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Assessing Consumer
Welfare Impacts ofAviation Policy Measures
Discussion Paper

180
Guillaume Burghouwt
SEO Amsterdam Economics

Roundtable

Corporate Partnership
Board

180
Guillaume Burghouwt
SEO Amsterdam Economics

Roundtable

Assessing Consumer
Welfare Impacts ofAviation Policy Measures
Discussion Paper

The International Transport ForumThe International Transport Forum is an intergovernmental organisation with 60 member countries. It
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Cite this work as: Burghouwt, G. (2019), “Assessing consumer welfare impacts of aviation policy
measures”, International Transport Forum Discussion Papers, No. 2019/06, OECD Publishing, Paris.

ASSESSING CONSUMER WELFARE IMPACTS OF AVIATION POLICY MEASURES | ITF ROUNDTABLE 180

Table of contents
Introduction......................................................................................................................................... 4
Direct demand and travel cost effects ............................................................................................. 4
What about airline supply responses? ............................................................................................. 4
Policy makers and regulators should be aware of potential second order airline supply effects .... 5
Examples of policy measures that can trigger second order impacts .............................................. 5
A model framework to take into account first and second order effects ........................................ 6
Scope of the paper .......................................................................................................................... 6
Measuring direct consumer welfare impacts ....................................................................................... 6
Consumer surplus ............................................................................................................................ 6
The issue of pass through ................................................................................................................ 8
The issue of capacity constraints ..................................................................................................... 9
Consumer welfare impacts of allocating additional traffic rights to a third country carrier .......... 10
Airline supply reactions...................................................................................................................... 11
Airline seat capacity is lumpy ......................................................................................................... 13
Base and route closure .................................................................................................................. 13
Dehubbing ..................................................................................................................................... 13
New airline entry ........................................................................................................................... 14
The hub rationalisation model ........................................................................................................... 14
Description of the model ............................................................................................................... 14
Hub carrier responses.................................................................................................................... 15
Simplified illustration of the Hub Network Rationalisation-model ................................................ 17‘Tipping points’ in the hub carrier’s network ................................................................................. 18
Other airline entry and capacity expansion ................................................................................... 19
Example of consumer welfare impacts of hub rationalisation at Amsterdam Schiphol....................... 20
Network scenarios ......................................................................................................................... 20
Non-hub scenario .......................................................................................................................... 20
Network impacts ........................................................................................................................... 21
Consumer welfare impacts ............................................................................................................ 26
Conclusions........................................................................................................................................ 27
Notes ................................................................................................................................................. 29
References ......................................................................................................................................... 30
Annex: NetCost generalised travel cost model ................................................................................... 32© OECD/ITF 20193

ASSESSING CONSUMER WELFARE IMPACTS OF AVIATION POLICY MEASURES | ITF ROUNDTABLE 180

Introduction
Assessing the potential economic impacts of aviation policy measures is a recurrent topic. Examples ofsuch policy measures are public investments in aviation infrastructure, the liberalisation of air transport
markets, the introduction of aviation taxes or environmental restrictions on airport growth. In many
countries, investments in aviation infrastructure are evaluated through Cost-Benefit Analysis (CBA) (e.g.
CEPA, 2007; Decisio et al., 2014; Forsyth, 2013; Jorge and De Rus, 2004; UK Airports Commission, 2015).
In addition, aviation policy measures/ reforms such as deregulation and environmental measures havebeen assessed using CBA/ welfare analysis (Booz, 2007; Forsyth, 2014a,b), although applications are lesswidespread.*Direct demand and travel cost effects
Within a CBA framework, an important part of the effects of aviation policy measures usually consists ofthe direct consumer welfare impacts or consumer surplus. In general, the focus in such analyses is on thefirst order consumer welfare impacts/ direct user impacts resulting from changes in travel costs (fares,
travel times) and associated changes in demand. For example, liberalising a passenger aviation market
may result in more airline competition. It creates a direct benefit to aviation users due to lower ticket
prices, higher frequencies, more direct routes, shorter travel times and new demand generated.
Alternatively, the introduction of an aviation tax is likely to have an upward impact on air fares. As aresult, travel costs will rise, demand will be lost and consumer welfare is negatively affected.

What about airline supply responses?
However, potential second order effects resulting from airline supply reactions are frequently not taken
into account in such analyses. These effects can be substantial. Using the example of the air travel tax
again, the decrease in demand may cause the occupancy rate of a particular flight to drop below the
break-even load factor. This could force the airline to cut frequencies or cancel the route altogether, asairlines are unable to adjust capacity continuously to demand because seat capacity is “lumpy”. The
airline’s flexibility to use smaller aircraft is generally limited and flight frequencies need to remain abovecertain thresholds to be able to compete successfully in a market.
The lumpiness of capacity can result in discontinuous supply effects that go beyond the individual flightand route level:

*

4



Airlines may decide to close operating bases in response to policy measures (see Ryanair’s
decision to close its Oslo Rygge base in response to the introduction of the Norwegian air travel
tax).



At hub airports, the typical characteristics of the airline hub operation can lead to a “domino
effect” when the hub airline decides to rationalise its route network at the hub. Closure of one
route results in less passenger feed to other routes to and from the hub. If the hub carrier

Author affiliation on the cover was provided at the time of drafting.

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cannot sustain these other routes, it will need to close them, which again affects the remaining
routes in its network, etc.


Other airlines may enter “abandoned” markets or expand capacity at existing routes.

In sum, discontinuous airline supply side reactions can leverage initial demand and consumer welfare
impacts.

Policy makers and regulators should be aware of potential second
order airline supply effects
An important message of this paper is that policy makers should be aware that policy interventions in theaviation market could potentially trigger airline supply responses, which may increase the demand andwelfare impacts beyond the direct, first order impacts from the policy measure itself.
Airlines themselves frequently use the argument of the risk of second order supply impacts in theiradvocacy and lobbying work against proposed policy measures. A proper assessment of the risk of suchsecond order impacts is therefore useful for policy makers in their discussions with industry
stakeholders.
Hence, the risk of second order supply impacts should at least be addressed qualitatively in a CBA, andpreferably be quantified.

Examples of policy measures that can trigger second order impacts
The areas in which policy measures could involve second order impacts are numerous. They cover for
example the following areas:
(De)regulation of aviation markets: deregulation of aviation markets has proved to be beneficial
for consumer welfare due to enlarged competition, lower fares, better service and stimulation ofdemand. However, additional competition in (transfer) markets affects hub carrier market sharesand profits and can trigger rationalisation/dehubbing of airline hubs.

Introduction of air travel taxes: the introduction of air travel taxes results in higher airline costs.
When these costs are (partially) passed through to the passenger by means of higher fares,
demand substitution and degeneration will occur. Airlines may react to cost increases anddemand reductions by moving aircraft elsewhere, up to the level of closing entire operatingbases.



Changes in airport charges, ATC costs, noise levies and security costs: if airlines at a specificairport are confronted with increases in charges/fees, the question is to what extent the airlines
will absorb the cost increase and to what extent they are able to pass these costs through to thepassenger. In both cases, route profitability may be affected, which can trigger airline supply
reactions. In particular, at hub airports, strong supply effects may arise when the hub carrier
starts to rationalise its network in response to increasing cost levels.

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A model framework to take into account first and second ordereffects
This paper presents an approach that estimates not only the first order demand and consumer welfare
impacts due to a certain policy measure, but also potential second order supply effects. It discusses how
policy makers may integrate the second order network effects in a CBA.
We first discuss the so-called NetCost generalised travel cost model to calculate first order demand andconsumer welfare impacts resulting from aviation policy measures. This model is a generalised travel
cost model at the individual origin-destination level, which allows for calculating demand and generalised
travel cost changes as a result of certain policy measures. Special attention need to be given to the issueof cost pass-through: to what extent are airlines likely to pass on a higher cost level resulting from a
policy intervention to their passengers? In addition, we argue that when assessing consumer welfare
impacts in aviation, both direct and indirect travel options and their associated generalised travel costs
need to be taken into account. Affected relevant markets need to be identified carefully.
Next, we argue that an assessment of consumer welfare impacts needs to determine if there are any
significant, potential second order effects at stake that may arise because of airline supply responses. We
introduce the Hub Network Rationalisation (HNR) model to estimate part of these second order impacts.
The HNR-model iteratively estimates further demand and supply effects, including the entry of new
airlines/ expansion of capacity by existing airlines as well as potential “cascade effects” at hub airports.
When a stable situation has been reached, results are fed again into the NetCost model to estimate final
consumer welfare impacts. The model is suitable for both non-hub and hub airports, but shows its realvalue when applied to typical hub airports such as Frankfurt, Atlanta, Dubai and Amsterdam.
Finally, we discuss how to integrate the presented framework in a CBA-context. We highlight importantcaveats as well as avenues for future work.

Scope of the paper
This paper is limited to consumer welfare impacts of policy measures. Other impacts within a CBAcontext (for example, producer surplus, wider economic benefits and environmental externalities) areoutside the scope of this paper. In addition, the paper focuses on the air passenger market and not on
the cargo market.

Measuring direct consumer welfare impacts
Consumer surplus
Consumer surplus or consumer welfare impact is a widely accepted way of quantifying changes in
welfare from policy interventions. In short, consumer surplus is a concept of monetised welfare. It is the6© OECD/ITF 2019ASSESSING CONSUMER WELFARE IMPACTS OF AVIATION POLICY MEASURES | ITF ROUNDTABLE 180

amount consumers are willing to pay for these policy interventions in excess of the actual price they pay
for the service without these interventions.
In the context of connectivity and air travel, consumer surplus relates to the change in welfare resulting
from a change in the generalised travel costs. The generalised travel costs include direct costs (such as
ticket prices) and a valuation of travel time. To estimate the economic benefits of a policy measure, forexample the introduction of an aviation tax, the change in consumer surplus/consumer welfare impact
for existing demand can be calculated as a result of a change in generalised travel costs. The consumer
surplus for lost or newly generated demand is calculated as the change in demand times the half of thewelfare gain per passenger (the “rule of half”).
There are various ways to estimate the consumer welfare impacts in aviation. They range from macro
approaches, which calculate at a fairly high level of aggregation (Jorge and De Rus, 2004) to micro
approaches at the individual origin-destination level (Lieshout and Matsumoto, 2012; Lieshout, 2012;
Lieshout et al., 2016; Veldhuis, 2011).
Box 1. Measuring consumer surplus using a generalised travel cost model
One example of a micro-level generalised travel cost model, specifically developed for the aviation
market is NetCost (Burghouwt et al., 2016; Lieshout and Matsumoto, 2012; Lieshout, 2012; Lieshout etal., 2016; Veldhuis, 2011).
The NetCost model first identifies available direct and indirect travel options from a certain (set of)originating regions to all possible final destination regions. For example, consumers travelling by air
from the region of Utrecht (The Netherlands) to Singapore have different options to travel to Singapore.
They can travel via Amsterdam Airport directly with KLM or Singapore Airlines to Singapore. However,they also have the possibility to choose a multitude of indirect travel options, such as with Lufthansa via
Frankfurt of with Emirates via Dubai. Travellers may also use foreign departure airports such as Brussels.
Direct travel options follow directly from the OAG schedules database. Using available schedules,
NetCost then builds the indirect travel options.
Next, the model estimates the generalised travel costs for each individual travel option. For each travel
option, the model distinguishes between the generalised travel costs for business and leisure
passengers, as these passenger segments have different time and price sensitivities. The generalised
travel costs consist of:

Air travel time costs: time costs of in-flight time and transfer time (in case of an indirectconnection) using Values of Time. An intermediate transfer does not only result in additional
travel time, but also in inconvenience. Hence, we apply an additional cost penalty to transfer
time, which has been based on a calibration of NetCost on revealed passenger choice data.

Airfare: NetCost estimates the airfare for each individual travel option based on a number ofvariables, including flight distance, market concentration/competition, carrier type (low-costcarrier or other) and type of connection (direct/indirect flight option). To estimate market
concentration levels, it is important to carefully delineate the relevant market. In case of themarket for the Utrecht/New York regions, we do not only consider the direct and indirectflights between Amsterdam Airport and New York JFK to be part of the relevant market, butalso travel options to New York Newark and travel options via other airports in the Dutch
catchment area, such as Rotterdam via Frankfurt to New York JFK. The air fare module has
been estimated using observed fare data.

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

Access time costs: access time costs are the access times multiplied by a Value of Time. Accesstimes from each NUTS3-region are determined using car travel times in Google Maps.



Access user costs: access user costs are the monetary costs for using the access mode. The
current model is limited to car mode. Access user costs have been determined by multiplying
costs per kilometre by access distance to the departure airport. Subsequently, we divide thisamount by the average number of travellers in the car. We use a car cost per kilometre of 0.30
euro. The average number of travellers per car is set at three for non-business purposes and
1.5 for business purposes.

The estimation of the generalised travel costs in combination with flight frequency results in a utility
valuation for each flight alternative. Based on the changes in these utility valuations, demand
substitution and degeneration are estimated. Finally, NetCost translates the change in generalised travel
costs in relation to the change in passenger numbers into a consumer welfare impact in each scenario,
in comparison to a reference situation. We refer to the Annex for a more detailed description of the
NetCost model.
Direct and indirect travel options, competition and consumer welfare

A major part of the worldwide air traffic is carried by network airlines. These airlines operate hub-andspoke networks. They serve markets with direct flights, but also with indirect flights (with a transfer attheir hubs). For example, Lufthansa and Singapore Airlines compete directly, head to head on the
Frankfurt-Singapore market, but also with carriers such as Emirates, KLM and Qatar Airways, which
provide travel alternatives with a transfer at their respective hubs.
In a “hub-and-spoked world”, policy interventions can affect multiple markets at the same time. For
example, liberalisation of air services between country A and country B, usually not only affects
competition in the origin-destination (OD) market between country A and country B, but also markets
between country A/B and third countries that carriers from one or both countries serve with (in)direct
services via their respective hubs. For example, liberalisation of the restricted Austria-UAE air service
agreement would not only affect the opportunities for Austrian Airlines/Emirates to compete in themarket between Austria and the UAE, but also markets that both carriers serve indirectly via Vienna andDubai respectively. It could allow Emirates to increase its service in the Vienna-via Dubai-Bangkok
market, for example. In this market, Austrian Airlines also offers a direct flight from Vienna.
When assessing consumer welfare impacts in aviation, both direct and indirect travel options and theirassociated generalised travel costs need to be taken into account. Affected relevant markets need to be
identified carefully.

The issue of pass throughIf airlines are confronted with a cost increase due to a certain policy measure, an important question isthe extent to which airlines can pass these higher costs through to the passengers via higher air fares.
In air transport studies, it is often assumed that airlines fully pass through higher costs to the consumeron the longer term (Koopmans and Lieshout, 2016). In reality, airlines will apply different pass-throughstrategies, but there is little empirical evidence in relation to pass-through strategies by airlines.
Koopmans and Lieshout (2016) conclude that the pass-through level strongly depends on the costchange and market conditions. In monopolistic markets, a large part of the price change may be passed

8© OECD/ITF 2019ASSESSING CONSUMER WELFARE IMPACTS OF AVIATION POLICY MEASURES | ITF ROUNDTABLE 180

through. In more competitive markets, sector-wide cost changes may also be passed through to a large
extent, but not fully. In competitive markets where cost changes only affect one competitor, passthrough levels are likely to be much smaller. The authors state that although a 100% pass-through islikely under perfect competition, most aviation markets are not perfectly competitive.
As there is quite some uncertainty in terms of the level of pass through of airline cost increases, it isuseful to use different pass through assumptions in evaluations of policy measures involving costchanges for airlines, at least as a sensitivity analysis.

The issue of capacity constraints
Policy measures and regulation can affect airport capacity. One can think of environmental caps that
restrict the airport’s annual capacity. When the demand for airport capacity exceeds the supply of
airport capacity, the introduction or abolition of such a cap is likely to have positive impacts on consumer
welfare.
In a situation where demand for airport capacity exceeds the supply of airport capacity, prices can beused to balance the level of demand with the capacity available. If the airport prices efficiently throughits airport charges, scarcity will be reflected in higher (peak period) charges, hence in higher costs to theairlines and, in turn and depending on the market situation, in higher fares charged to passengers fortravel at peak periods.
Nevertheless, regulation often prevents congested airports from clearing the market through airport
charges. The sensible airline will maintain its fares at market clearing levels where airport charges arecapped at a level below the market clearing rate. This will result in higher air fares. In other words,
airlines will charge what the market can bear and may make excess profits on the use of scarce slots(Starkie 1998). It does not make sense for the airlines to pass on sub-optimally low airport charges in theform of lower fares for passengers (Starkie, 1998). Therefore, airlines have no incentive to compete thelow airport costs away (Forsyth, 2004; Gillen and Starkie, 2016).
Hence, restrictions on airport capacity may result in negative welfare impacts for consumers due to thescarcity rents generated in case demand starts to exceed declared capacity, in addition to the impact of
non-accommodated demand on consumer welfare. The other way around, lifting constraints cangenerate consumer benefits, as fares are expected to decrease and more demand can beaccommodated.

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Figure 1. Excess demand may generate economic rents

Source: ITF and SEO (2015).

Consumer welfare impacts of allocating additional traffic rights to athird country carrier
As a simple example, we analyse the consumer welfare impacts of a policy intervention in a long-haulcountry-pair aviation market between a European country and a third country. The bilateral air service
agreement allows the governments of both countries to designate one airline from each country tooperate services on the country-pair. The bilateral agreement does not allow them to fly more thanseven times per week, but they are free to set fares and choose the capacity of their aircraft.
Assume that two carriers (one from each country) compete with a daily flight on the country-pair. They
operate each a transfer hub at one end. Hence, apart from the origin-destination traffic between bothcountries, both carriers carry a significant amount of transfer traffic beyond their own hubs. As such,they also compete on a number of markets indirectly served via their hubs. The third country carrier has
requested traffic rights to operate an additional daily frequency on the route. In preparation of the airservice negotiations, the government of the European country has gained insight into the potential
welfare impacts of an additional daily frequency by the third country carrier.
Using the NetCost generalised travel cost model, Table 1 shows that the European country may see a
consumer benefit of EUR 10 million for its own residents for a number of reasons. First, additionalfrequencies to the third country increase flexibility for travellers. Second, more indirectly served
destinations via the third country hub become available for residents of the European country, who will
benefit from shorter travel times to these destinations, as well as more flexibility. Third, fares decreaseslightly due to changing competition levels in the various markets affected. Fourth, total demand in themarket will be stimulated because of overall lower generalised travel costs in the market. At the sametime, load factors and yields of the European country’s carrier are reduced.

10© OECD/ITF 2019ASSESSING CONSUMER WELFARE IMPACTS OF AVIATION POLICY MEASURES | ITF ROUNDTABLE 180

Table 1. Direct consumer welfare impacts of the allocation of
additional traffic rights to a third country carrier (first year)

Third country carrier
Flights / year
Passengers / year

Reference
situation

Scenario 1

Change365
150 000730
243 250365
93 25030 000120 000

43 250200 000

13 25080 000365
589 800365
556 8000-33 000

572 000

547 000-25 000

17 8009 800-8 000Of which are:Direct origin-destination pax
Beyond the hub pax
European carrier
Flights / year
Passengers / year
Of which are:Direct origin-destination pax to the third country + pax tocompeting destinations beyond the third country carrier hub
Pax travelling indirect to third country
Consumer welfare impacts (x mln)
Consumer welfare impact all passengers travelling from/tothe European country
Of which are:On local origin-destination market
On markets beyond the third country carrier hub
Consumer welfare impact residents European country
Of which are:On local origin-destination market
On markets beyond the third country carrier hub
Impact on revenues European country carrier

EUR 20.0
EUR 2.3
EUR 17.7
EUR 10.0
EUR 1.2
EUR 8.8
EUR -36.0

Source: SEO NetCost model; OAG
Note: Total passengers per year for the European carrier relate to all passenger traffic in the markets that are
affected by the entry of the third country carrier. These include the directly travelling origin-destination
passengers to the third country, the passengers travelling to destinations also served indirectly by the third
country carrier and passengers travelling indirectly with the European carrier (and partners) to the third
country.

Airline supply reactions
Generalised travel cost modelling can be used to estimate consumer welfare impacts of certain policy
measures, as shown in the example above. However, a policy measure may also affect the revenues/
profitability of an airline supplier in the market. This could potentially trigger supply side reactions, which
affect service levels, fares and demand in the market.
Let us consider again the simple example of the long-haul country-pair aviation market, where a third
country carrier expands its service with seven frequencies per week (Table 1). The European carrier faces
a substantial reduction of traffic and revenues, which could force the European carrier to reduce service
levels or even cancel its service all together. In the case of such a second order supply response,© OECD/ITF 201911

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consumers travelling in the market between the European country and the third country will beconfronted with 1) lower frequency levels on the local origin-destination market and 2) increase in fares
as the third country carrier is now the only supplier of direct services. Both effects increase generalised
travel costs and lower the initially estimated direct consumer welfare gain.
Table 2 shows both the initially estimated consumer welfare impact in comparison to the reference
situation (scenario 1), as well as the consumer welfare impacts after the second order supply reaction by
the European carrier (scenario 2). Including the potential second order impacts reduces the initiallyestimated impacts. As the European carrier carried substantial transfer traffic on the route over its own
hub and relatively limited local origin-destination traffic, the reduction of initially estimated direct
consumer benefits is about 5%. This percentage can be higher on routes with more local demand or
where the European carrier carries substantial traffic beyond a partner’s hub as well. In addition, higher
order impacts may arise if the cancelled route affects the viability of other routes in the European
carrier’s network because of a decrease of transfer traffic feed.
Table 2. Direct consumer welfare impacts of the allocation of additional traffic rights
to a third country carrier (first year): first and second order impacts

Third country carrier
Flights/ year
Passengers / year

Reference
situation

Scenario 1

ChangeScenario2

Change365
150.000

730
243.250365
93.250730
256.350365
106.350

30.000120.000

43.250200.000

13.25080.00056.350200.000

26.35080.000365589.800365
556.8000-33.000

0541.600-365-48.200

572.000

547.000-25.000

531.800-40.200

17.8009.800-8.0009.800-8.000Of which are:Direct origin-destination pax
Beyond the hub pax
European carrier
Flights/year
Passengers/ year
Of which are:Direct origin-destination pax to the third country +pax to competing destinations beyond the third
country carrier hub
Pax travelling indirect to third country
Consumer welfare impacts (x mln)
Consumer welfare impact all passengers travellingfrom/to the European country
Of which are:On local origin-destination market
On markets beyond the third country carrier hub
Consumer welfare impact residents European
country
Of which are:On local origin-destination market
On markets beyond the third country carrier hub
Impact on revenues European country carrier

EUR 20.0

EUR 19.0

EUR 2.3
EUR 17.7

EUR 1.3
EUR 17.7

EUR 10.0

EUR 9.5

EUR 1.2
EUR 8.8
EUR -36.0

EUR 0.7
EUR 8.8
EUR -49.0

Source: SEO NetCost model; OAG
Note: Total passengers per year for the European carrier relate to all passenger traffic in the markets that are
affected by the entry of the third country carrier. These include the directly travelling origin-destination passengers
to the third country, the passengers travelling to destinations also served indirectly by the third country carrier andpassengers travelling indirectly with the European carrier (and partners) to the third country.

12© OECD/ITF 2019ASSESSING CONSUMER WELFARE IMPACTS OF AVIATION POLICY MEASURES | ITF ROUNDTABLE 180

Discontinuous supply side reactions are generally not taken into account in a CBA, in which a more orless linear relationship between forecasted demand and the underlying network supply is assumed in thelong term. In the context of consumer welfare impacts, there are a number of lumpy airline supply
reactions that are important to consider as they have longer-term impacts on supply and demand at theindividual airport level.

Airline seat capacity is lumpy
In principle, airlines can reduce capacity in two ways: by reducing frequencies (including route closure)and by using smaller aircraft. As Starkie and Yarrow (2013) point out, possibilities for using smaller
aircraft are generally limited due to the available fleet mix and commitments on other routes. Airlines“do not have a continuous, smooth supply function, but one that is lumpy and discontinuous”. Thismeans that airlines –when confronted with a reduction in demand cannot continuously adjust seatcapacity (Starkie and Yarrow, 2013, p.10).

Base and route closure
Supply side adjustments largely come from cutting frequencies, closing routes and sometimes closing
entire bases. Low-cost carriers have shown to be particularly footloose in terms of base rationalisationand closure in response to changing policy and market conditions. Malighetti et al. (2016) found thatover the period 1997-2014, at 109 out of 813 low-cost operating bases, low-cost carriers decreased theirpresence by at least 50%. For example, early 2016 Ryanair announced it would close its Oslo Rygge
operating base in response to the Norwegian government’s policy initiative to introduce an air travel tax
in Norway1. The air travel tax is expected to result in higher total fares, affect passenger demand as wellas airline revenues. Ryanair’s move resulted in the airport operator’s decision to fully close down the
airport by 1 November 2016, because of insufficient market opportunities without the Ryanair base2. The
Rygge example shows that policy initiatives can have consequences that are discontinuous at theindividual airport level and reach further than the initially expected consumer welfare impacts.

DehubbingA specific type of base rationalisation concerns hub airports. At a hub airport, a “home based” hubcarrier combines various origin-destination passenger flows from, to and via (with a transfer at) the hubon the same flight. It does so with the objective of to create route density and achieve economies ofdensity, scope and aircraft size. At hub airports, there is a possibility that initial demand effects andsupply effects are further leveraged if they induce a so-called hub rationalisation effect.
At hub airports such as Frankfurt, Amsterdam, Atlanta or Dubai, the hub carrier combines various
passengers travelling in a multitude of origin-destination markets on the same flight and transfers themat the central hub airport. When the hub carrier is forced to decrease capacity as a result of cost
increases/demand loss, the hub carrier network may end up in a negative spiral. Cutting capacity on aroute negatively affects other routes to which it provides feed. This may induce further capacity
reductions on the affected routes, etcetera.

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Hence, a hub rationalisation/dehubbing effect may result in less directly served destinations at the hub
airport, higher travel costs for local consumers to reach affected destinations and negative consumer
welfare impacts.

New airline entry
Other airlines may intervene as a response to airline decisions to rationalise capacity in certain markets.
However, entry of a new home-based hub carrier after dehubbing is not a very likely scenario. Redondi
et al. (2012) have shown empirically that airports that lose their hub status do not regain their hub
position in the short-medium term. This can be explained by the large (sunk) investments required andaeropolitical constraints, as well as the quick entry of low-cost carriers that occupy attractive slot pairs
and create a lock-in situation. Dehubbed airports have lower traffic levels in comparison to airports thathave not lost their hub function.
However, on individual markets, barriers to entry are much lower and new airline entry may take place
on routes with enough demand. The same holds true for capacity expansion by competing airlines
already serving an affected route. This means that second order impacts due to hub
rationalisation/dehubbing/base closure on demand/consumer welfare may be softened or fully
recovered because of entry of/ expansion by other suppliers.

The hub rationalisation model
Description of the model
We have developed a Hub Network Rationalisation (HNR) model to estimate the impact of potential
lumpy airline supply decisions at a specific hub airport on consumer welfare. To our best knowledge, noother academic work has been carried out to model such effects. The model can be applied to both hub
and non-hub airports, but shows its real value at typical hub airports. In this paper, we discuss the model
for a typical hub airport.
The HNR-model simulates iteratively the supply reactions of the hub carrier at routes from/to the hub
airport when confronted with lower passenger demand or higher costs in a single market or multiple
markets it serves from, to or via the hub (i.e. local and transfer markets). In addition, the model
simulates the entry of new airlines as well as capacity expansion from other airlines on routes at whichthat the hub carrier may cut capacity. Finally, the model calculates connectivity and welfare changes
using the NetCost model, discussed earlier in this paper (see Annex).
The model consists of four main elements:
1. Input from so-called “network rationalisation scenarios”. These are scenarios in which the hub
carrier is faced with reduced demand in (part) of its network due to of a policy measure (e.g. anaviation tax, loss of market share due to liberalisation). As a result, it is forced to a lumpy supply
decision because it cannot operate certain routes profitably anymore. In short, the hub carrier

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rationalises its network. The demand reduction is exogenously given or can be estimated using
the NetCost model;
2. The HNR-model iteratively simulates the hub carrier’s capacity and fare decisions in response tothis network rationalisation scenario. As the hub carrier carries both local origin-destination
traffic and transfer traffic on the same flight, demand reduction at a specific route resulting in
supply reactions on that route, will have consequences for the passenger demand level at otherroutes in the network. Also on these routes, the hub carrier needs to adjust supply/ fares todeal with decreased demand. This process continues until a stable situation is reached in which
all remaining routes are operated with a viable load factor;
3. If the network rationalisation scenario results in capacity reductions by the hub carrier, themodel assesses the feasibility of new airline entry to the abandoned route or capacity expansion
by other carriers already operating on the route;
4. When a stable situation is reached, the generalised travel cost model NetCost calculates the net
impacts on the airport’s total traffic, in terms of passenger numbers, number of routes andnumber of flights. Furthermore, the NetCost model estimates changes in generalised travel
costs in the network rationalisation scenario compared to the “reference situation” andcalculates consumer welfare changes.

Hub carrier responses
The HNR-model simulates which route(s) in the hub carrier’s network are not profitable anymore in the
network rationalisation scenario. For this purpose, we first construct the hub carrier’s network3 andpassenger flows (both local origin-destination and connecting traffic) to/from and via the hub, using OAG
airline schedules data and adjusted passenger booking data. As reliable revenue, cost and profit data at
the individual route level are hardly available, we assume that a route becomes unprofitable when
passenger demand is below the critical load factor. Hence, we feed the model with load factors, which
are derived from passenger booking data and OAG airline schedules data. Critical load factors have been
set at 65% and 75% for short and long-haul routes respectively, but can be adjusted if applicable4.
The hub carrier may restore load factors by either reducing fares or capacity.
Fare adjustments

Generally, the hub carrier will first try to restore load factors by adjusting airfares downward. Farereductions will stimulate passenger demand and market share. However, there is a limit to the extent
that fares can be reduced (if at all). In monopolised markets, there will be more room for carrier-specificfare reductions than in heavily contested markets. As we do not have insight into the (network)
profitability of individual routes, we assume that the hub carrier can reduce its fares by five % inmonopolised markets and by one % in heavily contested markets. These markets are defined as markets
with a Herfindahl-Hirschman Index (HHI) of 0.5 or less. The maximum fare decrease increases linearly
from 1% to 5% in markets with an HHI of 0.5-1.
The impact of small fare reductions in transfer markets can be substantial, as the cross-price elasticities
are often high in the transfer market (SEO 2011). In contrast, in markets with low price elasticity, theimpact of fare decreases will be limited.

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Capacity adjustments

If the hub carrier fails to sufficiently restore load factors by means of fare adjustments, additional
capacity reductions are required. The hub carrier can reduce capacity on the route, either by using
smaller aircraft or by decreasing the flight frequency (Starkie and Yarrow, 2013).
The possibilities to adjust aircraft size are limited, because the hub carrier is constrained by the available
aircraft in the fleet and commitments on other routes, at least on the short term. Hence, we do notconsider the use of smaller aircraft as a general applicable, feasible response in the short term.
Instead, we assume that the hub carrier will decrease the flight frequency at the route until load factors
are restored. However, a lower frequency level also results in a lower attractiveness of the hub carrier in
the OD and transfer markets involved, which in turn can lead to a decrease in market share and lower
passenger volumes. This is taken into account by applying frequency elasticities of demand.
However, there is a limit to which frequencies can be reduced. Too low frequencies are not likely to be
acceptable for business passengers in particular. Business passengers attach considerable value toflexibility. Hence, we assume that minimum frequencies need to be maintained in the short and longhaul network if a hub carrier wants to compete in the business market successfully. On short-haul routes(up to 3 000 km), the minimum competitive frequency has been set at a default value of ten flights per
week. On long-haul routes (3 000 km or more), the minimum competitive frequency has been set atthree flights per week5. It is assumed that a route is cancelled altogether when the frequency level drops
below the minimum competitive frequency.
Second order network effects

When a carrier adjusts fares and capacity on a certain route, not only passenger demand on this specificroute is affected, but also on other routes to which it feeds (Figure 3). Because of capacity reductions at
one route, the hub carrier may therefore need to restore load factors on other routes as well. The HRMmodel iteratively tries to restore the load factors throughout a hub carrier’s network by fare and capacity
adjustments. It does so, until equilibrium has been reached and all remaining routes are above thecritical load factor.

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Figure 2. Modelling scheme of first and second order network effects

Source: Burghouwt et al. (2016).

Simplified illustration of the Hub Network Rationalisation-model
To illustrate the workings of the HNR-model, let us assume that a hub carrier at a European hub airport(H) offers three destinations:

London Heathrow (LHR): 14x/week with 100 seats per flight;

Toronto (YYZ): 7x/week with 250 seats per flight;

Delhi (DEL): 4x/week with 250 seats per flight;

The hub carrier at hub H is faced with increasing competition on the H-DEL route due to a government
initiative to liberalise the market. The table below provides the passenger demand, capacity and loadfactor figures per market. For example, on the H-LHR route, 520 local and 600 transfer passengers travel
weekly on the route, generating a load factor of 80%.

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Table 3. Passenger demand, capacity and load factors in a
simplified hub network (for illustration purposes only)
London Heathrow(LHR)

Toronto
(YYZ)

Delhi
(DEL)

Passengers weekly
Local passengers
Transfer passengers
LHR-H-XXXYYZ-H-XXX
DEL-H-XXX

520
600
500
100

575
500
500
500

100
600
100
500
-

Total number of passengers

1 120

1 575

700

Capacity
Load factor

1 400
80%

1 750
90%

1 000
70%

Source: Burghouwt et al. (2016).

Because of increasing competition, the load factor on H-DEL decreases to 70%, well below the criticalload factor of 75% for long-haul routes. Fare and frequency reductions are insufficient to restore loadfactors. The hub carrier therefore cancels the Delhi-route. As a result, the London and Toronto routes nolonger receive feed from Delhi, which reduces passenger demand on these routes. The load factors on HLHR and H-YYZ decrease to 73% and 61% respectively. Because the load factor on the London route
remains above the critical load factor for short-haul routes, no further action is required by the hub
carrier. However, the load factor at the Toronto route decreases below the critical level for long-haulroutes. Additional fare and possibly capacity reductions are required to restore load factors. In casecapacity reductions are needed, this will also lead to reduced feed to the London flights.
Table 4. Cancellation of the H-DEL route will have impacts on the other two routes as well
London Heathrow(LHR)

Toronto
(YYZ)

Delhi
(DEL)

Passengers weekly
Local passengers
Transfer passengers
LHR-H-XXXYYZ-H-XXX
DEL-H-XXX

520
600
500
100

575
500
500
500

100
600
100
500
-

Total number of passengers

1 020

1 075

700

Capacity
Load factor

1 400
73%

1 750
61%

1 000
70%

Source: Burghouwt et al. (2016).

‘Tipping points’ in the hub carrier’s network
Figure 3 shows the potential risk of a “cascade” or “domino” effect in the hub carrier’s network when we
exogenously reduce transfer demand of the hub carrier at Amsterdam Airport in a stepwise mannerusing the HNR-model. The hub carrier’s network is fairly robust in terms of the number of flights andnumber of routes up to a reduction in transfer demand of 30%. Around a 40% transfer demand
reduction, a tipping point is reached, and the number of viable flights and destinations decreases quickly.
When 80% of the transfer traffic is removed from the carrier’s network, the impact of a further reduction

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in transfer demand is much less dramatic: the situation reflects a network that is mainly supported by
local origin-destination demand.
Figure 3. Tipping points in the hub carrier network rationalisation process: the example of Amsterdam
80

3000

70
60

2500

50

2000

40

1500

30

1000

20

500

Number of routes

Number of flights hub carrier and partners at
Amsterdam Airport

3500

10

0

0
0%

-10%

-20%

-30% -40% -50% -60% -70% -80%
Initial decrease in transfer volume# flights: intercontinental (lef axis)-90% -100%# flights: European (left axis)

Source: HNR-model; MIDT adjusted passenger booking data for Amsterdam Schiphol; OAG data; SEO (2015)

Other airline entry and capacity expansion
Route cancellations by the hub carrier may offer opportunities for other airlines. They can decide toenter abandoned routes or increase capacity on routes, which they already operate. Dehubbing casesaround Europe (e.g. Milan Malpensa, Barcelona, Budapest) show that substantial low-cost carrier entry is
likely to take place on short-haul routes (Bilotkach 2014; Lieshout et al. 2015; Redondi et al. 2012). On
long-haul routes, entry and capacity expansion is more challenging, due to potential bilateral restrictions
and greater need for beyond/ behind transfer passenger feed to operate a route profitably6.
The HNR-model simulates new airline entry and capacity expansion on routes abandoned by the hub
carrier and partners. The model determines if airlines are likely to take over capacity on routes
abandoned by the hub carrier, based on the available local demand and potential behind and beyond
transfer passenger demand. An algorithm identifies candidate airline(s) and selects the airline that canpotentially deliver the highest frequency level.
Finally, this results in a new airport network in terms of routes, airlines and frequencies. This network isthen compared to the reference situation to derive consumer welfare impacts, using the NetCost
generalised travel cost model.

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Example of consumer welfare impacts of hub
rationalisation at Amsterdam Schiphol
This section is draws on Burghouwt et al. (2016) and Lieshout et al. (2015).

Network scenarios
We have applied the HNR-model framework to Amsterdam Airport Schiphol to determine to what extent
hypothetical network rationalisation due to a policy intervention can potentially affect the airport’s
connectivity and consumer welfare7. For this purpose, two hypothetical network rationalisation scenarios
have been defined, which are the input to the HNR-model8.

Non-hub scenario
In the first scenario, the hub carrier decides to close its entire hub operation at Amsterdam Airport.
Amsterdam Airport becomes a non-hub airport. The remaining route network is based on the local
origin-destination market. New airline entry and capacity expansion of existing airlines takes places onmarkets with enough demand. The operations of Air France and SkyTeam partners remain at the airport,
to the extent to which these operations are still feasible without the extensive KLM-feed to the Air
France and SkyTeam partner flights.
Partial dehubbing

In the second scenario, the hub carrier rationalises its network in response to increasingly challenging
market conditions. A smaller-scale hub operation is maintained. Based on Redondi et al. (2012) and the
dehubbing cases of Brussels and Zurich, we assume that the initial (but not final) frequency loss in the
hub carrier’s network is 50%. We assume that frequency losses mainly take place at the least profitable
routes. These are in our case those routes with the lowest load factors.
For each scenario, we estimate the second order network impacts using the HNR-model. In addition, we
determine to what extent other carriers (incumbents and new entrants) can increase their capacity at
Amsterdam Airport in response to the network rationalisation by the hub carrier, based on the available
local origin-destination demand. Subsequently, we calculate total changes in consumer welfare
compared to a reference scenario. The reference scenario is the network at Amsterdam Airport Schiphol
in 2013, the latest year for which sufficient traffic data were available to carry out the analysis. Hence,
we implicitly assume that the network rationalisation scenarios would have taken place in 2013. In
addition, this means that we estimate the immediate (single year) impacts of these scenarios incomparison to the reference scenario.

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Network impacts
Non-hub scenario

In the non-hub scenario, the number of SkyTeam-destinations at Amsterdam Airport will decrease by
82% (Table 5). However, as other airlines will enter abandoned routes and increase capacity on existing
routes (+61% in number of destinations), only a few European destinations in the 2013-network at
Amsterdam will not be served anymore with direct flights (6% reduction in destinations). These aremainly low demand routes with low load factors, which provide little potential for other carriers to takeover from the hub carrier. However, most of the European destinations remain available after
dehubbing.
This result is in line with previous European dehubbing cases, such as Brussels, Barcelona and Milan
Malpensa. Competing carriers on the hub carrier’s routes continue operating after dehubbing or they
may increase capacity. On other routes, new airline entry takes place. However, as Figure 4 shows, theweekly flight frequency decreases on many routes, as it was the hub operation that delivered sufficienttransfer demand for multiple daily services to complement origin-destination traffic. Whereas thenumber of European destinations at Amsterdam Airport only decreases by 6%, the number of weekly
flights decreases by 39%.
Figure 4. European hub carrier network at Amsterdam in the non-hub scenario

Note: Green: frequency SkyTeam maintained; orange: frequency SkyTeam decreases; yellow: former hub
carrier/SkyTeam destination now only served by other carriers; red: direct service from AMS cancelled
Source: HNR-model

For Amsterdam’s intercontinental network, implications are much more severe (Figure 5): a substantial
number of intercontinental destinations will not be served directly anymore, in particular destinations in
Africa and Asia. These routes heavily relied on the transfer demand carried over Amsterdam Airport,
which constituted on average a 70% of total passenger volume at the intercontinental hub carrier routes.
A number of destinations are only expected to be served by competing airlines following dehubbing,such as United to the United States and Emirates to Dubai. In total, we estimate that Amsterdam will lose
26% of its intercontinental direct destinations and 38% of intercontinental frequencies in the non-hub
scenario.

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Figure 5. Intercontinental hub carrier network at Amsterdam in the non-hub scenario

Note: Green: frequency SkyTeam maintained; orange: frequency SkyTeam decreases; yellow: former hub
carrier/SkyTeam destination now only served by other carriers; red: direct service from AMS cancelled
Source: HNR-model

Table 5. Number of directly served destinations andfrequencies in the non-hub scenario at Amsterdam Airport
RoutesType of route

Number of weekly flights

Hub carrier andpartners

Other carriers

Total

Hub carrier andpartners

Other carriers

Total

Hub carrier andpartners

Other carriers

Total% change

Total

Other
routesAbsolute number

Other carriers

Routesserved by
hub carrier
andpartners% change

Hub carrier andpartners

Absolute number

Number of destinations

Europe

315

1 051

1 366

-84%

90%

-46%

11

55

62

-85%

67%

-13%

Intercontinental

100

222

323

-80%

75%

-48%

15

32

44

-80%

52%

-42%

Subtotal

415

1 273

1 688

-83%

87%

-47%

26

87

106

-82%

61%

-28%

Europe

485

485

0%

0%

69

69

0%

0%

Intercontinental

162

162

0%

0%

46

46

0%

0%

Subtotal

647

647

0%

0%

115

115

0%

0%

315

1 536

1 851

-84%

48%

-39%

11

124

131

-85%

22%

-6%

100

384

484

-80%

33%

-38%

15

78

90

-80%

16%

-26%

415

1 920

2 336

-83%

45%

-39%

26

202

221

-82%

20%

-16%

EuropeTotal routes
Intercontinental
AMS
Total

Note: The number of destinations offered by the hub carrier and partners and by other carriers does not add up to
the total number of destinations offered at the airport. This is because there is overlap in destinations served by the
hub carrier and partners and other carriers.
Source: HNR-model

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The analysis demonstrates that in particular for a large long-haul destination portfolio, a hub operation isindispensable, but less so for the European portfolio. In addition, the hub operation contributes to amuch more frequent European and long-haul network than would be possible based on the local origindestination demand alone.
Partial dehubbing

In the partial dehubbing scenario, we estimate that the number of destinations at Amsterdam will drop
by 9% and the number of frequencies by 21% in comparison to the reference scenario. In case of partial
dehubbing, the hub carrier will rationalise part of its network at Amsterdam Airport, resulting in adecrease in the number of frequencies of 50%. This initial decrease will have second order impacts on
the rest of the hub carriers and partners’ network. In addition, it will trigger capacity growth or entry byother carriers on routes from/to Amsterdam Airport.
The model results show that the hub carrier and partners are able to maintain a major share of theEuropean destinations, although a number of hub carrier route cancellations are expected in the
United Kingdom and Scandinavia (Figure 6). The model shows that most of the destinations will be takenover by competing (low-cost) carriers. In total, the number of European destinations served by the hub
carrier and partners decrease by 42%, whereas the number of European destinations served by othercarriers from Amsterdam Airport increases by 27% (Table 6). The net impact on the direct European
network at Amsterdam is a decrease in 5% in the number of destinations. As in the non-hub scenario, theimpact on the European flight frequency is more severe, with a net decrease of 23% in total number ofEuropean flights. The average European flight frequency of the hub carrier and partners decreases froman average of 36 flights per week to 29 times per week.
Figure 6. European hub carrier network at Amsterdam in the partial dehubbing scenario

Note: Green: frequency SkyTeam maintained; orange: frequency SkyTeam decreases; yellow: former hub
carrier/SkyTeam destination now only served by other carriers; red: direct service from AMS cancelled
Source: HNR-model

Again, the connectivity impacts of a non-hub scenario are much larger for the intercontinental network
than for the European network. As Table 6 shows, we estimate that the number of intercontinental
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scenario. The number of flight frequencies drops by 15%. Amsterdam Airport will in particular lose
destinations in Africa. In addition, the hub carrier will be forced to decrease frequencies on a number ofroutes due to the lower transfer demand in a rationalised network. Competing carriers take over orincrease capacity at routes to North America, the Caribbean and the Middle East.

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Figure 7. Intercontinental hub carrier network at Amsterdam in the partial dehubbing scenario

Note: Green: frequency SkyTeam maintained; orange: frequency SkyTeam decreases; yellow: former hub
carrier/SkyTeam destination now only served by other carriers; red: direct service from AMS cancelled
Source: HNR-model

Table 6. Number of directly served destinations and frequencies
in the partial dehubbing scenario at Amsterdam Airport
RoutesType of route

Number of weekly flights

Hub carrier andpartners

Other carriers

Total

Hub carrier andpartners

Other carriers

Total

Hub carrier andpartners

Other carriers

Total% change

Total

Other
routesAbsolute number

Other carriers

Routesserved by
hub carrier
andpartners% change

Hub carrier andpartners

Absolute number

Number of destinations

Europe

1 095

759

1 855

-45%

37%

-27%

41

42

64

-42%

27%

-10%

Intercontinental

335

163

498

-32%

29%

-19%

48

28

59

-37%

33%

-22%

Subtotal

1 430

922

2 352

-43%

35%

-26%

89

70

123

-39%

30%

-16%

Europe

485

485

0%

0%

69

69

0%

0%

Intercontinental

162

162

0%

0%

46

46

0%

0%

Subtotal

647

647

0%

0%

115

115

0%

0%

Europe1 095 1 245 2 340

-45%

20%

-23%

41

111

133

-42%

9%

-5%

335

660

-32%

13%

-15%

48

74

105

-37%

10%

-14%

1 430 1 570 3 000

-43%

18%

-21%

89

185

238

-39%

9%

-9%

Total routes
Intercontinental
AMS
Total

325

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Consumer welfare impacts
Generalised travel cost changes

In both network rationalisation scenarios, connectivity levels at Amsterdam Airport are substantiallyaffected, both in terms of the number of destinations offered, as well as the average flight frequencies.
A decrease in connectivity will result in higher generalised travel costs for consumers due to:

The need for more indirect travel: A larger share of passengers will travel indirectly from
Amsterdam as the number of direct travel options decreases. Indirect instead of direct travel
involves higher time costs due to longer transfer and detour times.



Lower frequencies: which imply a decrease in flexibility and lower available capacity.



Increase in access costs (time and money) as passengers may have to use alternative airports inthe catchment area.

The impact on fares is less straightforward: when the hub carrier and partners decrease capacity at aroute or close the service, market concentration may increase, implying higher fares. When on the other
hand, the hub carrier decreases capacity on markets where it had a dominant position and new (lowcost) entry takes place, fares may also decrease.
Higher net generalised travel costs will negatively affect passenger demand (distribution) at Amsterdam
Airport: more passengers will use other airlines than the hub carrier, more passengers will use
alternative airports in the catchment area and due to the overall increases in the cost of air travel,
market degeneration will occur.
Results

The impacts on consumer welfare are most negative in the non-hub scenario (EUR -590 million per year).
The impacts in the partial dehubbing scenario are much less severe (EUR -145 million per year). Notethat consumer welfare impacts relate to impacts for Dutch local residents only.
The major part of the consumer welfare change is caused by longer landside access times. Due to thelower frequencies at Amsterdam Airport, more passengers will travel to other airports in the catchment
area, involving longer access times. Another part of the passengers will decide not to travel at all.
A smaller share of the welfare impact of dehubbing, but still substantial, is caused by a decrease inconnectivity. More passengers will travel indirectly in one of the network rationalisation scenarios, incomparison to the reference situation. Indirect travel involves more time in the air and at the airport.
Finally, the net impact on airfares is positive. Due to the decrease in capacity of the hub carrier, market
concentration rises and fares increase9.

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Table 7. Consumer welfare changes in the network rationalisation scenarios(x EUR million/ year)
Non-hub scenario

Partial dehubbing
scenario

-66

-20

-154

-46

-370

-78

-590

-145

Fare/
competition
Effects for Dutch users of air transport Connectivity
services
Landside access
Total

Note: Consumer welfare changes refer to a single year and are for Dutch consumers only.
Source: Burghouwt et al. (2016).

Conclusions
First order consumer welfare impacts in aviation can be estimated using the usual transport model
formulations. However, we also argue that airlines can and do make supply decisions that may have longterm discontinuous effects on generalised travel costs/ consumer welfare.
Airline seat capacity is lumpy, which may leverage demand and welfare effects. In other words, airlines
cannot respond continuously to changing demand at the route level. In addition, airlines may choose to
close or downsize operating bases. A specific type of operating base is the transfer hub. At hubs, network
rationalisation can trigger a so-called hub rationalisation process or domino effect, as the hub carrier’s
flights to and from the hub are highly interrelated in terms of passenger flows. Finally, rationalisation ofairline networks can trigger responses from other airlines (entry/ capacity expansion), that can alleviate
initial supply effects, but may also pre-empt entry of a new hub carrier. We argue that policy makers andregulators should be aware of the risk of second order network impacts of policy interventions, address
them at least qualitatively and preferably quantitatively.
This paper has presented a model framework that can be used to estimate second order network effects
at hub and non-hub airports as well as the resulting consumer welfare impacts. It allows policy makers
and regulators to look beyond the initial demand and welfare impacts and identify risks associated with
their policy interventions, which may arise through the supply side. Figure 8 provides a general overview
of how to integrate of the second order network impacts in the assessment of consumer welfare
impacts.

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Figure 8. Integration of airline supply responses in the assessment of consumer welfare impacts
Policy measure

Consumer
welfare impact

Impact on airline
cost level

Impacts on
generalized
travel costs
Model iterations
on second order
network impacts

Passenger
demand impacts

Airline supply
decisions

In a regulatory/ policy context, applications of the approach are numerous and include consumer welfare
impacts resulting from:(de)regulation of aviation markets

greater airline competition

changes in airport charges, ATC costs, noise levies and security costschanges in taxing regimes.

A number of caveats and avenues for further research apply. First, the HNR-model in its current form issuitable to analyse the impacts of network rationalisation, but not for analysing airline network
expansion. This is a clear limitation to the current model, which can and needs to be addressed withfurther investments in the model.
Second, the HNR-model does not consider the opportunity costs for an airline of deploying aircraft at a
certain route or base. For example, it is quite likely that Ryanair did not decide to close its Oslo Ryggebase because it would not be profitable after the introduction of the Norwegian air travel tax. Rather, it
has decided so because it can allocate its capacity more profitably elsewhere in the network. Answeringsuch a question would require a full network and profitability analysis for every airline affected by apolicy measure, which is clearly outside the reach of most modelling exercises. Instead, a scenario-wiserisk analysis can be performed if there are early indications that an airline may close its operating basefollowing a certain policy intervention.
Third, airlines have multiple instruments to restore load factors, when faced with decreasing demand.
These include pricing and capacity decisions. Further research is needed to determine to what extent
various types of airline use these instruments and in which order.
Finally, the HNR-model is most useful when policy measures are likely to result in discontinuous supply
effects that have long-term effects on demand and consumer welfare. Such supply effects are most likely
to occur where policy measures significantly increase costs and/or competition for a hub carrier which is
to a large extent dependent upon transfer traffic.

28© OECD/ITF 2019ASSESSING CONSUMER WELFARE IMPACTS OF AVIATION POLICY MEASURES | ITF ROUNDTABLE 180

Notes

1

http://corporate.ryanair.com/news/news/160601-oslo-rygge-base-closure-16-route-cancellations-after-norwegian-govt-introducesenvironmentally-friendly-tax/?market=en2

http://www.en.ryg.no/mainpage/about-rygge/rsl

3

As well as that of the hub carrier’s partner airlines.

4

An analysis of OAG and MIDT data shows that in the case of Amsterdam Airport Schiphol, the load factors of the hub carrier are generally
above 65% and 75% for short- and long-haul routes respectively.

5

Based on a case study for Amsterdam Airport. See Burghouwt et al. (2016).

6

As stated earlier, “rehubbing” (or entry of a new hub carrier that replaces the old hub carrier after closure of a hub
operation) is not likely for various reasons.
The other way around, the analysis demonstrates the current connectivity and economic contribution of the hub operation at the airport.

7
8

The network rationalisation scenarios in this case have not been related to a specific policy measure, but illustrate the working andapplication of the HNR-model. In other cases, the HNR-model has been used to evaluate welfare impacts of policy interventions.

9

As mentioned above this needs not be the case in each and every market. However, overall competition decreases and fares increase.

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ASSESSING CONSUMER WELFARE IMPACTS OF AVIATION POLICY MEASURES | DISCUSSION PAPER NO | ITF

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Annex: NetCost generalised travel cost model
The SEO NetCost model is a generalised travel cost model. It estimates the important inconveniences(time costs, out of pocket costs, schedule delay costs) that passenger experiences when travelling from A
to B and expresses these inconveniences in monetary terms. These monetised inconveniences arelabelled as the generalised travel costs.

Construction of travel alternatives
The model uses OAG flight schedules to construct the feasible direct and indirect travel alternatives in
the relevant origin-destination markets. The model then estimates the generalised travel costs for eachtravel alternative in an OD-market. The model distinguishes between the generalised travel costs forbusiness and leisure passengers, as these passenger segments have different time and price sensitivities.
In the context of a CBA, NetCost compares the generalised travel costs of a future airline network anddemand forecast in a reference situation with the generalised travel costs of a future airline and demand
forecast in various policy scenarios. NetCost translates the changes in generalised travel costs (andchanges passenger numbers) into a consumer welfare impact in each policy scenario, in comparison to areference situation.
The NetCost model was first presented in Heemskerk and Veldhuis (2006a, 2006b) and further
developed by Veldhuis and Lieshout (2009). We provide a brief description of the model at an airportpair market basis below. We refer to Lieshout et al. (2016) for a more detailed description of the NetCost
model version used in this paper.

Market shares
NetCost contains a disaggregated passenger itinerary share module, which measures the attractivenessof individual travel alternatives and hence their market share. The market share of a specific hub or
airline in a specific market depends on the available travel alternatives and on the relative attractivenessof these alternatives vis-à-vis competing alternatives.
The attractiveness of individual travel alternatives is expressed by the frequency levels and the“generalised travel costs” of all the alternatives. Generalised travel costs are representations of all travelinconveniences the passenger perceives while travelling.

Generalised travel costs
The generalised travel costs consist of air fares (fare), time costs (GCtime ) and schedule delay costs and(GCfrequency ).
GC=fare+GCtime +GCfrequency
Air fare component of the generalised travel costs

32© OECD/ITF 2019ASSESSING CONSUMER WELFARE IMPACTS OF AVIATION POLICY MEASURES | ITF ROUNDTABLE 180

A fare module determines the expected average fares for each travel option, for both business andleisure trips. The fare module was econometrically estimated using MIDT fare data for the markets
from/to and via Amsterdam Airport for 81 million leisure and 63 million business passenger trips.
Fare=β0 +β1 ⋅tnonstop +β2 ⋅t2nonstop +β3 ⋅ ln(freq) +β4 ⋅HHI+β5 ⋅DLCC +β6 ⋅Dindirectwhere tnonstop is the (theoretical) non-stop travel time, freq is the offered frequency of the connection,
HHI the Herfindahl-Hirschman market concentration index using the offered frequency of eachconnection, DLCC is a dummy if the travel option is offered by a low-cost carrier and Dindirect is a dummy if
the travel option is an indirect option with a transfer at an intermediate hub airport.

Travel time component of the generalised travel costs
The travel time costs for direct flights are sourced from the OAG schedules database. Indirect travel
alternatives involve additional travel time compared to direct alternatives. Both because of the circuitous
flight and the transfer at the intermediate hub. Circuity flight time is defined as the time difference
between the total indirect flight time and the (hypothetical) non-stop travel time. The transfer time isdefined as the time spent at an intermediate airport. The transfer time follows from the airline
schedules. Circuity time and transfer time are perceived with a higher degree of inconvenience than inflight time, therefore penalties are applied to both circuity and transfer time. These penalties are
assumed to decrease with distance. This means that a larger penalty factor remains for short-haul routes
and a smaller one for long-haul routes. The difference between the penalty factors for short and longhaul routes is justified by the fact that one hour of circuity leads to relatively more inconvenience on ashort-haul than on a long-haul travel option.
The same argument holds for connecting time, although the penalty factor for connecting time is overallslightly higher, as connecting time is perceived to be more inconvenient than circuity time.
GCtime =VoT*(tnonstop +ϕcircuity ⋅(3-0,075⋅tnonstop )⋅(tfly -tnonstop )+ϕtransfer ⋅(3-0,075⋅tnonstop )⋅ttransfer
where VoT is the Value of Time, tnonstop is the (theoretical) non-stop travel time, tfly is the total in-flighttime of the travel alternative, ϕtransfer is an additional penalty of circuity of indirect connections, ϕtransfer
is an additional penalty for the discomfort of waiting at the hub airport and ttransfer is the transfer time.
The values of time for business passengers are higher than those of leisure passengers. The penalty
factors and values of time were calibrated using MIDT data for the markets served from and via
Amsterdam Airport Schiphol.

Schedule delay cost component of the generalised travel costs
Flights usually do not depart exactly at the time desired by the passenger. The schedule delay is thedifference between the departure time preferred by the passenger and the actual departure time.
Schedule delay decreases when the flight frequency increases. The costs associated with schedule delay
equal the schedule delay (in hours) time multiplied by the value of waiting time for the next flight. We
estimate the schedule delay or generalised travel cost component of frequency as follows:
GCfreq =VoWT⋅(3,96-0.07⋅freqtotal ) if freqtotal <28© OECD/ITF 201933

ASSESSING CONSUMER WELFARE IMPACTS OF AVIATION POLICY MEASURES | ITF ROUNDTABLE 180

GCfreq =VoWT⋅ (56) if freqtotal ≥28
freqtotal

Where GCfreq is the schedule delay component of the generalised travel costs, VoWT is the Value of
Waiting Time and freqtotal is the total offered frequency in the origin-destination market.

Consumer value
After the generalised travel costs (GC) are calculated, a utility function is used to determine theConsumer Value (CV), having as base the frequency (f). A cost sensitivity parameter α is included. Aftercalibrating the model, we find that α=0.01 for business passengers and α=0.015 for leisure passengers
are the most appropriate values. The consumer value for route alternative i (CVi ) is given by:
CVi =freq⋅e-α⋅GCi
Market shares of route alternatives are estimated using these consumer values. The market share of aroute alternative i is given by:
MSi =

CVi
∑j CVjConsumer welfare changes
Consumer welfare changes in the two scenarios in comparison to the reference scenario are estimatedat the origin-destination market level. The difference in generalised travel costs between the two
scenarios gives the welfare gain per passenger in each market. We compute the total consumer welfarechange for each OD market by applying the so-called “rule of half”: the welfare change per passenger ismultiplied by the number of OD-passengers in the respective market in the reference scenario. The
number of new passengers – which do not in the reference scenario but do not travel in the network
rationalisation scenario – is multiplied by half of the welfare gain per passenger.

34© OECD/ITF 2019

Assessing Consumer Welfare Impactsof Aviation Policy Measures
This paper presents a model framework for estimating second-order
network effects and the resulting consumer welfare impacts at
hub and non-hub airports. It emphasizes the benefits of looking
beyond the initial demand and welfare impacts and identifying risks
associated with policy interventions which may arise through the
supply side.

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Published: 11/2019 | Photo credit: Robert_Chlopas/Shutterstock

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