Rail Efficiency: Cost Researchand its Implications for Policy

22

Discussion Paper 2014 • 22
Andrew S.J. Smith andChristopher Nash

Institute for Transport Studies
Leeds University, United Kingdom

Rail Efficiency: Cost Research and Its
Implications for Policy

Discussion Paper 2014-22
Prepared for the Roundtable:

Efficiency in Railway Operations andInfrastructure Management
18-19 November 2014,
International Energy Agency, Paris, France

Andrew S.J. Smith
Christopher Nash1
Institute for Transport Studies
Leeds University,
United Kingdom

December 20141

We also acknowledge the contribution of Phil Wheat to material on which this paper is partly
based.

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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

Table of Contents

ABSTRACT ............................................................................................................................................. 4
1. INTRODUCTION ................................................................................................................................ 4
2. TECHNICAL EFFICIENCY ............................................................................................................... 5
Introduction .......................................................................................................................................... 5
Outputs.................................................................................................................................................. 7
Inputs .................................................................................................................................................... 8
Problems in measuring technical efficiency ......................................................................................... 9Concluding comments .......................................................................................................................... 9
3. COST FUNCTION ESTIMATION ................................................................................................... 10
Introduction ........................................................................................................................................ 10
Cost function estimation ..................................................................................................................... 10
Stochastic frontier methods: Introduction........................................................................................... 11
Stochastic frontier methods: Panel data approaches ........................................................................... 13
Conclusion .......................................................................................................................................... 14
4. RAIL PRIVATISATION IN BRITAIN ............................................................................................. 15
Introduction ........................................................................................................................................ 15
British passenger train operating costs ............................................................................................... 17
Infrastructure costs.............................................................................................................................. 23
5. EUROPEAN RAIL SYSTEMS ......................................................................................................... 27
6. CONCLUSIONS ................................................................................................................................ 29
BIBLIOGRAPHY .................................................................................................................................. 31

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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

Abstract

In this paper we first consider alternative measures of efficiency. We explain why simple partial
productivity measures are inadequate as the basis of overall measures of efficiency, and outline twoalternative approaches. The first is technical efficiency – the degree to which output is maximised fora given level of inputs (or conversely inputs are minimised for a given output) – and the second is cost
efficiency, the degree to which costs are minimised for a given level of output. Cost efficiency implies
technical efficiency but also allocative efficiency – choosing a cost minimising mix of inputs. We
explain why we prefer to measure cost efficiency, both in terms of what governments and regulators
are interested in and in terms of practical data problems.
We then examine applications of cost function analysis to two areas. The first is rail privatisation in
Britain. British experience has seen a large increase in traffic, but also a similar increase in costs. We
review attempts to understand and explain both the increase in passenger train operating cost andinfrastructure cost using cost function analysis. The second is European rail reform. Countries in
Europe have adopted a wide variety of approaches to rail reform, and studies using a mix of European
and other countries should be able to shed light on the important question of what works best indifferent circumstances. Finally we consider how efficiency analysis techniques need to develop in
future to address current weaknesses and tackle new challenges.

1. Introduction

Studies of rail efficiency usually have one of two motivations. Firstly they may aim to identify which
railways are efficient and which are not, in order to draw lessons as to the level of improvement that
may be required. An example of this is the benchmarking studies conducted on behalf of the British
rail regulator in deciding on the financial requirements of Network Rail, the infrastructure manager
discussed below (see, for example, Smith et al., 2010). Secondly studies may seek to draw policy
conclusions about which policies regarding industry structure, competition and regulation will be mostbeneficial. We consider both aspects in this paper.
In sectors of the economy in which markets are a reasonable approximation to perfectly competitive, areasonable measure of overall efficiency may simply be the profitability of the firm. Under perfectcompetition, prices are not influenced by the individual firm and therefore the more profitable thefirm, the more it has been able to minimise costs of production and to produce the most valuable
combination of goods in the eyes of consumers.
But rail is a long way from being a perfectly competitive industry. In some sectors (e.g. coal,commuters) rail operators still have considerable monopoly power, whilst for this and other social
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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

reasons rail prices are often regulated by governments, who also play a key role in specifying
passenger sector outputs. If the aim is to examine the efficiency of railway management, these factors
must be allowed for. To the extent that railway managers (at least in the European passenger sector)
have limited control over their outputs, the key issue is whether they produce them at minimum cost.
Economists are very used to distinguishing between technical efficiency and allocative efficiency.
Technical efficiency is measured by whether output is maximised for a given level of inputs (orconversely inputs are minimised for a given output). The standard economic approach to examine thisis to estimate a production function using econometric methods, although non-parametric methods –such as data envelopment analysis (DEA) – have also been used. This approach is considered further
in the next section.
Allocative efficiency considers whether the correct mix of inputs is used to minimise cost for a given
level and quality of output. Cost efficiency, which is the product of technical and allocative efficiency,
and thus takes both into account, is the subject of the following section. We then examine evidence on
both the efficiency impact of the approach to railway reform taken in Britain, and the relative
efficiency impact of the variety of approaches taken around Europe before seeking to reachconclusions.

2. Technical Efficiency

Introduction
The material in this section draws partly on Smith (2004) and Nash and Smith (2007).
Before turning to discuss more advanced measures it is first worth considering why it is important togo beyond simple partial productivity measures - such as train kilometres per member of staff or per
locomotive - which are often used as measures of technical efficiency. The first point to note is thatthey are partial measures and cannot therefore lead to conclusions about overall efficiency (unless thesame railway is more efficient on every measure). Further, they are often impacted significantly bycapital substitution effects (where capital is substituted for labour, therefore improving labour
productivity). In a multi-input, multi-output environment such as the railways, the concept of totalfactor productivity (TFP) provides a more informative indicator of technical efficiency.
TFP is a measure of the ratio of all outputs to all inputs (with the different inputs and outputs weighted
in some way). Under perfect competition, prices may be used as the weights so the measure returns to
one of the ratio of the value of outputs to the value of inputs, or in other words profitability. Of course,
the underlying assumptions of constant returns to scale/density and marginal cost pricing are highly
restrictive, especially in the context of the world’s railway systems, which are characterised by highquasi-fixed costs and are heavily regulated. These restrictive assumptions mean that TFP measures areunable to distinguish changes in technical efficiency from underlying technical change and fromchanges in TFP resulting from scale or density effects or departures of prices from marginal cost.
Figure 1 illustrates, for the single input, single output case, how inefficiency (the gap between a firm’s
input-output combination and the frontier) differs from productivity effects (moving along the
production frontier). Compared to input level x1, it can be seen that productivity changes if there is a
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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

move along the frontier to x3. Likewise, a move to x2, which represents an inefficient level ofproduction, also results in a change in productivity. Overtime the production frontier may shift
outwards (to g (x) in the diagram), driven by technical change.
Figure 1. Single input production frontier

y

g(x)
f(x)

y

0

x3

x1

x2

x

One way of dealing with this problem is to estimate a cost function using econometric methods (seesection 3 below). This approach allows the estimation process to calculate the elasticities of cost withrespect to the outputs (and in turn the extent of returns to scale and density), productivity growth
resulting from technical change, and changes in efficiency. Data envelopment analysis (DEA) is analternative index number method for deriving productivity and efficiency measures. It is a nonparametric approach, in which the efficiency frontier is computed using linear programming
techniques (rather than estimated, using econometric methods; see section 3). The method permits theassumption of variable returns to scale and Malmquist productivity indices can be computed from suchmodels that also allow the decomposition of TFP changes into technical efficiency, scale efficiency
and technical change.
One challenge that the DEA approach faces is its difficulty in characterising the technology, since theintroduction of numerous inputs and outputs into the DEA tends to result in all firms being on thefrontier. It can thus be hard to distinguish between, for example, economies of scale and density. One
way in which the approach can be augmented to deal with this criticism is through the use of a second
stage, in which the efficiency scores from the first stage are regressed on a range of factors, such as
train density and load factor (and indeed policy variables, such as the degree of competition).
However, the approach then starts to look much more like an econometric model, and it raises aquestion as to why adopt a two stage approach, and instead simply estimate an econometric model. A
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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

remaining, major weakness of DEA is its inability to take account of random noise, meaning thatmeasures of inefficiency can be overstated (or efficiency understated).
One supposed advantage, historically, of DEA has been its ability to deal with multiple inputs andoutputs without recourse to potentially restrictive behavioural assumptions (for example, cost
minimisation, as required in econometric cost function estimation). Another is the fact that it does not
require the specification of a functional form for the underlying technology. However, both theseadvantages have been largely eliminated by the widespread use of flexible functional forms (e.g. thetranslog) and by the development of econometric methods for estimating distance functions (seeGroskopf et al, 1997).
Overall, the most advanced approaches and widely used approaches in the academic literature for
estimating technical efficiency are econometric distance functions (see for example, Kennedy andSmith, 2004 and Coelli and Perelman, 1999) and DEA (see, for example, Cantos et. al., 2010.
However, as will be discussed in section 3, econometric cost function / cost frontier estimation has
numerous benefits, and this approach dominates the empirical discussion in this paper.
Importantly, whatever approach is adopted, a decision is needed as to what should be the relevant
outputs and inputs, as well as associated output characteristic variables.

Outputs
At its simplest, transportation output may be regarded as the transport of passengers or freight. Thus
measures such as passenger kilometres and freight tonne-kilometres are the usual starting point for
output measurement. However, there are grave shortcomings with such simple measures of output.
Multiplicity of outputs is a common feature of transport firms. Strictly, an output needs to be describedin terms of the provision of transport of a specific quality from a specific origin to a specificdestination at a specific point in time. Thus an operator of rail passenger services running trains
between ten stations ten times per day and offering two classes of travel is already producing 1800
different products. A large European railway will have literally millions of products on offer. Of
course, it is not possible to provide cost or performance measures that separately identify eachproduct.
This is only really a problem if the different products have significantly different cost characteristics,
and traffic on them is growing or declining at different rates. For instance, if it costs a similar amountto transport passengers (per km) between London and Leeds and London and Manchester, thenperformance measures will not be distorted by regarding these as the same product. On the other hand,
failure to identify different traffic having very different costs will be very distorting. For instance, partof the rapid improvement in productivity of British Rail freight wagons in the 1980s was because of
the decline and eventual abolition of movement of single wagonloads in favour of movement of traffic
in full trainloads.
In passenger rail transport, longer distance, faster moving traffic and traffic moving in large volumes
generally costs less per passenger-kilometre to handle than short distance traffic or traffic that mustmove slowly and in small volumes. This is because of the spreading of terminal costs and theeconomies of operating longer trains. Peaks in demand also lead to poor productivity by requiring theprovision of a lot of resources that are only used for a small part of the day. Such peaks in demand arelikely to be an issue in urban public transportation operations, with some services being more peaked
than others. Thus a fundamental distinction is between different types of rail passenger traffic such as
inter-city, suburban and regional. Economies of density occur when adding more traffic to the same
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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

network leads to a less than proportionate increase in cost, whereas economies of scale occur when agiven increase in both network size and traffic leads to a less than proportionate increase in cost.
In any event, frequency of service is an important quality attribute. A transportation manager who was
simply wishing to minimize costs – for a given number of passenger kilometres - might run one highcapacity service per day, but this would not be very attractive to customers. No sensible transportation
manager will provide the frequency of service that minimizes costs if a more frequent service will
improve net revenue or benefits. This suggests that, unless a way can be devised of adjusting
passenger and freight-tonne-kilometres for the quality of service provided, a more radical change tothe output unit to train-kilometres rather than passenger- or freight-tonne-kilometres might bedesirable (it will still be necessary to disaggregate train-kilometres according to their costcharacteristics, as it costs much more to shift a 5000 tonne freight train than a two-car branch line
passenger train). The use of vehicle-kilometres in place of train-kilometres may be a helpful further
refinement of the train-kilometre measure, although this measure will still not correct for different
weights of train. Certainly, to regard operations where rolling stock is grossly overloaded, as for
instance in some developing countries, as therefore performing well - even if they are producing theservice itself very inefficiently - seems mistaken.
Freight traffic is particularly complex because of the lack of a homogenous unit of measurement; atleast in passenger transport we are always dealing with people. A tonne of freight may cost very
different amounts to transport according to whether it is a dense product or not (for a dense product asingle wagon will contain far more tonnes than for a product that is not dense) and the form it is in(bulk solids or liquids may be loaded and unloaded much more simply than manufactured goods,
although the latter will be easier to handle if they are containerized). It follows that loaded-wagonkilometres may be a better unit of measurement than tonne-kilometres, and that distinctions may beneeded between trainload, wagonload and container or intermodal traffic. If tonne-kilometres are used,a distinction by commodity is important; for instance, a railway that has declining coal traffic andrapidly growing intermodal traffic will almost certainly show declining productivity if tonnekilometres are the measure.

InputsProviding a rail service requires locomotives, passenger coaches or freight wagons (or self-powered
vehicles), track, signalling, terminals and a variety of types of staff (train crew, signalling, track androlling stock maintenance, terminals and administration). While ultimately all may be regarded asforms of labour and capital, the length of life of the assets and government intervention over
employment and investment will often mean that at a particular point in time an undertaking will nothave an optimal configuration of assets and staff. This renders attempts to measure inputs simply as
labour and capital difficult, as measures of the value of capital stock will need to allow for excess
capacity and inappropriate investment. An alternative is to simply look at physical measures of assets(e.g. kilometres of track, numbers of locomotives, carriages and wagons for railways), but thisobviously makes no allowance for the quality of the assets. Further, and importantly, physicalmeasures such as staff numbers can be greatly affected by contracting out and, unless a balancing item
such as other costs is included as an input, substantially distort measures of productivity andefficiency.

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Problems in measuring technical efficiency
A key problem in measuring technical efficiency is that of joint costs and economies of scale anddensity. For instance, a single-track railway may carry both passenger and freight traffic, a passenger
train first- and second-class passengers, and a freight train a variety of commodities. In this situation,
only some of the costs can be specifically attributed to one of the forms of traffic; the remaining costs
are joint. The result is that railways typically are characterized by economies of scope; i.e., the costs ofa single railway handling a variety of types of traffic are less than if each distinct product were to behandled by a different railway. Moreover, most evidence suggests that railways are subject toeconomies of traffic density. Putting more traffic on the same route generally reduces unit costs andraises measures of total factor productivity, unless the route is already heavily congested.
The result is that apparent rises in productivity may be caused by diversification into new products orby increased traffic density rather than being relevant to the measurement of performance. Of course,
under conditions of economies of density, running more services (and possibly different types ofservice) on the network does lead to a genuine improvement in productivity. The argument here,
however, is that the improvement in productivity arises naturally as a result of the shape of the costfunction, and not because of any improvement in working practices.
The operating environment will also exert a strong influence on railway performance through its
impact on the nature of the traffic carried. This has already been considered above. However,geography has other influences as well; gradient, climate and complexity of the network are all likely
to influence costs. The quality of the service delivered will also impact on costs, for example in termsof the rolling stock used (e.g. air conditioned trains; trains that give greater access for disabled users),
and more widely the punctuality and reliability of services. Other factors, such as the extent ofpassenger information provided and the quality of on-board catering services for rail will also affectcosts. Particularly in the case of rail, the quality of the service will depend critically also on thecapability of the infrastructure.
Of course, to the extent that the method used contains relevant measures of outputs and output
characteristics such that it can capture some of these features of the technology (e.g. scale and density
effects; quality; network complexity), then it should be possible to obtain measures of technicalefficiency after having taken account of these effects. We consider that econometric methods, as
opposed to DEA or partial or TFP measures give the best opportunities for getting at underlying
technical efficiency; though as noted, DEA combined with a second stage econometric model is a
useful alternative. As we will argue in section 3, we consider cost econometric models to be the mostsuitable to achieving the objectives set out earlier, namely assessing relative efficiency, andunderstanding the impact of industry structure.

Concluding comments
There are many studies of railway technical efficiency using total factor productivity or DEA
approaches. However, in addition to the problems of measuring outputs and inputs, there is a severe
difficulty in terms of the data for this form of analysis. Typically, such approaches include physicalmeasures of the labour input, combined with physical measures of the infrastructure and rolling stock
measured in simple terms (track-km and number of rolling stocks). Usually other inputs are excluded
which, given that there are varying degrees of subcontracting in the rail sector (rolling stock may beleased, maintenance and cleaning of rolling stock and stations may be contracted out, as may track
maintenance and renewals and so on), this risks giving misleading results. For that reason as well as
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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

the technical reasons given above, we prefer studies based on costs, which should be comprehensive
measures including all contracted out items. Moreover, it is costs ultimately that governments andregulators are most interested in, not measures of physical productivity.

3. Cost Function Estimation

Introduction
In addition to the reasons outlined for preferring the approach of estimating cost functions, there is anadditional practical reason, namely that data is more reliable, although there remain problems ofinconsistencies in treatment of costs such as depreciation and interest, particularly in international
comparisons, but even in one country over time. The problem is not simply different assumptions
about asset lives. In some cases, where assets are purchased with grants, no depreciation or interest isentered into the accounts. In some cases historic debts have been written off; in other cases interest is
still charged on them. Getting consistent data remains a challenge.
The problems regarding how to measure inputs and outputs also remain, as does the issue of theoperating environment. How these problems may be handled in cost function analysis is consideredbelow. However, overall, the cost function approach does at least ensure that all inputs are considered
and the allocative efficiency (or inefficiency) associated with using different input combinations
accounted for. This compares to technical efficiency analysis which first of all does not, of course,
take account of allocative efficiency, but perhaps more importantly for analysis of railways, often
neglects some inputs (i.e. those represented by other costs; see section 2).

Cost function estimation
Derived from assuming cost minimisation in a production process, the cost function relates costs (C)to the level of outputs (Y) and input prices (P) and, where data is available over time, some measure of
how costs change over time (t) as a result of technical change. Thus

Cit  C Yit , Pit , t (1)It therefore automatically allows for one key issue in comparing costs between railways in different
countries, and in a single country over time, namely different input prices.
There has been a vast array of functional forms proposed for the cost function. Notable developments
include the constant elasticity of substitution (CES, Arrow et al (1961)) and generalised Leontief(Diewert, 1971). The most widely employed cost function is the Translog (Christensen, Jorgenson andLau 1971, 1973). The Translog nests the simpler, and widely-used Cobb Douglas function (see, forexample Beattie and Taylor, 1985) as a special (restricted) case, however it is not derived from any
production function using duality theory. Instead the Translog cost function is usually presented as afunctional form which is a second order approximation to any cost function rather than being derived
directly from economic theory. The general form of the Translog cost function for m outputs and n
inputs is represented as:
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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

m

n

i 1

i 1

ln C   0   i ln y i    i ln w i n

1 m m ij ln yi ln y j
2 i 1 i 1

n

1
1 m n
   ij ln w i ln w j   ij ln y i ln w j
2 i 1 i 12 i 1 i 1

(2)

The function includes both first and second order terms in all variables. Importantly, the Translog
allows for elasticities and marginal costs to vary flexibly with the level of outputs and prices. In thissense the Translog does have appealing economic characteristics, such as the ability to deal withvarying degrees of returns to scale and density as firm size varies.
Finally we note that the Translog cost function is often estimated along with the factor shareequations. Factor share equations are expressions for the proportion of total cost used by each input
and are derived using Shephard’s (1953) lemma as the partial derivative of the cost function withrespect to each input price. Estimation can then proceed using Zellner’s (1962) Seemingly Unrelated
Regression (SUR) which is more efficient (in terms of estimation) than single equation ordinary leastsquares.
When it comes to measuring outputs, the need to distinguish between scale and density effects or the
choice between passenger and freight tonne or train or vehicle km is only part of the wider issue of
how to account for the heterogeneity of railway outputs. One way to deal with the heterogeneity in
outputs is to group outputs (denoted y) into m groups and include a further set of r variables whichcharacterise the outputs (denoted q):Cy1 ,, y m , q1 ,, q r , w1 ,, w n (3)

The move from potentially hundreds or thousands of outputs to a more manageable number of m
outputs is obviously a simplification. However the inclusion of output characteristic variables is anattempt to reintroduce heterogeneity in outputs back into the model. Such variables may include
revenue measures (such as passenger-km and freight tonnes-hauled) where availability measures suchas train-km are adopted as output and vice-versa. As such it can become a little ambiguous as to what
variables represent outputs versus output characteristics versus network size.
Wheat and Smith (2014) is an attempt to introduce heterogeneity by means on a hedonic cost function(Spady and Friedlaender, 1978 and Bitzan and Wilson, 2008). Under this approach there is only one
output as opposed to n outputs (this is relaxed in some applications, such as Wheat and Smith, 2014).
Second the output and output characteristic variables enter into their own function and this then enters
into the general cost function. The benefit of this approach is a more parsimonious model. It is perhaps
surprising that there have not been too many applications of hedonic cost functions in transportation
operations. In Wheat and Smith (2014), the model included three outputs (train hours, route length andnumber of stations operated) and many characteristic variables relating to the train hours output. This
analysis provided rich insights into the impact of output heterogeneity on economies of scale anddensity (see section 4), whilst enabling a parsimonious model.

Stochastic frontier methods: Introduction
The above discussion has focused on the relationship between costs, outputs, output characteristics
and input prices. As noted earlier, a key motivation for policy makers is to understand the relative cost

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efficiency of transport operators. The cost function relationships discussed above can be augmented toallow the relative efficiency of companies to vary and for this degree of variation to be estimated.
The efficiency measurement literature cites three functions which may be estimated, depending on theappropriate behavioural assumption: cost functions, production functions or distance functions (thelatter two are focused on technical efficiency; see section 2). Most applications in railways are basedon cost functions, reflecting the fact that, due to the highly regulated environment in which mostrailways operate, it is appropriate to view railways as seeking to minimise cost for a given level of
output (where the latter is more or less determined by government). In this section we focus on costfunction relationships or, more precisely now that we are introducing inefficiency into the approach,cost frontier relationships.
The simplest econometric approach is to use the method of corrected ordinary least squares (COLS).
This method proceeds by ordinary least squares (OLS), but then shifts the regression line down by theamount of the largest negative residual (for the cost function case), thus translating an “average” costline into a cost frontier. However, like DEA, the COLS method is a deterministic approach which does
not distinguish between genuine inefficiency and statistical noise when looking at deviations from thefrontier. It is however, with suitable adjustments, widely used by UK economic regulators, in part due
to its simplicity.
The alternative and more widely used method in the academic literature (and increasingly byeconomic regulators) is stochastic frontier analysis (SFA); see equation (4) below. The stochastic cost
frontier model can be represented as:

Cit  f (Yit , Pit , N it , t ;  )  vit  uit(4)

where the first term ( f (Yit , Pit , Qit , t ;  ) ) is the deterministic component, and Yit is a vector of
output measures, Pit is a vector of input prices, N it is a vector of exogenous network characteristic
variables,  t is a vector of time variables which represent technical change and  is a vector ofparameters to be estimated. C it represents the cost variable to be explained. The i and t subscripts
refer to the number of firms and time periods respectively. Whilst some applications may use only
cross sectional data, most railway applications utilise panel data, and this type of data greatly expands
the possibilities for increasing the richness of the analysis in a number of ways as discussed further
below. The vit term is a random component representing unobservable factors that affect the firm’s
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 ( uit ).
For cross-sectional data, it is necessary to make distributional assumptions concerning the one-side
inefficiency term, and the estimation proceeds via maximum likelihood. This is a significant limitation
as these assumptions may not be valid. For panel data, there are additional estimation possibilities.
Before turning to the panel data approaches it is worth summarising the benefits of the econometricmethods for studying the structure of railway costs and relative efficiency performance.
Compared to cost function (or average response function estimation) it is clear that frontier methods
are a significant development since they explicitly allow for the possibility of variation in efficiency
performance between railways and over time. Compared with the DEA approach, econometricmethods provide estimates of the underlying structure of production/costs, for example, the elasticity
of costs with respect to different cost drivers, such as traffic volumes - which DEA does not. As noted,
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the study of these elasticities allows us to say something about the scale and density characteristics of
the industry.
In addition, through the development of stochastic frontier analysis, econometric techniques are alsoable to distinguish between random noise and underlying inefficiency effects. However, econometricapproaches do require the choice of an appropriate functional form, and the more flexible forms (suchas the translog) are not always straightforward to implement due to the large number of parameters tobe estimated. In addition, the choice of distribution for the inefficiency term in stochastic frontier
analysis is arbitrary. The precise method that researchers should use will therefore depend on a range
of factors, and in many academic papers more than one method is used in order to provide a crosscheck against the other approaches.

Stochastic frontier methods: Panel data approaches
The existence of panel data offers a number of important benefits. First of all, by combining crosssectional and time series observations it provides additional degrees of freedom for estimation. Thismay be very important, particularly if the number of companies for which data exists is small as it
often is for economic regulators. Second, it provides an opportunity to simultaneously investigate
inter-firm efficiency disparities, changes in firm efficiency performance over time, as well as industrywide technological change over the period of the study. Third it can, for some models, permit theestimation of firm efficiency without recourse to potentially restrictive distributional assumptions.
Finally, it offers the prospect of disentangling inefficiency from unobserved factors. This latter benefit
may be particularly important for railways, where substantial differences exist between railways bothwithin and between countries, but where it is hard to capture these differences in a set of variables tobe included in the model.
One way of dealing with a panel is to treat each data point as a separate firm. In this case, eachobservation, including observations for the same firm over multiple time periods, is given a separate
efficiency score. In the case of econometric estimation this assumption may not be appropriate, since it
assumes that inefficiency is independently distributed across observations, even though it might be
expected that an inefficient firm in one period is likely to retain at least some of that inefficiency in thenext period.
The alternative and more usual approach is explicitly to recognise the panel nature of the data set.
Within this alternative, there are two further options. Firstly, to estimate the model using traditional
panel data methods (fixed effects or random effects (GLS)); see Schmidt and Sickles (1984).
Alternatively, Pitt and Lee (1981) offer a maximum likelihood version of the same approach. In bothcases, inefficiency is assumed to be “time-invariant” and each firm is given one efficiency score forthe whole period, rather than one score per firm for each period as in the simple pooled approach. The
advantage of the traditional panel approach (fixed and random effects) is that it does not require
distributional assumptions concerning the inefficiency term as in the maximum likelihood equivalent.
This benefit does come at a cost though, as it requires the assumption that inefficiency does not varyover time, which is restrictive.
For long time periods, the assumption of time invariant inefficiency is clearly problematic, and anumber of approaches which allow for inefficiency to vary, whilst retaining some structure to thevariation, have been developed. Time varying models have been developed for both the traditional
panel data methods (e.g. Cornwell, Schmidt and Sickles, 1990, and the maximum likelihood approach(e.g. Battese and Coelli, 1992; Cuesta, 2000. Kumbhakar and Lovell (2000) describe these approaches
in detail. A key distinction in the literature is between those models which make the assumption of
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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

independence in inefficiency over time (e.g. pooled SFA; Battese and Coelli, 1995) and those which
permit firm inefficiency to change in a structured and not random way over time (Cuesta, 2000). The
latter seem to have advantages from a regulatory and economic perspective.
An important and relatively recent development in the literature has revolved around the problem ofdisentangling inefficiency from unobserved heterogeneity. In the standard panel literature, fixed andrandom effects are assumed to represent unobserved, time invariant factors that vary between firms.
As noted, in the efficiency literature, these models have been applied as efficiency estimation
approaches, with the firm effects re-interpreted as inefficiency. This approach risks badging
unobserved factors – genuine heterogeneity between railways – as inefficiency. Methods havetherefore been developed in the literature to address this (Greene, 2005; Farsi et al., 2005; Kumbhakar
et al., 2014; Colombi et al., 2014). One version of Greene’s approach includes a firm-specific dummy,
to capture unobserved heterogeneity between firms, which is assumed to be time invariant (e.g.
environmental factors, such as topography or climate) as well as the one-side inefficiency term (which
varies over time). The decomposition therefore relies on the assumption that inefficiency varies
randomly over time whereas unobserved heterogeneity is time invariant (as well as on the
distributional assumptions of the model). The model is then estimated via maximum likelihood. This is
one of the so-called “true” models, and there is also a random effects version of this approach.
The Farsi et al. (2005) approach separates inefficiency from unobserved heterogeneity by making theassumption that the former is assumed not to be correlated with the regressors whilst the latter may be(inefficiency being a function of the ability of management to control costs given the exogenous set of
output requirements and input prices that it faces – hence this would not be expected to be correlatedwith the regressors). Finally, the approaches set out by Kumbhakar et al., 2014; Colombi et al., 2014seek to go further and separate the model residual into four components: random noise, time varying
inefficiency, time invariant inefficiency and time invariant unobserved heterogeneity. This model
relies entirely on distributional assumptions to make this separation, which is a limitation. It further
assumes that unobserved heterogeneity is uncorrelated with the regressors, which may not be valid. It
is worth noting that these are relatively new approaches with relatively few applications as yet, andnone, to our knowledge in railways.

Conclusion
To conclude, then, there exists a wide range of methods for estimating cost inefficiency in theliterature, some of them relatively simple and widely used, particularly by regulators, and others more
complex. However, some of the more complex methods are now entering the economic regulation
sphere in the UK, for example, ORR has adopted a range of advanced methods, and others, such as
OFWAT, have considered these approaches at least, though to date has fallen back on simpler
methods, given the data and results obtained. Panel methods offer much more scope for a rich analysisof the cost structure (economies of scale and density) and inefficiency and these are the most widelyused in railways. The question of dealing with random noise and heterogeneity (observed andunobserved) remains a key issue for all regulators in railways and other sectors. Ultimately, the choiceof technique will depend on a number of factors, including the number of data points, availability of
cost driver data, model performance, economic theory and practical considerations. Usually, it isappropriate to run a range of approaches and compare the results and in some cases it will not be
possible easily to choose between them. Economic regulators in that case tend to average the fficiency
results across a range of models.

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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

4. Rail Privatisation in Britain

Introduction
Over the period 1994-97, the British rail system underwent the most radical transformation of any
European railway. Infrastructure was separated from train operations, and the new infrastructurecompany (Railtrack) privatised by sale of shares. The freight sector was privatised essentially as twocompanies and open access for new entrants permitted. The passenger sector was largely franchisedout in the form of 25 franchises, offered for a period in most cases of 7-10 years, on a net cost basis
but with requirements as to what services should be operated and restrictions on the levels of somefares.
In the period since privatisation (1995 to 2010) passenger-km growth has been faster than in all othermajor European railways (Brown, 2013). To provide a few specific examples, between 1995-2010,
passenger km increased 84% (Britain); 65% (Sweden); and 17% (Germany)2.
In the passenger market, many studies have shown that economic growth is the main driver ofincreases in demand. However, one major change which started in the 1990s was that the degree towhich car was increasing its competitiveness declined markedly. Growth in car ownership and use
greatly slowed down (and actually fell amongst men under 30), car journey times (which had greatly
improved with construction of the motorway network) worsened as road building slowed andcongestion worsened, car operating costs rose after decades of decline (partly due to government
taxation). Thus the degree to which increases in traffic due to economic growth were offset by loss of
traffic to car was greatly reduced. Even to the extent that increases in rail traffic were brought about by
improvements in services, reductions in regulated fares and new rolling stock, in a franchise systemmuch of this improvement was prescribed by and paid for by government. A residual effect attributed
to privatisation is a small share of the total (see Wardman, 2006).
Freight market growth has also been strong. From 1996-67 to 2012-3 rail freight grew from
15.1 billion tonne-kms to 21.5 billion tonne-kms – i.e. by 6.4 billion tonne-kms. Of this increase,
3.6 billion was coal and from 1998-99 (earlier data not available), 2.8 billion was domestic intermodal(the name is slightly confusing as this is nearly all containers to and from ports). In other words, thesetwo commodities account for all the growth – collectively other commodities stayed constant.
Over this period, coal tonnes transported declined from 52.2 m to 52.0 m – in other words the growth
was entirely increased length of haul. This was brought about by changes in supply of coal fromdomestic pits to imported coal brought in at deep water ports remote from the power stations. The
growth in containers was partly due to increases in overall international container movements,
although rail has increased its market share.
So whilst restructuring and privatisation may have contributed to the strong performance of the British
system, the major causes of it lie elsewhere. However, the story regarding passenger train and
2

Source: European Commission Transport in Figures.

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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

infrastructure costs is not so positive (Table 1). Both remain higher per train kilometre than at the startof the process, when one might have expected that economies of traffic density would have led them
to fall. The biggest increase is in infrastructure costs. Under strong regulatory pressure, Network
Rail’s costs have been falling in recent years, although the company is not improving efficiency as
quickly as the regulator would like.
To understand the drivers behind these trends we will first consider studies of passenger train
operating costs and then infrastructure costs to try to understand the reasons for this increase.

Table 1. Rail Industry Costs in Britain (Infrastructure andPassenger Train Operations): 1997 to 2012

Costs (£b2011/12 prices)

1996/97 2005/6

2011/12

Infrastructure expenditure
Maintenance

1.1

1.4

1

Renewals and enhancements

1.5

3.7

4.6

1

1.4

1.5

3.5

6.5

7.1

TOC costs less access charge
payments

4.2

5.8

5.9

Total passenger rail costs

7.7

12.3

13

Total passenger rail costs perpassenger train km

20.2

27

25.4

Infrastructure costs perpassenger train km

9.2

14.4

13.9

TOC costs (excluding accesscharges) per passenger train km

11

12.6

11.5

Other operating costs

Unit cost measures (£)

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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

British passenger train operating costs
There have been many studies of passenger train operating costs in Britain, including, Affuso,
Angeriz, and Pollitt (2003); Cowie (2002a, 2002b, 2005 and 2009); Smith and Wheat (2012a), Wheat
and Smith (2014) and Smith, Nash, and Wheat (2009). Preston (2008) provides a review of, inter alia,
previous cost studies of the British rail sector. A variety of methods including non-parametric DEA(Affuso et al., 2003; Cowie, 2009, Merkert et al., 2009) and index number approaches (Cowie, 2002a;
Smith et al., 2009), as well as parametric estimation of cost functions (Cowie, 2002b; Smith andWheat, 2012a, Wheat and Smith, 2014), production functions (Cowie, 2005) and distance functions(Affuso et al., 2003). Clearly the former methods (DEA and index number approaches) can only
consider cost or technical efficiency and produce no estimates regarding the actual cost structure.
Importantly, of the British TOC cost studies, only four cover the crucial period after 2000 when TOC
costs started to rise substantially, these being Cowie (2009), Smith et al. (2009), Smith and Wheat(2012a) and Wheat and Smith (2014). Cowie (2009) covers the period to 2004, whilst the two further
papers cover the period to 2006. Wheat and Smith (2014) provide analysis up to 2010.
An important issue is whether to include an infrastructure input in any analysis of train operating
costs. Clearly the infrastructure input may be an important part of the transformation function and soshould be considered for inclusion in any analysis. The four papers by Cowie all include somemeasure of infrastructure input in the analysis which is some combination of route-km and accesscharges paid by operators to the infrastructure manager (to form a price if applicable).
This in turn raises two important and related problems: (1) the infrastructure input is hard to measure(this is particularly the case in Britain where access charges change significantly from year to year
depending on the degree of subsidy); and (2) the inclusion of this input turns the analysis into anassessment of rail industry costs/production, rather than being targeted on the TOCs. In their study,
Affuso et al. (2003) produce two models: one including the infrastructure input, and one not. The
results differ as a consequence, although this problem is less severe during the early period after
privatisation (which the study covers), since access charges and infrastructure costs were fairly stable
during that period. Whilst there are good reasons for capturing the infrastructure input in a study of
TOC performance, to capture the possibility that this input affects the TOC transformation function,
Smith and Wheat (2012a) and Wheat and Smith (2014) argue that, given the measurement problems
noted above, infrastructure inputs are best left out of the analysis. The dependent variable in theirpaper is thus defined as TOC costs, excluding fixed access charges. Route-km is also included as an
explanatory variable in their model, not as a measure of the infrastructure input, but to distinguishbetween scale and density effects.
The focus of Smith and Wheat (2012a) was on the impact on cost efficiency of contract regimes after
several renegotiations and temporary contracts being introduced following franchise failure. It used apanel data stochastic frontier framework which allowed efficiency to evolve over time (based on
Cuesta, 2000; see section 3). They also included dummy variables in the cost function to allow theextent of cost effects of different contract types to be directly estimated.
The focus of the Wheat and Smith (2014) work, in contrast, was how to best model the cost structureof the industry. This work utilised a hedonic cost function (see section 3.2) and the description of thedata used is given in Table 2. In particular they defined three generic outputs (route-kms, train-hours
and number of stations operated) and defined nine characteristics of train services which go into thetrain-hours output function. These characteristics control for the heterogeneity in train service
provision. They also define two inputs and associated prices.

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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

Table 2. Data used in Wheat and Smith (2014)
Symbol Name
Route-kmy1

y2

Train Hours

q12

Average vehicle lengthof trains
Average speed

q 22
q32

q 42
q52
q 62
q 72
q82
q92

y3

Description
Data Source
Length of the line-km operated by National Rail Trends
the TOC. A measure of thegeographical coverage of the
TOC
Primary driver of train operating
National Modelling
cost
Framework Timetabling
ModuleVehicle-km / Train-km
Network Rail
Train-km / Train Hours

National Modelling
Framework Timetabling
ModulePassenger Load Factor Passenger-km / Train km
Passenger-km data from
National Rail Trends. Trainkm data from Network Rail.
Intercity TOC
Proportion of train services
National Rail Trends for theintercity in naturecategorisation of TOCs into
London South Eastern Proportion of train services into and intercity, LSE and regional.
Where TOCs have merged
indicator
around London
across sectors a proportion(in general commuting services)
Interaction between Intercity and allocation is made on an
q 42 q52
approximate basis with
LSE proportions
reference to the relative size
Interaction
betweenintercityandq42 1  q42  q52 of train-km by each preregional (non-intercity and nonmerged TOC
LSE services) proportions
Interaction between LSE andq52 1  q42  q52 
regional proportions
Number of rolling stock Number of “generic” rolling stock National Modellingtypes operated
types operated
Framework Rolling StockClassifications
Stations operated
Number of stations that the TOC
National Rail Trends
operates

PricesP1

Non-payroll cost per unit
rolling stock

P2

Staff costs (on payroll)

TOC accounts for cost,
Platform 5 and TAS Rail
Industry Monitor for rollingstock numbers
TOC accounts (both costs
and staff numbers)

Source: Reproduced from Table 1 in Wheat and Smith (2014).

Findings on the cost structure of passenger train operations
In this sub-section we present some results of the work undertaken to date to illustrate the richness andusefulness of the methods employed. Returns to scale (RtS) and returns to density (RtD) can be
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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

defined specifically for operations (as distinct from infrastructure). RtS measure how costs change
when a firm grows in terms of geographical size. RtD measures how costs change when a firm grows
by running more services on a fixed network.
The DEA analysis yields few results with relation to economies of scale or density. Indeed the paper
by Cowie imposes constant returns to scale. Merkert et al. (2009) did estimate a variable returns toscale model and found that British and Swedish TOCs were below minimum efficient scale, while thelargest German operators were above. Of the parametric papers, Cowie (2002b) estimates a cost model
which provides evidence on economies of scale. Cowie finds evidence for increasing returns to scale
and that these are increasing with scale. There is no attempt to differentiate between scale and density
economies in the analysis.
Smith and Wheat (2012) put forward a model which yields estimates of the extent of both economies
of scale and economies of density, where the primary usage output is train-km. They found constantreturns to scale and increasing returns to train density. The policy conclusion of this finding is thatwhilst there would not be scale benefits from merging franchises, such mergers may reduce costs by
allowing greater exploitation of economies of density (a single operator running trains more
intensively down a given route); though see further discussion on economies of density andheterogeneity below. One limitation of the Smith and Wheat (2012) work was the inability to estimate
a plausible Translog function. Instead, a restricted variant was estimated, selected on the basis ofgeneral to specific testing and on whether key elasticities were of the expected sign. This implicitly
restricts the variation in economies of scale and economies of density.
Further work by Wheat and Smith (2014) estimated a Translog simultaneously with the cost shareequations and adopt a hedonic representation of the train operations output in order to include
characteristics of output in a parsimonious manner. This work provides new insights into the scale anddensity properties of train operations, since it allows RtS and RtD to vary with the heterogeneity
characteristics of output. Figures 2 and 3 summarise the findings on RtD and RtS.
Figure 2 shows that all TOC types exhibit increasing RtD and that this does fall with density, although
RtD are never exhausted within the middle 80% of the sample. At any given train hours per route km
level, intercity TOCs exhibit the lowest RtD, while LSE (commuter services into London) exhibit thestrongest (and indeed even at the 90th percentile density in sample the RtD estimate is in excess of
1.2). Intuitively, the curve for mixed TOCs is somewhere in-between the curves for intercity andregional. The policy conclusion from the analysis of RtD is that most TOCs should be able to reduce
unit costs if there is further growth in train hours (on a fixed network) in response to future increases
in passenger demand.
Figure 3 provides a similar plot for RtS. This shows that for all of the central 80th percent of the train
hours distribution, intercity (and mixed) TOCs exhibit decreasing RtS. LSE TOCs exhibit increasing
returns to scale only for the very smallest in sample, whilst regional TOCs are the only TOC type tohave an appreciable range exhibiting increasing returns to scale. The results are consistent with a ushaped average cost curve, although it would appear that most TOCs are operating at or beyond theminimum unit cost point. This finding has important implications for examining the optimal size of
TOCs and is relevant to the recent franchise policy change that has resulted in substantial franchisere-mapping, and in turn larger franchises.

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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

Figure 2. Returns to density for different TOC types holding other variables constant
1.6
1.4

1.2
1
0.8
0.6
0.4
0.2
0
0

0.5

1

1.5

2

2.5

3

3.5

Train Hours per Day per Route-km
Intercity

Regional

LSE

Mixed

Source: Reproduced from Wheat and Smith (2014).

Figure 3. Returns to scale for different TOC types holding other variables constant
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0

500

1000

1500

2000

2500

Train Hours per Day

Intercity

Regional

LSE

Mixed

Source: Reproduced from Wheat and Smith (2014).
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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICYThe overall conclusion from this section is that modelling returns to scale and density in passenger
train operations potentially requires a rich model to fully capture the effects. The initial work
published in Smith and Wheat (2012) based on a restricted translog model suggested broadly constantreturns to scale combined with fairly strong economies of density. This may suggest that there could
be a case for making franchises smaller, which could additionally help in reducing the risk of franchise
failure, which has been a key problem in Britain. Britain’s franchises are already considerably larger,
in general, than those elsewhere in Europe. However, if reducing the size of franchises also increases
the degree of franchise overlap, then important economies of density may be lost in the process, so it is
not a clear policy conclusion. Turning the argument the other way round, larger franchises, that result
in reductions in franchise overlap and the exploitation of economies of density may reduce costs.
That said, Wheat and Smith (2014) develop a richer model, which takes account of service
heterogeneity (in particular, in terms of train speed and TOC type) in relation to returns to scale anddensity. In that later paper it is found that the ability to exploit economies of density may beconstrained by service heterogeneity. Likewise, the losses of economies of density from reducing
franchise size might be smaller than indicated above. It is further found that some franchises in Britain
are operating at decreasing returns to scale, and may therefore be too large.
What the above research suggests is that it is possible to shed new light on the structure of costs ofpassenger train operations, and draw broad conclusions about the economies of scale and density of
those operations. The most recent work suggests that there could be cost savings from reducing
franchise size (because of scale diseconomies) and that losses in economies of density might be
reduced by service heterogeneity. Whilst we can draw these general findings, we would however
recommend more detailed, bottom-up modelling work be carried out on a case by case basis if adecision is required on franchise mergers / de-mergers. It may not be possible in an econometric tofully reflect the service and rolling stock possibilities that result from such changes.
Findings on efficiency variation
The purpose of this section is to illustrate the potential of the methodologies with respect to measuring
efficiency of railway operators. There is an extensive literature analysing the efficiency andproductivity 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., 2010; Asmild et al.,2009; Growitsch and Wetzel, 2009; Cantos et al., 2010 and 2011; Mizutani and Uranishi, 2013; Nashet al., forthcoming; van de Velde et al., 2012); although some studies considered evidence from North
America (e.g.Bitzan, 2003). Overall, the results seem inconclusive, suggesting that much depends onthe circumstances of the country concerned and the way in which the system is managed. However,
we consider the recent work by Nash et al., forthcoming and van de Velde et al., 2012 to offer
interesting new insights on the circumstances in which vertical separation and the holding company
model might result in lower or higher costs. We review this work in more detail in section 5 below.
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 theexperience of competitive tendering has generally been positive, with the evidence suggesting thatsavings 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 andNilsson, 2009). Even here though, some franchises have failed to achieve favourable results. 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 (and also freight) rail franchises have also
been signed in Latin America, generally leading to radically improved performance, although in most
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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

cases re-negotiation has been required due to changed economic circumstances (in particular thesevere economic recession in the late 1990s; see Kogan, 2006).
Turning to studies of British TOCs, Affuso et al. (2003) and (Cowie, 2002a, 2002b, 2005) study theearly 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 post2002. Smith and Wheat (2009) report productivity levels falling as early as 2000 and not recoveringover the remainder of the sample (to 2006). Smith et al. (2010) reviews this literature. Thus Britain's
franchising experience appears to be the outlier (at least within Europe), with costs rising rather thanfalling as in Germany and Sweden.
Smith and Wheat (2012) investigate the impact of the response of the franchising authority in Britain
to franchise failure. Two approaches were adopted. First, most operators were placed onto annuallynegotiated management contracts (similar to cost-plus contracts). The second saw some operatorsplaced onto newly-negotiated short-term franchise arrangements. Figure 4 summarises the findings on
the cost effect of different franchise contracts. This shows that the franchising authority’s decision toplace 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 tocosts 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
the sample period. The short-term franchise agreements which, once signed, retain incentives toreduce costs, saw a more positive pattern, with costs remaining in line with other operators (those thatremained on their original franchise agreements throughout).
The overall conclusion from this section is that whilst competitive tendering / franchising appears tohave reduced costs (or at least subsidies) in Germany and Sweden (Nash, Nilsson and Link, 2013)
costs have risen in Britain. Whilst the management contracts put in place following numerous
franchise failures explains part of the problem, Smith and Wheat (2012) also report a general upward
trend in train operating costs in Britain (affecting all operators). It does appear that some British
franchises may be inefficiently large.
However a major report on rail costs commissioned by the British Office of Rail Regulation (ORR)
and the Department for Transport in 2011 (McNulty, 2011) concluded that a further factor leading tocost increases in Britain was a misalignment of incentives between infrastructure manager and train
operating companies in the vertically separated structure of railways in Britain. They advocated closer
working arrangements, including a high level Rail Delivery Group representing all parts of theindustry, and alliances between the infrastructure manager and train operating companies or evenleasing of the infrastructure to franchisees at the regional level. These issues are considered further at
the European level in section 5.

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Figure 4. Findings for TOCs in Britain that were subject to short-term management or
renegotiated contracts relative to other TOCs
1.3

Cost frontier level relative to Other TOCs

Last year ofmanagement
contract *Management
and renegotiatedcontracts start

1.25
1.2

1.15
1.1
1.05
1

0.95
0.9
0.85
0.8
1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Year
Other TOCs

Management TOC

Renegotiated TOC

Management TOC Continuing* 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 theperiod.
Source: Reproduced from Smith and Wheat (2012).

Infrastructure costs
As noted above, infrastructure costs have risen even more than train operating costs. A major reason
for this was the expansion of maintenance and renewals activity following the fatal accident at
Hatfield in 2000 which was caused by a broken rail, but also the cost of the West Coast Mainline
renewal and upgrade programme rose to some £8 billion from an original estimate of £2 billion. The
result of these two events was that the privately owned infrastructure manager, Railtrack, was placedin administration in 2001, and was ultimately succeeded in 2002 by Network Rail, a company limited
by guarantee, responsible to its members rather than shareholders and with its debts guaranteed by thegovernment. The Office of National Statistics has recently ruled that, under current EU regulations,
Network Rail is to be regarded as a public sector organisation.
How did these events affect the efficiency of the British infrastructure manager over time, and how
does it compare with its European peers?
As noted above rail infrastructure costs rose substantially after the Hatfield accident. Whilst part ofthis increase was driven by the need to increase maintenance and renewal activity in the light ofgenuine concerns over the quality of the network, a wide range of evidence has shown that Network
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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICYRail’s efficiency performance deteriorated sharply over that period. Strong regulatory action has
therefore resulted, with Network Rail being tasked with making large efficiency gains in recent andthe coming years.
Kennedy and Smith (2004) showed that Railtrack delivered substantial real unit cost reductions in theearly years after privatisation (6.4% to 6.8% for overall maintenance and renewal activity between
1996 and 2002). However, these improvements were more than offset by the post-Hatfield cost
increases, which resulted in unit cost increases of 38% for overall maintenance and renewal activity.
Indeed, costs continued to rise after 2002 (see Smith et al. 2009). A further finding of Kennedy andSmith (2004) is that there was scope for the infrastructure manager to reduce its costs by reduce intracompany performance differences.
ORR carried out a review of Network Rail’s efficiency performance, in the light of the large cost
increases, and commissioned a wide range of studies for its 2003 Interim Review of the company’s
finances. A key weakness of the 2003 review though was that the ORR’s efficiency determination was
ultimately based on two bottom-up consultant reviews of Network Rail’s business plan (LEK, TTCIand Halcrow, 2003 and Accenture, 2003). These results were supplemented by internal benchmarking,which indicated the kind of savings that could be achieved if Network Rail implemented its own bestpractice consistently across the network.
Ultimately then, the 2003 Interim Review was unable to provide a clear, empirically based assessmentof Network Rail’s relative efficiency position based on hard data from external sources. ORR
nevertheless set a tough efficiency target of 31% over 5 years (2004-2009). However, costs werestarting from a very high base. Thus, although costs then started to fall as Network Rail set about
delivering its efficiency targets, by the time of the next periodic review in 2008, the scene was set totake the benchmarking approach a step forward by attempting international comparisons.
Two approaches were adopted during the 2008 review. The first used a panel of thirteen European
infrastructure managers over an 11-year period. The data was provided from the Lasting Infrastructure
Cost Benchmarking (LICB) project undertaken by UIC. The dataset included data on costs (adjusted
based on PPP exchange rates), traffic volumes (by type), network length, and a range of othervariables characterising differences between the companies (for example, extent of electrification,
network density).
A structured inefficiency model (Cuesta, 2000; see section 3 above) was used that permits inefficiency
to vary by firm over time, but in a structured way that recognises the panel nature of the dataset. The
results are shown for Network Rail in Figure 5 (other companies cannot be shown for confidentiality
reasons); see Smith (2012). Results are shown for maintenance only, and for maintenance andrenewals, with the additional model variant to allow for Network Rail’s renewals costs to be reduced
downwards prior to modelling to allow for the fact that the company was renewing at above steadystate levels in terms of renewal volumes. The overall message of Figure 5 is that Network Rail’s
efficiency deteriorated sharply after 2000, compared to its European comparators, leaving thecompany with an efficiency gap of around 40% by the end of the period. The analysis was carried out
by the University of Leeds, with ORR and in conjunction with Network Rail and UIC.

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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

Figure 5. Profile of Network Rail Efficiency Scores: Preferred Model
Profile of Network Rail Efficiency Scores:
Flexible Cuesta00 Model (with and without steady-state adjustment)
1

0.9

Without steady-stateadjustment

0.8

With steady-stateadjustment

Score against frontier

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0
1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Source: Reproduced from Smith (2008); see also Smith (2012).

In a separate, supporting study, ORR and University of Leeds, collected a new dataset comprising fiveother rail infrastructure managers in Europe and North America. This includes data on costs, outputs,
and network characteristics at the regional level within each country. Thus, although the number ofcompanies included was smaller than in the LICB dataset, the sample size was expanded via the use ofregional data within companies (sub-company data structure). The dataset also allowed ORR to study
within-country variations in inefficiency. The results broadly confirmed the results of the main study
using LICB data (see Smith et al., 2010; Smith and Wheat, 2012b).
It is further worth noting that the ORR carried out a range of other studies, principally based onbottom-up evidence. These confirmed the existence of a substantive gap, supported by examples ofbest practice in other countries (see Table 3).
Although ORR carried out/commissioned a wide range of studies – all of which pointed in the
direction of a large efficiency gap – it was the output of the LICB-based econometric model which
was used to set Network Rail’s efficiency targets. ORR chose to compare Network Rail against theupper quartile of the peer group, rather than the frontier, thus meaning that the starting efficiency gap
for its analysis – based on the preferred econometric model from the analysis of the LICB data – was
37% rather than 40%. ORR also gave the company ten years to close the gap, with only two-thirds ofthe gap targeted to be closed during the immediate control period (control period 4 (CP4); 2009-2014).
In the next periodic review (PR13), ORR shifted the emphasis of its approach to bottom-up methods.
This was driven by a number of factors, but in part reflected increased doubts after 2008 about thequality of the LICB data and the commitment of the different companies to providing accurate
information. A re-run of the sub-company approach was also attempted, but again it was consideredthat there was insufficient time to get enough certainty about the quality and comparability of the data
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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

received. Therefore, although Network Rail acknowledged the size of the efficiency gap resulting
from the PR08 econometric modelling, emphasis switched in the PR13 review to bottom-up analysis.
Whilst new econometric modelling with an updated LICB dataset was carried out and reported, in theprocess also applying more advanced techniques (including the more recent methods set out in section
3), the econometric modelling played a supporting role to the bottom-up analysis (thus reversing theapproach taken in PR08; see ORR, 2013).

Table 3. Examples of European best practice
Asset inspection
and asset
management.

In general best practice European railways undertake fewer track inspections but
inspections are generally of higher quality. It is estimated that similar techniquesapplied in Britain could reduce foot patrolling inspection costs by around 75%
and tamping expenditure by 20%

Recycling
components

This is common European practice. In Switzerland, for example, rail, point
motors, sleepers and signal heads are regularly refurbished then cascaded fromhigher to lower category routes. Cascaded rail on lines re-laid with steel sleepers
could lead to savings. Additionally ballast cleaning (partial renewal) as opposedto traxcavation (complete renewal) could reduce ballast renewal cost in Britain
by 40%High output rail
stressingStressing continuously welded rail by heating it rather than physically stretching
it is a process discontinued in Britain in the 1960s and 1970s. Some European
networks (using modern equipment) have re-introduced this method which
doubles on-site productivity and, if applied to the renewals re-railing workbank
in CP4, could lead to significant annual savings for Network Rail

Formation
rehabilitation
trainsModern high output European plant is regularly used to undertake formation andalso ballast renewals. If applied to Network Rail’s CP4 category 7 and 12 trackrenewals RailKonsult estimate that it could reduce unit costs for both activities
by around 40%

Lightweightstation platforms

The use of modular construction polystyrene station platforms in the
Netherlands could provide opportunities in Britain, given the substantial CP4
platform extension workbank. Analysis suggests a unit cost saving of around
25% in Britain

Efficient
European rerailingtechniques.

This particular study brought together many themes from the previous
RailKonsult work by focussing upon the Swiss re-railing method. Bespoke
plant, high output welding techniques and dedicated teams are applied routinely.
Put together for basic re-railing work alone this method is around 40% more
efficient than current Network Rail practiceUse of dedicatedteams

Contractors are widely used by most continental railways, as they are in Britain.
However there is generally a greater degree of specialisation by activity inEurope (such as S&C renewal or tamping). This ensures a highly skilled andproductive workforce dedicated to particular tasks in contrast to the situation in
Britain where contractors are often not even dedicated to rail.

Source: Taken from Smith et al., 2010.
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Perhaps one of the lessons that may be learned here is that international benchmarking is problematic
because it takes considerable time and commitment from a group of countries to make the analysis
credible and usable. In PR08, ORR had the advantage of a ready-to-go dataset, produced by UIC, andthis enabled top down, econometric international benchmarking to play a more significant role than it
has in other regulated sectors. A further factor at play in PR08 was the sheer size of the cost increases
that had occurred and the scale of the cost challenge, and given the lack of domestic comparators,
there was a strong need imperative to use this kind of approach. In PR13, with Network Railacknowledging the size of the gap, with the gap closing already, and with new uncertainties arising in
the LICB dataset, ORR has become more cautious about top down international benchmarking, andarguably the need to rely on it has become less. ORR also considers that its leading role in collecting
new data for the sub-company modelling approach is questionable – and that perhaps this kind of work
should be led and funded by companies rather than regulators.
One consideration going forward, however, is the extent to which ORR, and regulators in general, canavoid the use of top-down benchmarking. Further, if international benchmarking is to work, then it
may require concerted efforts by regulators/governments across Europe working together to establish acommon benchmarking framework against which all companies can be compared, thus also implyingthat data can be requested and audited by regulators and policy makers. Finally, a further opportunity
for benchmarking remains the notion of internal benchmarking. Whilst not without its problems it
remains a useful part of a regulators toolkit as it establishes the savings that could be achieved if bestpractice (within-country) is consistently applied. The existence of disaggregation into units that havemanagerial autonomy (at least to some degree), as with Network Rail’s routes, is of course a prerequisite for such an approach, but these groupings/disaggregations do also exist in other railways.

5. European Rail Systems

The previous section considered the impact of reforms on costs in Britain and attempts to benchmark
Network Rail (and to an extent the TOCs) with a view to challenging their costs and improve
efficiency. In this section we consider wider European experience. Most past studies on the impact ofreforms at the European level have applied data envelopment analysis to physical data; our problems
with that approach have been outlined above. Moreover they have usually used the data published by
the Union International des Chemins de Fer (UIC)3, data which has been shown to containinconsistencies (van de Velde et al., 2012). Moreover this source only contains data on UIC members,
generally the incumbent but not new entrants, and in some cases covers their activities in a number ofcountries rather than just their home country. A rare example of the estimation of cost functions tostudy the impact of European reforms is Asmild et al. (2008); they also went to considerable efforts toclean up and supplement the UIC data. They found that competitive tendering for passenger, open
access for freight services and accounting separation of infrastructure from operations all improved
efficiency, but could find no further effect of complete separation of infrastructure from operations.
However, their data series ended in 2001 before many reforms took place.
A recent example of the use of a translog cost function with panel data from a large number ofcountries is Mizutani and Uranishi (2013). They used data for 30 railway companies from 23 OECD
3

Note this is published data as distinct from the confidential data from the LICB project described earlier.

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RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

countries for the years 1994 to 2007, giving 420 observations. Whilst most of the observations werefrom Europe, they included the vertically integrated passenger railways of Japan, and also South
Korea and Turkey. Where vertical separation had been implemented, they added together the
infrastructure manager and the train operating company to form a single observation. The basic sourceof data was UIC, but this was supplemented as necessary by data from company annual reports.
Two separate models were estimated, one using passenger kilometres and freight tonne kilometres as
outputs and the other total train kilometres, with the share of passenger revenue to total revenue,
passenger load factors and length of haul and freight number of cars per train to reflect differences in
the characteristics and therefore costs of the train kilometres. Factor prices for labour, track, rollingstock and other materials were estimated. Finally route kilometres, train kilometres per route kilometre
and the percentage of line electrified were included as descriptors of the network.
The rail reform variables were dummies reflecting complete vertical separation (the holding company
model being regarded as integrated) and horizontal separation of passenger and freight operations. It
was found that whilst horizontal separation unequivocally reduces costs, vertical integration only
reduced costs for densely trafficked railways; for most European railways it increased them. Given
that there are no separate variables representing the degree to which competition is permitted or
actually takes place, it must be assumed that these impacts are the net effect of any additional costs
directly caused by vertical separation and of the impact of competition which in most of Europe must
presumably be sufficient to outweigh these costs. The explanation given for the impact on costs
varying with density is that given above, that the transactions costs caused by vertical separation will
be much greater in densely trafficked networks than in less densely trafficked ones.
Van de Velde et al. (2012) takes this work further by updating and improving the data set andintroducing separate dummy variables for holding companies and complete vertical separation (thiswork also later published in the academic literature, see Nash et. al. forthcoming and Mizutani et al.,2014). They also added in Britain, the country in which the most radical reforms had taken place, butwhich had been excluded from most previous studies due to lack of data. Finally, they introduce
dummy variables representing passenger and freight market competition.
They confirm the previous finding that, compared with complete vertical integration, verticalseparation reduces costs at low levels of density but increases them at high; at mean European density
levels costs are not affected by the change. This effect is not likely to be one of pure transactions costs(negotiating and enforcing contracts), which have been shown to be a relatively small proportion of
total systems costs (Merkert et al., 2012) but is more likely a problem of misalignment of incentives
leading to poor integration of infrastructure and operations in circumstances (dense traffic) when thisis particularly important. They find weak evidence (significant at 10% only) that the holding company
model reduces costs compared with vertical integration, but this does not vary with density, so theholding company would be preferred to vertical separation at high levels of density but not at low.
Within the range of the data, the introduction of competition seems to have had no effect on costs.
Horizontal separation of freight and passenger undertakings seems to have sharply reduced costs(perhaps because this has typically been associated with preparation of the freight undertaking forprivatization), whilst a high proportion of revenue coming from freight rather than passenger tends toincrease the costs of vertical separation (perhaps because planning freight services efficiently requires
closer day to day working than passenger, since freight services vary from day to day whereas
passenger services are generally fixed for the duration of the timetable). The paper also provides
qualitative evidence on the issue of how misalignment of incentives may raise costs and show how,whilst efficiently set track access charges and performance regimes are important, they do not provide
incentives for railway undertakings to assist infrastructure managers in seeking the minimum cost
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solution to infrastructure provision. Only a mechanism for sharing of changes in costs and revenues, as
provided for in some of the alliances now being negotiated in Britain, will achieve that.
The conclusion of the studies in this section is that there is no one size fits all policy for European
railways. Based on a mixture of qualitative and quantitative research the evidence suggests thatvertical separation may perform less well than the Holding company model for intensely used
networks, whilst being the structure of choice for less dense networks. Whilst this is in part intuitive, it
is not totally clear why separation reduces costs for lightly used networks, particularly if there is little
competition. It is further disconcerting that it has not been possible to find clear competition effects inthe data. To date no research has yet been published to consider the impact of regulation on costs,
though research is ongoing at University of Leeds in this respect. A final note must be that although
we consider the cost function approach to be the best, and with the van de Velde et al. (2012) study
incorporating new data from CER members to supplement published data, there nevertheless remains
work to be done on the data side to improve its comparability.

6. Conclusions

There are various measures of efficiency available, and all have their uses. Partial productivity
measures may have their value in shedding light on utilisation of particular resources, but they are
inadequate as measures of overall efficiency, such as are needed in econometric studies of theinfluences of alternative policies on efficiency of the rail industry as a whole. For this there are anumber of possibilities, including total factor productivity measures and Data Envelopment Analysis,
but for various practical reasons we prefer the econometric estimation of cost functions.
Detailed studies of Britain suggest that the reforms in Britain did not achieve lasting improvements incost efficiency. In the case of the infrastructure manager, the events surrounding the placing ofRailtrack into administration brought about a major reduction in efficiency from which the current
infrastructure manager, Network Rail, is only gradually recovering. Surprisingly, competitive
tendering did not achieve a reduction of train operating costs, which have risen substantially. Thereappear to be various reasons for that, but the use of management contracts to deal with cases offinancial failure, the use of too large areas as the basis for franchising (from a cost and risk of failure
point of view) and the loss of economies of density through overlapping franchises all seem to befactors in this (though it must be noted that splitting franchises to reduce their size increases theprospect of franchise overlaps). Most continental franchises are smaller and subject to less overlap ofhomogenous services, and they appear to be more successful. However, it must still be noted there thatthe studies carried out other than in Britain are based on data on subsidies and not cost, and it may bethat costs have been reallocated elsewhere in the system.
Studies of European-wide reforms suffer even more data problems than studies in a single country, but
a recent study which sought to overcome these was Van de Velde et al. (2012) using part publisheddata and new data provided by CER members. They found that complete vertical separation only
reduces cost in low density countries; at higher densities it raises cost. The reason for this is likely to
lie in the misalignment of incentives, a factor considered important in Britain by the McNulty report(McNulty, 2011). The level of competition in both freight and passenger markets appears to be an
insignificant factor in determining costs.
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It is clear from all these studies that much still needs to be done to understand the determinants of rail
cost efficiency. The most fundamental requirement, whether within or across countries, is for goodquality consistent data on the variables we are seeking to measure (in cost analysis, a key area here isthe costs of depreciation and interest). Better measures of policy variables are needed, including inparticular the quality of regulation and the degree of competition. At the same time, the measurementof service quality is an important factor – as noted above, no sensible operator minimises costs whollyat the expense of service quality.
The data challenges are unlikely to be overcome based on published data. Experience suggests thatsome progress can be achieved by working with companies; however, this takes time andcommitment, and sometimes companies may start involvement in a benchmarking project, only towithdraw later. Long-term commitment to developing a framework and data collection exercise isrequired. Ultimately it may be that data issues cannot be fully addressed without concerted, Europeanwide effort by multiple policy makers and regulators to require data to be collected to a common set ofdefinitions, with appropriate audits in place to achieve comparability.
Whilst the data will never be perfect and there will never be data on every possible variable that may
drive differences between companies, methodological advances mean that, with enough data points(and with panel data), there are ways of disentangling inefficiency from unobserved factors that cause
costs to vary between railways. Thus we consider that it is both possible and sensible to continue todevelop our understanding of railway costs and efficiency performance using existing datasets,
improved where possible, and applying state of the art methods.
Finally we note that the challenges facing policy makers seeking to compare performance are further
hampered by the increased need for railways to improve the quality of what they deliver, whilst
expanding capacity, reducing carbon, and also responding to the increased challenges posed byclimate change. All of these raise new data and methodological challenges which researchers andpolicy makers will need to grapple with in the coming years.

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