Privatization, Commercialization, Ownership Forms
and their Effects on Airport Performance
Tae H. Oum
The Air Transport Research Society, and
Sauder School of Business
University of British Columbia, Canada
(with Nicole Adler and Chunyan Yu)
www.ATRSworld.org
Outline
Introduction
‹ Privatization, Ownership and Firm
Performance:
‹
– General Literature; Airports
‹ Modelling
the Effects of Airport Ownership
‹ Data Sources, Sample Airports and
Variable Construction
‹ Empirical Results
‹ Summary and Further Research Needs
**Comments on performance of privatized
ANS
Introduction
‹
‹
‹
Worldwide Trend - Corporatization, commercialization, and
privatization of airports
• Access to private sector capital
• To improve productive efficiency
Different models of ownerships and governance:
• 100% privatized
• mixed ownership with private majority
• mixed ownership with government majority
• public corporation
• independent airport authority
• multi-level government corporation
• Government branch ownership and operations
U.S. Airports have remained mostly as government-owned and
operated
Objective of the Paper
To examine the effects of ownership
forms, and institutional structures on
airport performance in terms of their
productive efficiency, profit, and user
charges.
Literature Summary on
Firm’s Performance
‹ Agency theory and strategic management
literature suggest that ownership form
influences firm performance because different
owners
pursue different goals;
– design different incentives for managers;
–
‹A
common view: government-owned firms
are less efficient than their private sector
counterparts operating in similar environment.
Literature summary on firm’s performance – cont’d
‹
Government firm’s efficiency performance depends
heavily governance structure, and degree of
management autonomy;
– E.g., Some say that many US airports enjoy high degree of
autonomy, and thus, are “among the most privatized in the
world”.
‹ Privatized
firm’s efficiency performance depends
heavily on whether or not its product market is
competitive.
– privatized airlines and telecom firms became very efficient
since they face competitive market;
– but not sure about monopoly infrastructure like airports,
ANS; performance depends partly on type of regulation
Modelling the Effects of Airport Ownership
and Governance Structure
‹ Productivity levels as a function of:
•
•
•
•
‹
Ownership and Governance Form
Management Strategy Variables
Airport Characteristics and Business Environment
The remainder: Technical (residual) efficiency
Ownership forms investigated:
government department, public corporation,
independent airport authority, mixed ownership with
a government majority, mixed ownership with
private sector majority, multi-level government
corporation.
Modelling the Effects of Airport Ownership – cont’d
‹ Management Strategies
– Extent of business diversification (non-aviation
commercial activities)
– Degree of outsourcing
‹ Airport
Characteristics (beyond managerial
control):
–
–
–
–
Airport size
Average size of aircraft
Composition of airport traffic.
Capacity Constraint
Variable Factor Productivity (VFP)
Index number approach:
TFP = Output Index / Input Index
VFP = Output Index / Variable Input Index
* Alternative methods of measuring efficiency are being
compared for our 2006 airport benchmarking report.
Airport Inputs and Outputs
Inputs
- Labor
- Other non-capital
(soft cost) inputs
Outputs
- Aircraft movements (ATM)
- Passengers handled
- Non-aviation output
including commercial
services
- (Cargo could not be
included – revenue not
available separately)
Data Sources
‹ Airport
Annual Reports and direct requests;
‹ US FAA, DOT statistics;
‹ ICAO Digest of Statistics:
– annual financial data -- not for all airports
‹ ACI; IATA
– annual traffic statistics
– Capacity information
‹ IMF and World Bank – various price indices
including GDP deflators for service sectors and
PPP
Selective
Airport Characteristics
YYZ
YU L
YVR
JFK
M IA
YYC
LAX
EW R
HNL
IA D
YO W
SF O
BO S
IA H
YEG
YH Z
O RD
PH L
DFW
SEA
DTW
F LL
AT L
MCO
C LT
C VG
LG A
M SP
BW I
BN A
PH X
P IT
M EM
LAS
T PA
D EN
RDU
ST L
SJ C
O AK
C LE
DCA
SAN
PD X
IN D
MDW
SLC
M SY
MCI
SM F
AM S
BR U
PR G
G VA
C IA
V IE
DUB
ZRH
C PH
CDG
LH R
LG W
W AW
M AN
BH X
ST N
M XP
L IS
DUS
H EL
MUC
AR N
IS T
CG N
H AM
FCO
BC N
M AD
O SL
T XL
ORY
ED I
A e r R ia n ta
FRA
BAA
AN A
C A A ( F IN )
AD R
C AA(SW E)
BER
MFM
H KG
IC N
S IN
T PE
NRT
BKK
K IX
KU L
AKL
MNL
PVG
PEN
D EL
H KT
BO M
SYD
PER
CNS
CG K
PEK
CHC
BN E
M EL
HDY
C AN
W LG
CNX
AD L
SEL
SH A
Sample airport characteristics
Share of International Passengers (2003)
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0
JFK
MCO
ATL
LAX
SFO
MSY
MIA
SEA
ORD
LAS
DEN
SAN
IAH
EWR
DFW
PHX
DTW
YYZ
TPA
MSP
BWI
STL
FLL
HNL
LGA
BOS
YVR
MDW
DCA
SMF
SJC
PHL
MCI
CLT
IAD
SLC
PDX
YOW
YEG
CVG
CLE
PIT
OAK
YYC
IND
RDU
BNA
YUL
YHZ
MEM
LHR
LGW
HEL
STN
ORY
AMS
MAN
CDG
MAD
DUB
FCO
IST
LIS
TXL
MXP
BCN
OSL
DUS
HAM
BHX
EDI
CPH
MUC
ARN
VIE
PRG
ZRH
BRU
GVA
CGN
CIA
WAW
CAA(SWE)
BAA
ADP
FRA
Aer Rianta
ANA
ADR
CAA(FIN)
BER
BNE
KIX
NRT
ICN
BKK
HKG
HKT
SIN
HDY
CNX
KUL
TPE
SEL
PVG
CGK
PEK
PER
SYD
MFM
SHA
PEN
MNL
CNS
AKL
CHC
ADL
WLG
Sample airport characteristics
Passengers per Aircraft Movement (2003)
200
180
160
140
120
100
80
60
40
20
80%
70%
60%
50%
40%
30%
20%
10%
PH L
M EM
JFK
P IT
SF O
M SY
EW R
LG A
D EN
YYZ
ORD
C LE
C VG
M IA
ST L
PD X
YEG
DFW
IA H
SEA
IA D
YH Z
YO W
YYC
SLC
DTW
C LT
DCA
BW I
SAN
BO S
M SP
YVR
IN D
LAS
MDW
O AK
BN A
LAX
PH X
HNL
MCO
RDU
F LL
ATL
SJC
SM F
YU L
T PA
MCI
L IS
W AW
CGN
ED I
PR G
C PH
ZRH
O SL
LH R
G VA
AM S
LG W
V IE
H AM
STN
MUC
C A A ( F IN )
C AA(SW
B e r lin
AN A
F raport
AD R
BAA
Aer
K IX
NRT
W LG (04)
PEK
T PE
IC N
H KG
S IN ( 0 2 )
AD L
SYD
CHC
PER
SEL
AKL
BN E
CNS
AAI
AO T
M AH B
M E L /A P A
200
180
160
140
120
100
80
60
40
20
0
JFK
MCO
SFO
ATL
LAX
IAH
SEA
ORD
DFW
YYZ
MIA
MSP
TPA
LGA
FLL
BOS
PHL
CLT
YVR
SLC
IAD
CVG
CLE
RDU
YHZ
YYC
LHR
LGW
ORY
FRA
AMS
CDG
FCO
MXP
OSL
DUS
MUC
ZRH
CPH
EDI
ARN
VIE
PRG
HAM
GVA
LIS
WAW
NRT
ICN
SIN
BKK
HKG
KIX
SEL
KUL
PEK
MEL
PER
PVG
SYD
BNE
AKL
WLG
Sample airport characteristics
Figure S-5: Aeronautical Revenue Shares (2003)
Passengers per Aircraft Movement 2002
90%
Europe
Asia
Pacific
North
America
0%
Figure S-6 Revenue per Passenger (2003)
US$
60
50
North America
40
30
20
10
0
Europe
Asia Pacific
Results on Operating Efficiency -VFP
Table 3: Variable Factor Productivity Regression Results – Log-Linear Model
(Base ownership: airport with a private majority)
Model
Dependent Variable
Intercept
Output Scale (Index)
Runway Utilization
(ATM per Runway)
Aircraft size (Pax /ATM)
* Europe
* Asia-Pacific
%International
* Europe
* Asia-Pacific
%Non Aviation
%Cargo
Asia
Europe
Oceania
2002
2003
1
VFP
Coeff.
t-stat
0.776
0.080
1.99
2
VFP
Coeff.
t -stat
-0.531
0.029
0.58
3
VFP
Coeff.
t-stat
0.689
0.076
1.56
-
-
0.101
1.71
0.045
0.80
-0.161
-0.010
0.574
0.019
-0.623
-0.453
0.410
-0.066
-0.081
1.94
0.51
9.04
0.65
4.60
3.40
2.72
1.35
1.66
-0.128
-0.008
0.565
0.021
-0.612
0.234
0.432
-0.060
-0.069
1.51
0.38
8.92
0.74
4.52
0.55
2.86
1.22
1.40
-0.303
0.599
0.628
-0.035
-0.316
0.139
0.504
0.013
-3.403
-2.720
0.508
-0.054
-0.067
3.19
3.74
2.83
1.65
1.96
3.52
7.70
0.45
3.17
3.03
3.58
1.18
1.45
Ownership/Governance Form Dummy Variables:
U.S. Govt Department
N. America Airport
Authority
100% Public Corporation
Mixed Ent. (majority-gov)
Multi-Gov shareholders
R2
Adjusted R2
Log-Likelihood Value
Observations (n)
-0.046
0.026
0.34
0.18
-0.031
0.047
0.24
0.34
-0.056
0.0176
0.44
0.13
-0.047
-0.341
-0.287
0.54
2.95
2.91
-0.038
-0.303
-0.264
0.44
2.58
2.65
-0.012
-0.225
-0.331
0.14
1.98
3.51
0.6846
0.6647
-57.27
254
0.6885
0.6674
-55.71
254
0.7336
0.7107
-35.84
254
Private majority airports focus more on commercial revenue
Table 4 Ownership Form vs Shares of Non Aeronautical Revenue
Groups
N. American Airport Authorities
Public Corporation
Government majority
Private-Majority
Multi-Government.
US Government Dept
Count
78
44
14
32
16
70
Sum
35.87
21.02
5.25
18.20
8.72
34.65
Average
46%
48%
37%
57%
55%
50%
Variance
0.016
0.020
0.014
0.013
0.018
0.014
Source of Variation
Between Groups
Within Groups
Total
SS
0.510
3.928
4.439
df
5
248
253
MS
0.102
0.016
F
6.447
Private majority airports achieve higher operating margin
Table 5 The Effects of Ownership on Operating Margin
Groups
N. American Airport Authorities
Public Corporation
Government majority
Private-Majority
Multi-Government.
US Government Dept
Count
27
16
5
16
6
26
Sum
10.62
5.80
0.98
9.02
1.37
8.09
Average
39%
36%
20%
56%
23%
31%
Variance
0.012
0.153
0.092
0.016
0.082
0.041
Source of Variation
Between Groups
Within Groups
Total
SS
0.975
4.653
5.628
df
5
90
95
MS
0.195
0.052
F
3.771
Private majority airports do not charge higher aeronautical fees
Table 6b The Effects of Ownership on Airport Charges
Aeronautical Revenue per Work Load Unit*
Groups
Count
Sum
Average
N. American Airport Authorities
26
123.55
4.75
Public Corporation
16
125.67
7.85
Government majority
5
35.91
7.18
Private-Majority
15
90.07
6.00
Multi-Government.
5
49.56
9.91
US Government Dept
26
129.43
4.98
SS
df
MS
Source of Variation
Between Groups
206.019
5
41.204
Within Groups
1919.567
87
22.064
Total
2125.586
92
* A Work Load Unit (WLU) defined as one passenger or 100 kg of cargo.
Variance
6.93
47.13
11.53
9.20
28.30
30.05
F
1.867
Empirical Results on Ownership Forms
‹
‹
‹
‹
Corporatized Airports owned/operated with govt majority
is less efficient than those owned by private majority or
100% gov’t corporation;
NO statistical evidence indicating that airports with
private majority are more efficient than airports
owned/operated by US. Government departments or
100% public corporations (note: privatized airport also
has monopoly power, not necessarily more efficient).
Airports operated by U.S. and Canadian Airport
Authorities are no more efficient than the airports operated
by US government departments;
Airports with government majority and airports
owned/operated by multiple governments are the least
efficient.
Empirical Results on Ownership Forms – cont’d
‹ Airports
with private majority achieved
significantly higher profit margins than others,
despite the fact that they generally charge lower
aeronautical charges (because of their vigorous
pursuit of commercial opportunities).
‹
(Airports with extensive outsourcing
achieve higher efficiency)
‹Further
research:
– Building more time-series data (longer
panel data);
– Use of alternative measurement methods
such as stochastic frontier cost functions,
other cost function methods, and DEA.
– Structural modeling and other model-based
research needed
Thank You
2006 ATRS World Conference:
Nagoya Japan, 26-28 May;
2007 ATRS World Conference in Asilomar, Monterey, CA – to
hosted by UC-Berkeley (Mark Hansen and Nextor), 21-23 June
ATRS Global Airport Benchmarking Reports – 5th Year
www.ATRSworld.org