Airport and Airline Economics and
Policy: An Interpretive Review of
Recent Research
Anming Zhang
Sauder School of Business, University of British Columbia /
Shanghai Advanced Institute of Finance (SAIF), SJTU
Achim I. Czerny
WHU – Otto Beisheim School of Management
May 28, 2012 @ IFSPA
1. Introduction
• Research has focused on airline sector
• With airports undergoing policy reforms
worldwide and the relative maturity of
research on airline service, airport sector has
recently experienced increased attention
• Long tradition of airport research (1960s …)
- treat airport like roads: “atomistic” flights
Externalities as a Divergence of Social and Private Costs
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• Distinct characteristic of recent airport research: Explicit
recognition of airline markets being oligopoly or other forms
of imperfect competition
Borenstein (1989), Daniel (1995), Brueckner (2002):
airlines at hubs are imperfectly competitive
• Central insight: Airport economics (and policy) should
incorporate strategic interactions between airlines, and
integrate airports and airlines research
• “Vertical structure” approach:
Airports reach final consumers (passengers)
through airlines
Input
service to
carriers
Airport
Charges,
Capacity
Airline 1
Airline 2
Airline 3
…..
Airline N
Passengers
• This survey consists of:
Airport congestion and pricing
Airport regulation and privatization
Airline alliances
• New, and important, insights have been
derived in these areas
• This survey seeks a unifying framework
2. Airport Congestion and Pricing
2.1 Delays and airport congestion
2.2 Congestion toll: Basic model
2.3 Discussion/Extensions
Empirical results
Internalization or not?
Pricing or slots?
Capacity investment and cost recovery
2.4 Main insights
2.1 Delays and airport congestion
• Air travel delays: a major problem
• In United States:
- Total cost of delays in 2007 was $31.2
billion (Ball et al., 2010)
- Reducing airport congestion is DOT’s No.
2 management challenge (behind safety)
• Large traffic volume (relative to airport
capacity) is a major cause
 airport congestion
• Add capacity
• Demand management:
1) Pricing;
2) Quantity restraint
2.2 Congestion toll: Basic model
• Early congestion-pricing models treat flights as
atomistic
• Unlike roads, congestion pricing has not really been
implemented at airports
Several (failed) attempts:
- too high
- uniform charge
- resisted by large airlines
• Congested airport dominated by only a few carriers,
each running a large number of flights
• Daniel (1995, Econometrica): first economist to raise
possibility of “self internalization”:
A large airline consider that scheduling one more
flight generates extra congestion costs for its other
flights (and its passengers)
- Idea demonstrated using queuing models and
simulations
• Brueckner (2002, AER) referred back to a static
congestion model to address the internalization issue,
and drew attention of a wider group of researchers
• Basic model of airport congestion charge:
Passenger quantity q
their benefits from flying B(q)
per-passenger delay cost vC(q)
C(q) = average delay; v = VTTS
• At the demand equilibrium, marginal benefits
of flying equal the “full price”:
• Welfare-optimal airfare is
which equals the part of marginal
congestion costs that are not internalized
by passengers
• Airport authority does not directly control
airfares; fare decisions are made by carriers
• Welfare-optimal airport charge?
– Need the “airport demand” function
– Same as the “passenger demand for transport”?
Implications of the vertical structure:
 The demand for airport services is a derived
demand:
There is a consumer demand for air transport (“final
demand”), which leads to a demand for airport
services
 Specifically, an airport is an input provider that
reaches final consumers (passengers) only through
airlines
 From the equilibrium output of airlines, we obtain
the derived demand for the airport:
which is a function of airport charge
• Welfare-optimal airport charge derived as
(1)
with being the (positive and endogenous)
price elasticity of passenger demand,
Equation (1):
• “Atomistic charge” is given by the part of
congestion costs not internalized:
when
• Airlines choose the passenger quantity just right
from social viewpoint when
and
,
which represents a situation with a monopoly
airline that possesses no market power
• However, if
and
, full internalization
cannot always be achieved  the optimal charge
becomes positive
2.3 Discussion/Extensions
Empirical results (for)
• Brueckner (2002) finds empirical
evidence of the inverse relationship
between delays and airline concentration
and so self-internalization
• Mayer and Sinai (2003) provide further
evidence for self-internalization
Empirical results (against)
• Daniel (1995) found no evidence for
internalization of self-imposed delays; indicated
the atomistic equilibrium is correct model
Daniel and Harback (2008) with a much
broader data base confirmed Daniel (2005)
• Morrison and Winston (2007) concluded welfare
loss would be small if airline market structure
were treated as atomistic
Internalization or not?
• At a theoretical level, argument by Daniel (1995) and
Daniel and Harback (2008) for non-internalization is
based on behavior of “competitive fringe”, a set of
carriers operating a small portion of an airport’s flights
• If a large carrier cuts back its flights to reduce selfimposed congestion, fringe carriers will fill the resulting
gap, eliminating any benefits of self-internalization by
reestablishing the initial combined traffic pattern
• Brueckner and Van Dender (2008): Stackelberg model
• Consider a Stackelberg leader serving passengers and n
Cournot followers serving a total of passengers
• Change of
when
increases is
(2)
• When homogeneity is given, RHS of (2)  -1 when the
number of Cournot followers reaches infinity (competitive
fringe), so no self-internalization exists
• When outputs of the Stackelberg leader and Cournot
followers are imperfect substitutes, the absolute value of
RHS is reduced  room for self-internalization
• Czerny and Zhang (2011) provide an alternative explanation
based on different passenger types with distinct time
valuations, for observation that atomistic tolls may lead to
surprisingly good welfare results even though airlines selfinternalize
- Czerny and Zhang (2012) relax the assumption of “no
price discrimination” by carriers
• Silva and Verhoef (2011) consider a differentiated Bertrand
duopoly:
Airlines internalize little self-imposed congestion relative
to Cournot
Pricing or “slots” (quantity constraint)?
• Complication with pricing: Need for carrier specific tolls and
smaller carriers pay higher toll
• Airport under a slot system decides on how many slots to make
available, then followed with slot trading
• Brueckner (2009) shows that slot system can lead to an efficient
outcome
- Since congestion impacts cease to be a carrier’s concern,
need for carrier specific tolls vanishes
- Slots distributed for free, or via auction
Capacity investment and cost recovery
• Well known from road-pricing literature: Atomistic toll exactly
covers the cost of welfare-optimal capacity when
i) capacity is divisible
ii) capacity construction is constant returns to scale
iii) C(q, K) is homogenous of degree zero in q and K
• Welfare is given by
capacity is determined by:
, and welfare-optimal
(3)
• Atomistic toll given by uninternalized part of congestion cost,
leading to “optimal” (per passenger) revenues
• With the constant returns to scale, “optimal” capacity cost is
• C is homogenous of degree zero,
; so cost
recovery is achieved by the welfare-optimal toll when capacity
reaches the optimal level
• Since welfare-optimal congestion toll may be reduced relative
to atomistic toll when carriers have market power but
capacity choice is independent of the market structure (see
(3)), cost recovery may not be achieved
2.4 Main insights
• One controversial issue in airport congestion pricing has been
whether carriers internalize self-imposed congestion (mixed
empirical evidence)
 theoretical attempts to resolve the issue
• Passenger types provide an explanation for why welfare loss
could be small if airline market structure were ignored (even
though carriers do self-internalize)
• Slots can actually provide an effective, and perhaps “fairer”
alternative policy, approach relative to congestion pricing
– Slots auction generates revenue for capacity expansion
• The “infrastructure-operator” vertical
structure is relevant for many transport cases:
 Air cargo terminals – airlines/integrators – shippers
 Ports (terminals) – shipping lines – shippers
 Railway tracks – carriers (railway companies) –
passengers/shippers
• … and other industries:
 Phone and, more generally, telecom
 “Platforms”: 4PL; e-Commerce; …
28
3. Airport Regulation and
Privatization
3.1 Public vs. private airport behavior
3.2 Regulation forms
3.3 Concessions
3.4 Single-till vs. dual-till
3.5 Empirical studies
3.6 Main insights
Regulation, Privatization, and Airport Charges:
Panel Data Evidence from European Airports
Volodymyr Bilotkach, Newcastle Business School
Joseph Clougherty, UIUC
Juergen Mueller, HWR-Berlin
Anming Zhang, Sauder School of Business, UBC
Introduction
• Airports have traditionally been viewed as an
essential transportation infrastructure, and
owned/managed by governments
• Yet, over last two decades, increasingly recognized as
business enterprises:
– Should private or public entities own/manage airports?
– How do we contain potential market power of airport?
-- A ‘local monopoly’ candidate?
-- By regulation; if so, which form?
31
Airport literature -1
• Consists of mainly descriptive studies on the
evolution of airport regulation
• Emerging theoretical work on airport regulation
– Czerny (2006, JRE): non-congested airports
– Yang & Zhang (2011, JRE): congested airports
• Empirical ‘benchmark studies’, started in late 1990s:
airport (technical) efficiency and its determinants
32
Airport literature -2
• Yet, very little has been done on analysis of
relationship between ownership/regulation and
airport pricing
- Striking in view of vast literature on airline pricing
• Two studies:
- van Dender (2007, JUE)
- Bel & Fageda (2010, JRE)
33
Airport literature -3
• van Dender (2007): 55 US passenger-oriented
airports
- Simultaneous-equations model of charges, concession
revenue, airfares, departures, passenger volume, delays
- Airport charge defined as aeronautical revenue per aircraft
movement (a landing or a take-off)
34
Airport literature -4
• US airports are publicly owned, and forced to do
cost-based charges via ‘Airport Improvement
Program’ grants
• Our study differs from van Dender’s, as we look at
airports subject to various regulation regimes and
ownership types
 European airports
35
Airport literature -5
• Bel & Fageda (2010): study of charges at 100 largest
European airports
– Cross-section
36
This study
• Uses data from the German Airport Efficiency Project
(an airport efficiency study)
• Unbalanced panel, with 61 airports and up to 18
years deep (1990-2007); annual observations
• First study looking at relationship between
ownership, regulation, and charges in the panel
data setting
- van Dender’s data spanning 1998-2002, but unable to fully
employ panel-data econometric techniques due to nonvariation in key variables
37
Dependent variable
• Airport charge
– Defined as aeronautical revenue per aircraft movement (a
landing, or a take-off)
– Same as one used by van Dender (2007)
– Shortcomings
• It lumps together different aircraft types, and we don’t have data
of aircraft mixes for our sample airports
• It lumps together passenger and cargo flights
• Some airports differentiate between origin-destination and
transfer passengers
38
Hypotheses -1
• ‘Single till’ vs. ‘dual till’ regulations
- Aeronautical service: runway; aircraft parking;
terminal
- Non-aeronautical (commercial) service: e.g. dutyfree shops; other concessions; car parking; car
renting
• Single-till regulation leads to lower airport
charges, owing to ‘cross subsidization’
39
Hypotheses -2
• Privatization and charges:
– Private airport: market-power effect (+)
– Private airport: efficiency effects (-)
-- more cost efficient
-- stronger incentive to attract traffic due to more
profitable commercial activities
– Complex relationship, and an empirical one
40
Hypotheses -3
• Regulation and charges
– Sample airports with cost-based regulation or
“price cap”
– Sample airports with ex post regulation
– Ex post regulation ≠ no regulation:
• Ex post regulation  Lower charges: more operational
freedom and efficient (no ex ante regulation, but threat
of re-regulation)
• Ex post regulation  Higher charges, owing to less
monitoring
41
List of Airports
IATA Code
Airport / Country
IATA Code
Airport / Country
ABZ
Aberdeen, UK
LHR
London Heathrow, UK
AHO
Alghero, Italy
LIL
Lille, France
AMS
Amsterdam, The Netherlands
LJU
Ljubljana, Slovenia
ATH
Athens, Greece
LPL
Liverpool, UK
BFS
Belfast, UK
LTN
London Luton, UK
BHX
Birmingham, UK
LYS
Lyon, France
BLQ
Bologna, Italy
MAN
Manchester, UK
BRE
Bremen, Germany
MLA
Malta International, Malta
BRS
Bristol, UK
MME
Durham Tees Valley, UK
BRU
Brussels, Belgium
MRS
Marseille, France
BTS
Bratislava, Slovakia
MUC
Munich, Germany
CAG
Cagliari, Italy
NAP
Naples, Italy
CGN
Cologne/Bonn, Germany
NCE
Nice, France
Newcastle, UK
CPH
Copenhagen, Denmark
NCL
CTA
Catania, Italy
NUE
Nuremberg, Germany
CWL
Cardiff, UK
OLB
Olbia, Italy
DTM
Dortmund, Germany
OSL
Oslo, Norway
DUS
Düsseldorf, Germany
PMO
Palermo, Italy
EDI
Edinburgh, UK
PSA
Pisa, Italy
EMA
East Midlands, UK
RIX
Riga, Latvia
FLR
Florence, Italy
SCN
Saarbruecken, Germany
FRA
Frankfurt, Germany
SOU
Southampton, UK
GLA
Glasgow, UK
STN
London Stansted, UK
GOA
Genoa, Italy
STR
Stuttgart, Germany
GVA
Geneva, Switzerland
SZG
Salzburg, Austria
HAJ
Hanover, Germany
TRN
Turin, Italy
HAM
Hamburg, Germany
TRS
Trieste, Italy
LBA
Leeds Bradford, UK
VCE
Venice, Italy
LCY
London City, UK
VIE
Vienna, Austria
LEJ
Leipzig, Germany
ZRH
Zurich, Switzerland
LGW
London Gatwick, UK
42
Descriptive Statistics
Mean
Median
Maximum
Minimum
Std. Dev.
Aeronautical revenue, thousand Euros
(year 2000 prices)
103,907
39,385
1,287,187
3,633
176,848
Non-aeronautical revenue, thousand Euros
(year 2000 prices)
77,286
28,151
889,370
627
144,556
Passengers
9,084,926
4,439,804
67,869,000
419,680
12,340,683
Cargo (metric tons)
135,769
19,678
2,190,461
0
318,072
Aircraft Movements
110,819
69,600
492,569
4,113
108,939
Charge per aircraft movement (year 2000
prices)
712.51
651.59
3,005.96
136.47
408.90
Cost-Based regulation
0.35
0.00
1.00
0.00
0.48
Price-Cap regulation
0.17
0.00
1.00
0.00
0.38
Single-till regulation
0.34
0.00
1.00
0.00
0.47
Ex post regulation
0.48
0.00
1.00
0.00
0.50
Private ownership share
38.38
8.00
100.00
0.00
42.79
43
Estimation Results
Base Model
Regression Specification:
Price-Cap regulation
Single-Till regulation
Ex-Post regulation
Private Ownership Share
Hub
Nearby Airports
Log(non-aeronautical revenue
per pax)
Log (Real GDP per capita)
Log (Population)
Log(Passengers/
Aircraft Movements)
Log (cargo volume)
Lagged dependent variable
Dynamic Panel Data Model
System GMM Dynamic Panel Data
Model
#1
FE
#2
FE + IV
#3
FE
#4
FE + IV
#5
IV for Yt-1
0.0030
(0.0322)
-0.1223**
(0.0500)
-0.2592**
(0.0957)
-0.0027**
(0.0004)
0.1308**
(0.0547)
0.0067
(0.0322)
0.0290
(0.0397)
-0.1288**
(0.0598)
-0.2703**
(0.1112)
-0.0029**
(0.0005)
0.1880**
(0.0483)
-0.0030
(0.0353)
-0.0019
(0.0195)
-0.0719**
(0.0257)
-0.0719
(0.0853)
-0.0009**
(0.0003)
0.0523**
(0.0218)
0.0206
(0.0255)
0.0121
(0.0281)
-0.0844**
(0.0317)
-0.1679**
(0.0698)
-0.0014**
(0.0005)
0.1095**
(0.0339)
0.0040
(0.0308)
0.0228
(0.0724)
-0.0993**
(0.0465)
-0.0858
(0.0557)
-0.0009**
(0.0003)
0.1000**
(0.0489)
0.0117
(0.0109)
#6
IV for Yt-1 and
more
0.0719
(0.0871)
-0.0765*
(0.0476)
-0.1493**
(0.0543)
-0.0014**
(0.0005)
0.1242*
(0.0702)
0.0261
(0.0372)
0.1367
(0.0888)
0.3721**
(0.1393)
-0.1481**
(0.0617)
0.0362
(0.1304)
-0.0999
(0.0644)
-0.0024
(0.0954)
0.2212*
(0.1170)
0.0199
(0.0776)
0.2002**
(0.0942)
-0.0046
(0.0092)
0.2355**
(0.0946)
0.3093**
(0.0718)
0.2280*
(0.1345)
0.0303*
(0.0176)
---
---
0.1527*
(0.0865)
0.1622**
(0.0414)
-0.1971**
(0.0835)
-0.0003
(0.0060)
0.7683**
(0.0397)
0.0760
(0.1838)
0.1781**
(0.0476)
0.0700
(0.1838)
0.0230*
(0.0135)
0.6241**
(0.1346)
0.1156
(0.0726)
-0.0861
(0.0579)
-0.1535**
(0.0365)
0.0003
(0.0034)
0.7459**
(0.0448)
0.0596
(0.0823)
0.1589*
(0.0819)
-0.2211**
(0.0538)
0.0146*
(0.0090)
0.7188**
(0.0602)
44
Conclusions
• We studied determinants of airport charges in a
panel of 61 diverse European airports
• We find:
– Hub airports charge more (Expected, yet studied)
– Single-till regulation yields lower charges (Expected)
– Airport appears to be a ‘local monopoly’
– Privatized airports have lower charges (Need to identify
the channels?)
45
3.1 Public versus private airport
behavior
Airport pricing
• A general setting with airport’s objective as:
, where is the airport profit, CS is
consumer surplus and is carrier profits. Total surplus
is maximized if ownership parameter is equal to one,
while only airport profit is maximized when is zero
• The per-passenger airport charge is determined by
• Since it is natural to assume
and
, the
private airport charge is excessive from social
viewpoint
• If differences in time valuation exist, it can
thus be that is positive in sign;
consequently, it is unclear whether private
airport charges are excessive or not from the
social viewpoint
• Basso and Zhang (2008) develop a theoretical
model and show that private profitmaximizing airports use PLP, irrespective of
the airline market structure, whilst public
welfare-maximizing airports may not.
Capacity choice
• If airport charge is given by , capacity choice is:
• Since it is natural to assume
, private
airport capacity is socially insufficient
• Zhang and Zhang (2003) found that a profitmaximizing airport is less inclined towards
capacity expansion than a welfare-maximizing
airport
• On the contrary, Zhang and Zhang (2006b) find a
private airport overinvests in capacity when
carriers have market power and the passenger
quantity is given (Figure 1)
• If there is no congestion (after increasing
capacity), the airport charge must be increased
until up to in order to keep the passenger
quantity at the given level in the monopoly
case. The increase of revenues is greater than
the reduction of congestion cost . This shows
the airport’s incentive to invest is excessive
Figure 1: Price effects of an increase in capacity depending on carrier market power
3.2 Regulation forms
• Two principal types of economic regulation: the
traditional cost-based (that is, rate of return) regulation
and the more incentive-minded price-cap regulation
• Under ROR regulation, the airport is allowed to charge
a price equal to efficient costs of production plus a
market-determined rate of return on capital
investment
• Price-cap regulation adjusts an airport’s charges
according to the price-cap index that reflects the
overall rate of inflation in the economy and the ability
of the airport to gain efficiencies relative to the
average firm in the economy
Yang and Zhang (2012):
• find that for a monopoly profit-maximizing infrastructure
(e.g. airport), its capacity and service quality levels are the
highest under cost-based regulation, which is followed by
ROR regulation, no regulation, and price-cap regulation
• Whilst cost-based regulation leads to lower productivity
than price-cap regulation, it is associated with higher
service quality
• Another more-recent regulatory mechanism that has been
employed in the airport sector is ex post regulation, i.e., no
regulation is applied to the airport unless the “regulated”
airport sets prices, earns profits, or reduces service quality
beyond certain critical levels
3.3 Concessions
• Airports worldwide currently derive as much
revenue, on average, from concession services
(retailing, advertising, car rentals, car parking,
and land rentals) as from aeronautical ones
• More importantly, concession operations tend
to be more profitable than aeronautical
operations
• With concessions, the airport’s objective is:
with being the profit derived
from aeronautical services, the profit derived
from concessions, and the surplus parameter
• The airport behavior is given by:
(6)
for
, where denotes the aeronautical
charge while denotes the charge for airport
concession services
Private airport pricing
• Assuming
, concessions reduce the private
aeronautical charge
• It is based on the assumption that the
aeronautical charge can impact passenger
demand and concession demand, while prices for
concession services have no effect on the
passenger quantity
• However, when concession services shift
passenger demand, Czerny (2006) shows that the
private aeronautical charge is increased by the
existence of concession revenue
Public airport pricing
• Czerny (2012) shows that the public and private
aeronautical charges can be the same when a part of
the concession demand is independent of traveling
(
)
• If the passenger quantity is independent of concession
services, then the private concession price will exceed
the welfare-optimal concession price; if an increase of
concession price reduces the passenger quantity, both
the private and public airport operators have further
incentives to reduce concession prices
Capacity choice
• Assuming that airport capacity K is perfectly
divisible and that an increase of K increases the
passenger quantity, the private capacity is
increased by concession revenues, since adding
capacity increases concession revenues
• The private incentives are insufficient from the
social viewpoint because the private airport
ignores the positive effects of a capacity increase
on passengers and carriers
Empirical evidence
• Geuens et al. (2004) find that airport shopping can elicit
travel-related needs such as airport-atmosphere-related
and airport infrastructure-related motivations
• Van Dender (2007) found empirically that the perpassenger concession revenues are declining in the
passenger quantity
• Altogether, there is only little empirical evidence on
whether, and to what extent, airport concession services
can change the passenger quantity
• Another open empirical question is to what extent the
demand for airport concession services is independent of
traveling activities
3.4 Single-till versus dual-till
• Under the single-till approach, operating
profits from both the aeronautical and
concession operations are considered in the
determination of regulated aeronautical
charges (more traditional)
• Under the dual-till approach, the aeronautical
charges are determined solely on the basis of
aeronautical activities (relatively recent)
• Aeronautical charges are likely set lower under singletill price-cap regulation than under dual-till price-cap
regulation, due to the cross-subsidy from the usually
(largely unregulated) profitable commercial operation
• Zhang and Zhang (1997) and Czerny (2006) point out
that such cross-subsidization can be welfare enhancing
at a non-congested airport
• A major critique of the single-till approach is that
aeronautical charges are set too low at congested
airports
• Yang and Zhang (2011) show that dual-till price-cap
regulation can be more desirable than single-till pricecap regulation at a congested airport
3.5 Empirical studies
A relative scarcity of literature on airport pricing:
• Van Dender’s (2007) study shows that that market
structure may affect airport charges
• Bel and Fageda (2010) find that airport charges are
higher at larger airports in terms of passenger quantity
• Bilotkach et al. (2011)’s empirical findings indicate the
that the onset of single-till regulation generates lower
aeronautical charges and that airport privatization
leads to lower aeronautical charges on average
• Yan and Winston (2011) indicates that private airports
would be profitable and would improve the welfare of
commercial carriers and travelers
3.6 Main insights
• A private and monopolistic airport may charge excessive
prices to airlines and passengers, while capacity
investments may be too low. However, if the passenger
quantity is fixed, the private airport may overinvest in
capacity when carriers have market power
• Price-cap regulation may be preferred to ROR regulation
• Airport concession revenues can reduce the private
airport’s incentive to charge excessively high prices
• If there is plenty of capacity, single-till should be used. By
contrast, if capacity is scarce, dual-till may be appropriate
4. Alliances
• A major strategic action taken by airlines postderegulation involves the proliferation of
alliances, both international and domestic
• The result: three global alliances – Star Alliance,
oneWorld, SkyTeam – made up 73.6% of world
market in 2008
• Prevalence of international alliances are due in
large part to constraints of existing international
regulatory regime
4.1 Alliance: Link of complementary networks
4.2 Competition policy issues
4.3 Rivalry between alliances
4.4 Main insights
4.1 Alliance: Link of complementary
networks
• Network complementarity is fundamental to
explaining those alliances that result in the
extension of an airline’s network
• Consider Figure 2: Airline 1 uses city H as its
hub, operates routes to the domestic
endpoints A and B as well as an overseas route
to city K, which serves as the hub for the
airline 2
Figure 2: Network structure (Brueckner 2001)
• Under Cournot competition, Brueckner (2001)
shows alliance reduces interline fares owing to
elimination of “double marginalization” in
vertical integration
• Using a Bertrand competition approach,
Bilotkach (2005) also predicts alliance reduces
fares for the interline trips
4.2 Competition policy issues
• Antitrust immunity allows alliance carriers to
practice cooperative pricing without being
subject to antitrust law
• For: power to resolve the double
marginalization in interline markets
• Against: slot constraint; the potential effect on
non-interline passengers and competition on
“parallel routes”
Airport slots
• Alliance may reduce competition via
concentration of airport slots and other airport
facilities
• Ciliberto and Williams (2010) investigate role of
limited access to airport facilities as a
determinant of airfares (“hub premium”): 8% for
tickets out of a hub; 6.4% for tickets into a hub
Non-interline passengers
• Bilotkach (2005) observes “code sharing” may allow
partners to price discriminate spoke-to-hub passengers
from “connecting” spoke-to-spoke passengers (“bundling”)
• Czerny (2009) shows: Whilst the price for spoke-to-spoke
passengers falls following code-sharing, the fares for spoketo-hub passengers can rise and as a consequence, total
welfare can be reduced by code-sharing activities
• Armantier and Richard (2006, 2008) empirically found that
code-share agreements between Continental and
Northwest reduced average prices for interline passengers
but increased average price paid for non-stop flights
Parallel routes
• Alliance, by reducing competition, is likely to raise fares
in “parallel” markets
• Brueckner’s (2001) simulation analysis indicates that
both consumer and total surplus typically rise following
formation of an alliance despite harm to inter-hub
passengers
• Adler and Hanany (2010) consider not only airfares but
also schedule delays, showing competitive code-share
agreements increase, in all cases, welfare relative to
the competitive outcome when there are business
passengers with a high time valuation relative to
leisure passengers
Carve outs
• A “carve out” is a type of regulation that prohibits
cooperation of alliance partners in hub-to-hub market
while allowing cooperation elsewhere
• Brueckner and Proost (2010) use a network structure
similar to Figure 2:
A carve-out imposed on non-JV alliances always
improves welfare.
For the JV case, however, the net effect is in
general ambiguous, since although carve-out mitigates
anti-competitiveness in hub-to-hub market, it also
restricts cost synergy that can arise in this market
Route choices
• Researchers typically assume that passengers
travel as long as possible with their home
carrier irrespective of airfares
• By contrast, Czerny et al. (2012) elaborate on a
symmetric environment where the route
choices depend on airfares and show that a
symmetric solution does not exist when
economies of traffic density exist
Route choices
• To illustrate Czerny et al. (2012), assume airline 1 charges
for the AH-connection and
for the AK-connection, while
airline 2 charges
for the KD-connection and
for the HDconnection
• If route choices depend on fares and airline services are
perfect substitutes on the hub-to-hub part, a symmetric
demand equilibrium can only exist if
• the carriers’ profit margins must be the same for both routes
in a symmetric equilibrium, i.e.,
and
• Carrier 2 could reduce
by a small amount. All passengers
would then travel the long distance with carrier 2, which
increases margins due to economies of traffic density and
profit. There is no symmetric equilibrium in this scenario for
this reason
Figure 2: Network structure (Brueckner 2001)
4.3 Rivalry between alliances
• Alliance rivalry has been analyzed in Zhang and Zhang
(2006a) with a simple four-firm, four-product model.
• The inverse demand function is pi = pi ( q1 , q2 , q3 , q4 )
with p (Q ) > 0, p (Q ) > 0, p (Q ) > 0, p (Q ) > 0 , indicating demand
complementarity between goods 1 and 2 (3 and 4).
• Firms 1 and 2 contemplate to form a (potential)
alliance, with 3 and 4 forming the other pair (see
Figure 3). Alliance behavior is modeled as:
1
2
2
1
3
4
4
3
Max π 1 + απ 2 ≡ Max π 12 (Q;α )
q1
q1
Max π 2 + απ 1 ≡ Max π 21 (Q;α )
q2
q2
with α being the degree of cooperation between Firm
1 and Firm 2 ( β for Firm 3 and Firm 4).
Figure 3: Competition between two alliances (Zhang and Zhang 2006a)
• It can be shown that:
φα1 + φα2 = (1 − α )(π 12 qα1 + π 21qα2 ) + [(π 31 + π 32 ) qα3 + (π 41 + π 42 ) qα4 ] (9)
with φ i denoting firm i’s profit in the first stage.
• Expression (9) shows that the effect of a change in α on profit
can be split into two parts: i) a direct effect of the shift on the
alliance pair’s profit (first term), and ii) an indirect effect of the
shift in the marginal profits which in turn changes the
equilibrium (second, bracketed term), both of which are
positive.
• The indirect effect is unique to competing alliances – it works by
indirectly influencing the behavior of the rival firms (which in
turn improves own profits) and so may be referred to as the
“strategic effect” of alliance.
• Zhang and Zhang’s (2006a) analysis therefore
suggests that an alliance may confer a strategic
advantage by allowing the partners to credibly
commit to greater output levels, owing to both
within-alliance complementarities and cross-alliance
substitutabilities
• Rivalry between different alliances tends to improve
welfare because it would, owing to the strategic
effect, result in greater output levels than would be
found in the absence of the rivalry
4.4 Main insights
• Cooperative pricing can reduce airfares and increase welfare in
interline markets
• Alliances may lead to collusion on potentially competitive parts of
the networks, but carve-outs can be a useful measure
• Seat swaps can improve welfare even on parallel routes when
frequencies and passenger types are incorporated
• Welfare effects of cooperative pricing can depend on the
passengers’ route choices, and abstracting away from route choices
may underestimate social benefits of airline alliances
• Theoretical and empirical results that cooperative pricing can
increase airfares for non-interline passengers
• Account for the interaction between rival alliances
5. Concluding Remarks
5.1 What we have learned
• Carriers may internalize self-imposed congestion
at a congested airport, and the incentive for selfinternalization depends crucially on existence of a
competitive fringe
• Slots may reach welfare maximum without an
extra burden on small carriers, and airport
revenues can be higher with slots, contributing to
airport cost recovery
• There may be a discrepancy between the welfareoptimal and private airport behaviors; price-cap
regulation may be preferred to rate-of-return
regulation
• When airport concession revenues exist, there
might be little welfare gains from regulation
• Single-till should be used if there is plenty of
capacity, while dual-till may be appropriate at
congested airports
• Cooperative airline pricing can increase welfare in interline markets
• Seat swaps can improve welfare even on hub-to-hub routes when
passenger types with distinct time valuations exist
• Carve-outs may be used to resolve collusive pricing behavior on
these markets
• If route choices are endogenously determined by airfares, the
positive welfare effects of alliances can be further increased
• Cooperative pricing can increase airfares for non-interline
passengers and reduce the social benefits of alliances
• It is important that the social evaluation of airline alliances
incorporate the interactions between rival alliances
5.2 Avenues for future research
• Interactions between 5 elements:
(i) carrier market power and structure
(ii) network complementarities
(iii) schedule delays
(iv) passenger types
(v) third-degree airline price discrimination
• “Land value capture” mechanisms through
which internalization of the positive
externalities could be realized
• Variations of social objective function to
derive results that better match real-world
policy problems
• Consider “countervailing power” of dominant
carriers over airports
• Influence of airports on airline performance?