CPB

Assessing the Net Overall
Distributive Effect of aCongestion Charge
Discussion Paper

170
Jillian Anable
University of Leeds

Phil Goodwin
University College London

Roundtable

Corporate Partnership
Board

CPB

Assessing the Net Overall
Distributive Effect of aCongestion Charge
Discussion Paper

170
Jillian Anable
University of Leeds

Phil Goodwin
University College London

Roundtable

Corporate Partnership
Board

The International Transport ForumThe International Transport Forum is an intergovernmental organisation with 59 member countries. It
acts as a think tank for transport policy and organises the Annual Summit of transport ministers. ITF is
the only global body that covers all transport modes. The ITF is politically autonomous andadministratively integrated with the OECD.
The ITF works for transport policies that improve peoples’ lives. Our mission is to foster a deeper
understanding of the role of transport in economic growth, environmental sustainability and social
inclusion and to raise the public profile of transport policy.
The ITF organises global dialogue for better transport. We act as a platform for discussion and prenegotiation of policy issues across all transport modes. We analyse trends, share knowledge andpromote exchange among transport decision-makers and civil society. The ITF’s Annual Summit is theworld’s largest gathering of transport ministers and the leading global platform for dialogue on transport
policy.
The Members of the Forum are: Albania, Armenia, Argentina, Australia, Austria, Azerbaijan, Belarus,
Belgium, Bosnia and Herzegovina, Bulgaria, Canada, Chile, China (People’s Republic of), Croatia, Czech
Republic, Denmark, Estonia, Finland, France, Former Yugoslav Republic of Macedonia, Georgia, Germany,
Greece, Hungary, Iceland, India, Ireland, Israel, Italy, Japan, Kazakhstan, Korea, Latvia, Liechtenstein,
Lithuania, Luxembourg, Malta, Mexico, Republic of Moldova, Montenegro, Morocco, the Netherlands,
New Zealand, Norway, Poland, Portugal, Romania, Russian Federation, Serbia, Slovak Republic, Slovenia,
Spain, Sweden, Switzerland, Turkey, Ukraine, the United Arab Emirates, the United Kingdom and theUnited States.
International Transport Forum
2 rue André Pascal
F-75775 Paris Cedex 16
contact@itf-oecd.org
www.itf-oecd.org

ITF Discussion Papers
ITF Discussion Papers make economic research, commissioned or carried out in-house at ITF, available toresearchers and practitioners. They describe preliminary results or research in progress by the author(s)and are published to stimulate discussion on a broad range of issues on which the ITF works. Any
findings, interpretations and conclusions expressed herein are those of the authors and do notnecessarily reflect the views of the International Transport Forum or the OECD. Neither the OECD, ITF
nor the authors guarantee the accuracy of any data or other information contained in this publication
and accept no responsibility whatsoever for any consequence of their use. This document and any map
included herein are without prejudice to the status of or sovereignty over any territory, to thedelimitation of international frontiers and boundaries and to the name of any territory, city or area.
Comments on Discussion Papers are welcome.
Cite this work as: Anable, J. and P. Goodwin (2018), “Assessing the Net Overall Distributive Effect of aCongestion Charge”, International Transport Forum Discussion Papers, OECD Publishing, Paris.

Acknowledgements
The authors are grateful to their co-authors in the papers cited in this report. In particular, much of theempirical work is an outcome of the MOT project (EP/K000438/1), funded by the UK Engineering andPhysical Sciences Research Council under the Research Councils UK Energy Programme, leading on from
Research Councils UK Energy Programme research grant EP/J004758/1. The presentations can be viewed
on the ITF website at https://www.itf-oecd.org/road-pricing-roundtable, the input papers are published
as companion reports also available on the ITF website.

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

Table of contents

Introduction ........................................................................................................................................ 5
The founding economic principles of road pricing and distributional impacts ..................................... 6
The classical case............................................................................................................................. 6
Why the classical case stopped working ......................................................................................... 7
A general approach to assessing the distributional consequences of road pricing ............................... 8Interim conclusion......................................................................................................................... 10
The role of distributional issues in practical discussions on congestion charging in the UK................ 10
Micro-simulation as a method of exploring the effects..................................................................... 12
Enhancing microsimulation with novel data and area classifications ............................................ 13
Discussion.......................................................................................................................................... 16
Distributional impacts must be analysed in terms of the net impacts .......................................... 16
Net impacts will change over time depending on behavioural response ...................................... 17
Adaptive response is not in itself an indicator of a gain or a loss .................................................. 17
Distributional analysis requires understanding the joint effect of different factors ..................... 17
Conclusions ....................................................................................................................................... 18
Notes................................................................................................................................................. 20
References ........................................................................................................................................ 21

4© OECD/ITF 2018

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

Introduction
It is widely accepted that congestion is a serious problem, causing economic and social stress. In recent
years there has been much interest in seeking solutions by more and better infrastructure of roads
and/or their alternatives, clever management of demand, regulation, transport and land-use planning,
better understanding of the economic, social and psychological reasons for choices, publicity, ‘nudges’,
and marketing. Among these instruments is the use of economic prices set at a level where the users oftransport systems broadly pay for the resources their choices require: among these resources, the use oftime by other travellers is an important case. Each driver using the road system in congested conditions
causes delays to all other road users, and the idea of congestion charging is that if these delays are paid
for, the system overall will operate in a more efficient way.
This is a simple economic principle, but implementing it requires charging for something which has forgenerations been considered, in a sense, ‘free’, and therefore some people will pay more than they did
before for their travel. Others, when the reduction in congestion is taken into account, will pay less.
However unfair the present situation might be considered to be, new patterns of unfairness arepolitically sensitive, and therefore it is sensible to have a good understanding of who gains and who
loses: it is not adequate simply to say that the economy ‘as a whole’ benefits, without also analysing whogets that benefit.
This paper is structured in five sections as follows: Firstly, we outline how economists have classicallytreated this issue, with special attention to understanding why it has often been hardly considered at all.
Secondly, we outline some reasons why the approach above breaks down in current circumstances,putting greater pressure not only on distributional effects, but on a wider definition of those effects toinclude the distribution of benefits as well as the distribution of payments.
Thirdly we reflect on some practical experience of how the discussion on congestion charging has
broadened the definitions beyond the payment of a congestion charge, to include issues of taxation,complementary policies in the context of transport strategic thinking, and the effects of congestion
charging on the incidence of health and environmental benefits.
Fourthly, we describe some empirical analyses exploring distributional effects by simulation, and byanalysis of the geographical patterns of car ownership and use.
Finally, we draw together the methodological considerations of assessing the ‘Net Overall Distributional
Effect’ of a charging intervention.

© OECD/ITF 2018

5

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

The founding economic principles of road pricing
and distributional impacts
The classical case
Until recently, the economic case for road pricing has focussed mainly on congestion (hence ‘congestion
charging’), and has always depended on strong assumptions about its distributional effects, but that has
not always been recognised. In one of the seminal papers in the field, Walters (1961) wrote briefly:“Problems of the distribution of income – who would and who would not be harmed by
the policy advocated – will not be considered here. The general ramifications of such a policy
are reasonably clear, but the detailed analysis would be cumbersome and boring.”
This approach, albeit not always stated in such a provocative manner, underpinned much of thetechnical and policy studies of the 1960s through to around the mid-1980s. However, it is important tonote that this was not due to a lack of interest, but was on the basis of a theoretical rationale whichsought to justify ignoring distributional consequences by demonstrating that the economic advantages
applied independently of the effects on distribution. This conclusion derived from the development ofwelfare economics in the 1930s and subsequently, where a ‘Pareto Optimum’ was defined in terms of
the possibility that efficiency improvements would enable losers to be compensated by gainers, whether
or not that compensation was actually made.
This is seen in the Smeed Report of 1964, the first explicit formal technical and policy study by aGovernment1 (Ministry of Transport, 1964). A series of technical appendices demonstrated, though using
a calculus presentation which was not widely accessible in policy discussion, that the direct effects of acongestion charge would be to reduce the consumer surplus (economic welfare) enjoyed by two
important groups of road users – both those who continued to make their journeys, with less congestion
but at a higher money cost larger in magnitude than the congestion benefits, and those whose journeys
were deterred by the extra cost. Critical to the existence and calculation of benefit was the proposition(Figure 1) that the loss in utility to these groups would be more than offset by the increase in utility
contained in the revenue collected from the charge, after allowing for the administrative or technicalcosts of the system. The entire discussion on this point is contained in one page of the Appendix, where
the crucial single sentence is:“However, the amount…is a transfer payment and represents no real cost to thecommunity”
In other words, the amount of welfare surplus lost by the initial payers of the charge is exactlycompensated by the amount gained by those who receive the revenue, whoever they may be, leaving
the reduction in congestion as the net efficiency benefit. This principle has underpinned all subsequentappraisals of the economic benefit of congestion charging, from the simplest theoretical case to themuch more complex considerations of different types of road users and wider categories of benefit
beyond congestion. The relevant page of the Smeed Report is shown in Figure 1.

6© OECD/ITF 2018

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

Figure 1. Smeed’s estimate of the economic benefits from direct road pricing

Source: Extract from the “Smeed Report”, Ministry of Transport, 1964, p50.

The main text of the report therefore gave no great attention to the question of what should be donewith the revenue, since by axiom it made no difference to the economic benefit. There were a few
sentences exploring the possibility of using it in part for the provision of parking spaces, with amoderately favourable treatment, but it was probably to clarify the ‘transfer payment’ proposition,rather than a policy recommendation in itself, that the report:“assumed that the introduction of road prices would be accompanied by acorresponding reduction in road taxes.”
The basic contention was that calculation of the real economic benefits of congestion charging can be
carried out independently of any consideration of distributional effects or wider policy context.
Therefore distributional effects were a subordinate question, and if discussed at all, were mainly in thecontext of the amount of the charge which would be paid by different groups of motorists, mostly
classified by income group.

Why the classical case stopped working
This emphasis was, however, challenged by the experience of policy discussions centred specifically oncongestion charging as they got closer to practical application, as this in turn affected how the theory© OECD/ITF 20187

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

was described – see, for example, Nash (2001), Calthrop and Proost (2003) and Ubbels and Verhoef(2007). This was because:
Arguments of ‘integrated transport policy’ and a more strategic view of the interaction between public
and private transport, especially in urban contexts where congestion was most severe, triggered studiesof the relative benefits of, for example, investment in new light rail systems, priority systems favouring
buses, cycling and walking, and land use planning. These often had greater benefits than construction of
new road capacity which provided only temporary relief to congestion (see Goodwin (2010)).
The range of problems from excessive traffic, and the use of transport charging to tackle them, went
beyond the traditional concerns with congestion and traffic accidents, to include emissions of harmful
pollutants, emissions of carbon dioxide contributing to climate change, effects on health of transportrelated activity, and interactions with social and community life. This put more attention on theimportance of behavioural change including greater reliance on active modes, walking and cycling, withmeasures which would reinforce the use of road pricing by more favourable conditions for alternativetransport choices and provide additional, and different, benefits.
It became obvious in many political discussions and debates that public acceptability of new systems ofcharging for road use was very dependent on what other policies would be implemented – at the sametime, and as part of the same package of measures – to improve the alternatives. Although public
opinion can be volatile and will vary in different contexts and places and times, one particular strand wasthat new road charges would only be acceptable if accompanied by better public transport, especiallyrail. Since congestion charging could produce a cash surplus, and such investments depended on new
funding sources, it was a logical step to suggest that revenue received from road user charges could be
used to improve the alternatives, thus giving a carrot as well as a stick and making behavioural change
advantageous rather than forced. See, for example, Jones (1995), Musselwhite and Lyons (2009),
Goodwin and Lyons (2010).

A general approach to assessing the distributional
consequences of road pricing
The result of the above discussions was that the policy context within which it was realistic to think thatnew charging systems could be implemented, radically changed. The original single policy assumption,
made partly to simplify the argument, of ‘tax neutrality’ defined as a compensating reduction in roadtaxes, became a policy alternative to be chosen or rejected, not an axiom. An alternative was todeliberately use the revenue collected for other specific purposes: such purposes are varied, but broadgroups of objectives can be distinguished. The important point is that each of these has different
distributional consequences which will offset, reinforce, or cut across the distributional pattern of who ispaying the charge. Therefore we are led to the idea of a net distributional effect, for each individual,family or segment of the population, consisting of what they pay out offset by what they receive, inmoney and material benefit. Once it is decided that use of the revenue is part of the charging policy, it
only makes sense to see distributional effects as the combined effects of both.

8© OECD/ITF 2018

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

Therefore we first consider, in very broad brush qualitative terms, what sort of pattern of distributionalcosts and benefits we might see for each of the four main groups of charging strategy.
Return of charging revenue to road users in the form of reductions in other roadtaxes.

We note that if this is done (a) there will be no additional revenue available for other transport or social
improvements, and (b) there is not a direct match between the amount paid by individual road users on
existing road taxes and the amount they would pay in congestion charges, so the approach would
necessarily involve a shift in the distribution of income between some groups of road users and others,
even if road users as a whole (or the owners of tax-paying vehicles) were deemed to be collectively
compensated for the charge. There will still be the benefits directly following from the charge, but they
will not accrue to the same motorists in proportion to the charge paid.
Therefore it is likely that the main payers would be those who travel in the most congested conditions,and continue doing so. There would be a reduction in congestion on the most congested streets, which
would accrue as an incidental side-effect in part to bus users or any services that might be exempt frompaying. The residents in those areas would benefit from a reduction in emissions, and this would include
children living there, whose own car use would be small. Car ownership in such areas is lower than
average, so residents would benefit from some reduction in travel by motorists living outside the area.
There might be some shift in net benefit to those car-owning residents in rural areas who did notcommute to towns. All the effects would be rather small as there would be no additional investment inalternative travel.
Spending the revenue on increased road capacity

This would provide some benefit to some of the road users paying the charge. In earlier days there
would have been plans for large scale urban road building – ring roads etc. – which might more closely
have benefitted the main payers in terms of their car travel. Nowadays, there is little appetite for suchpolicies because additional traffic induced by the new construction would tend to reduce the period of
benefit considerably. There could be additional emissions in the urban areas themselves, and a reductionof the services offered by public transport, so the main losers would be residents in those areas (both car
users and non-car users, but more so for the latter, especially children living close to the expanded
roads). Those who gain the most would be the car-owning residents of non-urban areas who drive
largely in congested areas in towns, or more likely on trunk roads and motorways outside towns, which
are the roads which would most likely be expanded. The resulting reduced congestion would also reduce
the revenue from charges.
Spending the revenue on public transport, walking and cycling

This policy would see motorists on congested urban roads and congested trunk roads paying more.
Those who live in the towns would see benefits in the form of reduced pollution, and those who
continue to drive would see some benefit in the form of reduced congestion. However, this would (bythe Smeed report mathematics) be of less value than the charge they were paying on average, thoughwith a net benefit to those with high values of time and low values of money, being wealthy and busy.
The main beneficiaries in this case would be (a) those who changed their travel behaviour to the now
improved alternative modes, benefitting from better health and more pleasant surroundings; (b) those
who did not previously drive and now have better environmental conditions and improvements to theirown travel. Drivers who continue to drive, and pay the charge, might receive a bigger congestion benefit© OECD/ITF 20189

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

than the road-building case, since there would be a greater number of their fellow drivers transferring toother modes.
Putting the revenue in to general taxation

In this case the main beneficiaries could be different groups entirely, who have a reduction in someother class of taxation, eg on income, or increases in public expenditure e.g. on health services or
education. The principle involved is a simple one – increase tax on activities which cause damage andreduce taxes on activities which cause good – independently of what sector they are in. If it is possible toidentify the spending, a distributional analysis can be carried out, but otherwise it probably comes closer
to the ‘neutral’ assumption of compensation. In terms of perception, it is not a given that drivers who
pay the charge would be happier if the money were returned to other drivers than to their children atschool or employees where they work, or themselves when sick.

Interim conclusion
We argue that there are two important consequences of these considerations.
First, any attempt to explore the distributional consequences of a road pricing intervention solely interms of the incidence of payments cannot produce a useful picture of who gains and who loses. The
distributional effects must be analysed in terms of the net impacts of both the new charges and of the
new patterns of the benefits arising from plans about use of the revenue (including any related changes
in regulation, control and operations).
Second, there is a crucial methodological difficulty in that the salient dimensions which affect variationsin the incidence of payment will, in general, not be identical to the salient dimensions which affect theincidence of transport and other benefits and losses. Thus for example distributional analysis oftenfeatures distinctions by income group, but the amount of payment made will vary according to thepattern of journeys made by car users especially, while the incidence of changes in air pollution, say, will
be affected by the geographical location of residents living near a road which will include people ofdifferent incomes and car use patterns. Statistically, the type of disaggregation of the population by
those who gain benefits and those who suffer losses demands a complex pattern of joint distributions by
economic, social, travel and geographical variables which stands in danger of being intractable to carryout and complex to understand.

The role of distributional issues in practical
discussions on congestion charging in the UK
Important political lessons were developed in the preparations for what became the 1998 UK Transport
White Paper (DETR, 1998) and the Transport and Greater London Acts (1999). The basis for a new roadpricing for urban transport was a major part of preparatory discussions among departments and withoutside advisors, intended to lay the basis for the authorities in large urban areas to be able (but not
10© OECD/ITF 2018

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

required) to charge motorists in proportion to use, mostly either by new charges for movement or for
workplace parking.
Specific aspects of the wider question of distribution – relevant to the idea of the Net Overall
Distributional Effects (NODE) – was the question of what to do about taxation, and what to do with therevenue. These were crucial to the decision, but were actually not discussed mostly using the languageof equity, but the language of political powers, public opinion, and transport strategy. The politicaldecision was to be led by the local authority (held responsible to its own electors), and a crucial –indispensable – reward for doing so was that they would retain control over the revenue, with a form of
hypothecation not reserved for roads, but for all transport modes in accordance with their own
preferred transport strategy. It was judged that a hypothecation purely for roads would be rejected bymost if not all authorities, and an equivalent reduction in other taxes was not a live issue. It was judgedthat a more balanced inclusion of public transport and the new priorities for walking and cycling would
command more public support. This was supported by a wide range of (generally convincing) public
opinion research, and in any case sat sensibly within a planning approach based on the interaction
between all important methods of transport.
A wide range of transport stakeholders (including motoring organisations and, cautiously, freight movers)
bought into this idea, as well as town planners, environmentalists, and those most informed about theeconomic successes of reducing traffic in European town centres by other means. The two key sources ofresistance were the evident and understandable caution from the UK Government’s advisors about therisk of having a row with motorists, and an official reluctance in the Treasury to give way on theirobjection to the principle of hypothecation. This was resolved at high level politically, but at official level
by what became a significant agreement on words: the new system was to be described as a charge, or
price, for services, explicitly not a tax. In this way, Government financers could accept the fact ofhypothecation, while retaining opposition to the principle, a tidy solution for all.
In the Government’s Ten Year Plan for Transport (2000) provision was made for 20 of the largest cities tosign up for road user charging in some way by 2010. Apart from London (which was certainly important),
and a few minor initiatives later, it did not happen. Most local authorities either came to the view thatthe amount of money available from small scale town centre schemes was not enough to justify thepolitical hassle or would be rejected by voters, or their objectives could more easily be achieved byvarious other more classical funds and revenue sources that they could apply for. The particular elementof the Ten Year Plan to 2010 which depended on hypothecated road user charging simply did not happen
and as a live document of transport strategy, the Plan was no longer discussed after about 2004.
In London, however, the strategy was successful more or less as planned. It was labelled a congestion
charge (not road pricing), it applied only to the central area, it provided rather less money than forecast(but a greater impact on traffic volume), and the revenue was a helpful addition to the cost of improving
other modes, which was indeed done. It is interesting that these different strands have come together
very strongly in the current plans to implement an emissions-based extension (the ‘T-Charge’, fortoxicity) to the congestion charge in Central London, an example of the increasing importance of wider
considerations than congestion alone. Discussion in the London newspaper, the Evening Standard (2017)
has placed as much focus on the distributional effects as on air quality. The paper’s editorial stated that:“Like the original C-charge, it is regressive – falling hardest on the poorer car owners.
But it is those on the lowest incomes who suffer the greatest health problems”
and the Mayor of London also emphasised that:“it is the poorest and most disadvantaged who are being disproportionately affected”,© OECD/ITF 201811

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

while at the same time supporting a compensation scheme for those motorists (mostly not the poorest)
who bought diesel cars ‘in good faith, based on government advice’3.
Thus we argue that the evolution of experience, especially in London, illustrates the themes discussed
above – an active consideration of the implications for where the money would be spent, in the context
of a more integrated and coherent transport strategy, and a broader consideration of effects other thancongestion. Distributional issues have been at the heart of the decisions, though formal distributional
appraisal has been very sketchy indeed.
Therefore it does seem to follow that the development of more formal appraisal tools would be useful,which we now address.

Micro-simulation as a method of
exploring the effects
The use of micro-simulation techniques offers a way to explore these consequences. In these, apopulation of simulated individuals is generated which corresponds with the overall statistical propertiesof the real population of a city, region or country. Estimation techniques typically involve cloning or
matching households in surveys (which often have the spatial information removed) with small-area
census data. From this, any subgroup of interest can be considered at fine spatial resolutions in terms ofthe charging and its related effects on travel and other potential benefit and losses. Such a method gives
a direct (approximate) way of counting up the overall numbers of gainers and losers (generally based on
individuals or households) in each sector of the population or area of a city or country. Each sector orarea has a complex set of characteristics and varies in terms of the average and variation of theadvantages, disadvantages and vulnerability to road pricing.
Previous work at the Leeds University Institute for Transport Studies (ITS) used a synthetic population toestablish the impacts on at-risk groups of congestion charging in the city (Bonsall and Kelly, 2005). Usinga case study for Leeds, UK, the authors generate a synthetic population from probabilities of traveller
characteristics derived chiefly from the UK Census. This was then linked to a traffic assignment package
to identify the spatial patterning and characteristics of those impacted by different charging regimes andpolicies. They defined ‘at risk’ groups as those on low incomes, carers, mobility impaired, elderly andsocial minorities with no realistic alternatives to the car for particular journeys for a variety of reasons
including health restrictions. The simulation highlighted the sensitivity of these groups to the locationand extent of the charge, but also whether exemptions can be targeted efficiently at them.
In the next sub-section we describe two very recent studies which use novel data sets and spatial
analysis that would complement the microsimulation approach specifically in ways that would assist in
the evaluation of distributional impacts.

12© OECD/ITF 2018

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

Enhancing microsimulation with novel data and area classifications
Birkin and Clarke (2012, p515) note:“When [microsimulation] model estimates are benchmarked against real-world data,
the models are typically well behaved and very robust, but they can struggle to capture thediversity of spatial variations shown by observed data.”
The authors suggest that one reason for this is the often considerable variation of household types in thesurveys being reweighted and that, in any case, similar household types may show different behavioursor have different attributes depending on geographical factors not contained in surveys.
By starting with the full population of cars and vans in the UK, Mattioli et al. (2017) developed an areaclassification of vulnerability (in this case to motor fuel price increases) which allows investigation of how
different dimensions of vulnerability correlate spatially as potentially useful input into a microsimulation
study. The data was obtained from a novel anonymised vehicle inspection record dataset (MOT data, see
Cairns et al., 2016) which provides vehicle characteristics, annual mileage and vehicle keeper location for
all registered cars. From this, estimates of per household fuel consumption and expenditure for all
journey purposes by car/van could be estimated at a spatially disaggregated level (Lower-layer Super
Output Area4). By combining these estimates with income data (median household income5) and data toassess the ability of households to shift to other modes (using the UK Department for Transport’s‘Accessibility Statistics’ on travel time to key services by public transport and walking6), three dimensionsof vulnerability were calculated: (i) exposure (the cost burden of motor fuel) (ii) sensitivity (income) and(iii) adaptive capacity (accessibility with modes alternative to the car). These were combined into aspatial metric of social vulnerability to fuel prices for England. The most vulnerable areas were defined as
those with a high cost burden of motor fuels, low income and high levels of car dependence.
The results show diverse spatial patterns and processes of vulnerability both at the national level andwithin city-regions, and the complex interplay between the constituent dimensions of the index. Figure 2
shows the spatial patterns of the three components of the index, with five different colours
corresponding to quintiles in the distribution (in each case from lowest (blue) to highest (red)vulnerability). Figures 3 shows the distribution of vulnerability for separate city regions.
The first map in Figure 2 shows the cost burden ratio. On average, this is around 3.7% - meaning that perhousehold expenditure on fuel typically corresponds to around 3.7% of the median income in the area.
This is lower in most urban areas, although is much more pronounced in London than in city regions in
the North of England. The second map shows income. Here colours other than red tend to dominate,even though the same colour-scheme based on quintiles is used. This is because lower income areas areconcentrated in dense, inner-urban areas, which are smaller in land area on the map. Note that this is
not true for London or the peri-urban areas in between cities in the North, which are mostly in the higher
income classes, along with most of the South-East. Outer rural areas (e.g. Cornwall, the east coast) tend
to be middle income. The third map shows accessibility - that is time taken to travel to essential services
by public transport or walking. This is very high in rural areas and is lower in and around major cities andalong some (rail) corridors. The values can then be mapped as a composite index. The map (not shownhere) is dominated by areas in the highest quintile of vulnerability as these tend to have a larger landarea because of a lower population density and thus inferior accessibility. On the other hand, many
suburban and peri-urban areas around London have low levels of vulnerability, despite high car
dependence and relatively high expenditure on fuel, because of very high (average) income. This bufferzone around cities is less prominent in the North of England, where the areas between cities tend to behigh vulnerability, despite relatively high income, possibly due to very high levels of expenditure and very

© OECD/ITF 201813

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

poor accessibility. Inner cities, even in the North, tend to be low vulnerability but show very highvulnerability in the interstitial spaces between the major urban areas.
Figure 2. Mapping the three components of the vulnerability index

Source:Mattioli et al. (2017), p7.

Figure 3. Mapping the composite vulnerability index for four city regions in England

Note: the ranking is based on a national, not regional, benchmark.
Source: Mattioli et al. (2017).

This fine spatial scale sheds light on Bonsall and Kelly’s conclusions about the sensitivity of the at-risk
groups to the geographical boundaries of any road user charge. In Figure 3, it can be seen that whilst
London is skewed towards a generally low level of vulnerability, it has pockets of high vulnerability. If thisinformation were to be coupled with a house affordability factor, the pattern of vulnerability would be
14© OECD/ITF 2018

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

very different with a high likelihood of much more red on this map. Other city regions show immense
diversity such as the general mosaic pattern in Birmingham or the north/south split in Greater
Manchester. This, comprehensively of the underlying vehicle fleet and mileage data means that it can beused to assess vulnerability with respect to the vehicle efficiency and to shed light on issues such as theequity implications of restructuring a whole gamut of charges related to vehicle emissions, road tax, fueltax as well as the structure of the road user charge, as was indeed the proposition in the recent Wolfson
Prize winning entry (Raccuja, 2017).
Even without the vulnerability index being used as a relevant constraint in a population synthesis, it
could contribute to assessing the social impacts of road user charging if coupled with origin-destination
data and cross tabulated with the costs to residents in different zones. Availability of the fleet
composition at small geographical scales combined with the ability to calculate annual mileages, allows
the emissions generated by all cars registered in an area to be estimated. In turn, this can be related tojourney patterns and mapped against data on actual pollution concentrations or exposure levels. This
allows analysis of the relationship between the polluters (i.e. locations where those vehicles responsible
for the highest emissions are registered) and the polluted (those living in the pollution hotspots).
Chatterton et al. (2015) found that those living in the most polluted areas are the least responsible forthe production of those pollutants. This is largely because those living in the most polluted areas tend todrive older, smaller petrol cars which are less polluting than larger, newer diesel cars in higher income
areas. And, crucially, they drive fewer miles. Something similar is being carried out with the same vehicle
registration/testing data in order to assess implications of a new cordon-type charge around a Clean Air
Zone relating to the highest NOx polluting vehicles in Edinburgh (Morton et. al, 2018). In the Edinburgh
City Region, information on total vehicle mileage was augmented with information about where cars areused. This was based on locally modelled Origin-Destination matrices to reveal where the vehicles thattravel through the pollution hotspots in the city tend to come from. Each local in the region was gradedaccording to the proportion of cars in that area that comply with Euro 4 Petrol and Euro 6 Diesel
standards7 and according to the proportion of traffic it supplies to the city centre in the morning peak.
This analysis illustrated that traffic in the pollution hotspots is comprised of a combination of city andregional flows and that the majority of vehicles originate from areas with high rates of non-compliance.
Overall, this work clearly shows that targeting the cars that reside in the polluted central areas would nottarget the majority of cars responsible for those emissions. The Chatterton et al. and Morton et al.
studies also clearly highlight the vital importance of having a detailed understanding of the vehicle fleet
as well as total mileage and journey patterns - as it is this which ultimately corresponds to emission
levels. The analysis also highlights how any social impact analysis is very sensitive to assumptions about
how an emissions related charge would be structured. For instance, as discussed above, in November
2017 London) introduced a new T-charge which bases its criteria for high/low polluting vehicles on thedate of first registration, which it is assumed crudely relates to a Euro-standard8. Not only is this methodin danger of misclassifying a car due to common mismatches in registration dates and Euro-standards,
there are very large discrepancies between the test-cycle allocated emissions factors and real world
emissions (Fontaras et al., 2017). Overall, given Chatterton et al.’s conclusions cited above, such a charge
is in danger of penalising the very people who are also suffering the most from the poor air quality, yet
who are not causing the bulk of it. The exact impact would be sensitive to journey patterns and thestructure of any charge including exemptions for those living ‘locally’. The net impact would depend on
the degree to which the air they breathe is eventually improved by the charge. If the charge is structured
with the polluter/polluted relationship in mind, it could be that the groups currently most vulnerable tothe poor air quality may actually receive an enhanced benefit if the charge is based on a sophisticatedmethod of classifying vehicles that also accounts for new data on real-world emissions (e.g. Tietge et al.,© OECD/ITF 201815

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

2017), as well as benefitting from air pollution improvements. The next stage of this research is tocombine the vulnerability index with the evaluation of the Clean Air Zone charge work to include capacity
for drivers to adapt in terms of using alternative modes. However, adaptation can also take the form ofchanging the vehicles used. Work has shown that high income areas are much more likely to have thelower fuel efficient cars and in this sense, have more “steps of adaptation” that they could adopt.

Discussion
The purpose of this paper has been to explore the methodological challenges that flow from intervention
design considerations that account for the ‘Net Overall Distributional Effect’ (NODE) which will not only
vary by income group, but also by age, gender, car ownership and use, and geographical area ofresidence, work and activity. Specifically, we have profiled data and methods that measure small-area
spatial variability in travel behaviour, opportunities and household characteristics and could be used todefine, measure and operationalise dimensions of social vulnerability to some form of road user orcongestion charge. In doing so, we have identified a set of concerns to be addressed in the assessmentand evaluation of the distributional consequences of a road pricing intervention.

Distributional impacts must be analysed in terms of the net impacts
Any attempt to explore the distributional consequences solely in terms of the incidence of payments
cannot produce a useful picture of who gains and who loses. The distributional impacts must be analysedin terms of the net impacts both of the new charges and of the new patterns of the benefits arising fromplans about use of the revenue (including any related changes in regulation, control and operations).
This approach is in sympathy with that proposed by Tovar-Reanos and Sommerfeld (2017) but not
identical to it. They propose a consumer surplus method to calculate the distributional effects of various
ways of implanting fuel tax changes, including compensation, with a focus on distinguishing ‘progressive’and ‘regressive’ effects related essentially to income: They conclude that:“…an additional tax on conventional fuel is regressive. However, returning theadditional tax revenue via lump-sum transfers can alleviate this effect. Second, when the
additional revenue is also used to finance subsidies for electrical and compressed natural gas(CNG) vehicles, households that own such vehicles experience welfare gains. However, thispolicy also increases income inequality and decreases social welfare”.
There would seem to be no reason why this method should not be extended to the more complex issuesof distribution when location, access and the range of choices available may be as important dimensions
as income, though (as noted above) the question of a lump sum compensation in practice does not lend
itself to correcting unfairness at the level of small groups or individuals, which in turn affects judgements
about whether equality has increased or reduced. But overall this approach reflects a similar judgementthat distributional effects cannot be determined from the first round of price changes alone.

16© OECD/ITF 2018

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

Net impacts will change over time depending on behavioural
response
Bonsall and Kelly (2005) identify the need to extend their work to investigate behavioural responses toallow consideration of the second order impacts of charges and linked policies. However, they note thetechnical challenge and express concern that this could be counterproductive in terms of introducing
uncertainty. They suggest this effort would be considerably aided by adding characteristics to any
modelling.

Adaptive response is not in itself an indicator of a gain or a loss
Impact is related to adaptive capacity; but adaptive response is not in itself an indicator of a gain or aloss. Just because people currently use cars a lot, cannot be taken as measure of car dependence(defined as the need to travel by car) in and of itself. In other words, it cannot be assumed that impacts
of a charge on car users will be directly in proportion to how much the car was used preceding thecharge. This is because car users have differential opportunities to escape, reduce and adapt to thatcharge. Therefore, a NODE assessment requires some measure of adaptive capacity as well as
understanding of dynamic behavioural responses – both of which are big methodological challenges inthemselves. For one thing, accessibility indicators do not measure lived accessibility well, tending to rely
on crude assessments of the quantity rather than the quality of services (Curl et al., 2017) and focus on
modal shift, ignoring the many other adaptations that could take place (timing, destination, car type etc).
Most importantly, measures of behavioural response need to be disaggregated and longitudinal not only
because different behavioural adaptations take different lengths of time to embed, but also because thetarget groups will themselves change as previous groups adjust (Sloman et al., 2010). Perhaps mostchallenging of all in terms of evaluation is the question that Bonsall and Kelly (2005, p417) pose:“The prediction of response would however bring an additional issue to the fore, namely
do travellers who change their behaviour in response to policy gain or lose more or less thanthose who, because they regard the alternatives to be less desirable, choose to retain their
existing pattern of behaviour?”Distributional analysis requires understanding the joint effect ofdifferent factors
The salient dimensions which affect variations in the incidence of payment will in general not be identicalto the salient dimensions that affect the incidence of transport and other benefits and losses. Thus, forexample, distributional analysis often features distinctions by income group. Yet, whilst the amount of
payment made will vary according to the pattern of journeys made by car users especially, the incidenceof changes in air pollution, say, will affect the geographical location of residents living near a road which
will include people of different incomes and car use patterns.
The vulnerability analysis summarised above by Mattioli et al. (2017) offered some insight into how thedifferent dimensions of vulnerability interact with each other. Other work on Australian cities has showna ‘regressive’ urban structural effect, whereby low income and high cardependence are strongly colocated in the urban periphery (Dodson and Sipe, 2007). Similar work on city-regions in New Zealand(Rendall et al., 2014) and continental Europe (Büttner et al., 2013) has highlighted different patterns(Mattioli & Colleoni, 2016). Similarly, previous research on Australian cities has suggested that areas with© OECD/ITF 201817

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

high vulnerability are also characterised by lower rates of diffusion of diesel and other alternative energy
vehicles, and thus worse average fuel economy of the vehicle fleet. This further affects their overall levelof wellbeing (Li et al., 2017). However, work by Chatterton et al. (2015) summarised in the previous
section to this paper showed that, in the UK, those living in the most polluted actually tend to own
relatively less polluting cars (because they are smaller and not diesel), even though they are not thenewest cars. This in turn poses a design challenge for the design of a graduated charge that may try toaddress not only congestion but both fuel economy (CO2) and local air pollution. In addition, income is
not itself necessarily a good indicator of ‘exposure’ to any charge as this and housing costs or job
insecurity may impact vulnerability to increased motoring costs.

Conclusions
Congestion charging is not a single well defined intervention. It involves a wide range of different
projects that differ in terms of the financial instruments used and the principle of their application.
Congestion charging also requires defining the primary and secondary objectives of the scheme
especially in terms of the benefits sought (economic, social, environmental, political, engineering,
pragmatic, legal and indeed distributional), the time horizon of assessment, and unintended
consequences. Therefore there is no generic well-defined distributional impact, but a series of different
distributional impacts specific to the policy and design decisions taken.
Distributional consequences will be primarily impacted by:Whether a scheme is designed to be revenue neutral in terms of public accounts or designed tointernalise external costs (which will, in some important contexts with high levels of congestion andenvironmental damage, generally not be revenue neutral but initially generate a surplus).
Decisions about the use of that revenue. Whether revenue neutral or not in overall terms, all charging
schemes generate revenue held initially by the charging authority or agency. For practical reasons, theformation of a political consensus strong enough to achieve implementation will require promises andplans about the spending. Options may include (a) return to general tax revenue, (b) spending on road
construction aimed at the classes of vehicles and users more or less in proportion to their payments (c)spending on alternative means of transport, e.g. rail, other public transport, walking and cycling tofacilitate reductions in congestion and pollution and provide alternatives to the now more expensive
road vehicle use (d) use of revenues collected in richer areas to improve the travel opportunities inpoorer areas and e) return of the revenue collected to taxpayers in the form of reductions in specifictaxes or generally across the board. In the case of a scheme run by a private company for the benefit of
its shareholders, the distributional consequences will be a product of its calculations of a profitmaximising design, which may be modified by public regulation.
Deciding which impacts are most important and what benefits are being sought. This is not just about
congestion but requires some form of hierarchy or possible weighting of a set of costs and benefits
resulting from specific vehicles at specific times, or cumulatively and dynamically over time, in specificareas to specific people or sets of people.

18© OECD/ITF 2018

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

The sensitivity of the charge and revenue allocation to changes over time. Dynamic effects ofredistributing the revenue will take time to build up, with intended and unintended longer term
consequences. In addition, individuals do not stay in the same classification used for the initial analysis,
and therefore people who may be initial beneficiaries may be losers in the longer term, and vice versa.
All these considerations are underpinned by an important feature of the classical economic case for roadpricing, which shows theoretically that in general a large part of the potential benefit of marginal social
cost pricing is captured in the revenue collected, and is only made concrete when that revenue is spent.
As well as affecting the distributional consequences, proposed decisions on the pattern of spending willaffect the political and attitudinal responses of individuals and organised interests – partly as a result of
their own separate expectations of distributional impacts (‘how will this affect me?’) which may notcorrespond with the formal judgments of the official assessment.

© OECD/ITF 201819

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

Notes
1

The report was commissioned by one UK Government but swiftly dismissed by following Government at the time of its publication – thePrime Minster responded by vowing ‘never again’ to commission such a study. In fact successive Governments have many times
commissioned such studies.
It can be seen that if the amount of charge paid were exactly to be returned to each individual paying it – were such a thing practicallypossible – nobody would be paying out any more or less than previously, and it is difficult to see what behavioural consequences there
could be in the longer run. There would be little or no effect on congestion or anything else. Any compensation scheme relies on someindividuals getting back more than they paid, and some less. It is not at all obvious that a class like ‘motorists’ for collective compensation
is fairer than a class like –say – ‘urban residents’.
The paper’s editor is George Osborne, a former member of the Conservative Government and Chancellor of the Exchequer, i.e. theMinister of Finance. The Mayor, Sadiq Khan, is the candidate of the Labour opposition in Government, defeating Boris Johnson, theformer Mayor, who is (at the time of writing) a member of the Government. Support for expansion of diesel cars was initiated by theformer Labour Government, now widely thought to have been a mistake, and the current reversal is based on the EU test-regime results
related to age of vehicle, which, as discussed below, does not convey an accurate picture of the relationship between vehicle age andemissions.
As defined for the 2011 Census.1 In England and Wales, there are 34,753 of these areas. They have been designed to be relatively similarin population size, containing 1,000–3,000 people, or 400–1,200 households, and to represent people with relatively similar
characteristics. In Scotland, the equivalent unit is the Data Zone, of which there are 6,976, with a population of 500–1,000 residents.
Median annual household income in 2011, from Experian Ltd.
https://www.gov.uk/government/collections/journey-time-statistics
However, as explained in the paper, this parameter is estimated from the date of first registration and fuel type which is a proxy for Euro
standard and the accuracy of this method of approximation is being assessed.
https://tfl.gov.uk/modes/driving/emissions-surcharge/emission-standards-and-the-t-charge-zone?intcmp=49129

2

3

4

5
6
7
8

20

© OECD/ITF 2018

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

References
Birkin, M. and Clarke, G. (2012) The enhancement of spatial microsimulation models using
geodemographics. Annals of Regional Science, 49, pp. 515-532.
Bonsall, P. and Kelly, C. (2005) Road user charging and social exclusión: The impact of congestion charges
on at-risk groups. Transport Policy, 12:, pp.406-418.
Büttner, B., Wulfhorst, G., Crozet, Y. and Mercier, A. (2013) The impact of sharp increases in mobility
costs analysed by means of the Vulnerability Assessment. 13th World Conference on Transport Research,
Rio de Janeiro, July 15-18, 2013.
Cairns, S., Emmerson, P., Ball, S., Anable, J., Chatterton, T., Wilson, R. E., Philips, I. & Barnes, J. (2016).
Using Vehicle Inspection Data to Understand Personal Car Mileage. Proc. 44th European Transport
Conference, 5–7 October 2016, Association for European Transport, Barcelona.
Calthrop, E. and Proost, S. (2003) Environmental Pricing in Transport. In Hensher, D. A. and Button, K. J.
Handbook of Transport and the Environment, pp 529-545, Elsevier.
Chatterton, T., Anable, J., Barnes, J. & Yeboah, G. (2016a). Mapping Household Direct Energy
Consumption in the United Kingdom to Provide a New Perspective on Energy Justice. Energy Research &Social Science, 18, pp. 71–87.
Chatterton, T., Anable, J., Cairns, S. & Wilson, R. E. (2016b). Financial Implications of Car Ownership and
Use and the Drive to Work: A distributional analysis using income data and area classifications. Paper
presented to the DEMAND conference, Lancaster, 13–15 April 2016.
Chatterton, T., Anable, J., Cairns, S. & Wilson, R. E. (2018). Financial Implications of Car Ownership andUse: A distributional analysis based on observed spatial variance considering income and domesticenergy costs. Transport Policy, Volume 65, pp. 30-39.
Chatterton, T., Barnes, J., Wilson, R. E., Anable, J. & Cairns, S. (2015). Use of a Novel Dataset to Explore
Spatial and Social Variations in Car Type, Size, Usage and Emissions. Transportation Research D:
Transport and Environment, 39, pp. 151–164.
Curl, A., Nelson, J. and Anable, J. (2011) Does Accessibility Planning address what matters? A review ofcurrent practice and practitioner perspectives. Research in Business Transportation and Management, 2,
pp. 3 – 11.
Department of the Environment, Transport and the Regions (1998) A new deal for transport: better for
everyone. Cmnd 3950. The Stationery Office, London.
Dodson, J. and Sipe, N. (2007) Oil vulnerability in the Australian city: Assessing socioeconomic risks fromhigher urban fuel prices. Urban Studies, 44(1), p. 37-62.
Evening Standard (2017) issue of 23.10.2017, editorial and p17.

© OECD/ITF 201821

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

Fontaras, G., Zacharof, N-G. and Ciuffo, B. (2017) Fuel consumption and CO2 emissions from passenger
cars in Europe – Laboratory versus real-world emissions. Progress in Energy and Combustion Science, 60,
pp. 97-131.
Goodwin, P. (2010) Opportunities for improving transport and getting better value for money, bychanging the allocation of public expenditures to transport, Commission for Integrated Transport,
http://webarchive.nationalarchives.gov.uk/20110304135123/http://cfit.independent.gov.uk/pubs/2010/
tco/think03/02.htm
Goodwin, P. and Lyons, G. (2010). Public attitudes to transport: interpreting the evidence. Journal ofTransportation Planning and Technology, 33(1), pp. 3-17.
Tietge, U., Mock, P., German, J. Bandivadekar, A. and Ligterink, N. (2017) From laboratory to road: A
2017 update of official and ‘real-world’ fuel consumption and CO2 values for passenger cars in Europe.
White Paper. International Council on Clean Transportation (ICCT) Europe. November 2017, Berlin.
Li, T., Sipe, N. and Dodson, J. (2017) Motor vehicle fleets in oil vulnerable suburbs. A prospect oftechnology innovations. In Dodson, J., Sipe, N. and Nelson, A. (eds.), Planning after petroleum. Preparing
cities for the age beyond oil. Abingdon: Routledge.
Mattioli, G. and Colleaoni, M. (2016) Transport disadvantage, car dependence and urban form. In Pucci,
P. and Colleoni, M. (eds.), Understanding Mobilities for Designing Contemporary Cities. Wiesbaden:
Springer.
Mattioli, G., Philips, I., Anable, J. and Chatterton, T. (2017) Developing an index of vulnerability to motor
fuel price increases in England. Proc. 49th University Transport Studies Group Conference, January 2017,
Dublin.
Ministry of Transport (1964) Road Pricing: The Economic and Technical Possibilities (the ‘Smeed Report’),
Her Majesty’s Stationery Office, London.
Morton, C., Lovelace, R., & Anable, J. (2017). Exploring the effect of local transport policies on the
adoption of low emission vehicles: Evidence from the London Congestion Charge and Hybrid Electric
Vehicles. Transport Policy, 60, pp. 34–46.
Morton, C., Mattioli, G. and Anable, J. (2018) A Framework for Assessing Spatial Vulnerability in the
Introduction of Low Emission Zones: A case study of Edinburgh, Scotland. Proc. 50th University Transport
Studies Group Conference, January 2018, London.
Musselwhite, C. and Lyons, G. (2009) Exploring the public acceptability of road pricing. Proc. 41st
Universities Transport Study Group Conference, January 2009, London.
Nash, C. (2001) Equity versus Efficiency in Transport Systems. In Button, K. J. and Hensher, D. A. (eds)
Handbook of Transport Systems and Traffic Control, pp. 32-45, Elsevier.
Raccuja, G. (2017) Paying for road use could be miles better. Wolfson Economics Prize winner 2017.
Rendall, S., Page, S. and Krumdiek, S. (2014) Voila! A new measure of oil vulnerability for cities. 1st
International e-Conference on Energies, 14-31 March 2014.
Sloman, L.; Cairns, S., Newson, C., Anable, J.; Pridmore, A. and Goodwin, P. (2010) The effects of smarterchoice programmes in the Sustainable Travel Towns. Report to the Department for Transport, January
2010.
Tovar-Reanos, M. A. and Sommerfeld, K. (2018) Fuel for inequality: distributional effects ofenvironmental reforms on private transport. Resource and Energy Economics, Volume 51, pp. 28-43.
22© OECD/ITF 2018

ASSESSING THE NET OVERALL DISTRIBUTIVE EFFECT OF A CONGESTION CHARGE | DISCUSSION PAPER | ITF ROUNDTABLE 170

Ubbels, B. and Verhoef, E. (2007) The Economic Theory of Transport Pricing. In Threats from Car Traffic
to the Quality of Urban Life. In Garling, T. and Steg, L. (eds), pp. 325-345, Elsevier.
Walters, A. A. (1961) The theory and measurement of private and social cost of highway congestion.
Econometrica, 29, pp. 676-99.

© OECD/ITF 201823

Assessing the Net Overall Distributive
Effect of a Congestion Charge

International Transport Forum
2 rue André Pascal
F-75775 Paris Cedex 16
T +33 (0)1 45 24 97 10
contact@itf-oecd.org
www.itf-oecd.org

2018-10/ Photo credit: Nattapoom V

This paper offers new insights into the definition, measurement andoperationalisation of different dimensions of social vulnerability
to road user charges, using unique data sets available in the UK.
Assessing distributional effects of road pricing or congestion
charging schemes requires evaluating distributional patterns: who
receives the benefits of reduced congestion and who receives therevenues collected? How these impacts change over time also needs
consideration.