1. 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

2. 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)
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3. 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 extra-costs when increasing frequency: additional fixed costs, worse exploitation of density economies
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4. 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 optimal.
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5. 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
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)
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6. Theoretical part: utilities (1)
Flying:
Driving:
Staying:
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7. Theoretical part: utilities (2)1) 2) 3)
2 scenarios
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8. Theoretical part: scenarios (1)
Scenario 1 (with drivers):
Scenario 1 (with drivers):
Utility
“Stayers”
Drivers
Air travelers
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9. Theoretical part: scenarios (2)
Scenario 2 (without drivers):
Utility
“Stayers”
Air travelers
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10. Theoretical part: scenarios (3)
Lemma: there is a d* such that
d<d* implies Scenario 1
d>d* implies Scenario 2
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11. Theoretical part: Scenario 1 (1)
Demand:
Cost:
Profits:
Equilibrium:
Cf*
Cf*
Lf*
Lf*
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12. Theoretical part: Scenario 1 (2)
Lemma:
- f* falls with
- f* rises with
- finally
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13. Theoretical part: Scenario 2 (1)
Demand:
Cost and profits as in Scenario 1
Equilibrium:
Cf*
Lf*
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14. Theoretical part: Scenario 2 (2)
Lemma:
- f* falls with
- f* rises with
- finally
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15. Theoretical part: result
Proposition:
- for d<d*
- for d>d*
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*
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16. Theoretical part: social optimum Scenario 1
Welfare:
Optimum f:
Optimum alpha:
Lf*=LfSO
CfSO
These travelers “should” fly but they do NOT fly in equilibrium
Equilibrium air travelers
Travelers that “should” fly
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17. Theoretical part: social optimum
Lemma:
Scenario 1
Scenario 2
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18. 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
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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19. Empirical part (2): Spline of total frequency with respect to distance
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20. Empirical part (3): The regression model:
Frequencyijk = α+ β1Distanceij + β2Populationij + β3GDPCij + β4Dcapitalij + β5Dislandj + β6Tourismj + β7HHIij + β8DLCCk + β9DInterhubk + β10Airport-presencek + 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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21. Empirical part (4):
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22. Empirical part (5):
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23. Empirical part (6):
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24. 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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25. 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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26. Thank you for attending!!

27. Expected schedule delay

28. Expected schedule delayf f f f

29. Expected schedule delayf f H __ f f f

30. Expected schedule delayf f __ H H __
2 f f f f

31. Expected schedule delay__ H
4 f f f H __ H __
2 f f f f

32. ( Cost per seat: ) Cost Cost of a flight: 100% load factor:
Total cost:
Ecs. of traffic density

33. f-solution
Cf *
Lf *
f *

34. f-social optimum
Cf * Cf SO
Lf *= Lf SO
f * f SO