Those who fly more should pay more – 70% of the total number of flights are taken by only 15% of the population

But what most of us don’t know is that the majority of flights are taken by a very small and very rich segment of the population. It’s estimated that 70% of the total number of flights are taken by only 15% of the population, while 57% of the population took no flights abroad whatsoever in 2013. Those who do fly are significantly wealthier – for example, the average income for leisure passengers at Edinburgh Airport in 2013 was more than twice the average Scottish income.

Why should we allow a small number of rich people to take advantage of a global environment that belongs to all of us? And why should we assist those flights with fuel duty and VAT exemptions, as we do in the UK? It stands in stark contrast to those in low-earning countries who stand to suffer the worst effects of a changing climate.

The fair solution is obvious: those that fly more should pay more.

The majority shouldn’t have to subsidise the air miles lifestyle of the elite. That’s why we’re joining forces with the Fellow Travellers campaign in proposing that the current system of Air Passenger Duty, which levies the same small charge regardless of how frequently we fly, should be replaced witha Frequent Flyer Levy. A move to this system would see nothing charged on the first return flight that an individual takes each year, but would see it increase progressively the more flights the individual takes.

By doing this we can help limit the environmental impact from flying in a way that is fair and doesn’t penalise the majority for the actions of an elite. We can have a future in which flying is not reserved for the rich and we don’t have to keep building more runways for the benefit of the few. But to do that we need to correct the injustice in our tax system.

Our report, Managing aviation passenger demand with a Frequent Flyer Levy, forms part of the newly launched Fellow Travellers project. Together we are making the case for replacing the biased system of Air Passenger Duty with a fairer Frequent Flyer Levy. We show how this can both reduce the environmental impact of flying and redistribute flights away from the richest towards the rest of us.

**

Managing aviation passenger demand with a frequent flyer levy 

Fellow Travellers authors: stephen devlin and sandra bernick
New Economics Foundation www.neweconomics.org
info@neweconomics.org
© 2015

This paper considers the feasibility of managing aviation passenger demand through a reformed taxation regime for flights. Specifically, it is supposed that Air Passenger Duty (APD) be repealed and replaced by a Frequent Flyer Levy (FFL), which would vary depending on the number of previous flights taken by an individual.

The research question is what such a tax regime should look like in order to achieve four goals: (i) prevent passenger demand from increasing more than 60% by 2050, as recommended by the Committee on Climate
Change; (ii) be revenue neutral to the exchequer; (iii) obviate the need for new runway capacity; and (iv) reduce greenhouse gas emissions in line with a low probability of > 2°C warming.

The potential impact on businesses is also considered. It is found that a progressive tax on frequent flying could play a significant role in restraining demand for flights, while at the same time tending to distribute those flights more equally across the income spectrum. 

Introduction and motivation

Greenhouse gas emissions from aviation comprised 6% of the UK’s total in 2011.1 And demand for flights is expected to continue soaring, possibly increasing by 127% (i.e. more than doubling) between 2010 and 2050,2 thus dramatically increasing the associated environmental damage. Consumer surveys also indicate that most people plan to fly as much or more in the future.3

However, while the cost of this environmental damage will be spread across the global population it is only a relatively small proportion of UK society that makes frequent use of air travel. It is estimated that only 15%
of the population takes 70% of the flights,4 while 55% of the population took no flights abroad whatsoever in 2013.5 Even in terms of the global community, UK citizens are responsible for a disproportionate level of
aviation emissions – per capita emissions from air travel are much higher in the UK than anywhere else in the world, and twice as high as in the USA.6  Unlike many other sectors, aviation is not expected to make absolute reductions in its emissions between now and 2050. The sector’s allocation of the total UK carbon budget is expected to increase from 6% in 2011 to 25% in 2050.7 A whole quarter of our limited allowance to emit greenhouse gases will be devoted to aviation. This situation is peculiar, therefore, in that a small number of beneficiaries are causing a substantial degree of environmental damage and yet not being asked to reduce that damage in absolute terms. This can be contrasted with, for example, the energy sector, whose beneficiaries are extremely diffuse (indeed, they are everyone) and will be required to undergo significant changes in the coming decades.

Despite this rather extreme situation, discussion of the potential to limit aviation emissions has focused on only 15% of the population takes 70% of the flights6 increasing fuel efficiency, adopting lower-carbon biofuels and marginal technological substitutes such as better teleconferencing capabilities.

There has been little to no discussion of options for actively restraining the number of flights. For example, the interim report of the Davies Commission8 is quite clear that emissions from aviation will need to reduce and discusses a number of options for doing so, including greater fuel efficiency. Yet, despite this substantial discussion, the possibility that the number of flights might simply have to fall is not explicitly considered. We are failing to have an important debate. In particular, this paper considers an option that is not currently on the table: fiscal policy. The aviation sector is particularly privileged in this regard – it is exempt from fuel duty and zero-rated for VAT. It has been argued that such treatment represents a significant public subsidy, putting other forms of transport at a competitive disadvantage.9 In terms of absolute numbers, DfT’s aviation forecasts show that the expected growth in flights by 2050 will come largely from short-haul rather than long-haul flights (a ratio of roughly 4:1) – these are exactly the flights for which (electric aviation or) alternative forms of transport, such as rail, are most feasible.

This paper considers the potential for a fiscal reform that aims to reduce the environmental impact of flights from the UK by reducing their total number while incentivising a more equitable distribution of those flights
across the income spectrum. Specifically, it is hypothesised that Air Passenger Duty is repealed and replaced with a Frequent Flyer Levy that is zero for an individual’s first outbound flight in each year and increases
continually for each subsequent outbound flight. The research question is what such a tax regime should look like in order to achieve four goals:

The administrative practicalities of this reform are not considered at this stage.

1. Prevent passenger demand from increasing more than 60% by 2050, as recommended by the Committee on Climate Change.
2. Be revenue neutral to the exchequer
3. Obviate the need for new runway capacity
4. Reduce greenhouse gas emissions in line with a low probability of > 2°C warming.

Data and modelling methodology

Data

The National Travel Survey dataset was downloaded from the UK Data Service for the years 2002-2010. This is an enormous data set with entries for nearly 200,000 individuals including their household income quintile, although most of the information relates to domestic journeys conducted by personal transport and terrestrial public transport, rather than aviation.  Two survey questions are of relevance. One question asks how frequently the respondent takes a domestic flight and a second asks how many times the respondent has taken an international flight out of the UK in the last 12 months. As such, the format of these questions is different. International flight survey responses are available for 2006-2008 only. In both cases, the respondent must answer by choosing a category, rather than giving an absolute number – e.g. someone might answer “12-53” when the true value is 37. This means that the average number of flights taken by each income group cannot be calculated as a precise value without some modifications. An imprecise fix is to convert the response category variables into a numerical variable by assuming an absolute value for each category. For the most part this is simple because the category only includes one number, but for the higher range of responses it is necessary to
make a conversion, e.g. for international flights: “7-12” becomes 9.5; “13-52” becomes 33; and “53+” becomes 53. Choosing the mid-point of the range seems to be the least arbitrary option, although it might be expected
that the true value is skewed towards the lower end of each category. This potentially adds a substantial, but unavoidable, degree of error to the analysis. However, since the large majority of responses are within lower
single-number categories, the effect may not be so important.

The data is weighted using the sampling weights “W3” as recommended by the dataset guidance for individual level analysis. Using statistical software the mean number of domestic and international flights taken by each income quintile per year is obtained. As expected there is a significant increase in flights taken along the income spectrum. Table 1 details these statistics.

Scaling these figures by the UK population in 2010 gives a total value of 148 mppa. This is very close to the actual figure for 2010 of 142 mppa (which is the sum of total UK domestic leisure and business, UK international leisure and UK international business), as reported by the Department for Transport.10

Modelling methodology

A standard approach to modelling the demand response of a change in fiscal policy is to multiply the percentage change in unit price caused by the change by the estimated price elasticity of demand. In this case such a simple approach is not possible since there is no single percentage change in unit price: the percentage change depends on how many flights have already been taken (i.e. the price of your 5th flight will increase much more than the price of your 3rd flight).

Table 1 – average number of flights taken by each income quintile source: national travel survey income quintile average number int’l flights taken 2006-2008 average number domestic flights taken 2006-2008
total

Lowest real income .45 .79 1.24
Second level .60 .89 1.50
Third level .85 1.05 1.90
Fourth level 1.20 1.27 2.47
Highest real income 2.55 2.10 4.65
NB these figures may not sum exactly due to rounding

An alternative approach is necessary.

The approach taken breaks the demand for flights into a matrix of flight ranking by household income quintile, as illustrated in Table 2 for the base period of 2010.11 This matrix illustrates, for example, that all income groups take at least one flight (domestic and international) on average and the average person in the lowest income group takes 0.24 of a second flight. This breakdown allows demand responses to be estimated for 1st, 2nd, 3rd, etc., flights individually (since the price change is different for each) at each income group. The average cost of a flight fare is obtained from DfT’s aviation forecasts.12 This provides an average cost per flight (across international and domestic passengers) between 2008 and 2050, split by cost component, including Air Passenger Duty. No estimates were found for the average cost per flight at each income quintile (one would expect the lower quintiles to purchase cheaper flights on average). As such, the same average cost per flight is assumed for each income quintile – the sensitivity of the results to this assumption is tested below.

Since the price elasticity of demand might be expected to vary depending on both flight ranking and household income quintile, an elasticity matrix corresponding to the above flight matrix is assumed, illustrated in Table 3. DfT employs an overall price elasticity of demand of -0.6 for flights (an average across UK and foreign, business and leisure flights)13 and this is placed in the matrix under the second flight at the third income level, highlighted below (based on the fact that the average income is contained in the third quintile and the average number of flights taken is between 1 and 2). -0.6 is also the value recommended in a study commissioned by IATA (an aviation industry group) for pan-national level changes (that is, when a ‘set of routes (e.g., across a continent) experience an identical price change’) such as the one hypothesised in this paper.14 It is further assumed that elasticity is greater (further from zero) as flight ranking increases and as household income decreases, as illustrated below. These are somewhat arbitrary and the sensitivity of the results to these assumptions is tested below. The maximum range of elasticities is between -0.49 and -0.77 (the latter is for 9th flights taken by the lowest real income group).

Table 2 – flight matrix for base period 2010 flight rank 1st 2nd 3rd 4th 5th 6th Total

Lowest real income 1.00 0.24 0.00 0.00 0.00 0.00 1.24
Second level 1.00 0.50 0.00 0.00 0.00 0.00 1.50
Third level 1.00 0.90 0.00 0.00 0.00 0.00 1.90
Fourth level 1.00 1.00 0.47 0.00 0.00 0.00 2.47
Highest real income 1.00 1.00 1.00 1.00 0.65 0.00 4.65

After inputting a tax rate for each flight rank and calculating the resultant price per flight, a new matrix of percentage changes in price per flight is obtained, which can be multiplied by the elasticity matrix and used to obtain a new flight matrix. Comparing the original flight matrix with this new matrix gives the total expected change in demand for flights under a certain tax regime.

Table 3 – matrix of price elasticities of demand
flight rank 1st 2nd 3rd 4th 5th 6th
Lowest real income -0.69 -0.7 -0.71 -0.72 -0.73 -0.74
Second level -0.64 -0.65 -0.66 -0.67 -0.68 -0.69
Third level -0.59 -0.6 -0.61 -0.62 -0.63 -0.64
Fourth level -0.54 -0.55 -0.56 -0.57 -0.58 -0.59
Highest real income -0.49 -0.5 -0.51 -0.52 -0.53 -0.54

This procedure is repeated at four time periods – 2020, 2030, 2040 and 2050 – using DfT forecasts as a counterfactual in each case (i.e. the original flight matrix in each period is based on the forecasted growth in total flights from DfT aviation forecasts). For later periods the matrix is extended beyond the 6th degree (to a maximum of 9 flights) to accommodate the expected increase in flights person at the highest income quintile. Since these results are expressed in terms of changes in flight frequency per person, it is necessary to adjust for expected population growth (based on ONS forecasts)15. The counterfactual scenario from DfT already accounts for this impact.

Results

(i) Preventing passenger demand from increasing more than 60% by 2050

The Committee on Climate Change has estimated that ‘there is potential for aviation demand to increase while still meeting the Government’s target [for reducing carbon emissions by 2050] – in the most likely scenario, a 60% increase in demand is allowed.’16 It should be noted that this scenario makes assumptions regarding improvements in fuel efficiency, use of biofuels, carbon pricing, and increased use of videoconferencing and other forms of transport. Importantly, this allowable 60% increase in demand is relative to a 2005 base year. In the results that follow a 60% demand increase had been allowed from a base year of 2010, since this is the base year of the DfT forecasts used. Consequently, the results estimated correspond to some unknown figure slightly greater than 60% increase between 2005 and 2050.

Using the above methodology, the tax schedule in Table 4 and Figure 1 results in an increase in passenger demand between 2010 and 2050 of 60.4%. It is assumed that all tax rates increase by 5% each decade.17 Clearly, there are a multitude of tax rate combinations that would result in such a total increase – this is but one example.

Figure 1 – tax schedule for scenario (i) in base period table 4 – tax schedule for scenario (i) in base period flight rank 1st 2nd 3rd 4th 5th 6th 7th 8th 9th
Tax rate £0 £20 £60 £135 £210 £270 £330 £380 £420
1st
£450
£400
£350
£300
£250
£200
£150
£100
£50
2nd 3rd 4th 5th 6th 7th 8th 9th
tax rate
flight rank

Table 5 and Figure 2 illustrate the evolution of demand over time under this scenario. The counterfactual scenario is taken directly from DfT’s unconstrained aviation forecasts and the policy scenario is calculated as described in the methodology section above.

Figure 2 – total passenger volume for scenario (i) The policy scenario exhibits a 60.4% increase between 2010 and 2050, while the counterfactual scenario exhibits a 129.0% increase over the same period. Demand in the policy scenario is 30% lower in 2050 compared to the counterfactual.  Under this scenario, revenues to the exchequer are significantly greater in the policy scenario compared to the counterfactual, as illustrated in Table 6 and
Figure 3.

Table 5 – total passenger volume for scenario (i) total passengers (mppa) 2010 2020 2030 2040 2050

Counterfactual scenario 211 259 320 391 482
Policy scenario 211 230 268 306 338
2010 2020 2030 2040 2050
figure 2 – total passenger volume for scenario i

600m
500m
400m
300m
200m
100m
counterfactual
policy
passengers per year

Figure 3 – tax revenue for scenario (i)

Tax rates have been specified up to the ninth flight.  This is because the greatest average number of flights is 8.42 for the highest income group in 2050 under the counterfactual scenario. However, there will clearly be some number of individuals that take a tenth flight or more. Since the number of people at each flight frequency decreases as frequency increases (i.e. there are far fewer people taking 20 flights than 5 flights), the coverage of tax rates decreases beyond the ninth flight and therefore those rates become comparatively unimportant, though not necessarily negligible, for affecting behaviour. In principle, consistency would require that the tax rate continue to escalate with flight rank; however, a tax rate cannot be individually specified for all possible numbers of flights – there will need to be a limit beyond which the tax rate remains constant or increases at some automatically calculated rate (for example, each additional flight beyond the  counterfactual policy.

Table 6 – tax revenue for scenario (i)
tax revenue (millions) 2010 2020 2030 2040 2050
Counterfactual scenario £2,280 £3,480 £4,347 £5,430 £6,890
Policy scenario £2,280 £7,098 £9,703 £12,467 £14,955
2010 2020 2030 2040 2050
figure 3 – tax revenue for scenario i
£16bn
£14bn
£12bn
£10bn
£8bn
£6bn
£4bn
£2bn

tenth is charged an additional fee of £50). The most appropriate limit is a practical question that the current data is not suited to answer.

(ii) Revenue neutrality to the exchequer

As demonstrated above, the tax schedule required to reduce passenger demand in line with CCC recommendations potentially creates large additional revenues to the exchequer. A question that follows is: how could a tax regime be designed so as to manage aviation demand while taking broadly the same level of revenue? Using the above methodology, the tax schedule illustrated in Table 7 and Figure 4 results in a roughly neutral impact on exchequer revenues. It is assumed that all tax rates increase by 5% each decade.

Table 7 – tax schedule for scenario (ii) in the base year
flight rank 1st 2nd 3rd 4th 5th 6th 7th 8th 9th
Tax rate £0 £5 £15 £25 £35 £40 £45 £50 £55
figure 4 – tax schedule for scenario (ii) in the base year.
1st
£60
£50
£40
£30
£20
£10
2nd 3rd 4th 5th 6th 7th 8th 9th
figure 4 – tax schedule for scenario

ii in the base year tax rate flight rank

Table 8 illustrates the evolution of demand over time under this scenario. Again, the counterfactual scenario is taken directly from DfT’s unconstrained aviation forecasts and the policy scenario is calculated as described in the methodology section above.

While this tax schedule still achieves a redistribution of flights down the income spectrum, it has a much weaker impact on total demand compared to scenario (i). The policy scenario exhibits a 124.9% increase
between 2010 and 2050, while the counterfactual scenario exhibits a 129.0% increase over the same period. Demand in the policy scenario is 2% lower in 2050 compared to the counterfactual. Under this scenario, revenues to the exchequer are similar in both scenarios over the full time period (though they differ somewhat in any single period), as illustrated in Table 9 and Figure 5.

Table 9 – tax revenue for scenario (ii) tax revenue (millions) 2010 2020 2030 2040 2050
Counterfactual scenario £2,280 £3,480 £4,347 £5,430 £6,890
Policy scenario £2,280 £2,389 £3,765 £5,757 £8,967
table 8 – total passenger volume for scenario (ii)
total passengers (mppa) 2010 2020 2030 2040 2050
Counterfactual scenario 211 259 320 391 482
Policy scenario 211 262 321 389 474

Figure 5 – tax revenue for scenario (ii)

It should be noted that there are other ways of making this policy revenue neutral without changing the rate schedule, for example, some other tax could be reduced by an equivalent amount, or the revenues could be hypothecated towards making low-carbon transport more affordable by reducing the tax rates applied to them.

(iii) Obviating the need for new runway capacity

Under DfT’s “constrained” aviation forecasts it is assumed that:

• ‘no new runways are built in the UK;
• schemes already in the planning system and airport masterplans implemented by 2020;
• incremental growth to full potential long-term capacity by 2030 taking account of the airports’ own longer term plans, physical site constraints and up 13% capacity gain (where possible) through operational and technological improvement;
• terminal capacity increased incrementally to service additional runway capacity; and
• no changes after 2030.’18

2010 2020 2030 2040 2050
figure 5 – tax revenue for scenario ii
£10bn
£8bn
£6bn
£4bn
£2bn
counterfactual
policy

The resulting forecast is that passenger numbers will reach 445 mppa in 2050. It is assumed, therefore, that constraining passenger numbers to such a level by some means other than physical constraints would potentially obviate the need for significant extra capacity.

As shown in Table 5 above, scenario (i) constrains passenger demand significantly below the level of 445 mppa and is therefore consistent with this third objective. Scenario (ii) is not. Using the above methodology, the tax schedule illustrated in Table 10 and Figure 6 is consistent with keeping passenger demand just below the level forecasted in DfT’s “constrained” scenario. It is assumed that all tax rates increase by 5% each decade.

Table 10 – tax schedule for scenario (iii) in the base year flight rank 1st 2nd 3rd 4th 5th 6th 7th 8th 9th

Tax rate £0 £10 £30 £50 £70 £90 £110 £130 £150

Figure 6 – tax schedule for scenario (iii) in the base year.
1st
£160
£140
£120
£100
£80
£60
£40
£20
2nd 3rd 4th 5th 6th 7th 8th 9th
Figure 6 – tax schedule for scenario iii in the base year tax rate flight rank

Table 11 and Figure 7 illustrate the evolution of demand over time under this scenario. The counterfactual scenario is taken directly from DfT’s unconstrained aviation forecasts and the policy scenario is calculated as described in the methodology section above. The policy scenario exhibits a 106.8% increase between 2010 and 2050, while the counterfactual scenario exhibits a 129.0% increase over the same period. Demand in the policy scenario is 10% lower in 2050 compared to the counterfactual.

2010 2020 2030 2040 2050
Figure 7 – total passenger volume for scenario iii

600
500
400
300
200
100 million passengers per year counterfactual policy

Table 11 – total passenger volume for scenario (iii) total passengers (mppa) 2010 2020 2030 2040 2050
Counterfactual scenario 211 259 320 391 482
Policy scenario 211 254 308 368 435

Figure 7 – total passenger volume for scenario (iii)

(iv) Reducing greenhouse gas emissions in line with a low probability of > 2°C warming

It has been argued that the UK’s official target for reducing greenhouse gas emissions (80% in 2050 compared to 1990) is an insufficient contribution to achieving a reasonable likelihood of keeping temperature increases within +2°C, as committed to in the Copenhagen Accord. Anderson and Bows (2011)19 illustrate this discord by modelling the necessary reductions in cumulative20 emissions among Annex 1 countries (which includes the UK), given different pathways for non-Annex 1 countries. Their results imply that very rapid and immediate emissions reductions of 7-11% each year are necessary from Annex 1 countries to achieve an acceptable chance (approximately 37%) of not exceeding 2°C of warming.

This establishes a pathway of 7-11% annual reductions for the UK’s total carbon budget. The allocation of that budget to the aviation industry must then be decided. Current forecasts would see aviation’s allocation of
the total carbon budget increase from around 6%21 currently to 25% in 2050.22 In part this reflects the relative lack of substitutes for aviation and aviation fuels23 and in part it reflects special treatment of the aviation industry relative to others (which must pick up the slack by cutting emissions harder).

If the aviation industry were to take the same responsibility for emissions reductions as all other industries must (on average), this would imply an annual reduction in aviation emissions of 7-11%.  Conservatively assuming only 7% annual reduction (implying greater reductions required in other sectors) and accounting for expected reductions in carbon intensity of 0.9% per year, as estimated by the CCC,24 this implies the necessary pathway for total flight volume illustrated in Figure 8.

Figure 8 – necessary flight volume pathway for 37% chance of < 2 degrees warming
(index, 2010=1.00)
In 2050 flight volumes would have to be 92% lower compared to 2010 under these assumptions. It seems clear that any single fiscal instrument such as the hypothesised Frequent Flyer Levy would be insufficient
to effect such profound change – a much more radical approach would be necessary.

More broadly, this scenario clearly demonstrates the need for re-examining the adequacy of our existing targets and the degree to which we are willing to allow aviation to absorb a significant proportion of those
targets.

2010 2015 2020 2025 2030 2035 2040 2045 2050
Figure 8 – necessary flight volume pathway for 37% chance of < 2 degrees warming index, 2010=1.00)
1.2
1.0
0.8
0.6
0.4
0.2

Business Impacts

The proposed policy could potentially treat business flights in either of two ways.  Firstly, a system could be designed such that business flights face the same marginal rates of taxation as leisure flights. For example, a business might face a charge that depends on the ratio of total flights taken to total employees. The above results implicitly assume this scenario since the forecasts include both leisure and business flights and the calculations
employ elasticities that account for the appropriate mix of business flights in the total. To get an idea of the demand response of business customers to this policy change, rather than the overall response, the average percentage change in flight costs for business customers is assumed to be between that for the lowest and highest income quintile, a range of +3% to +122% in 2050 in scenario (i).25 Based on an elasticity of -0.2 for business passengers (as assumed by DfT) this implies a business demand response of between -0.5% and -24.5% in scenario (i). Intuitively, one might expect businesses that take very few flights per employee to benefit from this policy (since the first flight per employee becomes cheaper) and other businesses with a higher such ratio to reduce their demand. This impact is significantly less than the overall impact (-30% in 2050), as expected due to the lower elasticity. 

A second option is to exempt businesses from the tax regime altogether, in which case the trajectory for business flights would remain slightly higher, as per DfT forecasts.26 In order to still achieve the objectives described above it would be necessary to offset the increase in business passengers with a further decrease in leisure passengers. For a tentative understanding of the necessary change to the tax regime, it is assumed that, in this scenario, business passengers are taxed according to the previous APD regime. Thus, the trajectory of business passenger growth is assumed to be equal to that forecasted by DfT. Since we know the rough passenger numbers that satisfy a 60% increase by 2050, we can subtract business passengers from this figure to obtain the necessary trajectory of all other passengers. These trajectories are detailed in Table 12. To obtain an example of a tax regime that results in this trajectory for other passengers the model described above is applied with the following changes: (a) the starting volume of passengers is 168 mppa, rather than 211 mppa; and (b) the elasticity matrix is centred around a value of -0.7 (which reflects leisure passenger price elasticity), rather than -0.6 (which reflects overall price elasticity). The resulting matrix is shown in Table 13.

Table 12 – passenger volumes in business exemption scenario
passenger volume 2010 2020 2030 2040 2050
Business flights as per counterfactual (mppa) (A) 43 56 70 86 104
60% increase total trajectory (B) 211 230 268 306 338
Necessary trajectory for other
passengers (B – A)* 168 175 198 221 234
NB these figures may not sum exactly due to rounding
table 13 – elasticity matrix for business exemption scenario
flight rank 1st 2nd 3rd 4th 5th 6th 7th 8th 9th
Lowest real income -0.79 -0.8 -0.81 -0.82 -0.83 -0.84 -0.85 -0.86 -0.87
Second level -0.74 -0.75 -0.76 -0.77 -0.78 -0.79 -0.8 -0.81 -0.82
Third level -0.69 -0.7 -0.71 -0.72 -0.73 -0.74 -0.75 -0.76 -0.77
Fourth level -0.64 -0.65 -0.66 -0.67 -0.68 -0.69 -0.7 -0.71 -0.72
Highest real income -0.59 -0.6 -0.61 -0.62 -0.63 -0.64 -0.65 -0.66 -0.67

An example of a tax schedule that obtains the necessary trajectory above (i.e. passenger numbers in 2050 ≤ 234 mppa) is shown in Table 14 and Figure 9. It is assumed that all tax rates increase by 5% each decade.
The resulting overall trajectory is detailed in Table 15. Passenger numbers increase by 59.8% between 2010 and 2050 in the policy scenario. 1st
£500
£450
£400
£350
£300
£250
£200
£150
£100
£50
2nd 3rd 4th 5th 6th 7th 8th 9th

Figure 9 – tax schedule for business exemption scenario in base period
tax rate
flight rank
Table 15 – total passenger volume in business exemption scenario
total passengers (mppa) 2010 2020 2030 2040 2050
Counterfactual scenario 211 259 320 391 482
Policy scenario 211 223 262 302 336
table 14 – tax schedule for business exemption scenario in base period
flight rank 1st 2nd 3rd 4th 5th 6th 7th 8th 9th
Tax rate £0 £40 £90 £155 £220 £290 £350 £405 £435
figure 9 – tax schedule for business exemption scenario in base period

Sensitivity tests

There are two key assumptions made as part of this modelling that are tested for sensitivity. These are: (a) that ‘other flight costs’ (i.e. the cost of a flight excluding APD or FFL) are invariant with household income; (b) price elasticities of demand vary both by household income and by flight rank. The sensitivity of the results obtained to these assumptions is tested by changing them by a given percentage and observing the magnitude change in the results obtained.

(a) Other flight costs

The a priori expectation is that when lower income households purchase flights they will purchase cheaper flights on average compared to higher income households. However, in the absence of evidence to substantiate this expectation the same cost is applied across the income spectrum in the above analysis. To take a large deviation from this assumption the costs are changed in the following way: for each time period analysed the third income quintile takes the average flight cost from DfT forecasts; the first quintile costs are 60% of that value, second quintile 80%, fourth quintile 120% and highest quintile 140%. To illustrate the change, this implies that in 2050 the average flight cost for the lowest income quintile is £96.25, while the cost for the highest quintile is more than double that at £224.57.

This changes the results of scenario (i) as follows: the increase in demand between 2010 and 2050 in the policy scenario increases from +60.4% to +69.4% the difference between the counterfactual and policy scenarios
decreases from 30% to 26%/

This is a considerable change in the assumptions and the resulting impact is significant, though not especially large. Since the true values of flight costs across the income spectrum are likely to lie in between the main assumptions and this sensitivity test, the two results might be interpreted as a range.

(b) Price elasticities of demand

In the main analysis price elasticities of demand are assumed to vary as described in the elasticity matrix detailed above. The rationale for these assumptions is described in the methodology section.

To test the possibility that price elasticities are in fact less variant than assumed, the entire elasticity matrix is set to -0.6, the DfT overall value. This changes the results of scenario (i) as follows:

the increase in demand between 2010 and 2050 in the policy scenario decreases from +60.4% to +57.8%
the difference between the counterfactual and policy scenarios
increases from 30% to 31%
To test the extreme case in which behaviour is much less
responsive to changes in the price of flights, it is assumed that
no element of the elasticity matrix is greater (further from zero)
than -0.6, while the higher income quintiles have a lower demand
response, as illustrated in Table 16.
This changes the results of scenario (i) as follows:
• the increase in demand between 2010 and 2050 in the policy
scenario increases from +60.4% to +69.5%
• the difference between the counterfactual and policy scenarios
decreases from 30% to 26%
This is considered an extreme assumption – as such, the results
obtained are similarly extreme. As with the previous sensitivity
test, in response to relatively large changes in the assumptions,
the results change significantly, but not with a particularly large
magnitude.
table 16 – elasticity matrix for sensitivity scenario
flight rank 1st 2nd 3rd 4th 5th 6th 7th 8th 9th
Lowest real income -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6
Second level -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6
Third level -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6
Fourth level -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5
Highest real income -0.4 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4
28
discussion
These results provide some tentative ideas as to the magnitude
of change required to the tax regime in order to achieve the
four goals described above.
In the first three scenarios the tax schedule would change from one that is flat over
the flight rank and relatively low (around £13 on average at present) in magnitude
to one that increases steeply over the flight rank from a very low base (zero) to a
high rate (though actually slightly lower than the very highest current rate of APD).
In the policy scenarios considered the first flight that an individual takes becomes
cheaper and the second flight is roughly similar (higher in scenario (i) and lower in
scenario (ii)). Since the majority of flights that are taken are either a first or second
flight (73% in 2010 and 50% in scenario (i) in 2050) a large proportion of flights
will not become more expensive.
The impact of the policy is predominantly to discourage high flight frequency.
Since it is the highest household income quintile that flies the most frequently, it
is this group that is expected to change behaviour most significantly in response
to the policy. Figure 10 illustrates the modelled change in flights taken per person
by income quintile in 2050 for scenarios (i) and (ii). Notably, in scenario (ii) the
majority of people actually slightly increase the number of flights they take, but this
is offset by a larger reduction in flight frequency by higher income groups.
Even with this policy, which has the impact
of reducing flight frequency, between 2010
and 2050 the number of flights taken per
person would be expected to increase
from 2.35 to 2.98 on average. Figure 11
illustrates the percentage of flights that are
a first, second, third, etc. flight for 2010 and
2050 under policy scenario (i). Although
the policy attenuates a trend it does not
on average decrease flight frequency
in absolute terms, only relative to the
counterfactual trend.
The important result from this analysis is
that, given the assumed elasticities and
counterfactual forecasts, a progressive tax
on frequent flying could play a significant
role in restraining demand for flights, while
at the same time tending to distribute those
flights more equally across the income
spectrum.
29
figure 10 – change in average number of flights taken by income quintile
figure 11 – percentage of flights by flight rank
9
8
7
6
5
4
3
2
1
2nd
level
3rd
level
4th
level
lowest
real income
highest
real income
2nd
level
3rd
level
4th
level
lowest
real income
highest
real income
flights per person
figure 10 – change in average number of flights taken by income quintile
scenario i
2050
scenario ii
2050
1st 2nd 3rd 4th 5th 6th
figure 11 – percentage of flights by flight rank
flight rank
2010
2050 policy
percentage of flights
45%
40%
35%
30%
25%
20%
15%
10%
5%
30
issues for further
consideration
Data quality
As noted in the data and methodology section, the
data set that has been used is highly imperfect. It
contains information only on the frequency of flights
taken by respondents. It does not capture purpose
(i.e. business or leisure) or distance travelled.27 What’s
more, the responses given are grouped in categories, so
that analysis using absolute numbers requires arbitrary
transformation of categories into single figures. As such,
the data this modelling relies on is considered relatively
poor and the results should be treated with suitable
caution. However, rather than the precise figures
that have been presented, the important result of this
modelling is a test of feasibility. It has been shown
that significant demand restraint in the aviation sector
is possible with a change to tax rates that are not
unfeasibly extreme.
In order to model more specific and nuanced outcomes
of imposing such a regime it would be necessary to
obtain a richer data set.
Applying the tax to non-UK residents
Practical implementation issues have not been
thoroughly explored in this paper. A particularly
important such issue is the question of residency. The
impact of a Frequent Flyer Levy has been modelled
based on data from UK survey respondents. Therefore,
it is implicitly assumed that the tax can be designed
in such a way that all passengers taking flights out of
the UK respond in a similar way to UK residents (who
comprise around two thirds of flights taken).28
Tourism
The tourism industry is particularly dependent on
aviation to provide customers. As such, one concern
31
with the hypothesised tax regime might be that this
industry would disproportionately suffer. However, there
are two clear reasons why the opposite may, in fact, be
true:
1 The UK operates a significant “tourism deficit” – in
other words, more money is removed from our
economy by UK residents taking trips abroad than
is brought in by foreign visitors.29 As such, any
measure that increases the tendency of residents
to stay in the UK will reduce that deficit. Figure 12
shows the expenditure data30 for both overseas
visitors to the UK and UK visitors abroad, illustrating
this deficit.
figure 12 – UK tourism deficit
2 The FFL penalises frequency of flying. UK residents
may be quite likely to make a number of trips out
of the country in any year, and would incur the
associated cost. However, visitors to the UK are
much less likely to make more than one such trip.
As such, they will not be penalised to the same
degree.
2011 2012 2013 2014
figure 12 – UK tourism deficit
overseas residents’
visits to the UK
UK residents’
£3.5bn visits abroad
£3.0bn
£2.5bn
£2.0bn
£1.5bn
£1.0bn
£0.5bn
32
Waiting times and delays
An incidental benefit of reducing the volume of flights
that pass through UK airports would be to significantly
alleviate the problem of congestion, both in airport
terminals and on runways, which may result at our
current level of airport capacity. A lower throughput of
passengers for a fixed air travel infrastructure could be
expected to lead to significant improvements in customer
satisfaction due to a lower likelihood of delay to
any given flight and shorter waiting times for passing
through check-in and security.
Tax revenues
The preceding analysis finds that, in the process
of constraining passenger demand, significant tax
revenues would accrue to the pubic purse. These
taxes could be used to reduce tax rates in other areas
of the economy, such as VAT or income taxes, with
potentially beneficial effects. An alternative would be to
use the increased revenues to further research in lowcarbon
substitutes for aviation fuel. If such research
yielded successful results it would eventually remove
the environmental requirement to constrain absolute
demand for flights.
Uncertainty over the long term
Long term impacts are inherently unpredictable because
they are a result of random processes of trial and
error, adaptive responses and path-dependent choices.
There is a cumulative effect of innovation and research
on the possibilities available in the long term.
Putting in place a clear and long-term framework in
the current day that provides the incentives to move
towards a more grounded economy will cause gradual
and cumulative changes in institutions and technologies.
Such a change will re-orient the path of the
economy in a way that could make existing aviation
forecasts meaningless, or at least highly incomplete. It
is not meaningful to think of the economy as having a
single “optimal” path along which it ought to progress,
with the government tasked with keeping it as close to
33
that path as possible. There are innumerable potential
pathways, some of which will be objectively bad, but
many of which might be considered good.
In a long-run scenario, the change in the number of
flights taken resulting from the proposed policy reform
is unknowable with any high degree of certainty. However,
it is reasonable to expect that the reduction could
be significantly greater than implied by a simplistic elasticity
response estimate. The long-term consequences
are cumulative and potentially large. The economy will
be directed down a path in which technologies, institutions
and behaviours adapt, emerge and disappear
in an unpredictable way, resulting in an economy and
society that differs in quality, not just quantity.
34
endnotes
1 Committee on Climate Change (2013). Factsheet: Aviation. Retrieved from
http://www.theccc.org.uk/wp-content/uploads/2013/04/Aviation-factsheet.pdf
2 Department for Transport (2013). UK Aviation Forecasts. Retrieved from https://www.gov.uk/
government/uploads/system/uploads/attachment_data/file/223839/aviation-forecasts.pdf, p66
3 Ipsos MORI (2007). Attitudes to Aviation and Climate Change. Retrieved from https://www.ipsosmori.
com/Assets/Docs/Publications/attitudes-to-climate-change-and-aviation-2007-report.pdf
4 Department for Transport (2014). Public experiences of and attitudes towards air travel: 2014. Retrieved
from https://www.gov.uk/government/statistics/public-experiences-of-and-attitudes-towards-airtravel-
2014
5 Data retrieved from National Travel Survey table NTS0316 at
https://www.gov.uk/government/statistical-data-sets/nts03-modal-comparisons
6 TGI (n.d.). Green values: Consumers and branding. Retrieved from http://www.wpp.com/wpp/
marketing/reportsstudies/greenvalues/ [accessed on 22.10.2014], p9
7 Committee on Climate Change (2009). Meeting the UK aviation target – options for reducing
emissions to 2050. Retrieved from http://www.theccc.org.uk/publication/meeting-the-uk-aviationtarget-
options-for-reducing-emissions-to-2050/, p2
8 Airports Commission (2013). Airports Commission: Interim Report. Retrieved from https://www.gov.uk/
government/uploads/system/uploads/attachment_data/file/271231/airports-commission-interimreport.
pdf, p44-7
9 Lockley, P. & Dresner, S. (2012). Flying in the face of fairness: Intergenerational inequities in the
taxation of air travel. Retrieved from http://www.if.org.uk/wp-content/uploads/2012/11/Aviation_
Report_Intergenerational_Foundation_FINAL.pdf
10 Department for Transport (2013). UK aviation forecasts 2013 data annexes. Downloaded from
https://www.gov.uk/government/publications/uk-aviation-forecasts-2013
11 It is assumed for simplicity that the flight matrix for the base period (2010) is the same as the flight
matrix for the data period (2006-2008).
12 Department for Transport (2013). UK aviation forecasts 2013 data annexes. Downloaded from
https://www.gov.uk/government/publications/uk-aviation-forecasts-2013
13 Department for Transport (2013). UK Aviation Forecasts. Retrieved from https://www.gov.uk/
government/uploads/system/uploads/attachment_data/file/223839/aviation-forecasts.pdf, p18
14 InterVISTAS (2007). Estimating Air Travel Demand Elasticities. Retrieved from
https://www.iata.org/whatwedo/Documents/economics/Intervistas_Elasticity_Study_2007.pdf
15 Office for National Statistics (n.d.). National Population Projections. Downloaded from http://www.ons.
gov.uk/ons/taxonomy/index.html?nscl=National+Population+Projections [accessed on 22.10.2014]
16 Committee on Climate Change (2009). Meeting the UK aviation target – options for reducing
35
emissions to 2050. Retrieved from http://www.theccc.org.uk/publication/meeting-the-uk-aviationtarget-
options-for-reducing-emissions-to-2050/, p2
17 This implies that the real tax rate increases by around 22% over the 40-year period.
18 Department for Transport (2013). UK Aviation Forecasts. Retrieved from https://www.gov.uk/
government/uploads/system/uploads/attachment_data/file/223839/aviation-forecasts.pdf, p56-57
19 Anderson, K. & Bows, A. (2011). Beyond ‘dangerous’ climate change: emission scenarios for a new
world. Philosophical Transactions of the Royal Society, Vol. 369, pp 20-44
20 Anderson and Bows make very clear the point that what matters to the climate is not only achieving
the single end-point target (in our case <60% demand increase), but also the cumulative emissions over
the time period (i.e. the total area under the emissions graph).
21 Committee on Climate Change (2013). Factsheet: Aviation. Retrieved from
http://www.theccc.org.uk/wp-content/uploads/2013/04/Aviation-factsheet.pdf
22 Committee on Climate Change (2009). Meeting the UK aviation target – options for reducing
emissions to 2050. Retrieved from http://www.theccc.org.uk/publication/meeting-the-uk-aviationtarget-
options-for-reducing-emissions-to-2050/, p7
23 Although arguably this, in turn, reflects a failure to invest in developing substitutes.
24 Committee on Climate Change (2009). Meeting the UK aviation target – options for reducing
emissions to 2050. Retrieved from http://www.theccc.org.uk/publication/meeting-the-uk-aviationtarget-
options-for-reducing-emissions-to-2050/, p10
25 Calculated as the average increase in price weighted by the original flight matrix
26 Department for Transport (2013). UK aviation forecasts 2013 data annexes. Downloaded from
https://www.gov.uk/government/publications/uk-aviation-forecasts-2013
27 The International Passenger Survey provides data on distances travelled by respondents; however, this
data does not include information on the income of the respondent or the frequency with which they
fly.
28 Department for Transport (2013). UK aviation forecasts 2013 data annexes. Downloaded from
https://www.gov.uk/government/publications/uk-aviation-forecasts-2013
29 Rhodes, C. (2014). Tourism: statistics and policy. Retrieved from
http://www.parliament.uk/briefing-papers/sn06022.pdf, p12
30 ONS (2014). Overseas Travel and Tourism – Monthly Release, August 2014. Data downloaded at
http://www.ons.gov.uk/ons/publications/re-reference-tables.html?edition=tcm%3A77-318870
36
fellowtravellers.org
there’s a way
fairer way
fellow
travellers