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Road Pricing in Great Britain: Winners and Losers

Technical Report

Stephen Glaister Dan Graham Department of Civil and Environmental Engineering Imperial College London March 2006

Research commissioned by the Independent Transport Commission. It was funded by the Rees Jeffreys Road Fund, the Joseph Rowntree Foundation and the

Esmee Fairbairn Foundation

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CONTENTS

Introduction 3

Revisions to the economic parameters of the model 4 Traffic and speed-flow relationships 5 Vehicle occupancies 5 Values of time 5 Vehicle operating costs 5 Environmental costs 6

Time Switching 6 The new algorithm for congestion costs 16

Response of car occupancy 17

The alternative policies 19 Exemptions and discounts. 19 How would the revenues be used? 20

Results 22 Calculating results at ward level 23

The effects on various type of area 33

Variation in traffic by time of day, by road type, by area type and by region 37

The relationships between road pricing and deprivation 40 Income deprivation 48 Crime deprivation and living environment deprivation 49 All the domains together 51

Modelling average effects for typical trips 52 Spatial variance in urbanisation 54

Effects on household budgets 67

Summary and Conclusions 74

References 80

Acknowledgements. This research was commissioned by the Independent Transport Commission. It was funded by the Rees Jeffreys Road Fund, the Joseph Rowntree Foundation and the Esmee Fairbairn Foundation. We are grateful for guidance and comments from the members of the Independent Transport Commission and from its Secretary, Terence Bendixson. We are grateful to Dr. Mohammed Quddus and Miss Grace Kwan for research assistance.

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Introduction The UK Government has recently indicated that the possibility of a national,

comprehensive system of road pricing should be investigated as an important

component of future transport policy (DfT 2004a, 2004b). However implemented, the

basic principle would be to charge road users according to the use they actually

make of the network at different times and in different places. One important

question surrounding the introduction of such a charging scheme is how would it

bear on the population: who would stand to win or lose? The distributional

consequences of road user taxation have attracted interest in the literature over the

years (e.g. Poterba 1991, Evans 1992, Blow & Crawford 1997, Richardson & Bae 1998,

Santos & Rojey 2004). It is, however, difficult to generalise about whether road user

taxation is regressive or progressive per se because so much depends on the limits

and design of any one particular system and on the specific context within which it

is implemented.

Glaister and Graham (2003, 2004, 2005, 2006) and Graham and Glaister (2006)

developed a model to study the potential implications of various systems of national

road user charging. For small areas of England they estimate the effects of different

pricing scenarios on traffic volumes, user charges and fares, subsidies,

environmental costs, benefits to consumers, government revenue, and overall net

benefits.

In this paper we extend and update our model to explore the distributional

consequences of national road user charging in Britain1. In contrast to previous

studies our analysis is mainly based on spatial units, not on individuals or

households. We do not have access to measures representing variance in individual

incomes or in wages and salaries to provide a good match with the output from our

road pricing model. Instead, we consider traffic, price, speed, and cost changes from

charging scenarios in relation to the spatial distribution of measures of deprivation

for small areas of Britain. In effect, we use the deprivation measures as a proxy for

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spatial variance in relative poverty and affluence. An important consequence of using

spatial units rather than individuals or households is that we cannot offer

conclusions about whether any pricing scheme is truly regressive or progressive. But

what we can show is how road users living in different areas of the country,

experiencing different levels of deprivation, are likely to fare under national pricing

schemes. We also make a preliminary exploration of the implications of the fact that

households which are not private car users will be differently affected.

This is a report on research building upon that reported in Glaister and Graham (2003).

It should be read in conjunction with that document. The previous work related to

England only, with traffic conditions as found in the year 2000, with 2003 price levels.

In this research the previous version has been modified as follows:

• traffic flows now represent traffic as it is expected to be in 2010, allowing for

such increases in capacity as are expected to become available by then;

• Scotland and Wales are added;

• speed-flow relationships are adjusted;

• economic values, such as values of time are adjusted to what they might be

expected to be in 2010, allowing for growth in GDP. Financial quantities are

expressed in spring 2005 prices;

• some allowance has been added for the tendency of road users to vary their

time of travel in response to differences in charges by time of day;

• similarly, allowance has been added for the tendency of the number of

occupants in cars to change in response to charges paid by the vehicles for

using the roads.

Revisions to the economic parameters of the model

The structure of the data and the philosophy behind the computations remain almost

unchanged. The following changes have been made to the data and the parameters.

1 Britain comprises the countries of Scotland, England and Wales.

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Traffic and speed-flow relationships

As before, estimated traffic flows for cars, commercial vehicles and buses together with

corresponding speed-flow relationships were kindly supplied by the Department for

Transport. These represent forecasts of the situation in 2010, assuming traffic increases

likely to be generated by normal growth in economic activity but mitigated by the

deterrent effect of worsening congestion, taking into account such extra capacity as is

expected to become available by then (file R_2010SR045DDCen_IC).

Vehicle occupancies

Occupancies for 2000 were derived from TAG Unit 3.5.6 (DfT, 2004), Table 4. Table 6

of that document gives estimates of annual decline in occupancies, which were used

to compute 2010 values shown in the second row of Table 1.

Table 1. Vehicle occupancies

Occupancy HBW HBEB HBEO HBDO NHBWEB NHBO LGV Rigid Artic

2000 1.14 1.2 1.85 1.85 1.14 1.85 1.25 1 1

2010 1.13 1.19 1.80 1.80 1.13 1.80 1.25 1 1

Values of time

Values of time and annual growth in values of time were derived from TAG 3.5.6

(DfT, 2004) section 1.2. The results were as shown in Table 2.

Table 2. Values of time per vehicle (£ per hour)

HBW HBEB HBEO HBDO NHBWEB NHBO LGV Rigid Artic PSV pax

RAIL pax

7.17 33.95 10.10 10.10 32.32 10.10 13.73 10.98 10.98 6.32 11.62

Vehicle operating costs

Vehicle operating costs were taken from TAG Unit 3.5.6 (DfT, 2004), Tables 10, 11, 12

and 13 and converted to 2005 prices. This includes an assumption that vehicles will

become more fuel efficient by 2010 as shown in Table 3.

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Table 3. Assumed vehicle fuel efficiency improvements 2003-2010 (%)

Average car LGV Rigid Artic

20.9 10.3 7.7 7.7

Fuel was assumed priced at £0.80 per litre for cars and commercial vehicles and at

£0.34 per litre for public service vehicles, after fuel duty rebate.

Environmental costs

The environmental costs used in the previous version of the model were pro-rated with

expected real GDP growth 1998 to 2010 and expressed in 2005 prices: a compound

factor of 1.45.

Road costs (pence per vehicle km), Great Britain, 2010 at 2005 prices.

Cost category

Infrastructure operating costs and

depreciation

External accident costs

Air pollution

Noise

Climate change

0.61

1.19

0.49

0.03

0.22

Source: Sansom et al (2001).

Time Switching A limitation of our previous work (Glaister and Graham, 2003) was that we assumed

that road users would not change the time at which they chose to travel in response to

changes in speeds or charges. In the current work we have added an algorithm to

represent the phenomenon of time switching.

For each region, area type and road type the model represents travel at nineteen

different time of the week as shown in Table 4.

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Table 4. Times of the week represented in the model. Period Day Time Period Day Time

1 Mon-Fri 00:00 - 06:00

2 Mon-Fri 06:00 - 07:00 12 Saturday 00:00 - 09:00

3 Mon-Fri 07:00 - 08:00 13 Saturday 09:00 - 14:00

4 Mon-Fri 08:00 - 09:00 14 Saturday 14:00 - 20:00

5 Mon-Fri 09:00 - 10:00 15 Saturday 20:00 - 24:00

6 Mon-Fri 10:00 - 16:00

7 Mon-Fri 16:00 - 17:00 16 Sunday 00:00 - 10:00

8 Mon-Fri 17:00 - 18:00 17 Sunday 10:00 - 15:00

9 Mon-Fri 18:00 - 19:00 18 Sunday 15:00 - 20:00

10 Mon-Fri 19:00 - 22:00 19 Sunday 20:00 - 24:00

11 Mon-Fri 22:00 - 24:00

We wanted to model the propensity of car users to switch their journeys from one

period to another in response to relative changes in time or money cost whilst

maintaining the general structure of the existing model. This was achieved by adding a

preliminary stage to the choice model.

The existing model takes the form

xi = x°i exp { Σj λij (gj - g°j)}

where xi is the number of passenger trips per hour and x°i is the base number of trips.

Here the λij are constant parameters determining the responses of demand to changes

in generalised cost. They relate changes in demand for any one mode to changes in

generalised costs (including prices and taxes) for all modes. There is a simple

relationship between the λ’s and the respective elasticities which enables the one to be

calculated from the other. The base numbers of trips are constants set by the flows in

the “base” situation.

In the new version of the model the “base” values, x°i , were themselves allowed to

vary in response to generalised costs relative to those at neighbouring times:

x°i = b°i exp { Σj µij (gj - g°j)}.

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Here the b°i are the “raw” base values determined, as before, as the base values in the

data: if all the gi take their base values, g°i , then the x°i take their base values, b°i

and, in turn, the xi take their base values, x°i . However, as the generalised costs, gi ,

deviate from their base values, g°i, the x°i respond in accordance with the parameters,

µij . This new response in the base values is normalised in such a way that for each trip

type the switching does not change the total base number of vehicle kilometres for each

region, area type and road type. Thus the net effect of a new money charge or speed

change on final demand at any time period, xi is now a compound result of switching

between times of the week and, as before, an elasticity with respect to generalised cost.

Our greatest difficulty is that we have not been able to find good evidence to guide us

on the magnitudes of time switching likely to occur in practice. Small (1982) and Burris,

Konduru and Swenson (2004) report some relevant empirical evidence but it is not a

great deal of help in our context. Therefore our approach has been to postulate several

alternative magnitudes of switching and to investigate the sensitivity of our results.

We have imposed some a priori restrictions which are summarised in Table 5. In this

Table a blank indicates that transfer is not possible and an “x” shows that it is

possible. For example, transfer is assumed not to occur into or out of the very early

mornings (period 1). But it does occur on week days between the pre-morning peak

(period 2), the first morning peak hour (07:00 to 08:00, period 3), the second morning

peak hour (08:00 to 09:00, period 4) and the first inter-peak hour (period 5). There is

a similar (though simpler) pattern in the week day evenings. Transfer is possible

between weekend mornings and afternoons, and between Saturday and Sunday

during the day.

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Table 5. The times of week between which switching is permitted Period 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1

2 X

3 X X

4 X X

5 X

6

7 X

8 X X

9 X

10

11

12

13 X X X

14 X X X

15

16

17 X X X

18 X X X

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Commercial vehicles are assumed not to switch times of travel. This is a

simplification because, in reality, commercial vehicles do have substantial flexibility.

Some current night-time deliveries could revert to day time to take advantage of

lower labour costs and greater convenience for customers. Equally, some peak

deliveries could divert to off-peak times to take advantage of lower road charges.

The equation above shows how different values for µ represent different

propensities of drivers to switch times. In order to investigate the sensitivity of the

system to different magnitudes of this switching parameter the following tables

summarise the effects on the total numbers of the various types of car trip, of levying

a flat rate charge of £0.01 per vehicle km. in periods 4, 8 and 13 (that is, the second

morning peak hour, the evening peak and Saturday mornings). This is done for all

values of µ at 0 (no time switching), 0.1, 0.5 and 1.0. Note that costs of fuel for cars in

the base are of the order of £0.05 per vehicle km. so the additional charge used here

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is equivalent to approximately a 20% increase in fuel prices. The results are not the

same as the “pure” fuel price elasticities because the system has been equilibrated:

an additional charge reduces traffic, which increases speeds, which reduces time

costs, which induces some new traffic. The extent to which the time reduction

generates new traffic depends on the respective values of time. As the Tables

illustrate, the consequence of making a money charge is to change the mix of journey

types in favour of those with higher values of time savings, as well as to reduce the

total of traffic.

Table 6. Changes in traffic, µ = 0 (no time switching) HBW HBEB HBEO HBDO NHBWEB NHBO ALL CARS

1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%2 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%3 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%4 -4.4% -1.9% -6.9% -5.2% -2.7% -4.9% -4.3%5 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%6 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%7 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%8 -4.3% -2.3% -6.8% -5.4% -2.7% -5.1% -4.5%9 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

10 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%11 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%12 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%13 -4.6% -1.9% -7.3% -5.4% -1.9% -5.3% -5.8%14 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%15 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%16 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%17 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%18 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%19 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

-1.3% -0.4% -1.2% -0.6% -0.2% -0.8% -0.9%

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Table 7. Changes in traffic, µ = 0.1 HBW HBEB HBEO HBDO NHBWEB NHBO ALL CARS

1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%2 -0.2% 0.0% -0.1% 0.0% 0.0% -0.1% -0.1%3 0.6% 0.0% 0.2% 0.1% 0.0% 0.2% 0.3%4 -4.9% -1.8% -7.2% -5.6% -2.7% -5.3% -4.7%5 0.6% 0.1% 0.2% 0.1% 0.0% 0.2% 0.3%6 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%7 0.3% 0.0% 0.1% 0.2% 0.0% 0.3% 0.2%8 -4.8% -2.2% -7.1% -5.9% -2.6% -5.6% -4.9%9 0.3% 0.1% 0.1% 0.2% 0.2% 0.3% 0.3%

10 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%11 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%12 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%13 -5.3% -1.9% -7.9% -6.0% -1.8% -5.9% -6.4%14 0.1% 0.1% 0.1% 0.2% 0.1% 0.1% 0.1%15 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%16 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%17 0.4% 0.3% 0.3% 0.1% 0.6% 0.2% 0.2%18 0.5% 0.3% 0.4% 0.1% 0.7% 0.2% 0.2%19 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

-1.3% -0.4% -1.2% -0.6% -0.2% -0.8% -0.9%

Table 8. Changes in traffic, µ = 0.5 HBW HBEB HBEO HBDO NHBWEB NHBO ALL CARS

1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%2 -0.9% 0.2% -0.4% -0.2% 0.3% -0.4% -0.4%3 2.8% 0.2% 1.2% 0.3% 0.2% 1.0% 1.6%4 -7.0% -1.7% -8.4% -6.7% -2.6% -6.6% -6.3%5 2.9% 0.4% 1.2% 0.3% 0.4% 1.0% 1.7%6 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%7 1.7% -0.1% 0.7% 1.2% -0.2% 1.3% 1.1%8 -6.9% -2.1% -8.2% -7.5% -2.3% -7.2% -6.5%9 1.9% 0.4% 0.8% 1.3% 0.4% 1.4% 1.3%

10 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%11 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%12 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%13 -8.0% -2.0% -9.7% -7.9% -1.8% -7.9% -8.3%14 0.7% 0.2% 0.4% 1.0% 0.1% 0.7% 0.7%15 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%16 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%17 2.2% 0.4% 1.6% 0.5% 1.3% 1.0% 0.9%18 2.6% 0.3% 1.9% 0.6% 1.2% 1.2% 1.0%19 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

-1.3% -0.3% -1.1% -0.6% -0.1% -0.8% -0.9%

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Table 9. Changes in traffic, µ = 1.0 HBW HBEB HBEO HBDO NHBWEB NHBO ALL CARS

1 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%2 -1.6% 1.0% -0.6% -0.3% 1.5% -0.6% -0.6%3 5.6% 0.1% 2.2% 0.5% 0.1% 1.8% 3.1%4 -9.6% -1.2% -9.9% -7.6% -1.8% -8.2% -8.1%5 5.7% 0.4% 2.2% 0.5% 0.3% 1.9% 3.2%6 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%7 3.3% -0.2% 1.3% 2.3% -0.1% 2.5% 2.2%8 -9.3% -1.7% -9.5% -9.5% -1.5% -9.1% -8.3%9 3.7% 0.5% 1.6% 2.7% 0.5% 2.8% 2.6%

10 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%11 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%12 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%13 -11.3% -1.3% -11.7% -10.1% -0.1% -10.1% -10.4%14 1.2% 1.2% 0.9% 2.0% 1.3% 1.3% 1.5%15 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%16 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%17 4.5% 0.6% 3.2% 0.9% 2.4% 2.1% 1.7%18 5.2% 0.3% 3.7% 1.2% 2.2% 2.4% 2.0%19 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

-1.2% -0.2% -1.1% -0.6% 0.0% -0.8% -0.8%

Consider first row 13 of these Tables, which corresponds to Saturday mornings. In

the case where µ = 0 and there is no time switching, we see a 5.8% reduction in car

traffic, which is what we would expect from approximately 20% increase in fuel

costs and the fuel price elasticity of around 0.3. In rows 4 and 8 we see a smaller

overall reduction because at these times (weekday peaks) congestion is more of a

problem so some of the traffic deterred by the new charge is replaced by traffic

taking advantage of the improved speeds. This is apparent in the smaller reductions

in the columns for Home Based Employers’ Business (HBEB) and Non Home Based

Non Work/Employers Business (NHBWEB) where the values of time are much

higher.

In the case where µ = 0.1 some switching occurs. The overall traffic reduction in row

13 is greater at 6.4%, but there have been small increases in traffic on Saturday

afternoons, Sunday mornings and afternoons. These phenomena are much more

marked in the case where µ = 1. In each case tested the direct impact on the time

charged is nearly twice as high as it was with no time switching. There is substantial

transfer to the neighbouring periods.

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Notice that when a charge is added in the later weekday morning peak, the traffic in

the preceding peak hour rises as expected, but traffic in the hour before the peak falls

slightly. This may be because of some users in the early morning switching into the

charged peak to take advantage of the clearer roads.

Table 10. Environmental charges and congestion charges: effects of time switching

Col. No 1 2 3 4 5 6 7

µTotal tax take

£mCar tax

£mLGV tax

£mRigid tax

£mArtic tax

£m Passenger km bnChange in pass. km from base %

0.0 19,386 14,782 2,926 861 817 815 -11.00.1 18,895 14,307 2,917 856 765 821 -10.40.5 18,600 14,063 2,932 848 757 823 -10.21.0 18,115 13,676 2,869 832 738 826 -9.8

Table 10 summarises the outcome for the scenario with environmental charges and

congestion charges, added to existing fuel duties, with the several values for the time

switching parameter, µ.

As the previous discussion demonstrates, time switching makes demand more

responsive to price at the time the price is raised. Therefore, charges to deal with

congestion do not need to be so high. This is illustrated in the columns 2 to 6 Table

10. Also, as column 7 shows, the reduction in passenger km is less: some trips that

are deterred from peak times reappear at other times and so they are not lost to the

system.

These data are plotted in Figures 1 and 2. It is apparent that there is greatest

sensitivity to the move from µ = 0 to µ = 0.1.

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Figure 1. Extra charge revenues at various values of µ (time switching parameter)

18

18

19

19

20

20

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

µ

£ bn

Figure 2. Passenger km at various values of µ (time switching parameter)

800805810815820825830835840845850

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

µ

bn k

m p

a

Table 11 shows our estimates of costs and benefits for a scenario with environmental

charges and congestion charges additional to existing tax levels. This shows that

there is some sensitivity to the propensities to switch time of day. Figure 3 plots net

benefit against µ.

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Table 11. Economic appraisal for various values of µ (time switching parameter)

(£ bn.)

µChange in

traveller benefit

Saving in environmental

costsChange in tax & charge revenue Net benefit

0 -13.17 1.83 19.29 7.950.1 -12.55 1.77 18.80 8.020.5 -12.25 1.73 18.51 7.98

1 -11.79 1.66 18.03 7.90

Figure 3. Relationship between net benefits and values of µ (time switching

parameter)

Comparing row 4 of Table 6 (µ = 0) with Table 8 (µ = 0.5) we see that with no time

switching car traffic fell by 4.3% and with it it fell by 6.3%. Therefore the switching

accounts for a 2% reduction over and above the pure price effect. Since this is caused

by a charge approximately equivalent to a 20% increase in fuel costs, this represents

elasticity due to switching of approximately 0.1. This is the same order of

magnitude as the long term effects found by Burris et al (2004) – although their

results are not definitive.

This analysis suggests that time of day switching could be a significant—though not

overwhelming—factor in designing road pricing schemes. In practice substantial

benefits can be obtained through persuading a few users to change their time of

travel, thereby securing a more efficient use of the limited highway capacity.

7.07.27.47.67.88.08.28.48.68.89.0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

µ

£ bn

16

In what follows we use of the case µ = 0.5.

The new algorithm for congestion costs

The introduction of switching of time of day introduced a new complexity into the

computation of the costs of congestion. In the original version of the model an

additional vehicle km at a particular time (row of the matrix or “case”) only affected

other vehicles at that time: vehicles at other times would be unaffected. In the new

version, an extra vehicle in, say, the second workday morning peak hour would

slow down traffic at that time and therefore cause some traffic to switch to a

neighbouring time. So there would be disbenefits both to traffic at the time the

additional vehicle was travelling and to traffic in the neighbouring time periods. The

computation of the cost of all delay caused by one extra vehicle (or, equivalently, the

total benefit of removing one vehicle by increasing the road user charge) must take

account of these effects on neighbouring periods. And this required that a new

equilibrium be computed as the effects of the added vehicle “rippled” backwards

and forwards through the neighbouring periods.

In order to represent this new and complex algorithm was developed which varied

the charge at one time of day only and traced through the effect of that on the time in

question and on all the other times by iteratively establishing a new “micro-

equilibrium”. The result then indicated whether that particular charge should be

increased or reduced slightly. Then the charge at the next time of day was tested in

the same way, progressing through all nineteen times of day. This whole process

was then repeated as many times as it took to establish an overall, “macro-

equilibrium”.

The computational demands of this are very much greater than they were with the

simpler version of the model.

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Response of car occupancy In our previous work we assumed that the number of individuals occupying each

vehicle varied by vehicle type and by trip purpose, but does not respond to changes

in the charges for using the roads. In reality increasing charges would give an

incentive to increase average occupancies. This could be an important phenomenon

because increased average occupancies mean that the same number of people would

be carried whilst consuming less road space and therefore causing less congestion.

The Department for Transport’s Feasibility Study (2004) confirmed that this

consideration should not be neglected.

As with time of day switching we do not have suitable empirical evidence to guide

us as to the propensity of people to switch between being drivers and being

passengers – though casual observation suggests that it may be quite low.

Experience on car sharing in California is said to indicate that sharing rises with

journey distance – because the benefits of cost saving rise too. It is also claimed that

Heathrow workers living out at Swindon share to save fuel costs and the fatigue of

driving. But sharing takes place (for practical reasons) only where the origins and

destinations of potential sharers are concentrated. All this clearly limits the scope for

sharing but it may be greater at greater distances – bearing in mind that, with road

charging, distance could cost a lot at peak times.

In this work we have approached the problem by hypothesising several different

propensities and evaluating the difference it makes to our results.

We assumed that occupancies of all commercial vehicles stay fixed.

For private cars we have assumed that the average occupancy is related to the

occupancy in the base and money cost difference between the current situation and

the base according to the following relationship:

Occupancy = 1 + 2(base occupancy - 1)/( 1 + e λ{cost – base cost})

18

where λ is a negative constant.

If the current cost is equal to the base costs then the occupancy is equal to the base

occupancy. As the current cost rises above the base cost, so the average occupancy

rises. The occupancy can never fall below one and it never rises above twice the base

occupancy. Figure 4 shows the relationship for three values of the parameter, with a

base occupancy of 1.8 people per car.

Figure 4. Relationship between average occupancy and cost differences.

Table 12 compares the outcomes with four values for the parameter, λ. It suggests

that the results are, indeed sensitive to the propensity to share cars. The higher it is:

the less overall disbenefit there is to road users from road user charging, the greater

the environmental benefits, the less the charge revenues (because congestion is

relieved with lower charges) and the greater the overall net benefit from the scheme.

1.75

1.77

1.79

1.81

1.83

1.85

-0.90 -0.80 -0.70 -0.60 -0.50 -0.40 -0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Cost change from base (£)

Ave

rage

car

occ

upan

λ = -0.1λ = -0.05

λ = 0

19

Table 12.Economic appraisal for various values of occupancy parameter (£ bn.)

λChange in

traveller benefit

Saving in environmental

costsChange in tax & charge revenue Net benefit

0 -13.17 1.83 19.29 7.95-0.05 -9.33 1.88 16.56 9.12-0.1 -8.18 2.10 15.77 9.68

-1 -4.39 3.72 10.87 10.20

In the absence of empirical evidence we have chosen the value λ = -0.1 throughout

the remainder of our work. However, these sensitivity tests do suggest that if it were

thought that a different value was more appropriate then that would make an

important difference to the overall results.

The alternative policies In our previous work we considered the general implications of a range of policies

towards road pricing. In this work our focus is more specifically on the incidence of

road pricing on who would gain and who would lose. To identify this clearly we

have chosen to analyse two “polar extreme” alternative policies in order to highlight

the main issues.

Our representation simplifies any real implementation in several ways.

Exemptions and discounts.

In order to analyse gainers and losers we have to make assumptions about what

exemptions or discounts would be offered. Any practical policy will have these. The

London Congestion Charging scheme has many exemptions including a 90 per cent

discount to residents in the charged area. There will always be a long list of people

arguing for concessions, including:

• residents

• the less able

• older persons

• police and emergency services

20

• “essential service workers”

• utilities’ vehicles

• the unemployed

• motor cycles

• public transport vehicles

• commercial vehicles

• taxis

• alternative fuel vehicles.

For the purposes of this exercise we assume that no concessions are given except to

public service vehicles. Each vehicle is to be charged per kilometre an amount that

represents the congestion delay it imposes on other road users plus an estimate of its

environmental damage costs at that time and location. We are therefore implicitly

assuming that any concessions the authorities wish to give for reasons of general

policy are achieved by means different than concessions on road charges.

Whatever concessions are proposed in practice will have direct consequences for our

conclusions. They will also have administrative implications and require

enforcement.

How would the revenues be used?

An issue even more important than concessions is what it is proposed will happen to

the charge revenues. In general debate one hears propositions such as it will be used:

• to improve road maintenance and road capacity;

• to improve public transport alternatives;

• to defray the investment and operating costs of the pricing system;

• for other local or national public expenditure purposes;

• to reduce fuel duty or Vehicle Excise Duty (the tax disc).

Since the money can only be spent once it would not be possible—as is sometimes

implied—to spend it all on two or more of these.

21

In this exercise we concentrate on the last two items: either the revenue is all held for

general local or national expenditure purposes (we call this “revenue additional”) or

it is all returned to the national community of charged road users by rebating fuel

duties (“revenue neutral”).

The tax revenue neutrality is calculated from a national Exchequer viewpoint. The

charges would not be neutral from the point of view of most individuals or groups

of individuals. For instance, it would change the balance between cars and

commercial vehicles. With the revenue additional policy some of the money would

undoubtedly be used for transport purposes such as those at the top of the above list

but we assume that the benefits are general. They are taken to be £1 per £1 of

revenue. Under the revenue neutral policy there is, by definition, no new money

available.

Current legislation requires that all revenues raised by a local authority through

congestion charging must be spent on transport purposes within the area for a

period of ten years. In particular, net revenue from the London Congestion Charging

scheme is a contribution to Transport for London’s general budget: it is not

specifically rebated to the road users that pay it and it is not, in our sense, revenue

neutral.

Under both policies we are neglecting the important issue of the cost of

implementing and operating the charging system. In practice this is a very

significant issue. Work for the DfT Feasibility Study (DfT, 2004a) demonstrates that

costs could be prohibitively high unless great care is taken. It is likely that the costs

can be mitigated by piggy-backing road charging onto other services and

technologies. Even so, implementation and enforcement costs are likely to consume

a significant proportion of the gross revenues. Glaister and Graham (2004) discuss

the implications of cost structures for sensible geographical coverage.

22

Results Table 13 summarises the results of the two “polar” policies.

Table 13. Economic performance of revenue additional and revenue neutral

policies (£ billion per annum)

Change in traveller benefit

Saving in environmental

costs

Change in tax & charge revenue Net benefit

Revenue additional -8.18 2.10 15.77 9.68Revenue neutral 6.32 0.46 0 6.77

Both policies produce overall net benefits, the revenue additional policy rather more.

They both produce a saving in environmental costs, the revenue additional policy

substantially more because, in addition to achieving a more efficient (that is, lower

environmental cost) pattern of usage of the road network, it reduces total national

traffic.

There is a crucial difference between the two policies: with revenue additional policy

motor vehicle users as a group are definitely considerably worse off. The

environmental cost savings and the tax revenues both represent benefits to others

(and to road users in the other aspects of their lives) and they are more than

sufficient to outweigh the disbenefits to road users. This illustrates the basic

economic efficiency proposition in favour of road pricing—that, in principle, the

benefits represented by the environmental savings and the revenues are more than

sufficient to compensate those who pay the charge. The proposition stands

irrespective of whether compensation is actually made. Within the population of

road users there will be some, typically those with high values of time savings, for

whom the benefits of higher traffic speeds exceed the money charges so they will be

better off even though not compensated. However, as a group, road users are made

worse off.

By contrast, with revenue neutrality road users as a group are made better off. In

effect the compensation is made. Some—those with high valuation of time savings—

23

will be made considerably better off. Some road users will be made worse off but,

overall, the gains will outweigh the losses.

Calculating results at ward level

The 10,070 CAS wards of Britain form the basic unit of analysis used in the following

representation. However, as mentioned above, our road pricing model is based on

road traffic flow data for Britain which are disaggregated in an entirely different

way. The flow data represent a sample of road links from the UK network and are

arranged in a matrix of 11,120 rows or “cases”. Each row in the matrix corresponds

to a type of road, in a particular type of area, in one of the 11 Standard Regions of

Britain, at some time of the day, and in a busy or non-busy direction.

The ward level results are derived from the matrix results as follows. For traffic data

we take passenger car unit (PCU) per hour values for each type of road averaged

over defined periods of the day, and allocate this value to the wards in

correspondence to their associated region and area types. We then multiply the ward

PCU / hr values by the length of each road type in the ward to calculate PCU

kilometre per hour values. This is our measure of ward traffic flows.

For speed and price data the procedure differs because we have to account for the

fact that speeds and prices are associated with different traffic flows. There are two

steps involved in deriving the ward data. First, we calculate weighted-average speed

and price values over defined periods of the day for the 7 road types from our model

results using the following formulae:

1−

=∑∑

ii

i i

i

TvT

v and

1−

=∑∑

ii

i i

i

TpT

p , (1)

where v is speed and p is price, T is traffic flow (PCU kilometres per hour) and the

subscript i refers to a particular time of day. Second, we calculate aggregate speed

24

and price values for the wards by performing the same weighted average calculation

over road types (i.e. where the subscript i then refers to type of road).

Note that in the illustrative maps that follow there has been considerable averaging,

particularly by time of day: the changes displayed relate to a weighted average

across the week. The changes are substantially greater at peak times (this is

illustrated below).

Figure 5 displays an estimate of the average traffic volume changes experienced in

each census ward in Great Britain under a revenue additional policy. Figure 6

displays the corresponding revenue neutral policy.

Two points are immediately apparent from these two Figures. First, under either

policy there is a marked difference between the impact on urban areas and the much

larger rural areas. Secondly, there is a contrast between the experiences of the south

east region and the north and west of the country.

The busy urban areas experience similar traffic reductions under either policy—

congestion is treated aggressively in both cases. But at the other end of the scale the

policies have very different implications. With the revenue additional policy the

rural areas experience a small reduction in traffic: there is no congestion charge but a

relatively small charge reflecting the environmental damages. But with the revenue

neutral policy the rural areas experience a 22 to 26 percent increases in traffic. This is

because the revenues earned in the urban areas are used to reduce the cost of fuel.

This estimate of the traffic increase is a simple arithmetic consequence of the form of

the demand relationship assumed (see above) and the empirical estimates we have

used of how motor vehicle users respond to changes in fuel prices. However, a

change in fuel price to the user of this magnitude is outside the range of historical

experience so the estimates should not be taken too literally.

25

Figure 5. Average percentage traffic changes by census ward, GB, 2010

Additional revenue: £16 billion per annum

Note: this map is designed to be viewed in 16 colours

%

26

Figure 6. Average percentage traffic changes by census ward, GB, 2010

Revenue neutral

Note: this map is designed to be viewed in 16 colours

%

27

This points up the major feature of the revenue neutral policy: it would transfer

considerable sums of money from urban areas to rural areas. Unless compensation

were made through a major change in the local government finance regime the

residents of the urban areas would, as a group, be made worse off—particularly

most of those paying the road charges. Since a majority of the population lives in or

near the urban areas the consequence would be that a large number of people would

be made worse off and a small number would be made better off, some of them

considerably so. In England 21per cent of the resident population would live in

areas where traffic increased and the 79 per cent would have a traffic reduction. In

Great Britain the corresponding figures are 23 per cent and 77 percent.

There are important differences between the policies in the suburbs. For instance, in

the areas just outside Greater London, under the revenue additional policy traffic

would fall by around 20 per cent. Under the revenue neutral policy it would only

fall by around 8 per cent. There are similar implications in the suburbs of the West

Midlands and in the Liverpool-Manchester-Leeds-Sheffield area. This distinction is

particularly significant if it is expected that there will be long term growth of

population in areas such as this.

A large increase in traffic in the rural areas should not necessarily be regarded as a

bad thing on the crucial proviso that this traffic is genuinely paying the full cost of

the congestion and other damages inflicted on others. Then, the benefits to the extra

traffic must outweigh the costs to others. However, there is a distributional question

if, as in our revenue neutral scenario, the revenues are disbursed to the road-using

community in the form of rebates on fuel duties. Some of the damage costs—noise,

air pollution, climate change—fall on people other than road users and they would

not be compensated. As we have already noted, so far as damage to the environment

is concerned, the revenue additional policy is a more effective remedy.

Under current policy the Office of the Deputy Prime Minister is committed to a

‘compact city’ policy in the belief that, compared with traditional development, it

28

would be more fuel efficient, promote healthy walking and cycling life styles and

enable the poor to be better served by services including public transport. Whether

such policies are fully supported by evidence is an open question (see Cheshire,

2006). But if a revenue neutral policy made rural driving cheaper than now, it might

attract additional people to enjoy it and so might be in conflict with compact city

policy.

Figures 7 and 8 display the effects on average speeds. In the revenue additional case

speeds all improve. But in much of the country the change is negligible because the

traffic is free-flowing so change in traffic causes little change in speed. The situation

is actually very similar with the revenue neutral policy because the large traffic

increases occur on uncongested roads. There are a few places where speed do fall as

the traffic increases (for example in the area around Cambridge and other parts of

East Anglia) but the average speed reduction is more than one per cent in only about

two per cent of the GB wards.

29

Figure 7. Average percentage speed changes by census ward, GB, 2010

Additional revenue: £16 billion per annum

Note: this map is designed to be viewed in 16 colours

%

30

Figure 8. Average percentage speed changes by census ward, GB, 2010

Revenue neutral

Note: this map is designed to be viewed in 16 colours

%

31

Figure 9. Average percentage price changes by census ward, GB, 2010

Additional revenue: £16 billion per annum

Note: this map is designed to be viewed in 16 colours

%

32

Figure 10. Average percentage price changes by census ward, GB, 2010

Revenue neutral

Note: this map is designed to be viewed in 16 colours

%

33

Figures 9 and 10 show the corresponding percentage money price changes for

motorists. These relate to the vehicle operating costs (including fuel purchase) and

the road user charges. The traffic changes displayed in Figures 5 and 6 are not only

brought about by these money price changes. They take into account changes in the

money value of time spent travelling, which depend upon speeds. Further, changes

in traffic include changes in commercial vehicles which constitute a significant

proportion of all traffic in some places. Heavy commercial vehicles burn much more

fuel than do private cars and they generally have higher values of time. Therefore,

the private car price changes shown in Figures 9 and 10 are only part of the cause

behind the traffic and speed changes. However, they do reflect the average changes

in money costs of motoring to private individuals. Again, these are averages across

the week: within that there will be times when there are much lower charges and

peak periods when they are much higher.

The effects on various type of area Table 14 displays the definitions of the ten area types used in our modelling

Table 14. Area types

Area types Description Population

1 Central London

2 Inner London

3 Outer London

4 Inner Conurbation

5 Outer Conurbation

6 Urban Big > 250,000

7 Urban Large >100,000

8 Urban Medium > 25,000

9 Urban Small > 10,000

10 Rural

34

Figues 11 and 12 classify the census wards by these area types and then relate each

type to the percentage traffic change.

Figure 11. Census wards and traffic change by area type, GB, 2010

Revenue additional

-45

-40

-35

-30

-25

-20

-15

-10

-5

00 1 2 3 4 5 6 7 8 9 10 11

Area Type

% T

raffi

c C

hang

e

Figure 12. Census wards and traffic change by area type, GB, 2010

Revenue neutral

-40

-30

-20

-10

0

10

20

30

0 1 2 3 4 5 6 7 8 9 10 11

Area Type

% T

raff

ic c

hang

e

35

In these diagrams each point represents one of the 10,072 census wards positioned to

show the area type in which it is situated and the average traffic change it would

experience.

There is not much difference between these two figures, except that the revenue

neutral one is shifted vertically relative to the revenue additional one. Referring to

the revenue neutral case, the traffic reduction in outer London—the outer boroughs

such as Hillingdon and Croydon—is the greatest in GB and typically greater than in

inner London. There is significant traffic reduction in other inner conurbations.

Most outer conurbations also have traffic reductions but a few have increases. The

four types of urban area (as distinct from conurbation) generally have small traffic

reductions on the average. There is a large population resident in areas of this kind.

Finally, the rural areas experience a traffic increase.

Figure 13. Census wards and traffic change by area type, Scotland, 2010

Revenue neutral Scotland

-40

-30

-20

-10

0

10

20

30

0 1 2 3 4 5 6 7 8 9 10 11

Area Type

% T

raff

ic C

hang

e

36

Figure 14. Census wards and traffic change by area type, Wales, 2010

Revenue additional Wales

-40

-30

-20

-10

0

10

20

30

0 1 2 3 4 5 6 7 8 9 10 11

Area Type

% T

raff

ic C

hang

e

For comparison Figures 13 and 14 show the picture for revenue neutral policy for

Scotland and Wales separately. These simply confirm the greater predominance of

smaller towns and rural areas in these two devolved administrations, with the

implication that a GB-wide revenue neutral policy would benefit them relative to

England.

Figure 15 shows a similar plot, but for the price change rather than traffic flow.

Comparing this with the traffic changes in Figure 12 above, it is interesting to note

that the price increases in central, inner and outer London are much higher than

elsewhere and high relative to the traffic reduction achieved. This is because traffic

speeds are already low in the London area so time costs are high and money price is

a smaller proportion of total cost. Therefore price must be raised more in absolute

terms in order to secure a given traffic reduction.

37

Figure 15. Census wards and price change by area type, GB, 2010

Revenue neutral

-60

-40

-20

0

20

40

60

80

100

120

140

160

0 1 2 3 4 5 6 7 8 9 10 11

Area Type

% P

rice

chan

ge

Variation in traffic by time of day, by road type, by area type

and by region Figures 16 and 17 display histograms of the effects of the revenue additional and

revenue neutral policies on nine characteristics of the 10,071 cases relative to the 2010

base. For each of the nine panels we adopt the convention that a value of unity

signifies that the variable in question takes the same value as it did in the base. A

value of 1.1 implies that the variable is ten percent higher than in the base and a

value of 0.95 implies that it is five per cent lower. This is the only place in which we

report the effects on bus and rail use and the effects on the three types of commercial

vehicle.

Figure 16 shows that traffic is reduced by an average of 11 per cent for the revenue

additional case but Figure 17 shows that it increases by 6 per cent in the revenue

neutral case.

Our software enables to plot these histograms for any combination of period of the

week, road type, area type and region.

38

Figure 16. Summary of the distribution of effects for all periods, road types, area types and regions. Great Britain

Revenue additional

39

Figure 17. Summary of the distribution of effects for all periods, road types, area types and regions. Great Britain

Revenue neutral

40

The relationships between road pricing and deprivation A major interest of this study is the extent to which road pricing might benefit or

disbenefit disadvantaged people. The maps above display results for 10,071 census

wards and we have other evidence about the characteristics of the households that

inhabit the wards. We now relate our results to government’s official measures of

deprivation.

Note that we draw conclusions about the relationships between changes in traffic the

level of deprivation of wards as a whole—we cannot deduce that this will be the

common experience of all the individuals in the ward. There will, of course, be

considerable variation in circumstances of individuals within any ward. In

particular, some of them will be private car users and some will not. We address the

point that car users will be differently affected than—say—public transport users in

later sections. In this section we are effectively relating the experience of road users

in wards with various levels of deprivation, irrespective of whether or not those

users are themselves deprived. In drawing conclusions we are implicitly assuming

that car users living in deprived wards tend to suffer more deprivation than car

users living in less deprived wards.

The 2004 Index of Deprivation is a composite of seven domains

• Income deprivation

• Employment deprivation

• Health and disability deprivation

• Education, skills and training deprivation

• Barriers to housing and services deprivation

• Crime rates

• Living Environment deprivation (which includes air quality and road traffic

accidents)

To represent spatial variance in poverty we use the income deprivation measures

which form one component (or domain) of the Indices of Deprivation. These are

41

produced separately and on a different basis for England, Scotland and Wales. The

latest indices were published in 2004 for England and Scotland and in late 2005 for

Wales. For each country the specific calculations used to derive the income

deprivation domains differ which makes it almost impossible to compare levels of

deprivation across the counties. But the general principle underlying each is much

the same. Detail on the specific calculations used to construct the income

deprivations indices, and the deprivation indices more generally, can be found in

ODPM (2004), Scottish Executive (2004) and Statistics for Wales (2005).

The latest income deprivation indices are reported for very small geographical areas.

For England and Wales the income domain indices have been constructed at the

Super Output Area level, which defines approximately 32,500 spatial units in

England and 1,900 in Wales. The Scottish index has been constructed at the Data

Zone level which defines 6,500 zones. This is a much finer level of spatial

disaggregation than we can achieve in the spatial representation of results from our

pricing model. For this reason, we have constructed weighted average income

domain scores for larger geographical units defined by the Census Area Statistic

(CAS) ward disaggregation of Britain. There are 7,970 CAS wards in England, 1,219

in Scotland and 881 in Wales. To construct the aggregated scores we use the ward

share of population in each of the smaller areas as the weight.

In addition to the deprivation indices we also make use of data from the UK Census

of Population 2001 on ward resident population, the number of households and on

car ownership. Ward employment data is taken from the Annual Business Inquiry.

Figure 18 displays the geographical distribution of the deprivation index for

England. (As in all the maps in this document each colour shade represents

approximately the same number of census wards.)

42

Figue 18. Deprivation Index for England. Low numbers – blue – indicate low

deprivation. High numbers – red – indicate high deprivation

Note: this map is designed to be viewed in 16 colours

43

Since England, Scotland and Wales each compiles its own set of deprivation indices

according to somewhat different methods we have made our investigations

separately. The general picture does not appear to differ much across the three

administrations so we do not report all the results here.

In the case of income deprivation it is a measure of the proportion of the population

receiving some form of income ‘benefits’ from the state. These benefits essentially

include income support for the unemployed and for working households below a

low income threshold. The fact that the basic concept underpinning measurement of

the income deprivation indices is the same for each country means that we would

expect them to represent broadly the same phenomenon, even though the units of

measurement differ. So, in this one case of income deprivation we have contrived a

single index covering the whole of England and Scotland.

In much of the following discussion we present the relationship between percentage

traffic change and deprivation, rather than price change or speed change. This is

because, as noted above, traffic change encapsulates the consequences of the

composite effects of money charges and time changes.

Throughout low values of the deprivation indices represent little deprivation and

high values correspond to high deprivation.

Figure 19 illustrates the relationship between the degree of deprivation of census

wards in England and area type. We had expected to find a strong relationship,

with considerably more deprivation in the large conurbations and less in the rural

areas. Since road pricing would definitely involve higher charges in large urban

areas there would be a strong relationship between road pricing and deprivation.

44

Figure 19: Compound index of deprivation of census wards and area type

England.

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7 8 9 10 11

Area Type

Com

poun

d in

dex

of d

epriv

atio

n

The Figure suggests that there is indeed such a relationship, but that it is not a very

marked one. High deprivation is to be found in most types of area. Figures 20 and

21 suggest that there is a more marked relationship for crime deprivation and living

environment deprivation, with the big urban areas showing more deprivation.

Figure 20: Index of crime deprivation of and area type England.

-4

-3

-2

-1

0

1

2

3

0 1 2 3 4 5 6 7 8 9 10 11

Area Type

Crim

e

45

Figure 21: Index of living environment deprivation and area type, England.

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7 8 9 10 11

Area Type

livin

g en

viro

nmen

t

We now translate this into a relationship between deprivation and traffic change

under road pricing. Figures 22 and 23 show the relationship for the compound

index of deprivation under the revenue additional and revenue neutral policies.

Figure 22: Percent traffic change and compound deprivation, England.

Revenue additional.

-45-40-35-30-25-20-15-10

-50

0 10 20 30 40 50 60 70 80 90

Compund deprivation index

Traf

fic c

hang

e %

46

Figure 23: Percent traffic change and compound deprivation index, England.

Revenue neutral.

-40

-30

-20

-10

0

10

20

30

0 10 20 30 40 50 60 70 80 90

Compund deprivation index

Traf

fic c

hang

e %

In both cases the wards fall into two groups. One group has a relatively small traffic

reduction, or a traffic increase in the revenue neutral case, and it is at the less

deprived end of the scale. The other group has a bigger traffic reduction, spreads

across the scale of deprivation and does not appear to have any particular

relationship to deprivation. The implication appears to be that the rural areas tend

to have less deprived wards and suffer less reduction in traffic under road pricing.

But, once the rural areas are excluded, there is no obvious, systematic relationship

between deprivation and the degree of traffic reduction.

Figure 24: Percent price change and compound deprivation index, England.

Revenue additional.

0

20

40

60

80

100

120

140

160

0 10 20 30 40 50 60 70 80 90Compound index of deprivation

Pric

e ch

ange

%

47

-35

-30

-25

-20

-15

-10

-5

00 20 40 60 80 100

Traf

fic c

hang

e %

eccc traffic % change

Figure 24 is similar to Figure 22 but with price changes rather than traffic changes. It

implies similar conclusions. Whilst the relatively small price increases tend to apply

in the least deprived wards the highest price increases (mainly in London) apply at

most levels of deprivation.

Figure 25: Percent traffic change and compound deprivation index of census

wards, Scotland. Revenue additional.

-35

-30

-25

-20

-15

-10

-5

00 10 20 30 40 50 60 70 80

Compund deprivation index

Traf

fic c

hang

e %

Figure 26: Percent traffic change and income deprivation index of census wards,

Wales. Revenue additional.

48

Figures 25 and 26 illustrate that the relationships are similar in Scotland and Wales.

In most of the following we only present the results for England because the

separate results for Wales and Scotland do not add much.

Income deprivation

Figure 27 presents the relationship between traffic changes and income deprivation.

It adds little to Figure 23 for the compound index of deprivation.

Figure 27: Percent traffic change and income deprivation index of census wards,

England. Revenue additional.

Income deprivation

-45

-40

-35

-30

-25

-20

-15

-10

-5

00 0.1 0.2 0.3 0.4 0.5 0.6 0.7

eccc traffic % change

The income deprivation index was one example where we were able to approximate

a consistent index for the whole of England and Scotland. Figure 28 shows the result.

49

Figure 28: Percent traffic change and special income deprivation, England and

Scotland. Revenue additional

-45

-40

-35

-30

-25

-20

-15

-10

-5

00 20 40 60 80 100

Special income deprivation index

Traf

fic c

hang

e %

This relationship appears to be similar to that for each of England, Scotland and

Wales separately using the officially published indices so we are reassured that we

are unlikely to mislead by only displaying results for England in what follows.

Crime deprivation and living environment deprivation

As already indicated the most interesting differences in the relationship between

traffic change and the several domains of the deprivation indices occur with crime

deprivation (meaning deprivation associated with high crime rates) and living

environment deprivation, shown in Figures 29 and 30.

50

Figure 29: Percent traffic change and crime deprivation index, England.

Revenue additional

-45

-40-35

-30

-25

-20-15

-10-5

0-4 -3 -2 -1 0 1 2 3

Figure 30: Percent traffic change and living environment deprivation index of

census wards, England. Revenue additional

-45

-40

-35

-30

-25

-20

-15

-10

-5

00 10 20 30 40 50 60 70 80 90

Living environment

Traf

fic c

hang

e %

In the case of the crime deprivation index there is a slight suggestion that the largest

traffic reductions would tend to occur where deprivation is higher. No doubt this

reflects the tendency for crime rates to be higher in large urban areas.

51

On the other hand there does not seem to be as strong an association between traffic

reduction and living environment deprivation as Figure 21 would lead us to expect.

All the domains together

The several domains of deprivation are not, of course, independent of one another.

Table 15 displays the matrix of correlations between them

Because of these inter-correlations, some of them quite high, it can be misleading to

consider the relation between traffic change and any one of the domains in isolation

as we have been doing: the one domain may be acting as a proxy for one or more

other domains with which it is correlated. In Table 16 we display a multiple

regression analysis of all of the domains on the percentage traffic change in the

revenue additional case. The multiple regression identifies the separate relationship

between traffic change and each of the domains holding all the others constant.

Table 15. Correlations between measures of deprivation

Index Income Employment Health Education Housing Crime Living Env

Compound

index 1.00

Income 0.96 1.00

Employment 0.94 0.91 1.00

Health 0.87 0.82 0.90 1.00

Education 0.83 0.82 0.79 0.73 1.00

House -0.05 -0.14 -0.23 -0.27 -0.27 1.00

Crime 0.70 0.68 0.60 0.64 0.59 -0.26 1.00

Living

Environment 0.65 0.58 0.50 0.50 0.36 0.09 0.52 1.00

52

Table 16 Multiple regression of deprivation domains on traffic change. England

Dependent variable: Traffic change %

Coefficients Standard

Error t Statistic

Intercept -24.51 0.40 -61.37

Income -63.36 2.95 -21.47

Employment 75.30 4.56 16.53

Health -1.40 0.24 -5.77

Education 0.12 0.01 11.10

Housing 0.23 0.01 27.80

Crime -3.98 0.16 -24.87

Living

Environment -0.05 0.01 -6.05

Adjusted R2 = 0.37, 7969 observations.

Using this technique employment, housing and education deprivation all show a

highly significant positive relationship with traffic change. Thus wards showing high

deprivation on any of these three measures will, other things being equal, tend to

have smaller traffic reductions—because of smaller price increases. In the case of the

revenue neutral policy they are more likely to enjoy price reductions and traffic

increases. To the extent that reduced travel costs are helpful in mitigating these types

of deprivation, road pricing will be more helpful on these measures than as

measured by the other domains.

Modelling average effects for typical trips The procedure just outlined yields a set of results that describe changes in traffic

flows, speeds and price within each of the 10,070 wards of Britain. Obviously the

trips made by the residents of any ward will necessarily not be contained within the

boundaries of that ward. So if we want to analyse the effects of pricing in relation to

deprivation, we need to represent price, speed and generalised cost changes over a

wider area than the ward of residence.

53

In fact the wards of Britain are relatively small; the average radius is approximately

two kilometres. Table 17 shows average trip lengths by trip purpose for Britain. Of

course these average figures will vary considerably across the country, for instance,

commuting lengths in London and the South East are typically very much larger

than in other regions of Britain. But the table does demonstrate that average trips

lengths for most purposes will tend to take travellers outside of their ward of

residence.

Table 17: Average trip lengths by trip purpose, Britain, 2004.

Source: DfT (2005).

To represent the implications of pricing for trip patterns that take place over a wider

spatial area than the ward of residence we have calculated traffic, speed and price

changes averaged over broad areas ‘captured’ within specified radii from the

centroid of each ward. The procedure works as follows. Using the Cartesian

coordinates of the centroid of a ward we search to find the coordinates of all other

wards that fall within a given radius from this centroid. The values of all wards

within the radius are then summed and divided by the total number of wards to

produce an average figure. This procedure is repeated for each of our 10,070 wards

purpose kms

Commuting 13.7 Business 34.4 Education 4.8 Escort education 3.5 Shopping 6.8 Other escort 8.4 Personal business 7.1 Visiting friend at home 14.8 Visiting friends elsewhere 9.7 Entertainment 12.6 Sport: participate 10.1 Holiday: base 82.2 Day trip 23.5 Other 1.8

all 11.1

54

and for three radii corresponding to 15 km, 30km and 50km. Implementation of the

algorithm in C++ provides a very fast execution of these calculations.

Spatial variance in urbanisation

One additional spatial effect we have represented is the level of urbanisation of each

of the British wards. This is a useful measure because the level of road user charging

is strongly influenced by the level of congestion, and the most urbanised areas often

tend to be the most congested.

To measure urbanisation we have constructed measures of the effective density of

people and jobs that are accessible from each ward. The urbanisation variable (Ui) is

calculated for each ward i as follows:

( ) ∑≠

++

+=

ji

j ij

jj

i

iii d

PE

APE

Uπ

, (2)

where Ei is ward employment, Pi is ward population, Ai is the area of the ward and

dij is the distance between ward i and ward j.

Note that this is not the same thing as the crude classification of area type used

above: a ward could be classified as rural but be located quite close to centres of

employment and population and therefore be expected to experience quite high

levels of through traffic.

We present results in full for the English wards, since these form the majority of

wards within Britain, but also provide a summary of how the relationships in

Scotland and England compare.

Our previous work on the spatial implications of transport pricing has shown that

under today’s overall rates of fuel tax, city areas and major inter urban routes tend to

be relatively under-charged whilst the country areas are significantly over-charged.

55

In other words, broadly speaking we might expect a positive association between

prices based on some measure of marginal social cost and the level of urbanisation.

Figure 31 shows such a relationship for the wards of England. Note that since this

scenario assumes additional taxation, prices are increased everywhere. It graphs

ward urbanisation, as defined in equation (2) above, against the absolute price

change that would result under the revenue additional pricing scenario. Since the

vertical axis represents an index it is hard to relate the values it takes to explicit

locations. The lower end of the scale clearly relates to deep rural areas. The upper

end relates to particularly heavily urbanised places high resident population and

high employment close by. Many, but not all of these will be London wards.

Figure 31. Ward urbanisation and price change (pence per vehicle km.) under the

revenue additional scenario.

The Figure confirms that there is a strong relationship between the level of

urbanisation of the ward and the price change that would result given a system of

marginal social cost pricing. The line fitted to the data has a t-statistic of 117.5 and

0

500000

1000000

1500000

2000000

2500000

3000000

0 0.05 0.1 0.15 0.2 0.25

price change (£)

urba

nisa

tion

0 5 10 15 20 25 Price change (pence)

56

the R2 value for this bivariate regression is 0.63. In other words, just under two thirds

of the variation in price change is associated with the level of urbanisation of the

ward. It worth noting that there are groupings of observations shown in the data

which correspond to broad area types. For instance, the most heavily urbanised

wards sit in a group on the right hand side of the chart. The average price increases

experienced in these areas is somewhere between 13p and 23p per car kilometre.

Rural areas, on the other hand, are clustered in a group on the left hand side of the x-

axis and the price change experienced here is very small.

Conducing the same association for Scotland also produces a relatively large R2

value, 0.54, but less so for Wales, 0.25. The lower Welsh correlation is caused partly

by the fact that so much of the country is rural and registers a small price change

(less than ten percent increase in average motoring costs), but also because Wales

contains a much more restricted range of values for urbanisation.

Figure 32 shows a scatterplot of urbanisation and deprivation for the English wards.

Figure 32: Income deprivation and urbanisation, English wards.

0

500000

1000000

1500000

2000000

2500000

3000000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

income deprivation

urba

nisa

tion

57

The graph does not show a strong relationship between income deprivation and the

level of urbanisation. Fitting a linear curve to the data does indicate a positive

relationship, or in other words, that the least deprived wards tend to be the least

urbanised2, but the R2 value for this line is only 0.05 and the fit appears

unconvincing. In fact it seems reasonably clear from the Figure that the level of

deprivation is not distributed systematically with urban densities. Consequently, it is

unlikely that our previous results showing a spatial association between the price

and the level of urbanisation will have a simple bearing on equity considerations.

For Wales and Scotland the correlations between urbanisation and income

deprivation are also weak, with R2 values of 0.007 and 0.13 respectively.

The measure of urban density we use, which is essentially based on a gravity

calculation, is likely to provide a reasonably good proxy for the volume of traffic

flows in any ward (e.g. Graham et al 2003). Therefore, Figure 32 could also be

interpreted as showing that in general there is no tendency for deprived areas to have

more traffic than non-deprived areas. Thus, whilst there are certainly heavily

urbanised, heavily trafficked wards which have high income deprivation, equally

there are heavily urbanised wards that have low income deprivation. For instance it

is easy to think of deprived wards in Westminster and the eastern edges of the City

of London which share boundaries with some of the least income deprived wards in

the country.

A large proportion of trips made from any ward will take travellers outside the

boundaries of that ward. So here we are interested not so much in price and speed

changes in each of the wards but in how prices and speeds will change on average

around the wards. Figure 33 shows for England the relationship between average

price changes based on the revenue additional scenario at a distance of 15 kilometres

from each ward and the level of income deprivation.

2 High values of the deprivation indices indicate higher levels of deprivation.

58

The graph shows no clear relationship between price change and income

deprivation. If we fit a linear curve to the data we get an R2 value of only 0.09. The

spread of values from the middle to the top of the x-axis are the London wards

which tend to experience relatively high rises in price, but the graph also shows a

fairly diverse spread of levels of income deprivation for these wards. Similarly, for

wards outside London the spread at deprivation values at each level of price change

is diverse. Overall, there is no evidence of a systematic relationship between price

change and deprivation. The Scottish and Welsh data show similarly weak

associations with R2 values of 0.07 and 0.04 respectively.

Figure 33. Income deprivation and average price change (£) from the revenue

additional scenario at 15km.

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

income deprivation

pric

e ch

ange

(£) a

t15k

Table 18 shows deciles based on the level of income deprivation and average price

changes for each of these deciles. The table shows average price changes under the

revenue additional and revenue neutral scenarios.

Price changes are actually relatively consistent across these deciles under both

scenarios, although the least two deprived deciles do have higher price changes. A

59

closer examination of the data shows that these are highly urbanised central and

inner London wards which are not deprived and tend to have much higher levels of

traffic congestion, hence the increased magnitude of the charge.

In addition to price changes we have also calculated average speed changes for the

wards relating to our defined distance bands. Figure 34 shows a scatterplot of speed

changes for England under the revenue additional scenario averaged at a distance of

15 kilometres from each ward and the level of income deprivation.

Table 18. Average price changes for income deprivation deciles, revenue

additional and revenue neutral scenario.

Decile Average income

deprivation score

Average price

change (£)

revenue additional

Scenario

Average price

change (£)

revenue neutral

Scenario

I 0.030 0.030 0.002 II 0.044 0.028 0.000 II 0.055 0.029 0.001 IV 0.067 0.029 0.001 V 0.080 0.029 0.001 VI 0.096 0.031 0.003 VII 0.119 0.035 0.007 VIII 0.152 0.039 0.011 IX 0.203 0.046 0.018 X 0.312 0.055 0.028

60

Figure 34. Income deprivation and average speed change (%) from the revenue

additional scenario at 15km

0

2

4

6

8

10

12

14

16

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

income deprivation

spee

d ch

g (%

)

Again, the data show no clear relationship between income deprivation and speed

change. The R2 value for this bivariate regression is 0.08, indicating a very low

correlation between the two variables. In fact, the association shown in Figure 34 is

very similar to that demonstrated between price change and income deprivation.

This is of course unsurprising: high price changes lead systematically to large

improvements in speed. Thus, regressing speed change data on price changes

produces R2 values of over 0.90. Again the data show the same basic lack of

association for Scotland and Wales with R2 values of 0.06 and 0.03 respectively.

So overall there is little evidence of a systematic relationship between income

deprivation and price and speed changes from marginal social cost based pricing

system of road user taxation. Table 19 summarises regression results for the English

wards of average prices and speeds on income deprivation for each of our distance

bands and for both the revenue additional and revenue neutral scenarios. The R2

values shown in the table are low indicating it is not possible to reject the hypothesis

of no systematic relationships. The Scottish and Welsh data also support this general

61

conclusion. The high t-statistics show that the slope coefficients are highly

significantly different from zero, but they are small (that is, the line is almost

horizontal, but not quite).

We have shown above that there is a strong positive association between the level of

urbanisation of the wards and the price and speed changes that would result from a

system of national road user charging. This is because congestion, which has the

largest influence on price change under the revenue additional and revenue neutral

scenarios, tends to be highest in the most urbanisation locations: the more urban a

place, the more traffic congestion, the higher the road pricing charges and the

greater the subsequent increase (in percentage terms) in traffic speed. But can a

deprivation effect be identified having controlled for differences in the level of

urbanisation? In other words, recognising the great influence that congestion has on

price, if we account for the fact that different wards have different levels of

urbanisation can we then find any association between levels of deprivation and

price and speed changes.

62

Table 19. Results from regression of income deprivation on average price and

speed changes (distance bands of 15k, 30k and 50k and revenue additional &

revenue neutral scenarios).

Dependent variable R2 b t-stat

Av price chg 15k (revenue additional) 0.088 0.101 27.587 Av price chg 15k (revenue neutral) 0.083 0.104 26.784 Av speed chg15k (revenue additional) 0.085 11.383 27.113 Av speed chg 15k (revenue neutral) 0.093 11.588 28.439 Av price chg 30k (revenue additional) 0.030 0.050 15.608 Av price chg 30k (revenue neutral) 0.029 0.052 15.363 Av speed chg 30k (revenue additional) 0.030 5.159 15.709 Av speed chg 30k (revenue neutral) 0.036 5.325 17.293 Av price chg 50k (revenue additional) 0.002 0.011 4.238 Av price chg 50k (revenue neutral) 0.002 0.012 4.180 Av speed chg 50k (revenue additional) 0.003 1.112 4.521 Av speed chg 50k (revenue neutral) 0.006 1.489 6.640

Notes: 1. The independent variable in all regressions is the ward weighted average income deprivation score 2. The number of observations is 7925.

Table 20 shows a regression of income deprivation and urbanisation on speed and

price changes for the English wards under the revenue additional and revenue

neutral scenarios. The coefficient βu is associated with the urbanisation variable and

βi with the income deprivation variable.

The table shows that the level of urbanisation is positive and significantly associated

with ward price and speed changes under both scenarios and for each of our

distance bands. Note that the R2 values are lower for larger distance bands and this

is because the variance in the data is reduced due to averaging. Regarding the effect

of income deprivation the table shows some conflicting evidence. For the distance

band of 15 km there is a positive association between price and speed changes and

income deprivation. Thus, other things being equal, more deprived areas will tend to

pay more and have greater increases in speed. For distance bands at 30km and 50km

the data show a negative association and it is therefore the least deprived areas that

63

tend to pay more and have the highest speed increases. So the table shows some

inconsistent evidence, but, despite the significance of the t-statistics, overall the

existence of any deprivation effect is weak. The R2 values increase by a small amount

going from a model based on urbanisation alone to one based on urbanisation and

income deprivation.

Table 20. Results from regression of ward income deprivation and urbanisation on

average price and speed changes (distance bands of 15k, 30k and 50k and revenue

additional & revenue neutral scenarios).

Dependent variable R2 βu t-stat βi t-stat

Av price chg 15k (revenue additional) 0.786 7.85x10-8 160.65 0.035 19.25 Av price chg 15k (revenue neutral) 0.780 8.25x10-8 158.51 0.034 17.57 Av speed chg 15k (revenue additional) 0.764 8.83x10-6 151.07 3.935 17.99 Av speed chg 15k (revenue neutral) 0.700 8.14x10-6 126.62 4.724 19.64 Av price chg 30k (revenue additional) 0.736 6.64x10-8 145.60 -0.006 -3.66 Av price chg 30k (revenue neutral) 0.728 7.01x10-8 142.88 -0.007 -3.90 Av speed chg 30k (revenue additional) 0.776 7.03x10-6 162.61 -0.773 -4.78 Av speed chg 30k (revenue neutral) 0.695 6.22x10-6 130.86 0.082 0.46 Av price chg 50k (revenue additional) 0.602 4.92x10-8 109.32 -0.031 -18.10 Av price chg 50k (revenue neutral) 0.594 5.21x10-8 107.45 -0.032 -17.83 Av speed chg 50k (revenue additional) 0.639 4.79x10-6 118.05 -2.932 -19.29 Av speed chg 50k (revenue neutral) 0.552 4.06x10-6 98.38 -1.935 -12.53

Notes: 1. The independent variables in all regressions are the level of urbanisation of the ward (see equation (2)) with associated estimate βu and the ward weighted average deprivation score with associated estimate βi 2. The number of observations is 7925.

So far we have looked at deprivation in relation to price and speed changes. When

prices rise speeds tend to increase and so there is an offsetting effect that is

encapsulated in the change in generalised cost. We can use ward level data on

changes in price (money costs) and speeds to calculate average changes in the

generalised cost of making a trip. The generalised costs (g) are calculated as follows:

ts

pg v +

+=

1τ (3)

64

where p is the money price, τv is the value of time, s is the speed and t is the charge.

We calculate this formula for each ward in the base and for revenue additional and

revenue neutral scenarios. The value of time figure used is an average over all

journey purposes at all times of day expressed in 2010 values in 2005 prices. For the

base the value it is £11.85, for revenue additional £12.41 and for revenue neutral

£12.07.

Figure 35 shows a scatterplot for the English wards of generalised cost changes

under the revenue additional scenario averaged at a distance of 15 kilometres from

each ward and the level of income deprivation.

The range of values for change in generalised cost runs from approximately 1p to

9.5p compared to money prices change ranging from 1p to 14.5p (see Figure 33

above). This demonstrates the compensating effects of speeds and price changes;

where the price changes are highest increased speeds reduced the time component

of generalised cost. The evidence for a systematic relationship between income

deprivation and change in generalised cost is also weak. It is hard to determine any

clear association from the data in Figure 35. Table 21 reports regression results of

average changes in generalised cost on income deprivation for each of our distance

bands and for both the revenue additional and revenue neutral scenarios. The R2

values shown in the table indicate no evidence of a correlation between changes in

generalised cost from marginal social cost charging and income deprivation. The

Scottish and Welsh data offer the same conclusion.

65

Figure 35: Income deprivation and average change in generalised cost (£) from the

revenue additional scenario at 15km

0

0.02

0.04

0.06

0.08

0.1

0.12

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

income deprivation

gene

ralis

ed c

ost c

hg (£

)

Table 21: Results from regression of income deprivation on average generalised

cost changes (distance bands of 15k, 30k and 50k and revenue additional &

revenue neutral scenarios).

Dependent variable R2 �gc t-stat

Av GC chg 15k (revenue additional) 0.090 0.059 27.93 Av GC chg 15k (revenue neutral) 0.074 0.055 25.12 Av GC chg 30k (revenue additional) 0.030 0.035 15.69 Av GC chg 30k (revenue neutral) 0.026 0.034 14.50 Av GC chg 50k (revenue additional) 0.003 0.009 4.68 Av GC chg 50k (revenue neutral) 0.002 0.008 3.79

The regressions and association presented in this section are based on spatial areas,

not on people or groups of people. The indication is that there is no systematic

relationship between ward income deprivation and the speed and price changes that

might arise from marginal social cost pricing. One important factor that we have not

considered in the above analysis is that the number of car owning households, and

66

therefore the number of people affected by the price and speed changes, may vary

across the wards.

To take account of this important factor we have constructed a dependent variable

that scales the change in generalised cost for the ward at each distance band by the

number of car owning households. Figure 36 shows a scatterplot of the scaled

generalised cost variable against income deprivation for the English wards.

The scatter shown in Figure 36, which scales the change in generalised cost by the

number of car owning households, is quite different from the unscaled scatter shown

in Figure 35. In particular, the clustering of the London wards to the top end of the y

axis is made less obvious because while these wards typically have the highest

increases in generalised cost, they tend to have less residential and more commercial

land use and car ownership is low in London. Consequently the scaled values

appear less extreme because there are fewer car owning households.

Figure 36. Income deprivation and average change in generalised cost (£) from the

revenue additional scenario at 15km, times the number of car owning households

0

100

200

300

400

500

600

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

income deprivation

(gc

chg

(£) x

car

ow

ning

hou

seho

lds)

67

Fitting a linear curve to the data we find a positive association between the scaled

change in generalised cost and the deprivation index; more deprived areas are

relatively worst off. But as we have found consistently in this section, the

relationship is weak. Table 22 summarises R2 values obtained by regressing ward

level income deprivation on scaled change in generalised cost for each distance band

and for the revenue additional and revenue neutral scenarios.

Table 22. Results from regression of income deprivation on average changes in

generalised cost time the number of car owning households (distance bands of

15k, 30k and 50k and revenue additional & revenue neutral scenarios).

Dependent variable R2 βgc t-stat

Av GC chg × no. car owning households 15k (revenue additional) 0.058 203.107 22.049 Av GC chg × no. car owning households 15k (revenue neutral) 0.042 116.185 18.672 Av GC chg × no. car owning households 30k (revenue additional) 0.036 157.716 17.317 Av GC chg × no. car owning households 30k (revenue neutral) 0.019 79.313 12.352 Av GC chg × no. car owning households 50k (revenue additional) 0.023 109.847 13.528 Av GC chg × no. car owning households 50k (revenue neutral) 0.004 30.211 5.679

Effects on household budgets As we have already noted, the effect of road pricing on any individual will depend

upon the extent to which they happen to be car users: individuals that do not own

cars and do not use them as passengers will be much less affected. Indeed, they are

more likely to be public transport users and therefore more likely to benefit from

improvements in speed and reliability that bus services can offer if congestion is

reduced.

This section is adapted from Glaister and Graham (2000). It uses data from the 1996-

1997 Family Expenditure Survey (FES) to address these issues. This is the most

recent, suitable FES data that we have to hand—and the following can only be

regarded as the roughest of sketches because of its age. These FES data give detailed

information on all items of household expenditure, the households being classified

68

Res

t of S

E

£446

Gre

ater

Lon

don

£42

4

East

Ang

lia

£390

UK

£3

88

Nor

th W

est

£37

6

Sout

h W

est

£37

6

Hum

ber &

Yor

ks

£372

Wal

es

£368

East

Mid

land

s £

368

Wes

t Mid

land

s £

363

Scot

land

£3

61

Nor

th E

ast

£342

05

101520253035404550556065707580859095

100

Per c

ent

in a variety of ways. They have the important advantage that they record the

proportions of households that record zero spending on each item, so that we can

differentiate those that would be affected by a road charges from those that would

not.

Figure 37 displays the proportion of households that bought motor fuel in the FES,

categorised by standard region. The bars are ordered in declining total weekly

household expenditures (in 1996-97), shown in £ on each bar is that total. The

expenditure figures have been converted from May 1996 prices to May 2005 prices

using the Retail Prices Index. Note that this will understate total 2005 expenditures

because of growth in real incomes. Over this period the real price of fuel rose by

about 16 per cent, a little less than the growth in real incomes. It is likely that

incomes and expenditures will have grown in some areas (such as London) relative

to those in other areas.

Figure 37. Percentage of households buying fuel, FES, 1996-97.

We take the fact that a household does not record any expenditure on fuel in the

survey period to be an indication that they are not frequent car users and we assume

their expenditures would be unaffected by the introduction of road charging. In

69

Greater London about half of all households would not be directly affected, in spite

of relatively high incomes. At the other extreme 100 – 72 = 28 per cent of households

in the South West would not be affected. This is probably because of the much

superior availability of public transport in London so car use is lower even though

incomes are higher. Further, those households that do buy fuel in London spend less

on it than those in any other region: see Table 23, below.

However, road charges in London would be much higher than elsewhere. We now

treat a new, revenue additional per kilometre road charge as if it were an equivalent

per kilometre increase in the cost of fuel—they are both cash out of the pocket. First,

we calculated the absolute and proportionate increase in direct money expenditures

on using cars (fuel plus the revenue additional charge) for each region. Then, using

the FES information on weekly household spending on fuel as a base we worked out

the implied increase in weekly spending per household, shown in Figure 38.

Figure 38. Increase in weekly spending per household due to road charges,

average for all households, by region (£ per week). Revenue Additional.

Figure 39 expresses the same information as a proportion of average total weekly

household spending.

Res

t of S

E, 2

Gre

ater

Lon

don,

10

Eas

t Ang

lia, 2

UK

, 3

Nor

th W

est,

1

Sou

th W

est,

1

Hum

ber &

Yor

ks, 1

Wal

es, 2

Eas

t Mid

land

s , 2

Wes

t Mid

land

s, 2

Sco

tland

, 1

Nor

th E

ast,

3

0

5

10

£ pe

r wee

k

70

Res

t of S

E, 2

Gre

ater

Lon

don,

20

Eas

t Ang

lia, 2

UK

, 4

Nor

th W

est,

2

Sou

th W

est,

2

Hum

ber &

Yor

ks, 2

Wal

es, 3

Eas

t Mid

land

s , 3

Wes

t Mid

land

s, 3

Sco

tland

, 2

Nor

th E

ast,

6

0

5

10

15

20

25

£ pe

r wee

kFigure 39. Increase in weekly spending per household due to road charges,

average for all households, as a proportion of total spending (%), by region.

Revenue Additional.

Figures 40 and 41 repeat the same pair of calculations but allowing for the variations

in the proportions of households that do not use cars. Thus these figures relate only

to those households that would be likely to pay the new road charges. But note that

Figure 41 will overstate the proportion of household spending to the extent that

those spending on fuel have higher than average household incomes.

Figure 40. Increase in weekly spending per household due to road charges, for

households paying road charges, by region (£ per week). Revenue Additional.

Res

t of S

E, 0

.4

Gre

ater

Lon

don,

2.4

UK

, 0.7

Nor

th W

est,

0.2

Eas

t Ang

lia, 0

.4

Hum

ber &

Yor

ks, 0

. 3

Eas

t Mid

land

s , 0

.5

Sco

tland

, 0.3

Sou

th W

est,

0.4

Wal

es, 0

.4

Wes

t Mid

land

s, 0

.5

Nor

th E

ast,

0.9

0

1

2

3

Per c

ent

71

Figure 41. Increase in weekly spending per household due to road charges, for

households paying road charges, as a proportion of total spending, by region (%).

Revenue Additional.

It is very important to note that these results only relate to cash outgoings and they

make no allowance for the offsetting benefits in terms of time savings. Also, they

ignore the values of the tax revenues which, in principle would be available in

compensation. Both of these are particularly important considerations in the case of

London where the average charges are so much higher.

Table 23 shows the estimated levels of weekly spending by households that bought

road fuel in 2005, together with the additional spending .

Table 23. Estimated household spending on motor fuels by households that buy it in 2005, and additional spending on roads charges (£ per week), by Region. Revenue Additional.

Rest of SE

Greater London

East Anglia

UK North West

South West

Humber & Yorks

Wales East Midlands

West Midlands

Scotland North East

Spend on fuel 25 21 25 24 21 23 22 26 23 25 24 23

Additional spend on charges

2 20 2 4 2 2 2 3 3 3 2 6

Res

t of S

E, 0

.5

Gre

ater

Lon

don,

4.8

UK

, 1.1

Nor

th W

est,

0.4

Nor

th E

ast,

1.7

Wes

t Mid

land

s, 0

.8

Wal

es, 0

.7

Sou

th W

est,

0.5

Sco

tland

, 0.6

Eas

t Mid

land

s , 0

.8

Hum

ber &

Yor

ks, 0

. 5

Eas

t Ang

lia, 0

.6

0

5

Per c

ent

72

The striking thing about all of these figures is the extent to which charges are higher

in London than anywhere else. Although about half London households would be

unaffected (and would benefit from improved public transport), the other half

would, on average be spending an additional 4.8 percent of their total household

budget on the charges: £20 per week out of a total of £424 per week (and would

benefit from less traffic congestion). This approximately doubles their current

outgoings on fuel.

Aside from London there is considerable variation across regions. Road users in the

North East appear to spend a particularly high proportion of their household

budgets on the charges. This is the consequence of car use being relatively rare in

this region and those who have cars spending more on fuel than the national

average.

There is no obvious, simple relationship between ranking of regional incomes

(corresponding to the order of the bars in these diagrams) and the proportion of

incomes that would be spent on revenue additional road charges. London and the

North East are almost at opposite ends of the spectrum.

Figures 42 to 44 show the proportions of households shown in the 1996-97 FES as

spending on motoring fuel classified by economic status, occupation and household

composition. In each case the bars are arranged in order of reducing household

incomes.

We are not able to relate the rates of road charges to these classifications on the basis

of our modelling. However, in the most general terms the Figures give some

indication of which kinds of households would be least affected because they are

less likely to be paying for car use.

73

Figure 42. Proportions of households not recording spending on motoring fuel, by

economic status.

Figure 43. Proportions of households not recording spending on motoring fuel, by

occupation

Self

empl

oyed

, 21

Full

time

empl

oyee

s, 1

9

All h

ouse

hold

s, 3

8

Part

time,

41

Uno

ccup

ied,

52

Une

mpl

oyed

, 51

Econ

omic

ally

inac

tive,

63

05

101520253035404550556065707580859095

100

Per c

ent

Empl

oyer

s an

d m

anag

ers,

21

Prof

essi

onal

, 13

Inte

rmed

iate

non

-man

ual,

20

Skille

d m

anua

l, 19

Juni

or n

on-m

anua

l, 34

Sem

i-ski

lled

man

ual,

33

Uns

kille

d m

anua

l, 52

0

5

10

15

20

25

30

35

40

45

50

55

60

Per c

ent

74

Figure 44. Proportions of households not recording spending on motoring fuel, by

household composition

Summary and Conclusions The results presented in this report illustrate a well-known proposition: that altering

charges to make them reflect social costs more accurately can generate new

economic value. In our context they do this by making road users face up to the

congestion and environmental costs they impose on others and by giving road users

incentives that guide them towards more intelligent use of scarce highway capacity.

Most conventional taxes are imposed because of a need to raise revenues and they

have the distinct disadvantage that they distort the relationship between cost and

value to the end user—leading to a “deadweight loss” of the tax which is additional

to the costs of collection. So the overall cost of such a tax is greater than the benefit

represented by the revenue raised. By contrast, well designed road charges with low

enough collection costs improve the match between social cost and value to users so

that the overall cost is less than the benefit represented by the value of the revenue

raised.

So, in principle, unlike most taxes road pricing can both raise revenue and “do

good” in the round. But, as with a tax, who gains and who loses depends crucially

on who pays the tax and how the benefits of the revenues are disbursed. The main

3+ad

ults

with

chi

ldre

n, 1

5

3+ad

ults

no

child

ren,

12

Two

adul

ts, o

ne c

hild

, 20

Wor

king

cou

ple,

21

Ret

ired

coup

le n

on s

tate

pe

ns.,

32

One

adu

lt +

one

child

, 66

One

adu

lt +

two

child

., 65

One

non

-ret

ired

adul

t, 51

Stat

e pe

nsio

n co

uple

, 60

One

adu

lt no

n st

ate

pens

., 72

One

adu

lt st

ate

pens

., 93

05

101520253035404550556065707580859095

100

Per c

ent

75

thrust of this research has been to throw light on the kinds of people that might

benefit or disbenefit from the introduction of a national system of road pricing.

Who might gain and who might lose plainly depends crucially on a number of

characteristics of the policy. We analyse a simple policy of making a charge per

vehicle kilometre for the use of all roads at a rate that reflects the level of congestion

and environmental damages. This charge rate will vary by the current traffic level,

the size of the vehicle, capacity of the road and the nature of the locality.

This is an idealised scheme and practical schemes would probably be simpler. The

nature of the simplification - such as a cordon scheme or an area scheme like the

London Congestion Charge - could significantly alter the incidence on particular

individuals. Plainly, any concessions granted in a practical scheme will also have

direct implications. We abstract from this issue by assuming that there are no

concessions. Also we have ignored the issue of how much these charges might cost

to collect. This must not be neglected in practice – every £1 spent on hardware or

administration is £1 of benefit lost, to be set against the traffic and environmental

gains. In an earlier study (Glaister and Graham, 2003, 2004) we showed how

different technologies dictate different relationships between geographical coverage

and cost. We argued that rather than attempting to do everything it might be better

to accept a part of the available gross benefits with a less than complete geographical

coverage. This is an issue we do not consider in this report, but it is a vital

component of practical policy design: and the tradeoffs change rapidly as technology

advances.

In this research we have developed our previous work by representing traffic as it is

expected to be in 2010 rather than 2000; adding Scotland and Wales; investigating the

effect of the tendency of the number of occupants in cars to change in response to

charges paid by the vehicles for using the roads; and investigating the effect of the

tendency of road users to vary their time of travel in response to differences in charges

by time of day.

76

Not surprisingly, a propensity to switch time of day makes an important difference

to the outcomes of road pricing. Time switching makes demand more responsive to

price at the time the price is raised. Therefore, charges to deal with congestion do not

need to be so high. This allows pricing to achieve a more efficient use of existing

capacity more easily by encouraging some users out of the most congested times to

periods when there is spare capacity. It also generates benefits by allowing those

with the highest valuations of the peak capacity to use it and pay for it whilst those

who do not mind switching so much can respond to the financial incentive to do so.

Similarly, quite modest propensity to increase average car occupancy (to “car share”)

in response to road pricing in congested conditions make an important difference.

The higher it is: the less overall disbenefit there is to road users from road user

charging, the greater the environmental benefits, the less are charge revenues

(because congestion is relieved with lower charges) and the greater the overall net

benefit from the scheme.

Whilst we have succeeded in modelling both time switching and changes in

occupancy we face the problem that there is a paucity of empirical data on the

magnitudes of these effects in practice. We have demonstrated the sensitivity of our

results to these factors and then proceeded by assuming what we believe to be

reasonable degrees of response.

We have abstracted from issues of concessions and costs of collection to enable us to

concentrate on the biggest single issue: what is to be done with the revenues and

how does that affect who gains and who loses? Would they be used by the

Exchequer for general purposes, made available in the locality in which they are

collected for use for transport purposes or returned to the generality of road users

across the nation in some form? To point up the differences we have considered two

alternatives. In one, the “revenue additional” case, the revenues are either used by

the Exchequer for the general benefit, or they are used by an administration local to

77

the area in which the revenues are collected for local benefit. In the other, the

“revenue neutral” case, fuel duties are reduced in such a way that the sum of fuel

duties and road pricing revenues is held constant: thus the national road using

community (including freight vehicles) would pay the same in total with or without

road pricing.

Economic performance of revenue additional and revenue neutral policies in

Great Britain (£ billion per annum)

Change in traveller benefit

Saving in environmental

costs

Change in tax & charge revenue Net benefit

Revenue additional -8.2 2.1 15.8 9.7Revenue neutral 6.3 0.5 0 6.8

The Table summarises the estimated economic performance of the two alternative

policies. In the case of a revenue additional policy, road users as a group would be

worse off. The extra revenues would amount to about £16 billion per annum—

though concessions would reduce this. If these revenues were returned to the local

communities from which they came then road pricing could lead to important

overall gains for the communities, though the net effect on road users or transport

users generally clearly depends upon what the money is spent on. So long as the

costs of collection do not consume too much of the revenues, there would be a new

and significant steam of annual income that local authorities could use either for

revenue support or to service Prudential Borrowing. That could be used for capital

finance for some of the items they cannot fund presently.

The revenue neutral policy would generate somewhat less overall net benefit. But it

would make road users as a whole better off because the revenues are returned to

them and the road network is more efficiently used. A major feature of the revenue

neutral policy is that it would transfer considerable sums of money from urban areas

to rural areas, particularly from London. Unless compensation were made, such as a

change in the local government finance regime, the residents of the urban areas

would, as a group, be made worse off. Since a majority of the population lives in or

78

near the urban areas a consequence could be that a large number of people would be

made worse off and a small number would be made better off. 23 per cent of the

resident population would live in wards where traffic increased on average because

travelling costs (money cost reductions net of the value of time lost because of

increased congestion) had reduced and 77 per cent would have a traffic reduction

because costs had increased on average. These average calculations need to be

treated with caution because they conceal important variations. For instance, under a

revenue neutral scenario car users in urban areas at uncongested times would be

paying less, even though, averaged across the week, car users in urban areas were

paying more.

The revenue neutral proposal has important presentational attractions. However,

there would be no net revenue to defray the costs of the scheme or to spend on the

“complementary measures” that are important in winning general support.

There are differences between the two policies in the suburbs. For instance, in the

areas just outside Greater London, under the revenue additional policy traffic would

fall by around 20 per cent. Under the revenue neutral policy it would only fall by 8

per cent. There are similar implications in the suburbs of the West Midlands and in

the Liverpool-Manchester-Leeds-Sheffield area.

The revenue additional policy does more to reduce accidents, fuel consumption and

vehicle emissions (listed as “savings in environmental costs” in the Table) because,

in effect, it increases the average money cost of using roads compared with the

revenue neutral policy

We have analysed the impact of road pricing by the degree of urbanisation. The

traffic reduction in outer London is the greatest in Great Britain and typically greater

than in inner London. There is significant traffic reduction in other conurbations.

Under a revenue neutral policy smaller urban areas (as distinct from conurbations)

generally have small traffic reductions on the average. There is a large population in

79

areas of this kind. The rural areas experience a traffic increase. A revenue neutral

charging scheme could be greatly beneficial for poor rural car users and given that

car-ownership rates are high in rural areas, in rural areas the poor do run cars. A

revenue additional scheme, by contrast, would be no better for poor rural drivers

than existing taxes and charges. And given the impossibility of providing more than

sketchy rural bus services, this would mean that a revenue additional scheme would

hit the rural poor perhaps harder than their urban counterparts who may be able to

walk the shorter distances or catch a bus and who might benefit from the revenues

being channelled back into their areas.

A major interest of this study is the extent to which road pricing might benefit or

disbenefit disadvantaged people. To represent spatial variation in disadvantage we

use the deprivation measures which form the components of the official Indices of

Deprivation. Road pricing would definitely involve higher average charge rates in

large urban areas where there are also concentrations of deprivation so we had

expected to find a relationship between the rates of charge and levels of deprivation.

There is indeed such a relationship, but across England as a whole it is not a very

marked one. This is because high deprivation is to be found in most types of area, in

the remote parts of the country as well as in the large urban areas.

Employment, housing and education deprivation all show a significant positive

relationship with traffic change. Thus wards showing high deprivation on these

measures will, other things being equal, tend to have smaller traffic reductions—

because of smaller price increases. Indeed, in the case of the revenue neutral policy

they are more likely to enjoy price reductions. To the extent that reduced travel costs

by car are helpful in mitigating these types of deprivation road pricing will be less

damaging on these measures than on the other measures of deprivation.

The true effects on households would be determined by the charges in the areas

through which they drove, rather than where they live. Therefore, for each

residential location we investigated conditions within circles of radius 15 km, 30 km

80

and 50 km. The indication is that there is no systematic relationship between ward

income deprivation and the speed and price changes that might arise from road

charging. The same appears to be true for Wales and Scotland. Adjusting for

variations in rates of car ownership does not change this result.

Not everybody is a car user and those that are not would stand to benefit from the

clearer roads and improved bus services. Car use in London is much lower than the

national average because of the superior public transport. But road charges would,

on average, be substantially higher in London. The combined effect is that under

either kind of revenue policy private car users in the London area would spend a

higher proportion of their household budgets on motoring: the extra might be up to

a doubling: an increase from the five percent of household budgets presently spent

on fuel to ten percent. This neglects important benefits in terms of the value of

higher road speeds and it does not take account of the benefits from the charge

revenues if spent in London. For those who are not car users there would be no

increase in charges, and the benefit of clearer roads. Under revenue additional

policy, for most of the other regions of GB the additional spending would be

between one and two percent of household budgets for those that use cars, but it

might be three percent in the North East.

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