the effect of governance structures on airport efficiency
TRANSCRIPT
The Effect of Governance Structures on Airport Efficiency Performance
– The North American Case
by
QI ZHAO
B.A. in International Economics and Trade, Zhejiang University, 2006
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE IN BUSINESS ADMINISTRATION
in
THE FACULTY OF GRADUATE STUDIES
THE UNIVERSITY OF BRITISH COLUMBIA
(Vancouver)
March 2011
© Qi Zhao 2011
ii
Abstract
Over the last two decades, there have been widespread moves to corporatize, privatize, and
deregulate airports around the world. These changes have created a great diversity of airport
ownership and governance structures. Against this backdrop, this paper applies a stochastic cost
frontier model to examine how the two dominant governance forms of publically owned airports
in North America, namely operation and governance by a government branch and by an airport
authority, affect airport efficiency performance. The data for this study is taken over the 2002-
2008 period from 54 airports in Canada and the US and provided for this thesis in confidence by
the ATRS Global Airport Performance Benchmarking Project.
This study sets out to prove that these two types of governance structures can have significant
effects on the efficiency performance of airports in North America, with the results showing that
(1) the airports operated by an airport authority achieve higher cost efficiency than those
operated by a government branch; and (2) the airports operated by a government branch tend to
have lower labour share than those operated by an airport authority. Moreover, by separating
Canadian and US airport authorities, our study also attempts to determine whether Canadian and
US airport authorities differ in their impact on airport (cost) efficiency performance and hence
should be considered as different types of airport governance. However, our regression models
have not discerned there is any statistically significant difference as to the efficiency
performance between airports operated by US and Canadian airport authorities. It seems
therefore that US and Canadian airport authorities are similar in nature and should not be
considered as different types of airport governance.
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Table of Contents Abstract...................................................................................................................... ii
Table of Contents................................................................................................ iii
List of Tables............................................................................................................ iv
List of Figures ......................................................................................................... v
Acknowledgements ........................................................................................... vi
1 Introduction ............................................................................................................................ 1
1.1 Literature Review ............................................................................................................. 3
1.1.1 Debate over Ownership Efficiency Problem in a Broad Economic Context ........... 3
1.1.2 Efficiency and Productivity of Airports: Different Methodologies .......................... 4
1.1.3 Determinants of Airport Productivity and Efficiency: Ownership and Governance
Structures ............................................................................................................................... 6
1.2 Scope and Objective ......................................................................................................... 9
2 Background Information on Airport Governance Structures ............................................ 11
2.1 Overview of Airport Governance Structures .................................................................. 11
2.2 Airport Governance in Canada ....................................................................................... 12
2.3 Airport Governance in the United States ........................................................................ 14
2.4 The Effects of Governance Structure on Airport Performance ...................................... 17
2.5 Conclusion ...................................................................................................................... 25
3 The Econometric Model ....................................................................................................... 26
3.1 Model of Airport Performance ....................................................................................... 26
3.2 The Framework of the Stochastic Cost Frontier Model ................................................. 28
3.3 The Specifications of the Stochastic Cost Frontier Model ............................................. 29
3.3.1 Specification of the Cost Frontier ........................................................................... 29
3.3.2 Effects of Governance Structures on Airport Cost Efficiency ................................ 32
3.4 Conclusion ...................................................................................................................... 37
4 Sample and Variables ........................................................................................................... 39
4.1 Variable Construction ..................................................................................................... 39
4.1.1 Outputs and Inputs .................................................................................................. 39
4.2 Characteristics of Sample Airports ................................................................................. 41
4.2.1 Summary Statistics of the Airports Operated by the Government Branch ............. 42
iv
4.2.2 Summary Statistics of the Airports Operated by the Airport Authority ................. 43
4.3 Conclusion ...................................................................................................................... 45
5 Empirical Results and Discussion ....................................................................................... 47
5.1 Model Issues and Alternative Considerations ................................................................ 47
5.2 Empirical Results and Discussion .................................................................................. 51
5.2.1 The Effects of Airport Characteristics on Cost Frontier ......................................... 51
5.2.2 The Effects of Airport Governance Structures ....................................................... 59
5.3 Hypotheses Tests for Effects of Airport Governance Structures .................................... 63
5.4 Conclusion ...................................................................................................................... 67
6 Conclusion ............................................................................................................................ 68
6.1 Summary of Key Findings .............................................................................................. 68
6.1.1 Effects of Other Airport Characteristics ................................................................. 68
6.1.2 Effects of Airport Governance Structures............................................................... 69
6.2 Suggestions for Further Research ................................................................................... 69
6.3 Conclusion ...................................................................................................................... 70
Bibliography ................................................................................................................................. 71
Appendices .................................................................................................................................... 77
Appendix A: Classification of Airport Governance Structures ............................................. 77
Appendix B: The Sample Airports ......................................................................................... 78
v
List of Tables
Table 1 Effects of Governance Structure on Airport Operating Total Cost ................................. 21
Table 2 Effects of Governance Structure on Airport Non-aeronautical Revenue ........................ 22
Table 3 Effects of Governance Structure on Shares of Non-aeronautical Revenues ................... 22
Table 4 Effects of Governance Structure on Airport Labour Share ............................................. 23
Table 5 Effects of Governance Structures on Airport Labour Price ............................................. 24
Table 6 Summary of Average Statistics -- Airports Operated by Government Branch ............... 43
Table 7 Summary of AverageStatistics -- Airports Operated by Airport Authority ..................... 45
Table 8 Explanatory Variables and Regression Model for Stochastic Frontier Analysis ............. 50
Table 9 Stochastic Cost Frontier Model A Results ....................................................................... 52
Table 10 Stochastic Cost Frontier Model B Results ..................................................................... 53
Table 11 Stochastic Cost Frontier Model C Results ..................................................................... 54
Table 12 Stochastic Cost Frontier Model D Results ..................................................................... 55
Table 13 Hypothesis Tests for Stochastic Frontier Model A ........................................................ 65
Table 14 Hypothesis Tests for Stochastic Frontier Model B ........................................................ 65
Table 15 Hypothesis Tests for Stochastic Frontier Model C ........................................................ 65
Table 16 Hypothesis Tests for Stochastic Frontier Model D ........................................................ 65
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List of Figures
Figure 1 Non-aeronautical Revenue for Sample Airports in 2008 ............................................... 56
Figure 2 Share of Non-aeronautical Revenue for Sample Airports in 2008 ................................. 56
Figure 3 Labour Cost Share for Airports Operated by Government Branch in 2008 ................... 60
Figure 4 Labour Cost Share for Airports Operated by Airport Authority in 2008 ....................... 61
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Acknowledgements
I would like to express my sincerest gratitude to my supervisor, Professor Tae H. Oum, for all of
his support and encouragement during my master studies and throughout all stages of this thesis.
This thesis would not be completed without his guidance and support. I would like to extend my
appreciation to Professor Anming Zhang for serving on my committee and for his continuous
encouragement and help during my years at UBC. I would like to thank Professor Chunyan Yu
for serving on my committee and for her invaluable advice along the way.
Special thanks to Professor David Gillen for serving as my university examiner and his
great experience and support for my thesis. I would also like to thank Prof. Keth Head, Prof.
Vadim Marmer and Prof. Shinichi Sakata for their help and advice during my study. In addition,
I will be always grateful to Zian Zhao and Natalie Anderson for their attention and help during
my study.
Special thanks to my friend, Nancy Roberts, for helping me go through the darkest time
in my life.
1
1 Introduction
As the most important infrastructure for air transport systems, airports play a critical role in both
the modern economy and society. From the earliest years of airport transport system
development, it was natural for governments to own and operate airports around the world.
Therefore, public ownership and governance of airports were not questioned throughout most of
the history of aviation. Even though economists started expressing concerns about the overall
inefficiency of air transport systems in the 1970s, the initial efforts focused more on the
deregulation and privatization of airlines than on airports or air traffic control systems. Their
reasons for focusing on airlines over airports underline the reality that more efficiency gains
could be made from the former than the latter. However, as deregulation and privatization of the
airline industry and its markets have become the norm, attention has slowly been shifting to
questioning the efficiency of airports and air traffic control systems that have been operating in a
near monopoly market for a long time.
Since the British government privatized their airports in 1987, a great diversity has
emerged in the ways which countries tackle the airport ownership and governance issues. In
Europe and Australia, lots of airports have been fully privatized and governed under different
regulatory structures. However, in Canada and the United States (henceforth referred to as
―North America‖ for convenience), the majority of commercial airports are still publicly owned ,
and operated either by a government branch (mostly city or state governments) or an airport
authority that is also viewed as a quasi-governmental organization.
Despite the uniqueness of the North American airport governance structures, few studies
have investigated on their effects on airport efficiency performance. Many insightful studies,
2
such as that of Oum, Zhang and Zhang (2004), are devoted to examining the effects of ownership
forms and regulatory policies on airport efficiency. Nonetheless, by far the most extensively
studied influence on airport efficiency performance has been that of ownership forms rather than
governance structures. Indeed, there has been little discussion about the way in which different
governance structures affect airport efficiency or productivity performance. Furthermore, very
few studies have focused solely on measuring and comparing the effects of airport governance
forms on the efficiency performance of North American airports. To fill theses gaps, this study
attempts to examine how the two dominant types of governance forms (i.e., a government branch
vs. an airport authority) affect the cost efficiency performance of airports in North America.
We apply a stochastic cost frontier model to investigate the effects of these two types of
airport governance structures, not only on technical inefficiency but also on allocative
inefficiency, in particular, variable input usage. This study sets out to prove that these two types
of governance structures can have significant effects on the efficiency performance of airports in
North America, with the results showing that (1) the airports operated by an airport authority
achieve higher cost efficiency than those operated by a government branch; and (2) the airports
operated by a government branch tend to have lower labour share than those operated by an
airport authority. Moreover, by separating Canadian and US airport authorities, our study also
attempts to determine whether Canadian and US airport authorities differ in their impact on
airport (cost) efficiency performance and hence should be considered as different types of airport
governance. However, our regression models have not discerned there is any statistically
significant difference as to the efficiency performance between airports operated by US and
Canadian airport authorities. It seems therefore that US and Canadian airport authorities are
similar in nature and should not be considered as different types of airport governance.
3
To set the stage, this chapter starts off an overview of the academic discussion about the
relationship between ownership reform and productivity or efficiency performance. Based on
recent economic literature, we summarize the impact of ownership and governance structure on
productivity and efficiency in the airport sector. In section 2, we outline the scope and objective
of our study.
1.1 Literature Review
It seems that various countries have tackled the airport ownership and governance issue in a wide
variety of ways. How do such different packages adopted by countries affect airport efficiency?
At first glance, it seems a relatively straightforward question, but the answer is neither simple
nor straightforward, and any attempt to answer it almost immediately reveals a history of
prolonged controversy.
1.1.1 Debate over Ownership -- Efficiency Problem in a Broad Economic Context
The 1970s saw the resurgence of the notion that transferring assets from the public to the private
sector would raise both allocative and technical efficiency, leading to greater economic well-
being for all. Some early economic studies enthusiastically reported that the evidence stood in
favour of private-sector performance, as found, for instance, by De Alessi (1980) and Bennert
and Johnson (1980). Later studies, however, have been more cautious. In their widely quoted
1983 survey, Millward and Park (1983) arrive at the conclusion that there is no systematic
difference in performance under public or private ownership. Further, by assuming that both
public and private firms implement the optimal contract, De Frajia (1993) demonstrates that
public ownership yields a higher level of productive efficiency than private ownership. In
addition, many scholars also point out that complementarities exist between the optimal choice
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for the form of privatization and the design of the legal framework within which privatized firms
will operate. Indeed, the evidence provided by Vickers and Yarrow (1988) casts doubt on
whether regulating a private firm by strictly defined rules leads to better performance efficiency
than the internal regulation that characterizes public ownership.
Meanwhile, the presence of mixed private and public ownership only further complicates
the debate over the relationship between ownership and efficiency. Although several interesting
properties of mixed enterprises have been revealed, the answer to the impact of mixed ownership
is not obvious and has not fully been settled. For instance, in their 1986 survey, Boardman, Eckel
and Vining (1986) argue that mixed enterprises tend to be more efficient in production than
public enterprises, but less profitable than those totally owned by the private sector. However, by
solely focusing on firm’s profitability, Boardman and Vining (1989) arrive at somewhat different
conclusion and suggest that the mixed enterprises perform no better and, in fact, often worse than
the private or public enterprises.
1.1.2 Efficiency and Productivity of Airports: Different Methodologies
Prior to 1990, there was relatively little attention paid to questions of airport performance, in
terms of productive efficiency. In the latter half of the 1990s, however, there has been a small
boom in modeling airport efficiency. Different methodologies have been proposed to provide an
adequate measure of airport performance. These methods can be broadly classified into non-
parametric and parametric. Non-parametric methods include indexes of partial and total factors
productivity (TFP), and data envelopment analysis (DEA). Parametric methods involve the
estimation of neoclassical and stochastic cost or production function. At least in part, these two
methods can be reconciled, and it is important to do so.
5
Thus far, much of the work which has been done on airport efficiency and productivity,
such as that of Doganis (1992), has taken the form of partial productivity measures. Partial
productivity measures are useful to compare performance across airports operating in similar
operating environments or over time within an airport when the operating environment and input
prices remain relatively stable. However, partial productivity measures related an airport’s output
to a single input factor. The productivity of any one input depends on the level of other inputs
being used; high productivity performance in one input may come at the expense of low
productivity of other inputs. Therefore, it is desirable to have a more comprehensive measure of
productivity in order to make more confident assessments of airport performance.
As an alternative to construct indexes of partial productivity, a Total Factor Productivity
(TFP) index has been widely used in measuring airport productivity performance. It is
noticeable, however, that a TFP index is a ratio of a total (aggregate) output quantity index to a
total (aggregate) input quantity index. The TFP index, itself, only yields a ―gross‖ measure of
productivity changes. It does not distinguish among sources of productivity growth. Thus, in
order to make inference about airport efficiency it is necessary to separate out the influences,
such as operating environments and scale of outputs, on the ―gross‖ measure of TFP. Hooper and
Hensher (1997), Nyshadham and Rao (2002), and Yoshida (2004) have used regression analysis
to decompose a TFP index and further investigate the productivity efficiency of airports from
different regions.
Nonetheless, since the TFP method requires detailed specifications on both an airport’s
outputs and inputs, the lack of accurate and consistent capital input measures1 inevitably limits
1 Since different countries often have different accounting convention and construct official data in different ways,
it is nearly impossible to compile accurate and consistent capital input measures.
6
the application of this method, as the results of its analyses of airport performance over time and
across countries are unreliable. To overcome this problem, the Air Transport Research Society
(ATRS) proposes a Variable Factor Productivity (VFP) method, which only requires the data on
variable input factors. This method has been applied to measuring the level of productivity in
ATRS annual airport benchmarking reports. Further, many researchers, such as Cooper and
Gillen (1994), Gillen and Lall (1997, 2001), Sarkis (2000) and Barros and Dieke (2007), have
also applied a Data Envelopment Analysis (DEA) method to evaluate the efficiency performance
of airports under different circumstances.
More recently, a Stochastic Frontier Analysis (SFA) has been incorporated to analyze
airport efficiency performance. As an econometric technique, SFA specifies the underlying
production/cost function when conducting efficiency analysis. In contrast to the conventional
econometric approaches, SFA introduces an additional one-side error term, alongside the
traditional symmetric noise term, to capture unexplained inefficiency in the production/cost
frontier. Barros (2008) and Martin et al. (2009) have used this method (SFA) to investigate the
cost efficiency performance of Spanish airports.
1.1.3 Determinants of Airport Productivity and Efficiency: Ownership and
Governance Structures
From the 1990s on, the momentum to privatize airports has been gaining strength throughout
most of the world. These changes in airport ownership are usually accompanied by explicit price
regulations. This subject at hand has attracted the attention of a wide range of researchers, who
focus on the effects of ownership and associated regulations on airport efficiency performance.
There are many fine studies already on this topic: for the UK, Beesley (1999) and Starkie (2001);
7
for Australia, Forsyth (1997, 2002) and Hooper et al (2000). Nevertheless, these studies have
chosen a geographical structure, which highlights diversity rather than drawing out unifying
themes. A highly influential study, by Oum, A. Zhang and Y. Zhang (2004), investigates 60
airports worldwide under different ownership forms and provides both theoretical and empirical
evidence on the impact of different economic regulations on airport efficiency performance.
Their results support the argument that dual-till regulation would be more economically efficient
than the single-till regulation, especially for large, busy airports. Moreover, their empirical
results indicate that privately owned airports do not necessarily achieve higher capital input
productivity or total factor productivity than publicly owned airports do.
However, it is not only ownership and its associated regulations that determine
performance but also airport governance structures themselves can have significant effects on
performance. In his insightful work, Gillen (2010) not only adopts a descriptive approach to
examine the evolution of airport governance but also proposes two-sided platforms2 to consider
airport governance structure issues. Apart from this work, a limited number of studies have
explicitly analyzed the impact of governance structures on airport efficiency performance.
Moreover, it remains inconclusive as to how different governance structures affect the efficiency
and productivity of North American airports. Although most empirical evidence suggests that
airports operated by a port authority are less efficient than those run by either a government
branch or an airport authority, it has not been fully determined how the airports operated by the
latter two governance structures differ in their efficiency and productivity performances.
2 This is a new study, with its initial stream of articles emerging in 2003. Some economists would argue that two-
sided platforms (airports and airlines) internalize usage externalities that agents cannot internalize efficiently Rochet
and Tirole (2005) define two sided markets as a situation in which the volume of transactions between end-users
depends on the structure and not only on the overall level of the fees charged by the platform (Gillen 2010).
8
On the one hand, some studies argue that the difference in efficiency between airports
operated by these two governance structures (i.e. a government branch vs. an airport authority) is
negligible in North America. For instance, by using the VFP method to measure the productivity,
the study by Oum, Adler and Yu (2006) examines the impact of six different governance
structures3 on the productivity performance of 116 airports worldwide. One of their finding
suggests that, in terms of productivity of North American airports, there is no significant
difference between the airports operated by an airport authority and airports operated by a
government branch.
On the other hand, other studies, such as Oum, Yan and Yu (2008) and Craig, Airola
and Tipu (2005) detect a better performance from the airports operated by an airport authority.
Oum, Yan and Yu (2008) compare the cost efficiency among 109 airports worldwide with
respect to seven different airport governance structures4. By estimating a stochastic cost frontier
via a Bayesian approach, they argue that the airports run by an airport authority perform far more
efficiently than those operated by a government branch in North America. Based Solely on US
airport data, Craig, Airola and Tipu (2005) find consistent results as Oum et al (2008) that
airports operated by airport authorities outperform those operated solely by the government
branches in term of technical efficiency. Nevertheless, the omission of airport non-aeronautical
service may bias the results from Craig et al (2005), since the majority of US airports generate a
high proportion of their total revenues from non-aeronautical services.
3 See appendix A
4 See appendix A
9
1.2 Scope and Objective
While it is clear that there has been a worldwide move towards privatizing airports over the last
two decades, not all countries have joined this push towards privatization; the United States and
Canada have done little in the way of privatization and keep their airport sectors mainly under
government ownership. Given the recent growth in the privatization of airports, it is perhaps
more interesting to ask how airports are governed and operated in countries in which
privatization has not been undertaken. Also, the literature on airport governance structures
provides few concrete, quantitative analyses of how different governance structures affect airport
efficiency or productivity. Especially in North America, there has been no consensus, so far, as
to which airport governance structure is better to foster airport efficient performance.
Therefore, this paper seeks to present new empirical evidence on the way in which two
different governance structures—a government branch and an airport authority—affect the cost
efficiency performances in North American airports. To achieve this objective, we apply a
stochastic cost frontier model to an unbalanced panel of 54 airports over 2002-2008. In
particular, our model will not only capture unobserved airport inefficiency, but also examine
whether different airport governance structures can explain them. Moreover, we argued that
governance structures will exert influence on airport operation or production in a non-neutrally
input-augmenting fashion and thus employed a parametric method to measure the impact of
governance structures on airport variable input usage.
To achieve these objectives, the structure of this paper is as follows: In Chapter 2, we
begin with a short section on reviewing the evolution of airport ownership and governance
around the world. Then, we turn on exploring fundamental characteristics of airport governance
structures adopted in North America. Chapter 3 starts by comparing and contrasting different
10
models used for measuring airport efficiency performance. After explaining motivations for
using stochastic frontier model, this chapter further discusses the specification of our stochastic
cost frontier model. Chapter 3 contains a summary of the data, where the sample airports are
categorized into two groups: those operated by a government branch and those run by an airport
authority. We present the empirical results and a discussion of our findings in Chapter 4. Finally,
Chapter 5 summarizes our study and considers some potential follow-up studies.
11
2 Background Information on Airport Governance Structures
It is not an easy question to determine whether the form of governance makes a significant
difference in achieving airport operational efficiency in North America. This question is
complicated because: (1) the definition of airport governance varies across countries; (2) it
remains a challenge to provide an adequate measure of airport efficiency; (3) airport
performance is the result of multiple factors, governance structure being one of the factors. Since
the following two chapters will provide detailed answers to the latter two questions, we here
attempt to develop a basis of understanding the evolution of airport governance and the ways in
which different airport governance types may influence efficiency performance of North
American airports. This kind of scrutiny provides a factual profile of airport governance forms
in North America and fosters the identification and discussion of the most crucial matters: how
do governance structures affect airport efficiency performance in North America.
2.1 Overview of Airport Governance Structures
In the 1980s, long after a wave of privatization swept through utility industries around the world,
policy-makers began to turn their attention to reforming airport governance. At the time, most
airports round the world were owned and operated by the public sector. One potential catalyst for
airport reform in the 1970s and 1980s was the dramatic growth in air travel brought about by
deregulation in the airline industry in North America and elsewhere. Rising passenger demand
led to airport congestion and the need to invest in additional capacity, and, more importantly, to
increase the productivity of airports.
To respond to these needs, there was an extensive attempt to alter the governance
framework in which airports operate. Different regimes adopted by countries (Oum, Adler and
12
Yu, 2006, and Oum, Yan and Yu, 2008) can incorporate different degrees of private-sector
involvement, ranging from a complete sell-off of public airports to private investors
(privatization) to simply demanding more self-sufficiency with respect to public services. The
move towards full privatization has been strongest in the UK and, later, Australia and New
Zealand. In continental Europe, there has been a preference for partial privatization, with the
public sectors remaining with majority ownership.
Surprisingly, with all of the changes brought about by consolidation and restructuring of
the airline industry, Canada and the United States (henceforth referred to as ―North America‖ for
convenience) are closer to the opposite end of the spectrum. Even though there is a long tradition
of privately owned utilities and transport industries, virtually all major airports in North America
are publicly owned. What distinguishes North American airports is, therefore, not their form of
ownership but their unique governance structures.
2.2 Airport Governance in Canada
From the 1960s through the 1980s, airports in Canada were governed by the Canadian Air
Transportation Administration (CATA), a division of Transport Canada. In the 1970s, growth in
air transport together with technological changes placed substantial stress on the airport system.
Under the existing airport governance framework, public-sector managers had little incentive to
increase revenues or improve cost efficiency. To promote commercialization and efficiency, the
initiative to reform Canadian airports was first taken in 1987. The federal policy, ―A Future
Framework for Airports in Canada‖, allowed provincial, regional or local authorities to assume
financial responsibility for airports, and directly manage and operate airports by virtue of a long-
term ground lease drafted by Transport Canada. As a result, ―local airport authorities‖ (LAAs)
were established in Montreal, Calgary, Vancouver and Edmonton in 1992.
13
The Canadian government further advanced airport governance reform with the
introduction of the National Airports Policy in 1994. Under this policy, 64 regional airports and
30 small airports have sold to their communities, usually for a nominal amount. 8 arctic airports
have been transferred to provincial or territorial governments, whereas 13 remote airports are
remained under federal government operation. Most importantly, the National Airports Policy
guarantees the transfer of responsibility for the operation, and management of 17 large national
airports to ―Canadian Airport Authorities‖ (CAAs) on long-term leases.
The leased LAA and CAA airports comprise 22 airports that link Canada from coast to
coast and internationally. Currently, these 22 airports serve 90 per cent of all scheduled
passenger and cargo traffic in Canada and are the points of origin and destination for almost all
interprovincial and international air service in Canada. Given their importance to the country and
society, our following discussion centers on the governance regime under which both the LAAs
and CAAs operate.
In Canada, the concept of airport authority governance is that of a private sector
corporation which operates an airport. What differentiates an airport authority from the private
corporation is that an airport authority is not-for-profit and thus has no shareholders. Between the
LAAs and CAAs, there is a slight difference in terms of public accountability provisions5. The
CAAs must publish 60-days’ advance notice and justification for price increases in the local
media, while Transport Canada may audit the LAAs’ records and procedures at any time and
review the LAAs’ performance every five years. Despite of this, both LAAs and CAAs are
private, self-financing, not-for-profit, non-share-capital corporate entities that do not pay income
5 Canadian federal government has examined the principles under which the local airport authorities (LAAs) were
created and has revised them in the areas relating to the CAA's accountability to the communities they serve.
14
tax. Their leases on Canadian federal infrastructure are for 60 years with an option to renew for
an additional 20 years. Although some business practices are controlled through the lease
document, the LAAs and CAAs are not subject to economic regulation through legislation.
Furthermore, the LAAs and CAAs enjoy the freedom to set up the prices for various airport
activities (e.g., parking, rent, landing aircraft, terminal use, etc.) and determine service levels
within the safety regulatory framework. In addition, the LAAs and CAAs operate airports with
virtually no federal assistance or subsidy. To the contrary, with required ground lease payments,
the Canadian airport authorities have become a source of significant general treasury revenues
for the federal government.
2.3 Airport Governance in the United States
Governance and ownership of airports is actually quite complicated in the US, as it can differ for
each state. Therefore, we do not attempt to draw sweeping conclusions, but seek to depict
general pattern that has emerged in the types of airport governance in the US.
Although there is no single path by which US airports came to their present governance
form, state governments have exerted a strong influence on restructuring and forming airport
ownership and governance in the United States. In the first part of the 20th century, state
governments in the US began enacting legal statues that explicitly authorized local governments
to establish and operate airports. These statutes promoted a wave of lawsuit from nearby
communities, from different government subdivisions competing for control of the new airports,
and from taxpayers challenging the use of tax revenues to build and operate airports. By and
15
large, the statues survived these challenges. Nowadays, with few exceptions6, majority of
commercial airports in the US are owned by local governments.
As state governments experimented with different forms of airport governance, state
legislatures enabled state or local governments to establish their own aviation departments.
Moreover, these legislatures either created individual airport authorities, such as State of
Michigan and Minnesota, or authorized local governments to create individual airport
authorities. As a result, these two governance types have become the most common forms of
airport governance structure in the United States. The most recent ACI-NA survey, conducted in
2003 primarily among larger airports, has revealed that more than 60% of the airports responding
to the survey were operated either by a local/regional government branch or by an airport
authority. The rest of our discussion in this section therefore will focus on these two dominant
types of airport governance structures (i.e., a government branch vs. an airport authority).
It has long been the tradition that airports are operated by local or regional government
branches (i.e., a division or department of aviation). Such an aviation department is usually
separated from other departments, but often uses some functions of local government, for
example accounting services, or purchasing decisions. Within an aviation department, the board
directors are appointed by the chief executive officer of the local government and are ultimately
responsible to the councils. Generally speaking, these board directors in an aviation department
cannot enter into contracts without the approval of the (city) councils which literally own the
airport. Moreover, the annual budget, bond sales and other similar measures of an aviation
6 Two airports, Dulles and Reagan National Airports are owned by the federal government, while the following state
own and operate their own airports: Alaska, Arizona, Connecticut, Hawaii, Maryland, Minnesota, New Hampshire,
and Rhode Island.
16
department also need to be approved by the councils. In some states, such as Alaska and
Maryland, the aviation department can call upon general-fund revenues to subsidize airport
operations and capital development, whilst, in other areas, such as Illinois and Nevada, the
aviation department self-supports and operates without financial assistance from local/regional
government. In most areas, the aviation department not only administers all aspects of airport
operation and development, but also is responsible for setting, modifying and implementing rules
and regulations that will affect the airport. Elsewhere the aviation department operates the
airports directly but with the help of an advisory board as happens at Atlanta. It should be noted,
however, that the advisory board is only involved in setting and modifying rules and regulations
rather than operation or management of airports.
As an alternative to direct control by local/regional government, airport authorities were
first established to assume control over public airports during the 1950s and 1960s. Unlike those
in Canada, US airport authorities are considered as public agencies since they are created by the
local/regional governments that own airports. Although few airport authorities, such as the Great
Orlando Aviation Authority and the Metropolitan Washington Airports Authority, lease the
airport from the government, majority of local/regional governments directly transferred and
delegated all airport managerial responsibilities to an airport authority at no cost or nominal cost.
At large, an airport authority resembles an autonomous corporation with its own functional
departments, such as finance and procurement departments. While airport authorities are
structured as independent and self-supporting institutions, the board members of an airport
authority are always elected by the local/regional government. The boarder members are
authorized to appoint the chief executive officer of an airport authority and veto authority
decision. Therefore, the local government, to a greater or lesser degree, can exercise varying
17
levels of oversight and control via the makeup and structure of the board. In some states, such as
state of Florida, the elected public officials are allowed to serve and always server as the board
members, whistle, in other states, such as state of Michigan, state legislatures have ruled out the
elected public officials as the board member of airport authorities. For instance, Mayor of the
City Orlando and other public officials are serving as the board member of the Great Orlando
Aviation Authority, whereas the board of the Cincinnati/Northern Kentucky International Airport
only consists of both civic and business leaders.
2.4 The Effects of Governance Structure on Airport Performance
Of critical importance to our study is to differentiate airport governance structures adopted in
North America. As discussed previously, the model that airports are operated by local or regional
government branches is clearly defined and unique to the US. However, the terminology of
Airport Authority has some ambiguity to it. It has been used in Canada as a private sector
corporation, whilst the terms airport authority, in the US, is considered as a quasi-governmental
operation model. In previous research it is conventional to consider both Canadian and US
airport authorities as one type of airport governance and simply refer to them as ―airport
authority‖. This argument rests on the fact that these airport authorities are not-for-profit/non-
shareholder entities that re-invest retained earnings into future airport development programs and
are by-and-large financially self-sustaining. However, differences occur between US airport
authorities and their Canadian counterparts.
Regardless of governance structures, US airports have developed particular relationship
with their customers, airlines for example, and financial structures which distinguish them from
airports in Canada. For instance, US airports enter into legally binding contracts known as
airport-use agreements which detail the conditions for the use of both airfield and terminal
18
facilities. These contracts are negotiated between the airport and its airline customers. The
contracts will specify the fees and rental rates which an airline has to pay and the method by
which these fees are to be calculated. In respect to sources of capital investment, many US
airports are financed partly or largely from the private sector through the bond market.
Moreover, US airports are eligible to be funded by the federal government via the Airport
Improvement Program (AIP), which is administered by the Federal Aviation Administration
(FAA). In addition, while Canadian airports are not directly regulated, US airports are subject to
some general pricing rules. For instance, US airports are required to set aeronautical fees so as to
collect revenues that reflect the costs of providing the services.
Despite of their particular relationship with airlines and financial sources available, US
airport authorities are different from their Canadian counterparts as the selection of Board
members. As for US airport authorities, their board members have to be appointed by the state
and local government which own the airport, while the board members of the airport authority in
Canada are generally appointed by local community organizations. However, it remains a
question how such different selection processes affect airport efficiency performance. There is
no doubt that political motivated appointment of the Board members leaves US airport
authorities vulnerable to changes in administration and to the exertion of political decisions of a
business nature. It is noticeable, nonetheless, that the board members of US airport authorities
either serve on a voluntary basis or are paid by a small stipend for each official meeting or
activity. It is therefore possible that the Board members of US airport authorities are more likely
to represent the communities that airports serve and thus have a strong interest in the
performance of the airport. On the other hand, the Board members of Canadian airport
authorities receive compensation for their service on the Board and thus would be distant from
19
the communities that airports serve. It is then likely that Canadian airport authorities enjoy
greater autonomy from the electorate and from a single accountable body than US airport
authorities. Such greater autonomy may trigger greater rent seeking activities of bureaucrats in
the authority and potentially induce inefficiencies in airport operation.
While the definition of an airport authority varies, this model rivals direct control by
local/regional governments, i.e., an aviation department runs the airports owned by
corresponding local/regional governments. Compared to airport authorities, directors of aviation
departments may be more sensitive to voter demands but also consider airports as stimulating
development. Hence they would like to pursue more cost effective strategies and articulate clear
incentives for efficient performance.
The benefits of direct control often are balanced against the belief that greater autonomy
can lead to improved performance and greater efficiency. It is often noted that airport authorities
are less liable to political interference. To a large extent, airport authorities avoid many of the
civil services, contract approval, and other constraints of aviation departments. Moreover,
managers of airport authorities may have greater knowledge and expertise regarding the
specialized aviation industry. In addition, it has been long argued that there is some potential
inefficiency in procurement practices of aviation departments. As discussed in Section 2, airports
run by a government branch rely on the local government staff to make purchasing decisions
which often make the process longer and less efficient. In most cases, the political influence
form local government ultimately prevents aviation departments from procuring services from
the most cost-effective source.
20
Here, we adopt some simple assessments to see whether there are any clear patterns of
performance between different airport governance structures. For the assessment, we identify a
sample of 54 airports in North America (see appendix B). The data covers the time frame from
2002-2008 and all the monetary values have been converted into US dollars. The sample
airports have been split with 26 airports operated by a government branch and 28 airports
operated by an airport authority. We applied the one-way analysis of variation (ANOVA) to
compare the performance of the sample airports as regards the following statistics.
As a simple summary means of evaluating the overall (cost) efficiency performance, we
first compare the total operating cost of the sample airports by using a one-way ANOVA
analysis. Over 7 years, the average total operating cost of airports operated by a government
branch was US $ 131 million, as opposed to US $73 million for those operated by an airport
authority. The result from Table 1-A shows that airports operated by an airport authority have
significantly lower total operating cost than those operated by a government branch. To bring
more insights, we further break down airports operated by an airport authority for airports run by
US airport authorities and Canadian airport authorities. As shown in Table 1-B, there is
statistically significant difference between airports operated under these three governance types
with regard to their total operating costs. Airports operated by a government branch tend to have
higher total operating costs than those by either US or Canadian airport authorities.
21
Table 1 Effects of Governance Structure on Airport Total Operating Cost
Table 1-A Governance Structure vs. Total Operating Costs
Croups Count Average( Variance(
Airport Authority 192 7.32 3.80
Government Branch 175 13.2 12.4
DF Sum SQ( ) F Value
Government Structures 1 3.15 39.76
Residuals 365 28.9 Table1-B Governance Structure vs. Total Operating Costs (Alternative Classification)
Groups Count Average( Variance( )
US Airport Authority 143 8.38 44.1
Canadian Airport Authority 49 4.22 7.65
Government Branch 175 13.2 124
DF Sum SQ( ) F Value
Governance Structures 2 3.78 24.33
Residuals 364 28.3
Since many airports aim to increase revenues from commercial services and other non-
aeronautical activities, we also examine the effects of governance forms on airport non-
aeronautical revenue. Surprisingly, Table 2 –A and B indicate that airports operated by a
government branch tend to have significantly higher non-aeronautical revenue than those under
other governance structures. However, the picture looks different as to the percentage of non-
aeronautical revenue. It is clear from Table 3 A and B that airports operated by a government
branch tend to derive a lower percentage of their revenue from non-aeronautical activities than
their counterparts under other two governance forms.
22
Table 2 Effects of Governance Structure on Airport Non-aeronautical Revenue
Table 2-A Governance Structure vs. Non-Aeronautical Revenue
Croups Count Average( Variance (
Airport Authority 192 6.85 3.23
Government Branch 175 9.27 4.85
DF Sum SQ( F Value
Governance Structures 1 5.35 13.40
Residuals 365 146
Table 2-B Governance Structure vs. Non-Aeronautical Revenue(Alternative Classification)
Groups Count Average ( Variance (
US Airport Authority 143 7.30 3.60
Canadian Airport Authority 49 5.55 1.96
Government Branch 175 9.27 4.84
DF Sum SQ ( F Value
Governance Structures 2 6.46 8.12
Residuals 364 144
Table 3 Effects of Governance Structure on Shares of Non-aeronautical Revenues
Table 3-A Governance Structure vs. Shares of Non-Aeronautical Revenue
Croups Count Average Variance
Airport Authority 192 0.54 0.015
Government Branch 175 0.40 0.013
DF Sum SQ F Value
Governance Structures 1 0.182 12.95
Residuals 365 5.146
Table 3-A Governance Structure vs. Shares of Non-Aeronautical Revenue (Alternative Classification)
Groups Count Average Variance
US Airport Authority 143 0.55 0.018
Canadian Airport Authority 49 0.52 0.004
Government Branch 175 0.50 0.013
DF Sum SQ F Value
Governance Structures 2 0.206 7.32
Residuals 364 5.123
23
Finally, different governance structures may induce different levels of provisions over
airport hiring and procurement activities. Therefore, we also look at some statistics with regard
to airport personnel expenses. Although there is no statistically significant difference between
airports under different governance structures as regards their labour price (shown in Table 5),
airports operated by a government branch tend to have a lower labour share those operated under
other governance structures (shown in Table 4).
Table 4 Effects of Governance Structure on Airport Labour Share
Table 4-A Governance Structure vs. Labour Share
Croups Count Average Variance
Airport Authority 192 0.43 0.010
Government Branch 175 0.39 0.011
DF Sum SQ F Value
Governance Structures 1 0.14 13.46
Residuals 365 3.89
Table 4- B Governance Structure vs. Labour Share (Alternative Classification)
Groups Count Average Variance
US Airport Authority 143 0.44 0.010
Canadian Airport Authority 49 0.40 0.007
Government Branch 175 0.39 0.011
DF Sum SQ F Value
Governance Structures 2 0.18 8.64
Residuals 364 3.85
24
Table 5 Effects of Governance Structures on Airport Labour Price
Table 5-A Governance Structure vs. Labour Price
Croups Count Average ( Variance (
Airport Authority 192 6.88 2.90
Government Branch 175 6.92 5.11
DF Sum SQ ( F Value
Governance Structures 1 1.55 0.04
Residuals 365 14400
Table5-B Governance Structure vs. Labour Price (Alternative Classification)
Groups Count Average ( Variance (
US Airport Authority 143 6.83 2.78
Canadian Airport Authority 49 7.02 3.29
Government Branch 175 6.92 5.11
DF Sum SQ ( ) F Value
Governance Structures 2 1.55 0.20
Residuals 364 1440
Although we have detected some difference in the performance of airports under different
governance forms, these average and simple comparisons should not be viewed as conclusive.
Many other factors relating airport performance are not within the control. Therefore, more
rigorous statistical method should be used to establish a relationship between governance
structure and airport performance.
25
2.5 Conclusion
In this chapter, we briefly surveyed the current state of airport governance around the world and
shed more light on discussing the types of airport governance in North America. In addition, by
applying a simple ANOVA analysis, we have discerned some evidence from a cursory review of
different types of financial data that airports operated by a government branch perform
differently than those run by an airport authority. However, given the preliminary nature of these
assessments, it is too early to make any definite statement about the impact of governance forms
on airport efficiency performance. The inherent complexity of the airport industry calls for a
more rigorous analysis that can better control for the factors other than governance structures to
correlate airport governance and its efficiency performance.
26
3 The Econometric Model
In Chapter 2, we provided some preliminary analysis on the effects of governance structures on
the efficiency performance of North American airports. In this chapter, we will discuss how to
analyze such effects in a more sophisticated econometric manner. We start off by comparing and
contrasting different methodologies or models used to measure airport efficiency performance. In
Section 2, we review the basic framework of our stochastic frontier model. Based on this
framework, we then proceed, in Section 3, to discuss each constituent element of our model.
Particularly, we provide specific details on the formulations which permit us to examine the
effects of governance structures on both technical inefficiency and variable input usage.
3.1 Models of Airport Performance
As indicated in Chapter 1, a lot of efforts and resources have been expanded in exploring
measures of performance in air transport industry over the last two decades. The 1990s,
nonetheless, was a distinctly late time for there to be an awakening of interest in airport
performance. One reason why models of airport performance have been only relatively recently
developed lies with the difficulties associated with the task. It can be argued that it is more
difficult to develop satisfactory models than it is for other sectors in transport, airlines for
example. To provide a confident measure of airport performance, a model must solve the
aggregation problem, and ensure that the diverse outputs and inputs of the airport being
aggregated in a meaningful way. Furthermore, since there are many factors that affect the
relationship of inputs to outputs, it is necessary to be able to relate these factors to measured
productivity. Among the standard approaches introduced in Chapter 1, non-parametric
approaches, such as TFP and DEA, readily handle a large number of input and output categories,
27
more easily than they can be accommodated in econometric estimation methods. But
econometric estimation methods, if data are available, build on established economic theory
relationships and separate out the influences on cost/productivity. In this study, we argue that it
is not sufficient to simply describe airport performance but also to be able to assess and
understand how different governance structures can affect it. Hence econometric methods are
preferred since it is desirable to isolate governance structures from other sources which also
affect measured airport performance.
In regard to econometric methods, traditional econometric methods for estimating cost
or production functions implicitly assume that all firms are successful in reaching the efficient
frontier (and only deviating randomly). If, however, firms are not always on the frontier, then the
conventional estimation method would not reflect the (efficient) production or cost frontier
against which to measure efficiency. For this reason, we would like to estimate either frontier
production or cost functions recognizing that some firms may not be on the efficient frontier. The
use of frontier cost functions requires the specification of assumptions on firm behaviour and the
properties of functional forms. The assumptions on production technology depend on the
specification of the cost function, but in all cases the firm pursues the strategy of cost
minimization. As an alternative to cost functions, one could use production functions, for which
the assumption of cost minimization is not necessary. However, it is difficult to estimate a
production function when firms produce more than one output. Granted that multiplicity of
output is a key feature of airports, cost-function approach enables us to bypass the aggregation
problem but also incorporate the diverse outputs and inputs of the airport in our analysis. Taking
everything into consideration, our study will rest on a stochastic cost frontier model and
28
econometric techniques presented in the following sections will further explore the ways in
which we analyze the effects of governance structures on airport efficiency performance.
3.2 The Framework of the Stochastic Cost Frontier Model
A large body of literature commencing with work by Knight (1933), Debreu (1951), and Farrell
(1957) has argued that the conventional production/cost function is merely an efficient frontier
and that the existence of inefficiency necessitates the incorporation of a nonzero disturbance in
the function. In agreement with this argument, Aigner, Lovell and Schmidt proposed a so-called
Stochastic Frontier Analysis (SFA) in their pioneering empirical study in 1977. The basic
empirical framework for SFA is a regression specification involving a logarithmic
transformation that adds a positive random error term, along with the traditional symmetric noise
term, to capture unexplained inefficiency. Following this path, we outline the basic framework of
our stochastic cost frontier model as follow.
In the short run, if an airport tries to minimize its production cost (C) given the outputs
(Q), variable input prices (W), and capital inputs (K), then the cost minimization frontier in a
logarithmic form can be expressed as ,where i represents airport and t
represents time. In reality, airports may deviate from their cost minimization objective for
various reasons and such deviations indicate the existence of inefficiency. To reflect this reality,
for airport , we denote a positive random term as technical inefficiency, which indicates the
deviation of airport actual cost from its cost frontier. Further, since our interest centers on
determining whether and/or how governance structures can affect airport efficiency performance,
we assume that the technical inefficiency term depends on airport governance structure
variable , with the dependence expressed as . Moreover, it is possible that
29
governance structures can assert influence not only on airport technical inefficiency but also on
allocative inefficiency. Although we cannot fully analyze the impact of governance structures on
allocative inefficiency due to estimation problems, we model as the interaction
between airport governance structure variable and variable input price variables ( ). By
applying Shephard’s lemma, this specification will allow us to investigate how different
governance structures alter airport variable input usage. Taken together, the observed actual
production cost7 of airport i at time t can be expressed as
(3.1)
where represents the traditional symmetric noise term. In particular, our model includes
three outputs in vector (number of passengers ; number of aircraft movements ; and
non-aeronautical output ), two variable input prices in vector (labour price ; and
price of the soft cost input ), and three capital inputs8 in vector (number of runways ;
number of gates ; and terminal size ). We shall discuss these variables in Chapter 4.
3.3 The Specifications of the Stochastic Cost Frontier Model
3.3.1 Specification of the Cost Frontier
Stochastic cost frontier analysis begins with the selection of a functional form. In previous
literature, there are three ―basic‖ functional forms that have been applied with the stochastic cost
frontiers model: the Cobb-Douglas, Leontief and constant elasticity of substitution (CES)
7 From this point on, we will no longer make an explicit notational distinction between a variable and its logarithm.
To be consistent with most empirical specifications, we will assume all variables are measured in logs. 8 As pointed out in Chapter 2, there are no accurate and consistent measures of airport capital inputs available. We
have no choice but to replace the capital input measures with three physical measures of capital stocks.
30
specifications. Among all of these functional forms, the Cobb-Douglas is one of the most
commonly used. But, some scholars, such as Hasenkamp (1976), noted that a function having the
Cobb-Douglas form cannot accommodate multiple outputs without violating the requisite
curvature properties in output space. In addition, the simple functional form of Cobb-Douglas
can hardly capture the true complexity of the production technology, therefore leaving the
unmodeled complexity in the error term and biasing estimates of cost inefficiency. In this regard,
translog cost function has been incorporated into stochastic frontier analysis. There are some
difficulties attached to this function, however. For example, the translog multiproduct cost
function cannot deal with zero output (Caves et al., 1980). Moreover, some restriction may not
be met: a common finding in the applied economic literature is that an estimated translog
functional form is not globally concave (Diewert and Wales, 1987). Despite these difficulties, the
translog cost frontier is usually preferred in the literature over more simple specifications such as
the Cobb-Douglas function. Our study also adopts translog specification which can be expressed
as follows:
(3.2)
Given that out study rests on a translog cost-function specification, among all challenges
that have to be further addressed is how to measure and specify capital inputs in an airport. Due
31
to different accounting or reporting system across airports worldwide, transportation researchers
have long struggled for finding accurate and consistent measures of airport capital inputs. To
overcome the difficulty in data collection, we proxy capital inputs by some physical measures,
e.g., measuring airport capital input by the number of runways or total terminal size. Particularly,
we treat physical measure as a fixed input, i.e., the physical measure enters the cost frontier
rather than the price of capital. However, in practice airports may not be able to adjust their
capacity quantities as output changes. In order to account for the short-run disequilibrium
adjustment in capacity, we estimate the following restricted translog cost frontier, which is log-
linear in the physical measure of capital inputs.
(3.3)
Moreover, all airports are, to an extent, unique; no airport is simply a larger or smaller
version of another. In addition to governance structures, many other factors, such as location, are
important with airports but beyond airport managerial control. Therefore, we augment our cost
frontier in (3.3) to account for observed airport heterogeneity by adding to two sets of variables:
percentage of international passengers in total passenger traffic ( ), country dummy variable (
). In addition, worldwide economic climate is the most powerful driver of changes in air
transport industry at large. At its simplest, we include a set of time dummies ( ) in the cost
frontier to reflect the impact of general economic condition on airport operating cost.
32
Adjusted to these specifications, the stochastic cost frontier adopted in our study can be written
as follows:
3.3.2 Effects of Governance Structures on Airport Cost Efficiency
3.3.2.1 Effects of Airport Governance Structures on Variable Input Usage
Although the types of airport governance are generally beyond the control of airport managers,
airport governance structure indeed capture features of the environment in which airports
operate. It may influence the airport production process itself, as is the case with network
characteristics in transportation studies. If so, it would be entirely appropriate to include
governance structure along with other explanatory variables in a stochastic cost frontier, which
we write as
(3.5)
where represents airports, is the deterministic kernel of the
stochastic cost frontier, captures the effect of technical inefficiency ,
33
captures the effect of symmetric random noise term , and the parameters vector to be estimated
now includes both technology parameters and governance structure parameters. In this
formulation, we assume that airport governance structure influence airport operating cost
directly, by affecting the structure of the cost frontier relative to which the efficiency of airports
is estimated.
However, as noted in Chapter 2 (see Section 2.2), it is more likely that governance
structures will exert indirect influence on airport operation or production, through its effect on
the use of various airport inputs. Therefore, we seek to model the effect of airport governance
structure in a non-neutrally input-augmenting form rather than in a mere shift of cost frontier.
To analyze such an effect, we initially tried to break down the overall cost inefficiency into
technical and allocative inefficiency. We soon discovered, however, that there is an inherent
econometric challenge in estimating both technical and allocative inefficiency with panel data: it
either requires a restrictive functional form, or estimates a highly nonlinear specification
involving the allocative errors. To avoid these complexities, we narrow the scope of our
investigation to the effect of governance structure on airport variable input usage.
Further, to reduce the number of parameters to estimate, we model this effect ( ) as
an interaction between the dummy variable of airport governance structures ( ) and the
variable input prices ( ). This interaction term is expressed as
(3.6)
34
where stands for the price of variable input for the airport at the time , is a dummy
variable indicating the governance structure of the airport , and is the parameter to be
estimated. Our stochastic frontier model, therefore, can be rewritten as follows:
(3.7)
where represents airports, is the deterministic kernel of the stochastic
cost frontier, captures the effect of governance structures on airport variable input usage,
captures the effect of technical inefficiency ,
captures the effect of
symmetric random noise term , and the vector and are the parameters to be estimated.
3.3.2.2 Effects of Governance Structures on Technical Inefficiency
What is accomplished by the formulation (3.7) is a more accurate characterization of airport
production possibilities than would be provided by a formulation similar to (3.4) that excluded
the effect of governance structures from the model, and consequently more accurate estimates of
airport efficiencies. However variation in efficiency is left unexplained by the formulation (3.7).
As much of this paper addresses on whether airports operated under different governance
types differ in their efficiency levels, it is necessary for us to associate variation in estimated
efficiency with variation in airport governance forms. To do so, many empirical analyses have
proceeded in two steps. Had this two-step approach applied to our case, in the first step, one
would ignore the effect of airport governance structures and estimate the stochastic frontier
model and firm’s efficiency levels. In the second step, one would try to see how the estimated
35
efficiency levels varied with types of airport governance structure, perhaps by regressing a
measure of efficiency on variables representing airport governance structures.
Unfortunately such a two-step procedure is not appropriate for our analysis. First of all,
any attempt to calibrate the two-step procedure to our analysis has so far relied on the
assumption that other exogenous variables in the cost frontier should be uncorrelated to airport
governance structures. Contrary to this assumption, the formulation (3.6) has explicitly assumed
the input price variables are correlated to airport governance structures and thus prevents us from
applying the two-step formulation. Secondly, even if it is a judgment call in regard to our attitude
toward the effect of governance structures on airport input usage, there is a more serious
econometric problem with this two-step procedure. This method implicitly assumes that in the
first stage that the inefficiencies are identically distributed. But this assumption is contradicted in
the second-stage regression in which predicted efficiencies are assumed to have a functional
relationship with airport governance structures. In these circumstances it is not clear whether a
two-step formulation successfully contributes anything to our understanding of airport
governance structures on efficiency variation.
To overcome the drawbacks of the two-step approach, we adopt the formulation proposed
by Battese and Coelli (1995) in which the distribution of technical inefficiency depends on firm
characteristics, i.e. governance structures in our case. Specifically, we assume that technical
inefficiency may change linearly with respect to airport governance structures. is then
written as follows:
36
9
(3.8)
where is a dummy variable indicating what type of the governance structures the airport has
at the time , and is the parameter to be estimated, and is a random variable defined by
the truncation of the normal distribution with zero mean and variance , such that the point of
truncation is – , i.e., .
Based on these specifications, the stochastic cost frontier model adopted in our study can
be expanded as follows:
(3.9)
9 Not including an intercept parameter, , in the mean, may result in the estimator for the -parameter
associated with the governance structure variable being biased and the shape of the distribution of technical
inefficiency , being unnecessarily restricted.
37
where
As the cost function must be linearly homogeneous in input prices, the following
restrictions are required:
, , , , ,
,
,
Moreover, we also impose the symmetric restrictions which are given as
, , ,
3.4 Conclusion
In this chapter, we gave a detailed description of the stochastic frontier model adopted in our
study. Our model not only captured unobserved airport inefficiency levels, but also examined
38
whether different airport governance structures can explain them. Moreover, we argued that
governance structures will exert influence on airport operation or production in a non-neutrally
input-augmenting fashion and thus employed a parametric method to measure the impact of
governance structures on airport variable input usage. In addition, our model included three sets
of variables that represent the heterogeneity of cost frontier across individual airports.
39
4 Sample and Variables
In previous two chapters, we have clarified the definition of airport governance structures and
present our econometric model. We will now focus on the data used in estimation. Our sample
contains an unbalanced panel of 54 North American airports over the period of 2002-2008.
These airports are located across two countries (Canada and the United States) and governed /
operated under two different governance structures (a government branch and an airport
authority). The present chapter presents a brief overview of variable constructions and some
characteristics of the sample airports10
.
4.1 Variable Construction
4.1.1 Outputs and Inputs
In the field of transportation research nothing is more valuable yet simultaneously more limiting
to the validation of theory and models than are data. In dependence of the precise methodology
used, modeling airport performance requires the definition of inputs (or input prices in our case
of determining cost frontier) and outputs. Such definitions are not straightforward and give rise
to some controversy. On the output side, it is noticeable that airports do not provide final
services; they provide intermediate services to the airline and other related industries. This makes
it difficult to define the outputs of airport precisely. The output categories commonly employed
in economic analysis include the number of passengers, the volume of air cargo, and the number
of aircraft movements (ATM). In classical transport economic there have been little questions
raised as to consider the number of passengers as a separate output. Here we follow the
10
For a more detailed description of the data, readers should refer to a series of the ATRS Global Airport
Benchmarking Reports (2003-2009).
40
conventional approach and treat the number of passengers as one set of airport outputs. As for
the latter two output categories, airport cargo services are generally handled by airlines, third-
party cargo handling companies, and others that lease space and facilities from airports. In this
study, we do not consider airport cargo services as a separate output since airports derive a very
small percentage of their income indirectly from air cargo services. Are aircraft movements to be
regard as outputs of an airport? A contrary view might be that the airport is providing air-land
interchange services for passengers and freight, and that aircraft movements are not separate
outputs, but rather the means by which these interchange services are affected. However, granted
that a significant portion of air port activities are related to the movement of aircraft, we consider
the number of aircraft movements at an airport as an important indicator of airport activity and
thus a separate output in our analysis.
In addition to the three output categories mentioned above, airports also derive revenues
from concessions, car parking and other numerous services. These services are not directly
related to aeronautical activities in a traditional sense, but they are becoming increasingly more
important for airports around the world. For some airports, such as Richmond and Nashville
International airport, the revenues from non-aeronautical services account for as high as 70% of
their total revenue in 2008. Moreover, the airport inputs are not usually separable between the
aeronautical and the non-aeronautical activities. Any productivity measure which excludes the
non-aeronautical services as the output would, therefore, bias empirical results seriously against
the airports generating a large portion of their revenues from commercial activities. For this
reason, we construct the third output to take into account the revenues from all non-aeronautical
services. Further, since non-aeronautical services include various items and activities, it is
difficult to construct an ―exact‖ price index consistent across airports in different regions and
41
over time. At this point, we construct the non-aeronautical output index by deflating the total
non-aeronautical revenues by the cost of living index (COLI) for all sample airports.
On the input side, we initially considered four general categories: (1) labor, which is
measured by the number of employees who work directly for an airport operator; (2) purchased
goods and materials; (3) purchased services, including those contracted out to external parties;
and (4) capital, which consists of various facilities and infrastructure. In practice, however, few
airports provide separate expense accounts for input categories (2) and (3). We therefore
combine them to a single input category, which is referred as ―the soft cost input‖. This soft cost
input consists of all operating expenses not directly related to personnel and capital expenditures.
As such operating expenses are measured in different currencies and airports operate in regions
with very different price levels, we use the COLI as proxies for the soft cost prices of the sample
airports. Moreover, as indicated in Chapter 3, we use some physical measures as the proxies for
capital inputs. In particular, we consider three physical measures: the number of runways, the
number of gates and the terminal size.
4.2 Characteristics of Sample Airports
In this section we illustrate general data and statistics for the sample airports in terms of
governance structures, in both absolute and relative figures11
. Summary statistics are shown for
each governance structure, so as to allow an easy comparison between all variables by readers
themselves. Note that these summary statistics presented are based on raw data from 2002-2008
and provided in confidence by the ATRS Global Airport Performance Benchmarking project. In
11 Without additional notations, all monetary values are measured in US dollars.
42
the ensuring estimation, we normalize total operating costs, outputs, proxy capital measures and
variable input prices at their sample means.
4.2.1 Summary Statistics of the Airports Operated by the Government Branch
As is evident from Table 6, there are significant differences among the airports operated by a
government branch in terms of operation scale and orientation. For example, while the average
passenger traffic was 28 million in 2008, 30.8% of the airports had annual passenger traffic
below 10 million and 42.3% had annual passenger traffic above 30 million. The annual
passenger traffic ranged from 90.2 million for Hartsfield-Jackson Atlanta International Airport to
6.5 million for Albuquerque International Airport in 2008. Furthermore, with an average of 38%
in 2008, labour expense accounts for 56.8% of San Francisco International Airport’s total
operating costs in 2008, whereas comprising only 18% of the total operating costs at Louis
Armstrong New Orleans International Airport. Nonetheless, as for all the sample airports in this
category, a substantial portion of airport revenue comes from non-aeronautical activities. In
2008, the share of annual non-aeronautical revenue ranged from 31% for Miami International
Airport to 65% for Phoenix Sky Harbor International.
43
Table 6 Summary of Average Statistics -- Airports Operated by Government Branch
Airports Operated by Government Branch 2002 2003 2004 2005 2006 2007 2008
Output Measures
Number of Passengers (million) 23 (19) 24 (20) 26 (21) 27 (22) 25 (19) 28 (23) 28 (23)
Number of Aircraft Movements(000's) 328 (221) 335 (225) 343 (240) 347 (251) 334 (218) 361 (246) 349(245)
Non-Aeronautical Revenue
(million COLI deflated $) 74(51) 76 (53) 76 (55) 79 (56) 79 (55) 87(61) 91 (69)
Proxy Capital Measures
Number of Runways 3.4(1.2) 3.5(1.3) 3.5 (1.3) 3.5 (1.3) 3.5 (1.3) 3.6(1.3) 3.6 (1.2)
Number of Gates 73 (46) 77 (48) 76 (47) 79(47) 73 (41) 80(49) 79(48)
Terminal Size (000's Squared Meter) 200 (183) 220 (187) 244 (224) 224 (195) 217 (184) 230 (194) 212(159)
Variable Inputs' Prices
Wage (000's US$) 58 (16) 63(19) 64(18) 69(21) 68(22) 79(24) 86(27)
Soft Cost Input Price ( COLI) 1.04(0.2) 1.07(0.2) 1.12(0.3) 1.14(0.2) 1.19(0.2) 1.22(0.3) 1.26(0.3)
Variable Inputs' Share
Labour Cost Share (%) 39(10) 39(10) 40(9) 40(11) 38(11) 39(11) 38(11)
Other Characteristics
Percentage of International Passengers (%) 8(11) 8(12) 8(11) 8(11) 8(11) 8(11) 7(12)
Share of Non-Aeronautical Revenue (%) 49(11) 48(12) 49(12) 49(12) 50(12) 51(12) 50(11)
Number of Observation 26 25 26 25 25 25 24
Note: The numbers in parentheses are the standard errors.
4.2.2 Summary Statistics of the Airports Operated by the Airport Authority
In this category, the sample airports are located across two countries – Canada and the United
States – and the majority of these airports are in the US. For instance, in 2008, the US airports
comprised 75% of the sample airports, while 25% of the airports were Canadian.
Table 7 presents some summary statistics of the airports in this category. Overall, the
traffic volume of the airports in this category is relatively small. With an average 15 million, the
annual number of passengers ranged from 2.8 million for Albany International Airport (US) to
57.1 million passengers for Dallas Fort Worth International Airport (US) in 2008. 42.9% of the
airports had the passenger volume below 10 million, while 10.7% of the airports accommodated
more than 30 million passengers during the same period. Similar to the airports operated by a
44
government branch, the majority of the airports in this category also generate a large portion of
their revenue from commercial activities and facilities, such as concessions and car parking. For
instance, in 2008, the overall average share of non-aeronautical revenue reached 56%, ranging
from 28.6% for Memphis International Airport (US) to 85% for Richmond International Airport
(US). Moreover, within this category, labour expense accounts for almost 50% of the total
operating costs for most airports in this category over 2002-2008. For example, on average,
labour expense comprised 43% of the total operating expense for the airports in 2008. In the
majority of the airports (79%), the labour share in total operating costs varied from 47% to 66%
in 2008.
In comparison between Canadian and the US airports, Canadian airports accommodate
more international traffic. Whereas, on average, international traffic accounts for 30% of total
passenger traffic for Canadian airports in 2008, the average percentage of international traffic for
the US airports are significantly lower (4%).
45
Table 7 Summary of Average Statistics -- Airports Operated by Airport Authority
Airports Operated by Airport Authority 2002 2003 2004 2005 2006 2007 2008
Output Measures
Number of Passengers (million) 14(12) 13(12) 15(13) 15(14) 15(13) 15(13) 15(12)
Number of Aircraft Movements(000's) 245(162) 236(159) 252(171) 242(160) 232(143) 228(140) 222(134)
Non-Aeronautical Revenue
(million COLI deflated $) 52(39) 53(38) 59(47) 61(46) 67(53) 73(69) 75(75)
Proxy Capital Measures
Number of Runways 3.2(1.1) 3.1(1.2) 3.3(1.3) 3.3(1.2) 3.2(1.2) 3.2(1.2) 3.3(1.2)
Number of Gates 59(42) 60(43) 61(42) 58(39) 63(41) 65(44) 64(41)
Terminal Size (000's Squared Meter) 121(102) 119(101) 123(104) 126(100) 127(94) 134(10) 133(10)
Variable Inputs' Prices
Wage (000's US$) 55(12) 60(12) 64(12) 67(13) 72(17) 77(15) 84(19)
Soft Cost Input Price ( COLI) 0.99(0.1) 1.03(0.1) 1.06(0.2) 1.08(0.2) 1.13(0.2) 1.16(0.2) 1.18(0.2)
Variable Inputs' Share
Labour Cost Share (%) 46(10) 43(12) 43(10) 42(10) 42(9) 42(9) 43(9)
Other Characteristics
Percentage of International Passengers (%) 10(14) 10(15) 10(13) 10(15) 10(15) 11(15) 11(15)
Share of Non-Aeronautical Revenue (%) 52(13) 52(11) 53(12) 54(12) 55(12) 56(12) 56(13)
Geographic Distribution of Airports in Percentage
Canadian Airports (%) 26 27 26 26 25 25 25
US Airports (%) 74 73 74 74 75 75 75
Number of Observation 27 26 27 27 28 28 28
Note: The numbers in parentheses are the standard errors.
4.3 Conclusion
This chapter discussed our construction of the variables and presented a short summary of the
data used in our study. It is evident that the airports differ in the degree to which they engage in
aeronautical and non- aeronautical activities. Except that of governance structures, the
importance of other variables on airport performance has been acknowledged and illustrated in
the previous literature, such as Oum et al (2008). Therefore, it is important to control for the
effects of these variables when testing hypothesis concerning the effects of governance structures
46
on airport efficiency performance. Moreover, it would be also interesting to see how these
variables affect the observed cost performance of the airports.
47
5 Empirical Results and Discussion
In the preceding chapters, we have discussed the model and data issues put to our study about the
effects of governance structures on airport efficiency performance. In this chapter, we trace our
initial classification of airport governance structures and present our modified categories. We
then interpret our empirical results and critique the impact of governance structures on airport
cost efficiency performance in Section 2.
5.1 Model Issues and Alternative Considerations
The theme of this paper centers on examining the effects of airport governance structures, and
asking whether airports operated under different governance forms differ in their efficiency
performance. Thus far the spread of mathematical modeling to applied fields of transport
economics and the development of econometrics greatly facilitate our research. However, a
fundamental challenge to this study is how to define and classify governance of airports across
countries. As most of the changes in ownership and governance of airports (with the exception of
the UK) have taken place only within the last decade, there has been little scope for economic
researchers to systematize and synchronize the different models that countries have adopted. In
literally every circumstance different classification of airport governance forms has been
defended by its proponents based on various political or economic allegations. For instance, it
has been often argued that US and Canadian airport authorities are similar in nature as they are
not-for-profit and financially self-sustaining. But as we saw in our discussion in Chapter 2, US
airport authorities indeed differ from their Canadian counterparts in terms of their relationship
with airlines, financial sources available and selection of the Board members. The question
could, therefore, arise as so whether Canadian and US airport authorities differ in their impact on
48
airport (cost) efficiency performance and hence should be considered as different types of airport
governance.
For the sake of conceptual clarity, we experiment two alternative classifications of airport
governance structures in our analysis. In Model A, we postulate the differences between
Canadian and US airport authorities can be dismissed as without importance. Consequently all
sample airports are classified into two categories: (1) airports operated by a government branch
and (2) airports operated by an airport authority. In Model B, we separate Canadian and US
airport authorities in our classification, which is concerned with three types of airport governance
structures: (1) a government branch; (2) the US airport authority; and (3) the Canadian airport
authority.
Despite of unsettling classification of airport governance forms, another issue also needs
to be taken into account. In Chapter 3, our approach of modeling airport heterogeneities lies
within a simple framework: we established three sets of variables – country dummy variable,
time dummy variables and percentage of international traffic – to control observed airport
heterogeneities. Merely adding country dummy variable and the variable capturing the
percentage of international traffic in airport total traffic implies that traffic pattern is similar
among airports located in different countries. The statistic summary in Chapter 4, however, has
revealed that, on average, Canadian airports appear to have much higher percentage of
international passengers in their total traffic than US airports. Therefore, at least, the estimated
coefficient of percentage of international traffic would be biased as without allowing the
percentage of international traffic to differ across countries. To response to this finding, we
create Model C and Model D by adding an addition interaction term to Model A and Model B,
respectively. This new interaction term is the product of location (country dummy variable) and
49
the percentage of international traffic in airport total passenger traffic. By incorporating this new
interaction term, we assume that the percentage of international traffic in airport total passenger
traffic will differ between two countries.
The designs of the four stochastic frontier models enable us to examine a pooled
regression model for the two countries, in which the United States is the benchmark for the
country dummy variable. In Model A and C, we set up the airport authority as the benchmark for
the dummy variable of the governance structure, whereas the US airport authority is considered
as the benchmark for the dummy variable of the governance structure in Model B and D. We use
the Gauss maximum-likelihood computer program to obtain the empirical results. Table 8 shows
the four stochastic cost frontier models that are analyzed in this paper – Models A, B, C and D.
The econometric results for these four models are discussed in detail in the following two
sections.
50
Table 8 Explanatory Variables and Regression Model for Stochastic Frontier Analysis
Table 8- A Explanatory Variables for Stochastic Cost Frontier
Observed Airport Heterogeneities Model A Model B Model C Model D
Year 2003) × × × ×
Year 2004) × × × ×
Year 2005) × × × ×
Year 2006) × × × ×
Year 2007) × × × ×
Year 2008) × × × ×
(%International ) × × × ×
- - × ×
(Canadian dummy) × × × ×
Outputs
non-aeronautical output) × × × ×
passengers ) × × × ×
aircraft movements) × × × ×
Proxy Capital Measures
runway) × × × ×
number of gates) × × × ×
terminal size) × × × ×
variable input's prices
wage ) × × × ×
Interactions between Governance Structure and Variable Input Price
× × × ×
- × - ×
Interactions among Outputs
non-aeronautical * non-aeronautical) × × × ×
(passenger * passenger) × × × ×
aircraft movements * aircraft movements) × × × ×
non-aeronautical * passenger ) × × × ×
non-aeronautical * aircraft movements ) × × × ×
passenger *aircraft movements) × × × ×
Interaction between Inputs' Price
wage *wage ) × × × ×
Interaction between Outputs and Variable Inputs' Prices
× × × ×
passenger * wage) × × × ×
aircraft movement * wage) × × × ×
Table 8-B Explanatory Variables for Airport Technical Inefficiency
Governance Structures Model A Model B Model C Model D
(Government -branch dummy) × × × ×
) - × - ×
** X denotes the variables used for that particular regression model
51
5.2 Empirical Results and Discussion
This section presents our empirical results and their economic implications, based on the four
stochastic frontier models described in Section 1. As presented in Table 9-12, there is no
significant statistical difference among the results obtained from the four different model
specifications. Therefore, we will not discuss the empirical results obtained from each model
separately but interpret these results within a unifying framework which is compatible with the
four proposed scenarios. The first part of this section then discusses the effects of airport
characteristics on the stochastic cost frontier. The second part, which is of particular interest in
this paper, reveals the effects of governance structures on airport cost efficiency and evaluates
whether such effects vary under different model specifications.
5.2.1 The Effects of Airport Characteristics on Cost Frontier
As noted previously, translog cost frontier includes first-order and quadratic terms. Since total
operating costs and all regressors are in natural logarithms, and the regression has been
normalized at the mean data point, the first order coefficients of the translog cost frontier can be
interpreted as cost elasticities evaluated at the sample means. As it is difficult to interpret the
quadratic terms in the translog specification, we here draw more attention on discussing the first-
order coefficients of the cost frontier and the coefficients of explanatory variables of particular
interest in Table 9-12.
52
Table 9 Stochastic Cost Frontier Model A Results
Table 9-A Estimation Results for the Airport Characteristics
Parameters Coefficient t-statistics
constant) -0.934 -3.79**
Year 2003) 0.043 3.29**
Year 2004) 0.022 0.94
Year 2005) 0.048 1.89*
Year 2006) 0.066 2.82**
Year 2007) 0.085 3.01**
Year 2008) 0.117 2.61**
(%International ) 0.684 2.34**
(Canadian dummy) -0.241 -1.13
Coefficients of Outputs
non-aeronautical output) 0.325 3.36**
passengers ) 0.290 3.21**
aircraft movements) 0.081 1.17
Coefficients of Proxy Capital Measures
runway) 0.088 0.50
number of gates) 0.108 1.05
terminal size) 0.017 0.26
Coefficients of variable input's prices
wage ) 0.394 2.99**
Impact on Variable Input Usage
-0. 069 -1.57
Coefficients of Interactions among Outputs
non-aeronautical * non-aeronautical) 0.361 2.33**
(passenger * passenger) 0.276 0.61
aircraft movements * aircraft movements) -0.241 -0.88
non-aeronautical * passenger ) -0.247 -0.97
non-aeronautical * aircraft movements ) -0.075 -0.29
passenger *aircraft movements) 0.044 0.24
Coefficients of Interaction between Inputs' Price
wage *wage ) 0.172 0.76
Coefficients of Interaction between Outputs and Variable Inputs' Prices
-0.056 -0.28
passenger * wage) -0.393 -1.48
aircraft movement * wage) 0.511 2.30**
Table 9-B Estimation Results for the Impact of Airport Governance Structures
Parameters Coefficient t-statistics
-0.798 -3.1**
0.141 1.75*
variance parameter) 0.058 -
0.744 -
** Significant at ;* Significant at
53
Table 10 Stochastic Cost Frontier Model B Results
Table 10-A Estimation Results for the Airport Characteristics
Parameters Coefficient t-statistics
constant) -0.936 -3.47**
Year 2003) 0.040 3.21**
Year 2004) 0.021 0.87
Year 2005) 0.044 1.72*
Year 2006) 0.059 2.46**
Year 2007) 0.082 2.78**
Year 2008) 0.115 2.50**
(%International ) 0.652 2.32**
(Canadian dummy) -0.236 -1.12
Coefficients of Outputs
non-aeronautical output) 0.338 3.60**
2 passengers ) 0.269 3.18**
aircraft movements) 0.077 1.21
Coefficients of Proxy Capital Measures
runway) 0.095 0.61
2 number of gates) 0.111 1.06
terminal size) 0.013 0.21
Coefficients of variable input's prices
wage ) 0.416 2.91**
Coefficient for Interactions between Governance Structure and Variable Input Price
Government branch wage -0.058 -1.29
2 Canadian Airport Authority wage 0.014 0.31
Coefficients of Interactions among Outputs
non-aeronautical * non-aeronautical) 0.356 2.43**
22
(passenger * passenger) 0.243 0.54
aircraft movements * aircraft movements) -0.270 -1.02
2
non-aeronautical * passenger ) -0.232 -0.93
non-aeronautical * aircraft movements ) -0.026 -0.17
2
passenger *aircraft movements) 0.083 0.31
Coefficients of Interaction between Inputs' Price
2 wage *wage ) 0.185 0.97
Coefficients of Interaction between Outputs and Variable Inputs' Prices
non aeronautical wage -0.055 -0.29
2 passenger * wage) -0.305 -1.11
aircraft movement * wage) 0.505 2.18**
Table 10-B Estimation Results for the Impact of Airport Governance Structures
Parameters Coefficient t-statistics
0 Constant -0.766 -3.04*
(Government -branch dummy) 0.145 1.77**
2 Canadian Airport Authority dummy) 0.028 0.22
z2 + 2 (Variance Parameter) 0.061 - v
2 2
Ratio of the Variances) 0.755 -
** Significant at ; * Significant at
54
Table 11 Stochastic Cost Frontier Model C Results
Table 11-A Estimation Results for the Airport Characteristics
Parameters Coefficient t-statistics
constant) -0.802 -3.26**
Year 2003) 0.044 3.20**
Year 2004) 0.021 0.84
Year 2005) 0.049 1.67*
Year 2006) 0.065 2.53**
Year 2007) 0.086 2.81**
Year 2008) 0.118 2.47**
(%International ) 0.022 1.26
0.013 2.02
(Canadian dummy) -0.195 -0.49
Coefficients of Outputs
non-aeronautical output) 0.330 3.25**
passengers ) 0.283 3.31**
aircraft movements) 0.085 1.16
Coefficients of Proxy Capital Measures
runway) 0.061 0.44
number of gates) 0.105 1.21
terminal size) 0.020 0.45
Coefficients of variable input's prices
wage ) 0.395 3.01**
Impact on Variable Input Usage
-0. 071 -1.60
Coefficients of Interactions among Outputs
non-aeronautical * non-aeronautical) 0.363 2.30**
(passenger * passenger) 0.281 0.59
aircraft movements * aircraft movements) -0.222 -0.67
non-aeronautical * passenger ) -0.261 -0.96
non-aeronautical * aircraft movements ) 0.015 0.17
passenger *aircraft movements) 0.021 0.10
Coefficients of Interaction between Inputs' Price
wage *wage ) 0.165 0.71
Coefficients of Interaction between Outputs and Variable Inputs' Prices
-0.061 -0.28
passenger * wage) -0.385 -1.45
aircraft movement * wage) 0.508 2.26**
Table 11-B Estimation Results for the Impact of Airport Governance Structures
Parameters Coefficient t-statistics
-0.772 -3.11**
0.139 1.71*
variance parameter) 0.055 -
0.738 -
** Significant at ; * Significant at
55
Table 12 Stochastic Cost Frontier Model D Results
Table 12-A Estimation Results for the Airport Characteristics
Parameters Coefficient t-statistics
constant) -0.916 -3.53**
Year 2003) 0.040 3.19**
Year 2004) 0.020 0.84
Year 2005) 0.045 1.73*
Year 2006) 0.059 2.44**
Year 2007) 0.083 2.77**
Year 2008) 0.116 2.49**
(%International ) 0.017 1.64
0.007 2.11
(Canadian dummy) -0.181 -0.55
Coefficients of Outputs
non-aeronautical output) 0.332 3.46**
passengers ) 0.273 3.21**
aircraft movements) 0.080 1.22
Coefficients of Proxy Capital Measures
runway) 0.095 0.61
number of gates) 0.106 0.99
terminal size) 0.016 0.24
Coefficients of variable input's prices
wage ) 0.411 2.89**
Coefficient for Interactions between Governance Structure and Variable Input Price
-0.059 -1.33
0.014 0.32
Coefficients of Interactions among Outputs
non-aeronautical * non-aeronautical) 0.357 2.39**
(passenger * passenger) 0.247 0.54
aircraft movements * aircraft movements) -0.246 -0.91
non-aeronautical * passenger ) -0.247 -0.94
non-aeronautical * aircraft movements ) -0.003 -0.02
passenger *aircraft movements) 0.056 0.21
Coefficients of Interaction between Inputs' Price
wage *wage ) 0.179 0.93
Coefficients of Interaction between Outputs and Variable Inputs' Prices
-0.061 -0.33
passenger * wage) -0.289 -1.05
aircraft movement * wage) 0.495 2.11**
Table 12-B Estimation Results for the Impact of Airport Governance Structures
Parameters Coefficient t-statistics
-0.758 -3.08*
(Government -branch dummy) 0.142 1.84**
) 0.049 0.55
+ (Variance Parameter) 0.058 -
Ratio of the Variances) 0.731 -
** Significant at ; * Significant at
56
Generally speaking, the four stochastic frontier models convey remarkably similar
information as regards the effects of airport characteristics on the cost frontier. All first order
coefficients, except those for the three proxy capital measures, have the expected coefficient
signs in the cost frontier. The following overview highlights the effects of these variables:
Non-aeronautical output is one of the most statistically significant variables in all four
regressions and has a positive coefficient. In all four scenarios, the coefficient for non-
aeronautical output is very close to the value of 0.3, which indicates that a 1% increase in
non-aeronautical output, holding other variables constant, causes an increase in the total
operating cost of about 0.3%. Moreover, these results further demonstrate that non-
aeronautical activities have become one of the primary economic forces driving mordent
airport operation and have a substantial impact on the observed performance of an
airport (as shown in Figure 1 and 2). Thus, the omission of this important variable can
lead to misleading inferences concerning the effects of governance structures on airport
efficiency performance.
Figure 1 Non-aeronautical Revenue for Sample Airports in 2008
57
Figure 2 Share of Non-aeronautical Revenue for Sample Airports in 2008
Proxy capital measures include the number of runways, number of gates and terminal
size. Although not statistically significant, the coefficients of these three variables are all
positive and thus indicate upward shifts of the cost frontier. These counter-intuitive
results are in part due to the indivisibilities of airport investments in major infrastructure
facilities, and in part, perhaps, due to a lack of accurate and consistent measurement of
capital inputs12
.
Input Price (Wage) has a positive and significant coefficient. This first-order coefficient
of labour input price indicates that, at the sample mean data, labour input accounts for
almost 40% of the total operating cost, which leaves the soft cost input to account for
60% of the total operating cost.
Time Dummies capture the effect of time-specific variables, omitted in the model, which
vary over time but are constant across airports. In our regressions, all coefficients for time
12
As pointed out in Chapter 3, transportation researchers have long struggled to find accurate and consistent
measures of airport capital inputs. Given the data available, we have no choice but to replace the capital input
measures with three capital stock variables.
58
dummies are positive. Except the coefficient for year 2004, all coefficients for other time
dummies are statistically significant. These positive coefficients indicate upward shifts of
the cost frontier in the post-2001 period and further reflect the fact that airports in North
America have to bear the cost of the recently raised security threats and impacts of
recovery from the recent recession.
Percentage of International Traffic has a positive coefficient in all four regressions. In the
first two regressions—Model A and B— the coefficient for percentage of international
traffic reaches 0.6 and statistically significant. This result enthusiastically suggests that
accommodating international traffic imposes significant upward pressure on airport
operating cost. However, we know from our previous data examination that this result
may be attributed to the omission of the interaction between airport location (country)
and the percentage of international traffic in airport total traffic volume. As is evident
from Model C and D, the effect of percentage of international traffic has been reduced
after adjusting the percentage of international traffic to airport location (country). In
Model C and D, the coefficient of percentage of international traffic is still positive but
not statistically significant. On contrary, the cross term with Canadian dummy is
statistically significant with a positive coefficient. These results provide some evidence
that in Canada, airports with a heavy reliance on international traffic face a higher cost
frontier since international traffic requires more airport services and resources than
domestic traffic.
Country dummy has a positive coefficient but is not statistically significant in all four
models. This result provides weak evidence that Canadian airports face a lower cost
frontier than US airports. It is noticeable that, after incorporating the new interaction
59
terms between airport location (country) and the percentage of international traffic, the
coefficient for country dummy has become less significant (see Table 11 and Table 12).
Thus, it is probably much more difficult to conclude that airports located in these two
countries face different cost frontiers.
5.2.2 The Effects of Airport Governance Structures
In this paper, we seek to develop a linkage between airport (efficiency) performance and
governance structures, arguing that airport governance structures do not affect airport technical
inefficiency but also allocative inefficiency, in particular, airport variable input usage. By
comparing the results obtained from the four different model specifications, we found that
adding the new interaction term—the product of location and the percentage of international
traffic—does not highly influence the estimated effects of governance structures on either airport
technical inefficiency or variable input usage: Model A and Model C show the similar estimated
effects of governance structures on airport efficiency, as Model B and D do. It is therefore
interesting to see whether our empirical results can provide a convincing rationale for alternative
classification of airport governance structures in North America.
5.2.2.1 The Effects of Airport Governance Structures on Airport Input Usage
Recall from Chapter 3 that the impact of the airport governance structures on variable input
usage is identified via the coefficient of the input price variable interacted with the governance
structure dummy. Thus, by applying Shephard’s lemma, we are able to use this coefficient to
analyze the effect of the different governance structures on the level of airport input usage (labor
and soft cost input).
In Model A and Model C, the coefficient for government-branch with wage is negative
60
but not statistically significant. By applying Shephard’s lemma to the estimated cost functions,
the negative coefficient for this cross term suggests that the airports operated by a government
branch appear to have a lower labour cost share, or conversely a higher soft cost share, than
those operated by an airport authority (Figure 3 and Figure 4). As discussed in Chapter 2, this
result is partly because airports operated a government branch do not have some functional
departments (e.g accounting and security) and use these services from other local government
departments. Partly as a result of the procurement provisions of the local government, airports
run by a government branch may not purchase services from the most cost-effective source, and
thus tend to have a higher soft cost share than those run by an airport authority.
Figure 3 Labour Cost Share for Airports Operated by Government Branch in 2008
0.00
0.10
0.20
0.30
0.40
0.50
0.60
MS
Y
MD
W
SN
A
BW
I
CL
T
HN
L
PH
X
OR
D
SJC
PH
L
MC
I
CL
E
SM
F
MK
E
LA
S
IAH
MIA
AU
S
AT
L
SL
C
AB
Q
ON
T
LA
X
SF
O
Labour Cost Share for Airports Operated by Government Branch in 2008
Labour Cost Share Average Labour Cost Share
61
Figure 4 Labour Cost Share for Airports Operated by Airport Authority in 2008
The results obtained from Model B and D also provide some supporting evidence to the
argument as to potential inefficiency in procurement of airports run by government branches. As
presented in Table 10 and Table 12, the negative coefficient for government-branch with wage,
although not statistically significant, also indicates that the airports operated by a government
branch appear to have a lower labour cost share (and thus a higher soft cost share) than those
operated either by US or Canadian airport authorities. A interesting point brought out by Model
B and D is that , compared to airports operated by US airport authorities, airports operated by
Canadian airport authorities tend to have a high labour cost share. Provided that airport
authorities, in general, are less impaired by government-wide hiring and procurement rules, there
is a possibility that greater independence from the electorate and from a single accountable body
makes Canadian airport authorities more prone to higher pay for their employees. Nonetheless, it
requires more caution over this argument as the t-statistics of the cross term with Canadian
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
YY
C
FL
L
SA
N
MC
O
YV
R
DT
W
PIT
YU
L
IAD
DF
W
YO
W
ME
M
JAX
ST
L
DC
A
SD
F
YH
Z
AL
B
TP
A
MS
P
IND
BN
A
RD
U
RIC
CV
G
YW
G
YE
G
RN
O
Labour Cost Share for Airports Operated by Airport Authority in 2008
Labour Cost Share Average Labour Cost Share
62
airport authority is highly insignificant in both regressions.
5.2.2.2 The Effects of Airport Governance Structures on Technical Inefficiency
The part B of Table 9-12 reveals the effect of governance structures on airport technical
inefficiency. In Model A and C, the coefficient for airports operated by a government branch is
positive and significant at the 10% level. This result suggests that airports run by a government
branch perform less efficiently than those run by an airport authority. Consistent with the results
obtained by Craig et al (2005) and Oum et al (2008), our finding confirms that, independent
institutions, such as the airport authorities, achieve a higher efficiency performance since they
enjoy sufficient freedom to operate the airports in a commercially-oriented manner. While
separated from other government departments, the aviation branch operates under the general
requirements of the local government bureaucracy and thus is influenced by other political
activities. Such factors may hinder efficient airport operation.
Based on the alternative classification of airport governance forms, Model B and D
suggest that airports operated by either the US or Canadian airport authority indeed outperform
those operated by a government branch. These results further confirm that greater autonomy over
airport decision-making can lead to improved performance and greater efficiency. Moreover, the
results obtained from Model B and Model D raise doubts as to whether there is any efficiency
difference between airports operated by US airport authorities and those run by Canadian airport
authorities. As noted in Chapter 2, conventional wisdom has often in favour of Canadian airport
authorities since political motivated appointment of the Board members leaves US airport
authorities vulnerable to changes in administration and to the exertion of political decisions of a
business nature. Although not statistically significant, the empirical results obtained from Model
B and Model D do not support this argument but indicate that airports operated by US airport
63
authorities are more efficient than those run by Canadian airport authorities. However, t-statistics
from both regressions are too low to discern there are any clear patterns of performance between
airports run by US airport authorizes and those by Canadian airport authorities.
This section analyzes the effects of governance structures on the cost efficiency
performance of North American airports under four different model specifications. Moreover,
the views taken from Model B and Model D results include determine whether Canadian airport
authorities differ from US airport authorities in terms of their impact on airport cost efficiency
performance and hence should be considered as a separated category of airport governance. Our
estimation results13
, as shown in Table 10 and 12, did not provide a convincing empirical
rationale for this classification: there was no significant difference in either technical inefficiency
or variable input usage between the airports operated by US and Canadian airport authorities.
However, it is too early to arrive at the conclusion the airport authorities from both countries are
similar in nature. Although we have established country dummy variable to delineate the
country-specific effects on the cost frontier, the lack of data capturing the relationship between
US airports and airlines may inherently limit our analysis on the efficiency difference between
US and Canadian airport authorities.
5.3 Hypotheses Tests for Effects of Airport Governance Structures
Thus far we have not only examined how different governance structures affect unobserved
airport technical inefficiency, but also argued that the effect of governance structure is more
likely to be embodied in a non-neutrally input-augmenting fashion rather than in a mere shift of
cost frontier. However, it is not obvious whether the governance structure should be a
13
In our original regression, the United States is the benchmark for the country dummy variable and the US airport
authority is the benchmark for the dummy variable of the governance structure.
64
characteristic of airport production technology or a determinant of airport productive efficiency.
It is frequently a judgment call as regards how to model the effects of airport governance
structures. Therefore, after discussing and making inference from our empirical results, we shall
use a series of hypothesis tests to further examine our specifications on the effects of airport
governance forms.
Roughly speaking, the most commonly used the test procedures for maximum likelihood
estimation include: the likelihood ratio, Wald, and Lagrange multiplier tests. Based on ease of
computation, we here use the likelihood-ratio test. Specifically, let be a vector of parameters
to be estimated, and specify some sort of restrictions on these parameters. Let be the
maximum likelihood estimator of obtained without regard to the constraints, and be the
constrained maximum likelihood estimator. If and are the likelihood functions
evaluated at these two estimates, then the likelihood ratio test is based on
where the test statistic has approximately chi-square distribution with degrees of freedom equal
to the number of restrictions imposed in the null hypothesis , provided is true. The null
hypothesis is rejected if the value of exceeds the appropriate critical value from the chi-squared
tables.
Since the likelihood ratio test cannot be used to test a simple null hypothesis against a
simple alternative, the hypothesis with regard to the effects of governance forms on airport
variable input usage is only conducted based on Model B and Model D. Thus, Table 14 and 16
contain four sets of hypothesis and test statistics based on Model B and D, whereas Table 13 and
15, based on Model A and C, only present three sets of hypothesis and test statistics.
65
Table 13 Hypothesis Tests for Stochastic Frontier Model A
Table 13 Hypothesis Tests for Stochastic Frontier Model A
Null Hypothesis Log[Likelihood( )] -Value Test Statistics
- - - - -
261.19 264.39 5.99 6.38**
258.88 264.39 7.82 11.01**
259.71 264.39 5.99 9.35**
** Significant at ;* Significant at
Table 14 Hypothesis Tests for Stochastic Frontier Model B
Table 14 Hypothesis Tests for Stochastic Frontier Model B
Null Hypothesis Log[Likelihood( )] -Value Test Statistics
263.69 266.12 5.99 4.86*
262.10 266.12 7.82 8.04**
258.88 266.12 11.07 14.48**
259.71 266.12 9.49 12.82**
** Significant at ;* Significant at
Table 15 Hypothesis Tests for Stochastic Frontier Model C
Table 15 Hypothesis Tests for Stochastic Frontier Model C
Null Hypothesis Log[Likelihood( )] -Value Test Statistics
- - - - -
261.87 265.07 5.99 6.40**
258.96 265.07 7.82 12.21**
260.15 265.07 5.99 9.84**
** Significant at ;* Significant at
Table 16 Hypothesis Tests for Stochastic Frontier Model D
Table 16 Hypothesis Tests for Stochastic Frontier Model D
Null Hypothesis Log[Likelihood( )] -Value Test Statistics
263.97 266.78 5.99 5.62*
262.10 266.78 7.82 8.05**
258.96 266.78 11.07 15.64**
260.15 266.78 9.49 13.26**
** Significant at ;* Significant at
66
As shown in Row 2 of Table 14 and 16, the null hypothesis indicates that the effect of
governance types on airport variable input usage are absent from Model B and Model D,
respectively. The chi-square statistic with two degree of freedom is significant at 10% level for
Model B and D. As shown in Table 9-12, the t-statistics have not discerned any significant
deference between airports operated under different governance structures as to their variable
input usage. These results, however, indicate that the joint effect of governance structures on
airport variable input usage is significant and thus cannot be omitted after separating US and
Canadian airport authorities.
In Row 3 of the four tables above, the null hypothesis specifies that technical inefficiency
does not change linearly with respect to the governance structures but is half-normal distributed.
As for all the four models, this null hypothesis is rejected at the 5% level of significance. This
implies that, by incorporating difference between airport governance types, the proposed
stochastic frontier cost model is a significant improvement over the corresponding stochastic
frontier model without incorporating the effect of governance structures on technical
inefficiency. Based on these results, we can conclude that governance structures exercise
substantial influence on airport technical inefficiency and should hence be considered as factors
which affect airport productive efficiency.
In Row 4 of Table 13-16, the null hypothesis specifies that the effects of governance
structures on both airport technical inefficiency and variable input usage are absent from the
model and that the model collapses to the conventional half normal stochastic frontier specified
in Aigner, Lovell and Schmidt (1977). This null hypothesis is rejected at the 5% level of
67
significance for all the four regressions. In Row 5 of the four tables above, the null hypothesis
specifies that effects of governance structures on both airport technical inefficiency and variable
input usage are absent from the model but that the model collapses to the truncated normal
stochastic frontier model as specified in Stevenson (1980). This null hypothesis is also rejected at
the 5% level of significance. Although the impact of governance structures on airport variable
input usage, especially based on the t-statistics, is not statistically significant, the results of
these latter two hypotheses tests confirm that the effects of governance structures on airport cost
efficiency, namely both technical inefficiency and variable input usage, are indeed statistically
significant.
5.4 Conclusion
Based on different model specifications, we have estimated the impact of airport governance
structures on the cost efficiency performance of North American airports. Our findings indicated
that there was no significant difference between airports operated by US airport authorities and
Canadian airport authorities as to their impacts on airport efficiency performance. It seems
therefore that US and Canadian airport authorities are similar in nature and should not be
considered as different types of airport governance. Moreover, our findings revealed that the
airports operated by an airport authority indeed outperformed those operated by a government
branch. In addition, although not statistically significant, our findings implied that the airports
run by a government branch tend to have a lower labour cost share than those operated by an
airport authority. Based on a series of hypotheses tests, we further confirmed that governance
structures exercise substantial influence on airport efficiency and should thus be considered as
determinants of airport performance.
68
6 Conclusion
This paper assessed the ways in which governance structures could affect the airport cost
efficiency performance. With a focus on North America, we investigated the impact of two
dominant governance structures – a government branch and an airport authority – on not only
airport technical inefficiency but also airport variable input usage. To achieve this objective, we
applied a stochastic frontier model to the unbalanced panel data of 54 North American airports
over the time period 2002-2008. In this concluding chapter, we open with a summary of our key
empirical findings and their contributions to the air transportation field. Section 2 discusses some
potential extensions of this study.
6.1 Summary of Key Findings
6.1.1 Effects of Other Airport Characteristics
In addition to governance structures, a number of other airport characteristics have potential
effects on airport cost performance. For instance, we find that non-aeronautical output is
significantly related to the observed airport cost performance. 1% increase in percentage of non-
aeronautical output is expected to increase the airport’s total operating cost by 0. %. Therefore,
it is important to control for the effects of these airport characteristics when testing hypotheses
concerning the effects of governance structures on airport efficiency performance.
69
6.1.2 Effects of Airport Governance Structures
This study has confirmed that the two dominant forms of governance structures (i.e. a government
branch vs. an airport authority) indeed exercise substantial influences on the cost efficiency
performance of airports in North America. In particular, our findings suggest that the airports
operated by an airport authority outperform those operated by a government branch in terms of
technical efficiency. This result provides new supporting evidence for the argument that the
governance structures in which management can exercise a greater degree of autonomy and face
less political pressure are more likely to stimulate airport efficiency performance. Moreover, by
modeling the interrelationship between governance structure and airport variable input usage, our
study provides weak evidence that the airports run by a government branch tend to have lower
labour cost share than those run by an airport authority. Since little attention has been paid to the
influence of governance structures on airport inputs, this paper provides a fuller account of the
impact of governance structures on efficiency performance and offers a new platform for the future
study.
6.2 Suggestions for Further Research
The empirical constructs of the current study offer a useful starting point for more in-depth
analysis of the impact of airport governance structures. Based on the framework of our study,
incorporating the share equation could improve the efficiency in estimation. To further extend our
study, more flexible formulations can be applied that account for the presence of observed and
unobserved heterogeneity across individual airports. Since our hypothesized model structure was
reduced due to estimation problems, the interrelationship between governance structures and
airport input usage also have the potential to be further analyzed. In addition, the incorporation of
70
various regulatory and environmental factors, such as the degree of competition and contract
between airport and their airline customers, is another area that warrants further research. The
complex specification required for such work, however, may increase the computational difficulty,
if the stochastic frontier model is estimated via maximum likelihood.
6.3 Conclusion
In summary, this paper contributes to the discussion on the relative merits of the airport authority
over the government branch in simulating airport efficiency performance. Most previous studies,
on the other hand, have focused barely on variable input considerations. This paper assess the
broader extent to which governance structures affect airport operation efficiency by investigating
the impact of governance structures on airport variable input usage. The future possibilities of
constructing complex specifications and relating governance structure to various regulatory and
environmental factors are two related areas to this study that are rich in their potential to provide
additional insight into the issue of airport efficiency performance.
71
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Appendices
Appendix A: Classification of Airport Governance Structures
If one differentiates between the degree and mode of the shift of airports out of government
ownership, there are at least seven possible ownership/governance forms:
a) Government Ownership: operated by a municipal or regional government department;
(e.g. ATL)
b) Government Ownership: operated by a local-based and single-purpose management
authority via a long term lease; (e.g. YVR)
c) Government Ownership: operated by a local-based and multipurpose management
authority via a long term lease; (e.g. JFK)14
d) Government Ownership: operated by the airport operator owned by multiple governments;
(e.g. AMS)
e) Government Ownership: operated by corporatized public airport operators; (e.g. OLS)
f) Majority Government Ownership (Mixed Public-Private Ownership): public sectors
owning a majority share in the airport operator; (e.g.PEK)
g) Private Ownership: private sectors owning 100% or a majority share in the airport
operator;(e.g. SYD)
14 In Oum, Alder and Yu (2006), the authors combine category (b) and (c) as one category.
78
Appendix B: The Sample Airports
North America - United States
Code Airport Name Governance Structures
1 ABQ Albuquerque International Airport Government Branch
2 ALB Albany International Airport Airport Authority
3 ATL Hartsfield-Jackson Atlanta International Airport Government Branch
4 AUS Austin Bergstrom Airport Government Branch
5 BNA Nashville International Airport Airport Authority
6 BWI Baltimore Washington International Airport Government Branch
7 CLE Cleveland-Hopkins International Airport Government Branch
8 CLT Charlotte Douglas International Airport Government Branch
9 CVG Cincinnati/Northern Kentucky International Airport Airport Authority
10 DCA Ronald Reagan Washington National Airport Airport Authority
11 DEN Denver International Airport Government Branch
12 DFW Dallas/ Fort Worth International Airport Airport Authority
13 DTW Detroit Metropolitan Wayne County Airport Airport Authority
14 FLL Fort Lauderdale Hollywood International Airport Airport Authority
15 HNL Honolulu International Airport Government Branch
16 IAD Washington Dulles International Airport Airport Authority
17 IAH Houston-Bush International Airport Government Branch
18 IND Indianapolis International Airport Airport Authority
19 JAX Jacksonville International Airport Airport Authority
20 LAS Las Vegas McCarran International Airport Government Branch
21 LAX Los Angeles International Airport Government Branch
22 MCI Kansas City International Airport Government Branch
23 MCO Orlando International Airport Airport Authority
24 MDW Chicago Midway Airport Government Branch
25 MEM Memphis International Airport Airport Authority
26 MIA Miami International Airport Government Branch
27 MKE General Mitchell International Airport Government Branch
28 MSP Minneapolis /St. Paul International Airport Airport Authority
29 MSY Louis Armstrong New Orleans International Airport Government Branch
30 ONT Ontario International Airport Government Branch
31 ORD Chicago O'Hare International Airport Government Branch
32 PHL Philadelphia International Airport Government Branch
33 PHX Phoenix Sky Harbour International Airport Government Branch
79
North America - United States (cont.)
Code Airport Name Governance Structures
34 PIT Pittsburgh International Airport Airport Authority
35 RDU Raleigh-Durham International Airport Airport Authority
36 RIC Richmond International Airport Airport Authority
37 RNO Reno/Tahoe International Airport Airport Authority
38 SAN San Diego International Airport Airport Authority
39 SAT San Antonio International Airport Government Branch
40 SDF Louisville International Airport Airport Authority
41 SFO San Francisco International Airport Government Branch
42 SJC Norman Y. Mineta San Jose International Airport Government Branch
43 SLC Salt Lake City International Airport Government Branch
44 SMF Sacramento International Airport Government Branch
45 SNA John Wayne Orange County Airport Government Branch
46 STL St. Louis-Lambert International Airport Airport Authority
47 TPA Tampa International Airport Airport Authority
North America - Canada
Code Airport Name Governance Structures
48 YEG Edmonton International Airport Airport Authority
49 YHZ Halifax International Airport Airport Authority
50 YOW Ottawa International Airport Airport Authority
51 YUL Montreal-Pierre Elliot Trudeau international Airport Airport Authority
52 YVR Vancouver International Airport Airport Authority
53 YWG Winnipeg International Airport Airport Authority
54 YYC Calgary International Airport Airport Authority