop non-airport related activities and attempt to attract cargo

throughput. The latter strategy may increase runway utilization and seems reasonable given

the high level of connectivity of the city and the lack of night flights restrictions due to their

location.

46 Coefficient of alienation is 0.137 and average of correlations is 0.829.

Airport Benchmarking from a Managerial Perspective

130

Fig. 17: Catchment area of Hanover airport (2 hour drive)47

Our final analysis presents the benchmarking results for Lyon Saint-Exupéry which is the

fourth largest airport in France, with a passenger throughput of 7.9 million in 2008 (ACI

2009). Like the majority of French regional airports, Lyon is fully publicly owned and

operated by the regional Chamber of Commerce. The airport became a major regional hub

airport for the national carrier Air France at the end of the nineties and today Easyjet is their

second largest customer (Lyon Aéroport 2008). In the independent revenue maximizing

formulation (4.01), Lyon improved in aeronautical efficiency (θ2) from a score of 2.09 to 1.02

between 1998 and 200548, benefiting from a change in the tariff structure similar to that of

Vienna airport. The important peer airports include privatized BAA Glasgow and Basel-

Mulhouse, both of which focus on low cost carrier traffic. Glasgow serves Easyjet and

Scottish Loganair in competition with Ryanair at Prestwick and Basel-Mulhouse serves

Easyjet, which achieved a market share of almost 50% in 2007 (Flughafen Basel-Mulhouse).

With respect to non-aeronautical activities, Lyon increased its efficiency (θ1) from 1.68 in

1998 to 1.41 in 2005, in part due to the large increase in car parking revenues, rents and

utilities which contributed to a 67% increase in overall commercial revenues (see Table 16).

Benchmarks on the non-aeronautical side include Basel-Mulhouse and Marseille, with the

former generating more than 50% of their revenue from commercial sources, the majority of

which are derived from retail sales, rents and utilities.

47 Source: adapted from ADV (2010). 48 Lyon’s benchmark clusters are stable over time according to formulation 4.01.

Airport Benchmarking from a Managerial Perspective

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Tab. 16: Output benchmarks for Lyon airport

Airport Domestic passengers

International passengers Movements Cargo Aeronautical

revenues (US$) Non-aeronautical revenues (US$)

DMUs under review

LYS1998 2.478.508 2.742.712 106.170 39.749 25.552.145 33.325.320

LYS1999 2.565.033 2.935.515 116.894 39.050 29.032.107 37.403.371

LYS2000 2.715.196 3.311.666 129.373 40.126 35.476.207 40.519.260

LYS2001 2.714.678 3.393.929 132.903 38.902 41.348.698 44.483.257

LYS2002 2.523.982 3.254.242 120.529 35.349 48.933.813 44.982.158

LYS2003 2.571.177 3.368.718 118.489 35.494 58.338.026 47.915.432

LYS2004 2.633.962 3.594.650 123.958 34.874 61.171.391 50.077.362

LYS2005 2.682.123 3.879.242 128.868 38.725 68.845.652 55.678.876

Output benchmarks for Lyon airport in 2005

Benchmark Intensity (λ) Domestic passengers

International passengers Movements Cargo Aeronautical

revenues (US$)

Non-aeronautical

revenues (US$)

Short-term benchmark for non-aeronautical activities (model 4.02, θ1=1.15)

MLH2004 0.98 651.102 1.893.772 57.915 34.227 30.882.599 38.867.805

GLA2000 0.29 3.568.259 3.453.741 92.000 10.000 69.790.936 38.013.125

GLA2005 0.16 4.604.022 4.237.878 97.610 9.461 76.125.667 63.356.734

NCE2006 0.06 4.332.382 5.615.653 164.617 13.940 100.059.952 83.755.849

Medium-term benchmark for non-aeronautical activities (model 4.01, θ1=1.4)

MLH2004 1.00 651.102 1.893.772 57.915 34.227 30.882.599 38.867.805

MLH2002 0.61 792.765 2.264.199 88.000 31.285 34.865.786 45.079.574

MRS1998 0.39 3.943.382 1.568.411 87.030 55.993 19.795.312 31.681.597

Medium-term benchmark for aeronautical activities (model 4.01, θ2=1.03)

MLH2004 0.96 651.102 1.893.772 57.915 34.227 30.882.599 38.867.805

GLA2000 0.52 3.568.259 3.453.741 92.000 10.000 69.790.936 38.013.125

NCE2006 0.05 4.332.382 5.615.653 164.617 13.940 100.059.952 83.755.849

Long-term benchmark for both activities (basic DEA, θ=1.4)

LCY2002 0.4 417.551 1.187.449 53.000 1.000 38.509.245 12.353.716

MLH2002 0.35 792.765 2.264.199 88.000 31.285 34.865.786 45.079.574

OSL2007 0.19 9.477.511 9.566.489 223.000 97.000 177.975.321 245.588.135

ATH2007 0.08 5.953.814 10.571.571 205.295 119.000 453.224.152 137.319.560

Given that aeronautical revenue maximization is not necessarily an optimal policy,

irrespective of ownership form, the combined formulation (4.02) defines aeronautical revenue

as a non-discretionary output and maximizes commercial revenue alone. The results of this

model suggest that Lyon’s short-term, commercial revenue target should be $63 million, an

increase of 15%, given current aeronautical revenues. The medium-term target (formulation

4.01) suggests an increase of 40% in non-aeronautical revenues to become efficient and the

longer term, standard DEA target requires the same increase of 40% both on the commercial

and aeronautical side respectively (Figure 18). In the combined model, Basel-Mulhouse

appears as an important benchmark and Glasgow acts as a benchmark of increasing intensity

over the years. As also shown in Figure 19, Lyon airport is moving in the direction of Basel-

Mulhouse and Glasgow hence is increasing in efficiency over time. Marseille no longer

Airport Benchmarking from a Managerial Perspective

132

appears as a benchmark in the results of the combined model since the airport generates

substantially lower aeronautical revenues in comparison.

Fig. 18: Current and target output values for Lyon

Non-aeronautical revenues

20.000.000

30.000.000

40.000.000

50.000.000

60.000.000

70.000.000

80.000.000

90.000.000

1998 1999 2000 2001 2002 2003 2004 2005

current value independent model (4.01) combined model (4.02)

Aeronautical revenues

20.000.000

30.000.000

40.000.000

50.000.000

60.000.000

70.000.000

80.000.000

90.000.000

1998 1999 2000 2001 2002 2003 2004 2005

current value independent model (4.01) combined model (4.02)

In summary, were Lyon to adopt the strategies of the short-term benchmarks, aeronautical

revenues were sufficiently high in 2003 and with respect to medium-term benchmarks, the

aeronautical charges were sufficiently high in 2005 (Figure 18). However, Lyon could still

optimize commercial revenues in order to increase managerial productivity. The targets

obtained from the basic DEA results would be very challenging in the short- or medium-term

and should therefore be considered only as a long-term target if at all. As shown in the graphs

of Figure 18, several paths to the Pareto frontier can be defined for the airport in which both

revenues can be expanded equi-proportionally or with greater emphasis on the non-

aeronautical revenues such that the airport remains profit maximizing.

Airport Benchmarking from a Managerial Perspective

133

Fig. 19: Co-plot of output maximization results with emphasis on Lyon49

4.6 Conclusion

DEA has been ubiquitous in the study of airport productivity however the basic DEA

models treat the airport technology as a black box which reduces the usefulness of the model

for purposes of benchmarking. The focus of this paper is to model the airport production

process from a managerial perspective in order to provide a set of models that would aid

benchmarking by applying a network DEA model. Usually network-DEA is applied to

determine the efficiency of sub processes and overall efficiency whereas in our research,

network DEA helps decision-makers to describe the production process, demonstrating the

sequential effects separating final and intermediate outputs including those under partial

managerial control and those that are known to be non-discretionary. Consequently, the

approach connects aeronautical and commercial activities via intermediate products.

To improve the set of peer airports chosen, a dynamic clustering mechanism limits DEA’s

dual variables (benchmark intensities), ensuring appropriate comparability within the dataset.

The dynamic clustering approach proposed by Golany and Thore (1997) restricts the selection

of best practice DMUs according to predefined boundaries within the basic DEA framework.

We extended this method by using integer linear programming which forms reference sets

based on similar mixes of inputs or outputs and intermediate products. As a result each DMU

49 Coefficient of alienation is 0.107 and average of correlations is 0.815

Airport Benchmarking from a Managerial Perspective

134

optimizes only the last stage of the network, taken into account the information from previous

stages. In addition, PCA-DEA is applied to reduce the number of variables when clusters are

too small to avoid the curse of dimensionality. By identifying individual reference sets using

dynamic clustering we provide individual benchmarks for inefficient DMUs, permitting

identification of strategy changes over time and uniqueness with respect to economic

regulation and airport infrastructure. The formulation was further adapted to ensure partial

flexibility with respect to an expensive and complicated infrastructure system. Finally, the

provision of ground handling was shown to severely affect efficiency estimates leading to a

separation in the comparison of those airports that undertake the process in-house compared

to those that outsource.

Data proved to be the most difficult issue for this application. After defining salient

variables (as in Figure 11), we were then forced to reduce the model drastically in the light of

data availability issues (as in Figure 13). It would be extremely helpful were various

government organizations to publicly publish the data that they already collect. However, the

results have shown that compared with the basic DEA approach, network DEA formulations

provide more appropriate benchmarks which may enable airport managers to improve

performance in the short and medium-term. In the case of Hanover, we show that in the short

or medium term it is sufficient to close one of the three existing runways or expand their

cargo operations to increase utilization, whereas basic DEA benchmarks require the airport to

close the equivalent of two runways. In the case of Lyon we demonstrate that in the short-

term the airport earns a sufficient level of aeronautical revenues and simply needs to focus on

improvements on the commercial side. In comparison, the results of basic DEA require Lyon

to increase aeronautical revenues by 40% in order to operate efficiently.

To be in a position to undertake benchmarking exercises requires the collection and

publication of airport related data openly at the federal level since such information would be

of public interest. Furthermore, an airport also produces undesirable outputs such as delays.

Besides the capacity utilization which has been considered in our research, delay substantially

affects airport and airline efficiency and should clearly be included in a benchmarking study.

For improved managerial benchmarking, disaggregated data with regard to non-aeronautical

activities would help to identify successful strategies on the commercial side. Other factors

that are beyond managerial control include the competitive environment, ownership structure

and economic regulation. These aspects influence managerial behaviour and accounting for

them may further improve comparability and permit the relevant authorities to analyze the

impact of cost or incentive based regulation on managerial efficiency.

Airport Benchmarking from a Managerial Perspective

135

4.A Appendix

Tab. 17: Airport dataset50

Code Airport Country Time Period Ground Handling

ABZ Aberdeen UK 1999 not provided AMS Amsterdam Netherlands 1998-2007 not provided ATH Athens Greece 2005-2007 not provided BHX Birmingham UK 2005 not provided BLQ Bologna Italy 2000-2005 provided BRE Bremen Germany 1998-2007 provided CGN Cologne-Bonn Germany 1998-2007 provided CPH Copenhagen Denmark 1998-2007 not provided DRS Dresden Germany 1998-2004 provided DTM Dortmund Germany 2001-2007 provided DUS Dusseldorf Germany 1998-2007 provided FLR Florence Italy 2000-2005 provided FRA Frankfurt Germany 2002-2007 provided GLA Glasgow UK 1999-2006 not provided GOA Genoa Italy 2000-2005 provided GVA Geneva Switzerland 1998-2007 not provided HAJ Hanover Germany 1998-2007 provided HAM Hamburg Germany 1998-2007 provided LBA Leeds-Bradford UK 1998-2006 not provided LCY London-City UK 2002 not provided LEJ Leipzig Germany 1998-2002 provided LGW London-Gatwick UK 1998-2002 not provided LHR London-Heathrow UK 1998-2006 not provided LJU Ljubljana Slovenia 2004-2007 provided LTN London-Luton UK 1998-2005 not provided LYS Lyon France 1998-2005 not provided MAN Manchester UK 1998-2006 not provided MLA Malta Malta 2005-2006 not provided MLH Basel-Mulhouse France 1998-2007 not provided MME Durham Tees Valley UK 2002 not provided MRS Marseille France 1998-2006 not provided MUC Munich Germany 1998-2007 provided NCE Nice France 1998-2006 not provided NUE Nuremberg Germany 1998-2007 provided OSL Oslo Norway 1999-2007 not provided RIX Riga Latvia 2004-2006 provided STN London-Stansted UK 1998-2006 not provided STR Stuttgart Germany 1998-2007 provided SZG Salzburg Austria 2004-2007 provided TLL Tallinn Estonia 2002-2007 provided VCE Venice Italy 2000-2005 provided VIE Vienna Austria 1998-2007 provided ZRH Zurich Switzerland 1998-2007 not provided

50 Source: adapted from SH&E (2002) and Airport Websites

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So which airport is really efficient?

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