Institute for Transport Studies
FACULTY OF ENVIRONMENT

Commissioned title: Assessing the
distributive Impacts of a CC using a
synthetic population model
ITF Roundtable Social Impact of Time and Space-Based
Road Pricing 30 November – 1December 2017 Auckland

Professor Jillian Anable (ITS Leeds) & Emeritus
Professor Phil Goodwin (UCL & UWE)
E-mail: j.l.anable@its.leeds.ac.uk

Institute for Transport Studies
FACULTY OF ENVIRONMENT

Final title: Assessing the Net
Overall Distributive Effect of acongestion charge
ITF Roundtable Social Impact of Time and Space-Based
Road Pricing 30 November – 1December 2017 Auckland

Professor Jillian Anable (ITS Leeds) & Emeritus
Professor Phil Goodwin (UCL & UWE)
E-mail: j.l.anable@its.leeds.ac.uk

Paper objectives

1. To offer new conceptual andmethodological insights into the definition,measurement and operationalisation ofdimensions of social vulnerability to roaduser charges
2. To explore the methodological challenges
that flow from intervention designs that
account for the ‘Net Overall Distributional
Effect’Assessing SDIs is too hard …
“Problems of the distribution of income – whowould and who would not be harmed by thepolicy advocated – will not be considered here.
The general ramifications of such a policy arereasonably clear, but the detailed analysis
would be cumbersome and boring.”
Walters (1961)

Walters, A. A. (1961) The theory and measurement of private and social
cost of highway congestion. Econometrica, 29, pp. 676-99.

The classical case
The classical economic case for road user charging:
1.Focuses on congestion
2.Has depended on a theoretical rationale which justified
ignoring distributional consequences because theeconomic advantages apply independently of the effects on
distribution
Any loss in utility to the ‘losers’* will be exactlycompensated by those who receive the revenue, who everthey might be.
* i) those who continue to make journeys and pay the charge albeit withless congestion (ii) those whose journeys are deterred by the extra costBut, the classical case breaks
down in practice
Challenges to the classic case:• Studies of the relative benefits of investment in different
modes leads to a more strategic view of the interaction
between public and private transport• The range of problems goes beyond traditional
concerns of congestion and traffic accidents. This puts
more attention on behaviour change and the need toreinforce the case for road pricing through investment indifferent modes• Public acceptability becomes dependent on what otherpolicies would be implemented

Various types of charging
strategy are now considered
Consequently the ‘Pareto Optimum’ assumption is no longer
universally accepted. Instead, various options are considered,each with different distributional consequences:• Return of charging revenue to road users in the form ofreductions in other road taxes• Spending the revenue on increased road capacity• Spending the revenue on Public Transport, Walking andCycling• Putting the revenue in to general taxation

Return of charging revenue to road users in
the form of reductions in other road taxes• Road users as a whole would be collectively compensatedfor the charge.
• Reduction in congestion on most congested streets, thus
benefitting bus users, local residents• Possibly some shift in net benefit to those living in rural
areas who did not commute into towns

But• No direct match between the amount paid by individual roadusers on existing road taxes and amount in the C-charge =distribution of income• No additional revenue available for other transport or social
improvements

Spending the revenue on increased roadcapacity• Some benefit to the road users paying the charge• Particular gain to car owning residents in non-urban areas
who drive on the expanded roads

But• Induced demand could lead to additional emissions in urban
areas and reduction in PT services• Reduced congestion would reduce the revenue from thecharges

Spending the revenue on Public Transport,
Walking and Cycling• Urban drivers will benefit from reduced congestion andemissions (more so than in the road building case)• Those who changed modes would benefit from better health• Those who did not drive would have better environment andtravel conditions
But• Benefits to urban motorists would be less in value than thecharge paid on average (with a net benefit to those with highvalues of time)• Motorists on congested roads paying more

Putting the revenue in to generaltaxation
Increase tax on activities which cause damage andreduce taxes on activities which cause good –independently of what sector they are in• Main beneficiaries could be different groups entirely to thecharge payers as they have a reduction in some other classof taxation
But• Drivers who pay the charge may not be happier

NODE (Net Overall
Distributional Effect)
Assessment of the distributional impacts involves evaluating
the pattern of:• Who pays and who receives the revenues collected• Who receives the benefits of reduced congestion• Consequential changes in pollution, quality of life + others
impacts• How these impacts change over time
NODE itself will vary by income, gender, car ownership,
location of home, work and other activities

The methodological challenge
1. Assessing who pays/ does not pay is not enough– need an assessment of the net impacts of both the new charges andthe new patterns of benefits from the revenue spend

2. Burden of the charge is determined not only by ability topay (income) but also the pattern of journeys made &,ability to adapt--yet the benefits (eg air quality, property value increase) will beaffected by geographical location which will affect people of different
incomes and car use patterns

Assessment = complex disaggregation of distributions byeconomic, social, travel and geographical variables

Microsimulation• Generation of a population of simulated individuals whichcorresponds with the overall statistical properties of the real
population• Clone or match households in surveys with small area
Census data
• From this: the RUC and its related effects on travel andother benefits/ losses can be considered for any subgroup ofinterest at fine spatial resolution

Bonsall & Kelly (2005) (1 of 2)• Generated a synthetic populationfor Leeds (UK) from theprobabilities of traveler
characteristics from the UK
Census*• This was linked to a traffic
assignment package to identify
the spatial patterning andcharacteristics of those impactedby 6 different charging regimes• Looked at impacts on ‘at risk
groups’*Using Popgen-T proportional fitting and monte carlo simulation

Bonsall & Kelly (2005) (2 of 2)• Looked at absolute numbers &proportions of ‘at risk’ people
affected and the extent of theimpacts• Applied exemptions to those atrisk and set charges to meettarget revenues accordingly• Found ‘at risk’ groups to bespread across the city – thus
spatially specific solutions notpossible

Enhancing microsimulation
with novel data

“When [microsimulation] model estimates arebenchmarked against real-world data, themodels are typically well behaved and very
robust, but they can struggle to capture thediversity of spatial variations shown byobserved data.”
Birkin and Clarke (2012, p515)

“MOT” dataset

The (t)ERES project(2014-2016) (Giulio Mattioli, ITS)

‘Car-owning households who need to spend a
disproportionately high share of their income to
get where they need to go, with negative
consequences in terms of restricted activity
spaces and/or spending cuts in other essentialareas’≈ ‘forced car ownership’, ‘transport poverty’…
https://teresproject.wordpress.com/
Note: Special Issue of Transport Policy“Household transport costs, economic
stress and vulnerability”

3 spatial components of vulnerability
to fuel price increases - England
1. Exposure:

2. Sensitivity

3. Adaptive capacity

Cost burden ratio = perhousehold expenditureon fuel / median income

Median householdincomeTravel time to 8 key
services by public
transport / walking(Anonymised MOT tests and results)(Experian Median Income data)

20 Accessibility Statistics)(UK GovernmentA spatial index of vulnerability to fuelprice increases - England, 2011
• Standardise each component variable (z-scores)• vulnerability to fuel price increases (VFP)• VFP = f(Exposure , Sensitivity , Adaptive Capacity)• VFP = cost burden – income + travel timeA spatial index of vulnerability tofuel price increases - England, 201122

English city regions, 2011
LondonWest Midlands

Greater Manchester

Sheffield CRHypothetical Low Emission
Zone for Edinburgh

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.

The polluters (those responsible for emitting high amounts of pollutants from vehicles)
are generally not co-located with the polluted (those exposed to high ambientconcentrations of pollution).
Emissions of nitrogen oxides
from all private vehicles byregistered keeper location

Ambient concentrations ofnitrogen dioxide (from allsources)

Where mean ambientNO2 concentrations
are higher, the:• mean age ofvehicles is higher• proportion of diesels
is lower• Average NOxemissions factor islower• Average distanceper vehicle is lower

Private car fleet compliance tothe LEZ emission standard

Car trips in the AM Peak toEdinburgh City Centre

Key methodologicalconsiderations (1)The distributional impacts must be analysed in terms ofthe net impacts• Just looking at incidence of payments is insufficient• Need to look at net impacts of new charges and patterns ofbenefits arising from use of revenue• Not just about how progressive or regressive the charge is inrelation to income• Also have to look at cost burden, location, access and rangeof choices

Key methodologicalconsiderations (2)Net impacts will change over time depending on
behavioural response• Allow consideration of second order impacts of charges andany linked policies• But this will add uncertainty – so need to add second order
impacts to modelling

Key methodologicalconsiderations (3)Impact is related to adaptive capacity; but adaptive
response is not in itself an indicator of a gain or a loss
• Impacts of the charge on car users is not directly inproportion to how much cars were used before the charge• Car users have different opportunities to adapt• NODE assessment must include some measure of adaptive
capacity• And some understanding of dynamic behavioural responses• Accessibility indicators assess quantity rather than quality ofservices and focus on modal shift and not other adaptations• Different behavioural responses take different times toembedKey methodologicalconsiderations (4)The identification of gainers and losers demands acomplex pattern of joint distributions by economic,social, travel and geographical variables• Income is not the best way to identify gainers/ losers• Amount of payment will vary according to patterns ofjourneys• Benefits (eg less congestion, pollution) will impact people on
certain roads with a different income profile• Low income and high car dependence can be located at theurban periphery – but patterns vary
• Becomes far more complex if the charge has multiple
objectives e.g. congestion + emissions reduction

Conclusions
There is no generic well-defined distributional impact of CC,
but a series of different distributional impacts specific to thepolicy and design decisions taken. Distributional
consequences will be primarily impacted by:• Whether a scheme is designed to be revenue neutral• Decisions about the use of that revenue• Deciding which impacts are most important/ the benefits
sought• The sensitivity of the charge and revenue allocation tochanges over time
Methodological innovation can only go so far as toinform/determine these political decisions

So do you agree thatassessing SDIs is too hard?
“Problems of the distribution of income – whowould and who would not be harmed by thepolicy advocated – will not be considered here.
The general ramifications of such a policy arereasonably clear, but the detailed analysis
would be cumbersome and boring.”
Walters (1961)