Reports:

Bus subsidy simulation study

4: Results of the Modelling Work

4.1: Introduction

Following the description of the bus simulation model in Chapter 3, we report the results of the modelling work in the present chapter.

The impacts of the proposed change in subsidy system can be thought of as occurring in a two-stage process:

• Initially, the change in subsidy system will have an impact on route profitability, with costs increasing due to the loss of FDR and revenues increasing as a result of IPP. We refer to this as the "Day 1" impact. This is the direct impact of the subsidy change on a route's profitability when the operators do not change their behaviour;
• However, the operator may review the route and adjust prices, service and/or quality levels on it in response to the change in subsidy and in response to the changes in incentives which result. Thus it should be possible to increase profits from the levels achieved as a result of the "Day 1" changes by changing the service's fare, frequency and/or quality level. In addition, there may be a need for service cuts if route profits have fallen or there may be need to adjust frequency levels in response to a perceived increase in the likelihood of entry by other operators. We refer to this as the "impact from optimising". These adjustments may only take place after the subsidy system has been changed, though it is possible that operators might make some adjustments in anticipation of subsidy changes that they know are going to take place.

It is important to note that the proposed change in the subsidy system will only result in a change in the number of bus passengers if the operator actually changes his behaviour. If the subsidy change results in a large change in route profits but the operator nevertheless chooses not to react (for example because the operator accepts a degree of cross-subsidy in its system), then there will be no impact on patronage whatsoever.

The structure of the remainder of this chapter is as follows:

• In Section 4.2, we summarise the key characteristics of the routes that we have examined as part of the study. As all the operators have supplied the data under strict confidentiality restrictions, it not possibly to identify the routes, to indicate their location the operator or to provide more detailed information on the routes' characteristics;
• In Section 4.3, we discuss the impact of the proposed change in subsidy system on route profitability ("Day 1" impact), before taking any change in fares, service or quality levels as a result of the subsidy change into account;
• In Section 4.4, we discuss how operators will respond, by modelling the impact of the changes in incentives on their behaviour. We present the results of this exercise in several ways, including scenarios in which either fares or frequencies are constrained at their present levels;
• In Section 4.5, we set out how we believe operators will respond in practice by taking account of a number of issues not covered by the formal model, in particular the impact by time of day and time of week; and
• In Section 4.6, we set out the conclusions of our modelling work.

To minimise the burden on bus operators, they have supplied us with data for a four-weekly period, to which all tables and figures in this chapter refer. Due to the seasonality of demand on most routes, the profit levels on the routes that we report in this Chapter may be different from the average profit levels over a whole year. However, seasonal changes in demand do not significantly affect marginal incentive impacts.

We also note that the analysis is based on patronage data as supplied by the operators. If recorded passenger numbers are below the actual passenger numbers (as operators have suggested), then the Day 1 impact on profits as a result of the subsidy change will be somewhat more favourable than our results suggest. This might also impact on the magnitude of the incentive changes, though it is unlikely that the sign of the incentive changes would be affected.

4.2: The Routes Examined

A total of nine routes were examined as part of the study. These were:

• Two busy profitable urban routes with frequencies of between six and eight buses per hour (Busy Urban 1 and 2);
• A profitable urban route with a bus every 20 minutes (Medium Urban);
• A marginal urban route with a bus every 12 minutes. This service was unprofitable in the period for which the operator supplied the data, though the route breaks even in some periods of the year (Marginal Urban);
• Two profitable interurban routes with around two buses per hour; both with a somewhat more rural part at one end of the route (Busy Interurban 1 and 2);
• A marginally profitable interurban route with two buses per hour; running beyond the bus station through several areas of the town at one end of the route. (Marginal Interurban);
• A loss-making (not covering its direct costs) unsubsidised interurban route with predominantly end-to-end traffic and an hourly frequency (Loss-Making Interurban); and
• A profitable (when taking tendered revenue into account) route of a more rural nature between two medium-sized towns and little in between (Rural).

The routes were selected by asking the operators for typical examples of the route types under consideration. We have not been in a position to verify whether these routes can indeed be regarded as typical examples, though we have no reason to believe that they would be atypical. We note that none of the operators had any difficulty with suggesting examples of "typical" routes on their networks.

All routes are commercial, though some are in part tendered. We have not analysed routes that are entirely tendered since on such routes, operators will have little or no freedom to set fares, service and quality levels. The results of our analysis suggest though that entirely tendered services will normally become more loss-making when FDR is replaced by IPP, so that tender prices for such routes can be expected to increase.

Key characteristics of the routes that we have examined are summarised in Table 4.1. This table contains the following:

• Average revenue per passenger journey: revenue (including any revenue from multi-journey tickets and concessionary fares reimbursement, but excluding any tendered revenue) divided by the total number of passenger journeys;
• Frequency: the number of buses per hour during daytime on weekdays;
• The quality level on the route in descriptive terms;
• The total number of passenger journeys on the route in the 4-week period;
• Current profit levels, including any tendered revenue (tendered revenue is not shown separately to protect the anonymity of the routes); and
• Current profit margins in terms of return on sales.

Table 4.1 - Characteristics of Routes Examined

Route	Average Revenue per Passenger	Frequency (Buses per Hour)	Quality Level	Passengers	Profit (£)	Return on Sales
Busy Urban 1	0.63	8	Low-Floor Buses; Infrastructure of Varying Quality	256,919	+50,364	+29.9%
Busy Urban 2	0.93	6	Low-Floor Buses	51,583	+3,108	+6.5%
Medium Urban	0.90	3	Low-Floor Buses	37,978	+2,983	+8.7%
Marginal Urban	1.02	5	Basic Quality	31,238	-2,957	-9.3%
Busy Interurban 1	0.86	1	Basic Quality	43,812	+4,457	+11.6%
Busy Interurban 2	1.23	2	Basic Quality	67,888	+26,299	+31.4%
Marginal Interurban	0.82	2	Low-Floor Buses with CCTV	59,465	+1,223	+2.5%
Loss-making Interurban	2.28	1	Coach Buses	9,334	-3,989	-18.7%
Rural	0.88	1	Low-Floor Buses with CCTV	19,206	+3,170	+16.4%

Source: Operator Data

4.3: The "Day 1" Impact on Profits

As discussed in Section 4.1, a move from FDR to a IPP will initially simply result in a change in route profitability, as costs increase due to the loss of FDR and revenues increase by the total IPP received. This "Day 1" impact is very important as it is straightforward to calculate and we therefore believe that operators will respond primarily on the basis on this indicator.

Table 4.2 shows the Day 1 impact on profits on each of the routes that we have examined, both in absolute and relative terms.

Table 4.2 - The "Day 1" Impact on Profits

Route	Current Profit (£)	Day 1 Profit (£)	Absolute Difference (£)	Percentage Difference	Absolute Difference (£) with LEK safety net[14]
Busy Urban 1	+50,364	+62,840	+12,476	24.7%	+12,476
Busy Urban 2	+3,108	+3,367	+259	+8.3%	+259
Medium Urban	+2,983	+4,203	+1,220	+40.9%	+1,220
Marginal Urban	-2,957	-4,115	-1,158	Loss increases by 39%	-1,158
Busy Interurban 1	+4,457	+3,293	-1,164	-26.1%	0
Busy Interurban 2	+26,299	+22,586	-3,713	-14.1%	0
Marginal Interurban	+1,223	+952	-271	-22.2%	0
Loss-Making Interurban	-3,989	-7,929	-3,940	Loss increases by 99%	0
Rural	+3,170	+2,964	-206	-6.5%	0

Source: NERA Estimates

As expected, it is mainly the urban routes that gain and the interurban/rural routes that lose (excluding the safety net) from a move from FDR to IPP. One of the busy urban routes in fact achieves a very substantial gain, whereas by contrast the other busy urban route (which has a high frequency level relative to the number of passengers carried) gains only marginally. The medium urban route also gains, but the marginal urban route loses. The already existing losses on this route increase by almost 40 per cent. All of the interurban and rural routes are worse off as well, with profits on these routes falling by between 6 and 25 per cent, and in one case the already substantial losses almost doubling. We note that there are no routes that were profitable and become loss-making, although the profit losses on the interurban and rural routes may mean that the routes would no longer meet minimum profit targets.

Whereas the above result that few routes gain and many lose might appear to be a result of the combination of routes that we have selected, we would expect that this result would in fact also hold in general. Busy urban routes carry very high passenger volumes and in combination with the short average journey distance on these routes, they will gain disproportionately from a flat IPP. Routes that are more dependent on FDR, such as interurban and rural routes, will tend to have lower passenger volumes and a longer average journey length, both of which adversely affect them in the context of a flat IPP . The marginal urban route in our sample is also worse off.

Revenue Neutral^[15] (without a safety net for inter-urban and rural services)

We predict that a revenue-neutral change in subsidy system will result in the majority of routes in the country being worse off, with most of the subsidy being reallocated to a relatively small number of busy urban routes that will see their profits increase disproportionately.

Increased Revenue (with a safety net for rural and interurban services)

When a safety net (as envisaged by the LEK recommendations) is provided to safeguard profits on rural and interurban routes only the marginal urban route loses profit. So we predict that with this safety net, most routes will have increased or unchanged profits, and only marginal loss-making urban ones will lose. Subsidy will be reallocated to busy urban routes, while the safety net will make good the losses on inter-urban and rural routes.

4.4: How Will the Operators Respond?

4.4.1 Introduction

This Section of our report considers how operators would respond to the changes in route profitability and incentives resulting from the proposed change in subsidy system from FDR to IPP. It does so by analysing the results of the simulation model for each of the different routes selected. In Section 4.5 we will bring in other factors, arising from our interviews and other considerations, which are relevant to considering how the model results may be influenced by additional factors.

The calculation of the modelled operator response involves the following three steps:

• Calculation of the modelled profit-maximising combination of price, frequency and quality in the current situation with FDR. The modelled profit-maximising position is normally different from the actual position, which may be the consequence of any of the following factors:
• The model is based on a number of assumptions and so, like all models, has some margin of error;
• Operators do not set their price, service and quality on the basis of detailed analyses but use informal procedures; and/or
• Operators deliberately refrain from maximising their profits, for example with a view to deterring entry by potential competitors.
• The calculation of the modelled profit-maximising position is necessary to provide a valid benchmark for comparison, since the model will be calculating (in step 3) the profit-maximising position under IPP.
• Calculation of the "Day 1" impact on route profits as a result of the change in subsidy system. Since the modelled profit maximising position is normally different from the actual position, the Day 1 impact on the basis of the modelled profit maximising position is also different from the impact on the basis of the actual position.
• Calculation of the profit maximising position of price, buses and quality under IPP.

We first present the results of the analysis as set out above without imposing further constraints. We then analyse cases where either prices or buses are constrained at their current levels, for reasons that we will set out below.

4.4.2 No Specific Constraints

Table 4.3 contains the profit-maximising operator response to the change in subsidy system. At this stage, only the following constraints were imposed:

• prices and quality to be greater than zero; and
• frequency to be at least one (i.e. one bus per hour).

We note that although in practice, frequency levels tend to be integer (i.e. an operator runs four or five buses per hour, not 4.5, for example), we have not imposed such a constraint in the final version of the model. The reason for this is that constraining the frequency level to integer values only will almost always result in no predicted change in frequency as a result of a change in subsidy system.

Yet, as we will see, a move from FDR to IPP does change the incentives in regard to frequency levels, and it is instructive to see the direction and magnitude of these changed incentives. In Section 4.4.4, we report the results of an analysis where frequency is constrained at present (integer) levels.

We note that during our second round of interviews with operators, they were shown an earlier version of the model with an integer constraint on frequency levels.

Table 4.3 contains the following:

• the change in average fare, compared to the modelled profit-maximising average fare;
• the change in frequency, compared to the modelled profit-maximising frequency;
• the change in quality, compared to the modelled profit-maximising quality level. Quality is expressed on a scale from 1 (no specific quality levels) via 2 (low-floor buses with CCTV and driver training) to 3 (as 2, plus comfortable shelters with CCTV).
• the "Day 1" change in profit under the modelled profit-maximising position. As indicated above, this change is different from the actual "Day 1" change in profit;
• the additional profit that results from the adjustment of fares, service and quality levels; and
• the percentage change in bus patronage that results from the profitmaximising adjustment of fares, service and quality levels.

Table 4.3 - Modelled Operator Response - No Constraints

Route	Fare Change	Frequency Change	Quality Change	Profit Change
				Day 1 Change Under "Profit-Maximising Position (£)	Additional Change From Optimising (£)	Patronage Change
Busy Urban 1	-4.9%	+4.8%	+0.06	+9,016	+541	+8.9%
Busy Urban 2	-2.3%	-0.4%	+0.01	-20[16]	+38	+2.5%
Medium Urban	-2.9%	+1.8%	+0.02	+689	+41	+5.1%
Marginal Urban	-1.1%	-3.0%	0.00	-742	+21	-1.1%
Busy Interurban 1	-2.2%	-3.3%	0.00	-1,314	+70	0.0%
Busy Interurban 2	-1.0%	-4.8%	-0.01	-4,494	+184	-1.8%
Marginal Interurban	-3.3%	-1.3%	+0.01	-689	+82	+2.7%
Loss-Making Interurban	-2.1%	-12.2%	-0.04	-2,853	+180	-12.8%[17]
Rural	-3.2%	-2.0%	0.005	-326	+28	+2.0%

Source: NERA Estimates

On the basis of Table 4.3, the following general observations can be made:

• The modelled operator response would involve a fall in prices on all routes examined, with the percentage reductions in fares varying from -1.0 to -4.9;
• Predicted changes in frequency levels are either positive or negative. This was to be expected: the IPP provides an incentive to increase frequency, but the loss of FDR has an opposite effect. On the busiest urban route, the optimal number of buses per hour increases by 0.3 (from the profit-maximising 6.1 per hour to about 6.4; note that the actual number of buses is 8). On routes that see their Day 1 profits fall, the optimal response is generally to slightly cut frequency levels, though the proportionate reduction is small except on the loss-making interurban routes (if this service were still to be run commercially - but we believe that this will not be the case in practice);
• Incentives to change quality levels are generally very small. They can be either positive or negative. Although IPP will also provide an incentive to increase quality, this may be mitigated by lower fares (thereby possibly reducing the marginal revenue of a quality improvement) and by lower frequency levels (these reduce the number of passengers, thereby also reducing the marginal revenue of a quality improvement);
• The additional profits that operators can earn by adjusting fares, service and quality levels are relatively small in all cases. As a percentage of the "Day 1" impact on profits, the additional profits that can be earned by optimising vary with one exception between 3 and 12 per cent. As a percentage of current profits (in Table 4.2), the additional profits from optimising are generally in the order of 1 or 2 per cent;
• On the busiest urban route, the modelled operator response (if implemented) would result in a patronage gain of about 9 per cent. The other busy urban route has a high frequency level relative to the number of passengers carried, so here the incentive would be to cut frequency with a lower net patronage growth as a result. The medium urban route achieves a patronage growth of about 5 per cent. On interurban and rural routes, the combined impact of lower fares and lower frequency levels results in either a small patronage gain or a small patronage loss. On the loss-making interurban route, the modelled frequency cut would be quite severe, resulting in a predicted patronage loss of almost 13 per cent. In practice, this route would cease to be operated commercially.

4.4.3 Prices Constrained at their Present Level

As already noted, the changes shown in Table 4.3 above are changes from the modelled profit-maximising level. In practice, operators may choose not to maximise their profits for political or commercial reasons, e.g. to deter entry by potential competitors.

In the present subsection and the next one, we therefore present alternative analyses where we constrain price and the number of buses at their present levels, both in calculating the current profit-maximising benchmark position, and in determining the operator's response.

The analysis with prices constrained at their present levels is shown in Table 4.4. One of the reasons for analysing such a situation is that we believe that in practice, many prices will prove to be "sticky" for one or more of the following reasons:

• operators may not be aware that they can increase profits under IPP by lowering fares;
• even if operators were aware that they can increase profits by lowering fares, the additional profit increase that they can earn is too small for them to make any adjustment in fares; or
• operators are not currently pricing up to the profit-maximising position, for example for political or public opinion reasons, and use the IPP to reduce the gap between their actual and profit-maximising prices.

When comparing Table 4.3 with Table 4.4, it should be kept in mind that the changes refer to different modelled profit-maximising benchmark positions. In Table 4.3, the current profit-maximising position is calculated without imposing specific constraints. In Table 4.4, prices have been constrained at their present level, resulting in a different current profit-maximising benchmark position.

Table 4.4 - Modelled Operator Response - Fare Constrained at Current Level

Route	Fare Change	Frequency Change	Quality Change	Profit Change
				Day 1 Change Under "Profit-Maximising Position (£)	Additional Change From Optimising (£)	Patronage Change
Busy Urban 1	Constant	+2.0%	+0.16	+13,549	+98	+1.5%
Busy Urban 2	Constant	+0.2%	+0.02	+432	+1	+0.3%
Medium Urban	Constant	+2.9%	+0.05	+1,153	+11	+2.2%
Marginal Urban	Constant	-1.4%	+0.01	-394	+3	-2.4%
Busy Interurban 1	Constant	-4.1%	-0.01	-1,009	+27	-3.0%
Busy Interurban 2	Constant	-4.6%	-0.04	-4,334	+124	-2.7%
Marginal Interurban	Constant	-0.9%	+0.04	-187	+6	-0.5%
Loss-Making Interurban	Constant	-13.2%	+0.01	-2,587	+207	-18.5%[18]
Rural	Constant	-2.0%	+0.01	-175	+4	-1.6%

Source: NERA Estimates

Table 4.4 shows that without the beneficial impact of lower prices, both the other incentive and the overall impact on demand fall significantly. On the busiest urban route, the incentive to increase frequency falls, but by contrast the incentive to improve other dimensions of quality increases. The combined impact of these changes is that the modelled patronage increase falls from 8.9 to just 1.5 per cent.

On the other urban routes, the modelled patronage gains also fall. On the interurban and rural routes, constant fares do not change the incentives in regard to buses or quality by a substantial extent. However, in the absence of the beneficial impact of lower fares, all interurban and rural fares would see patronage falls as a result of the move from FDR to IPP; mainly caused by the incentive to reduce frequency slightly.

4.4.4 Frequencies Constrained at Their Present Level

Our analysis of the response of operators with frequency constrained at the present level is contained in Table 4.5. The relevance of keeping frequency constant at the present level is related to the way competition in the bus market works: operators typically deter entry by avoiding leaving gaps in which competitors can enter profitably. We believe that the analysis with constant frequency is highly relevant for the present study: it became very clear in our interview programme with operators that cutting daytime frequencies is not on the agenda of operators, neither under FDR nor IPP.

Table 4.5 - Modelled Operator Response-Frequency Constrained at Current Level

Route	Fare Change	Frequency Change	Quality Change	Profit Change
				Day 1 Change Under "Profit-Maximising Position (£)	Additional Change From Optimising (£)	Patronage Change
Busy Urban 1	-4.9%	Constant	+0.04	+10,384	+640	+6.0%
Busy Urban 2	-3.2%	Constant	+0.02	-258[19]	+74	+3.8%
Medium Urban	-3.5%	Constant	+0.02	+808	+63	+4.2%
Marginal Urban	-2.2%	Constant	+0.01	-1,396	+30	+2.7%
Busy Interurban 1	-2.8%	Constant	+0.02	-1,459	+52	+3.4%
Busy Interurban 2	-1.8%	Constant	+0.01	-3,982	+43	+2.2%
Marginal Interurban	-3.5%	Constant	+0.02	-857	+95	+4.3%
Loss-Making Interurban	-0.5%	Constant	0.00	-3,902	+1	+0.6%
Rural	-3.6%	Constant	+0.01	-411	+31	+4.3%

Source: NERA Estimates

When keeping frequency constant at their present levels (i.e. not at the modelled current profit-maximising level), the incentive to cut fares improves on most routes that would otherwise have seen slightly falling frequency levels, i.e. the interurban and rural ones, as well as the "Busy urban 2" route. As a result, modelled patronage gains on the routes are now positive in all cases. As in the previous scenarios, the additional profit from optimising is small, most extremely on the lossmaking interurban route.^[20]

4.5: How We Believe Operators Will Respond in Practice

4.5.1 Introduction

Although the modelling work described above yields very interesting and valuable insights, we believe for a number of reasons that the actual consequences of the proposed change in subsidy system, if implemented, will be different than our model predicts. These reasons include:

• Bus operators run their business on the basis of imperfect and incomplete information. Fares, service levels and quality levels are set on the basis of informal procedures and are not based on detailed analyses;
• Some but not all operators that we have interviewed primarily evaluate the impact of the proposed change in subsidy system on the basis of the impact on their entire business. These operators are prepared to accept a degree of cross-subsidy in their operations and may use any increases in profits on busy routes to compensate any shortfalls on quieter or interurban routes that may result from a move to a per-passenger subsidy;
• Operators may in practice not maximise their profits but set prices, service and quality levels on the basis of different considerations, such as avoiding public or political controversy, or trying to deter entry by potential competitors;
• The model is a simplified version of reality, so there will be a margin of error in its predictions. However, the sign of the modelled impacts will normally be correct, except when predicted changes are very small.

We believe that there are a number of key issues that will influence how operators will respond to the proposed change in subsidy system in practice. These are:

• The extent to which they are prepared to accept an increasing degree of cross-subsidy in their system;
• Whether the predicted fall in prices will materialise; and
• The impact of potential changes in the perceived likelihood of entry onto certain routes.

We discuss each of these in turn on the next subparagraphs.

4.5.2 The Impact of Cross-Subsidy

Our interview programme has confirmed that cross-subsidy between routes is currently widespread in the bus industry. Profit margins on routes that operators run vary very substantially: some routes clearly perform very well, whereas others barely cover their direct costs.

The proposed change in subsidy system will, in the absence of any adjustments, increase the extent of these cross-subsidies. Most routes that currently perform very well will become even more profitable (except certain profitable routes in interurban areas), whereas the routes that are currently performing poorly will see their financial performance fall further.

A number of operators have indicated that they would review routes individually and act accordingly. Other operators however have suggested that provided they would overall not be worse off under the new subsidy structure they may not want to make any change to their business whatsoever. The impacts of such behaviour can only be predicted with knowledge of the mix of route types under the control of an individual operator, and the extent to which they cross-subsidise between different route types. It is likely, however, that cross-subsidy by operators would lessen the impact of the subsidy change, particularly without a safety net for interurban and rural services. These operators did indicate however that such a strategy would only be possible if the likelihood of competitive entry onto their profitable routes did not increase, an issue to which we return in Section 4.5.4.

4.5.3 Will the Predicted Fall in Prices Materialise?

As we have seen in Section 4.4, the predicted patronage increases (if any) as a result of the proposed change in subsidy system are to a substantial extent driven by the predicted fall in fares that would result from the introduction of IPP. We believe it to be very unlikely that the introduction of IPP will result in an actual overnight fall in prices. In our view, prices will display a degree of "stickiness" and will not actually come down in absolute terms. Rather, a number of operators have suggested that they may want to moderate or defer a fare increase. At a time when bus costs are rising rapidly, this is likely to be a short-run effect.

It is important to note in this context that operators do not normally set prices by route, but instead use standard increases, although these increases will vary by area (different increases may be set for different towns, for example). Given that the modelled impacts vary by route type, this will in our view make operators even more reluctant to cut fares or to defer a fare increase.

Again, an important factor is also whether operators primarily analyse the impact of the proposed subsidy change in terms of their whole business or in terms of individual routes. If they evaluate the impact in terms of their whole business, and if they are worse off, then we believe a fares cut to be even less likely. In fact, such operators may decide to put up fares to compensate for the overall negative impact on their business.

4.5.4 The Impact of Perceived Changes in the Likelihood of Entry

On routes that become more profitable as a result of the change in subsidy structure, the perceived or actual likelihood of entry by a competing operator may increase, so that the incumbent operator may want to adjust its price or service level with a view to deterring this.

Increased likelihood of entry may be the result of:

• operators redeploying vehicles from interurban or marginal urban routes onto busy urban routes; or
• smaller operators entering the market.

We believe that redeployment of vehicles may occur to a modest extent but do not regard it as likely that this will occur on a significant scale. Reasons for this include:

• The proposed safety net that we have assumed as given in our study will safeguard most interurban and rural services, so that vehicles deployed on these routes will continue to be used there.
• In urban areas; routes that are adversely affected by the change in subsidy will primarily be returned to (sufficient) profitability by removing marginal services from them, as mentioned above. This will in itself not result in any peak vehicles being freed that could then be deployed elsewhere.
• Operators belonging to large groups are reluctant to enter an area served by an operator belonging to a competing large group.

Whether smaller operators will enter the market on a significant scale is difficult to predict. Our interview programme did find that operators do perceive an entry threat from smaller operators and in certain cases set in particular their frequency levels with a view to avoiding gaps in which such operators can profitably enter. Whereas we therefore believe that there are many bus routes in the country where operators set frequency levels higher than they would have done in the absence of an entry threat, we doubt whether the predicted increase in profits on the routes that we have examined (e.g. the 25 per cent predicted increase on the busiest urban route) will be such as to induce operators to increase frequencies further on entry-deterrence grounds (they may wish to do so on commercial grounds).

In summary, we believe that the impact of the (perceived) likelihood of entry will materialise mainly in operators being very reluctant to cut daytime frequencies. We do not therefore believe that daytime frequencies will be cut even on routes that are worse off under the new subsidy system. On routes that will gain from the proposed change in subsidy system, frequency levels will typically be above the profit-maximising levels already in order to avoid leaving profitable grounds. We doubt whether the threat of entry alone would induce operators to increase frequencies further on such routes.

4.6: Overall Conclusion of Simulation Modelling

The simulation modelling has shown the initial impact of the switch in subsidy system from FDR to IPP. This shows how, in the absence of the safety net, most of the routes in our sample lose, including two urban routes. Two other urban routes gain, one of them disproportionately.

After looking at the immediate effect of the subsidy switch, we then consider the incentive effects of the switch. These are the same irrespective of the existence or size of the safety net^[21], provided that the operator continues to run the service. Identification of the incentive effects involves modelling how the switch affects bus operators' behaviour. We do this under three circumstances: with fare, frequency and service quality all free to vary; with fares fixed; and with frequency fixed. When all aspects of bus operators' behaviour change, fare will fall, and this will increase demand. But frequency also falls on all but two of the urban routes, and the impact of this on patronage works in the opposite direction to the fares reduction, causing demand to fall on three of the routes (and causing it to be unchanged on another one of the routes). When fares are sticky the beneficial effect on patronage of fares reduction is lost - frequency rises on three of the urban routes but falls on all other routes. Although there are some quality improvements, bus patronage falls on all interurban and rural routes, as well as on one urban route. On the other urban routes, patronage still rises but by less than in the previous scenario. However, where frequency is fixed, patronage rises on all routes as a consequence of reductions in fares, reinforced by an improvement in quality. A situation where frequency is fixed but fares fall appears to be the best situation - but controlling frequency in practice may be difficult to achieve - the danger is that while some operators may not make small reductions in frequency intervals, some others may make significant reductions.

In the final part of this chapter we consider additional factors that may influence the way in which bus operators may react to a change in subsidy. These include the extent to which operators are prepared to accept an increasing degree of crosssubsidy, the extent to which fares will be reduced, and the impact of changes in the perceived likelihood of entry onto certain routes. Entry may be a threat on the busiest routes, though it is less likely to be so on thinly-served routes. Although fares may be sticky in a downward-direction, operators may be prepared to defer increases where there are incentive effects to do so. Although some operators might be willing to accept an increasing degree of cross-subsidy to preserve their network and deter competition, we think it more likely that they would instead wish to seek profit opportunities, especially in the longer term.

Aggregate Passenger Growth

We have aggregated our results to the national level (for England excluding London) using information supplied by LEK from their bus industry database. The information supplied consisted of numbers of passengers carried on services in different types of area using the following six categories:

• large urban areas with over 250,000 population;
• medium urban areas with between 100,000 and 250,000 population;
• medium urban areas with between 25,000 and 100,000 population;
• market town;
• deep rural; and
• interurban.

We then applied the passenger growth rates for our routes estimated in Table 4.3 across these routes, interpolating between categories to derive broad estimates of growth rates for route types not directly modelled in our study. Our estimate of overall bus passenger growth is 4.7 per cent, or 114 million passenger journeys, though this estimate should be regarded as indicative only given the broad interpolation procedures we have used.^[22]

Our traffic growth estimate of 4.7 per cent generated across the country compares with the LEK estimates based on per passenger subsidies of 9.2p and 10.4p shown in Table 3.1. These LEK growth rates imply a short run growth rate of 3.9 per cent and a long run growth rate of 11.9 per cent for a 10p per passenger subsidy.^[23] Our estimate is therefore somewhat above LEK's short run estimate, but less than half their long run estimate. Our estimates are primarily based on long run elasticities, and the main reason for the difference is that LEK have identified specific quality improvements, namely CCTV and driver training, which they believe will be particularly cost-effective in increasing bus patronage. In addition, as discussed earlier, LEK's assumed quality elasticities are significantly above the values that we have used on the basis of the views of operators.^[24]

We have undertaken a more complete analysis of the differences in methodology between our study and that of LEK, and this is attached as Appendix E. All relevant tables from the main text of this report are reproduced within Appendix E to avoid the need for cross-referencing.

Aggregate Trends in Profitability

In regard to profitability trends, it is useful to make a distinction between the "Day 1" impact on profits and the additional impact on profits from optimising. The "Day 1" impact on individual routes can be either positive or negative; the additional impact from optimising is necessarily positive.

When estimating the "Day 1" impact on total profitability in the industry, it should be kept in mind that the "Day 1" impact only involves a reallocation of subsidy. The IPP of 10 pence has been calculated on the assumption that the entire FDR budget is converted into IPP. Given that the Day 1 impact assumes that all current services would continue to be operated (at least initially), the "Day 1" impact on profits would in the absence of a safety net be zero.^[25] There would be a greater degree of cross-subsidy in the system than at present. In practice, though, operators would want to immediately withdraw certain services, which is why a safety net is needed. The safety net may increase the net subsidy payable to the industry, depending whether an "increased revenue subsidy" or "optimised revenue subsidy" system is adopted. Since (by assumption) nothing else will have changed on Day 1, the Day 1 impact on profits can be expected to be equal to any net increase in subsidy.

Given the changed incentive structure, operators may be able to further improve their profits by changing fares, frequency and quality levels. The additional profit that may be earned, as a percentage of the Day 1 profit, is shown in Table 4.6. It can be seen that with two exceptions, the additional profit that can be earned from optimising is in the order of 0.5 to 1.0 per cent. It should be noted that these are changes from the profit-maximising Day 1 position. Since the actual Day 1 profits may be lower than that (operators may not profit maximise in practice), the percentage additional profit that can be earned from optimising may in practice be somewhat higher than these figures.

Table 4.6: Additional Profit from Optimising

Route	Additional profit as percentage of profit-maximising Day 1 profit
Busy urban 1	+0.7%
Busy urban 2	+0.5%
Medium urban	+0.6%
Marginal urban	+0.7%
Busy interurban 1	+1.1%
Busy interurban 2	+0.6%
Marginal interurban	+3.4%
Loss-making interurban	+3.9%
Rural	+0.7%

The exceptions relate to the marginal and loss-making interurban routes but are somewhat hypothetical: both of these routes are likely to become tendered routes where the operator would no longer be in a position to optimise its fares, service and quality levels itself.

For these reasons, we estimate that the bus industry as a whole would be able to increase its profits by optimising by about 1 per cent. This increase would be over and above the profit increase as a result of any net increase in subsidy.

It is possible to put these estimates in context by providing some estimated numbers, shown in Table 4.7. This Table shows that, when taking LEK recommended option and their central estimate of the safety net in that option, bus industry profitability might increase by 11.8 per cent on Day 1 and a further 1.0 per cent as a result of optimising.

Table 4.7: Indicative Impacts on Bus Industry Profitability

	Profit (£m)	Change from 2000/01 profit
Bus industry profitability in England (outside London), 2000/01[26]	197.2	-
Day 1 impact under assumed safety net (increased revenue scenario, LEK central estimate)	+23.3	+11.8%
Additional profit from optimising	+2.0	+1.0%

Cross-Modal Effects

The stated preference work for LEK estimated that about 40 per cent of additional bus passengers can be expected to come from the car mode. On the basis of this, an estimated 46 million out of our own estimate of the total passenger increase of 114 million would be expected to switch from car to bus. There is some uncertainty about such diversion factors, and we believe that the diversion factor could be lower, perhaps as low as 20 per cent, in which case only 23 million of the total of 114 million extra bus passengers would be diverted from car.

It is useful to analyse these numbers in the context of the total proportion of car and bus trips. In Great Britain between 1998 and 2000, there were on average 639 car trips per person (411 as driver, 228 as passenger) and 58 bus trips.^[27] If the total number of bus trips is expected to increase by 4.7 per cent, then the number of bus trips per person will increase to 60.7. If 40 per cent of the increase come from car, then the total average car trips per person can be expected to fall from 639 to 638 per year, or by 0.16 per cent.

In large urban areas, where most of the modal shift occurs, the impact on car traffic is slightly higher. Between 1998 and 2000, there were on average 623 car trips per person in these areas (396 as driver, 227 as passenger), and 70 bus trips. The total number of bus trips in these areas is expected to increase by 7.0 per cent, which will bring the total number of bus trips per year in these areas to 74.9. If 40 per cent of this increase comes from car, then the total of average car trips per person in these areas can be expected to fall from 623 to 621 per year, or by 0.32 per cent.

Comparison of FaberMaunsell/NERA and LEK Bus Costs

We have been asked to indicate whether the bus costing methodology used in our study is consistent with that used by LEK. Professor Mackie has confirmed that both use the CIPFA costing methodology. A comparison between the unit costs in ITS's and our models is contained in Table 4.8.

Table 4.8: Comparisons between Unit Costs in ITS and FM/NERA Routes

	Cost per hour	Cost per km	4-weekly cost per PVR	Total cost per km
ITS Large radial	£13.10	£0.26	£1,290	£1.25
ITS Medium radial	£12.91	£0.25	£1,170	£1.63
ITS Interurban	£12.23	£0.19	£1,061	£0.68
ITS Rural radial	£12.93	£0.11	£1,018	£0.78
Busy urban 1	£18.62	£0.13	£808	£1.25
Busy urban 2	£19.91	£0.15	£712	£1.28
Medium urban	£19.91	£0.15	£712	£1.75
Marginal urban	£21.93	£0.12	£702	£1.14
Busy interurban 1	£19.77	£0.11	£903	£1.00
Busy interurban 2	£14.19	£0.23	£1,944	£0.91
Marginal interurban	£19.81	£0.10	£1,062	£1.08
Loss-making interurban	£14.19	£0.18	£2,026	£0.72
Rural	£18.84	£0.10	£1,015	£1.06

It can be seen that although the total cost levels per vehicle kilometre are not dissimilar (in both approaches, costs per vehicle kilometre in urban areas are higher than in interurban areas), there are significant differences in the allocation of these costs.

In part, these differences are caused by differences in data availability. ITS did not have route specific cost data and had to calculate these from the model timetable and aggregate LEK cost parameters, whereas our cost data are based on data directly supplied by operators.

As noted in the report, a number of operators provided data showing their costs broken down between time related costs, distance related costs or maximum peak vehicle requirements or overhead related costs, in which case these costs have been allocated to the relevant cost driver. Other operators provided more detailed data, which have been allocated as indicated in the report.

Due to different accounting methodologies between operators, there are significant differences in cost allocation between operators. We would note however that operators will base their response to the change in subsidy system on their accounts, based on their accounting policy. Since the study was concerned with modelling operator responses using data specific to them, we did not consider it to be appropriate to adjust their cost allocation in order to arrive at a more uniform allocation of costs.

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14: Note that the final column represents our understanding of the manner in which the safety net has been applied in the work undertaken by LEK. This understanding of the LEK safety net underlies our subsequent discussion of safety net issues in this report.
15: Revenue-neutral in this context refers to the Day 1 position, in which FDR is simply switched to IPP on the basis of the patronage at that time.
16: Under the current position, the day 1 impact on profits on this route is positive. The fact that the day 1 impact on profits under the profit maximising position is slightly negative is due to the fact that the impact of fewer buses under the profit-maximising position (hence a lower negative impact of the loss of FDR) is outweighed by the impact of fewer passengers under the profit-maximising position (hence a lower positive impact from IPP).
17: This sharp fall in patronage is caused by a modelled fall in the number of buses per hour from 0.7 in the profit-maximising position under FDR to 0.6 in the profit-maximising position under IPP. See Table 4.5 for an analysis with the number of buses constrained at the present level. In practice, the losses on this service would no longer be acceptable to the operator so that the service would no longer be run commercially.
18: See footnote 8.
19: Under the current position, the day 1 impact on profits on this route is positive. The fact that the day 1 impact on profits under the profit maximising position holding frequency constant is negative is due to the fact that under the profit-maximising position, fewer passengers are carried, so that the positive impact from the per-passenger subsidy is no longer sufficient to compensate for the negative impact as a result of the loss of FDR.
20: The reason for this is that the IPP of 10 pence does not materially change the incentives on the revenue side on this route with a current average fare of £2.28, but the loss of FDR does heavily affect the cost base of this route. When holding prices constant, the additional profit from optimising on this route is large by cutting frequency levels. When holding frequency constant, however, prices can be reduced somewhat but this has virtually no effect on profit levels.
21: This is because we are looking at the marginal effects on profitability of changes in fare, frequency and quality for a given service. The safety net is the equivalent of a block subsidy based on the loss in profits due to the subsidy change, and is assumed to be independent of the operator's responses to changed incentives to maximise profits.
22: It may be noted that the growth rate in passengers just for the nine routes modelled in Table 4.3, but weighting by route size, is 4.4 per cent.
23: These estimates are based on our simple pro rata calculation from Table 3.1.
24: See the discussion in Section 3.3.3.
25: To the extent that the 10 pence figure is a rounded figure, there will in practice be either a small positive or negative Day 1 impact on aggregate profits.
26: Source: TAS (2002) Bus Industry Monitor 2002.
27: Source: Department for Transport (2001) Focus on Personal Travel.

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