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Scheduled service vs
personal transportation:
the role of distance
Volodymyr Bilotkach, Xavier Fageda and Ricardo
Flores-Fillol
XXXIII Simposio de Análisis Económico
Zaragoza - December 2008
Introduction (1)
• This paper presents a theoretical and empirical analysis to examine the
interaction between scheduled and private transportation services
• Scheduled versus personal transportation: delay cost but higher speed
• We show that frequency choices of scheduled carriers are dependent
on the substitutability with personal transportation, using distance as a
proxy.
• Concerning choices of scheduled carriers, our main result is that:
1) frequency increases with distance when driving is a relevant option (i.e;
short-haul routes).
2) frequency decreases with distance when driving is a dominated
alternative (i.e; long-haul routes)
1/24
Introduction (2)
• We propose a theoretical model to examine fares and frequency choices
of a monopoly firm providing high-speed scheduled services (i.e; airlines).
It builds on Brueckner (2004), Brueckner & Flores-Fillol (2007) and
Bilotkach (2006)
• We find that the monopolist’s choice depend on whether driving is a
dominated option or not: the relationship between frequency and distance
changes for short-haul (+) and long-haul routes (-)
• Our result is explained by a trade-off between two forces
1) frequency and costs: The scheduled carrier will always incur in extracosts when increasing frequency: additional fixed costs, worse exploitation
of density economies
2/24
Introduction (3)
2) frequency and demand
-
An increase in distance may boost the demand for high-speed
scheduled services in short haul routes where cars are a relevant option
for users; scheduled carrier may increase fares and frequencies
-
An increase in distance does not imply an increase in demand for
scheduled services in long-haul routes. For remoter destinations,
traveler may prefer to stay at home.
• Our theorerical model shows that, on short haul-routes, the positive
effect of distance on frequency derived from charging higher fares
outweigths the negative effect derived from incurring extra costs.
• Additionally, from the perspective of the social optimum, we find that a
monopoly carrier provides lower frequency of service than is socially
3/24
optimal.
Introduction (4)
• We test the predictions of our theoretical model using data on annual
airline frequencies at the route level.
• Our sample includes about 900 routes of the European market in 20062007.
• Controlling for several factors (route demand shifters, airline attributes,
intensity of competition), our empirical application shows that:
- frequency increases with distance for routes < 500 kms....
- but it decreases with distance for routes > 500 kms. The latter result is
consistent with previous empirical works (Wei & Hansen, 2007; Pai, 2007)
4/24
Theoretical part: utilities (1)

Flying: u f  y  p f   / f   [T  d / V ]

Driving: ud  y  cd  [T  d /(V )];   (0,1/ 2)

Staying: uo  y
5/24
Theoretical part: utilities (2)
1) u f  ud requires   
2) u f  uo requires   
2 scenarios
3) ud  uo requires   ˆ
6/24
Theoretical part: scenarios (1)
Scenario 1 (with drivers): 0  ˆ    1
uf
Utility
ud
uo
Value of time

0
“Stayers”

Drivers

1
Air travelers
7/24
Theoretical part: scenarios (2)
Scenario 2 (without drivers): 0    ˆ  1
uf
Utility
ud
uo
Value of time
 
0
“Stayers”

1
Air travelers
8/24
Theoretical part: scenarios (3)
Lemma: there is a d* such that
d<d* implies Scenario 1
d>d* implies Scenario 2
9/24
Theoretical part: Scenario 1 (1)
1

Demand: q f   d  1 


Cost: c   (d ) f   q f

Profits:  f  ( p f   )q f   (d )

Equilibrium:
2 (d )d (1   ) 3 
d (1   ) 
f   cd   
 f 
V
V 

Cf*
Lf*
10/24
Theoretical part: Scenario 1 (2)
Lemma:
- f* falls with  ,V and 
- f* rises with  and c
- finally df * / dd  0 for suff. low distances
11/24
Theoretical part: Scenario 2 (1)
1

Demand: q f   d  1 


Cost and profits as in Scenario 1

Equilibrium:
2 (d )(TV  d )

Cf*
f 3  TV  d   V  f   V
Lf*
12/24
Theoretical part: Scenario 2 (2)
Lemma:
- f* falls with 
- f* rises with  , V and T
- finally df * / dd  0
13/24
Theoretical part: result
Proposition:
- for d<d* df * / dd  0
- for d>d* df * / dd  0
Escenario 1 (with drivers, d<d*):
- Negative direct effect of d: Air services demand rises
- Positive indirect effect of d: pf* rises with d and f* rises with pf*
Escenario 2 (without drivers, d>d*):
- Direct effect of d: Air services demand may not rise
- Negative indirect effect of d: pf* falls with d and f* rises with pf*
14/24
Theoretical part: social optimum
Scenario 1

Welfare: W  u f  ud  uo   f

Optimum f:
 (d )d (1   ) 3 
d (1   ) 
f   cd   
 f 
V
V 

CfSO

Optimum alpha:
0
 SO  
Lf*=LfSO
These travelers
“should” fly but
they do NOT fly
in equilibrium
 SO
Equilibrium air travelers

1
Travelers that “should” fly
15/24
Theoretical part: social optimum
Lemma: f *  f SO and  SO  
*
SO
f

f
and  SO  
Lemma:
Scenario 1
Scenario 2
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Empirical part (1):
• Our sample includes 887 routes that link the ten largest airports in Europe
with all European destinations (EU27 + Switzerland and Norway) with
direct flights in 2006-2007
• Airlines data have been provided by Official Airlines Guide-OAG (Data
market analysis publication)
• Data of regional variables at NUTS 2 level have been provided by
Cambridge Econometrics (European Regional data base publication)
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Empirical part (2):
2000
1500
Median spline
2500
Spline of total frequency with respect to distance
150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 9501000
DIST
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Empirical part (3):
• The regression model:
Frequencyijk = α+ β1Distanceij + β2Populationij + β3GDPCij + β4Dcapitalij +
β5Dislandj + β6Tourismj + β7HHIij + β8DLCCk + β9DInterhubk + β10Airportpresencek + εijk,
• We make different estimations, considering routes < 500 kms and routes
> 500 kms.
Specification 1: OLS. Specification 2,3,4 5: ZTP
Baseline: 2, 4, 5: Destinationk as indicator of airport presence. In 3,
Airport sharek.
In 4 and 5, we exclude routes with islands as endpoints. In 5,
additionally, we include a dummy variable for the presence of high-speed
trains (DHSTij)
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Empirical part (4):
Table 1. T-test for mean differences in frequency and aircraft size choices
Average frequency
Average seats per flight
Routes
All
Hubbing LCC
All
Hubbing LCC
airlines airlines
airlines airlines
< 500 kms (1)
1477.95 1962.04 665.47 116.93
100.63 160.58
Number obs.
> 500 kms (2)
365
769.64
Number obs.
T.statistic mean
differences
(1) – (2)
1163
367
11.98***
8.08***
176
57
1127.79 380.52
365
152.25
175
165.47
57
166.61
343
1163
367
343
4.81***
-1.43*
-1.20
-0.84
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Empirical part (5):
Table 2. Frequency equation estimates (Routes <500 kms)
Distanceij
Populationij
GDPCij
Dcapitalij
Dislandj
Tourismj
HHIj
DLCCk
Dinterhubk
Destinationk
Airportsharek
Dhst
Intercept
N
R2
(1): OLS
1.62**
0.048*
-1.00
453.09*
169.14+
-1,107.31
-1,165.64***
-196.49
2,167.59***
25.27***
570.37
365
0.35
(2): ZTP
0.0010**
0.000027*
-0.00033
0.25**
0.09
-0.65
-0.87***
-0.33**
1.01***
0.016***
994.02***
365
0.45
(3): ZTP
0.0009**
0.000022+
-0.002
0.29***
0.07
0.64
-1.01***
-0.15
1.10***
1.97***
6.93***
365
0.48
(4): ZTP
0.0014***
0.000033**
-0.00097
0.29*
4.77*
-0.66***
-0.37*
1.05***
0.017***
6.07***
276
0.44
(5): ZTP
0.0014***
0.000028+
-0.0010
0.28*
4.79*
-0.67***
-0.37*
1.05***
0.017***
0.12
6.08)***
276
0.44
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Empirical part (6):
Table 3. Frequency equation estimates (Routes > 500 kms)
Distanceij
Populationij
GDPCij
Dcapitalij
Dislandj
Tourismj
HHIj
DLCCk
Dinterhubk
Destinationk
Airportsharek
Intercept
N
R2
(1): OLS
-0.30***
0.017*
6.41***
-58.16
60.63
-855.55+
-228.46***
-149.10***
661.67***
11.13***
113.94
1,163
0.38
(2): ZTP
-0.0006***
0.000022**
0.0070***
-0.016
0.035
-1.28
-0.36***
-0.34***
0.76***
0.012***
6.12***
1,163
0.49
(3): ZTP
-0.0006***
0.000022**
0.006***
0.000026
0.025
-0.49
-0.42***
-0.21***
0.83***
1.58***
6.18***
1163
0.52
(4): ZTP
-0.0006***
0.000017+
0.008***
-0.017
0.75
-0.26***
-0.42***
0.78***
0.013***
5.87***
769
0.49
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Conclusion (1)
• The main contribution is to underscore that the presence of the personal
transportation option crucially affects frequency choice by a provider of
scheduled transportation services.
• Analysts and policy-makers should consider it when analyzing
investment in transportation infrastructures and regulation of scheduled
services.
•Alleviating road congestion as a priority of transportation policies (US
Department of Transportation, European Commission).
• Since road and airport infrastructures are communicating vessels, policy
makers could take into account capacity at airports as instrument to
reduce road congestion( high-speed train lines with high frequency may
also be useful to alleviate road congestion)
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Conclusion (2)
• Our analysis has policy implications for transport markets (inter-urban,
urban) having private and scheduled services.
• Additionally, the logic of the model goes beyond transportation.
We can analyze the behavior of a firm where better alternatives in some
dimensions for potential customers are either present or absent
• Natural extensions of our theoretical model: introduce competition
across scheduled carriers, including inter-modal competition between
airlines and high-speed trains.
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Thank you for attending!!
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Cost

Cost of a flight: c flight   (d )   s

100% load factor: s  q f / f
( Cost per seat:

c flight
s

 (d )
s
Total cost: c   (d ) f   q f

Ecs. of traffic density
)
f-solution
Cf *
Lf *
f*
f-social optimum
Cf *
Cf SO
Lf *= Lf SO
f * f SO