JOURNAL OF TRANSPORT ECONOMICS AND POLICY.pdf

8/23/2019 JOURNAL OF TRANSPORT ECONOMICS AND POLICY.pdf

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Journal of Transport Economics and Policy

This article is the final accepted version to be published in a

forthcoming issue volume to be determined later.


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EVALUATING ALTERNATIVE POLICY RESPONSESTO FRANCHISE FAILURE: EVIDENCE FROM THE

PASSENGER RAI L SECTOR IN BRITAIN

Dr Andrew, S.J. Smitha

and Phill Wheatb

a Address for correspondence: Institute for Transport Studies, Room 213, University

of Leeds, Leeds, LS2 9JT, UK.

bInstitute for Transport Studies, University of Leeds.

Acknowledgements

The authors are grateful to the UK Engineering and Physical Sciences Research

Council (EPSRC) for funding this research, via Rail Research UK (RRUK), the

universities centre for railway systems research. We would also like to acknowledgethe support and advice offered by Professor Chris Nash, as well as the helpful

suggestions and data provided by many people within the rail industry. Finally, we

would like to thank the reviewers for their helpful comments. All remaining errors are

the responsibility of the authors.

Abstract

One potential problem with franchising (competitive tendering) is how to deal with

situations where the franchisee is unwilling to continue operating the franchise within

the contract period. This paper studies the effects of the franchising authoritys

response to franchise failure in passenger rail in Britain, which saw the affected

operators placed onto management or short-term re-negotiated contracts for an

extended period. We find that operators on management contracts saw a sharp

deterioration in efficiency. Further, the contract inefficiency persisted, though was

eliminated by competitive re-franchising. In contrast, costs for re-negotiated

franchises were no higher (statistically) than industry best practice.

Date of receipt of final manuscript: February 2011


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1.0 Introduction

Franchising (or competitive tendering) has become an important method for

introducing competition for the market where competition in the market may be

undesirable. Economic theory predicts that franchising should result in the tender

being awarded to the most efficient operator. When introduced to a service previously

operated by a state-owned monopoly, substantial cost reductions are therefore

expected, driven by the fixed nature of the contract over a given period, and the profit

maximising objective of the privatised firm. Of course, there are many problems in

practice, most notably that the tender process may result in overly-optimistic bids,

either due to winners curse or strategic bidding, meaning that the most efficient

operator may not be selected, and the expected cost reductions may not be achieved

(Vickers and Yarrow, 1988 and Viscusi et. al., 2005). Ultimately, franchise failure

may result, requiring a policy response from the franchising authority.

In railways, starting with the 1991 European Commission Directive 91/440,

Europe has embarked on a process of regulatory reform, progressively opening up rail

markets to competition (both in and for the market). Via successive legislation,

Europes rail systems have been required to separate train operations and

infrastructure (at least into separate divisions with their own accounts), which has

been achieved via full institutional separation, organisation into separate subsidiaries

within a holding company structure, or the separation of the key functions of train slot

allocation and infrastructure charging into a separate body. Based on this separated

model, competition in the market has been allowed to develop via third-party open-


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access to the infrastructure (mainly for freight traffic), whilst competitive tendering,

though not yet formally required by EU regulation, has been the chosen means of

introducing competition to passenger services. Competitive tendering in rail has also

been used outside Europe, for example in Melbourne, Latin America and for some

North American commuter services.

However, the expected productivity gains have not materialised in the British

case. Despite some reported train operating company (TOC) cost savings during the

early years after the completion of franchising in 1997, (see Affuso et. al., 2002;

2003), Smith et. al. (2009) find that TOC costs increased by around 45 per cent

between 2000 and 2006 (or 35 per cent on a cost per train km basis; Figure 1). This

cost rise is equivalent to around 1.5bn per year and, as a result, state subsidies to

passenger train operators increased substantially over this period (see Smith et. al.,

2009, Tables 2 and 7).

[Figure 1 here]

Importantly, during the period of our sample the franchising authority

performed mid-term re-negotiations with a number of operators, resulting in half of

the sector being placed on management or short-term re-negotiated contracts during

the second half of our sample (see Smith et. al., 2009). The purpose of the paper is to

test the impact of the British franchising authoritys approach to dealing with failing

franchises and the effects of the temporary contractual arrangements that were put in

place. Of course, the franchising authoritys response may have wider impacts on the

bidding process through the signal that it gives regarding the likelihood that contracts


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will be re-negotiated in future. Here we focus solely on the efficiency impact of the

arrangements as they affected costs for the period of their duration and directly

afterwards.

There is an extensive literature analysing the efficiency and productivity

performance of vertically integrated railways around the world (Oum et. al. , 1999;

Smith, 2006). More recently there has also been an interest in understanding the

impact of vertical separation on total industry costs, mainly focussed on European

evidence (Friebel, et. al., 2008; Asmild et. al., 2009; Growitsch and Wetzel, 2009;

Cantos et. al., 2010); although one study considered evidence from North America

(Bitzan, 2003). Overall, the results seem inconclusive, suggesting that much depends

on the circumstances of the country concerned and the way in which the system is

managed.

There have also been a small number of studies focusing on the impact of

competitive tendering on one part of the rail industry, namely passenger train

operations. In Germany and Sweden the experience of competitive tendering has

generally been positive, with the evidence suggesting that savings in the region of 20-

30 per cent can be achieved, alongside increased patronage (see Brenck and Peter,

2007; Lalive and Schmutzler, 2008; Alexandersson and Hulten, 2007; and Nash and

Nilsson, 2009). Even here though, some franchises have failed, so our work is

relevant for those countries. Kain (2009) describes the major problems that emerged

in Melbourne, though the impact of the policy response is not described in any detail.

Long-term passenger rail franchises have also been signed in Latin America,

generally leading to radically improved performance, although in most cases re-


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negotiation has been required due to changed economic circumstances (in particular

the severe economic recession in the late 1990s; see Kogan, 2006).

Turning to studies of British TOCs, Affuso et. al. (2002; 2003) and (Cowie,

2002a, 2002b, 2005) study the early years after privatisation (prior to the major cost

rises) and all find improving productivity during this period. Only two studies cover

the post-2000 period, after which costs started to rise. Cowie (2009) finds declining

productivity growth after 2000, with the absolute productivity level falling post-2002.

In a paper presented at the Thredbo 11 conference, Smith and Wheat (2009) report

productivity levels falling as early as 2000 and not recovering over the remainder of

the sample (to 2006). Smith et. al. (2010) reviews this literature.

The latter paper focused on the impact of franchising on total factor

productivity (TFP) and efficiency since privatisation. It included a simplified

treatment of management and re-negotiated contract effects, but importantly did not

adequately address the inherent problem of endogeneity bias when testing the impact

of contractual or institutional arrangements on productivity (the TOCs that ran into

trouble may always have had characteristics which made them likely to end up as

distressed franchises; see section 2). In the present paper we guard against the

endogeneity problem, and thus present a robust and comprehensive treatment of the

temporary contract arrangements put in place by the franchising authority following

franchise failure. The findings, as compared to the earlier simplified treatment, are

different as a result.


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This paper is therefore positioned within a broader literature on rail efficiency,

and a relatively small number of studies on the efficiency of passenger train

operations. Specifically, we focus on the franchising authoritys response to failing

franchises on costs and efficiency, rather than the broader picture of sector

productivity performance which has been covered by the aforementioned studies. We

also use a new dataset which is more robust than that used in earlier work, having

been derived from industry sources. Importantly, our data allows us to separately

identify TOC costs from track access charges paid to the infrastructure manager, and

thus focus attention closely on the costs under the direct control of TOCs. It also

contains new data on important cost drivers, most notably vehicle-km1 (we are not

aware of any previous studies of rail costs that has utilised vehicle-km data).

The analysis contained in this paper is important from a policy perspective

both in the UK, but also more widely internationally, where franchising or

competitive tendering arrangements have been put in place or are being considered.

Given the importance of franchising (competitive tendering) as a policy device in rail

and other sectors, it is important to understand the efficiency impact of alternative

policy responses to franchise failure.

The remainder of the paper is structured as follows. Section 2 formalises our

research questions regarding the impact of management contracts. Sections 3 and 4

detail the methodology and the data used for the study. The results are shown and

discussed in section 5. Section 6 offers some conclusions.

1Total vehicle-kms is defined as the distance travelled by all of the vehicles operated by a TOC (where

a vehicle is a sub-set of a train). For example, a 3 car diesel multiple unit, travelling one km, would be

counted as 3 vehicle-km.


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2.0 Research Questions

Passenger rail franchises in Britain were generally awarded on the basis of

minimum subsidy (or exceptionally highest premium for profitable franchises) and the

winning subsidy profiles generally declined sharply over the course of the franchise as

a result of assumed cost savings and/or revenue growth. However, despite strong

revenue growth, and some cost reductions, those operators where farebox revenue

was small relative to costs, and where therefore cost reduction was the key to success,

ran into difficulties within a few years of the franchises being let. The evidence

suggests that the problems were more to do with unrealistic assumptions, particularly

on the cost side (winners curse), rather than being a deliberate strategy of low-balling

with a view to subsequent re-negotiation (see Smith et. al., 2009).

In response, the then franchising authority (SRA) had to perform mid-term re-

negotiations with the affected operators, which made up around half of the 25

franchises let (Table 1). These franchises were mainly placed onto cost-plus type

management contracts with higher subsidy (the level of subsidy payment was

negotiated annually on the basis of projected costs, so the affected TOCs therefore

retained some cost risks during the year). For a smaller number of operators the

original franchise agreements were simply re-negotiated for a short period (typically

2-3 years), again with higher subsidy (see Smith et. al., 2009). Post re-negotiation,

these franchises therefore faced the same incentives as other operators continuing on

their original franchise agreements.


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It should be noted of course that in the event of franchise failure, the

franchising authority faces a number of alternatives. An operator of last resort could

be set up to step-in when a franchise fails2. Some use of a short-term management

contract with the incumbent is an alternative while a new franchise competition is

organised. However, the distinguishing feature of the British case is that TOCs were

put onto management contracts for an extended period (several years); although it

should be noted that this situation came about during a period of considerable

uncertainty, where refranchising had been temporarily halted because of lack of

funding resulting from cost increases on the infrastructure side, and due to a desire to

redraw the franchise map (see Smith et. al., 2009).

[Table 1 here]

Whilst there may have been good reasons for the franchising authoritys

response to franchise failure, given the circumstances, our aim is to determine

whether the chosen policy response weakened cost minimising incentives such that

costs under the temporary contract arrangements increased over and above the

changes in costs for unaffected TOCs, and whether costs of these operators was then

higher than best industry practice for the duration of the contracts (as well as after

competitive re-franchising of the affected franchises).

2During the sample period covered by this paper, one TOC, South Eastern, was moved onto such an

arrangement after a period on a re-negotiated contract.


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We break this objective down into a number of specific research questions:

IA Did the costs of TOCs on temporary contract arrangements increase in the first

year of those arrangements over and above the change in costs for other operators

over that period?

IB Were costs for the affected operators higher than industry best practice during the

first year of the temporary contract arrangements?

IIA Did costs fall back to the best practice level over the duration of the temporary

contract arrangements?

IIB Did costs fall back to the best practice level once the franchise had undergone

competitive re-franchising?

Research questions IIA and IIB are important given that some TOCs spent

four or five years on management contracts and so persistency of any contract effect

is very important from a policy perspective.

Finally, we specify two further research questions concerning the period prior

to franchise failure.

IIIA Were the costs of TOCs that subsequently failed higher, other things equal, to

industry best practice at privatisation (the start of our sample)?


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IIIB Did the TOCs that subsequently failed cut costs prior to franchise failure such

that their costs were lower than the other TOCs?

Research questions IIIA and IIIB are important since failure to explicitly

account for any prior systematic cost differences between the problem TOCs and

other TOCs could introduce endogeneity bias into our estimated parameters because

the problem TOCs may always have had characteristics which made them likely to

become a problem TOC. We guard against this bias by including a problem TOC

specific dummy variable and problem TOC time trend for the period proceeding

movement to a problem contract. Our approach is analogous to that adopted by

Domberger et al (1987) whom studied the efficiency performance of hospital ancillary

services. As noted earlier, Smith and Wheat (2009) contained a simplified treatment

of the contract effects, and did not adequately address the endogeneity problem.

Ultimately, our approach enables us to estimate and plot the time profile of costs of

TOCs which spent some time on management or re-negotiated contracts, relative to

other TOCs, over the whole period of our analysis. Thus we are able to highlight the

specific impact of the franchise authoritys response to franchise failure, and how that

impact changed over the duration of the temporary contract arrangements, as well as

after competitive re-franchising.


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3.0 Methodology

3.1General model

We investigate our research questions through estimation of a cost frontier.

Our model can be represented as:

itittititititit uv);,Z,Q,P,Y(fC (1)

where the first term ( );,Z,Q,P,Y(f titititit ) is the deterministic component,

and itY is a vector of output measures, itP is a vector of prices of the variable inputs,

itQ is a vector of output characteristic variables (for example, dummy variables

denoting commuter versus intercity services), itZ is a vector of exogenous policy

related influences on firms costs and contain the modelling of contract effects (see

section 3.2), t is a vector of time variables which represent technical change and

is a vector of parameters to be estimated. A translog functional form is used (see

section 5).

itC represents total controllable TOC costs, and so excludes track access

charges, which are outside the control of the operators. We are therefore estimating a

total cost function in the sense that TOCs are assumed to minimise all costs under

their control (see section 4). However, in the context of the wider literature on rail

cost function estimation, our cost function could be thought of as a variable cost


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function, in the sense that the size and associated costs of the rail infrastructure are

assumed fixed in our analysis.

The itv term is a random component representing unobservable factors that

affect the firms operating environment. This term is distributed symmetrically around

zero (more specifically assumed to be normally distributed with zero mean and

constant variance). A further one sided random component is then added to capture

inefficiency ( itu ). The specification of the inefficiency term is discussed in section 3.3

below.

Technical change over time (frontier shift) is modelled as 2t tt ,

where t is a time index and is a dummy variable which takes the value unity for

years after 2000. The motivation for the dummy variable comes from the analysis of

the raw data (Figure 1; see section 1) which indicates a cost shock after 2000. The rail

industry in Britain has seen a sharp rise in costs since 2000. Whilst the infrastructure

cost rises resulted from a re-appraisal of maintenance and renewal activities after the

Hatfield accident3

in October 2000, the reasons for the cost rises in passenger train

operations are less well understood. This paper considers one reason for the increase

in train operating costs, namely the impact of the franchising authoritys response to

franchise failure (see also Smith et. al., 2009).

3A train de-railment at a town called Hatfield, just north of London, caused by defective track, which

led to the death of four people.


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In respect of input prices we include a labour price, together with variables

that capture the characteristics of the rolling stock (rolling stock age and rolling stock

type). Obtaining input prices for rolling stock and other costs proved problematic for

two reasons. First, there are some classification issues between rolling stock and other

costs which mean that our rolling stock price variable reported rolling stock lease

costs divided by number of rolling stocks - is imperfectly measured. Second, it is

problematic to select an appropriate denominator for other costs, since there is no

associated physical input. Some previous studies have used train-km as the

denominator (for example Sanchez and Villarroya, 2000), which is an output, not an

input, and therefore runs the risk of capturing deterioration in efficiency as a rise in

input prices.

We therefore estimate a model that includes a labour price variable combined

with a set of rolling stock price hedonic4

variables, rolling stock age and rolling stock

type variables as noted5. In the final, preferred model, only the rolling stock variable

was statistically significant, with the other variables being dropped (though the results

of this paper are not sensitive to the decision to drop these variables).Further, we

include TOC sector dummy variables that should capture systematic differences in

rolling stock prices (and for that matter other costs) between the three TOC sectors. In

this respect, the time invariant nature of rolling stock prices (fixed for the duration of

franchises) matches with the time invariant nature of the sector dummies. As a final

cross-check we note that a model comprising a labour price and our rolling stock

input price variable gives almost identical results to those of the preferred model.

4These variables play a similar role to rolling stock input prices, since the characteristic variables

recognise that different types of rolling stock have different costs.5

We also experimented with economy wide fuel price indices but these performed badly, mainly due to

their invariance across TOCs.


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3.2 Contract variable specification

In order to capture the effects subsumed within the three research questions

outlined in section 2, we model the contract effects as follows:

jAjjOjjOjjBjjBjjit D*TDDTDDZ 54321 (2)

where jiD are dummy variables corresponding to whether the TOC was

eventually subject to a management contract (j=M) or re-negotiated contract (j=R).

The i subscript denotes whether the time period is before or after the shift to the

temporary contract arrangements (i=B refers to the time period prior to the temporary

contract arrangements; i=O denotes the time onwards from the contract, extending

also to the period after contract was terminated following competitive re-franchising;

and i=A denotes the period after the contract was terminated). T is a time trend, whilst

T* is a time trend starting at unity for the year that the TOC was placed onto the

management or re-negotiated contract. These time trends are interacted with the

relevant contract dummies to chart the progress of costs over time, both before, during

and after the contract arrangements. jk are a set of parameters to be estimated

(k=1,,5).

As discussed above, this specification is adopted in order to avoid endogeneity

bias, which means that we need to look at the cost characteristics of the problem

TOCs both before and after the contract arrangements; and we also want to look at the

path of costs whilst on the contract, and post re-franchising.


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Using (2) we can test the following hypotheses which address research

questions I to III6.

IA) If 02143 T*T. jjjj , for T*=1 (first year of the temporary

contract) and for T in the last year prior to the commencement of the

temporary contract), then we can conclude that the problem TOCs costs rose

as a result of the introduction of the temporary contracts relative to other

operators.

IB) If 043 *T.jj , for T*=1 (first year of the temporary contract) costs

are found to be higher for the problem TOCs following them being placed on

to the temporary contract relative to the cost for the other TOCs.

IIA) If 043 *Tjj for T*=1 to 5 (corresponding to the duration of the

contract arrangements), we can conclude that costs were still higher for the

affected TOCs than best practice. This finding would suggest that some part of

the contract effect is persistent.

IIB) If 0543 jjj *T for T* after the completion of competitive re-

franchising, then we can conclude that costs did not fall back to the best

practice level once the franchise had undergone competitive re-franchising.

Note that there was insufficient data to include the 5R dummy and thus

6Note j=M,R, that is there are two hypothesis; one for each group of problem TOCs (management

contracts and re-negotiated contracts).


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conduct this hypothesis for the re-negotiated TOCs (there are only four such

operators, and only one saw re-franchising over the period of our analysis).

IIIA) If 021 T.jj for T=1 (first year of the sample) costs are found to

be higher for the problem TOCs at the start of the sample relative to the other

TOCs.

IIIB) If 021 Tjj for T in the year directly preceding the move to the

temporary contract, then we can conclude that problem TOCs costs were still

above other TOCs costs prior to the introduction of problem contracts. If

021 Tjj then we can conclude that problem TOC costs were below

those of other TOCs costs and so this is evidence that the problem TOCs cut

costs to an unsustainable level and (at least part of) the cost increase following

introduction of problem contracts was simply a reversion to a sustainable cost

level.

In all of the above cases we use t tests based on linear combinations of the

relevant coefficients.

To complete this section we note that the complete vector itZ comprises

*ZZZZ itRitMitit (3)


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where MitZ , RitZ are as described in (2), and *Zit comprises other policy

variables that apply to all TOCs and not just the problem TOCs. We tested the

inclusion of planned franchise length, and a dummy variable denoting the last year of

a franchise contract; however only the last year of franchise dummy variable was

found to be remotely statistically significant and so only this variable is retained in

this vector. Given that the focus is on contract effects for this paper we do not

consider these other policy variables further in the discussion.

3.3 Model Estimation

We estimate the model as a stochastic frontier. This approach was first

proposed independently by Aigner et. al. (1977) and Meeusen and van den Broeck

(1977) for the cross sectional case. The advantage of the approach over conventional

average response cost function techniques is that it permits the possibility that some

firms may be inefficient.

Since we have panel data there are a range of possible assumptions concerning

the path of the inefficiency ( itu ) over time available from the literature. In this paper

we adopt a model from a more general and flexible class of time varying efficiency

models that allow for firm-specific time paths for inefficiency. These models

therefore allow for the possibility that some firms may be getting more efficient,

whilst others may be falling further behind. The general model can be written as:

)t(uu iiit

20 ui ,N~u (4)


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where )t(i is some function describing the variation in inefficiency over

time, and iu is a non-negative random variable. The model estimated in this paper is a

special case of equation (4), and takes the following form (see Cuesta, 2000 and

Alvarez et. al., 2006):

)texp()t( ii T,,1t (5)

where the i are a set of firm specific parameters to be estimated. If i is

positive for an individual firm, this indicates that efficiency is improving for that firm

over time, and vice versa for a negative i .

We do recognise however, that while this model is quite general, other non-

nested specifications of inefficiency are possible. Particular mention should go to the

class of models where inefficiency observations are assumed iid (independently and

identically distributed) over time even though time or policy variables influence the

mean and variances of the inefficiency distribution (models of this class include those

proposed by Battese and Coelli (1995) and Alvarez et al (2006)). We have examined

some variants of these classes of models and found the story regarding the impact of

contracts to be similar to our preferred specification.

As described in Section 3, a central aim of the paper is to understand the

impact of franchise contract changes and Section 4.2 details how our formulation

allows us to test a series of hypotheses about the effect of different contracts. We


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include these terms in the itZ vector within the deterministic frontier function. This

has the implication of implying that those TOCs were problem TOCs had different

deterministic frontiers than those that were not. The inclusion of these terms in the

frontier is a common approach in the literature for handling such variables (see for

example, Coelli, Rao, ODonnell and Battese, 2005).

4.0 Data

Table 2 shows the data used for the analysis. The dependent variable is TOC

variable cost, defined as All TOC expenditure (excluding exceptional items), less any

transfers to Network Rail (access charges and performance penalties / payments).

Thus variable cost comprises staff costs (32 per cent), rolling stock leasing charges

(27 per cent) and other TOC expenditure (41 per cent). Our sample covers a ten year

period (1997 to 2006) covering 26 TOCs (since not all TOCs appear in all years, the

total number of observations is 238). Note that the period 1997 to 2006 refers to the

financial years 1996/97 to 2005/06.

[Table 2 here]

Given the highly regulated environment in which TOCs operate the companies

are highly constrained in their ability to adjust prices to maximise passenger-km. We

thus consider that TOCs produce train-km. This is consistent with other studies in this

area (Oum et. al., 1999). In addition, TOCs also operate stations. In order to

distinguish between the cost associated with running more trains and the cost of


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lengthening trains, we define two separate outputs variables: train-km per route-km

(train density) and average length of train. The third output variable is then the

number of stations operated. The route-km variable is included alongside the other

variables in order to distinguish scale and density effects, and passenger-km is also

included in order to capture the separate cost impact of carrying more passengers on

existing services. Thus we see our main output vector as comprising train-km, average

length of train and number of stations; combined with the output characteristics

variables route-km and passenger-km. Table 2 also contains data on output quality

(public performance measure, which measures reliability and punctuality, and signals

passed at danger which is a safety measure).

Regarding input prices, we include a cost per worker measure derived from

the TOC accounts. As noted in section 3, we seek to control for the factors likely to

affect the price paid by TOCs for rolling stock by testing the impact on cost of a set of

rolling stock characteristic variables, as shown in Table 2. We also include, as noted

previously, TOC sector dummy variables that should capture systematic (time

invariant) differences in rolling stock prices and other costs between the three TOC

sectors7.

Two aspects of the data are worth noting. First, we have utilised industry-sourced data

on track access charges in order to obtain a measure of those costs that are directly

under the control of the TOCs (that is, track access charges, which are not controlled

by the TOCs are excluded). Our paper therefore focuses attention closely on the costs

7As noted in section 3, an alternative model that includes a rolling stock price variable in place of the

rolling stock characteristics variables produces very similar results to those of our preferred model.


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under the direct control of TOCs. The majority of the previous literature has focused

on overall industry costs including infrastructure (see for example, Cowie, 2009 and

Growitsch and Wetzel, 2009 in respect of British and European rail studies). Whilst

the latter papers offer an industry-wide perspective, through capturing both

infrastructure and operations, they do face the problem of measuring the capital input,

which has to be proxied either by track length, or by track access charges, both of

which are imperfect measures.Second, we have obtained data on vehicle-km as well

as train-km, which allows the model to take account of both distance travelled and

length of train. We are not aware of any previous studies of rail costs in other

countries that has utilised vehicle-km data.

5.0 Results and Discussion

This section outlines the econometric results and verifies that they are robust.

We then we report on the results of our hypothesis tests relating to the imposition of

problem contracts and discuss the implications.

5.1 Econometric results

Table 3 shows the results of our preferred model. In general, this model

performs well in terms of the signs and significance of the parameter estimates in

respect of both the explanatory variables and the efficiency specification. We adopt a


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(restricted) translog functional form, with squared and interaction terms on the main

output vector, comprising train density, train length and number of stations operated.

The translog is restricted in the sense that there are no second order terms for

the output characteristic terms. Likewise second order terms for the input price

variable are excluded. Our aim is to estimate a set of frontier parameters that are

plausible and represent a good approximation to the underlying technology. We can

then have confidence in the findings concerning the contract effects. A full translog

model was estimated. However this model was unsatisfactory in several respects

(translog estimation is often problematic, see for example, Morrison, 1999). The

elasticity of cost with respect to stations at the sample mean is implausibly high and

that on route is implausibly low (the stations elasticity also having a relatively tight

confidence interval associated with it). We also note that the wage elasticity is

negative for half of our observations, which violates economic theory.

We therefore retain our restricted translog model as the preferred model. Even

this restricted model includes squared and interaction terms for the key output vector

(density, train length and stations). The restricted translog is also preferred to the

Cobb-Douglas specification based on a likelihood ratio (LR) test (the Cobb-Douglas

restriction is rejected at the 1 per cent level of significance)8.

[Table 3 here]

8It should also be noted that a full TL produces a broadly similar profile of contract effects to that of

the preferred model.


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In general we find the coefficients to be of the expected sign and statistically

significant. At the sample mean, the model exhibits broadly constant returns to scale

and increasing returns to density which is in line with the general literature on rail

costs. Since all data is transformed by the sample mean, the scale elasticity is

computed as the sum of the elasticities on the first order route-km and stations

variables; variables ROUTE and STAT1 in Table 3 (Scale Elasticity = 1.015). The

density elasticity is derived from the first order coefficient on the train density

variable (TDEN in table 3; Density Elasticity=0.776).

Of the variables listed in Table 2 the planned franchise length, SPADs (safety)

and PPM (punctuality and reliability) measures were excluded due to the very low t-

statistics associated with the estimated coefficients. For the rolling stock characteristic

variables, most of the parameter estimates were statistically insignificant and the signs

on the variables were not intuitively plausible. The results in terms of our findings on

the contract effects were little affected by the inclusion or exclusion of these variables

and we thus decided to exclude them from the final model, with the exception of the

age of rolling stock. The coefficient on this variable has the expected negative sign.

As noted earlier, our preferred model produces a very similar result to an alternative

which includes a rolling stock price in place of the rolling stock age variable (see

sections 3 and 4).

As described above, the main aim of the paper is to study the performance of

three groups of TOCs: failing TOCs that were placed on management contracts;

failing TOCs that were placed on re-negotiated contracts; and other TOCs that

remained on their original franchise agreements. These effects are measured via


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dummy variables included in the deterministic part of the model, which therefore

imply three separate frontiers for these groups. As is standard in the literature we also

include a one-sided inefficiency term ( itu ) to pick up variation between firms within

each group.

The null hypothesis that there are no inefficiency effects ( itu ) is rejected,

giving us confidence that a stochastic frontier model is appropriate in this case. The

nested time invariant efficiency model (Pitt and Lee, 1981) and the simpler time

varying efficiency (Battese and Coelli, 1992) model can also both be rejected in

favour of our preferred model (again based on LR tests; in all cases at the 1 per cent

level of significance). The latter tests show that the time variation in efficiency (for

most firms) is found to be significant, and that it is important to allow for different

extents and directions of efficiency change between firms. In addition to the preferred

model, we also estimated several alternative models which assume inefficiency to be

iid across time periods (but does allow for such correlation through the means of the

inefficiency distributions). Overall these models provide similar results to those of our

preferred model with regard to the key issue of the contract effects.

For the remainder of the paper we concentrate on the contract group effects, as

identified by the dummy variables in the deterministic frontier for two reasons. Firstly

we are interested in the effects of the contract types, and therefore the performance of

the different groups, rather than the differential performance of firms within the


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groups (and in any case the itu should be independent of the contract effects9).

Secondly, the one-sided inefficiency effects ( itu ), are not very large and do not

change much over time (though as noted the effects are statistically significant, and

the time variation is also statistically significant; hence why we retain a stochastic

frontier model in preference to a standard cost function model).

5.2 The impact of temporary contract arrangements

We now discuss the results of our hypothesis tests for the temporary contract

effects (see Table 4). We also illustrate our findings graphically see Figure 2 -

which shows the extent to which the costs of management and re-negotiated contract

TOCs exceed those of industry best practice (as represented by other, unaffected

TOCs). Two profiles are shown for the management TOCs, one for a typical

management TOC that entered into a management contract in 2002 and saw

competitive re-franchising in 2005; and the remaining management TOCs, which

continued on these contract arrangements until the end of the sample (dotted line in

Figure 2). Given the way the model is constructed (see section 3), the results for these

two sub-groups are identical up to and including 2004, but diverge after that when

some of the firms are re-franchised.

[Table 4 here]

[Figure 2 here]

9The appropriateness of the random effects specification, where the inefficiency effects are

uncorrelated with the regressors is confirmed by a Hausman test (which we fail to reject).


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Taking the management contracts first, we find that management TOCs costs

rose 21.6 per cent in the first year on the management contract, compared to the

previous year, over and above the change in costs for other (unaffected) operators

(Hypothesis IA reject the null; significant at the 0.01 per cent level). This is shown

by the sharp upward shift in costs for these operators following the shift to

management contracts in year 6 (2002). The relevant test statistic in Table 4 is

0.19548. Further, following the shift to management contracts (in 2002), these TOCs

were found to be 23.2 per cent more expensive than industry best practice (Hypothesis

IB reject the null; significant at the 0.01 per cent level). The relevant test statistic in

Table 4 is 0.20935 which translates into an index number in Figure 2 of 1.232910

.

During the subsequent years costs fell for management TOCs relative to other

TOCs, albeit from a very high base. For those management TOCs that saw re-

franchising during our sample, by the end of the management contract period (year 8;

2004), costs were still 14.8 per cent greater than best practice (Hypothesis IIA reject

the null for this group of TOCs; significant at the 1 per cent level). The relevant test

statistic in Table 4 is 0.1382 which translates into an index number in Figure 2 of

1.1482. Thus while TOCs were on management contracts, we find that they had a

sustained negative effect on performance. However, once the management contract

TOCs were re-franchised (year 9; 2005), we find a sharp fall in costs, such that this

group of management TOCs did not have statistically different costs to the other

10The relationship between the index calculation in Figure 2 and the test statistics in Table 4 is as

follows: index = exp (test statistic).


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TOCs (Hypothesis IIB fail to reject the null; index of 0.9947 in year 911

).

Competitive re-franchising thus resolves the problem of higher costs.

Those management TOCs that did not see re-franchising during our sample

follow a path shown by the dotted line in Figure 2. Thus costs continue to fall, but not

as quickly as for TOCs that were re-franchised. The costs for this group of

management TOCs also remain above the costs of the other TOC category

throughout the sample (see Figure 2), this finding being statistically significant up to

and including 2005. However, by the very last year of the sample, the excess cost over

other TOCs, though positive, is no longer statistically significant (see Table 4). In

respect of Hypothesis IIA, we can therefore reject the null for the period from 2002 to

2005, but cannot reject the null for this group of TOCs in the final year of the sample.

That is, costs for this group did eventually return to normal levels whilst on the

management contract arrangements.

Taken together, from a policy perspective, these results suggest that

management contracts were bad for efficiency, particularly given that they were

allowed to persist for several years. However, following competitive re-franchising,

costs returned to industry best practice levels, which is reassuring. Whilst it appears

that after the initial cost rise, the franchising authority did have some success in

bringing downward pressure on costs through the management contract arrangements,

they did so from a very high base, and also costs came down much more slowly for

those operators that continued on the arrangements as compared to those that saw re-

11Test statistic of -0.00527 in Table 4.


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franchising. Thus costs for management TOCs were persistently, substantially higher

than other TOCs over a number of years.

Further, it may be that the cost falls among this group of management TOCs

was driven in part by the anticipation of future re-franchising as much by any pressure

brought to bear under the management contracts. The threat of competition has been

found to have impacts in a wide range of cases, quite apart from the impact of actual

competition (see for example, Evenden and Williams, 2000).

As noted earlier, to guard against endogeneity bias, we need also to consider

the profile of costs prior to the contract arrangements. We find that those TOCs which

subsequently ended up on a management contract had costs that were 19.0 per cent12

higher than best practice at privatisation (Hypothesis III A reject the null;

statistically significant at the 1 per cent level). We also find that these costs fell up to

the period directly preceding the contract shift. However we do not find that costs

were cut to levels below those for the other TOCs (Hypothesis IIIB fail to reject the

null; index of 1.0140 in year 5 (2001)13

). Thus while the TOCs which subsequently

were placed onto management contracts did make large cuts in their costs prior to

getting into difficulties, we find no evidence that they cut costs below an efficient

(and thus sustainable) level (see also Figure 2).

As also noted earlier, we would expect a different story to emerge for the re-

negotiated contracts. Once signed, these contracts should have the same incentive

12Test statistic of 0.17410; see Table 4.

13Test statistic of 0.01387; see Table 4.


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properties as the standard franchise contracts which other, unaffected operators

continued operating under. On the other hand, the annually negotiated management

contracts might be thought to have weaker incentive properties.

Figure 2 shows a similar cost profile for the TOCs which entered into re-

negotiated contracts, as management contracts. However, importantly, the cost

changes and differences are less dramatic and for most of the hypotheses we cannot

reject the null14

. In particular we do not find evidence that following re-negotiation

these TOCs had costs greater than the other TOCs (Hypothesis IB fail to reject the

null), although we do find that the costs for re-negotiated problem TOCs did increase

from the year preceding the renegotiation (Hypothesis 1A reject the null; significant

at the 0.01 per cent level). As for the management TOCs, costs started higher than

best practice at privatisation (Hypothesis III A reject the null; index of 1.2332 in

199715

; significant at the 2 per cent level), and were not cut below those of other

TOCs (Hypothesis III B fail to reject the null).

Our findings are therefore in line with prior expectations. It appears that TOCs

on re-negotiated contracts did see an increase in costs initially, which may have

resulted from the improvements in quality demanded by the franchising authority at

the time of re-negotiation. However, their costs were not statistically higher than best

practice during the period of the arrangements.

14We could not include a variable picking up the impact of competitive re-franchising on re-negotiated

contract TOCs due to parameter significance and model convergence issues most likely generated bythe small number of re-negotiated contract TOCs which were re-franchised into comparable TOCs.15

Test statistic 0.20965; see Table 4.


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Overall, our findings suggest that the decision to place TOCs onto

management contracts for an extended period led to a substantial and statistically

significant deterioration in efficiency for the affected TOCs relative to best practice.

Through careful specification of the contract dummy variables, before and after their

onset, we can reject the alternative possibility that the affected TOCs cut costs too far

during the early period to try and ensure their survival, with the consequences felt in

the later period. The TOCs which ended up on management contracts did not cut costs

below those of other TOCs prior to the onset of the temporary contract arrangements

(Hypothesis IIIB).

Rather, the evidence instead supports the hypothesis that these TOCs started

the period being less efficient than other TOCs, and then achieved partial catch-up

savings relative to other operators this being the very result that franchising would

be expected to deliver. The onset of the management contract arrangements then

weakened incentives for cost control among the affected operators and thus caused

efficiency and costs to diverge further from those of other TOCs (Figure 2). As

expected a priori, whilst the pattern of cost change is directionally similar for the re-

negotiated contracts, the effects are not statistically significant, since these contracts

retain the strong incentive properties of standard franchise agreements.

At this point we note an alternative interpretation of the above which is as

follows. The problem TOCs may have started with higher costs because of the nature

of their operations, rather than due to relative inefficiency. In bringing their costs

down to the levels of other operators, they thus reduced costs to unsustainable levels,

thus causing costs to rise later. However, we do not think that the evidence supports


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this interpretation. First of all, there is no reason to presume, a priori, that the problem

TOCs should have had higher costs than other operators (they are a mix of London

commuter, regional and intercity operators). Second our model contains a wide range

of variables to deal with heterogeneity between operators. Finally, by the end of the

sample, the cost gap between the problem TOCs and other TOCs has been closed, and

there is no evidence to date to suggest that this position is unsustainable.

Of course, from a policy perspective, when an operator runs into problems, the

franchising authority always faces a choice between taking control of operations

itself, an eventuality for which it may have call-off arrangements in place, or allowing

the incumbent to run services on a management or short-term re-negotiated contract

pending re-franchising. The option of allowing the incumbent to continue in the short

term on some kind of temporary arrangement is, of course, likely to be more

advantageous if a number of operators fail simultaneously as occurred here.

The difference in this case, however, is that the temporary contract

arrangements were allowed to persist for several years, not just for a few months

whilst waiting for the outcome of a new competitive franchise competition. As noted

earlier, there were good reasons for delaying competitive re-franchising (lack of

funding and a desire to re-draw the franchise boundaries). However, our analysis

shows that this was a costly decision. Further, given the different experience of the

two types of temporary contract arrangements employed, our findings suggest that if

the intention (a priori) is to delay competitive re-franchising, a re-negotiated contract

is preferable to an extended period on a management contract.


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As noted in the introduction, the results with regard to the effects of contracts

differ from those reported in an earlier paper by the current authors as part of a wider

study on the impact of rail franchising on productivity (Smith and Wheat, 2009).

Importantly, that paper did not adequately address the inherent problem of

endogeneity bias when testing the impact of contractual or institutional arrangements

on productivity. As a result, Smith and Wheat (2009) were not able to discern any

significant difference between the management and re-negotiated contracts which

were both found to have a substantial negative impact on productivity. Further, due to

the simplified treatment of the contract dummies, it was not possible to determine

whether costs fell following re-franchising.

Thus the comprehensive and robust approach to modelling the contracts in this

paper has advanced our understanding of their effects, in particular in respect of the

differential effects of the two alternative contract types, and the unwinding of the

effects following competitive re-franchising.

Finally, Smith and Wheat (2009) also report a general deterioration in

productivity across the sector, even the frontier firms, which is in addition to the

contract effects reported here. Cowie (2009) also reported a sector wide deterioration

in TFP over this period. Whilst not central to this paper, our model likewise shows a

deterioration in the frontier after 2000 in line with previous studies. Thus, whilst our

paper has focussed on the contract effects, further research is needed to understand the

wider trends as well.


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6.0 Conclusions

This paper has applied econometric techniques to test the impact of the British

franchising authoritys approach to dealing with failing passenger rail franchises and

the effects of the temporary contractual arrangements that were put in place. We

utilise a unique dataset which separates TOC costs from track access charges paid to

the infrastructure manager, and thus focuses attention closely on the costs under the

direct control of TOCs. We have controlled for a wide range of variables that capture

the heterogeneity between TOCs, in particular through the inclusion of train-length

alongside train-km.

We find that the franchising authoritys decision to place a large number of

TOCs on management contracts for an extended period led to a substantial

deterioration in efficiency relative to other TOCs. Furthermore, this effect was

persistent and led to costs being considerably higher than other TOCs for several

years. However, the relative inefficiency was eliminated by competitive re-

franchising for those TOCs that were subject to this process during our sample, which

is reassuring.

In contrast, we found that where the franchising authority used short-term re-

negotiated contracts, costs for these operators were not (statistically) higher than best

practice for the period of their duration. This finding is expected, since these contracts

retain the strong incentive properties of standard franchise agreements.


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Whilst the use of management contracts, pending re-franchising, may be a

useful short-term expedient following franchise failure, the analysis in this paper

shows that such arrangements can be bad for efficiency, particularly if allowed to

continue for long periods of time. Therefore, if the intention is to delay competitive

re-franchising for an extended period, for example to facilitate the re-drawing of

franchise boundaries, our analysis suggests that a re-negotiated contract is likely to be

preferable to a management contract.

In the British context, the Department for Transport has stated that it will not

re-negotiate with TOCs that run into difficulty which so far it has not done. Whilst

GNER continued to run the East Coast inter-city franchise pending re-franchising

after it ran into trouble in 2006, this arrangement was short lived and appeared to

preserve economic incentives during this period. In 2009, the Department also refused

to re-negotiate with GNERs successor, National Express, in respect of the same

franchise. The Department therefore appears to have learned the lessons of the past,

although following two franchise failures within a very short time frame, and given

the policy of not re-negotiating with private firms, the East Coast inter-city franchise

is currently (2010) being run as a nationalised firm. It remains to be seen whether this

arrangement proves to be a more satisfactory than that which would have resulted

from a management or re-negotiated contract with a private operator.

Our findings in respect of the impact of franchise contract re-negotiation on

efficiency performance have implications beyond the British rail sector, extending to

a wide range of policy situations internationally where franchising or concession

arrangements have been put in place or are being considered. Further, given the


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unenviable choice faced by franchising authorities in cases of franchise distress,

perhaps the wider lesson here is that policy makers should aim to avoid the problem

of franchise failure in the first place, through focusing on improving the bid

evaluation process to ensure deliverability, and considering changes to the way in

which risk is shared between franchisee and government. Franchise failure in

passenger rail has been much less prevalent elsewhere in Europe, for example in

Germany and Sweden, and British policy makers are considering changes to the

franchising process as part of the 2010/11 Value for Money review of the industry,

drawing, where relevant, on international best practice.

Finally, we consider that future research should focus on understanding the

reasons for more general increases in TOC costs in Britain post-2000, in addition to

those resulting from the temporary contract arrangements. Future research should also

consider how British TOC productivity trends and levels compare against

international comparators operating under different franchising regimes and, more

widely, alternative rail industry structures.


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Tables

TABLE 1

LIST OF PROBLEM TOCS

POST 2001

Cardiff Railways

Central Trains

South Central

South Eastern

Virgin Cross country

C2CMerseyrail

Northern Spirit

North Western

Scotrail

WAGN

Wales & West

Virgin West Coast

Management contract

Re-negotiated contract

Management contract

Re-negotiated contract*

Management contract

Re-negotiated contractManagement contract

Management contract

Management contract

Re-negotiated contract

Management contract

Management contract

Management contract

Source: own compilation based on SRA annual reports and TAS rail monitors

* This operator was subsequently run temporarily by the Operator of Last Resort (see section 2

above).


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TABLE 2

DATA AND SOURCES

Data SourceCosts

All TOC Expenditure (1) TOC AccountsRolling Stock Leasing Charges (2) TOC AccountsStaff Expenditure (3) TOC AccountsInfrastructure Access Charges (except electric tractioncharges) (4)

Network Rail

Other Expenditure (1)-(2)-(3)-(4)TOC Controllable Cost (dependent variable) (1) - (4)

Outputs and output characteristics (Y it; Qit)Train Density (Train-km per route-km) Network RailAverage Length of Train (Vehicle-km / Train-km)Route-kmNumber of Stations OperatedPublic Performance Measures (Delays and Cancellations)Signals Passed at Danger (SPADs)

Network RailNational Rail TrendsNational Rail TrendsNational Rail TrendsRSSB

Prices (Pit) and rolling stock characteristics (proxy for prices)Average SalaryAverage Age of Rolling StockRolling Stock Type

- EMU- DMU- Electric Locomotive- Diesel Locomotive

TOC Accounts; TASRail Monitor andPlatform 5 books

Policy variables

Dummy for One Year of Franchise RemainingProblem TOC Dummy VariablesTime Trend VariablesPlanned Franchise Length

Constructed fromOPRAF, SRA and DfTsources


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TABLE 3

PREFERRED MODEL RESULTS

Coeff. Std.Err. t-ratio P-value

Deterministic frontier parameters

ONE 5.0119 0.1239 40.4575 0.0000ROUTE 0.6946 0.0359 19.3488 0.0000

TDEN 0.7760 0.0652 11.9061 0.0000

STAT1 0.3207 0.0502 6.3916 0.0000

TIME -0.0276 0.0177 -1.5588 0.1191

INP 0.3349 0.1005 3.3316 0.0009

TDEN2 0.0382 0.0311 1.2267 0.2200

STAT12 -0.0058 0.0113 -0.5179 0.6046

TIME2 0.0020 0.0012 1.6746 0.0940

TLEN2 0.2980 0.0663 4.4957 0.0000

DENSTAT1 0.0708 0.0501 1.4127 0.1577

TDENLEN -0.1861 0.0570 -3.2645 0.0011

STAT1LN 0.0385 0.0651 0.5913 0.5543

TLEN 0.4484 0.0811 5.5274 0.0000

LFAC 0.1367 0.0722 1.8933 0.0583

ONWARDS2 0.1741 0.0195 8.9460 0.0000

_1_YEAR_ -0.0289 0.0187 -1.5408 0.1234

MANBF 0.2142 0.0709 3.0225 0.0025

MANAF 0.2449 0.0622 3.9406 0.0001

RENBF 0.2806 0.0967 2.9010 0.0037

RENAF 0.1264 0.0860 1.4691 0.1418

INTERCIT 0.4757 0.0755 6.3007 0.0000

LSE 0.1416 0.0795 1.7822 0.0747

MANBFT -0.0401 0.0116 -3.4496 0.0006

RENBFT -0.0709 0.0149 -4.7502 0.0000

MANAFTR -0.0356 0.0141 -2.5213 0.0117

RENAFTR -0.0353 0.0199 -1.7743 0.0760

LNAGE -0.0334 0.0235 -1.4207 0.1554

MANAF11 -0.1079 0.0493 -2.1872 0.0287


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TABLE 3

PREFERRED MODEL RESULTS (continued)

Coeff. Std.Err. t-ratio P-value

Inefficiency Distribution Parameters

Lambda 3.3057 0.0721 45.8449 0.0000Sigma(u) 0.2151 0.0027 79.8522 0.0000

X101T -0.3778 0.3315 -1.1395 0.2545

X102T 0.2046 0.0747 2.7402 0.0061

X103T 0.1330 0.0975 1.3643 0.1725

X104T -1.3183 1.7778 -0.7415 0.4584

X105T -0.9466 0.8498 -1.1139 0.2653

X106T -0.0508 0.1651 -0.3075 0.7585

X107T -1.1452 1.3123 -0.8726 0.3829

X108T -0.3084 0.2119 -1.4554 0.1456

X109T -0.1902 0.0629 -3.0258 0.0025

X110T -28.8320 471307 -0.0001 1.0000

X111T -0.1282 0.0567 -2.2624 0.0237

X112T 0.0882 0.0994 0.8872 0.3750

X113T 0.0676 0.0343 1.9707 0.0488

X114T -0.8042 0.4183 -1.9222 0.0546

X115T -0.3943 0.6799 -0.5799 0.5620

X116T -1.1755 1.4636 -0.8032 0.4219

X117T -0.2744 0.2727 -1.0062 0.3143

X118T -0.0798 0.0656 -1.2164 0.2238

X119T 0.1038 0.0910 1.1406 0.2540

X120T 0.1312 0.0318 4.1193 0.0000

X121T -0.2845 0.1599 -1.7798 0.0751

X122T -0.2047 0.0933 -2.1949 0.0282

X123T 0.1781 0.0853 2.0876 0.0368

X124T -0.0060 0.0652 -0.0914 0.9272

X125T 0.1568 0.0861 1.8213 0.0686

X126T -0.1243 0.1505 -0.8257 0.4090

The definitions of the variable names shown in Table 3 are as follows*:

ONE=CONSTANT

ROUTE=LN(ROUTE-KM)

TDEN=LN(TRAIN-KM/ROUTE-KM)

TLEN=LN(VEHICLE-KM/TRAIN-KM)

STAT1=NUMBER OF STATIONS**

TIME=TIME TREND VARIABLE

INP=LN(AVERAGE SALARY)

TDEN2=TDEN^2


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STAT12=STAT1^2

TIME2=TIME^2

TLEN2=TLEN^2

DENSTAT1=TDEN*STAT1

TDENLEN=TDEN*TLEN

STAT1LN=STAT1*TLEN

LFAC= LN(PASS-KM/TRAIN-KM)

ONWARDS2000=DUMMY FOR YEARS AFTER 2000

_1_YEAR_=DUMMY FOR ONE YEAR OF FRANCHISE REMAINING

INTERCIT=DUMMY FOR INTERCITY OPERATION

LSE=DUMMY FOR LONDON AND SOUTH EAST TOC OPERATION

LNAGE=LN(AVERAGE AGE OF ROLLING STOCK)

MANBF, MANAF, MANAF11, MANBFT, MANAFTR, RENBF, RENAF,

RENAFTR = CONTRACT DUMMIES AND TIME/DUMMY INTERACTIONS

SET OUT IN SECTION 3.2

X1IT = ETA PARAMETER FOR FIRM I (SEE EQUATION (5))

* Note that all data is transformed by dividing by the sample mean prior to taking logs, in order that the

first order coefficients can be interpreted as elasticities at the sample mean.

** This variable contains zero observations so could not be logged. For ease of interpretation this

variable was computed as (Number of Stations / Mean) -1.


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TABLE 4 HYPOTHESIS TESTS ON PROBLEM CONTACT EFFECTS

Hypo-thesis

Description Column 1 Column 2 Column 3Management

contract TOCs(re-franchisedduring sample)

Managementcontract TOCs(continuing on

contracts)

Re-negotiatedcontract TOCs

Testcoeff.

P value Testcoeff.

P value Testcoeff

P value

IA Did problem TOC costs

increase in the first year of

those arrangements over andabove the change in costs

for other operators? (Null:no difference in costchange between problemand other TOCs)

0.19548 0.0000 As

column 1

As

column 1

0.16514 0.0000

IB Were problem TOC costs

higher than best practice in

the first year of the

temporary contracts? (Null:

problem TOC costs notdifferent from bestpractice)

0.20935 0.0001 As

column 1

As

column 1

0.09113 0.2690

IIA Did costs fall back to the

best practice level over the

duration of the temporarycontract arrangements?

(Null: problem TOC costsfell back to best practicelevels)

Y6 0.2094

Y7 0.1738

Y8 0.1382(note a)

0.0001

0.0002

0.0022

Y6 0.2094

Y7 0.1738

Y8 0.1382Y9 0.1026

Y10 0.067

(note b)

0.0001

0.0002

0.00220.0298

0.2068

Y6 0.0911

Y7 0.0559

Y8 0.0206

0.269

0.5037

0.8172

IIB Did costs fall back to the

best practice level once the

franchise had undergone

competitive re-franchising?

(Null: problem TOC costsfell back to best practicelevels)

-0.00527 0.9348 NA NA NA

(note c)

NA

IIIA Were problem TOC costs

higher than best practice at

privatisation? (Null:problem TOC costs in linewith best practice)

0.17410 0.0059 As

column 1

As

column 1

0.20965 0.0185

IIIB Did problem TOCs cut costs

below other TOCs prior to

the temporary contract

arrangements (Null:problem TOC costs in linewith best practice)

0.01387 0.7874 As

column 1

As

column 1

-0.07400 0.3649

Notes: The tests in the above table are based on t-tests for linear combinations of the estimatedcoefficients.a) For the purpose of the above hypotheses we consider a typical problem TOC which entered into a

management contract in 2002 (year 6) and was refranchised in 2005 (year 9).b) This group of TOCs remained on management contracts throughout the sample and we record test

statistics for each year of those arrangements.

c) We could not include a variable picking up the impact of competitive re-franchising on re-negotiated contract TOCs ( (due to both parameter significance and model convergence issues most

likely generated by the small number of re-negotiated contract TOCs which were re-franchised into

comparable TOCs).


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Figures

FIGURE 1: TRAIN OPERATING COMPANY COSTS

(EXCLUDING INFRASTRUCTURE ACCESS CHARGES)

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

0

1,000

2,000

3,000

4,000

5,000

6,000

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Unitcostindex:1997=100

Costs,m,

2006prices

Source: Compiled from Train Operating Company Statutory Accounts and access

charge data provided by Network Rail.


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FIGURE 2

FRONTIERS FOR THE PROBLEM TOCS

0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

1.25

1.3

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Costf

rontierlevelrelativetoOtherTOCs

Year

Other TOCs Management TOC Renegotiated TOC Management TOC Continuing

Management

andre

negotiated

contractsstart

Lastyearof

management

contract*

* Some TOCs saw re-franchising during this period and came off their management contracts. Other

TOCs (dotted line in the above chart) continued on their management contracts to the end of the period.

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