efficiency and productivity growth in the european airlines industry

205 Pages·2013·5.15 MB·English

EFFICIENCY AND PRODUCTIVITY GROWTH IN THE EUROPEAN
AIRLINES INDUSTRY: APPLICATIONS OF DATA ENVELOPMENT
ANALYSIS, MALMQUIST PRODUCTIVITY INDEX AND TOBIT
ANALYSIS
Thesis submitted for the degree of
Doctor of Philosophy
at the University of Leicester
by
Meryem Duygun Fethi MA BSc
Management Centre
University of Leicester
January 2000
UMI Number: U125158
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EFFICIENCY AND PRODUCTIVITY GROWTH IN THE EUROPEAN
AIRLINES INDUSTRY: APPLICATIONS OF DATA ENVELOPMENT
ANALYSIS, THE MALMQUIST PRODUCTIVITY INDEX AND TOBIT
ANALYSIS
Abstract
In its early phase of liberalisation process along with some privatisation experiences,
the European airlines industry provides a fascinating case study to investigate the
recent performance record and assess the determinants of performance. We aim to
analyse the performance of 17 European airlines over the period 1991 to 1995. We
utilise the DEA Windows analysis to capture efficiency changes over time and the
DEA based Malmquist productivity index to measure the productivity change and
decompose any change into efficiency and frontier shift effects. Further we use Tobit
analysis to determine the potential determinants of airline efficiency. We find that
results from windows analysis reveal an increasing trend in the efficiency scores for
most airlines in the sample whereas Malmquist analysis shows a decline in the first
two periods, but some evidence of turnaround in 1993-1994, probably with the
introduction of the third liberalisation package. The Tobit results show no significant
role for state ownership, but indicates the importance of subsidy and concentration
policies in explaining the inefficiency differences among airlines.
Keywords: Efficiency; DEA; Malmquist productivity index; Tobit; European airlines
Meryem Duygun Fethi
To my husband, Sami
and our son, Dervis D. Fethi
iii
Acknowledgements
It is a great honour for me to have this opportunity to extend my thanks and
acknowledge the contribution of many people who have helped me to produce my
PhD thesis.
I would like to begin by thanking those who have helped me financially to
start and complete my PhD thesis. I acknowledge a scholarship from the
Association of Commonwealth University for the first three years of my study. I
am grateful to Ms. Pat Greatorex and Professor Peter M. Jackson for providing me
with the tutorship which enabled me to maintain the remainder of my study.
Many thanks should go to Professor Jackson again who has been my
supervisor. Apart from supervising my thesis, I had the great opportunity to
produce papers with him and present them at the international conferences. I
thank him for his encouragement, advice and motivation.
The ten days I spent at the European Summer Institute (University of
Warwick) gave me the chance to make insightful discussions, in particular with
Professors Robert Dyson, Larry Seiford, Leopold Simar, Drs. Val Belton, Viktor
Podinovski and Thierry Post. I must acknowledge Muhittin Kaplan for helping me
with the econometrics and Recep Tekeli for helpful comments. I thank David
Middleton for proof-reading my thesis.
Kees Veenstra, Deputy Secretary General of the AEA introduced me to the
general problems of European airlines at the meeting we had in Brussels. Several
companies kindly sent me their annual reports which enabled me to complete the
empirical analysis. Joanne Grimble of IATA was kind enough to fax the relevant
data. Dervis K. Deniz helped me to sort out the information in the annual reports.
I thank them all.
Many friends need to be thanked, but in particular Ozlem Gorey and Cahit
Ezel who have given me enormous support and encouragement throughout my
study. Many thanks also go to the staff at the University of Leicester Management
Centre, in particular Jenni Austen and Anjula Mehra for their continuous
friendship.
Finally, I want to thank my parents and my in-laws for all their prayers, love
and support. In particular I am indebted to my mum who looked after my son in
order for me to complete this thesis. However, above all, my love and thanks go to
my husband, Sami for always being patient and there for me when I needed him
most. Now that I have completed, my son Dervis can have the time and attention
from his mum that he deserves!
TABLE OF CONTENTS
Title i
Abstract ii
Dedication iii
Acknowledgements iv
Table of Contents v-viii
Abbreviations ix
List of Tables x
List of Figures xi
CHAPTER 1. INTRODUCTION 1-7
1.1 Motive of the Thesis
1.2 Need for An Empirical Study
1.3 Contributions and Methodologies
1.4 Thesis Outline
CHAPTER 2. THEORIES OF OWNERSHIP AND REGULATION 8-28
2.1 Introduction
2.2 Property Rights Theories
2.3 Public Choice Theories
2.4 Regulation Theories
2.5 An overview of privatisation, liberalisation and deregulation
2.6 Conclusion
CHAPTER 3. PREVIOUS STUDIES 29-65
3.1 Introduction
3.2 Ratio Analyses
3.3 Multivariate Analysis
3.4 Value Added Analysis
3.5 Unit Costs and Labour Productivity Analysis
3.6 Total Factor Productivity (TFP) Applications
3.7 Parametric Applications
3.8 Non-parametric Applications
3.9 Conclusion
CHAPTER 4. METHODOLOGY 66-100
4.1 Introduction
4.2 Farrell’s Efficiency Measure
4.3 Data Envelopment Analysis (DEA)
4.4 Graphical Representation of the DEA
4.5 DEA Windows Analysis
4.6 Malmquist Productivity Index
vi
4.7 Graphical Representation of the Malmquist Productivity Index
4.8 Explaining Efficiency with the Tobit Analysis
4.9 Conclusion
CHAPTER 5. THE EUROPEAN AIRLINES INDUSTRY 101-129
5.1 Introduction
5.2 Characteristics of the Industry
5.3 Ownership and Recent Privatisation Practices
5.4 Mergers, Acquisitions and Commercial Agreements
5.4.1 Mergers and Acquisitions
5.4.2 Partnership Agreements Without Equity Participation
5.5 The Evolution of Regulatory Policy in European Aviation
5.5.1 Liberalised Bilateral Agreements in Europe
5.5.2 The Impact of the European Commission
5.5.3 The Impact of the US Deregulation
5.6 Conclusion
CHAPTER 6. MEASURING THE PERFORMANCE OF EUROPEAN
AIRLINES: A NON-PARAMETRIC ANALYSIS 130-145
6.1 Introduction
6.2 Data
6.3 Estimation of DEA Efficiency Scores
vii
6.4 Estimation results: DEA Windows Analysis
6.5 Estimation of Malmquist Productivity Indices
6.6 Conclusion
CHAPTER 7. THE DETERMINANTS OF PERFORMANCE FOR THE
EUROPEAN AIRLINES: AN APPLICATION OF
TOBIT ANALYSIS 146-161
7.1 Introduction
7.2 Determinants of Performance
7.3 Potential Determinants of European Airlines Performance
7.3.1 Competition
7.3.2 Managerial and Organisational Factors
7.3.3 Heterogeneity
7.3.4 Dynamic Factors
7.3.5 Public Policy
7.4 Data
7.5 Explaining the Inefficiency of European Airlines: Tobit Results
7.6 Conclusion
CHAPTER 8. CONCLUSION 162-167
References 168-181
APPENDICES
viii
Abbreviations
AEA Association of European Airlines KLM Dutch Airlines
ANA Ansett Australian National Airways L Labour
ATK Available Tonne Kilometre LOAD Load factor
BCC Banker, Chames, Cooper Model LP Linear programming
C Carrier M Malmquist productivity index
CAA Civil Aviation Authority MC Change in efficiency (Malmquist)
CAB Civil Aeronautics Board MF Change in frontier (Malmquist)
CCR Charnes, Cooper, Rhodes Model MMC Monopolies and Merger Commission
CRS Constant Returns to Scale NFA Non-flight Assets
DEA Data Envelopment Analysis NPA Non-passenger Revenue
DMU Decision Making Unit OC Operating Costs
DRS Decreasing Returns to Scale OECD Organisation for European Economic Cooperation
DUMCON Concentration Dummy OLS Ordinary Least Squares
DUMSUB Subsidies Dummy OWN State Ownership
DUMYE Year Dummy ROUT Route Network Density
EC European Commmission RPK Revenue Passenger Kilometre
ECAC European Civil Aviation Conference RPM Revenue Passenger Miles
EM Average of Entire Period RTM Revenue Tonne Miles
EU European Union SAS Scandinavian Airlines
EUR European Flights SCHED Scheduled flights
GLS General Least Squares STAGE Stage length
LATA International Air Transport Association TAA Trans Australian Airlines
ICAO International Civil Aviation Organisation TFP Total factor productivity
INEFF Inefficiency Score TFW Tactical fighter wings
INTER International Flights UK United Kingdom
IRS Increasing Returns to Scale US United States
ITC Inclusive Tour Charters VRS Variable returns to scale
rv Instrumental Variables WM Weighted mean
JAA Joint Aviation Authorities Y Output