Fare Levels

This measure was fully updated by INSTITUTE FOR TRANSPORT STUDIES (ITS) in 2014 under the CH4LLENGE project, financed by the European Commission.


Fares can be described as the monetary charge for making a trip by public transport, e.g. the price of a rail or bus ticket. Fare levels can be affected by subsidies provided (or taxes levied) by national or local authorities. Fares may be the main source of revenue for public transport operators. When fare levels change they influence the level of demand for public transport. In general, all other things being equal, an increase in fares will reduce patronage, whilst a decrease in fares will increase patronage. The size and direction of the change in demand following a change in fares can be expressed in terms of a fare elasticity. For example, if the fare elasticity of bus demand with respect to bus fares is –0.4, and all fares were to increase by 10% we would expect patronage to decrease by 4%. The fare elasticity is therefore a measure of the price sensitivity of bus passengers.

A wide range of factors influence the size of fare elasticities, e.g. current fare levels (the higher the current fare the more sensitive passengers will be to fare changes), size of the fare change, service quality (passengers may be less sensitive to fare changes if the quality of service is high). Whilst these factors can be discussed in isolation it is likely that more than one of them will exert an influence at the same time.

Fare levels tend to reflect the costs facing operators. As such, fare levels will tend to differ between modes and also within mode between different operators. Fare levels can also differ in the short run for the same service, for example peak and off-peak fares. This reflects a desire of the operator to maximise his profits by introducing price segmentation into the market place and charging what the market will bear. It also reflects a desire on behalf of the operator to spread their passenger loading throughout the day to ensure that every passenger who wishes to travel can do so, and that the level of service offered to those passengers is at least perceived to be of an acceptable standard. In some countries fares are highly regulated (e.g. France) and controlled by local government, whereas in other countries (e.g. UK outside London) they are largely determined by the operator. In the former subsidies tend to be high and in the latter low.

In the long run fare elasticities will increase, reflecting the greater range of responses open to passengers who have been affected by increases in fare. In the short run passengers facing an increase in fares can either switch modes or not travel. In the long run the number of options increases to include switching destinations, changing jobs, changing homes, purchasing a car etc. Whilst fares are an important factor in a person’s choice of transport mode they need to be viewed as a policy instrument that works best with other complementary measures.

Description

Fares can be described as the monetary charge for making a trip by public transport, e.g. the price of a rail or bus ticket. Fares may be the main source of revenue for public transport operators. Fare levels can be affected by subsidies provided (or taxes levied) by national or local authorities. 

Why introduce different fare levels?

Fare levels can reflect costs facing operators, although subsidies and profits for private providers may have the consequence that the relationship is not direct.  Fare levels will tend to differ between modes and also within mode between different operators. Fare levels can also differ in the short run for the same service, for example peak and off-peak fares. This reflects a desire of the operator to maximise his profits by introducing price segmentation into the market place and charging what the market will bear. It also reflects a desire on behalf of the operator to spread their passenger loading throughout the day to ensure that every passenger who wishes to travel can do so, and that the level of service offered to those passengers is at least perceived to be of an acceptable standard.

In countries and regions where fares are regulated and controlled by central or local government, fares may be changed to reflect a desire to improve accessibility/equity throughout the general population. A desire to reduce car use to reduce environmental externalities or to improve efficiency are also policy aims which can aided by reducing fare levels.

Demand impacts

When fare levels change they influence the level of demand for public transport. In general, all other things being equal, an increase in fares will reduce patronage, whilst a decrease in fares will increase patronage. The size and direction of the change in demand following a change in fares can be expressed in terms of a fare elasticity and is defined as,

01

For example, if the fare elasticity of bus demand with respect to bus fares is –0.4, and all fares were to increase by 10% we would expect patronage to decrease by 4%. The fare elasticity is therefore a measure of the price sensitivity of bus passengers.

The absolute size of the fare elasticity conveys information on the sensitivity of demand to changes in the factor affecting demand and its sign conveys information on the direction of the change. Fare elasticities are defined as inelastic if they are less than (-)1.0 and elastic if they are greater than (-)1.0. The larger the fare elasticity the more sensitive passengers are to changes in the fare.

A wide range of factors influence the size of fare elasticities. Whilst these factors can be discussed in isolation it is likely that more than one of them will exert an influence at the same time.  The principal ones are:

  • Fare levels – the higher the current fare the more sensitive passengers will be to fare changes.
  • Size of fare changes – the larger the change in the fare the more sensitive passengers will be to the fare change.
  • Income levels – those on high incomes are less likely to be sensitive to changes in bus fares, whilst those on low incomes will be more sensitive.
  • Service quality – passengers may be less sensitive to fare changes if the quality of service is high.
  • Competition from other modes – strong competition from other bus operators and from other modes of transport will make passengers more sensitive to fare changes.
  • Social factors – Males tend to be more sensitive to fare changes than females. The elderly and school children are also more sensitive to fare changes.
  • Journey purpose – travellers commuting to work or school tend to be less sensitive to fare changes, whilst leisure travellers are more sensitive.
  • Distance – passengers will be more sensitive to changes in fare if they are only travelling short distances since walking is always an option.
  • Urban vs Rural – passengers tend to more sensitive to fare changes in rural areas compared to passengers in urban areas.
  • Area - passengers tend to be less sensitive to fare changes in metropolitan areas compared with non- metropolitan areas.
  • Peak vs Off Peak – passengers tend to be less sensitive during peak periods of travel, compared with off-peak periods of travel.

A distinction can be made between short run elasticities, which reflect passengers’ immediate responses, and long run elasticities, which take account of other changes which passengers are able to make, such as changing destination, workplace, vehicle or home.  The publication, Demand for Public Transport Publication (TRL, 2004) compares short run bus, metro and suburban rail fare elasticities for both the UK and non-UK systems.

Table 1 - Public Transport Fare Elasticities (short run)

Mode

UK

Non-UK

Overall

Bus

-0.43

-0.37

-0.42

Metro

-0.31

-0.29

-0.30

Suburban Rail

-0.58

-0.37

-0.50

Overall Public Transport

-0.44

-0.35

-0.41

(TRL, 2004)

The UK fare elasticities are higher than the non-UK values, which may reflect the lower fare levels (due to higher levels of subsidy) and better quality of service found in many other countries. Metro (underground) has the lowest fare elasticities, hence metro passengers are least sensitive about a change in price. So for example a 10% increase in fare levels would reduce patronage on the metro by 3.1% as compared with a 10% increase in fare levels for suburban rail which would reduce patronage on the railway by 5.8%. Metro’s low elasticity reflects the main advantage it has over other modes in a major city environment, namely its ability to offer a fast method of travel between the city centre and outer urban areas. Road congestion prevents the bus or car from offering a comparable service, whilst suburban rail does not have the same network penetration and walking takes too long. Suburban rail has the highest elasticity and this may reflect the fact that the car is the next most preferred mode. The cost of suburban rail is quite high when compared with the car and a moderate change in rail costs might be enough to persuade a rail user to switch to the car.

This last point illustrates the interaction between modes and the choices faced by passengers when experiencing an increase in costs for the mode they currently use. The impact on the demand of one mode as a result of competition from another mode is measured by cross elasticities. These cross elasticities measure the change in demand for a given mode as a result of the change in one of the factors associated with another transport mode (mainly fare or service frequency). Cross elasticities tend to be very specific to the relative market share they are estimated from and so are not easily transferred across time and space.

The impact of cross elasticities can be complex as a change in one factor can impact very differently across different modes of transport. The bulk of the evidence suggests that the cross elasticity of car use with respect to changes in public transport characteristics is low. For public transport demand with respect to car characteristics the evidence suggests a somewhat larger elasticity, whilst the cross elasticities between public transport modes are also more substantial. The factors influencing cross elasticities are listed and outlined below:

  • Relative Market Shares - in practice if bus has a major share of an existing market then an improvement in the competition will have a smaller impact than if bus’s share was minor. That is to say that a large market share for bus would indicate that other modes are not perceived to be good substitutes to bus for a variety of reasons, quality of current service, price of current services, range of services etc.
  • Own Mode Elasticity – the higher the own mode elasticity the greater the scope for passengers to substitute it for other transport modes. Again this is related to the current price and service quality of a mode and would indicate that other modes are seen as good substitutes.
  • Substitution – Bus and metro (in cities) and inter-urban coach and rail are seen as close substitutes for each other and have similar sized cross elasticities. This contrasts with bus and car in both urban and non-urban areas, which are not seen as close substitutes.

Cross elasticities of demand are difficult to interpret, as they are partly dependent on modal shares. Dargay & Hanly (1999) suggest an elasticity of + 0.02 for car use with respect to bus fare. An earlier review by Dodgson (1990) found the most convincing elasticities for car used with respect to bus fare to be + 0.03 in London and + 0.01 in provincial UK cities. He reports that the low value outside London reflects the low modal share of public transport in non-London regions. Grayling and Glaister (2000) use a cross elasticity of + 0.09 for London, whilst TRL (2004) presents the cross-elasticities in Table 2 for London public transport modes.

Table 2 - London Transport Cross Elasticities

Mode

With respect to

Elasticity

Underground

Bus fare

+0.21

Underground

Rail fare

+0.18

Bus

Underground fare

+0.10

Bus

Rail fare

+0.05

Source: TRL (2004)

We now present the demand impacts on car kilometres from both an increase and a decrease in fare levels for public transport. It should be noted that the biggest impact on car travel would come from a change in the fare for rail travel since rail users are more likely to switch to car than bus users, who are less likely to have access to a car, and metro users for whom the car is not a viable option due to congestion levels in the cities.

Demand impacts

Responses and situations (fare increase)
Response Reduction in road traffic Expected in situations
Switch from peak period travel to off-peak travel for non-commuting trips.
Unlikely to change route.
Some journeys might become more local , e.g. food shopping. But purchase of a car would mean more non-local journeys.
Increase as some passengers with car access switch to car and some extra trips are generated.
Some passengers with car access shift to car.
Not in the short term.
Not in the short term.
= Weakest possible response = Strongest possible positive response
= Weakest possible negative response = Strongest possible negative response
= No response

 

Responses and situations (fare decrease)
Response Reduction in road traffic Expected in situations
May switch from off-peak period travel to peak travel for non-commuting trips.
Possibly change route as car users switch to public transport.
Some local journeys might be replaced with longer journeys , e.g. shopping trips.
Decrease as some car passengers switch to public transport service.
Some passengers with car access shift to public transport.
Unlikely.
Highly unlikely.
= Weakest possible response = Strongest possible positive response
= Weakest possible negative response = Strongest possible negative response
= No response

 

Short and long run demand responses

In the long run we would expect more public transport users to purchase a car or to move jobs/house in order to reduce the distance they have to travel, following an increase in public transport fares.

Demand responses (fare increase)
Response - 1st year 2-4 years 5 years 10+ years
Switch from peak to off peak.
  Not likely to change.
  Might change house or job.
  Passengers purchase and use a car. Additional trips are made.
  Passengers purchase and use a car.
  People purchase a car.
  Long run, may be a factor in looking at new houses.
= Weakest possible response = Strongest possible positive response
= Weakest possible negative response = Strongest possible negative response
= No response

In the long run we would expect more public transport users to make additional trips and for some car users to switch to public transport for some trips.

Demand responses (fare decrease)
Response - 1st year 2-4 years 5 years 10+ years
May switch from peak to off-peak.
  Likely to change for former car drivers.
  Slight change as local trips replaced with trips further afield.
  Some car users will switch to public transport and so make less journeys by car.
  Some car users will switch to public transport.
  Unlikely in short term.
  Unlikely.
= Weakest possible response = Strongest possible positive response
= Weakest possible negative response = Strongest possible negative response
= No response

Supply impacts

In the short term a change in public transport fares and the change in patronage it triggers are unlikely to have any impact upon the level of service provided by operators unless a large increase in passengers, following a reduction in fares, led to severe overcrowding.  Over the long-term operators are more likely to reconfigure their services to take into accounts overloading, different movements in populations and changes in land use. In reality any such changes would not be driven solely by changes in fares, but by a combination of factors of which fares would be one.

Financial impacts

Changing fare levels can affect income for public transport providers, although the level of change will depend on factors including public subsidies, and changes in running costs. Externalities associated with changes in fare levels will have financial impacts, e.g. costs associated with poor health or mortality from pollution brought by increased private vehicle use if fares rise. These externalities are likely to be borne by public bodies and citizens rather than transport providers.

Expected impact on key policy objectives

Contribution to objectives (fare increase)

Objective

Scale of contribution

Comment

  Increases congestion and delays due to public transport users switching to car.
  Increases community severance if additional car traffic passes through residential areas.
  Increased air and noise pollution.
  Low income users cannot afford to travel as often.
  Additional accidents from more traffic.
  Higher congestion might reduce time for more productive work although evidence is not strong
  Increased revenue for public transport operators.
= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

 

Contribution to alleviation of key problems (fare increase)

Problem

Scale of contribution

Comment

Congestion

By increasing traffic volumes.
Community impacts By increasing traffic volumes.
Environmental damage By increasing traffic volumes.
Poor accessibility Increasing cost of travel and therefore reduced accessibility.
Social and geographical disadvantage Increasing cost of travel will disproportionately affect the socially excluded with no car available, particularly in areas where access to employment and services on foot is not feasible or safe.
Accidents By increasing traffic volumes.
Economic growth Higher congestion may reduce productivity and, along with the increased cost of public transport, may deter people and businesses from locating in the area. On the other hand, a possible reduction in subsidy requirement and therefore taxes may stimulate economic growth.
= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

 

Contribution to objectives (fare decrease)

Objective

Scale of contribution

Comment

  Reduces congestion and delays due to public transport users switching to car.
  Decrease in community severance.
  Reduction air and noise pollution.
  Low income users can afford to travel more often.
  Reduction in accidents from reduced traffic.
  Lower congestion reduces time for more productive work.
  Reduced revenue for public transport operators.
= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

 

Contribution to alleviation of key problems (fare decrease)

Problem

Scale of contribution

Comment

Congestion

By decreasing traffic volumes.
Community impacts By decreasing traffic volumes.
Environmental damage By decreasing traffic volumes.
Poor accessibility By decreasing traffic volumes.
Social and geographical disadvantage Cost of travel increase will disproportionately affect the socially excluded with no car.
Accidents By decreasing traffic volumes.
Economic growth Reduced congestion may increase productivity and along with the reduced cost of public transport may encourage people and businesses to locate in the area. On the other hand a possible increase in subsidy requirement and therefore taxes may stimulate economic growth.
= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

 

Expected winners and losers

The main losers from an increase in fare levels will be people on low incomes and other road users in general. The former will have reduced access to public transport whilst the latter are likely to experience an increase in traffic levels and so journey times. The main winners will be the transport operators who will see an increase in revenue, despite experiencing a fall in patronage.

Winners and losers (fare increase)

Group

Winners/Losers

Comment

Large scale freight and commercial traffic

More traffic and congestion on roads, increases journey times.

Small businesses

Reduced affordability of travel to work. Increased congestion.
Cyclists including children More traffic on roads.
People at higher risk of health problems exacerbated by poor air quality More traffic on roads.

High income car-users

More congestion on the roads increases journey times.
People with a low income Reduces amount of travel they can afford.
People with poor access to public transport What transport is available will cost more.
All existing public transport users An increase in costs. Tempered somewhat by a reduction in journey times (reduced boarding/alighting) and overcrowding.
People living adjacent to the area targeted No change.
People making high value, important journeys More traffic and congestion on roads, so an increase in journey times.
The average car user More traffic on roads, so an increase in journey times.
= Weakest possible benefit = Strongest possible positive benefit
= Weakest possible negative benefit = Strongest possible negative benefit
= Neither wins nor loses

The main winners from a decrease in the fare level will be people with low incomes and road users in general. The former will be able to afford to travel more often and so access a wider range of goods, services and employment opportunity. The latter will experience a small reduction in traffic levels and so an improvement in travel times. The main losers will be transport operators who will experience a reduction in revenues despite an increase in patronage.

Winners and losers (fare decrease)

Group

Winners/Losers

Comment

Large scale freight and commercial traffic

Less traffic and congestion on roads, very slight reduction in journey times.

Small businesses

Employers may benefit if employees find travel to work more affordable. Small businesses may benefit from reduction in congestion.
Cyclists including children Reduction in road traffic.
People at higher risk of health problems exacerbated by poor air quality Reduction in road traffic.

High income car-users

Less congestion on the roads so a very slight reduction in journey times.
People with a low income Increases the amount of travel they can afford.
People with poor access to public transport No change.
All existing public transport users A reduction in costs. Tempered somewhat by an increase in journey times (increased boarding/alighting) and overcrowding.
People living adjacent to the area targeted No change.
People making high value, important journeys Less traffic and congestion on roads, so a very slight reduction in journey times.
The average car user Less traffic on roads, so a very slight reduction in journey times.
= Weakest possible benefit = Strongest possible positive benefit
= Weakest possible negative benefit = Strongest possible negative benefit
= Neither wins nor loses

 

Barriers to implementation

Scale of barriers
Barrier Scale Comment
Legal This depends on the extent to which services are provided by the private sector. In a publicly operated or franchised service, changes in fares are relatively easily made. In a deregulated environment, legal barriers can limit what can be achieved.
Finance Reducing fares has a large impact on costs. The key issue is whether such changes are self financing or not. In the case of deregulated bus industry such changes will tend to be self financing and the cost implications will fall upon the passenger and not tax payers. With franchised services the financial burden will tend to fall on both passengers and tax payers.
Governance Governance will be more straightforward where services are run by public sector organisations. Where services are franchised, governance arrangements may be complicated. Where services are deregulated governance is straightforward but political bodies have no means of influencing fares.
Political acceptability A reduction in fares is likely to be politically popular, unless the costs of doing so restrict other areas of public spending.
Public and stakeholder acceptability A reduction in fares is likely to be popular.
Feasibility There are no technical limitations on fare changes.
= Minimal barrier = Most significant barrier

The evidence relating to the impact of changes in fare studies tends to take three forms. The first is provided by simulation models that aim to simplify complex interactive situations and allow the researcher to test incrementally the effects of changes to key determinants such as fare and service levels. Such models use base data to seed the models (demand data, fares data, service data and cost data) and apply established elasticities (for fare, service, GDP etc) to estimate new levels of demand. The advantage of these models is that they can isolate the impact of different fare levels which in real life would be difficult because of changes to a host of other variables that take place at the same time, e.g. service levels, income, employment etc.

The estimation of fare elasticities themselves provides very useful information and can be seen as another form of evidence on performance. The data used for estimating the elasticities will take one of two forms: 1) actual data which reveals people’s choices when faced with a real life situation, often referred to as revealed preference (RP) data; or. 2) what people state they would do if faced with a choice between different scenarios, often referred to as stated preference (SP) data.

A third approach is to use case study data, preferably incorporating before and after studies. This is the most data intensive of the three approaches outlined and so the most expensive. It is also difficult to isolate the effect of reducing fares from others changes, such as service level changes.

Simulation Studies

LEK – Achieving Best Value for Public Support in the Bus Industry (2002)

Context

This study was commissioned by the UK Commission for Integrated Transport (CfIT) and attempted to assess the best use of public support (concessionary fares and fuel rebate) within the UK bus industry. To help in this assessment a bus model was constructed. Standard values of time, quality values, diversion factors and elasticities were used on the demand side. On the supply side bus operating costs were estimated using the CIPFA formula (CIPFA, 1974) and augmented with costs associated with different quality packages.

The model was provided with base data by operators which included details about service levels, journey times, passengers and fare levels. All the data provided was heavily anonymised. Data for a number of different types of route was provided but in this section we only report the results from the large radial route model which was based upon a busy major radial route of approximately 12 kilometres in length in a large city. The services operate along a single route with the following frequencies in each direction:

  • 10 buses an hour: Monday to Friday, peak and interpeak and Saturdays
  • 2 to 4 buses an hour: Monday to Friday evenings, Saturdays early and evenings, and Sundays

Thus the service runs every 6 minutes during the main operating periods. The services are paralleled for part of the route near to the city centre. In short a very well served bus route.

Impacts on demand

A number of scenarios were run using the model and these were based upon four key attributes of bus services. The attributes and their levels are outlined below and give a possible 189 combinations, however in this section we only report the scenarios that examined different fare changes.

  • 7 fare levels (+20%, +10% as now, -5%, -10% -20% and –50%);
  • 3 frequency levels (as now, +20%, +50%);
  • 3 journey time levels (as now, -5%, -10%); and,
  • 3 quality combinations (as now, medium and high quality packages)

The model outputs were a mix of financial and quantitative data and represent the change from the base case, which is presented in the table below.

Base Case Scenario (weekly data in £s)

Profits

Bus Revenue

Bus Cost

Bus Pax

Car Pax

Bus Pax Kms

Car Pax Kms

Bus Veh Kms

£16,934

£40,374

£23,440

82,166

889,625

412,515

3,869,986

20,445

The models runs were only the fare level was changed are reported in Table 15. The table reports the change of each indicator as compared to the base case. A number of abbreviations are used, these are:

  • CS (Consumer Surplus)
  • Car Pax (car drivers/passengers)
  • Bus Pax (bus passengers)
  • Bus Pax kms (bus passenger kms)
  • Car Pax kms (car driver/passengers kms)

Note that in these tables the change in net benefits is the sum of the changes in consumer surplus, profit and any investment costs to the Local Authority.

Changes from the base for Fare Change Scenarios (weekly data)

Run No.

Fare

Profits £

CS £

Bus Pax

Car Pax

Bus Pax Kms

Car Pax Kms

12

+20%

5049

-7828

-4,753

2,225

-27,347

13,262

21

+10%

2630

-3977

-2,413

1,130

-13,906

6,742

30

-5%

-1394

2033

1,232

-577

7,111

-3,446

39

-10%

-2846

4103

2,484

-1,164

14,354

-6,957

48

-20%

-5924

8345

5,046

-2,366

29,208

-14,155

57

-50%

-16664

21956

13,206

-6,202

76,792

-37,200

Impacts on supply

No impacts on supply were calculated.

Contribution to objectives

Contribution to objectives
Objective Scale of contribution Comment
 

Fare reductions are likely to lead to reductions in car use will have contributed to an efficiency improvement.

Fare increases are likely to lead to the opposite impacts.

 

Fare reductions are likely to lead to a reduction in car use which will contribute to a liveability improvement.

Fare increases are likely to lead to the opposite impacts.

 

Fare reductions are likely to lead to a reduction in car use and so a reduction in environmental impacts.

Fare increases are likely to lead to the opposite impacts.

  No attempt was made to estimate the impact on equity and social inclusion from either a fares increase or a reduction.
 

There was no identified impact on safety but it is likely that a reduction in fares will reduce car use and reduce accident incidence and cost.

A fares increase is likely to lead to the opposite impacts.

 

Efficiency improvements that are likely to occur from a fares reduction may help support economic growth.

A fares increase is likely to lead to the opposite effects.

 

Reducing fares is likely to lead to reduce bus revenues.

Increasing fares is likely to lead to increases in bus revenues.

= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

Elasticity Studies

Dargay & Hanly – Bus Fare Elasticities (2002)

Context

This study estimated bus fare elasticities on annual data taken from bus operators in Great Britain for years 1987 to 1996 on fares, bus demand and a number of other variables that influence bus use, e.g. demographics, GDP and motoring costs. The data was obtained from the STATS100A database provided by the government and includes data returns from all GB bus operators who are licensed for 19+ vehicles. Permission had to be obtained from the bus companies first and it was sought from English operators with fleets of 50 or more vehicles. Eventually, data was obtained from operators who made up 87% of bus vehicle kilometres and 93% of passenger journeys in England.

Impacts on demand

A variety of models were estimated and the key results are presented in the table below. In the table, the long run fare elasticities are greater than in the short run, illustrating that passengers have more options open to them for reacting to changes in fares (e.g. they can change jobs, move house or purchase a car) as opposed to the short run. It is also interesting to note that the fare elasticity increases as the fare does. This is to be expected as, in monetary terms, a 10% increase in a high fare will be greater than a 10% change for a low fare, making passengers more sensitive to fare changes.

Estimated Short-run and Long-run Elasticities Based on Pooled Data for English Counties

 

Fare

 

Short run

Long Run

Constant Elasticity
   

Constrained

Unconstrained*

-0.33

-0.43

-0.68

-0.74

Variable Elasticity
   

Constrained

Min. Fare = 17p

Ave Fare = 56p

Max Fare = £1

-0.13

-0.41

-0.74

-0.26

-0.86

-1.53

Unconstrained*

Min. Fare = 17p

Ave Fare = 56p

Max Fare = £1

Average GB

-0.13

-0.44

-0.79

-0.33

-0.23

-0.75

-1.35

-0.62

*average of individual elasticties for all counties (...) elasticities not significantly different from zero.
Source: Dargay and Hanly (2002)

Case Study Evidence

Sheffield Case Study

In this section evidence is presented from two different studies that concentrated on the bus fare freeze policy that was implemented in South Yorkshire between 1974 and 1984. The policy was supported by the County Council and the South Yorkshire Passenger Transport Executive (SYPTE) and resulted in a real fares fall of 69%. The main justifications for the low fares policy was (according to Hay, 1986) to:

  • Slow, halt or even reverse the decline in public transport;
  • Contribute to planning and environmental objectives by reducing road traffic and supporting retail and service activities in city centres (and selected suburban centres and small towns);
  • To contribute to social objectives by increasing the mobility of transport-disadvantaged groups, and by making a nonstigmatising income transfer to low-income households.

Context

This study analysed changes in travel behaviour in Sheffield-Rotherham (1971-1981) and Manchester-Salford (1976-1982) with special reference to the effect of bus fare levels in real terms, which fell by around 70% in South Yorkshire but remained constant in Greater Manchester. The study made use of weekday travel records that had been collected as part of land-use transportation studies in both South Yorkshire (in 1973) and Greater Manchester (in 1977). A repeat of these surveys was carried out in 1981/82 in both areas.

Impacts on demand

Analysis of the data enabled comparisons of bus trip rates per day to be made, which are outlined in the table below.

Global Comparisons of Bus Trip Rates per Day on a Standard Population Structure

 

Sheffield-Rotherham

Manchester-Salford

 

1972

1981

1976

1982

All Trips

0.681

0.710

0.598

0.494

Households:

without cars

with cars

0.873

0.421

0.957

0.509

0.738

0.372

0.663

0.302

Work

Education

Shop

Social

0.333

0.067

0.113

0.090

0.239

0.083

0.139

0.106

0.261

0.069

0.080

0.071

0.217

0.088

0.091

0.046

Source: Hay (1986)

In terms of overall trips it can be seen that the number of trips made by people in Sheffield-Rotherham has increased by just over 4% and fallen in Manchester-Salford by around 17%. Interestingly, the only category of trips to fall during the 1972-81 time period in Sheffield-Rotherham are those for work (by nearly a third). This suggest either a decline in employment within the region or that despite decreasing real bus fares, people were choosing to travel to work by another mode (mainly car). In fact if one looks at the percentage of motorised trips made by bus during that time the pattern is one of bus catering for fewer trips (in relative terms) in all categories (see table below).

Motorised Trips Made by Bus and Estimated Global Figures by Purpose

 

Sheffield-Rotherham

Manchester-Salford

 

1972

1981

1976

1982

% of motorised trips by bus

All

Work

Shop

Social

50

56

45

44

43

47

37

33

54

58

46

44

41

45

28

26

Source: Hay (1986)

The main conclusions of the study were that the low-fare policy had resulted in higher levels of bus use in Sheffield-Rotherham than might otherwise have been expected and that such levels cannot all be explained by short run elasticities (e.g. low fares over a long period of time had encouraged a bus travel culture). However, there was no evidence to suggest that the low fares policy had made any contribution to reducing traffic congestion or assisting in city centre activities.

Impacts on Supply

No impacts on supply were estimated.

Goodwin – 1983

Context

This study made use of the same data set utilised by Hay (1986) augmented by additional postal questionnaire data and also face to face interviews. The study placed much more emphasis upon assessing the social and travel changes brought about by the low fares policy, and the key results are outlined below.

Impacts on Demand

a) Effect On Other Methods of Transport

Car Ownership: This had grown in South Yorkshire, but at a lower rate than in the adjoining county of West Yorkshire. A small number of households found the high cost of motoring and low cost of bus a combination that meant they would not be purchasing a car. However, the few number of people who had actually forsaken their cars, had done so because of a change in family circumstances, not because of the low fares policy.

Car Passenger Trips: The low fares policy had not affected the number of car passenger trips. Lifts were mainly offered and accepted for reasons of convenience and time saving.

Walking: Fares were at such a level that they were not the main consideration when making a choice between walking and making a bus trip. More weight was given to speed, security, weather, convenience of timing and knowledge about the bus service.

b) Effect on Particular Groups

Employed: The purchase of a car appeared to be most influenced by the journey to work. There has been a shift from bus use to car use for the journey to work, as car ownership has continued to increase.

Shoppers: The use of bus for shopping had seen a large increase than for any other journey purpose, with 23% of the weekday bus journeys in 1981 compared to 17% in 1972. The frequency of shopping trips by bus was highest among non-car owners, the elderly and the unemployed. These groups often see shopping as a recreational or social activity. The one type of shopping where car still predominated was bulk shopping, e.g. weekly groceries.

Unemployed People: Buses were not seen as the key means of looking for work. They were however seen as important for facilitating other activities such as shopping, visiting town, the library, recreational facilities and friends. As such the low fares policy was seen to be helpful in assisting the unemployed to maintain a ‘normal life’.

Retired and Elderly People: There was still a significant number of people in this group who owned a car or had access to one. In some car owning households more use was made of bus for certain journeys and there was an appreciation that lower incomes and a reduction in savings might mean that bus became more favoured over time.

Children: There had been an increase in bus travel by children despite a decrease in the numbers of children born. The largest increase in trips has been experienced during the morning and evening peaks during school terms and also on weekends throughout the year.

In its conclusions, the study notes that the evidence of the impacts associated with the low fares policy was consistent with long term as opposed to short term fare elasticities. At the same time the policy appeared to have had a much greater impact on the young than the middle-aged and old sections of the population. This could be explained by the fact that children and young people are much more influenced by conditions of the time when forming habits and attitudes, compared to older people who experienced different conditions as they grew up.

Impacts on Supply

No impacts on supply were estimated.

Contribution to objectives

Contribution to objectives
Objective Scale of contribution Comment
  No suggestion that the low fares policy had reduced congestion during the peak periods and so improved efficiency.
  No evidence presented on this.
  No evidence presented on this.
  Evidence that the unemployed, the elderly and children were making considerably more trips than in comparable areas. This suggests that equity and social inclusion were improved.
  No evidence presented on this.
  No evidence to suggest that the low fares policy had made any contribution to reducing traffic congestion during the peak. There was evidence to suggest that it had helped to increase city centre activities particularly for shopping purposes.
  No evidence was presented on this, however a fares freeze policy is likely to have led to an increase in the amount of financial support required from local government.
= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

 

Contribution to objectives and alleviation of problems

The following tables are based on fare reductions.  Fare increases would be expected to have the opposite effect.

Contribution to objectives

Objective

Scale of contribution

Comment

  Reduces congestion and delays due to public transport users switching to car.
  Decrease in community severance.
  Reduction air and noise pollution.
  Low income users can afford to travel more often.
  Reduction in accidents from reduced traffic.
  Reduction in congestion might result from lower fares, however there is not strong evidence on links between this and economic growth. Case studies found some evidence that lower fares are beneficial for retail.
  Reduced revenue for public transport operators.
= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

 

Contribution to alleviation of key problems

Problem

Scale of contribution

Comment

Congestion

By decreasing traffic volumes – with case studies showing less pronounced effect than first principles assessment .
Community impacts By decreasing traffic volumes– with case studies showing less pronounced effect than first principles assessment.
Environmental damage By decreasing traffic volumes– with case studies showing less pronounced effect than first principles assessment.
Poor accessibility Decreasing cost of travel and therefore increased accessibility– with case studies showing less pronounced effect than first principles assessment.
Social and geographical disadvantage Cost of travel decrease will disproportionately affect the socially excluded with no car.
Accidents By decreasing traffic volumes.
Economic growth Reduced congestion may increase productivity and along with the reduced cost of public transport may encourage people and businesses to locate in the area. On the other hand a possible increase in subsidy requirement and therefore taxes may stimulate economic growth.
= Weakest possible positive contribution = Strongest possible positive contribution
= Weakest possible negative contribution = Strongest possible negative contribution
= No contribution

 

Appropriate contexts for lowering fares

Appropriate area-types
Area type Suitability
City centre
Dense inner suburb
Medium density outer suburb
Less dense outer suburb
District centre
Corridor
Small town
Tourist town
= Least suitable area type = Most suitable area type

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Dargay & Hanly (1999) suggest an elasticity of + 0.02 for car use with respect to bus fare. An earlier review by Dodgson (1990) found the most convincing elasticities for car used with respect to bus fare to be + 0.03 in London and + 0.01 in provincial UK cities. He reports that the low value outside London reflects the low modal share of public transport in non-London regions. Grayling and Glaister (2000) use a cross elasticity of + 0.09 for London, whilst London Transport

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