Assessing consumer welfare impacts ofaviation policy measures
Airline responses, lumpy capacity and hub
rationalization
Guillaume Burghouwt
Presentation for ITF Round Table:
Assessing regulatory changes in the transport sector
www.seo.nl - secretariaat@seo.nl - +31 20 525 1630

Message for today
1.

2.
3.
4.

Assessment of economic impacts of aviation investments and policy measures recurrent
topic E.g. Deregulation, aviation taxes, increases in competition, airport charges
Within a CBA framework, important part of effects are consumer welfare gains/ losses due tochanges in travel costs and passenger demand
Generally, these are first order impacts. Second order supply effects generally not taken into
account
But airline seat capacity is lumpy: airlines cannot adapt seat capacity continuously tochanging demand.


5.
6.

Capacity adjustments: aircraft type changes, frequency changes, route closures/ openings, base closures/openings
Second order impacts can be substantial as lumpiness may leverage initial demand effects

Policy makers and regulators should be aware of potential second order supply effects
We present a model to take into account first and second order consumer welfare impacts

OutlineConsumer welfare impacts
Airline supply responses
The Hub Network Rationalization Model
Case study: hypothetical rationalization of the Amsterdam
hub Conclusions

CBA and consumer welfare impacts
 Investments in aviation infrastructure as well as policy
measures increasingly assessed with Cost-Benefit Analysis(CBA) Direct consumer welfare impacts/ consumer surplus
generally important part of the equation
 Relate to the changes in generalized travel costs for gettingfrom A to B as a result of a certain policy intervention, as
well as the change in demand (market (de)generation)
 Generalized travel costs: out-of-pocket costs (e.g. ticket fare)+ valuation of timeTimecosts

Generalised Travel Cost of an air trip
In-flight time

Frequency

Out-ofpocketcosts

Transfer time*

Value of traveltime

Out-of-pocketcosts

Air fareWaiting time fornext flight

Time costs*

Value of waitingtime

Frequency costs

Total
Generalised
Travel Costs

NetCost model estimates changes in generalized
travel costs, demand and consumer welfare

Identifies all direct and indirect travel options in a certain market
Measures all inconveniences (=generalized travel costs) to getfrom initial origin to final destination
 In the base case (=reference situation) and in a policy scenario



NetCost estimates changes in:

Generalized Travel Cost
Total passenger demand
Demand distribution over various travel options
Consumer welfare

See Lieshout & Matsumoto (2012); Lieshout (2012)Illustration market distribution with NetCostParis CDG - Singapore market:
Frequency
Origin

HubDest.

Leg 1
CDGCDGSeats

Generalised travel costs (€)

CarrierSINSkyTeam

Leg 2

7Leg 1

Leg 2363
409

Est.
share

Fare

Time

Sch.
delay

995

474

41473

27%

Total

SIN

STAR

71009

474

31486

24%

CDG

CPH

SIN

STAR

225156

282

676

878

31557

5%

CDG

KUL

SIN

OneWorld

748459

158

750

841

61597

5%

CDG

MUC

SIN

STAR

44

7136

278

748

852

31603

4%

CDG

AMS

SIN

SkyTeam

816164

341

746

857

41606

4%

CDG

SGN

SIN

SkyTeam

715315

182

715

897

41616

3%

CDG

ZRH

SIN

STAR

4112129

335

812

850

31665

3%

CDG

RUH

SIN

SkyTeam

92196

341

564

969

41537

3%

CDG

BKKSINSTAR

865

448

307

767

881

31651

3%

CDG

CAISINSTAR

113258

285

606

963

31572

2%

CDG

CMBSIN

Srilankan Airlines

414272

141

650

936

141600

Other indirect travel alternatives
Note: for illustration purposes only

2%
19%

Example: consumer welfare impacts of allocation
additional traffic rights to a third country carrier
Reference
situation

ChangeScenario

Flights/ year
Passengers / year365
146 553365
58 060730
204 613

Direct origin-destination pax
Beyond the hub pax

45 041
101 5128 572
49 48853 613151 000

Passengers/ yearConsumer welfare impacts
Consumer welfare impact all passengers travelling from/to theEuropean country

595 351-26 969568 382

Third country carrier

Of which are:European carrier

EUR 19.7 million

Consumer welfare impact residents European country

EUR 9.9 million

Impact on revenues European country carrier-22%

Source: OAG; NetCost; Note: for illustration purposes only

Other issues to consider when estimating first orderconsumer welfare impacts
 It is a network industry!
Direct and indirect (transfer) travel options should be taken into account whenassessing the impacts in a certain market

 The level of pass through

To which extent do airlines pass through cost changes to their clients?

 Airport capacity constraintsWhen demand is larger than supply, scarcity rents may arise in the aviation value chain
Policy interventions that enlarge capacity at constrained airports may lead to reductionof scarcity rents and lower user prices
Increases in airline costs at constrained airports may be absorbed by the airlines at theexpense of scarcity rents

OutlineConsumer welfare impacts
Airline supply responses
The Hub Network Rationalization Model
Case study: hypothetical rationalization of the Amsterdamhub Conclusions

But what if airlines adjust capacity?






GTC modelling can be used to estimate first order consumer
welfare impacts
However, airlines may react to changing demand and routeprofitability
 Such supply reactions will affect generalized travel cost in themarket, and again, demand
Supply reactions are important to consider because airline seatcapacity is lumpy at various levels
 Airlines find it difficult to adjust capacity continuously to changingdemandErgo: airline supply function is not smooth but discontinuous(Starkie & Yarrow 2013)

Example: consumer welfare impacts of allocation
additional traffic rights to a third country carrier
Referencesituation

ChangeScenario

Flights/ year
Passengers / year365
146 553365
58 060730
204 613

Direct origin-destination pax
Beyond the hub pax

45 041
101 5128 572
49 48853 613151 000

Passengers/ year
Consumer welfare impacts
Consumer welfare impact all passengers travelling from/to theEuropean country

595 351-26 969568 382

Third country carrier

Of which are:European carrier

EUR 19.7 million

Consumer welfare impact residents European country

EUR 9.9 million

Impact on revenues European country carrier-22%

Source: OAG; NetCost; Note: for illustration purposes onlySecondorderimpacts?

Airlines can adjust capacity in various ways






Use of different aircraft
Adjust route frequency
Route closure/ opening
Base closure/ opening
Hub rationalization/ building

But: Flexibility within the own fleet generally limited
 Minimum competitive frequencies may be necessary to keep routes
profitable Eventual impact on demand/ welfare may be larger than the initial demand/
supply impacts
 Or, as Starkie & Yarrow (2013) put it: elasticities at airports can beleveraged because of the lumpiness of airline seat capacity

OutlineConsumer welfare impacts
Airline supply responses
The Hub Network Rationalization Model
Case study: hypothetical rationalization of the Amsterdamhub Conclusions

Hub Network Rationalization (HNR) Model to include theimpact of lumpy airline supply decisions on consumer welfare
1.
2.

Demand impacts of a policy intervention are estimated using
NetCost (or are exogenously given)
HNR model then simulates iteratively supply reactions of a (hub)carrier when it is confronted with lower passenger demandFare, frequency and route adjustments (including route closure)
HNR model simulates new airline entry (if feasible)

3.

When a stable situation is reached, the model estimates impacts
on demand, connectivity, generalized travel costs and consumer
welfare (in comparison to a reference situation)

HNR model can be used for any airport/ airline, but shows its real value attransfer hubs Frequency reductions at one route affect passenger numbers at otherroutes

HNR-model: estimate initial demand impacts
DemandPolicy scenario:
airport charges
increase

NetCostAssumptions
on level of passthrough

Scenario
Model input
Calculations
Actions
Output

1st order
demand effectHNR-model: assess potential airline
responses
DemandNetwork(supply)

Load factors
below criticallevel?
Yes

Policy scenario:
airport charges
increase

NetCostAssumptions
on level of passthrough

Scenario
Model input
Calculations
Actions
Output

1st order
demand effectLower loadfactors

Reduce airfares torestore loadfactors

NoNo furtheraction requiredHNR-model: assess potential airline
responses and impact on demand
DemandNetwork(supply)

Load factors
below criticallevel?

NoNo furtheraction required

Yes

Policy scenario:
airport charges
increase

NetCost

1st order
demand effectReduce air
fares torestore loadfactors

Lower loadfactors

Assumptions
on level of passthrough

Load factors
still below
critical level?

Demand andrevenue
losses

NoNo furtheraction
requiredYes

Scenario
Model input

No additionalfrequency
reduction

Reducefrequency
NoCalculations
Close routeActions
OutputYes

Frequency
belowminimum?

18

HNR-model iterates until stable situation isreached
DemandNetwork(supply)

Load factors
below criticallevel?

NoNo furtheraction required

Yes

Policy scenario:
airport charges
increase

NetCost

1st order
demand effectReduce air
fares torestore loadfactors

Lower loadfactors

Demand andrevenue
lossesTransfer flows
Assumptions
on level of passthrough

Scenario
Model input

Load factors
still below
critical level?

2nd and higher order demand effects onroute itself and feeding routes

No additionalfrequency
reduction

Reducefrequency
NoClose routeOutputNo furtheraction
requiredYes

Calculations
Actions

NoYes

Frequency
below
minimum?

19Calculate consumer welfare impacts in comparison
to reference situation
Consumer welfare
impacts in comparison
to reference scenarioHNR-model in particular suitable for hub airports: feeder
relations of the Amsterdam-Detroit (DTW) route
BOM
3,0%

IAD

DOH

5,7%

1,5,2%

15,1%

13

2,8%,8%

MEM
3,6%

BAH%

PRG%

3,5,011,413% passenger feed fromone route to another%

HYD
0,5%

DTW

8,9%

0,36,61,178%%

ABV%18,7%%
1,7CFE3500

80

3000

7060

2500

50
2000
40
1500
30
1000

20500

10000%

-10%

-20%

-30%

-40%

-50%

-60%-70%-80%-90% -100%

Initial decrease in transfer volume# flights: intercontinental (lef axis)# routes: Intercontinental (right axis)
Source: HNR-model; MIDT adjusted passenger booking data for
Amsterdam Schiphol; OAG data; SEO (2015)# flights: European (left axis)# routes: European (right axis)

Number of routes

Number of fligths hub carrier and partners
at AMS

Hub networks robust for rationalization up to acertain point, but there is risk of a ‘domino effect’OutlineConsumer welfare impacts
Airline supply responses
The Hub Network Rationalization Model
Case study: hypothetical rationalization of the Amsterdam
hub Conclusions

Example: rationalization of the SkyTeam hubat Amsterdam to illustrate HNR-model Welfare and network impacts of the hypotheticalrationalization of the SkyTeam network at Amsterdam
 Non-hub scenario: hub carrier and partners decide to close
entire hub operation at Amsterdam
 Remaining network will be supported mainly by local OD
traffic
 New airlines may enter the market
 Using the HNR-model, what network will remain and what
are the consumer welfare impacts?

European network in a non-hub scenario

SkyTeam frequency maintained
Direct AMS service cancelled
Destination served by other airlines
SkyTeam frequency decreases

Intercontinental network in a non-hubscenario

SkyTeam frequency maintained
Direct AMS service cancelled
Destination served by other airlines
SkyTeam frequency decreases

Decrease in the number of directly servedroutes and frequencies
Type of route

Number of weekly flights

TotalAMS

routesOther carriers

Total

Hub carrier andpartners

Other carriers

Total

315

1 051

1 366

-84%

90%

-46%

1155

62-85%

67%-13%

Intercontinental

100222

323

-80%

75%

-48%

1532

44-80%

52%-42%

Subtotal

415

1 273

1 688

-83%

87%

-47%

2687

106

-82%

61%

-28%

Europe

485

485

0%

0%

69

690%

0%

Intercontinental

162

162

0%

0%

46

460%

0%

Subtotal

647

647

0%

0%

115

115

0%

0%

Total

Other carriers

Hub carrier andpartners% change

Total

Other routes

Absolute numberEurope

Hubcarrier andpartners
Routes servedby hub carrier& partners% change

Other carriers

Absolute number

Number of destinations

Hub carrier andpartners

RoutesEurope315

1 536

1 851-84%

48%

-39%

11124

131-85%22%-6%Intercontinental

100384

484

-80%

33%

-38%

1578

90

-80%

16%

-26%

Total

415

1 920

2 336

-83%

45%

-39%

26

202

221-82%20%-16%

Consumer welfare impacts in a non-hub scenenario(x mln year) in comparison to the 2013 situation
Scenario

Fare/ competition
Connectivity
Effects for Dutch users of air
Landside accesstransport services
costs
Total

Non-hubPartial dehubbing-66-20-154-46-370-78-590-145

OutlineConsumer welfare impacts
Airline supply responses
The Hub Network Rationalization Model
Case study: hypothetical rationalization of the Amsterdamhub Conclusions

Conclusions
 First order consumer welfare impacts in air transport canbe assessed using the usual transport model formulations
 However, airline seat capacity is lumpy Airlines cannot adjust capacity continuously to changing demand Lumpiness can leverage initial elasticities Rationalization of airline hubs can eventually result in a‘domino effect’, although hubs are quite robust up to acertain level The HNR-model allows to estimate (part of) the second
order impacts

Policy recommendations


Policy makers and regulators shouldtake into account risk of potential
second order supply impacts
Applications of the presented
approach are numerous: (De)regulation of aviation markets
 Impact of greater airline
competition
 Introduction of air travel taxes
 Changes in airport charges, ATCcosts, security costs

Policy measure

Consumer
welfare impact

Impact on airline
cost level

Impacts on
generalized
travel costsModel iterations
on second order
network impacts

Passenger
demand impacts

Airline supply
decisions