Selección de Personal Para Celis (2006), la selección de personal es un

The study has hypothesised that there will be user benefits of the scheme, namely a welfare gain for individuals who currently travel for free and previously had to pay and an increase in accessibility (see Chapter 7), as a result of removing bus fares for 12- to 17-year-olds. This section uses data from TfL to estimate those user benefits.

Methodology

The methodology that will be used to determine the change in user benefits involves calculating the change in consumer surplus as a measure of welfare change, using the user benefits approach described in DfT.127_{The user benefit measure is designed to capture the benefits from an accessibility gain as}

measured by the generalised cost (GC) function including accessibility gains from reductions in cost, reductions in travel time, improvements in journey quality and benefits to any new users who travel more due to the reduction in GC.

Measuring GC from i to j will usually follow the structure below:

GCij¼ Fareijþ Time Costijþ Other Disutilityij ð1Þ

where:

l _{‘Fare’ is the money cost of a trip (e.g. the bus fare)}

l ‘Time Cost’ is an amount calculated using ‘values of time’ which represent the inconvenience of time spent travelling

l ‘Other Disutility’ is a term which includes the individual’s valuation of any other trip characteristics which are relevant (e.g. inconvenience of a highfloor to a bus user).

The relationship between GC and user benefits is shown schematically in Figure 17. Before a scheme is introduced the GC to the user is GC0and at this cost level T0trips are made. If, for example, the fare cost

is removed (as is the case in this scheme) this reduces the GC by (GC0– GC1). The result of this reduction

in GC per trip is an increase in trips made from T0to T1. The user benefit resulting from the introduction of

the scheme equates to shaded area A for current users and shaded area B for new users (increased travel TABLE 12 Calculation of the value to society from reductions in road traffic casualties as a result of

the intervention

Scenario

Values Change in casualties

PVB

Fatal Serious Slight Fatal Serious Slight

(A) (B) (C) (D) (E) (F) [(A × D) + (B × E) + (C × F)]

Counterfactual 2a: 25- to 59-year-olds in London

1,585,510 178,160 13,740 1.00 47.00 263.50 13,579,520

95% CI 1,585,510 178,160 13,740 –0.93 38.11 240.00 8,621,126

1,585,510 178,160 13,740 2.97 55.64 263.50 18,234,795

Counterfactual 2b: 12- to 17-year-olds in England

1,585,510 178,160 13,740 1.25 113 569 29,839,513

95% CI 1,585,510 178,160 13,740 –0.25 99.5 568.75 25,145,168

1,585,510 178,160 13,740 3 121.5 592.25 34,540,485

or accessibility). The demand curve is shown as a straight-line which is consistent with a common

assumption in transport appraisal known as the‘rule of a half’ which is a rule of thumb used to interpolate between two known points when the rest of the demand curve is unknown.

If all the required data points are known then the value of both of these areas can be calculated and the impact on accessibility determined. The calculation to determine the user benefits for areas A and B is completed using the following formula:

User Benefits ðA þ BÞ ¼ 0.5  ðGC0−GC1ÞðT0þ T1Þ ð2Þ

Data were collected from TfL on the number of free journeys in the 12–17 years age group, in the year 2009. There were 247,297,000 free journeys (T1). The single bus fare in 2009 was £1 (if paid using an Oyster

Card).128_{[It should be noted that the data used to populate the number of trips for 12- to 17-year-olds in}

2009 were taken from data collected by TfL through zip card use. This is a different data set from that used in Chapter 3, which drew on travel diary data relating to term time (Monday to Friday) only. The TfL data set was not available for the‘before’ period of the intervention, prior to data collection via zip card use.] It is assumed for this analysis that young people would have been charged half this fare (50p) in the absence of the intervention. Therefore the difference between GC0and GC1is £0.50. The key input that needs to be

determined to allow the user benefits to be calculated is T0. This is the trips that would have happened in the

absence of the introduction of free fares (the counterfactual).

Two counterfactuals were calculated to assess what would have happened over the same time frame in the absence of the intervention:

l Counterfactual 2a: assumes that expected bus travel is based on a statistical analysis of what happened in the same period to adults in the 25- to 59-year-old age group in London.

l Counterfactual 2c: assumes that expected bus travel is based on demand elasticities and trip generation factor (TGF).

Counterfactual 2a assumes that in the absence of the intervention the pre–post change in bus use would be similar to the pre–post change in bus use in adults in the 25- to 59-year-old age group. Data from the LATS were used to determine the change and are reported in Chapter 3. It should be noted that the LATS data only included data from term-time weekday trips. It is therefore likely that this has underestimated the change in trips for 12- to 17-year-olds, if an increase in trips (outside the journey to school) takes place at weekends and out of term time. The pre–post ratio of adults trips was 1.36 (95% CI of 1.25 to 1.46). This indicates that over the same period bus trips for adults increased by 36% (95% CI 25% to 46%). The number of bus trips made by the 12- to 17-year-old age group was not collected in the pre-period. Instead, the‘before’ trips have been derived from the LATS and are provided in Table 13. The resulting

GC , £ GC₀ GC1 T₀ T₁ Trips Demand A B

FIGURE 17 New user and current users’ benefits from the introduction/enhancement of a scheme that reduces GCs. DOES THE SCHEME REPRESENT VALUE FOR MONEY?

change in bus trips for the 12- to 17-year-old age group is 22 million within a 95% CI of between 5 million and 40 million bus journeys.

Counterfactual 2c uses the data reported in Balcombe and colleagues74_{to estimate the change in trips}

associated with the price elasticities of demand and implied trip generation rates from the change from a half-fare charge per trip to a zero charge per trip. (Note this scenario does not use LATS data results from Chapter 3. It is based on what is expected to happen when there is a price change using price elasticities.) Figure 18 shows the demand curve given the price levels. PA’ is the full fare that an adult would pay, PAis

the half-fare that a young person would pay. PBis the zero fare. Balcombe and colleagues74provide data

on expected changes in trips as a result of changing from a half-fare to a zero fare and the difference in elasticity of demand associated with a child compared with an adult.

For a reduction in child fares from aflat (or half) fare PAto free fare PB, TGF = B/A. Given TGFs for the

reduction from full fare toflat fare, and from full fare to free fare, the TGF of interest can be deduced:

TGFFullFare→FlatFare¼ A A′ ð3Þ TGFFullFare→FreeFare¼ B A′ ð4Þ ⇒TGFFlatFare→FreeFare¼ B A ð5Þ

Given values of 1.33 and 1.60 for (3) and (4) in general demand, we infer (5) = 1.20,74_{TGF = B/A for}

child = 1.20. This should be considered a broad-brush estimate. Therefore, it should be expected that a TABLE 13 Counterfactual 2a: expected bus journeys based on a statistical analysis of what happened in the same period to adults in the 25- to 59-year-old age group in London

Outcome 12- to 17-year-old trips in the pre-period (A) 12- to 17-year-old trips in the post-period (B) (2009) Counterfactual: pre_{–post ratio} of adult trips (C) (95% CI) Expected 12- to 17-year-old trips under counterfactual (A × C) Change in 12- to 17-year-old trips attributable to the intervention: observed_{– expected} (B_{– (A × C))} Bus trips 165,754,820 247,497,000 1.36 (1.25 to 1.46) 225,426,555 (207,193,525 to 242,002,037) 22,070,445 (5,494,963 to 40,303,475) Fare P, £ Trips T Demand D = f(P ) P_A' P_A A' A PB = 0 B

reduction from half-fare to free fare for a child would generate 20% more trips. It would imply that the whole change in number of 12- to 17-year-old bus trips could be attributed to the intervention. Under counterfactual 2c, T1is 247,497,000 and T0would be 206,247,500, with the increase in trips due to

reducing from half-fare to zero being 41,249,500.

Calculations

Applying the following formula [User Benefits (A + B) = 0.5 (GC0– GC1)(T0+ T1)] to the trips associated with

the intervention in counterfactuals 2a and 2c produces the following results. It was assumed that the half-fare of 50p would be applied for both the counterfactual scenarios. The results are provided in Table 14, which indicates that benefits to users from reduced fares and increased access to the bus network have generated user benefits to society of the order of £118M.

In summary, both of the methods used to determine the user benefits of the intervention estimated that there would be considerable positive user benefits. The majority of these benefits are the result of the population group (12- to 17-year-olds) that were using the bus prior to the intervention now not having to pay. In the absence of a true counterfactual, both the scenarios tested have generated similar levels of user benefits as shown in Table 14. Although the quantitative analysis (see Chapter 3) did not identify a

statistically significant change in bus use for 12- to 17-year-olds, it should be noted that these data were based on LATS, which only includes weekday term-time travel, which has a greater potential to

underestimate the changes in bus travel for the population group. By contrast, counterfactual 2c in Table 14 used the full TfL trip data set for 2009, and with elasticities to estimate trip generation, indicated that there was an additional 41.2 million trips annually as a result of this intervention.