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Evidence on performance 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 affects of changes to key drivers 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 affect 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 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 times, 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 routes 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: 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%);
Table 14 Base Case Scenario (weekly data in £s)
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: 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. Table 15 Results of Fare Change Scenarios (weekly data in £s)
The results illustrate that an increase in bus fare levels will, all other things equal, reduce bus passengers and increase car passengers & car travel. Although not shown in the table this will increase the level of environmental externalities. A reduction in bus fare levels will have the opposite impact with an increase in bus passengers and a reduction in car travel. Financially, the operator will benefit from an increase in the fare level and lose from a decrease. These results reflect the fairly low fare elasticity of demand in the short run. It should be noted that in the long run the elasticity of demand may well be closer to 1, which would reduce the profitability connected to price rises and the losses associated with price reductions. Impacts on Supply No impacts on supply were calculated. Contribution to Objectives
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 DETR 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 Tables 17 and 18. In Table 17 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. Table 17 Estimated Short-run and Long-run Elasticities Based
on Pooled Data for English Counties
*average of individual elasticties for all counties (...) elasticities
not significantly different from zero. Table 18 again illustrates that in the long run fare elasticities will increase over time. It also demonstrates how fare elasticities can vary between location. The Shire counties (rural counties – such as Oxfordshire) have higher fare elasticities than in the Metropolitan areas (large urban areas – such as Greater Manchester). This is to be expected as car use in the metropolitan areas is less advantageous given congestion, parking costs etc Table 18 also contains a number of other elasticities that provide a useful demonstration of additional impacts on bus demand. Service elasticities are positive implying that an increase in the service levels will increase demand. The motoring costs cross elasticity tells us that an increase in motoring costs will also increase the demand for bus. Table 18 Estimated Short-run (SR) and Long-run (LR) Elasticities Based on Pool Data for English Counties
Note: elasticities in parenthesis are not significantly different from
zero. Impacts on Supply - no impacts on supply were estimated. Contribution to Objectives
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; Hay - 1986 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 Table 19. Table 19 Global Comparisons of Bus Trip Rates per Day on a Standard Population Structure
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 (Table 20). Table 20: Motorised Trips Made by Bus and Estimated Global Figures
by Purpose
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. Contribution to Objectives
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
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