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Efficiency in Railway
Operations andInfrastructureManagement

177

Roundtable

Corporate Partnership
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Roundtable

Efficiency in Railway
Operations andInfrastructureManagement

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Acknowledgements
The authors of this report are Dejan Makovšek, Vincent Benezech and Stephen Perkins of theInternational Transport Forum (Chapter 1); Louis S. Thompson of Thompson, Galenson and Associates,
United States, and Heiner Bente of Civity Management Consultants, Germany (Chapter 2); Alain
Bonnafous and Yves Crozet of the Laboratoire d’Économie des Transports, France (Chapter 3); andAndrew S. J. Smith and Christopher Nash of Leeds University, United Kingdom (Chapter 4). The reportsummarises the findings of an ITF Roundtable which brought together 25 experts from 13 countries todiscuss how to improve assessments of railway performance. A full list of Roundtable participants isavailable in Annex A.

TABLE OF CONTENTS

Table of contents
Executive summary ............................................................................................................................. 7
Chapter 1. Summary of discussions ................................................................................................... 10
Introduction .................................................................................................................................. 10
Is there a simple approach to assessing railway efficiency?.......................................................... 11
Taking account of multiple railway efficiency dimensions ............................................................ 18
The policy maker and exogenous efficiency determinants ........................................................... 21
Conclusion ..................................................................................................................................... 23
Notes................................................................................................................................................. 25
References ........................................................................................................................................ 26
Chapter 2. What is rail efficiency and how can it be changed? ......................................................... 28
Defining efficiency in a general sense ........................................................................................... 28
Indicators available from published data ...................................................................................... 28
Initial rankings based on cross-sectional comparisons and initial discussion of time-series data . 30
How can efficiency be changed? ................................................................................................... 34
Did any of these changes work? .................................................................................................... 35
Conclusion ..................................................................................................................................... 41
Notes................................................................................................................................................. 45
Annex 2.A1. Tables........................................................................................................................ 47
Annex 2.A2. Data sources ............................................................................................................. 60
References ........................................................................................................................................ 61
Chapter 3. Efficiency indicators of railways in France ....................................................................... 62
Introduction .................................................................................................................................. 62
Efficiency of rail transport services ............................................................................................... 64
Network efficiency ........................................................................................................................ 70
Conclusion ..................................................................................................................................... 77
Notes................................................................................................................................................. 78
Annex 3.A1. The SNCF Group and the SNCF and RFF industrial and commercial public
undertakings ................................................................................................................................. 79
Annex 3.A2. Formal demonstration of optimal order of project implementation ........................ 80
Annex 3.A3. The 2014 rail reform ................................................................................................. 82
References ........................................................................................................................................ 83
Chapter 4. Rail efficiency: Cost research and its implications for policy ............................................. 86
Introduction .................................................................................................................................. 86

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TABLE OF CONTENTS

Technical efficiency ....................................................................................................................... 86
Cost function estimation ............................................................................................................... 91
Rail privatisation in Britain............................................................................................................. 96
European rail systems ................................................................................................................. 107
Conclusion ................................................................................................................................... 109
Notes............................................................................................................................................... 111
References ...................................................................................................................................... 112
Annex A. List of Roundtable participants ......................................................................................... 117

FiguresFigure 1.1. Evolution of maintenance costs of the Dutch railway network....................................... 15
Figure 1.2. Evolution of cost items in infrastructure spending in the Netherlands ........................... 16
Figure 1.3. Expenditure of the British railway system by cost item between 1996 and 2010 ....... 177
Figure 1.4. Life-cycle cost comparison between the Netherlands and a US railway company ......... 20
Figure 1.5. Example of technical failure performance of ProRail ...................................................... 22
Figure 2.1. US Class I Railroads operating ratio (%) and all commodity average .............................. 36
Figure 2.2. Canadian Freight Railways (Tariff index and labour productivity index, 1995=100) ....... 36
Figure 2.3. Changes at JNR at privatisation ....................................................................................... 38
Figure 2.4. Rail traffic in the United Kingdom ................................................................................... 39
Figure 2.5. UK passenger-km, tonne-km and GDP ............................................................................ 40
Figure 3.1. Relevance, consistency and efficiency of rail transport .................................................. 63
Figure 3.2. The decline of French rail freight (Base 100: year 2000)................................................. 67
Figure 3.3. Regional government subsidies for regional express services in France ......................... 69
Figure 3.4. Confederation subsidies to CFF in Switzerland ............................................................... 70
Figure 3.5. Additional welfare gain of programmes .......................................................................... 75
Figure 4.1. Single input production frontier...................................................................................... 87
Figure 4.2. Returns to density for different TOC types holding other variables constant ............... 101
Figure 4.3. Returns to scale for different TOC types holding other variables constant .................. 101
Figure 4.4. Findings for TOCs in Britain relative to other TOCs ....................................................... 103
Figure 4.5. Profile of network rail efficiency scores ........................................................................ 105

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TABLE OF CONTENTS

TablesTable 1.1. A balanced score card for railway efficiency .................................................................... 12
Table 3.1. Rail passenger transport in France ................................................................................... 65
Table 3.2. Unit-kilometres per capita in France from 1996 to 2013 ................................................. 67
Table 3.3. Unit-kilometres per capita in Germany and Switzlerland from 1996 to 2013 .................. 68
Table 3.4. Summary annual performance chart for 2012 ................................................................. 71
Table 3.5. Main indicators of annual reporting for the year 2012 .................................................... 72
Table 4.1. Rail industry costs in Britain, 1997 to 2012 ...................................................................... 97
Table 4.2. Data used in Wheat and Smith (2014) ............................................................................. 99
Table 4.3. Examples of European best practice .............................................................................. 106

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EXECUTIVE SUMMARY

Executive summary
What we did
Railway efficiency is a challenging concept due to the complexity of providing railway services. Thispublication examines the question of how to define and effectively measure the performance ofrailways: What is the proper level of detail at which the analysis should take place? What data needs to
be available so that policy makers can benchmark railway performance, evaluate impacts of pastinterventions and assess the benefits of future initiatives? This report assembles the results of an ITF
Roundtable which brought together 25 experts representing rail operators, regulators, transportministries, sector associations and researchers from 13 countries to discuss how to improve assessmentsof railway performance.

What we found
Efficiency entails maximising the outputs from a set of inputs (technical efficiency) or creating an optimal
mix of inputs to maximise output (allocative efficiency). A single description of efficiency relevant for
railway owners/operators, government, users or the regulator is difficult. Operators will define it interms of access to and cost of infrastructure. Users are interested in availability, reliability or speed. The
regulator will measure both technical and allocative efficiency, and will require different information to
examine both.
The simplest approach to conceptualise and measure railway efficiency is by deriving key performanceindicators (KPIs) from published data. This can be sufficient to develop a simple but balanced scorecard.
To monitor performance, both cross-sectional indicators (that compare systems) and time series
indicators (that measure change over time) are needed.
Both types of data have to be treated with caution. Cross-sectional data risk comparing systems where
outputs or inputs have been delineated differently. Time series indicators suffer from the risk thatcategorisation of inputs or outputs can change from one year to the next. Data on past performancemust therefore be adjusted to reflect change, e.g. organisational set-up or in accounting standards. For
cross-sectional benchmarking, the data from different systems must be standardised and normalised.
Measuring the efficiency of a railway system through demand/supply ratios (such as passengers carried
per total train kilometres) can also be misleading. Such ratios are influenced by factors like topography,
historic evolution, etc., and can be meaningless without adjustment for exogenous factors. The Dutch
and British examples demonstrate the difficulties of measuring what constitutes efficiency. Verticalseparation of the rail system in the Netherlands in the mid-1990s came with soaring maintenance costs
and was perceived as reduced efficiency. Yet most of the cost increase was due to stricter safety rules,
more night work and other exogenous factors. Moving to performance-based maintenance contracts
brought down maintenance costs over the next ten years.

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EXECUTIVE SUMMARY

In the United Kingdom, the rail system underwent full vertical separation around the same time. Costs
initially went down as a result of low bids to win regional rail franchises, creating the perception ofefficiency gains. Yet several years later, privatisation costs increased sharply as incidents forced astronger focus on quality, among other things.
The same approach – outsourcing – thus worked in opposite directions in the two countries. In the
Netherlands, it proved beneficial to efficiency after a running-in period, while the UK experienced costsavings over the short-term, but ultimately faced very costly accumulated maintenance and investment
bill. The difference lies in the way contracting was managed.
Capturing the full complexity of factors determining rail efficiency would require a very large dataset.
This is not easily available and would be subject to inherent limitations regarding data quality. It is also
not clear how indicators would have to be weighted to capture performance.
Two approaches exist to better capture the complexities of rail performance: Econometric techniquessuch as Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). DEA allows carrying out
some analysis even without a large dataset of control factors. However, it tends to overstate
inefficiencies. Econometric analysis is the better approach where large datasets exist. Panel data isparticularly useful in this regard for separating unobserved heterogeneity from inefficiency.
The top-down view offered by econometric modelling will benefit greatly from a bottom-up “engineeringnarrative” to provide insight into what actually determines performance. It can be further enriched bytrying to capture cultural differences between countries – whether organisational changes drive theoutcomes and to what extent deeper root causes influence results.

What we recommend
Build on simple indicators for an aggregate analysis of rail performance
A basic aggregate view of railway efficiency can be formed with a small set of cross-section and timeseries data producing a limited set of KPIs. The data needed is generally collected by railway
organisations as a matter of routine. Issues with data availability and the interpretation can be limited.
Developing indicators that require large amounts of (not readily available) data can undermine
performance monitoring over time, for instance by making it difficult to update those indicators.
Use econometric models to better capture the complexity of rail performance
The inherent complexity of measuring rail efficiency can be addressed through econometric techniquessuch as Stochastic Frontier Analysis (SFA) or Data Envelopment Analysis (DEA). DEA can carry out someanalysis even when a large dataset of control factors is not available, but it tends to overstate
inefficiencies. Econometric analysis is therefore to be preferred when large datasets are available.
Develop a narrative of the drivers of rail performance
The top-down view of rail efficiency gained through indicators and econometric analysis will benefit
greatly from a bottom-up “engineering narrative”. This can provide insights into additional factors that
determine performance and help shape the parameters used in the econometric model – for instance,engineering studies can provide insights why an intervention delivers different results in one country
compared to another. For instance, unit costs reflect complexity, purpose and usage of the network as
much as they give an indication of the efficiency of the maintenance manager.

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EXECUTIVE SUMMARY

Invest more into data and indicators related to service quality
Most studies of railway efficiency focus on technical cost efficiency and thus reflect the internal view. But
ultimately it is service to the customer that it is important. More effort needs to be invested in providing
data and KPIs on the service quality related to how users choose between transport modes, thus linking
output measures with input measures more directly.

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1. SUMMARY OF DISCUSSIONS

Chapter 1. Summary of discussions
Introduction
The International Transport Forum (ITF) has produced a series of reports and discussion papers
addressing the interrelated issues of railway structure and performance; see for example Beck, Benteand Schilling (2013), Thompson (2013), ECMT (2007) and Thompson (2007). The academic literature on
this subject is also significant, with good examples in Mizutani et al. (2014), Nash, Nilsson and Link
(2013), van de Velde et al. (2012) and Kirchner (2002, 2004, 2007 and 2011). All of these studies haveconfronted the question of how to measure the performance, or efficiency, of railways – both in thesense of how one railway compares with others (cross-section) and how railways have changed as aresult of policy interventions (time-series). The purpose of the roundtable discussions was to revisit theissue of defining and measuring efficiency at the proper level of detail and with reasonably available
data, so that policy makers can benchmark the performance of their railways, evaluate the impact ofpast changes in railway structure, ownership or regulation and assess the likely outcome of future
initiatives. The challenge is inherent in the phrases “proper level of detail” and “reasonably available
data”.
Efficiency entails maximising the outputs from a set of inputs (technical efficiency) or creating an optimal
mix of inputs to maximise output (allocative efficiency). When considering efficiency, one is inclined tothink in terms of a single dimension, a single number or a percentage. The railway business is not that
simple. Railways come in all shapes and sizes: vertically integrated, vertically separated, public andprivate, passenger- or freight-dominated or mixed, supported by subsidies or fully self-reliant. The
provision of railway services is multidimensional. In economic terms the railway company is a multiproduct firm. It is a very capital-intensive business, economies of scale and density can be relevant andsome natural monopoly characteristics are present. In most contexts and on most continents, acompetitive railway market is not a straightforward concept.
This complexity makes it difficult to produce a description of efficiency that is equally relevant fromdifferent viewpoints or to all stakeholders. The answers to questions on railway efficiency will depend on
who is asking. Primarily, apart from the owners of rail companies, the questions may come from threemain stakeholders:the governmentthe users of the network (in a vertically separated railway) or the users of railway services

the economic regulator, if there is one.

In most countries, governments pay large subsidies for railway infrastructure and passenger transport. In
these cases, the focus of the government will be on asking whether those subsidies are spent efficiently
or how, through higher efficiency, they could be reduced.

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1. SUMMARY OF DISCUSSIONS

Train operators will be interested in efficiency in terms of availability and access costs of railway
infrastructure, while the users of railway services will place their stress on punctuality/reliability or speedof transport.
The regulator will pursue both technical and allocative efficiency. This will require different information
to examine each aspect. Its focus will include rate setting, user charge price caps, safety andperformance in terms of operating and financial indicators.
Against this background, this chapter explores how railway efficiency can be conceptualised andmeasured at different levels of inquiry and depth, developing the work begun in the paper by Beck,
Bente and Schilling (2013).

Is there a simple approach to assessing railway efficiency?
A basic but balanced scorecard

The simplest approach would be to derive key performance indicators (KPIs) from published data.
Thompson and Bente (2014) provide an example of what sources and types of publicly available data
exist (supplemented by data they have collected themselves). Their sample includes some railways in theEuropean Union, plus the railways of Switzerland, Norway, the People’s Republic of China, theUnited States, Canada, Japan and Indian Railways.
They propose an approach that can be used to produce some information on railway efficiency in alimited timeframe with limited data. The indicators proposed refer to basic indices of size and scale, andfrom these parameters basic ratios of efficiency and productivity can be developed (e.g. traffic density,
wagon/coach productivity, etc.). Adding a few more data points, one might create a basic but balanced
scorecard of railway efficiency that would consist of six types of indicators (Bente and Thompson, 2014):
system scopeasset utilisation

human resource utilisation

operational performancefinancial performancecustomer-centric service quality.

An example of more detailed KPIs that could be included in each of these fields is presented in Table 1.1.

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1. SUMMARY OF DISCUSSIONS

Table 1.1. A balanced score card for railway efficiency
System scope

Asset utilisation

Passenger service scope
Fleet
Transport units (pass-km)
Train-kilometres
Staff
Freight service scope
Fleet
Transport units (tonne-km)
Train-kilometres
Staff
Infrastructure scope
Lines
Track
Stations

Infrastructure assets
Train-km/line-km per year
Train-km/track-km per year
Transport units/line-km per year
Transport units/track-km per year
Passenger fleet
Transport units/unit of fleet per year
Kilometres in service/unit of fleet per year
Freight fleet
Transport units/unit of fleet per year
Kilometres in service/unit of fleet per year

Human resources

Operational performance

Infrastructure, number of staff in:
Operations/traffic management
Asset maintenanceNetwork development
Total cost of labour by division
Passenger service, number of staff in
Sales and marketing
Operations
Asset maintenanceTotal cost of labour by division
Freight service, number of staff
Sales and marketing
Operations
Asset maintenanceTotal cost of labour by division

Unit cost, infrastructure
Asset maintenance (per line/track-km per year)
Operational (per line/track-km per year)
Unit cost, train operations
Passenger service (per train-km)
Freight service (per train-km)

Financial performance

Customer-centric indicators

Revenue (core, ancillary)
Cost (core, ancillary)
Asset values (book value, MEV, shadow price)
Infrastructure
Passenger service
Freight service
Subsidy (OPEX, CAPEX)(NEW) Investment/capital employed(Re-) Investment/capital employed
Indebtedness/EBITDA-ratio
Cash-flow from financing activity (depreciation – CAPEX +subsidies-net borrowing)

Modal competitiveness
Relative speed between modes
Relative performance between modes
Relative cost/price between modes
Modal split(the points above) subdivided by
Freight into business lines (combined/block/…;
container/bulk/…) Passenger into:
Between agglomerations
Cross-country
HSR

Source: Bente and Thompson (2014).

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1. SUMMARY OF DISCUSSIONSBoth cross-sectional (comparing systems) and time series (change over time) indices will be required tomonitor performance. Time-series analysis makes it possible to compare the performance of a railway
system compared to previous years but no matter how well the railway may appear to be performing
against itself, it may still be relatively inefficient when compared to other railway companies. Using crosssection (with time-series) one could try to assess whether some railway companies consistently comeout on top of the distribution in terms of efficiency over a number of these fields and over time. Bothtypes of data have to be treated with caution. Time series indicators suffer from the risk that thecategorisation of inputs or outputs changes from one year to the next, and they can be heavily
influenced by major organisational or economic changes, for instance since 2008. Outsourcing services,for example, can have a very large impact on indicators of the efficiency of using human resources.
Cross-sectional data similarly risk comparing systems where outputs or inputs have been delineateddifferently. The limits are addressed more thoroughly in the next section. It is clear that some subjective
judgements in the creation and use of these KPIs will have to be made and that an integral part of using
such an approach is significant input from railway experts in interpreting the results. A key weakness of
the use of KPIs is that different measures will give different results (for example cost per train-km andcost per track-km) and it is not clear which measure should be used.
The outcomes of such an exercise would be subject to multiple caveats, examined in the next section,
and would give an observer a first indication of the efficiency of a railway system without explaining why
observed efficiency differences arise, a point addressed later in the book.
Caveats to the simple approach to railway efficiency assessment

The simplest benchmarking approach is to compare a railway to its own past performance. Even in thiscase, however, all things may not be equal. Past data on financial performance may need to be adjusted
as a result of organisational changes in the company, changes in accounting standards or policy,
inadequate application of accounting standards1 or external policy decisions,2 where the State is theowner. There may also be times during which railway investment or maintenance is inadequate, thus
storing up problems for the future.
Cross-sectional benchmarking is equally challenging and requires two preparatory processes. The data
used from different systems must be standardised and normalised. Standardisation means making surethat the activities or materials compared are alike. Normalisation requires taking account of differencesin economies of scale and density. In railways, company size or scale of activity has a substantial impact
on the perceived efficiency (most notably asset utilisation).
A rich data set is required to enable standardisation for cross-sectional analysis. Comparison isincreasingly compromised by data availability as a result of organisational changes, changes in ownershipand integration of railways into larger logistics chains. The UIC Lasting Infrastructure Costs Benchmarking
initiative3 is an excellent example of how to undertake cross-sectional analysis and illustrates how manydifferent factors need to be taken into account before the cost of maintenance and renewals can be
examined on comparable grounds. Reported costs across different infrastructure managers have been
harmonised under this initiative to account for the complexity of the network, for example in terms ofthe number of switches per track-km.
Another challenge is the use of demand to supply ratios to compare the output of railways (passengers
carried) with inputs (e.g. train-kilometres). In principle in a fully competitive market there should be
convergence in these ratios among railways but in practice the demand or output of railway services isdependent on a number of exogenous factors and public policies. Having low traffic levels does notnecessarily imply that train operations are inefficient, and vice versa.

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1. SUMMARY OF DISCUSSIONSFor example:
Heavily subsidised passenger traffic provided in a thin market under public service obligations
might yield low passenger numbers per train-kilometre, even though the railway is managed
highly efficiently, simply because the market is so small.



A financially self-sustained, profitable freight railway business relying on high volume, regularfreight shipments and under no competitive pressures can still be managed below best-practiceefficiency standards.

Bonnafous and Crozet (2014) illustrate how demand/supply ratios can be misinterpreted and how theuse of a limited number of indicators can provide a misleading picture without thorough knowledge ofthe root causes of efficiency. The French rail system, when compared to its peers, seems to be fairly
successful in passenger transport. In fact, it ranks first in Europe with 406 million train-kilometres and an
average of 227 passengers per train. Other national incumbents like DB in Germany or SBB in Switzerland
lie far behind, with 100 and 130 passengers per train, respectively. But when one probes more deeply, it
is revealed that this performance is the result of structural differences – in France two-thirds ofpassenger traffic is carried by the TGV. The high-speed trains have a higher capacity, higher occupancy(due to use of yield management in setting fares) and cover long distances. The illustration suggests how
basic structural differences, which are ultimately the result of geography and public (transport) policy
decisions, can determine the outcomes of simple efficiency comparisons.
The observed efficiency of a railway system through demand/supply ratios is influenced by factors
exogenous to the system (topography, historic evolution, etc.), government policy and endogenous
factors, which are dependent on the efforts of management and policy makers. Without adjustment forexogenous factors, KPIs such as “cost per transport unit” may be useless.
Finally, a problem that requires special attention is data availability. Research efforts, especially in theEU, have been hampered by the lack of good quality data. As Thompson and Bente (2014) note, mostrailways do not see the need for detailed information for internal management purposes or do not think
it is in their interest to release such information to permit public comparisons to be made. Given thatmost railways in the European Union receive substantial financial support from government sources, it ismore than questionable to what extent such a practice is acceptable, although it is a well-documented
phenomenon in the broader context of state-owned companies, particularly where strong unions havedeveloped (e.g. Salinger, 1984; Rose, 1987; Hendricks, 1977; Savedoff and Spiller, 1999). These authors
point to concealing data as a strategy to protect the company’s cash flow. Better information on
efficiency might lead to the shedding of excess workforce or the substitution of labour with capital orother changes, which the unions would perceive as a threat to their bargaining power.
The US example illustrates that information can be systematically collected and publicly reported,
including from fully private businesses.4 Moreover, a lot of the collected information is publicly available,
while a small part of commercially sensitive information is reserved for regulatory purposes only.
Given the caveats discussed above, a general recommendation in developing “high-level” aggregate
analysis, especially when time and resources are limited, is to build on simple indicators, preferably thosethat the railway organisation already routinely collects. These may simply be unit costs, so long as they
are not dependent on external factors5 and are controllable by management. This should reduce issues
with data availability and the interpretation of data.6 Even in this case, it should be recognised thatexogenous factors and the level of demand7 will influence the outcomes of any comparison, especiallythe cross-section type. The example in Box 1.1 tries to follow this approach, as much of the information
mentioned is in many cases already publicly available. Developing indicators that require large amounts

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1. SUMMARY OF DISCUSSIONSof data that are not readily available can undermine transparency and may be counterproductive formonitoring performance over time, for instance by making it difficult to update those indicators.
The importance of the narrative

This section looks more closely at the evolution of railway systems in two countries, the Netherlands andGreat Britain, in order to illustrate the importance of the context in which a railway operates whenassessing the railway’s efficiency. In particular, structural reforms and changes in rail policy have affected
the railways over recent decades and these changes need to be accounted for.
The evolution of infrastructure costs in the Netherlands

The transformation of the railway sector in the Netherlands started in the 1990s. Full vertical separation
occurred in 1995 and by 1998 all network maintenance was outsourced. In 2005, infrastructure
maintenance, capacity management and traffic control activities merged to form ProRail BV (Public
Limited Liability Company), which became the infrastructure manager in a clear, vertically separated
framework.
At first sight, from Figure 1.1, it would appear that vertical separation led to a decrease in the efficiencyof the Dutch railway system. Maintenance costs soared over the period 1996-98, and the overall cost of
running trains in the country steadily increased from the time of the separation in 1993 (Swier, 2014).
However, the main reasons behind the surge in costs are not directly linked to technical inefficiency andoutsourcing; changing circumstances including a rapid increase in rail contact fatigue, stricter safety
rules, a shift towards night work schedules and an increase in track utilisation are all factors that explain
much of the cost increase.
Figure 1.1. Evolution of maintenance costs of the Dutch railway network

Fixed maintenance slot scheduleRail contact fatigue

Source: adapted from Swier (2012).

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1. SUMMARY OF DISCUSSIONSIn the first ten years after outsourcing, ProRail had input contracts for maintenance. Even though failure
rates became lower than before outsourcing, costs did not go down. The performance/price ratio really
started to improve after the introduction of powerful performance contracts precisely defining the
expected outputs without explicitly prescribing the technical methods of achieving these outputs.8

Million EUR

Figure 1.2. Evolution of cost items in infrastructure spending in the Netherlands

MaintenanceSource: Swier (2014).

Separation was accompanied by the establishment of more transparent financial reporting rules, withassets depreciated according to replacement value rather than historical cost accounting value, and theinclusion of traffic control and capacity management in the ProRail balance sheet. Arguably, there is a
noticeable increase in stewardship costs (such as new monitoring requirements, accounting rules,
different charging schedules, etc.) which is partly attributable to separation; however these costs remain
small compared to the increase in asset depreciation costs (see Figure 1.2).
When ProRail is compared to infrastructure managers in other countries, a mixed picture emerges. In
terms of quality, ProRail fares significantly better than other European railway infrastructure managers,
with less rail breaks and track or signal failures, but their maintenance unit costs are higher than theEuropean average (Hansen, Wiggenraad and Wolff, 2013), stressing the need for quality-adjusted
metrics.
The cost of the railway system in Great Britain

The British railway system underwent structural changes, including full vertical separation, around thesame time as the Netherlands. The Railways Act of 1993 established the privatised framework for British
railways, breaking up the historical BR into more than 100 separate companies, the relationships
between which were to be set in contracts or through regulatory mechanisms. Infrastructure was takenover by Railtrack, a newly established public sector company whose shares were subsequently sold tothe private sector, and train operations were divided among 25 geographical franchises. Maintenanceand renewal were subcontracted by Railtrack to several private engineering companies formed by

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1. SUMMARY OF DISCUSSIONS

privatising the relevant parts of British Rail. Railtrack went into administration as a result of thederailment of a train near Hatfield in 2001. The accident was caused by a faulty rail. Even thoughcasualties were limited (four deaths), the derailment had major repercussions, as it laid bare thecompany’s ignorance of the condition of its assets. In response, Railtrack imposed speed restrictions overlarge sections of the network. This resulted in the government compensating the train operating
concessions, providing grants for track renewals and allowing the infrastructure company to raise
additional debt. Railtrack was replaced and their activities taken over by Network Rail, a not-for-profit
company eventually defined as State-owned for national accounting purposes.
Figure 1.3. Expenditure of the British railway system by cost item
between 1996 and 2010

Notes: Train operating costs exclude access charges apart from traction electricity. ROSCO – Rolling Stock Leasing
Company.
Source: Network Rail regulatory accounts and other sources.

The dynamics of infrastructure spending in Great Britain can be divided into three periods. After
privatisation and before the Hatfield incident, maintenance costs went down and total expenditure wasstable. This was not only the result of efficiency gains but to a large extent due to less maintenance being
carried out by subcontractors, who worked on fixed-cost contracts. Moreover, even if investment rose
during this period, experts agree that it was inadequate considering the increase in passenger demand(Nash, 2002). This eventually led to a spiralling of costs after 2001, when Network Rail started making up
for the inadequate levels of maintenance and investment under Railtrack and adapted to tougher safety
requirements.9 Substantial inefficiency crept into Network Rail’s cost base as the regulatory mechanism
was temporarily suspended (see Office of the Rail Regulator, 2003). Since 2004-05, Network Rail, underregulatory pressure, has improved efficiency substantially. Unit costs are still higher than 1996-97 levels
and international comparison suggests that costs are still about 30% higher than what could be expected

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1. SUMMARY OF DISCUSSIONSof them (McNulty, 2011). Two main reasons are given in the McNulty report. The first one concerns alack of outsourcing, Network Rail having taken over all private contractors in 2003, both for safety
reasons and to provide better coordination and economies of scale, or at least according to thecompany. The second reason is the misalignment of incentives as a result of vertical separation. The
huge increase in indebtedness to support investment in the years 2001-04 also contributed to sustained
higher costs up to 2011, given the cost of financing.
The overall picture for train operating companies (TOCs) is similar. Immediately after privatisation, costs
went down as the result of TOC’s making low bids to win franchises; franchising was in this regard a
success, with passenger numbers increasing and government subsidies decreasing significantly. From2001 on, TOC costs increased sharply, with 35% unit cost growth from 2000 to 2006 (Smith and Wheat,2012). This was the result of several factors, including fuel prices, emphasis by franchises on quality(cleanliness, information, etc.) and an above-inflation increase in staff costs. Interestingly, despite thisincrease, train-operating costs in Great Britain are comparable and even slightly lower than in other
European countries (Civity, 2012). Once again, this figure needs to be interpreted looking at the railway
context. For instance, the incumbent Dutch operator also has among the lowest unit costs but with muchhigher traffic density (passenger-kilometre per track-kilometre), thus enabling large economies ofdensity.
Two main conclusions can be drawn from these two case studies. First, unit costs need to be interpreted
cautiously. In the case of infrastructure maintenance, unit costs are as much a reflection of the network
complexity, purpose and usage as of the efficiency of its maintenance manager. An in-depth knowledgeof the “engineering narrative” is required to carry out international comparisons. The same applies to
TOCs and overall industry costs, where costs per passenger-kilometre are likely to be more favourable on
systems with high track usage rates.
Second, when looking at the evolution of costs for a single country, the broader railway context needs toguide interpretation. For instance, even though the evolution of infrastructure costs is similar between
ProRail and Network Rail, the underlying mechanisms are very different. The role of outsourcing has
worked in opposite directions in the two countries. In the Dutch case, outsourcing proved beneficial toefficiency after a running-in period of ten years or so, while in the United Kingdom it provided short-term
benefits in terms of cost savings, but ended up being very costly due to the accumulated maintenanceand investment bill. The difference lies in the way contracting is managed. When applying econometricmethods, these elements need to be carefully taken into account.10 Moreover, not only the costs but
also the quality of the railway system needs to be investigated: differences in performance (capacity,
functionality, reliability, etc.) often explain cost differences, and if delaying maintenance can result insavings in the short run, it is often not economically justified in the long run.

Taking account of multiple railway efficiency dimensions
If the context plays such a big role in determining efficiency measures as well as their interpretation, tothe extent illustrated in the previous section, then a simple approach to railway efficiency will not yield avery accurate image. One could try to expand the set of the observed KPIs. However given the large
number of dimensions which affect railway efficiency, a very large dataset would be needed subject tothe limitations in data collection highlighted earlier. In addition, the issue of which KPI should receive
greater weight is not trivial, and establishing a hierarchy of efficiency measures is challenging.
There are two main approaches to addressing such complexity, as outlined by Smith and Nash (2014):
econometric techniques relying on statistical approaches and Data Envelopment Analysis (DEA) based on
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1. SUMMARY OF DISCUSSIONS

linear programming. The main advantages of the DEA method over econometric approaches (in terms ofhandling multiple inputs and outputs and not requiring the specification of a particular functional form)
have diminished over time as econometric approaches have evolved. A key advantage of DEA is thepossibility to carry out some analysis even when a large dataset of control factors is not available.
However the major weaknesses of DEA lie in its inability to take account of random noise, which maylead to inefficiency being overstated, and the lack of statistical tests to provide an objective justification
for the exclusion/inclusion of key variables. Econometric analysis is therefore to be preferred when large
datasets are available.
Econometric techniques for efficiency measurement rely on three approaches:cost functions

production functions



distance functions.

The cost function approach assumes cost minimisation in the production process and relates cost to thelevel of outputs and to input prices. It can deal with multiple outputs, as well as incorporate thetreatment of economies of scale and density. It also allows for cost changes over time, for instanceresulting from technological progress. It is the only one of the three approaches that accounts for bothtechnical and allocative efficiency. Railway efficiency measurement most commonly relies on costfunctions. The relative cost efficiency of transport operators is also the key focus of policy makers andregulators, hence also their reliance on this approach. Another reason for preferring the cost function
approach is that data on costs is more reliable.11 Even though some of the caveats evoked for the simple
scorecard approach to railway efficiency remain, one key advantage of the econometric method withcosts is that it produces a single efficiency indicator, controlling all the factors that can be modelled in
the regression (such as cost changes over time, or some of the differences between countries).
The cost function approach can be extended to allow the relative efficiency of companies to vary throughtime and allows an estimation of this variation. The extensions are termed SFA (Stochastic12 Frontier
Analysis). As the name suggests, the method is used to construct an efficiency frontier and to estimate
the distance of individual observations (railway companies in our case) from the frontier (graphicallysimilar to Data Envelopment Analysis). The approach can be and is used with panel data (cross-section
and time-series data).
SFA seeks to go beyond the cost function approach by recognising that firms’ costs will deviate from thefrontier due to random noise (e.g. random events such as bad weather) as well as inefficiency. SFA thus
provides a set of assumptions that decompose the residual in the model into two components (“trueinefficiency” and “other sources of error”), and therefore obtain a better measure of inefficiency, which
disentangles other confounding sources of error.
Econometric approaches do require the choice of an appropriate functional form, but the commonlyused Translog function is very flexible and Box-Cox transformations allow the data to dictate thefunctional form. The choice of distribution for the inefficiency term in stochastic frontier analysis isarbitrary. In the case of SFA, there is a risk that unobserved heterogeneity will be captured asinefficiency. More recent literature cited in and including Smith and Nash (2014), have proposed anumber of useful methods to address this issue when panel data is available. In practice, cost functions
and increasingly SFA are being used by regulators. Regulators usually apply a number of different
methods and approaches to derive an average indicator of efficiency across the methods in order toaccount for the inherent uncertainty in the data and the modelling process.

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1. SUMMARY OF DISCUSSIONS

Econometric models require a lot of data. Panel data is particularly useful in this regard for separating
unobserved heterogeneity from inefficiency. In addition to panel data, regional data within countries isuseful to examine within system performance, providing better like-for-like comparisons acrosscountries, improving capturing scale and density effects, and increasing sample sizes.
The econometric modelling approach produces a top-down view of efficiency that would benefit greatly
from a bottom-up engineering narrative to provide insight into what determines performance. For
instance, in its last review of pricing, the Office of Rail Regulation (ORR) in the United Kingdom
commissioned a number of engineering studies to help understand differences in indicators of efficiency
between Britain and other countries.
Figure 1.4. Life-cycle cost comparison between the Netherlands and a
US railway company

Source: Swier (2014).

In an international comparison exercise with a US railway company, Jan Swier (2014) shows an exampleof the importance of such an engineering analysis. He found that unit rail infrastructure costs (measured
on a life-cycle basis) were as much as five times higher in the Netherlands. This is where another aspectof the narrative comes in: what is the purpose and level of use of the tracks? A precise statistical analysisof these questions for the US railway company and ProRail allowed Swier to establish the cascade
diagram in Figure 1.4. Usage and complexity explain 60% of the cost difference. The biggest costelements in the Netherlands are the large number of switches and signals, as well as the extension ofelectrified tracks. In contrast, the US company benefits from having dedicated freight traffic and hightraffic density in tonne-kilometres with fewer trains.

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1. SUMMARY OF DISCUSSIONSThe policy maker and exogenous efficiency determinants
Exogenous determinants of costs can, as argued in a previous section, be a far greater cause ofdifferences in performance than railway company management. Of the three main stakeholders
mentioned in the introduction, the policy maker has the greatest power in determining the exogenous
circumstances. The policy maker may not be able to change the topography or the density of settlementof a country but is able to define national transport policy and can choose the structure of the railway
company, its ownership and the form of economic regulation. Although it may be debated whether the
structure, the ownership and the regulation of the railway company are exogenous factors, this paper
considers them as such, as they cannot be decided by the management of the railway company.
The private railways of the United States, Mexico and Canada are vertically integrated companies,
competing with other private companies across large areas of land and large markets. These railways are
considered to be performing efficiently, but their exogenous (and historical) circumstances are different
to other countries, those of Europe for example, where state-owned railway companies still carry mostof the traffic.
In the European Union, the dominant mode of private participation, where it has developed, is throughexclusive concessions for passenger transport (with competition in the market). Open access freight andmore recently passenger train operations (competition in the market / on the track) have gradually
expanded since the mid-1990s. The effects of competition on efficiency are not obvious. The EU15
railways do not show a particularly positive picture in terms of traffic growth or modal share. As
Thompson and Bente (2014) note, it is possible that the restructuring reforms in the European Union
have not been implemented to a sufficient extent to have a significant impact. It is also possible to argue
that the outcomes would have been worse without the reforms.
The organisational structure in the case of most railways in the European Union has not substantiallychanged. In some cases, a restructuring was made from a single company to a holding structure. There
are now 14 cases of full vertical and horizontal separation in Europe. Recently, France reintegrated theinfrastructure manager, Réseau Ferre de France (RFF), with the national train operator, SNCF, recreating
a fully vertically integrated company. Bonnafous and Crozet (2014) note this will lead to further growthof unit costs due to the balance of power being shifted in favour of the company and the unions rather
than the public regulatory authority. It will also likely undo any advances in infrastructure management
efficiency achieved through devolving the infrastructure management role to RFF.
The United Kingdom is the most analysed case of railway privatisation and vertical separation in the EU.
The process of the privatisation of the original British Rail was marked by a steep learning curve on thepart of the infrastructure manager and the regulator. Smith and Nash (2014), in their literature review,
show that not everything in the United Kingdom went “according to plan”. The events that led to thebankruptcy of the infrastructure manager and its administration resulted in a major loss of efficiency,from which they have not yet recovered. In freight transport, privatisation effects were overridden byexogenous developments in the economy and especially the structure and geography/distance of coaltransport.13 In passenger transport, the growth of traffic can be attributed to exogenous developmentsin the economy for the most part. The cost of operations in passenger franchising increased, partly due
to inadequate franchise size and overlap. These developments contrast with other countries like
Germany and Sweden, where savings of 10% to 30% are reported as a result of the introduction ofcompetitive tendering for exclusive concessions for passenger services.
Vertical separation adds transaction costs for coordination and internal accounting and negotiation,although the exact magnitude of these costs is debated. The McNulty study (2011) noted that the major

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1. SUMMARY OF DISCUSSIONS

issue of vertical separation in the United Kingdom was not the transaction costs but rather amisalignment of incentives between the operators and infrastructure manager, which increased the costof the latter from 2% to 20%. The most comprehensive study to date, EVES-Rail (van de Velde, 2012),
concluded that the vertically separated model works less well on intensely used networks and provides
the perhaps counter-intuitive result that vertical separation reduces costs on less intensely used
networks. Likewise, Mizutani and Uranishi (2013) find that while horizontal separation unequivocallyreduces costs, vertical integration only reduces costs for densely trafficked railways; the explanation isthat transaction costs caused by vertical separation will be much greater in densely trafficked networks
than in less densely trafficked ones.
But the study also provides no evidence that the introduction of competition had any effect on cost. The
analysis is subject to data issues and did not take account of some factors that may be important(e.g. differences in declared and effective economic regulation). Finally, the EVES study gathers datafrom a large sample of Asian and European railways so its results only reflect average behaviour. It is notable to explain some of the finer changes that have been observed, for instance in the Netherlands,
where vertical separation first introduced a disturbance in the system with a worsening of performance
before a net improvement became evident (Figure 1.5).
Figure 1.5. Example of technical failure performance of ProRailbefore and after vertical separation

Outsourcing Maintenance

Separation

1995

1998

ProRail BV

2005

2013

Source: Swier (2014).

Given this discussion, a further element that could be added to exogenous performance drivers might becultural differences between countries. This leads to further pertinent questions. To what extent do thechanges in organisation and ownership drive the outcomes and to what extent are there deeper root
causes that influence the results – is there a confounding problem? If this is the case, what can really besaid about railway efficiency, structure and ownership of railways in the European Union or elsewhere?
Clearly, such questions do not imply that no progress has been made in our understanding of the
problem, but a lack of good quality data is a major obstacle to informing policy.

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Conclusion
Railway efficiency is a challenging concept due to the complexity of the production process in the railway
business.
A basic view of railway efficiency can be formed with a small set of cross-section and time series data
producing a limited set of KPIs. Railways generally produce this data as needed for internal management,
but it is not publicly available in every jurisdiction. A basic responsibility of government is to requirecompanies to report key data, with collection and publication co-ordinated either by companies andtheir associations or by government. Railway experts will inevitably be required for interpretation of theresults, but large deviations from efficient operation should be evident. For some of the key
management and policy purposes the set of basic indicators identified in Box 1.1 will be sufficient.
For monitoring efficient use of public subsidies and regulating monopolistic infrastructure managers andexclusive train operating concessions, more sophisticated econometric techniques will need to be
employed. These have much greater data requirements but are essential to developing meaningfulbenchmarking outputs.
With both basic indicators and econometric analysis, a narrative of exogenous and endogenous drivers ofrailway performance will need to be developed in order to contribute to a fuller understanding ofdifferences. The narrative includes the key functions of the railway network (whether it carries mixed
traffic or is dominated by one traffic type), the topographical characteristics of the country, historicalpolicy decisions and other elements outside of the scope of influence of the company’s management.
Interpretation of any kind of efficiency analysis results without taking account of the narrative will lead tomisinformed decisions.
Along with econometric analysis, bottom-up engineering analysis can be employed to develop thenarrative in sufficient detail to shape the parameters used in the econometric functions modelled.
Bottom-up analysis can also shed light on whether the parameter estimates in a model make sense.
When benchmarking the performance of several countries, the use of KPI requires detailed
harmonisation of data, as exemplified by the UIC study. There is a link between this harmonisation
process and statistical methods: both are trying to take account of factors before reaching an efficiency
judgement. Further, econometric approaches could even inform the harmonisation process as it informs
on the factors that need to be controlled for.
As the brief review of the current state of the art in railway efficiency assessment revealed, both thesimple and econometric approaches are data intensive and suffer from problems with data availability
and quality. While there are positive exceptions in data collection and availability, inadequate dataexplains why questions with regard to railway efficiency often remain very much open. The problem is
not that it is unclear what data should be collected. In the United States, both accounting regulations forprivate companies and data reporting requirements imposed by the regulator, the Surface
Transportation Board, make most of the data required for construction of a “balanced scorecard”
publicly available. US and Canadian data reporting requirements provide a model for the data needed forregulation of vertically integrated freight-dominated railways elsewhere.
In Europe, good quality studies have been undertaken, in terms of data quality and common terms ofreference, in a number of EU research projects on railway infrastructure expenditure (e.g. the RailCalc
and GRACE projects). However, data is not collected routinely or, when it is, it is only made public after itis has been processed to mask the identity of individual railways, as the UIC is constrained to do by its
member companies. Such practice is at odds with the substantial share of public money involved in theoperating of national railways.
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1. SUMMARY OF DISCUSSIONS

Much work by regulators and supranational organisations will be required to ensure collection of
consistent data across countries. European data collection and dissemination could be greatly improved
if a regulatory body, such as the European Railway Agency, were mandated to specify data to bereported by every railway, requiring annual production and publication. A small number of national
regulators already do this and the annual network statements of some railways provide a wealth of
information. The European Commission is also currently improving the Rail Market Monitoring Report,
which it produces biannually for all Member States. When maintenance and construction is outsourced,
the necessary information is available in the contracts and invoices. While respecting subsidiarity andconfidentiality, the specification of a common set of information to be produced for all the railways
would be extremely useful for comparing performance.
Finally, most studies of railway efficiency are focused on technical cost efficiency. Performance on costs
reflects the inside view, but what is ultimately important is service to the customer. More effort needs to
be invested in providing data and KPIs on the service quality related to how users choose between
transport modes, thus linking output measures with input measures more directly.

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Notes

1 Nash and Smith (2014) cite examples where no depreciation or interest is entered into the accounts for assets that have been purchased with
grants.
2 E.g. prior to privatisation, the state might decide to treat railway infrastructure as a strategic asset and remove it from the balance sheets of
the railway infrastructure manager. This would, at least in part, change its borrowing capacity against its assets and influence all future businessactivity.
3 Information on the initiative is available here: http://www.uic.org/com/uic-e-news/367/article/15-year-report-on-infrastructure?
page=thickbox_enews.
4 The railway companies face charges in case of reporting inaccurate data. The incentive for accurate reporting is enforced through the factthat such data are often used in court proceedings with shippers or the regulator and are subject to intense scrutiny.
5 For example, if the infrastructure manager is state owned and subsidised, it is the decision of the state how much money it wants to makeavailable for maintenance or renewals so these costs are outside of the direct control of the infrastructure manager.
6 However, care should be taken when comparing different railway companies or countries because unit costs depend on economy of scale anddensity, which need to be controlled for.
7 Whether demand for rail services should be treated as wholly exogenous is a complex question. On the one hand, derived demand from
economic activity can be treated exogenously and rail systems with similar levels of demand can be more appropriately compared. On the other
hand, demand growth can be an internalised management objective for railway companies, as exemplified by net cost contracts in a franchisingsystem.
8 One of the biggest challenges when writing these performance contracts, especially those related to daily maintenance, is to set out outputs
that guarantee track quality in the long-term and prevent sub-contractors from opportunistically reducing costs by only carrying out short-term
maintenance.
9 The strengthening of safety requirements resulted from the public outrage over the Hatfield incident. However, it is now argued (Evans, 2007)
that safety statistics had not deteriorated. The very high level of maintenance required after the incident rather stemmed from the fact that
Railtrack did not know the state of their network and could thus not direct maintenance to the portions of the network that required it most.
10 As noted by Nash and Smith (2014), vertical separation or outsourcing “is not a dummy variable in the regression” i.e. a variable, which cantake the value one or zero (e.g. there is or is not vertical separation).
11 Getting comparable data may be difficult, for instance for financial costs, but at least cost data does not suffer from the problem ofoutsourcing.
12 “Stochastic” refers to something than can be estimated statistically but cannot be precisely predicted.
13 With the closing of the coal mines in the UK, coal power plants had to shift to imported coal. That meant that more than half of the increase
in tonne-kilometres in rail freight could be explained by the increased length of coal hauls from the ports to the coal power plants.

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Papers, No. 2013/12, OECD Publishing, Paris.
DOI: http://dx.doi.org/10.1787/5k46bj46ptkb-en
Bente, H. and L. Thompson (2014), “A balanced scorecard model for system efficiency”, Presentation
prepared for the OECD/ITF Roundtable “Railway Efficiency”, Paris. http://www.itf-oecd.org/sites/
default/files/docs/thompson-bente-presentation.pdf
Bonnafous, A. and Y. Crozet (2014), "Les indicateurs d'efficience du transport ferroviaire en France"(“Efficiency indicators of railways in France”), International Transport Forum Discussion Papers, No.
2014/24, OECD Publishing, Paris. DOI: http://dx.doi.org/10.1787/5jrw1knfjfs5-fr
Civity Management Consultants (2012), “European benchmarking of the costs, performance andrevenues of GB TOCs”, report prepared for the Office of Rail Regulation.
ECMT (2007), Competitive Tendering of Rail Services, OECD Publishing, Paris. DOI: http://dx.doi.org/
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Evans, A.W. (2007), “Rail safety and rail privatisation in Britain”, Accident Analysis and Prevention Journal,
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Hansen, I.A., P.B.L. Wiggenraad and J.W. Wolff (2013), “Benchmark analysis of railway networks andundertakings”, presented at the 5th International Conference on Railway Operations Modelling andAnalysis, Copenhagen, Denmark, 13-15 May.
Hendricks, W. (1977), “Regulation and labor earnings”, Bell Journal of Economics, Vol. 8, pp. 183-96.
Kirchner, C. (2011), Rail Liberalization Index 2011, IBM Global Business Services. Earlier issues are 2002,
2004 and 2007.
McNulty, Sir R. (2011), “Realising the Potential of GB Rail: Final Independent Report of the Rail Value forMoney Study”, Department for Transport and Office of Rail Regulation, London.
Mizutani, F. et al. (2014), “Comparing the costs of vertical separation, integration, and intermediateorganisational structures in European and East Asian railways”, Journal of Transport Economics and Policy(Fast Track Articles).
Mizutani, F. and S. Uranishi (2013), “Does vertical separation reduce cost? An empirical analysis of therail industry in OECD countries”, Journal of Regulatory Economics, Vol. 48 (1), pp. 31-59.
Nash, C. (2002), “Regulatory reform in rail transport: The UK experience”, Swedish Economic Policy
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Nash, C., J.E. Nilsson and H. Link (2013), “Comparing three models for introduction of competition into
railways”, Journal of Transport Economics and Policy, Vol. 47 (2), pp. 191-206.
Office of the Rail Regulator (2003), Access Charges Review 2003: Final Conclusions, London.
Rose, N.L. (1987), “Labor rent sharing and regulation: Evidence from the trucking industry”, Journal of
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Salinger, M.E. (1984), “Tobin’s q, unionization, and the concentration-profit relationship”, Rand Journalof Economics, Vol. 15, pp. 159-170.
Savedoff, W. and P. Spiller (1999), Spilled Water: Institutional Commitment in the Provision of Water
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2. WHAT IS RAIL EFFICIENCY AND HOW CAN IT BE CHANGED?

Chapter 2. What is rail efficiency and how
can it be changed?
Defining efficiency in a general sense
In the abstract, what we mean by “efficiency” or productivity (we will use these terms essentiallyinterchangeably) is maximising the outputs from a set of inputs or maximising the ratio of
inputs/outputs. Efficiency is not a stand-alone concept, however; efficiency is always dependent on acomparative context. We need to know how a given performance compares with others.
Defining and measuring efficiency or productivity in the railway context is a complex problem because:Size and scale matter. Large railways and highly dense railways have a potential advantage inefficiency because some parts of railway operations are subject to returns to scale, at least over
the range below the very largest systems.



The mix of services matters. Most measures of productivity appear to show that passenger
service is less “productive” than freight. That is, a passenger-km tends to require moreresources to produce than a tonne-km: after all, many countries operate 10 000 tonnes (orgreater) unit freight trains while passenger trains carrying more than 1 000 passengers are rare(see Mumbai commuter trains, however). Moreover, freight is generally considered to be“commercial” and market-driven, and managers have an opportunity to set reasonably clear
management objectives. Passenger services are typically justified by social as well as financial
performance, leading to political involvement and mixed, even contradictory management
objectives.

Evaluating railway efficiency therefore requires a number of different types of indices relating to scale,asset productivity (including labour), financial indices (revenue-cost) and economic measures that
include social costs and social benefits. No single index can ever be dispositive. Instead, we will need tolook at a collection of indices to see which railways tend to fall at the bottom of the pack and which tend
to rise to the top.
The complexity of measures makes it important to have two types of indices: cross-section (comparing
railway systems at a single point in time) and time series (change over time). There can well be reasonsfor a lower ranking on various cross-sectional indices, especially when some railways are forced by thegovernment to provide large quantities of politically driven regional or commuter services (whether or
not compensated by public service obligation payments), or where regulation suppresses tariffs andharms financial performance. Even where a plausible case can be made for lower comparative
performance, however, adverse changes over time are harder to explain.

Indicators available from published data
Indicators of efficiency or productivity can be developed at many different levels. The objective of thischapter is to identify indicators that can be developed from publicly available data 1. We recognise thatsome measures would require much more detailed information, such as a comparison of the costs of DB
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versus Network Rail in maintaining a kilometre of electrified line with comparable traffic levels.
Unfortunately, information at these detailed levels is either not collected or not reported publicly.2
Annex 2.A2 contains a detailed discussion of the sources of data used in this paper. The dataset
developed covers the period 1970 to 2011 (in some cases later) for time series purposes and furnishes acomplete cross-sectional set for 2011. The data set includes all EU railways (separated between the EU15
and EU10) along with Switzerland and Norway. In addition, for comparison we include China, theUnited States (Class I freight railways and Amtrak), Canada (freight railways and VIA), Japan and, in somecases, Indian Railways (IR).
The basic indices of size and scale are as follows (see Table 2.A1.1 for a key to the countries, railways andgroupings employed in this analysis and Table 2.A1.2 for summary data):


Passenger data: passengers carried,3 passenger-kms, gross tonne-kms for passenger trains,
passenger train-kms, coaches, DMUs, and EMUsFreight data: tonnes carried, tonne-kms moved, gross tonne-kms of freight moved, freight trainkms and freight wagons4Common or joint assets: locomotives, labour, kilometres of lineFinancial and economic performance: total operating cost; total operating revenue, passenger
revenue, freight revenue.

Ratios of efficiency and productivity developed from the measures above:

Average trip length for passengers (passenger-kms/passengers) and average length of haul for
freight (tonne-kms/tonnes). Table 2.A1.3.



Passenger share of Traffic Units (TU): passenger-kms/(passenger-kms + tonne-kms). Table 2.A1.4.



Passenger share of gross tonne-kms: passenger GT-km/(passenger GT-kms + freight GT-kms).
Table 2.A1.4.



Passenger share of train-kms: passenger train-kms/(passenger train-kms + freight train-kms).
Table 2.A1.4.



Traffic density: TU/line kms, gross tonne-kms/line kms and train-kms/line kms. Table 2.A1.5.



Coach productivity: passenger-kms/(coaches+ DMUs+EMUs). Table 2.A1.6.



Wagon productivity: tonne-kms/wagon. Table 2.A1.6.



Locomotive usage: TU/(locomotives + MU factor)5 Table 2.A1.6.



Labour productivity: TU/employees, gross tonne-kms/employees and train-kms/employees.
Table 2.A1.7.



Operating ratio: operating cost/operating revenue. This is a commonly used measure of financial
performance and an indication of the railway’s ability to cover its financial obligations.6 Table
2.A1.8.



Average revenue per passenger-km and per tonne-km. These are measures of the railway’s
average tariffs and give an indication of the railway’s cost levels combined with government
subsidy policy. These measures show performance from the customer’s point of view – howmuch do I have to pay? In addition, they give a good indication of the railway’s charges
compared with competing modes. These measures are presented in constant 2011 Purchasing

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2. WHAT IS RAIL EFFICIENCY AND HOW CAN IT BE CHANGED?

Power Parity (PPP) adjusted international dollars. This involves several revenue conversions: 1)
into constant local currency (which requires conversion from local currency to Euros in thosecountries joining the Euro); 2) into USD at 2011 conversion rates; and 3) into PPP USD. Although
this chain of conversions clearly introduces potential errors at every stage, we believe it isinteresting because it furnishes a general comparison of amounts that users actually pay invarious countries and especially because it shows the impact (if any) on railway users of thevarious reform programmes. Table 2.A1.9.


Market shares for passenger and freight from OECD data of freight and passenger traffic for allmodes since 1970. This is the best available measure of how the railway has performed incompetition with highway, water and air traffic and is a measure of the impact of reforms on therailway’s competitive position. Table 2.A1.10.

Initial rankings based on cross-sectional comparisons and initial
discussion of time-series data
The data available are far too extensive for a detailed review of every railway. Instead, we can briefly
summarise the highlights of the basic performance indices illustrated in Tables 2.A1.1-2.A1.10.
Table 2.A1.1 provides a listing of all railway entities on which at least partial data have been collected
and shows how the tables distinguish among EU15, EU10 (and Croatia), Norway and Switzerland, and allother railways. It also provides the railway abbreviations that are used throughout this paper.
Table 2.A1.2 shows employees (labour force), line kms, passenger-kms and tonne-kms. There are somerailways, notably China, US Class I freight, Indian Railways and Japanese railways that are immense
industrial undertakings by any measure. SNCF, DB AG, PKP, FS and the UK rail system appear at the upper
end of the ranges as well. By comparison, many of the EU’s smaller railways are 1/1 000 (or less) the sizeof the largest railways. Although there have been studies arguing that returns to scale in railways taper
off beyond a certain size (and some of the largest appear to be at or beyond this point), there is little
question that many of the smaller railways will inherently be on the less efficient end of the scale. Thishas to be considered when assessing their performance.
Table 2.A1.3 shows the average trip distance for passengers and the average length of haul for freight.
Railways with a longer average trip are in a different market segment than those with mostly short trips.
CR, Amtrak and VIA, for example, operate numerous long-haul trains with sleepers and diners and, for
Amtrak and VIA, are partly in the cruise business and partly compete with air travel. A criticalcharacteristic of most of the EU railways is their very short average length of passenger trips, whichmeans that they operate mostly short intercity trips or commuter services. At these trip lengths, autoand bus are the main alternatives. Somewhat the same phenomenon shows up even more strongly infreight, where US Class I, CR, Canada and IR operate with lengths of haul long enough to fully capture theeconomic advantages of long-haul, heavy loading freight traffic. By comparison, most of the EU railways
are constrained to operate at lengths of haul where trucking becomes more competitive. We highlighthere that there is a real possibility that the EU lengths of rail freight haulage (and passengers to a lesser
extent) may be distorted to appear lower than they actually are by double-counting of the tonnes
handled when traffic crosses national borders.7 This also highlights the need for better origin-todestination rail traffic data in addition to that reported by the individual railways.8
Table 2.A1.4 shows the role of passenger traffic in the total traffic of each railway, first as a percentage ofTraffic Units (the sum of passenger-kms plus tonne-kms), then as a percentage of gross tonne-kms and30

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then as a percentage of train-kms – three different aspects of rail service. Traffic Units give a basic
picture of the relative markets the railway serves, gross tonne-kms gives at least an indication of therelative maintenance burden imposed by each type of service, and train-kms gives a rough picture of therelative usage of line capacity, which is the basic limitation on the ability of the railway to provide service.
By these measures, the EU15 railways tend to be passenger-dominant, the EU10 railways less so. Japan ishighly passenger-dominant and the US, Canada and CR are freight-dominant. It is also significant to notethat the passenger share of train-kms tends to be higher than TU or gross tonne-km, indicating thatmeasures of efficiency of system use should look at all three measures in order to account for services,
wear and tear in the system, and usage of capacity.
Table 2.A1.5 then looks at measures of line traffic density according to TU/km, gross tonne-kms/km andtrain-kms/km. It is interesting that CR and US Class I tend to rank higher by the first two measures,
whereas the EU railways rank higher by the third. We could say that the US Class I railways, for example,
are more efficient at using their tracks to move volumes of freight, but the EU railways are more efficientat moving trains carrying passengers. From another viewpoint, we could argue that the focus in theEuropean Union on using line capacity to emphasise train-kms may well limit the ability of the systems tomove freight that requires fewer train-kms but can interfere with passenger trains because of the speed
difference between freight and passenger trains.
Table 2.A1.6 provides a series of measures of the productivity of rolling stock. The measure for coaches ispassenger-km/coaches including MU coaches. Wagon productivity is shown as tonne-km/wagon fleet.
Locomotive productivity is TU/locomotives plus an adjusted number of MUs to reflect the fact that MUs
provide tractive effort. The adjustment factor used divides the number of MUs by six: we recognise thisas, at best, an approximation. In fact, while the coach measure pertains only to passenger service andthe wagon measure pertains only to freight, making them both reasonably separable, the locomotive
measure necessarily includes both services (except for railways that provide only freight or only
passenger service) since locomotives are often used interchangeably. Once again, in terms of locomotive
usage intensity, the major freight railways tend to predominate. IR, CR, SBB and Japan stand well above
the rest in coach productivity.
Table 2.A1.7 shows output per employee as measured by TU/employee, gross tonne-kms/employee andtrain-kms/employee. The US Class I and Canadian freight railways stand far above the pack in TU andgross tonne-kms per employee, but are in the middle of the pack for train-kms/employee. This reflects
the same difference in focus where, in order to reduce labour costs, the United States and Canada run
fewer, but long and heavy trains whereas the EU systems run higher frequencies of shorter trains
primarily because passengers place a higher value on service frequency than do freight shippers.
Table 2.A1.8 shows the operating ratio, which is the ratio of total operating costs (excluding costs of debt
and equity) to total operating revenues and is a basic measure of financial performance. Railways
running an operating ratio above approximately 85% are much less likely to cover their total costs andwill require increasing outside support as the ratio becomes higher – they are financially “inefficient”(though they may be economically efficient if they are rendering a social service at low cost and withadequate compensation). By definition, an operating ratio above 100% means that the railway cannot
survive without outside assistance. The critical observation is how few railways even approach being
self-sufficient financially. This may be well within the fiscal boundaries established by governments, but it
does ensure that railways are enmeshed in the annual politics of public finance: note, for example, that
the US Class I railways are profitable (operating ratio of 73.2%) whereas Amtrak (operating ratio of
150.2%) is dependent on public finance. It is also interesting to see that the operating ratios of RHK(900%) and BV/Trafikverket (250%) reflect the stated policies of the Finnish and Swedish governments to

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collect only marginal costs of infrastructure provision from users. By comparison, an estimate of theoperating ratio for DB Netz is 86.9%, reflecting the stated goal of the government to collect the full costof operations from users. The reported operating ratio of RFF (78.7%) is also surprisingly low, andperhaps explains the complaints of SNCF that access charges were too high. It will be interesting to see
what happens to this ratio when RFF is re-merged with the SNCF parent company. The Annual Reports of
Network Rail stated an operating ratio of 64.5%, which would again reflect a policy of collecting full costfrom users. We emphasise, however, that these measures are particularly sensitive to accounting issues
and to transparent accounting (or lack thereof) for public support.
Table 2.A1.9 shows the most important index of efficiency from the point of view of the customer prices
charged. In Table 2.A1.9, we have converted average revenues per passenger-km and per tonne-km into
2011 USD at Purchasing Power Parity (PPP). Because this involves conversion of currencies, first into
constant terms, then into a common currency and then into PPP terms, it is clearly subject to a range oferror. With this acknowledged, it is interesting to see that the average passenger tariffs of many EU
railways are well into the range of low-cost airlines as well as costs of auto operation, which does notbode well for competition except in congested urban environments. Similarly, many of the EU railways
charge average freight tariffs that are roughly comparable to trucking costs and thus subject to intensecompetition. Extremely low passenger tariffs on some railways (IR) reflect a desire to use freight income
to pay for passenger losses caused by politically suppressed passenger fares.
Table 2.A1.10 shows the market share (percentage of passenger-km) of rail transport in the passenger
sector in competition with autos and buses. It also shows the rail market share (percentage of tonne-km)
in relation to the entire surface transport market (trucks, water and pipeline) and then the rail market
share compared to trucks alone. In a direct sense, this is not so much a measure of rail efficiency as it is ameasure of the result of rail efficiency (or lack thereof) in the overall market. An inefficient railway will
perform poorly; an efficient railway has a chance to perform well. We argue that the competition of rail
versus trucks is probably the best measure of rail’s performance in the transport markets. As this tableshows, rail plays a very different role in some countries than in others. For example, rail plays practicallyno role in US and Canadian intercity passenger transport but is predominant in Japan.
Because the amount of information to be presented would be too large, we selected a few indicators
and a few countries to display a sample of the time-series information that is available. We show only the
years 1970, 1975, 1980, 1985, 1990, 1995, 2000 and 2005-2011 (interim years are available in theunderlying database). We select France (SNCF), Germany (DB through 1995 and DB AG for 1995-2011),
and the United Kingdom (old BR before 1995, ATOC, UK freight and Network Rail afterward): theserailways together account for about 60% of all EU15 railway traffic. We show the Czech Republic (CD)and Poland (PKP), as these represent about 60% of traffic in the EU10 and because the data available are
not complicated by changes in corporate structure. We also show the US, Japan and Switzerland (SBB) torepresent railway activity outside the EU. We use 1980 and 1995 as base years: 1980 is a point in thedevelopment of the European Union when railways began to be affected by the overall economicchanges and is the year before deregulation in the United States; 1995 is close to the beginning of theEuropean Commission’s attempts to restructure the EU railways.
Table 2.A1.11 gives an overall picture of how railway traffic has developed over time. Notable from thistable is the fact that rail passenger traffic grew faster in the United Kingdom than for SNCF and DB,
especially after 1995. UK freight traffic also grew faster. Rail traffic has been shrinking in the EU10 andhad, at best, stabilised by 2011. Swiss traffic trends essentially mirrored those of the EU15, while
Japanese passenger and freight traffic were stagnant or slowly shrinking. US passenger traffic grewslowly while freight traffic grew strongly, especially from the base in 1980.

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Table 2.A1.12 shows the evolution in operating ratios and labour productivity (using TU/employee).
There is a mild improvement in operating ratio in most countries, with a marked improvement in US
Class I freight railways and in Japan. With this said, it is interesting to note the difference between the US
Class I railways (73%) and Amtrak (150%). Labour productivity improved in all countries, with thegreatest growth rate in the US Class I freight railroads, United Kingdom and Japan.
Table 2.A1.13 shows the side of the railways that the consumer sees, i.e. average tariffs. There was anapparent trend upward in average passenger tariffs in every country from 1980 and in all but one (Japan)
from 1995. Average freight rates were stable or trending downward in most countries; but only in the US
Class I railroads do they appear to be well below competitive trucking rates. We stress again here thatthe calculation of average rail tariffs is inherently an approximation because of all of the conversions
involved. We do believe that they are usefully indicative both as to levels and changes over time, butthey do need to be viewed with some caution.
Table 2.A1.14 shows the evolution in market shares in passenger and freight markets. The rail passenger
share of the EU15 railways (~7%) has changed little since 1980 and 1995 whereas the rail passenger
share in the EU10 countries has rapidly fallen to EU15 levels. Rail passenger traffic has an insignificant
share in the United States and that has not changed.9 Japanese rail passenger shares have been stable at
a level much higher than the EU, while Swiss rail passenger shares have grown slightly and are about
twice the EU levels. The picture for rail freight is quite different: EU15 rail freight shares have fallen since1980 but have remained stable since 1995. EU10 rail freight shares have fallen dramatically since 1980
and 1995, though they may now be stabilising at a level slightly above that of the EU15. Interestingly, theSwiss rail freight market share is much higher than in the EU, though it has fallen somewhat since 1980
and 1995. The US rail freight market share has stabilised since 1980, though it was falling rapidly before
then (it was 78% in 1950 and 67% in 1960).
At this point, we can answer the first issue posed in this chapter. Yes, there are measures of efficiency or
productivity that can be developed from publicly available data. The measures we have developed dogive an overall picture of the performance of the selected railways both in cross-section (2011) and overtime (1970 to 2011). It is possible from these measures to identify the more efficient railways: China inboth freight and passenger; US and Canadian Class I railways in freight; and Japan for passenger service.
Within Europe, SBB seems to measure up quite well while the EU15 and EU10 railways present a mixed
picture. It would also be possible to use the data developed to assess the efficiency of a specified railway
and track its progression over time, if that were desired.
With this said, these measures could be greatly improved in the European Union by having a regulatory
body that could specify the data to be reported by every railway, verify its accuracy and require its
production annually.10 It is possible that many of the gaps identified in the database could be filled byreference to Annual Reports or other national documents, but there is no single point of reference forcomplete and consistent reports.
In fact, the EU data gaps and consistency problems underline an important challenge in measuring andcomparing railway efficiency – most railways either do not see the need for detailed information forinternal management purposes or do not think it is in their interest to release such information to permit
public comparisons to be made. For example, as mentioned earlier, the data in “Railway Efficiency,”(Beck, Bente and Schilling, 2013) conceals the identity of the railways in the comparison, significantly
vitiating the use of the results. This has long been the practice of the UIC in making comparisons ofrelative performance of its members. Under what circumstances should public entities, supported bypublic funding, be allowed to conceal information that would facilitate public analysis and evaluation of
their performance? This will be a point to consider in the analysis of the interaction among ownership,

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2. WHAT IS RAIL EFFICIENCY AND HOW CAN IT BE CHANGED?

structure and performance measurement discussed below. It is also a critical point in assessing whether
the European Commission’s railway objectives – transparent accounting for infrastructure to ensure fair
access and financial stability of the infrastructure agency, accompanied by separated accounts forpassenger and rail services – can ever be met.
We argue that the information that the Commission would need to ensure implementation of its
Directives with respect to financial transparency of infrastructure, passenger and freight operations
simply does not yet exist, and should be added to the task of a designated authority. In addition, one
important piece of information – where do passengers and freight shipments actually originate andterminate? – is not yet available in the European Union and awaits collection of passenger ticket andwaybill information. The same issues were described in more detail in “Railway Accounts for Effective
Regulation” (Thompson, 2007).11 The data collected and reported by the US Surface Transportation
Board (STB), including “Analysis of Class I Railroads” and “Public Use Carload Waybill Statistics”, would bea useful model for EU agencies to consider.

How can efficiency be changed?
It is all very well and good to define and measure efficiency (however approximately) but the effort
expended in defining, collecting and reporting data will have no payoff if there is nothing that can bedone to change railway performance.12 Fortunately, if railways are willing, and the political will exists,
efficiency can be changed.
One way to change efficiency, much favoured by traditional, engineering-dominated railway
managements, is increased investment (increasing capital intensity). One of the arguments in favour ofadded investment – making up for deferred maintenance – can well have some justification, although it
sometimes simply reflects neglect of a facility that lost its economic role long ago and should be takenout of service. Where legitimate deferred-maintenance needs exist, good management (and good public
policy) will deal with it. Another argument – replacing old with new without regard to payoff – tends toappear when the railway does not face any commercial objectives. In either case, this chapter does notlook at increased investment alone, although we acknowledge its role in improving efficiency when agood financial or economic case can be made, especially when the success of a new structure depends
on a fresh start from years of past investment neglect.
We instead look at various structural or organisational innovations that have aimed at changing theunderlying objectives or incentives faced by railway management, and use the time series data inoutlining those changes that seemed to have “worked” and those that have not been as successful.
In general terms, we can identify changes in structure, ownership and incentives, though these can be
combined and can work together:

34

Structural change means movement along the spectrum that begins with monolithic form (allassets owned by the railway and all services provided by the railway). The Ministry of Railways in
China has long been an example of a monolith. China recently separated China Railways (CR)from a newly-created Ministry of Railways, leaving Indian Railways (IR) as the only remaining
major railway that is still fully monolithic. There are railway structures where the dominantoperator is in control of infrastructure while other operators are tenants on the infrastructure
and pay for access (either marginal costs or a negotiated fee). This can include either competing
operations in the same market (freight trackage rights on a freight operator’s lines, which covers
27% of US freight lines) or non-competing operators (passenger) on freight lines (Amtrak and

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VIA) or, indeed, freight operators on passenger lines (JR Freight). The United States, Canada andJapan are examples where the dominant operator controls the infrastructure and tenants pay foraccess. The complete form of structural change is full vertical separation, with an infrastructure
provider offering neutral access to all operators, in accord with published access charges. The EU
Commission’s Directives have been aimed at creating vertical separation of infrastructure but
the process has been fragmented, inconsistent across member countries and, in many cases,
remains incomplete.


Ownership change means movement along the range from fully public to fully private. US andCanadian freight railways are now fully private, though the Canadian National (CN) was only
privatised in 1995 and Conrail was privatised in 1987. Amtrak is a publicly-owned corporation.
The old Japanese National Railway was broken up (structural change) and the three largest
passenger operators privatised in 1987. Most EU railways remain fully public, but the private
sector is increasingly being allowed to provide some operating services, both in the passenger
and freight markets. The United Kingdom was, at one time, an extreme case of virtuallycomplete privatisation, but that has evolved back into a public/private balance.

Changes in incentives (“rules of the game”) include situations in which the management of therailway is given more freedom to operate commercially and is given objectives that include atleast some degree of risk for cost control or net revenue maximisation, or both. Management
contracting is a starting point, but the process can extend through gross cost or even net costfranchising.13 In the US context, deregulation completely changed the ability of freight railways
to work directly with shippers to set rates and services that met shipper needs without
interference from the regulator.

Did any of these changes work?
The reform process in the United States actually comprised three parts: (1) formation of Amtrak in 1972,
in order to free the private freight railroads of the burden of passenger deficits (and, in the minds ofsome, to free passenger service from the indifference of freight company management); (2) combining
the bankrupt freight railroads in the mid-west and north-east part of the country into one entity,refinancing and rebuilding it, and subsequently re-privatising it in 1987; and (3) deregulation in 1980 (theStaggers Act). As Tables 2.A1.11 and 2.A1.12, and Figure 2.1 show, these reforms were highly successfulin stabilising market share, lowering rates, increasing traffic and improving essentially all indices of
efficiency.14 The comparison with changes in Amtrak is interesting. Amtrak rates went up (Table 2.A1.13),
service grew slowly (Table 2.A1.11) and productivity was stagnant (Table 2.A1.12). Operating ratios
improved for freight and were stagnant (and high for Amtrak). With this said, the essential purpose of
Amtrak – to save the freight railways that were staggering under the burden of passenger deficits – wasachieved.
In Canada, privatisation of CN produced a change in the relative productivity of CN with CP (Canadian
Pacific, always private), though the shift was not dramatic. In sum, however, Canadian rail freight rates
declined steadily both before and after CN privatisation, while labour productivity improved rapidly.
Operating ratios also improved after 1995. Comparing Figure 2.2 with Figure 2.1, it is also apparent that
the Canadian experience was at least partly driven by deregulation of the US freight railways, with which
the Canadian railways both compete and co-operate.15 VIA offers the same comparison with theCanadian freight railways as Amtrak does with the Class I US freight railroads: VIA’s labour productivity islow (Table 2.A1.7) and is little changed since establishment in 1980. VIA’s operating ratio (185.5 – see

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2. WHAT IS RAIL EFFICIENCY AND HOW CAN IT BE CHANGED?

Table 2.A1.8) is high, although its average tariffs are well below Amtrak and are about the EU average,
but for a very different traffic mix (see Table 2.A1.3, where VIA has the third longest average length oftrip, reflecting the importance of long-haul trains).
Figure 2.1. US Class I Railroads operating ratio (%) andall commodity average revenue/tonne-mile

Avg. Revenue

Operating Ratio (%)

Note: US cents/tonne-mile
Source: Analysis of Class I Railroads and US Bureau of Economic Analysis (GDP Deflator).

In brief, the Japanese reforms involved breaking up the old, monolithic Japanese National Railways (JNR)
into six new passenger companies and a freight company that operates much like a “freight Amtrak”,
i.e. it pays access charges and uses the narrow-gauge lines of the passenger companies (the high-speedlines – Shinkansen – are standard gauge and are not used for freight). The three large passenger
companies (JR East, JR West and JR Central) were subsequently privatised by sale of their stock. An
explicit goal of the reform was to break the control of the unions over the politically oriented
management. As Figure 2.3 shows, the reforms were highly successful in improving labour productivity
and the operating ratio for the system.16 This was accomplished while tariffs were held stable(Table 2.A1.13) and total traffic actually remained almost the same over the last twenty years.
Performance of JR Freight is harder to pinpoint. What is clear is that traffic has declined while tariffs havebeen held stable, roughly at EU levels. In perspective, however, JR Freight has faced a problem similar tothat of Amtrak: as the traffic of the dominant operator has grown there is less room for the tenant. Thishas caused Amtrak’s on-time performance to plummet and has restricted JR Freight’s ability to handle its
traffic. It is probably a risk inherent to dominant/tenant schemes (or, arguably, where some operators
have closer linkage to infrastructure management than other operators).

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Figure 2.2. Canadian Freight Railways(Tariff index and labour productivity index, 1995=100)

Tariff and Labour Index

Operating Ratio

Source: Railway Association of Canada.

Experience in the European Union is much more complex to assess. In overall terms the Rail
Liberalisation studies by Kirchner17 suggest that the Commission’s structural reforms have gradually been
implemented, though the degree differs among members, as Table 2.A1.15 shows. Although the indices
are arguable on a number of grounds and are, in any case, only partly objective, Kirchner argues that themarket is now more liberal and that the degree of competition has increased.
Table 2.A1.15 does indicate that the Liberalisation Index as computed by Kirchner had improved over thetime period (2002, 2004, 2007 and 2011) studies. This appears to have been much more applicable tofreight service than passengers, probably because the interaction between public support and passenger
service is stronger than in freight. Governments find it hard to allow competition for their supported
services, though this has changed in some countries.
It is also significant that Kirchner divided his index into three parts: LEX (legal change); ACCESS (whether
the infrastructure agency actually allowed access to take place in accordance with the new laws); andCOM (a measure of the actual degree of competition that emerged). Looking at the COM index on Table
2.A1.15, even by 2011 there was only one country (United Kingdom) that had an “advanced” COM index,
and only four (Germany, the Netherlands, Denmark and Estonia) that were considered “on schedule”. It
is also interesting that DB AG owns the major freight carriers in Germany, the Netherlands and Denmark(and in the United Kingdom), so the apparent degree of freight competition in these countries may beless than indicated. Estonia essentially exchanges traffic only with Russia (its Baltic connections are either“delayed” or “pending departure”), so competition would be of limited value.

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Figure 2.3. Changes at JNR at privatisation(operating ratio (%) and labour productivity)

Output/Employee

Operating Ratio

2500

180

Breakup andPrivatisation(1987)

Labor (000 TU/Empl)
2000

Operating Ratio

160
140
120

1500

100
80

1000

60
40

500

20
0
1960

0
1970

1980

1990

2000

2010

Source: Author’s analysis and UIC Railway Time Series, 1970-2000.

The relatively slow development of intra-rail competition, combined with the slower pace ofliberalisation in the passenger sector, should alert us to have lower expectations for the impacts of the
EU reforms, especially in countries slower to adopt the reforms. This effect can be multiplied by the factthat a country might well be aggressive in its reforms, only to see the impact muted by slow change inthe countries to which it connects.
This overall picture of a slow pace of reform in the EU railways, developed by Kirchner, is supported by
the results in Tables 2.A1.11 and 2.A1.14. The EU15 railways do not demonstrate a particularly dynamic
performance, either measured by freight or passenger traffic growth or by market share. We
acknowledge that the outcome could have (we argue would have) been worse without reform, but it is
not possible to contend that the reforms have had (to date, at least) anything like the positive impact of
the reforms in the United States, Canada and Japan. It is also possible to argue (as the Kirchner indicessuggest) that the restructuring reforms have not actually been implemented yet to the degree necessary
to have an impact on efficiency.
The picture for the EU10 railways (and Croatia) is even harder to assess, partly because they are more
recent members and, more important, because they were subjected to the wrenching transition from
central planning to market structure, which would have had a devastating impact on both passenger andfreight traffic no matter what changes in structure had occurred. With this said, it is at least interesting
to point out that new, private freight operating companies are already carrying nearly 25% of freighttraffic in Bulgaria and are carrying about 50% of the freight traffic in Romania. Clearly, this would nothave happened without vertical separation. It will be interesting to see if these companies eventuallyoperate at higher levels of productivity and efficiency.

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Figure 2.4. Rail traffic in the United Kingdom(per 000 000 passenger-km and tonne-km)

Source: SRA, WDI, UIC, ORR.

It is difficult to use the efficiency indices to draw any clear conclusions about the performance of DB AG
and SNCF. They are both in the upper middle of the pack in size and outputs. Despite the emphasis on
developing HSR services, SNCF has an average passenger trip of only 79 km, while DB AG is even shorter,
at 40 km, suggesting that the efficiency of both is heavily influenced by the economics of short-haulpassenger service. Well over 70% of SNCF’s traffic output is passenger service, while DB AG’s passenger
service ratio is in the high 40% range. In operations, though, 89% of SNCF’s train-km are passengers, as
are 75% of DB’s operations: both railways are clearly using most of their capacity for passenger serviceand (as with the US and Japanese cases) when one service dominates, the others suffer for lack ofpriority access to capacity. Both are in the middle of the pack as to line traffic density, with DB AG slightly
above SNCF. SNCF appears to make somewhat better use of its rolling stock fleet, though neither is at
the top of the productivity rankings. However measured, the labour productivity of SNCF is lower than
DB AG, although the productivity measures for both SNCF and DB AG (especially) are probably reducedby the inclusion of non-rail employees in the totals.18 SNCF reported a better operating ratio than DB AG
in 2011, but this would not have been true in most of the earlier years reported. DB’s average passenger
fare is about 30% higher than SNCF, but its average freight tariff is about 10% lower than SNCF. SNCF’s
market share is higher than DB AG for passengers but lower for freight. SNCF’s passenger traffic hasgrown slightly faster than DB AG’s, but SNCF’s performance in the freight market has been very poor,
worse than DB AG and actually worse than the EU10 countries. DB AG’s improvement in labour
productivity has been significantly better than SNCF, but neither did as well in this index as any of theother railways listed in Table 2.A1.12 (except Amtrak). Passenger tariffs on both SNCF and DB AG are
higher than in 1990, by 50% for SNCF and 34% for DB AG. By comparison, both saw a significant
reduction in freight tariffs since 1990.

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Figure 2.5. UK passenger-km, tonne-km and GDP(Index 1994=100, GDP index constant GBP 1994-1995)
200

Passenger-Km

180

Ton-Km160

GDP (1994/1995)

140
120
100
80
60
40
1963

1968

1973

1978

1983

1988

1993

1998

2003

2008

Source: SRA and UK Treasury.

It has been shown that vertical separation adds some costs of co-ordination and reporting as well asinternal accounting and negotiation, although the exact degree of the added costs is around 5% or so.19
The counter question – have these costs produced offsetting benefits? – for example, through addedcompetition that reduces tariffs (as in the United States), certainly has an apparent answer: “no” forpassengers and “mixed” for freight. Essentially, every EU15 and EU10 railway has the same or higher
passenger tariffs as in 2000 or 1995. There is no discernible pattern in average freight tariffs, with somehigher and some lower in 2011 than in 1995 or 2000.
The United Kingdom presents a significantly different picture. Although we defer to the paper by
Nash et al. (2013) to survey the UK case in more detail, Figures 2.4 and 2.5 give a useful picture incomparison with other EU experiences. As shown in Figure 2.4, both passenger service and freight
service reacted strongly to the restructuring, with passenger service reaching levels not seen since theend of World War II. In fact, as Table 2.A1.11 shows, passenger service in the United Kingdom grewfaster since the restructuring in 1995 than either SNCF or DB AG, and far faster than the EU15 average.
The same is true for freight in the United Kingdom. The United Kingdom’s rail market shares for bothpassenger and freight increased faster than the EU15 average, while the average passenger tariff has
been nearly stable in constant terms.
There has been spirited debate in the economics academic community as to whether the positive UK rail
results have been due to privatisation or to restructuring, or were primarily driven by strong GDP growth.
This is an argument that cannot be resolved, but Figure 2.5 clearly shows that something positive
happened upon reform: it would be very difficult to attribute all of the change to growth in the economy.

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Conclusion
No simple attempt to measure railway system efficiency can be expected to provide meaningful answers,
both because of the ambiguity and inherent challenge in defining what is meant by the term “efficiency”
and because the structural complexity of rail organisations and the heterogeneity of railway services andofferings limits the value of any single index. Differing perceptions and purposes for attempts to measureefficiency will therefore require appropriate, tailored approaches.
Among the various purposes for measuring efficiency, the following need to be distinguished inparticular:a government’s interest in determining or monitoring the overall performance of its ‎railway
system, e.g. with respect to value for money, modal competitiveness, operational cost efficiency
or financial viabilitya government’s policy analysis, to define and review the success of railway restructuring or
market organisation initiatives

an audit of railway management performance ‎(be it in a domestic or an international context)an intergovernmental policy evaluation and benchmarking effort.

There are fundamental practical issues about efficiency measurement that need to be resolved before
more high-level conceptual questions can effectively be addressed, including:

Robust, internationally comparable reporting standards do not exist (note that while mandatory
standards apply in the United States and Canada, Europe has nothing close to a homogeneous
format). On a global scale, the UIC has the “best available” database, which could neverthelessbe improved. However, the UIC’s data may be at risk of losing quality and coverage.

Transparency – Railways frequently resist reporting data to their governments, even when (andthis appears to be the “default option” in Europe) ‎substantial amounts of taxpayers’ money isdeployed to fund infrastructure and “public-service obligations”.

Off-balance-sheet items – Subsidies paid to railway systems are in many cases very substantial,
but are not clearly reported. They typically come through one, or a combination of, thefollowing: infrastructure investment grants, passenger tariff ‎ ruuo snr ‎sta‎orrustions support,
as well as special-purpose vehicles for “legacy staff” obligations. Such items are often notincluded in railway balance sheets and official reports, and these off-balance-sheet items canhave a strongly distorting effect on financial “efficiency” measurements.



Last but not least, definitions of parameters, be they rather of a technical/operational, service
performance or financial nature‎, often lack clarity and uniformity, which is a prerequisite forvalid international comparisons.

As a consequence, and to the frustration of many industry observers, ‎cross-sectional measurements ofrailway “efficiency” are often more subject to distortion and misunderstandings than meets the eye inthe first place. This fact imposes a significant caveat on any interpretation of face-value comparative
measurements. With this is mind, time-series evaluations can strongly buttress comparisons of how
individual railways have developed over time and provide far greater reliability for interpretation. Evenso, discontinuities in reporting or the organisational set-up of railways over time can also be a source of
ambiguity (albeit less critical than in the case of cross-sectional comparisons).

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‎From a “good public corporate governance” perspective, full reporting, including “shadow assets” andfinancial flows to special-purpose vehicles, should be the norm. This is essential to provide fullaccountability to the public on the deployment of funds and to inform policy makers responsibly.
Acknowledgment of the above-mentioned limitations on data availability, quality and meaning leads to a
cautious note on the use of econometric models to describe railway efficiency, for a number of reasons:Inconsistency of input data, including unclear definitions.

Structural scarcity of data (“no big data”) due to small and unstable samples ofobserved/observable railways systems, with inevitably inadequate sample sizes for statisticalevaluations.



An inability of econometric models to discriminate between “good”‎ ou‎ “poor” corporate
governance and management, which in practice can have an overriding impact on actual railway
efficiency.

Most railway systems in the world show signs of protracted under-investment, especially ininfrastructure, because “pro-forma” statements of steady-state investment requirements‎ (i.e.
future cash flows to be set aside) are rarely reported accurately. As a result, such backlogs often
go undetected, leading to a real risk of a mis-assessment of the condition of infrastructure orother long-lived assets.

Qualified and informed judgment is always required in conjunction with even the best available and mostsophisticated supporting efficiency measurement analyses. ‎As a high-level common denominator (anentry point) to measuring railway “efficiency”, a balanced scorecard approach should be used that allows
for some standardisation and is broad enough to cover different aspects and measuring purposes in a
360-degree manner. A “Balanced Railway Efficiency Scorecard” (BRESC) should at least contain thefollowing elements on a first-tier level (each and all open for greater in-depth ‎analysis):

scope of the railway systemasset utilisation of infrastructure and fleethuman resource deployment

operational performancefinancials

customer-centric performance (i.e. performance in the market).

Railways are very asset-intensive systems, and economic analysis shows that under real-life conditions,asset utilisation, which is highly disparate for different systems around the world, has a major impact onoverall system profitability or “efficiency”. To a very large extent, asset utilisation is a result of historicallydeveloped networks with vastly different traffic density, coupled with above-rail operations that are
more or less focused on sufficiently high demand services (where “demand” can have both a political aswell as a market dimension). It is immediately and demonstrably clear that such disparate “operating
conditions” affect railways’ economics by orders of magnitude: asset utilisation is therefore a structural
determinant for a system’s (in)ability to make profits or losses.
No other single factor is more important for economic railway efficiency than asset utilisation. Hence,from an efficiency measurement purpose standpoint, it is vitally important to separate the impact of
those parameters that are primarily imposed by governments and other political stakeholders fromthose that are a good “proxy” for the performance of railway management.
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A good and highly aggregate efficiency measurement from an overall perspective is railway market share(“modal share”); however, in cases where public subsidies are applied to provide services (the norm in
Europe), subsidies can literally buy market share: thus, market share and system funding provisions need
to be understood in close connection. As a direct result, efficiency measurements of a railway system
may not suffice to describe the performance of railway management due to the overriding impact of
economic “legacy factors” – parameters, such as politics, which are exogenous to railway management.
Good proxies for direct management performance are the normalised full cost per ‎train-kilometre inabove-rail operations and the normalised full cost of maintaining and operating a unit piece of network
infrastructure (e.g. a kilometre of line or track) in infrastructure management organisations. Various
other dedicated or sometimes more global ‎analyses exist to measure management performance ininfrastructure and above-rail operations, many of them in confidential or anonymous form, but it is notalways clear that proper distinctions are made between what management can influence and what isprovided by “system legacy”. More work is needed if the effort to measure railway “efficiency” is to bepromoted further.
Last but not least, almost all of the global railway efficiency ‎measuring work is devoted totechnical/operational and financial aspects, and the customer perspective (which one could arguably
consider the ultimate measure of efficiency) appears to be a neglected area. Market-level questions tobe analysed are, for instance: how efficient is the travel or shipment solution offered by a railway in theeyes of the passenger or the shipper? Or, “how competitive is the price of using a railway service ascompared to other modes?” From a government perspective, this also means addressing aspects ofpublic welfare.
There is reason to assume that the customer perspective has been neglected so far because it poses achallenge to describe and measure; however, this should not be an excuse not to attempt it (note thatemerging “big-data” applications may represent breakthrough opportunities to capture customer-centric
information).
Looking at the data and indices per se, it is clear that the policy and structural changes in the
United States, Canada and Japan worked in almost all dimensions, and one can strongly argue that thechanges would not have occurred without the reforms.
It is far beyond the scope of this paper to review all of the EU railways individually. The experience in theEuropean Union is much more complex because most services at base are social rather than commercial,legitimately increasing the role of government, and there is no good annual reporting on the value ofsocial benefits and costs generated by the railways.20 The result has been a much less clear definition of
objectives and incentives along with unstable, often inadequate financial support, reflecting thevicissitudes of annual public budgeting. Attempts to change the situation have been impeded by politicalresistance from unions and other interest groups and, in many cases, a complete lack of transparency of
the actual performance (“efficiency”) of the railway, which has made scrutiny by the public, including theacademic sector, impossible. We also have to deal with the null hypothesis – what would have happened
without reform? – though SNCF performance may give an indication. It is also possible to argue thatDB AG has resisted the actual implementation of most of the significant aspects of the EU’s reform
objectives, at least with respect to railway structure in Germany.
It seems clear that the UK Government overshot its target by smashing the old BR and privatising itcompletely at the outset – but gradual reform since 1995 has produced a system that certainly seems
better than the old BR. In France, the attempts to reform (without actually doing so) have clearly not
been very productive. RFF never fully emerged from SNCF control, and recombining them into a newagency will mostly have the effect of turning back the clock. The DB AG holding company approach
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produced a conflict of interest between DB Netz and the operators with regard to potential entrants, aconflict that will remain until DB Netz is truly separated.

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Notes

1 Unless otherwise specifically indicated, all data are expressed in metric terms – tonnes and kilometres. Unless otherwise specified, tonnes
mean net tonnes.
2 The International Union of Railways (UIC) sponsored a series of studies on the relative efficiency of track maintenance among a number ofrailways. Unfortunately, the identity of the railways in the dataset was concealed, depriving outside analysts of the ability to put the relative
performance of each railway into context. This also deprived governments of the ability to assess the performance of their own railways and to
decide whether the public was getting value for money. Beck et al. (2012) suffer from the same “confidentiality” restrictions. An explicit
objective of this study is to rely only on data sets that are publicly available.
3 We highlight the fact that there can well be double-counting of passengers carried and freight tonnes carried, since the same passenger (ortonne) can cross a railway border more than once and be counted each time. Passenger-km and tonne-km are not subject to double-counting.
Given that the average trip length of most EU railways is quite short, this issue may not be as significant for passengers as for freight.
4 Numbers of freight wagons are also affected in countries where there are significant numbers of lessor- or shipper-owned wagons that do
not appear as railway-owned assets. For example, only one-third of US freight wagons are owned by railways.
5 Measuring locomotive productivity is complicated by the presence of DMUs (Diesel Multiple Unit) and EMUs (Electrical Multiple Unit) thathave their own tractive effort. We attempt to correct for this by calculating effective locomotives, by dividing DMU or EMU numbers by a factor
that represents the average length of a DMU or EMU train. We acknowledge that this is, at best, an approximation. Of course, on freight-only
railways or railways without MUs this is not a problem.
6 The operating ratio includes depreciation and amortisation but excludes payments to acquire and compensate sources of capital.
7 This could be corrected if railways distinguished between tonnes originated as opposed to total tonnes handled and tonnes originated offline and terminated off-line.
8 A similar problem appeared in the US Carload Waybill Statistics in the early years of waybill reporting, because each railway in a multiple
railway shipment could report the same tonnage. This has since been corrected. See McCullough and Thompson (2012) for a detailed
discussion of the issue.
9 This is, to some extent, the result of exclusion of the traffic of US commuter railways (which is included in the EU, Swiss and Japanese
results). US commuter railways carry slightly more passenger-km than Amtrak, so the United States’ share would double, but still remain below
1% if auto traffic is included.
10 For railways, this requirement might also be met by encouraging all railway service providers, including infrastructure entities, to complete
the existing data requirements of the UIC.
11 See also “Workshop Report – Measuring Investment in Transport Infrastructure”, ITF, Paris, France, 9 and 10 February 2012, where exactly
the same data issues arise.
12 Indeed, the experience of the authors suggests that railway management often resists collecting information, and especially reporting it, on
the grounds that they cannot do anything with the results anyway. Of course, it could also be because they are concerned that better
information might support efforts to change the rules of the game they face (or in fact change them). As a rule of thumb, public ownership andmanagement under political control seem to be antithetical to collection of transparent information, even where the information is for publicuse. To be fair, private corporations also try to restrict public reporting but, as the STB example demonstrates (Office of Rail Regulation [ORR] in
the UK is a demonstration of restrictions on passenger information), these objections can be overcome. Moreover, private corporations are notusually spending public money and, when they are, they are required to report in greater detail.
13 See ECMT (2007) for a discussion of gross cost and net cost franchising.
14 See McCullough and Thompson (2012) for a detailed discussion of the impact of the Staggers Act on US rail freight tariffs and on the
profitability of the Class I Railroads. Basically, rates went down and profits went up because productivity increased even more rapidly, especiallyas a result of contract tariffs.
15 A recent OECD report (ITF, 2014) showed that changes in the structure and ownership of the Mexican railways had a similar effect.
16 The operating ratios shown are actually for the entire system, and are lowered by the performance of the three smaller railways and thefreight company (JR Freight). The operating ratio for the three larger companies by themselves would be more favourable.

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17 Kirchner (2011), but also 2002, 2004 and 2007.
18 The SNCF would be raised by about 25% and DB nearly doubled if non-rail employees were excluded from the productivity measures.
Unfortunately, though the data exist to do this separation in later years, the information is not available for earlier years.
19 See e.g. Nash et al. (2013).
20 This information could be added to other reporting requirements, at least in a prescribed, approximate form.

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Annex 2.A1. Tables
Table 2.A1.1. Sample key
Country

Austria
Belgium
Denmark
FinlandFranceDB Germany
DR Germany
Germany
GreeceIreland
Italy
Luxembourg
Netherlands
Portugal

Spain

SwedenUnitedKingdom

Railway name and date ofinception
EU-15
GKB
ÖBB
SNCB/NMBSDSBBDK (1997)
VR
RHK/FTA (1995)
SNCF
RFF (1997)
VeoliaPrior to reunification
Prior to reunification
DBAG (1994)
OSE
CIE
FNM
FS
CFL
CFL Cargo (2007)
NS
Pro Rail (1998)
CPCP Carga
REFER (1997)
RENFE
ADIF (2005)
Euskotren
FEVE
FGC
SJ
Green Cargo (2002))
BV/Trafikverket (1988)
BR
ATOC (1995)
Freight (1995)
Railtrack/NR (1995)
NIR

Country

Railway name anddate of inception

EU-10 + Croatia
BDZ
BDZP
BDZ Cargo
Bulgaria
NRIC (2003)
BRC
Bulmarket
CD (2003)
Czech Republic
SZDC (2003)
ZSSK
Slovakia
ZSSK CargoZSR (2002)
Former Czech.
CSD (End 1992)
Estonia
EVR
Floyd
Gysev
Hungary
MAV
MAV Cargo (2006)
MAV Start (2007)
LatviaLDZ
Lithuania
LG
PolandPKP
CFR
CFR Calatori (2006)
CFR MARFA (2006)
CFR SA (2006)
Romania
GFR
Servtrans
TFG
UnifertransSlovenia
SZ
Croatia
HZ
Other Railways
Class I
United States
Amtrak (1972)
Freight
CanadaVIA (1980)
China
CR
Japan
All
India
IR
Norway and Switzerland
NSB
NorwayCargonet (2002)
JBV (1996)
BLS
Switzerland
BLS Cargo
SBB/CFF/FFS

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Table 2.A1.2. Basic Indicators of size and scale of railway operations

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Table 2.A1.3. Average length of haul in km
EU15
RailwayCR
Amtrak
VIA
LDZ
SJ
IR
CFR Calatori
OSE
PKP
LG
SNCF
FS
BDZ
VR
NS
ZSSK
NSB
EVR
MAV StartÖBBSZ
SBB/CFF/FFS
SNCB/NMBS
DSB
RENFE
CIE
Gysev
DBAG
CD
ATOC
HZ
CP
Japan
FEVE
CFL
BLS
Euskotren
FGC

EU10
Passenger
529
355
331
250
205
128
90
87
85
84
79
75
71
57
54
53
52
51
50
49
49
49
47
46
46
44
41
40
40
39
30
30
28
21
19
17
11
10

CH/NO
Railway
US Class I
Canada Frt
CR
IR
Japan
CFR SA
RENFE
GFR
SNCF
Green Cargo
BRC
DBAG
LG
LDZ
FS
VR
PKP
Bulmarket
DSB
BLS Cargo
Unifertrans
CP Carga
Freight
SZ
BDZ
Servtrans
HZ
EVR
MAV CargoÖBB
ZSSK Cargo
Floyd
CD
CFR MARFACIE
SBB/CFF/FFS
OSE
FEVE
Gysev
SNCB/NMBSFGC
CFL Cargo

All Other
Freight
1 477
1 199
805
679
654
483
437
392
371
353
324
318
288
279
276
270
265
259
240
237
237
226
222
220
215
207
207
200
200
199
194
190
182
181
172
163
147
142
141
138
63
32

Note: Data from 2011.

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Table 2.A1.4. Passenger shares (%) measured bytraffic units, gross tonne-kms and train-kms

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Table 2.A1.5. Measures of line traffic density

Note: Data from 2011.

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Table 2.A1.6. Measures of productivity of rolling stock

Note: Data from 2011.

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Table 2.A1.7. Indicators of output per employee

Note: Data from 2011.

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Table 2.A1.8.
OperatingRatios (%)
Table
8Operating Ratios (%)
EU10

EU15
CH/NO

All Other

Railway

Operating Ratio*

RHK/FTA

900.0

OSE

553.6

FEVE

442.5

BV/Trafik.

250.0

VIA

185.5

FGC

171.3

Amtrak150.2

CP Carga

143.5

REFER

143.4

CP

136.0

CFR SA

131.4

SNCB/NMBS

120.0

Servtrans

114.0

NRIC

112.9

BDZ Cargo

109.5

MAV Cargo

108.0

MAV

105.6

CFR MARFA

105.1

ZSR

105.0

HZ

104.1

ADIF

103.9

CIE

103.0

Green Cargo

103.0

JBV

103.0

DSB

101.3

ZSSK

100.3

PKP

100.2

NSB

99.7

SZ

99.6

SJ

99.2

CFR Calatori

98.8

BLS

98.3

VR

98.1

CD

97.4

ZSSK Cargo

95.4

DBAG

95.0

Canada Frt

94.8

SBB/CFF/FFS

93.4

Pro Rail

93.0

LDZ

92.8

FS

92.7

NS

92.5

GKB

91.8

SZDC

91.2

ÖBB

90.5

RENFE

90.3

Unifertrans

90.0

SNCF

89.3

LG

88.5

DBAG **

86.9

Japan

83.1

RFF

78.7

CFL

76.4

US Class I

73.2

Gysev

69.9

EVR

69.0

Network Rail ***64.5* Operating Ratio is defined as Operating Expenses/
Operating Revenues (%). Operating Expenses include
Depreciation and Amortization, but exclude
costs of capital (principal and interest on debt
and equity.
** Estimated from DB Annual Reports. See, e.g.
2010 Annual Report at pg 60*** Taken from Network Rail Annual Report. See pg 1 of 14
of 2010/2011 Annual Report.

Source: See Appendix A

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Table 2.A1.9. Average revenues expressed as constant 2011
US PPP $ per passenger-km or per tonne-km

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Table 2.A1.10. Rail market shares for passengers and freight

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Table 2.A1.11. Development of railway traffic over time

Table 2.A1.12. Evolution of operating ratio and TU/employee over time

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Table 2.A1.13. Evolution of railway average tariffs

Table 2.A1.14. Evolution of railway market shares

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Table 2.A1.15. Rail Liberalisation Index for EU railways

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2. WHAT IS RAIL EFFICIENCY AND HOW CAN IT BE CHANGED?

Annex 2.A2. Data sources
The good news with railway data – as opposed to trucking, air and water transport data – is that railways
probably report more information in more detail than other modes. Depending on the country and therailway (and the year) it is possible to collect all the data used in this chapter along with even moredetailed data on types of service, commodities, etc. The bad news is that data taken from different
sources purporting to represent the same thing (passenger-km in a particular year) are not always (oreven often) consistent. In addition, not all railways report all data in any given year and some railways do
not bother to report at all. In some cases, restructuring has meant that most information is lost on thoseparts of the railway that are established separately (Green Cargo and UK freight operators). The net
result is that most of the apparently precise information in rail data sets has to be taken with a grain ofsalt and that there is a real need for governments and the European Union to take action to improve thequality and amount of rail data reported to the public. Thompson (2007) discusses this issue in moredetail.
The basic source of EU railway information is the International Union of Railways (UIC). This includes “Railway
Time-Series Data 1970-2000”, “Railway Time-Series Data 2008” (the electronic form was used) and various
issues of the International Railway Statistics for 2002 through 2011. Some of these data were manuallytranscribed, which may have introduced errors attributable only to the authors and not the UIC.
The source of US data for Class I freight railways is “Analysis of Class I Railroads”, as published by the
Surface Transportation Board (STB). This report has existed essentially in its current form in an unbroken
series since the beginning of the 20th century. We have also used the “Public Use Carload Waybill
Sample”, with added calculations of variable costs at the two-digit Standard Transportation Commodity
Code (STCC) level, as furnished by the STB and processed by the Association of American Railroads (AAR).
In some cases, we have used data from “Railroad Facts”, a statistical compendium of Class I freightrailroad activity, published by the AAR. Amtrak data were taken from various Amtrak statistical reports,
notably the “Monthly Performance Report” for September of various years that contain annual fiscal
year data, along with various Amtrak Annual Reports.
Canadian data were taken from various issues of Railway Trends, published by the Railway Association of
Canada (RAC) and data taken from Statistics Canada as processed by the RAC.
UK data are taken from UIC reports and from various editions of National Rail Trends Yearbook,published by the Office of the Rail Regulator.
Chinese data are taken from China Railways Facts, 2008 edition, published by the Statistics Centre of the
Ministry of Railways, along with updated figures provided to us by the Ministry.
Data on tonne-kms and passenger-kms used for calculation of market shares were taken from the OECD
website. Data on inflation indices, currency values and PPP conversion factors are taken from theWorld Bank’s “World Development Indicators”, which generally cover all countries over the period 1960
to the present. For reasons of space and brevity, we have not included the full set of 33 Excel
spreadsheets covering 81 railway entities (26 existing or former countries) over 41 years. These areavailable on request from the authors (lou.thompson@gmail.com). The tables presented are extracted
from these supporting spreadsheets.

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References
Beck, A., H. Bente and M. Schilling (2013), "Railway Efficiency", International Transport Forum Discussion
Papers, No. 2013/12, OECD Publishing, Paris, DOI: http://dx.doi.org/10.1787/5k46bj46ptkb-en.
Crew, M.A. and P.R. Kleindorfer (eds.) (2004), Competitive Transformation of the Postal and Delivery
Sector, Vol. 46, Springer.
ECMT (2007), Competitive Tendering of Rail Services, OECD Publishing, Paris, DOI: http://dx.doi.org/
10.1787/9789282101636-en.
Gassner, K., A. Popov and N. Pushak (2009), “Does private sector participation improve performance inelectricity and water distribution?”, Trends and Policy Options, No. 6, World Bank.
ITF (2014), "Freight Railway Development in Mexico", International Transport Forum Policy Papers, No. 1,
OECD Publishing, Paris. DOI: http://dx.doi.org/10.1787/5jlwvzjd60kb-en.
Kirchner, C. (2011), Rail Liberalisation Index 2011, IBM Global Business Services. Earlier issues are 2002,
2004 and 2007.
McCullough, G.J. and L.S. Thompson (2012), “A further look at the Staggers Act: Mining the available
data”, Research in Transportation Business and Management, Elsevier, 10.1016/j.rtbm.2012.11.009.
Nash, C. et al. (2013), “Structural reforms in the railways: Incentive misalignment and cost implications”,
Research in Transportation Economics, Vol. 48, pp. 16-23.
Nash, C., J.E. Nilsson and H. Link (2013), “Comparing three models for introduction of competition into
railways”, Journal of Transport Economics and Policy (JTEP), Vol. 47 (2), pp. 191-206.
Parker, D. (2004), “The UK’s privatisation experiment: The passage of time permits a sober assessment”,
CESIFO Working Paper No. 1126, Category 9: Industrial organisation.
Thompson, L. S. (2007), Railway Accounts for Effective Regulation, ECMT, OECD Publishing, Paris,
http://www.itf-oecd.org/sites/default/files/docs/07railacc.pdf.
Thompson, L.S. (2013), “Recent developments in rail transportation services”, Issues Paper for OECD
Working Party No. 2 on Competition and Regulation, Directorate for Financial and Enterprise Affairs,
OECD, Paris.
van de Velde, D. et al. (2012), EVES-Rail: Economic Effects of Vertical Separation in the Railway Sector,
inno-V (Amsterdam) in co-operation with University of Leeds – ITS, Kobe University, VU Amsterdam
University and Civity Management Consultants.
Vasallo, J.M. and M. Fagan (2005), “Nature or nurture: Why do railroads carry greater freight share in the
United States than in Europe?” Harvard University Research Working Paper Series WP05-15.

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3. EFFICIENCY INDICATORS OF RAILWAYS IN FRANCE

Chapter 3. Efficiency indicators ofrailways in France
Introduction
France is a key player in the European rail system, far outstripping all other countries in high-speed rail
travel, with 53 billion passenger-kilometres travelled in 2013. Because of the size of this market segment,
France is the European country with the greatest number of passenger-kilometres per year (92.4 billion
in 2013). In terms of freight, France lies in third place in Europe, with 32 billion tonne-kilometres, behind
Germany and Poland (with, respectively, 112 and 40 billion tonne-kilometres in 2013). These figures
should be seen in light of French public policy for rail in the last several years.
The success of the TGV is undeniable (Crozet, 2013). Work started in September 1975 on thefirst high-speed rail (HSR) line, between Paris and Lyon, and it was inaugurated in September
1981. New high-speed lines were opened in 1989 (towards the south-west), in 1993 (towards
the north), etc. The high-speed network now covers 2 033 km, and will reach over 2 700 km in2017 with the opening of the four lines currently being built.

The regionalisation of intercity and local services was tested in 1997 and fully deployed in theearly 2000s. Since then, TERs (regional express trains) have seen traffic rise steeply (50%
between 2000 and 2013), as have services in the Ile-de-France region (25%), to a lesser extent.

Rail freight has been far less successful. The French network carried 55 billion t-km in 2001, but
this figure scarcely reached 32 billion t-km in 2013. This weak performance contrasts sharply
with the ambitious public policy of the last fifteen years. The Grenelle Environment Forum(2007-2010) oversaw the deployment of a costly freight plan that was no more effective than its
predecessors.

The main reason for exploring the differing trends of these three segments of the rail economy is tohighlight the fact that, in France and elsewhere, there exists more than one rail market. Performanceneeds to be measured by type of activity, especially since the theory of network goods (Katz and Shapiro,
1987) and EU Directive 1991/440/EEC showed that infrastructure management should be consideredseparately from the operation of trains. Regardless of its level of independence from rail operators, theinfrastructure manager is now a central player in rail performance. In the second part of this chapter, we
will therefore examine infrastructure performance and, since this is a highly capital-intensive activity, theefficiency of capital.
Investment in infrastructure, especially rail infrastructure, largely falls to the public authorities. Even
more than in other network industries, in rail the government plays a central role. With the exception ofa handful of lines, rail infrastructure in Europe consists of state-owned monopolies. As a legacy of priordecades of integrated monopoly, a country’s main rail operator is also generally owned by the State.
Even today, in France, the SNCF has a virtual monopoly over passenger traffic and receives subsidies for
its various missions that total EUR 9 billion. Its revenues in France come to EUR 23 billion. When the
EUR 4.2 billion paid in annual subsidies to the infrastructure manager is taken into account, total railsubsidies amount to EUR 13.2 billion.1

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Given this ubiquity of the State, we cannot restrict our examination to the private sector. Neither the
train operating companies (TOCs) nor the infrastructure manager are in the standard situation forbusiness activities in which strategic decisions are taken by the shareholders according to the
profitability of invested capital. By no means do all rail transport services constitute business activities. A
large part of the rail passenger transport offering comes under the public service obligations defined by
the EU regulation of that name. It is not appropriate, therefore, to talk about performance or efficiency
in unqualified terms. As shown in Figure 3.1, we need to define indicators that distinguish between
relevance, consistency and different kinds of efficiencies.
Figure 3.1. Relevance, consistency and efficiency of rail transport

Political goals (see the strategic objectives of the 2011
white paper, Grenelle Environment Forum on the
expansion of freight, high-speed rail, regional trains, etc.)
Relevance
National operational choices: infrastructure (SNIT scheme,
regeneration), relations between the infrastructure manager
and the TOCs, access pricing, competition, etc.
Operational
efficiency

Consistency

Resources deployed (TGV, regional
express trains, rolling motorway)

Indicators of operational efficiency:
subsidy/km, revenue/km, cost/km

Inputs: work, capital, energy, etc.

Demand: passenger-km, tonnes-km

Productive
efficiency

Economic
efficiency

Supply:train-kilometres, available
seat-kilometres

The top section of the figure shows two key ideas: relevance and consistency.

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3. EFFICIENCY INDICATORS OF RAILWAYS IN FRANCE



Relevance is about political goals and the operational objectives linked thereto. In 2009, theFrench parliament passed a national transport infrastructure bill (SNIT) which will see thecreation of 4 000 km of new high-speed rail lines by 2030. A committee of parliamentarians andexperts (Duron, 2013) subsequently stressed the need to prioritise network maintenance andinvestment in capacity in rail hubs. Extending the high-speed rail network may require large
amounts of public money, as pointed out by the Court of Auditors in 2014. Committing funds tothese projects could be considered injudicious at a time when the government is scarcely
managing to reduce the deficit.

Consistency compares resources deployed, in the widest possible sense, to operational
objectives. Until recently, France made money available to achieve its goals: generous subsidies
for regional express trains, expansion of the high-speed network, freight subsidies, funds for the
rolling motorway, etc. But worsening public finances have thrown the issue of consistency into
sharper relief. In 2013, the transport ministry announced the phasing out of rail freight subsidies.
After abandoning the proposed national truck road user charge, it is struggling to find thefunding required for the expansion of regional express services and still does not know how itwill pay for the new high-speed lines that ministers keep promising (Bordeaux-Toulouse,
Montpellier-Perpignan). This creeping inconsistency raises questions about the relevance of theobjectives.

This chapter, therefore, remains mindful of this context in which the question of the relevance andconsistency of public policy is raised time and again. With this in mind, we will, in the next section,
examine the different ways to assess the efficiency of rail transport services. And in the section that
follows it, we will turn to the efficiency of infrastructure management.

Efficiency of rail transport services
The lower section of Figure 3.1 shows two main kinds of efficiency. A third is shown to the top right. It is
worth examining in detail the reasons why we need to distinguish among them, beginning with economic
efficiency.

64

Economic efficiency compares technical outputs (train-kilometres) to economic outputs such as
passenger-kilometres and tonne-kilometres. A good level of productive output can be associatedwith poor economic efficiency if supply exceeds demand. Occupancy rates are the critical factor:
when occupancy rates are high, economic outputs can be directly compared to technical inputs
such as capital and work.

Productive efficiency is based on traditional economic calculations of productivity and looks at
the activity’s most basic ratio of output to input. Outputs can be either train-kilometres oravailable seat-kilometres. Inputs can be the quantity of work or capital, measured with a greateror lesser degree of complexity, resulting in the calculation of basic or far more sophisticatedindicators such as production frontiers.



Operational efficiency is the standpoint of the public authorities who subsidise rail transport
services. The rather simplistic question is this: is it worth it? If the trains are full but costs or
subsidies per passenger-kilometre are very high, is the community getting value for money? Thisidea has been much explored in the United Kingdom, and can be applied to France with a little
international benchmarking.

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This section will examine each of these three categories. Using a few simple indicators, we will see that
the French rail system, when compared to those in neighbouring countries, seems as first sight to be
fairly economically successful in passenger transport, although not for freight (see below). But if weprobe more deeply, other indicators reveal weaknesses and insufficient improvement in productivity.
This lacklustre performance increases pressure on public finances and ultimately leads to poor
operational efficiency that is closely linked to the organisational weaknesses in the French rail system.
Strong economic efficiency, but only for passenger transport

As stated in the introduction, France, and more specifically the SNCF2, ranks first for rail passenger
services in Europe, with 92.4 billion passenger-kilometres. Its economic efficiency is undeniable, since
this figure was achieved with just 406 million train-kilometres, i.e. an average of 227 passengers per
train. By comparison, Deutsche Bahn trains only carry a little over 100 passengers per train on average,
which is just under the 130 passengers on Swiss railways. To understand this performance, we need tolook at rail traffic across different market segments.
Table 3.1. Rail passenger transport in France(in million passenger-kilometres; annual change in percent)Annual change
2013

2009

2010

2011

2012

2013

2013/
2008

Long-distance rail transport(1)
High-speed rail
(1)(2)
Intercity services

61 25653 768
7 489

-1.4
-0.7
-4.9

-0.2
1.8
-10.6

3.6
2.4
10.7

-2.7
0.0
-17.6

-1.2
-0.5
-5.8

-0.4
0.6
-6.1

Local rail services(3)
Trains under regional council contracts(4)
Ile-de-France commuter trains and RER

31 18414 03717 147

-0.3
1.2
-1.4

0.7
0.2
1.1

3.7
4.5
3.1

3.6
5.5
2.0

0.1
-1.2
1.2

1.5
2.0
1.2

Total excluding Ile-de-France commuter trains andRER

75 293

-0.9

-0.1

3.7

-1.2

-1.2

0.0

TOTAL

92 440

-1.0

0.1

3.6

-0.7

-0.7

0.2

(1) Including international services.
(2) Under government contract and non-contractual trains, excluding high-speed trains.
(3) Under regional council contract (excluding Ile-de-France and Corsica).
(4) Including the RERs operated by the RATP and the T4 tram line.
Source: CCTN-CGDD (2014).

This table shows that two-thirds of traffic is carried by high-speed trains. So this economic efficiency is
the result of structural factors and has three major causes:First, depending on their destination, high-speed trains in France have between 450 and 1 000
available seats per train, which is far more than a regional train.

Second, highly effective yield management delivers average occupancy rates of over 70% for
high-speed trains.



Most important, however, is their high speed, which allows them to cover long distances andautomatically increases the number of passenger-kilometres.

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But the TGV’s success has come at some cost for other intercity services, which saw traffic fall by 6.1%
every year from 2008 to 2013. Since high-speed traffic only grew by 0.6% during this period, despite
additional services,3 total long-distance traffic fell by 0.4% every year between 2008 and 2013. This ispartly due to poor economic conditions, but there are other reasons, which cast doubt on the wisdom of
the tout TGV strategy of creating a high-speed network across the entire country (Crozet, 2010; Cour descomptes, 2014), especially since the price elasticity of high-speed traffic is relatively low. Lower prices
will not lead to a sharp rise in traffic (Crozet and Chassagne, 2013) because of competition from air travelon many routes and the recent growth of carpooling, which now accounts for almost 5% of long-haultraffic in France, i.e. the equivalent of 2 000 high-speed trainloads every month. High-speed travel istherefore facing an uncertain future in France. In order to increase the turnover generated by its
400 high-speed trains, the SNCF is considering changing its offering in order, for example, to keep thesetrains on high-speed lines only, with other trains, especially regional express trains, completing theterminal haul.
In fact, regional express trains saw the highest traffic growth (up 2% every year) in 2008-13, alongsideIle-de-France services (up 1.2% every year). For the former, however, economic efficiency is weak
(40 passengers on average per train), because many trains are small, and because occupancy rates are
low, at under 30% (Cour des Comptes, 2014). The Transilien commuter services in Ile-de-France, on theother hand, have high-capacity trains, which are often saturated during rush hour, and occupancy rates
are very high (over 70%). This reflects the findings of Banister and Ghivoni in 2012, who showed that
European countries that did not prioritise high-speed rail saw the highest traffic growth. This indicatesthat rail transport is most appropriate for everyday mobility in urban and peri-urban areas, which calls
into question the expansion of high-speed rail not only in France but also on a European level, whereas
the 2011 white paper forecasts a tripling of the high-speed network in Europe by 2030 (European
Commission, 2011).
For passenger transport, then, the French rail system is not as successful as it appears at first sight. Thisis even more striking in rail freight, in which the legacy operator has manifestly failed to perform, despitehigh expectations. At the turn of this century, Jean-Claude Gayssot, the then transport minister, set atarget of 100 billion tonne-kilometres by 2015. By 2013, rail traffic had collapsed to 32 billion t-km, just
two-thirds of which was generated by the SNCF. What happened? Did economic efficiency fall because of
de-industrialisation?
A report by the French Commission on Sustainable Development (CGDD, 2013) analysed freight by
category, taking account of trends in different sectors of industry before the 2008 financial crisis. This
analysis offers no reasons for the collapse in rail freight. Comparing rail freight to production in four key
sectors reveals a complete divergence between 2000 and 2006:
Food industry: production up 3%, rail freight down 37%

Energy: production up 5%, rail freight down 34%Manufactured goods: production up 3%, rail freight down 35%

Construction: production up 4%, rail freight down 13%.

As shown in Figure 3.2, freight traffic, and therefore economic efficiency, fell sharply despite rising GDP
and, at least until 2008, stable industrial output.

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150.0
140.0
130.0
120.0
110.0
100.0
90.0
80.0
70.0
60.0
50.0

Rail
Ferroviaire
Industrial

Industrie
output

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

PIB
GDP

2000

GDP

Figure 3.2. The decline of French rail freight(Base 100: year 2000)

Source: Eurostat.

The problem, then, is the relative competitiveness of the rail carrier, generally Fret SNCF, which was
almost the sole operator until 2007. Between 2004 and 2010, Fret SNCF’s average revenue per tonnekilometre rose by 11%, from EUR 0.039 to EUR 0.043, while traffic fell by 50%. During the same period,
Deutsche Bahn saw its average revenue fall by 8%, from EUR 0.043 to EUR 0.04 per tonne-kilometre, buttraffic grew by 27% (CGDD, 2013). We need to look more closely at the reasons for this loss of efficiency,
and they do not concern only freight.
Weak growth in productive efficiency seen only in France

To assess the performance of a rail system, indicators need to provide comparisons in time and space:efficiency is not measured in absolute terms but by comparing an indicator to different periods anddifferent countries. Take, for example, a simple productivity indicator and track it through time. The
numerator – “kilometric units” – will be the sum of passenger-kilometres and tonne-kilometres. The
denominator will be the total number of SNCF and Réseau Ferré de France (RFF)4 employees. Table 3.2,
below, shows that productivity per capita grew by 22% between 1996 and 2008 (pre-crisis), and thenremained flat at 0.73 million kilometric units per capita from 2008 to 2013. This stagnation is caused by
the sharp fall in tonne-kilometres in the numerator, which is not offset by a rise in passenger-kilometres,
as was previously the case.
Table 3.2. Unit-kilometres per capita in France from 1996 to 2013
1996

2008

2013

Passenger-kilometres (millions)

59 70082 40092 400Tonne-kilometres (millions)48 60037 30020 700Kilometric units (KU) (millions)

108 300

119 700

113 100

Employees

180 500

163 000

155 400

Millions of KU per capita

0.6

0.73

0.73

Source: CCTN-CGDD (2014).

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3. EFFICIENCY INDICATORS OF RAILWAYS IN FRANCE

We could settle for these results, observing that the decline in headcount and the level of traffic still
gives 22% productivity growth between 1996 and 2013. But is this sufficient by comparison with twoother giants of European rail freight: Germany and Switzerland or, more precisely Deutsche Bahn andChemins de Fer Fédéraux (CFF) (Table 3.3.)?
Table 3.3. Unit-kilometres per capita in Germany and Switzlerland from 1996 to 2013
Germany

1996

2008

2013

Switzerland

1996

2008

2013

p-km (millions)

71 00077 10080 200p-km (millions)

11 80017 70019 400t-km (millions

67 40091 20075 200t-km (millions)

8 500

12 26011 800kmu (millions)

138 400

168 300

155 400

kmu (millions)

20 30029 96031 200

Employees

288 700

240 200

155 000

Employees

32 00028 00027 000Millions of KU per
capita

0.48

0.70

1.00

Millions of KU per
capita

0.63

1.07

1.16

Source: DB and Swiss Federal Statistics Office.

Like France, both Germany and Switzerland saw the effects of the crisis on freight traffic in the years
following 2008 and, in Germany, the rise to prominence of competitors. But in both these countries,
passenger traffic continued to grow, and this, coupled with a lower headcount, supported continued
productivity gains. Between 1996 and 2013, productivity grew by 84% in Switzerland and by 108% in
Germany, figures that overshadow France’s result, which is almost four times lower.
Despite conditions that promote economic efficiency, therefore, (average of 230 passengers per train),
the French rail system is relatively inefficient when outputs – passenger-kilometres andtonne-kilometres – are compared to inputs – employees. Another way to reach the same conclusion is tocompare the number of train-kilometres (output) per employee (input) in 2010 (UIC data), which is 2 983
in France but 3 695 in Germany, or 24% more per employee. In other words, given the low growth intraffic in France, a far more drastic headcount reduction would have been necessary to prevent
productivity falling behind that of Germany and Switzerland. The result is that the SNCF now has very
little room for manoeuvre financially, since its payroll costs represent 47% of revenue, compared to 29%
for DB (UIC data). This was discussed recently at a meeting of the SNCF’s board of directors.5 The
presentation of an internal report highlighted the 5.5% rise in payroll between 2009 and 2013, whenheadcount declined by 4%, a paradox explained by the fact that average pay rose by 3.87% per year,
when inflation was just 1.56%. This poses two problems for the SNCF:

Low productive efficiency caused by overstaffing, which the company is struggling to combat in
the face of the sharp fall in freight traffic and zero growth in passenger traffic since 2008.



Additional unit costs generated by the balance of power being weighted in favour of thecompany and its staff rather than the public authority. This raises concerns over operational
efficiency.

Poor operational efficiency

We have already mentioned that the French rail system draws heavily on the public purse. It is not highspeed rail that has cost so much government money. Most TGV projects have been funded by leveraging
RFF and the SNCF. If we exclude the EUR 3.5 billion in annual subsidies that offset the severely
unbalanced pension system (the SNCF has 1.8 times as many retirees as workers), the main flows

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3. EFFICIENCY INDICATORS OF RAILWAYS IN FRANCE(EUR 5.2 billion in 20136) fund regional passenger transport. RFF receives public money from the central
government to cover regional express train infrastructure charges (EUR 1 941 billion in 2013). And the
SNCF receives public money from regional authorities to subsidise the operation of regional express
trains (EUR 3 263 billion in 2013). Given the rise in traffic on these services, one might think that public
subsidies per train-kilometre had plateaued or fallen.
This is not the case, however (see Figure 3.3). Not only has the public outlay increased significantly (by
80% in nine years); the subsidy per train-kilometre has grown by almost as much. As the contracts
between the regional authorities and the SNCF are cost plus, or fixed cost contracts, the cost per trainkilometre has grown by 60%, or 5.3% per year, which is three times higher than inflation. It seems as
though the organising bodies, for various institutional and political reasons,7 were simply unable toprevent the escalation of costs.
Figure 3.3. Regional government subsidies for regional express services in France(indexed to 2002)

190.0
170.0
Index

150.0
130.0
110.0
90.0
70.0
50.0
2002

2004

2006

2007

2008

2009

2010

2011

Public contribution Index, base 100 : 2002
2002
Per train-kilometre - Index, base 100 :en
2002

It is interesting to compare this picture to what was happening in Switzerland over the same period.
Figure 3.4 shows total public contributions rising somewhat before falling off slightly, but the subsidy per
train-kilometre fell substantially (-22%) because of the reduction of costs per train-kilometre.
As also shown by the example of Germany, it is thus possible to cut the cost per train-kilometre. The DB
has had to face the arrival of new competition on the market, including the SNCF’s subsidiary Kéolis.
Although its rivals have only taken a small proportion of traffic in regional services (12% in 2012), unit
costs per train-kilometre have recorded a significant fall, which can be broken down into two separate
phases:

Gains in market share made by DB’s competitors, in response to which DB improved operating
costs, management and the quality of rail service.

Market stabilisation based on copycat win-win approaches. For local authorities and thetaxpayer, the level of subsidies fell.

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Figure 3.4. Confederation subsidies to CFF in Switzerland(indexed to 2002)
120.0
110.0

Index

100.0
90.0
80.0
70.0
60.0
50.0
1999

2000

2002

2004

2006

2007

2008

2009

2010

2011

Public contribution Index, base 100 : 2000
2002
Per train-kilometre - Index, base 100 :en
2002

In 15 years, Germany has seen passenger-kilometres grow by 55% for regional services, reaching
47 billion p-km), but train-kilometres have risen by just 26% (to 630 million t-km). A 26% cost reduction
for organising bodies has been measured across all contracts. Federal subsidies fell by 6% in real terms
between 1996 and 2009. During this period, the service offered (in terms of t-km) by the Länder for
EUR 1 (in real terms) has increased by 37%.
Our conclusion, at the end of this section, is that the efficiency indicators used for transport services canbe established for chronological analysis and to produce series crossed with data that is generally
published by UIC. Data for benchmarking network efficiency is far less accessible, as we shall see.

Network efficiency
The 1997 French rail reform saw responsibility for rail infrastructure entrusted to Réseau Ferré de France(RFF), an industrial and commercial public undertaking (établissement public à caractère économique etcommercial – EPIC) which is completely independent from the SNCF but works in a very unusual way: theinfrastructure manager has to work with a delegated manager, which is none other than the SNCF. The
SNCF therefore retains the departments responsible for network maintenance and repairs (SNCF Infra)
and traffic management, which is ensured by a special division that was not properly transferred to RFF
until 2010.
The infrastructure manager’s efficiency problems are quite unusual in this kind of system, insofar as theefficiency of the delegated manager is clearly a factor.
The efficiency of basic operations

In theory, the 1997 French rail reform was supposed to promote efforts to manage the network well,since responsibility for infrastructure management had been clearly devolved to RFF. But therequirement to “subcontract” to the SNCF posed problems that were, at first, formidable.

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3. EFFICIENCY INDICATORS OF RAILWAYS IN FRANCERFF toiled endlessly to change a procedure whereby it received a lump-sum invoice for network
maintenance and repairs from the SNCF and had great difficulty in obtaining details on the state of the
network and the maintenance operations carried out. After assuming full responsibility for development
investment, RFF was only gradually able to implement a sensible policy and full ownership of major
maintenance and renewal operations.
Table 3.4. Summary annual performance chart for 2012
(last year of the first performance contract, 2008-2012)
Strategic objective 1: Adapting to market liberalisation and increasing business revenue
Six sub-objectives: mainly
focused on customer
satisfaction

Four sub-objectives achieved, two partially achieved:1) the quality of freight paths has not improved as fast as expected;2) costs are better reflected in charges.

Strategic objective 2: Modernising infrastructure and improving network performance
13 sub-objectives:maintenance, maintenancemanagement, safety

Seven sub-objectives achieved;
Five partially achieved: mainly concerning the elimination of level crossings (only half thetargeted number), the standard of programming and ensuring that renewal investment iseffective;
One failure: the multiannual view of renewals.

Strategic objective 3: Breaking even and establishing sustainable financing
Six sub-objectives:

Two sub-objectives achieved;

improving the coverage of costby revenue

One partially achieved: management control adapted to the strategic segmentation of
the network;
Three sub-objectives not achieved because of the freezing of EUR 341 million of theoperating subsidy: costs not fully covered by revenue (charges or balancing subsidy);
accounting targets consequently missed.

Strategic objective 4: Dynamic steering and responsible governance
Eight sub-objectives:

Seven sub-objectives achieved;

improving governance designand control

One partially achieved, concerning the slower-than-expected establishment of the liaison
with regional authorities (regional transport organising bodies).

The limited reform of 2010, which put the SNCF division in charge of capacity allocation under RFF’s
authority, led to major change in the establishment of train paths, and, by extension, timetables. For the
first time in sixty years, the 2011 timetables were radically transformed, with the first introduction ofclock-face scheduling, increased availability of freight paths and improved management of bottlenecks.
The progress made by the infrastructure manager in the exercise of its responsibility was recorded in acontract between RFF and the government, covering the period from 2008 to 2012. A new contract has
been signed for 2013 to 2017. The first, now complete, demonstrates the progress made in the controlof network management and maintenance efficiency and control of the main cost and revenue factorsthat determine its financial position.
The 2008-12 contract is based on four strategic objectives that are broken down into sub-objectives,
representing a total of 33 RFF commitments. These commitments are tracked in a performance chartthat is presented to the undertaking’s Board of Directors every year.

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Table 3.4 gives a condensed summary of the performance chart presented in 2013, which reports theresults for 2012, the last year of the first contract. Most sub-objectives or commitments are tracked byindicators; we will mention a very few of these.
Table 3.5. Main indicators of annual reporting for the year 2012
Strategic objective

Examples of indicators

1. Adapt to the opening-upof the market and growbusiness revenues

Proportion of satisfied customers
Tariff acceptance rates
Number of pathways affected by maintenance work
Punctuality within five min.

2. Modernise infrastructure
and improve network
performance

Length of track renovated (compared to five-year target)
Number of switches and crossings renovated (idem)
Number of level crossings removed (idem)
Cost of renewing one km of track (idem)% of network in poor condition
Centralised signals installed (compared to a programme)
PDCAs for investment (Plan-Do-Check-Act)% of investment programme completed% of investment completed without cost overshoots% of investments completed on schedule

3. Target economic
equilibrium and establish
sustainable financing% of revenues compared to business plan
% of State subsidies compared to amounts pledged
Cost fully covered by income% of accounting outcomes compared to business plan
Ex-post financial evaluation of major projects (one year, five years, ten years)

4. Dynamic oversight andresponsible governance

Provisions relate mainly to the organisation of the company and its management and are illsuited to the use of performance indicators other than ratings by specialised agencies.

For strategic objective 1, “clients” – e.g. rail operators, combined transport carriers and organisingbodies – are surveyed to establish satisfaction rates on issues considered to be sensitive; these rates
have improved significantly. For strategic objective 2, network maintenance and safety (elimination oflevel crossings) are tracked on the basis of the number of points and kilometres of track that have been
upgraded, compared to the forecast. For strategic objective 3, cost and income are tracked closely
through, for example, the achievement of targeted kilometric maintenance cost savings. Strategic
objective 4 is tracked through detailed analysis of the undertaking’s management and its relations withthe regulator.
The performance contract and charts reveal spectacular progress since the first years of RFF. It has notbeen possible to establish international benchmarks, however, which would be needed to measure therelevance of these indicators.

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This effort to evaluate and monitor performance nonetheless has the merit of being based on a numberof indicators that allow assessment of the extent to which strategic objectives have been achieved.
Examples of the main indicators employed for this purpose are presented in Table 3.5.
Obviously, these indicators need to be tested through time series and particularly cross-sectional
analysis, as the results can be interpreted for the purpose of international comparison.
Marginal capital efficiency

Investments are clearly of central importance to RFF, and this raises the issue of efficiency indicators tomeasure the marginal efficiency of capital.
Investments cover three categories of transaction that need to be distinguished. The most visible is theconstruction of new high-speed lines. Because of the size of these projects, they must be carried out insuccession, and only after a lengthy preparatory process and ex-ante evaluations have culminated in adecision taken at government level. Depending on the programme, financing can range from
EUR 1 billion to EUR 3 billion per year, a figure that can rise to over EUR 4 billion if we include
transactions by public-private partnerships (PPPs).8 The corresponding efficiency indicators can be taken
to be those resulting from project evaluations, i.e. essentially internal socio-economic and financial
profitability rates and the net present value per euro of public money.
The second category concerns investment in network expansion, excluding high-speed lines, andconsisting mainly of network modernisation (centralised points, electrification, etc.) or extensions (portservices). These usually require financing of around EUR 1 billion per year, and the same socio-economic
and financial assessments apply as for new high-speed lines.
The third category consists of renewal investments that keep the different parts of the network in good
operational condition. When RFF was set up in 1997 and during its first years of operation, the rate ofcoverage of full costs by charges was so poor (less than 20%) that these investments were kept relatively
low. An audit on the state of the network carried out by the Lausanne École Polytechnique Fédérale
(Rivier et al., 2005), highlighted their inadequacy. Since then, a policy to reverse the situation has pushed
investments back up to around EUR 2.5 billion per year. Neither RFF nor SNCF Réseau has its own cash
flow, so renewal investments are financed by a regeneration subsidy. There are specific problems
involved in evaluating these transactions, not all of which have been resolved.
One of the main aims of the 1997 reform was to curb the rail system’s escalating debt. In theory, thesolution that has been implemented prevents this escalation and, de facto, dominates the issue ofmarginal capital efficiency.
Article 4 and how it works

Article 4 of the Decree that created RFF in 1997 contains a debt-control rule for the rail industry thatconcerns new investments. It states that “RFF may not accept any project to invest in the national rail
network that has been assigned to a programme on the request of central government, local government
or a local or national public body, unless the petitioners have endowed it with financial resources that will
prevent any negative impact on the RFF’s accounts during the amortisation of the investment.”
Under this rule, then, the funding of any new project must be assessed to determine the return on
investment. This discounted revenue gives the amount that the infrastructure manager (RFF and, now,
SNCF Réseau) is likely to commit and, by extension, the subsidies required. These financial resources
correspond to a public financing rate for the project, sufficient to ensure that the financial profitability of

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the infrastructure manager’s commitment covers both the amortisation of capital and a risk premium. In
short, projects must cover the Weighted Average Cost of Capital (WACC).
To take an example, if long-term rates on the financial market9 are 4% and if the risk premium is also
estimated at 4%, the WACC for the funding secured by the public operator is 8%. As required by theapplicable rules, the infrastructure manager may not launch a project unless its profitability rate is equal
to or higher than 8%. If profitability is lower, additional funding is required to make up the shortfall.
Meeting the “Article 4” obligation therefore involves a particularly strict evaluation of every project, in
addition to which the contracting authority – the network company, in the case of the rail industry –must also carry out an assessment of the project’s socio-economic profitability. Under the official
instructions, the socio-economic net present value (NPV) and the ratio of NPV to subsidy must be
calculated for every project.
The issue of optimising investments and the marginal efficiency of capital is based on these evaluations.
Optimal investment programming

We can only define the right strategy for the system’s efficiency, especially investment selection, if weknow what we want to achieve. Do we need to minimise subsidies by looking for the highest possible
financial profitability? Or, in the tradition of the public sector, should we be trying to maximise socioeconomic NPV?
Factoring in the budgetary constraint answers the question. The network company’s investments are
rarely self-financing and almost always require some level of subsidy. The public body providing thesubsidy will expect positive externalities in return, which can be considered to reflect a change in welfare
and to be measured by socio-economic NPV. It therefore controls investment programming but has alimited financing capacity. Chronologically, the sequence of subsidies is clearly dictated by budgetary
constraints. In theory, then, the public authorities are attempting to optimise investments within specificconstraints. Put simply, for a given programme duration, the exercise is to determine which of thesuggested projects will be selected, and to establish their optimum completion dates in such a way as tomaximise the programme’s socio-economic NPV for the amount of public money available.
Subsidy rates naturally play a very important role in the issue of programming, since subsidies represent
the budgetary constraint. A project’s subsidy rate is given by the project’s inherent profitability and thefinancial profitability required by the investor, in the case of its being subject, for example, to an Article 4
obligation.
If we want to compare investment programmes subject to the same public financing constraint, we justneed to know the required subsidy and expected socio-economic NPV for the proposed projects. In theearly 1990s, this exercise was applied to a group of 17 proposed French motorways (Bonnafous andJensen, 2005). The results are instructive.
To compare competing programmes, several different orders for the completion of the projects were
tested, exhausting all available public money every year. The projects were ranked according to several
different orders of priority: decreasing order of socio-economic return rate; decreasing order of financial
return on investment; and decreasing ratio of NPV to public subsidy. The target function, i.e. the NPV of
the programme given according to this exercise, is calculated in each case. The exercise was repeated for
different levels of budget.
A first, counterintuitive, finding was that carrying out projects in decreasing order of financial return gavehigher overall NPV than ranking projects by decreasing socio-economic profitability. The reason is simply

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that when projects are carried out in decreasing order of financial profitability they can be launchedmore rapidly because their higher financial profitability decreases the public money required. For thesame public subsidy, the socio-economic NPV is therefore higher. This welfare gain is further improvedby the financial profitability criterion when public money is limited, a result that has obvious significancefor developing countries, where there is greater pressure on public finances.
A less surprising finding is that ranking projects by decreasing order of NPV to public subsidy ratio further
improves a programme’s welfare gain, whatever the budgetary constraint. On failing to find a clear
demonstration in the literature, the authors proceeded to demonstrate that this order did determine
optimal programming.
Figure 3.5. Additional welfare gain of programmes

Note: Additional welfare gain of programmes compared to the ranking by socio-economic profitability when ranked
by financial profitability (IRR), the numerical ideal and the NPV public subsidy ratio (o) under different financing
constraints.
Source: Bonnafous and Jensen (2005).

A set of 17 projects with 17 different permutations gives around ten34 possibilities, so finding the
optimum order calls for a sophisticated combinatorial search algorithm, used in experimental science todeal with this level of complexity. The programmes examined naturally exhaust the available publicfinancing. These simulations determined a numerical optimum that corresponds to a programme
automatically aligned with the NPV-public subsidy ratio, which supports the principle of value for money.
These results are presented in Figure 3.5, in which the x-axis shows different financial constraints, basedon <F>, an average value that has been empirically observed, and the y-axis shows the additional welfare
gain generated by a programme, over and above that generated by a programme in decreasing order ofsocio-economic profitability.

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This result has previously been formally demonstrated (Roy, 2005; Bonnafous, Jensen and Roy, 2006).
Under a few fairly weak conditions, it was shown that the NPV/public investment ratio was the projectranking criterion that maximised overall welfare within the budgetary constraint. This demonstration
appears in Annex 3.A2 as an excellent illustration of the opportunity cost of public funds.
An indicator of the efficiency of the investment programme

Now that we have established an optimal standard, and observed that it is consistent with the value-formoney criterion, we automatically find ourselves with an efficiency indicator, insofar as we can now
easily establish a virtual, optimal programme for any given period. For a duration of five years, forexample, which is the term of the last performance contract between the government and RFF(2008-12), we know, for the investments made:

the chronological order of the subsidies given to these investments every yearthe chronological order of the socio-economic NPV generated.

The decision maker had to choose among n projects i characterised by their net present value ΔUi, andtheir need for subsidies Subi, within a budget Bt limiting public spending to the year t. The welfare
function, W, generated by all projects implemented, can then be defined as follows:n

W x    xi t U ii 1ns.c. Bt   xi t Subi
i 1

The parameters xti are variables with a value of zero if the project is not implemented during the year t
and equal to one if the project is implemented during that year.
The value of W given for the term of the programme must naturally be compared to the value of W
generated by an optimal programme, or Wo. The optimal programme is easy to establish if we postulate
the implementation of projects in decreasing order of the ratio ΔUi/Subi and if we exhaust the Bt
budgetary constraint every year.
This gives a definition for the global programme efficiency indicator as follows:
IGE=W(x)/Wo .
This indicator can just as easily be used to measure the efficiency of investments committed during the
programme, with an ex-post evaluation of the loss of value compared to an optimal programme, as it canbe to measure the ex-ante efficiency of a planned programme in a given time frame. In the latter case,we could consider assigning it a minimal value, since priorities are not exclusively determined for the sole
purpose of generating value, as we shall see below.
Empirical illustration

The three major investment categories mentioned at the beginning of this section – high-speed lines,
development investments and renewal investments – are naturally themselves liable to be judged
according to profitability calculations, and our global efficiency indicator (GEI) could, in theory, beapplied to the chronological order of investments. There are, however, some unresolved methodologicalproblems with the evaluation of renewal investments, which do not generate the same kind of value as
development investments.10 It also seems reasonable to assume that high-speed lines can be considered76

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separately, if only because of the concession schemes and public-private partnerships that are in use
today.
This is, however, a useful exercise for development investments, which are usually programmed underplanning agreements between central and regional government, renamed “project contracts” in 2007.11
They cover all kinds of public investment, designed to meet social and territorial cohesion targets thatare not covered by the idea of a project’s socio-economic profitability, as measured in a standard
evaluation. Calculating a GEI offers a measurement of the total relevance from the point of view of theefficiency of the rail system.
We were unable to collect all the figures relating to current projects that were required for this exercise
in time for this report. However, we can propose an experimental evaluation based on this GEI concept
in that all figures are available (subsidies and net present values) for 40 investments by RFF scheduled
under planning contracts between central government and the regions between 1997 and 2007:


A programme that would have consisted in completing the ten contracts with the best ΔUi/Subiratio yields a GEI equal to 8.



A programme that would have consisted in completing the ten contracts with the worst ΔUi/Subiratio yields a GEI equal to 0.5.

Expressed in simple terms, these findings mean that for every EUR billion of public investment the firstprogramme creates value over time amounting to EUR 7 billion, and that for every EUR billion of public
investment the second programme destroys value over time amounting to EUR 0.5 billion.

Conclusion
By separating out the relevance and consistency of the rail system management, and distinguishing
between three different kinds of efficiency, we have arrived at some key indicators for measuring railsystem performance.
We are able to deal with and evaluate government decisions in terms of relevance and consistency,
especially in terms of investment, as described above. And more generally, comparing performance topublic subsidy produces indicators that are useful for evaluating most rail activities.
The question of operational efficiency allows us to take the examination of subsidies further, with simple
indicators such as cost or level of subsidy per train or passenger-kilometre. These indicators are mostuseful when benchmarking different rail operators and different countries. Similarly, we can use
straightforward indicators to evaluate productive efficiency and economic efficiency. What theseapproaches have in common is the value-for-money approach or, if we turn the problem around, theminimisation of cost for a given service.
Finally, this study is based on the French system, which has just been reformed and will be replaced by a
new system as of January 2015, under the law passed in 2014 (see Annex 3.A3). The new system is
officially expected to generate productivity gains across the entire system. Many observers are scepticalas to the outcome.

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Notes

1 French rail transport subsidies are distributed as follows: Infrastructure manager, EUR 4.2 billion; regional express trains, EUR 3 billion; Ile-deFrance, EUR 1.2 billion; Freight, EUR 0.3 billion; SNCF pension fund, EUR 3.4 billion (CCTN, 2014, pp. 29-31).
2 With the single exception of the Paris-Venice Thello train, all passenger services in France are operated by the SNCF or its subsidiaries, such as
Thalys and Eurostar. ICE trains between France and Germany are operated under a partnership with the SNCF.
3 Opening of the East high-speed line in June 2007 and the Rhine-Rhone high-speed line in December 2011.
4 This method allows for benchmarking against operators that remain integrated, like DB and CFF.
5 Lionel Steinmann, Les Echos, 22 October 2014, http://www.lesechos.fr/22/10/2014/LesEchos/21798-087-ECH_a-la-sncf--la-masse-salarialeprogresse-quand-les-effectifs-reculent.htm .
6 CCTN, 2014, p. 29 and p. 31.
7 The SNCF has a monopoly over the operation of regional express trains and opening these services to competition is not openly advocated by
local government representatives.
8 PPPs currently concern three high-speed lines: Tours-Bordeaux and two partnership contracts for the Brittany-Loire line and the NimesMontpellier Bypass.
9 For the purposes of our example, we have taken rates applied during the first half of 2012 in countries rated AAA or AA+ to long-termborrowings (20-35 or even 40 years).
10 RFF is currently attempting to resolve the methodological issue.
11 Project contracts are generally financed equally by central and regional government.

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Annex 3.A1. SNCF Group and the SNCF and RFF
industrial and commercial public undertakings
The Société nationale des chemins de fer français (SNCF) was created when the railways were
nationalised in 1937, and for many years operated as a commercial undertaking in which the State was
the majority shareholder. Its status did not change until the early 1980s, during the wave of
nationalisations launched under François Mitterand’s presidency, when the SNCF became an industrial
and commercial public undertaking (EPIC). The infrastructure manager RFF (Réseau ferré de France),
which was set up in 1997, has the same status. These undertakings are under full government control
and therefore enjoy a de facto State guarantee. They cannot fail, and this allows them to borrow on thefinancial markets at preferential rates. The SNCF’s main bank is the Banque de France.
The SNCF industrial and commercial public undertaking, which operates in France, is just one part of the
SNCF Group. In 2012, the SNCF Group, which operates in 120 countries and employs 250 000 people,
generated revenue of EUR 33 billion. The group’s international activities account for just over onequarter of this figure.
The group has five divisions:

SNCF Infra, RFF’s delegated infrastructure manager: EUR 5.5 billion in revenue



SNCF Proximités: EUR 12.2 billion in revenue, 17% of which is generated outside France, mainly
by Kéolis, a subsidiary dedicated to urban and regional passenger transport



SNCF Voyages: EUR 7.5 billion in revenue, dedicated to long-haul passenger transport (20% ofrevenue generated outside France)Gares et Connexions: EUR 1 billion in revenue, chiefly generated by the management of
3 029 stations in France.



SNCF Geodis: EUR 9.5 billion in revenue. SNCF Geodis is dedicated to freight transport. Railtransport accounts for only 17% of the total. Fret-SNCF – SNCF Geodis’s industrial andcommercial public undertaking – is part of SNCF Geodis. But it also has commercial subsidiaries,
such as CAPTRAIN, which operates in Europe; VFLI, which operates mainly in France; VIIA; and a
subsidiary dedicated to combined transport.

The development of private-sector subsidiaries, both in France and overseas, has allowed the SNCF tomove into new sectors: coach transport (IDbus); carpooling (IDvroom); online travel sales; hotel
bookings; and car rental (Voyages-Sncf.com), etc.
Source: www.sncf.com/fr/portrait-du-groupe/un-groupe-de-service.

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Annex 3.A2. Formal demonstration of optimal orderof project implementation
The theoretical model established by William Roy (2005) formally resembles the programme of theconsumer for discrete goods. Let us assume that the decision maker has to choose among n projects i,characterised by their net present value ΔUi, and their need for subsidies Subi, with ΔUi>0 and Subi >0 
i=1,…,n. The objective function is the welfare function W generated by all the projects, subject to thebudget constraint B capping public spending. The optimisation programme can then be written as
follows:n

Max W x    xi U i
x

i 1


 B   xi Subi  0
i 1
 xi  0, i  1,..., n1 - x  0, i  1,..., n
 i

n

s.c.

The parameters xi are continuous variables, which have a zero value when the project is not
implemented and are equal to one when the project is fully implemented. We shall assume that it ispossible to implement project k partially, with parameter xk then ranging between zero and one.
The solution vector x* is therefore constituted by a value set one (projects to be implemented), a value
set zero (projects not to be implemented), and a value ranging between zero and one for the“borderline” project k (which is very likely not to be completed if the constraint is totally inflexible).
Assuming that the projects are ranked by their implementation priority, we can write this solution vector
as:




x*   1,........
1,x,0,.......,
0
k



 

projects rejected
 projects accepted
The Lagrangian of the optimisation problem is written as follows:Lx1, ..., xn ,  , 1 ,...,  n , 1 ,...,  n  

n
n

 n
x

U


B

x
Sub


x

 i 1  xi  ii 
i
i
i
i
i 1
i 1
i 1

 i 1n

The Kuhn and Tucker conditions imply that, at optimum:80U i  .Subi   i  i  0, i  1,..., n  B   xi Subi   0



n





i 1



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3. EFFICIENCY INDICATORS OF RAILWAYS IN FRANCE i xi  0 et  i 1  xi   0, i  1,..., nThe economic interpretation of this optimisation is quite simple:  is the variation in the community
surplus generated by a loosening of the public-funding availability constraint. Being equal to the
maximum surplus amount that the community can hope to obtain from an additional budgetary unit, represents the opportunity cost of public funds. It is important to distinguish this opportunity cost fromthe shadow cost of public funds, which results from the costs of collecting taxes and the price distortions
associated with raising taxes by an additional unit. It is therefore not by chance that we are calling  ascarcity coefficient: as the dual value of the budget constraint, it really is the signal “price” of the scarcity
of public funding.
For the projects accepted (indexed j), the Kuhn and Tucker conditions imply that: The constraint xi  0 is not saturated, and therefore  j  0
 The constraint 1  xi  0 is saturated, and therefore  j  0
whence U j   .Subj  0



U j
Subj

 .

The set of acceptable projects is therefore composed of those having a ΔUj/Subj ratio higher than , theopportunity cost of public funds. For projects rejected or postponed (indexed l), the optimisation
conditions imply that:–

The constraint xi  0 is saturated, and therefore  l  0–

The constraint 1  xi  0 is not saturated, and therefore  l  0

whence U l  .Subl  0 

U l
 .
Subl

In all, the projects indexed j selected, and the projects indexed l not selected confirm the fundamental
relationship:

U j
Subj

 

U l
Subl

Accepted projects must always have a higher ΔU/Sub ratio than rejected projects. Here, then, is thedemonstration by numerical simulation presented previously: even if the decision maker ignores , he or
she must prioritise projects with the highest “collective relevance per euro of taxpayer’s money
invested”. This result confirms the wisdom of the widely accepted effectiveness of the value-for-money
criterion.

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Annex 3.A3. The 2014 rail reform
On 4 August 2014, the French Government’s official journal of legal notices published a new law
reorganising the French rail system, which will impact some of the changes made in 1997. In 2011, facedwith the poor performance of the French rail system (Crozet and Raoul, 2011) and the pressure on the
SNCF’s revenue from the rise in high-speed rail charges, the government held a rail conference(Les Assises du ferroviaire). At the request of the SNCF, the conference focused on the transaction costs
involved in separating the infrastructure manager and the rail operator. The reform is based on this workand on reports produced for the new administration since 2012 (Auxiette, 2013; Bianco, 2013). At theheart of the reform is the adoption of an institutional model, based on the Deutsche Bahn in Germany,
where the infrastructure manager, DB Netz, is part of the rail holding company.
As of January 2015, there is an infrastructure manager called SNCF-Réseau and a rail operator called
SNCF-Mobilité, with different entities for main lines, regional lines, stations and freight, etc. This is whereparallels with Germany end, because the new entity will not be a public limited company, but anindustrial and commercial public undertaking (EPIC). And there will be not just one EPIC, but three. The
two mentioned above will be subsidiary to a main EPIC that will handle the strategic functions. Why
three and not just one? And why no public limited company?
A single EPIC is not possible for financial reasons. Reconciling the RFF’s statement of financial position
and all its liabilities – including over EUR 33 billion in debt – with the SNCF’s income statement amounts
to an impossible consolidation. The SNCF’s surpluses, such as they are, cannot support the financial costsof the debt and the RFF’s chronic deficit, largely caused by regeneration costs. But the SNCF cannot
receive balancing subsidies because it is in competition. To prevent this, the State would have had toshoulder the rail debt twenty years ago, as it did in Germany, before merging RFF with SNCF – an option
it ruled out. Having a single EPIC would also infringe EU regulations and the rules of the fourth rail
package that requires complete separation – a so-called “Chinese wall” – between the infrastructure
manager and the rail operator.
So the three EPICs are there to ensure the financial separation of SNCF from RFF (no accounting
consolidation; the SNCF does not have to fund RFF’s deficit). They also help ensure the independence of
the infrastructure manager, as required by the EU. So the problem is this: what exactly does the main
EPIC do? Is it just a structure set up to co-ordinate the two separate entities and allow the Government
to keep a watchful eye on the whole of the public unit and settle disagreements by, for example,
entrusting its chairmanship to a political personage? It looks as if we are headed instead for an entitywith over 10 000 employees, responsible both for finance and the management of human resources(with rail worker status). The question of personnel is crucial, and a collective bargaining agreement isplanned for the rail industry that will define the HR framework applicable to these employees. The
unions are demanding that SNCF's competitors be forced to adopt the same practices as the historic
operator.
In its own way, in what it says and what it doesn’t say (it breathes not a word about competition), the rail
reform bill reveals the limits of public policy in the French rail industry. In focusing on institutional issues,
such as the number and remit of the three EPICs, the reform skirts around such organisational issues as
the incumbent operator’s stranglehold on power (Laffont and Tirole, 1991).

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Desmaris, C. (2014b), “Une réforme du transport ferroviaire de voyageurs en Suisse: Davantage deperformances sans concurrence ?” Les Cahiers Scientifiques du Transport, No. 65, pp. 67-97.
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3. EFFICIENCY INDICATORS OF RAILWAYS IN FRANCEUIC/IRU, Union Internationale des Chemins de fer (2012), International Railways Statistics 2010,
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Chapter 4. Rail efficiency: Cost research and its
implications for policy
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, to decide on the financial requirements of Network Rail – the infrastructure manager
discussed below (see, for example, Smith, Wheat and Smith, 2010). Secondly, studies may seek to draw
policy conclusions about which policies regarding industry structure, competition and regulation will bemost beneficial. We consider both aspects in this paper.
In sectors of the economy in which markets vary from a reasonable approximation to perfectlycompetitive, a reasonable measure of overall efficiency may simply be the profitability of the firm. Under
perfect competition, prices are not influenced by the individual firm and therefore the more profitable
the firm, 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, while – for this and other social 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 used to distinguishing between technical efficiency and allocative efficiency. Technicalefficiency is measured by whether output is maximised for a given level of inputs (or conversely inputs
are minimised for a given output). The standard economic approach to examining this is to estimate a
production function using econometric methods, although non-parametric methods – such as dataenvelopment 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 leveland 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 theefficiency impact of the approach to railway reform taken in Britain and the relative efficiency impact ofthe variety of approaches taken around Europe, before seeking to reach conclusions.

Technical efficiency
The material in this section draws partly on Smith (2004) and Nash and Smith (2007).

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Before turning to a discussion of more advanced measures, it is first worth considering why it isimportant to go beyond simple partial productivity measures – such as train-kilometres per member ofstaff or per locomotive – which are often used as measures of technical efficiency. The first point to noteis that they are partial measures and cannot therefore lead to conclusions about overall efficiency(unless the same railway is more efficient on every measure). Further, they are often impacted
significantly by capital 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 insome way). Under perfect competition, prices may be used as the weights, so the measure returns to
one of the ratio of value of outputs to value of inputs or, in other words, profitability. Of course, theunderlying 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 high,quasi-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 from changes
in TFP resulting from scale or density effects or departures of prices from marginal cost. Figure 4.1
illustrates, for the single input, single output case, how inefficiency (the gap between a firm’s inputoutput 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 move along
the frontier to x3. Likewise, a move to x2, which represents an inefficient level of production, also results
in a change in productivity. Over time, the production frontier may shift outwards [to g (x) in the
diagram], driven by technical change.
Figure 4.1. 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 (seebelow). This approach allows the estimation process to calculate the elasticities of cost with respect tothe outputs (and, in turn, the extent of returns to scale and density), productivity growth resulting fromEFFICIENCY IN RAILWAY OPERATIONS AND INFRASTRUCTURE MANAGEMENT © OECD/ITF 201987

4. RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY

technical change, and changes in efficiency. Data envelopment analysis (DEA) is an alternative index
number method for deriving productivity and efficiency measures. It is a non-parametric approach, inwhich the efficiency frontier is computed using linear programming techniques (rather than estimated,using econometric methods). The method permits the assumption of variable returns to scale, andMalmquist productivity indices can be computed from such models 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 difficult 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, theapproach then starts to look much more like an econometric model, and it raises the question: why
adopt a two-stage approach, instead of simply estimating an econometric model? A remaining major
weakness of DEA is its inability to take account of random noise, meaning that measures of inefficiency
can be overstated (or efficiency understated).
Historically, one supposed advantage of DEA has been its ability to deal with multiple inputs and outputs
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 thespecification of a functional form for the underlying technology. However, both these advantages havebeen largely eliminated by the widespread use of flexible functional forms (e.g. the Translog) and by thedevelopment of econometric methods for estimating distance functions (see Groskopf et al., 1997).
Overall, the most advanced and widely-used approaches in the academic literature for estimating
technical efficiency are econometric distance functions (see, for example, Kennedy and Smith, 2004 andCoelli and Perelman, 1999) and DEA (e.g. Cantos, Pastor and Serrano, 2010). However, as will be
discussed below, econometric cost function/cost frontier estimation has numerous benefits, and thisapproach 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 speaking, an output needs to be
described in 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
1 800 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 each product.
This is only really a problem if the different products have significantly different cost characteristics, andtraffic on them is growing or declining at different rates. For instance, if it costs a similar amount totransport passengers (per kilometre) 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,
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failure to identify different traffic having very different costs will be very distorting. For instance, part of
the rapid improvement in productivity of British Rail freight wagons in the 1980s was because of thedecline and eventual abolition of the movement of single wagonloads in favour of movement of traffic infull 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 the economies
of operating longer trains. Peaks in demand also lead to poor productivity by requiring the provision ofnumerous resources that are only used for a small part of the day. Such peaks in demand are likely to be
an issue in urban public transportation operations, with some services being more subject to peaks thanothers. Thus, a fundamental distinction is made between different types of rail passenger traffic, such as
inter-city, suburban and regional. Economies of density occur when the adding of more traffic to thesame network leads to a less-than-proportionate increase in cost, whereas economies of scale occur
when a given 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 minimise 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 minimises 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 to the output unit
to train-kilometres, rather than passenger- or freight-tonne-kilometres, might be desirable (it will still benecessary to disaggregate train-kilometres according to their cost characteristics, as it costs much more
to shift a 5 000 tonne freight train than a two-car branch line passenger train). The use of vehiclekilometres in place of train-kilometres may be a helpful further refinement of the train-kilometre
measure, although this measure will still not account for different weights of train. Certainly, to regard
operations where rolling stock is grossly overloaded as therefore performing well – as is the case in somedeveloping countries, despite very inefficient production of the service itself – seems mistaken.
Freight traffic is particularly complex because of the lack of a homogeneous 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 (bulksolids or liquids may be loaded and unloaded much more simply than manufactured goods, although thelatter will be easier to handle if they are containerised). It follows that loaded wagon-kilometres may be
a better unit of measurement than tonne-kilometres, and that distinctions may be needed between
trainload, wagonload, and container or intermodal traffic. If tonne-kilometres are used, a distinction bycommodity is important: for instance, a railway that has declining coal traffic and rapidly growingintermodal traffic will almost certainly show declining productivity if tonne-kilometres 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 and rollingstock maintenance, terminals and administration). While, ultimately, all may be regarded as forms oflabour and capital, the length of life of the assets, as well as government intervention in employment andinvestment, will often mean that at a particular point in time an undertaking will not have an optimal
configuration of assets and staff. This renders it difficult to measure inputs simply as labour and capital,

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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 this obviously makes no allowance forthe quality of the assets. Further, and importantly, physical measures such as staff numbers can begreatly affected by contracting out and, unless a balancing item such as other costs is included as an
input, substantially distort measures of productivity and efficiency.
Problems in measuring technical efficiency

A key problem in measuring technical efficiency is that of joint costs and economies of scale and density.
For instance, a single-track railway may carry both passenger and freight traffic, a passenger train firstand 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 characterised by economies of scope; i.e. the costs of a single railway
handling a variety of types of traffic are less than if each distinct product were to be handled by adifferent railway. Moreover, most evidence suggests that railways are subject to economies of traffic
density. Putting more traffic on the same route generally reduces unit costs and raises measures of totalfactor productivity, unless the route is already heavily congested.
The result is that apparent rises in productivity may be caused by diversification into new products or byincreased 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 of service)on the network does lead to a genuine improvement in productivity. The argument here, however, isthat the improvement in productivity arises naturally as a result of the shape of the cost function, andnot 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 hasother 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 terms of the rolling stock
used (e.g. air-conditioned trains and trains that give greater access for disabled users) and, more widely,
the punctuality and reliability of services. Other factors, such as the extent of passenger information
provided and the quality of on-board catering services for rail, will also affect costs. Particularly in thecase of rail, the quality of the service will depend critically also on the capability 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 usefulalternative. As we will argue later in this chapter, we consider cost econometric models to be the mostsuitable for 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

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terms of the data for this form of analysis. Typically, such approaches include physical measures of thelabour 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 risks giving
misleading results, given that there are varying degrees of subcontracting in the rail sector (rolling stock
may be leased; maintenance and cleaning of rolling stock and stations may be contracted out, as may
track maintenance and renewals and so on). For this reason, as well as the technical reasons given
above, we prefer studies based on costs, which should be comprehensive measures including allcontracted out items. Moreover, it is ultimately costs that governments and regulators are mostinterested in, not measures of physical productivity.

Cost function estimation
In addition to the reasons outlined for preferring the approach of estimating cost functions, there is anadditional practical reason; namely, that the 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 is enteredinto the accounts. In other cases, historic debts have been written off; in still others, interest is still being
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, takeaccount of allocative efficiency but, perhaps more importantly for analysis of railways, often neglects
some inputs (i.e. those represented by other costs).
Cost function estimation

Derived from assuming cost minimisation in a production process, the cost function relates costs (C) tothe 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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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 this sense, theTranslog does have appealing economic characteristics, such as the ability to deal with varying degrees ofreturns to scale and density as firm size varies.
Finally, we note that the Translog cost function is often estimated along with the factor share equations.
Factor share equations are expressions for the proportion of total cost used by each input and arederived using Shephard’s (1953) lemma as the partial derivative of the cost function with respect 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 least squares.
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 ,, ym , 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 such as
train-km are adopted as output and vice versa. As such, it can become a little difficult to distinguishwhich variables represent outputs, versus output characteristics, versus network size.
Wheat and Smith (2014) present an attempt to introduce heterogeneity by means of a hedonic costfunction (Spady and Friedlaender, 1978; 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 thenenters into the general cost function. The benefit of this approach is a more parsimonious model. It isperhaps surprising that there have not been too many applications of hedonic cost functions intransportation operations. In Wheat and Smith (2014), the model included three outputs (train hours,route length and number 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 and density (see below), whilst enabling a parsimonious model.
Stochastic frontier methods: Introduction

The above discussion has focused on the relationship between costs, outputs, output characteristics andinput prices. As noted earlier, a key motivation for policy makers is to understand the relative cost
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.

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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). Most applications in railways are based on cost functions,reflecting the fact that, due to the highly regulated environment in which most railways operate, it isappropriate to view railways as seeking to minimise cost for a given level of output (where the latter ismore or less determined by the government). In this section we focus on cost function 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). Thismethod 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 notdistinguish 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 toits simplicity.
The alternative and more widely-used method in the academic literature (and increasingly by economic
regulators) is stochastic frontier analysis (SFA); see equation (4) below. The stochastic cost frontiermodel 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 of parameters to be estimated.
Cit 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 symmetricallyaround zero (more specifically, assumed to be normally distributed with zero mean and constantvariance). 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 asthese 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 econometric methods
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 among railways and over time. Compared with the DEA approach, econometric methods
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provide estimates of the underlying structure of production/costs; for example, the elasticity of costswith respect to different cost drivers, such as traffic volumes – which DEA does not. As noted, the studyof these elasticities allows us to say something about the scale and density characteristics of theindustry.
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 offactors, and in many academic papers more than one method is used in order to provide a cross-checkagainst 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 industry-wide
technological change over the period of the study. Third, for some models it can permit the estimation offirm efficiency without recourse to potentially restrictive distributional assumptions. Finally, it offers theprospect of disentangling inefficiency from unobserved factors. This latter benefit may be particularly
important for railways, where substantial differences exist among railways, both within and between
countries, but where it is hard to capture these differences in a set of variables to be included in themodel.
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 both cases, inefficiency
is assumed to be “time-invariant” and each firm is given one efficiency score for the 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 vary over time, which is
restrictive.
For long time periods, the assumption of time invariant inefficiency is clearly problematic, and a number
of approaches which allow for inefficiency to vary, whilst retaining some structure to the variation, havebeen 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 and94

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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 independence ininefficiency 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 tohave 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 reinterpreted as inefficiency. This approach risks labelling unobserved factors –genuine heterogeneity among railways – as inefficiency. Methods have therefore been developed in theliterature to address this (Greene, 2005; Farsi, Filippini and Kuenzle, 2005; Kumbhakar, Lien andHardaker, 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 socalled “true” models, and there is also a random effects version of this approach.
The Farsi, Filippini and Kuenzle (2005) approach separates inefficiency from unobserved heterogeneity
by making the assumption 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 theexogenous set of output requirements and input prices that it faces – hence this would not be expected
to be correlated with the regressors). Finally, the approaches set out by Kumbhakar, Lien and Hardaker(2014) and Colombi et al. (2014), seek 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 thisseparation, which is a limitation. It further assumes that unobserved heterogeneity is uncorrelated withthe regressors, which may not be valid. It is worth noting that these are relatively new approaches withrelatively few applications as yet and none, to our knowledge, in railways.
Concluding comments

To conclude, then, there exists a wide range of methods for estimating cost inefficiency in the literature,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
United Kingdom. For example, the British Office of Rail Regulation (ORR) has adopted a range ofadvanced methods, and others, such as OFWAT (the economic regulator of the water sector in Englandand Wales), have at least considered these approaches, though to date have 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 choice oftechnique 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 is appropriate
to run a range of approaches and compare the results and in some cases it will not be easily possible to

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choose among them. Economic regulators in that case tend to average the efficiency results across a
range of models.

Rail privatisation in Britain
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, with 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 seven to ten years, on a net costbasis but with requirements as to what services should be operated and restrictions on the levels ofsome fares.
In the period since privatisation (1995 to 2010) passenger-km growth in Britain has been faster than in allother major European railways (Brown, 2013). To provide a few specific examples, between 1995-2010,
passenger-kms increased by 84% (Britain); 65% (Sweden); and 17% (Germany).1
In the passenger market, many studies have shown that economic growth is the main driver of increases
in demand. However, one major change which began in the 1990s was the degree to which thecompetitiveness of the automobile slowed markedly. Growth rates in car ownership and use abatedsignificantly (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 and congestion
increased; and 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 theautomobile 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. The residual effect attributed
to privatisation is a small share of the total (see Wardman, 2006).
Freight market growth has also been strong. From 1996-97 to 2012-13, 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 freight and, from 1998-99 (earlier data not available), 2.8 billion was domestic
intermodal freight (the name is slightly confusing as this is nearly all containers to and from ports). In
other words, these two commodities account for all the growth – collectively, other commodities stayed
constant.
Over this period, coal-tonnes transported declined from 52.2 million to 52 million – in other words, thegrowth was entirely due to increased length of haul. This was brought about by a shift in the supply ofcoal from domestic 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 increased its market share.
Thus whilst restructuring and privatisation may have contributed to the strong performance of the
British system, the major causes lie elsewhere. However, the story regarding passenger train andinfrastructure costs is not so positive (Table 4.1). 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 tofall. 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 theregulator would like.

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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 4.1. 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 (£)

Source: ORR.

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, among other things,
previous cost studies of the British rail sector. A variety of methods, including non-parametric DEA(Affuso, Angeriz and Pollitt, 2003; Cowie, 2009; Merkert, Smith and Nash, 2009) and index number
approaches (Cowie, 2002a; Smith, Nash and Wheat, 2009), as well as parametric estimation of costfunctions (Cowie, 2002b; Smith and Wheat, 2012a; Wheat and Smith, 2014), production functions(Cowie, 2005) and distance functions (Affuso, Angeriz and Pollitt, 2003). Clearly, the former methods

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4. RAIL EFFICIENCY: COST RESEARCH AND ITS IMPLICATIONS FOR POLICY(DEA and index number approaches) can only consider cost or technical efficiency and produce noestimates regarding the actual cost structure.
Importantly, of the British Train Operating Company (TOC) cost studies, only four cover the crucial period
after 2000 when TOC costs started to rise substantially; these being Cowie (2009), Smith, Nash andWheat (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 so should
be considered for inclusion in any analysis. The four papers by Cowie all include some measure of
infrastructure input in the analysis, which is some combination of route-km and access charges paid byoperators 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, Angeriz and Pollitt (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 studyof 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 their paper
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 distinguish between scaleand 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. They used apanel data stochastic frontier framework which allowed efficiency to evolve over time (based on Cuesta,
2000). They also included dummy variables in the cost function to allow the extent of cost effects ofdifferent contract types to be directly estimated.
The focus of the Wheat and Smith (2014) work, in contrast, was how to best model the cost structure of
the industry. This work utilised a hedonic cost function and the description of the data used is given in
Table 4.2. In particular they defined three generic outputs (route-kms, train-hours and number ofstations operated) and defined nine characteristics of train services which go into the train-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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Table 4.2. Data used in Wheat and Smith (2014)
SymbolName

Description

Data sourcey1

Route-km

Length of the line-km operated by the
TOC. A measure of the geographicalcoverage of the TOC

National Rail Trendsy2Train-hours

Primary driver of train operating cost

National Modelling Framework
Timetabling Module

q12

Average vehicle length oftrains

Vehicle-km/Train-km

Network Railq 22

Average speed

Train-km/Train-hours

National Modelling Framework
Timetabling Moduleq 32

Passenger Load Factor

Passenger-km/Train-km

Passenger-km data from National
Rail Trends. Train-km data from
Network Rail.

q 42

Intercity TOC

Proportion of train services intercity innatureq 52

London South Eastern
indicator

Proportion of train services into andaround London

National Rail Trends for the
categorisation of TOCs into
intercity, LSE and regional. Where
TOCs have merged acrosssectors, a proportion allocation ismade on an approximate basiswith reference to the relative sizeof train-km by each pre-merged
TOC.

(in general commuting services)
Interaction between Intercity and LSE
proportions

q 62

q 42 q52

q 72

q42 1  q42  q52 

q 82

q52 1  q42  q52 

q 92

Number of rolling stock
types operated

Number of “generic” rolling stock types
operatedNational Modelling Framework
Rolling Stock Classifications

y3

Stations operated

Number of stations that the TOC
operatesNational Rail Trends

Interaction between intercity andregional (non-intercity and non-LSE
services) proportions
Interaction between LSE and regional
proportions

PricesP1

Non-payroll cost per unit
rolling stock

TOC accounts for cost, Platform 5
and TAS Rail Industry Monitor for
rolling stock numbers

P2

Staff costs (on payroll)

TOC accounts (both costs andstaff numbers)

Source: Wheat and Smith (2014).

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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
and usefulness of the methods employed. Returns to scale (RtS) and returns to density (RtD) can be
defined specifically for operations (as distinct from infrastructure). RtS measures how costs change whena firm grows in terms of geographical size. RtD measures how costs change when a firm grows byrunning 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, Smith and Nash (2009) did estimate a variable
returns-to-scale model and found that British and Swedish TOCs were below minimum efficient scale,
while the largest German operators were above. Of the parametric papers, Cowie (2002b) estimates acost model which provides evidence on economies of scale. Cowie finds evidence for increasing returns
to 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 ofscale and economies of density, where the primary usage output is train-kilometres. They found constantreturns to scale and increasing returns to train density. The policy conclusion of this finding is that, whilst
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 agiven route); though see further discussion on economies of density and heterogeneity 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 of general to specific testing and onwhether key elasticities were of the expected sign. This implicitly restricts the variation in economies ofscale and economies of density.
Further work by Wheat and Smith (2014) estimated a Translog simultaneously with the cost shareequations and adopted 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 4.2 and 4.3 summarise the findings on RtD and RtS.
Figure 4.2 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 and regional. The
policy conclusion from the analysis of RtD is that most TOCs should be able to reduce unit costs if there isfurther growth in train-hours (on a fixed network) in response to future increases in passenger demand.
Figure 4.3 provides a similar plot for RtS. This shows that for all of the central 80 th percentile 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 to have anappreciable range exhibiting increasing returns to scale. The results are consistent with a u-shaped
average cost curve, although it would appear that most TOCs are operating at or beyond the minimum
unit cost point. This finding has important implications for examining the optimal size of TOCs, and isrelevant to the recent franchise policy change that has resulted in substantial franchise re-mapping and,in turn, larger franchises.

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Figure 4.2. Returns to density for different TOC types holding other variables constant

Source: Wheat and Smith (2014).

Figure 4.3. Returns to scale for different TOC types holding other variables constant

Source: Wheat and Smith (2014).

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101

4. 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 constant returns toscale combined with fairly strong economies of density. This may suggest that there could be a case formaking 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, thanthose elsewhere in Europe. However, if reducing the size of franchises also increases the degree offranchise 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 reductionsin 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 and density. In thatlater paper, it is found that the ability to exploit economies of density may be constrained by service
heterogeneity. Likewise, the losses of economies of density from reducing franchise size might besmaller than indicated above. It is further found that some franchises in Britain are operating atdecreasing 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 conclusions, we would, however,recommend that 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 model 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 extensive literature analysing the efficiency and productivity
performance of vertically integrated railways around the world (Oum, Waters and Yu, 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, Ivaldi and Vibes, 2010; Asmild etal., 2009; Growitsch and Wetzel, 2009; Cantos, Pastor and Serrano, 2010 and 2011; Mizutani andUranishi, 2013; Nash et al., forthcoming; van de Velde et al., 2012), although some studies consideredevidence from North America (e.g.Bitzan, 2003). Overall, the results seem inconclusive, suggesting thatmuch depends on the circumstances of the country concerned and the way in which the system ismanaged. 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 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, the experience ofcompetitive tendering has generally been positive, with the evidence suggesting that savings in theregion of 20-30% can be achieved, alongside increased patronage (see Brenck and Peter, 2007; Lalive
and Schmutzler, 2008; Alexandersson and Hulten, 2007; Nash and Nilsson, 2009). Even here, though,some franchises have failed to achieve favourable results. Kain (2009) describes the major problems thatemerged in Melbourne, though the impact of the policy response is not described in any detail. Long-

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term passenger (as well as freight) rail franchises have also been signed in Latin America, generally
leading to radically improved performance, although in most cases re-negotiation has been required due
to changed economic circumstances (in particular, the severe economic recession in the late 1990s: see
Kogan, 2006).
Turning to studies of British TOCs, Affuso, Angeriz and Pollitt (2003) and Cowie (2002a, 2002b, 2005)
study the early years after privatisation (prior to the major cost rises) and all find improving productivity
during this period. Only two studies cover the post-2000 period, after which costs started to rise. Cowie(2009) finds declining productivity growth after 2000, with the absolute productivity level falling post2002. Smith and Wheat (2009) report productivity levels falling as early as 2000 and not recovering over
the remainder of the sample (to 2006). Smith, Wheat and Nash (2010) review this literature. Thus
Britain’s franchising experience appears to be the outlier (at least within Europe), with costs rising rather
than falling, as in Germany and Sweden.
Figure 4.4. Findings for TOCs in Britain that were subject to short-term management or renegotiatedcontracts relative to other TOCs

* Some TOCs saw re-franchising during this period and came off their management contracts. Other TOCs (dottedline in the above chart) continued on their management contracts to the end of the period.
Source: Smith and Wheat (2012).

Smith and Wheat (2012) investigate the impact of the response of the franchising authority in Britain tofranchise failure. Two approaches were adopted. First, most operators were placed onto annuallynegotiated management contracts (similar to cost-plus contracts). The second approach saw some
operators placed onto newly-negotiated short-term franchise arrangements. Figure 4.4 summarises thefindings on the cost effect of different franchise contracts. This shows that the franchising authority’s
decision to place a large number of TOCs on management contracts for an extended period led to a

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substantial deterioration in efficiency relative to other TOCs. Furthermore, this effect was persistent andled to costs being considerably higher than other TOCs for several years. However, the relative
inefficiency was eliminated by competitive re-franchising for those TOCs that were subject to thisprocess during the sample period. The short-term franchise agreements which, once signed, retain
incentives to reduce costs, saw a more positive pattern, with costs remaining in line with other operators(those that remained on their original franchise agreements throughout).
Infrastructure costs

As noted above, infrastructure costs have risen even more than train operating costs. A major reason forthis was the expansion of maintenance and renewals activity following the fatal accident at Hatfield in
2000, which was caused by a broken rail, but the cost of the West Coast Mainline renewal and upgrade
programme also rose to some GBP 8 billion from an original estimate of GBP 2 billion. The result of these
two events was that the privately-owned infrastructure manager, Railtrack, was placed in administrationin 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 the Government.
The Office of National Statistics has recently ruled that, under current EU regulations, Network Rail is tobe regarded as a public sector organisation.
How did these events affect the efficiency of the British infrastructure manager over time, and how doesit compare with its European peers?
As noted above, rail infrastructure costs rose substantially after the Hatfield accident. Whilst part of thisincrease was driven by the need to increase maintenance and renewal activity, in the light of genuine
concerns over the quality of the network, a wide range of evidence has shown that Network Rail’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 and 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, Nash and Wheat, 2009). A further finding of Kennedy and Smith(2004) is that there was scope for the infrastructure manager to reduce its costs by reducing 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 ORR’s efficiency determination was ultimately based on
two bottom-up consultant reviews of Network Rail’s business plan (LEK, TTCI and Halcrow, 2003 andAccenture, 2003). These results were supplemented by internal benchmarking, which indicated the kindof savings that could be achieved if Network Rail implemented its own best practice consistently acrossthe network.
Ultimately then, the 2003 Interim Review was unable to provide a clear, empirically-based assessment of
Network Rail’s relative efficiency position based on hard data from external sources. ORR neverthelessset a tough efficiency target of 31% over five years (2004-2009). However, costs were starting from avery 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 to take the benchmarking
approach a step forward by attempting international comparisons.

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Two approaches were adopted during the 2008 review. The first used a panel of thirteen European
infrastructure managers over an eleven-year period. The data was provided from the Lasting
Infrastructure Cost Benchmarking (LICB) project, undertaken by the UIC. The dataset included data on
costs (adjusted, based on PPP exchange rates), traffic volumes (by type), network length, and a range ofother variables characterising differences between the companies (for example, extent of electrification,
network density).
A structured inefficiency model (Cuesta, 2000; see above) was used that permits inefficiency to vary byfirm over time, but in a structured way that recognises the panel nature of the dataset. The results areshown for Network Rail in Figure 4.5 (other companies cannot be shown for confidentiality reasons); see
Smith (2012). Results are shown for maintenance only and for maintenance and renewals, with theadditional model variant to allow for Network Rail’s renewals costs to be reduced downwards prior tomodelling, to allow for the fact that the company was renewing at above-steady-state levels in terms ofrenewal volumes. The overall message of Figure 4.5 is that Network Rail’s efficiency deteriorated sharply
after 2000, compared to its European counterparts, leaving the company with an efficiency gap ofaround 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.
In a separate, supporting study, ORR and the University of Leeds collected a new dataset, comprising fiveother rail infrastructure managers in Europe and North America. This includes data on costs, outputs andnetwork 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, Wheat and Smith, 2010; Smith and Wheat, 2012b).

Score against frontier

Figure 4.5. Profile of network rail efficiency scores: preferred model

Source: Smith (2008, 2012).

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It is further worth noting that the ORR carried out a range of other studies, principally based on bottomup evidence. These confirmed the existence of a substantive gap, supported by examples of best practicein other countries (see Table 4.3).
Table 4.3. Examples of European best practice
Asset inspection andasset management

In general, best practice European railways undertake fewer track inspections but inspections are
generally of higher quality. It is estimated that similar techniques applied in Britain could reducefoot 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 andsignal heads are regularly refurbished then cascaded from higher to lower category routes.
Cascaded rail on lines re-laid with steel sleepers could lead to savings. Additionally, ballast
cleaning (partial renewal) as opposed to 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 70s. Some European networks (using modern equipment)
have reintroduced 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 trains

Modern high output European plant is regularly used to undertake formation and also ballast
renewals. If applied to Network Rail’s CP4 category 7 and 12 track renewals, RailKonsult estimatethat it could reduce unit costs for both activities by around 40%.

Lightweight station
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 rerailing techniquesThis 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 anddedicated teams are applied routinely. Put together for basic re-railing work alone, this method isaround 40% more efficient than current Network Rail practice.

Use of dedicated teams

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

Source: Smith, Wheat and Smith (2010).

Although ORR carried out/commissioned a wide range of studies – all of which pointed in the direction ofa 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 the upper quartile of thepeer group, rather than the frontier, thus meaning that the starting efficiency gap for its analysis – basedon 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 of the 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. Thiswas driven by a number of factors, but in part reflected increased doubts after 2008 about the quality 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 considered that there wasinsufficient time to get enough certainty about the quality and comparability of the data 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 the process also
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applying more advanced techniques (including the more recent methods set out previously), theeconometric modelling played a supporting role to the bottom-up analysis (thus reversing the approachtaken in PR08; see ORR, 2013).
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, and thisenabled top-down, econometric international benchmarking to play a more significant role than it has inother 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 astrong imperative to use this kind of approach. In PR13, with Network Rail acknowledging the size of thegap, with the gap closing already, and with new uncertainties arising in the LICB dataset, ORR has
become more cautious about top-down international benchmarking and, arguably, the need to rely on it
has diminished. 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 bycompanies 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, itremains a useful part of a regulator’s 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.

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 improving efficiency.
In this section, we consider the wider European experience. Most past studies on the impact of reforms
at the European level have applied data envelopment analysis to physical data; our problems with thatapproach have been outlined above. Moreover, they have usually used the data published by the Union
International des Chemins de Fer (UIC),2 data which has been shown to contain inconsistencies (van de
Velde et al., 2012). Furthermore, this source only contains data on UIC members, generally theincumbent but not new entrants, and in some cases covers their activities in a number of countries
rather than just their home country. A rare example of the estimation of cost functions to study the
impact of European reforms is Asmild et al. (2009); they also went to considerable efforts to clean up
and supplement the UIC data. They found that competitive tendering for passenger services, open accessfor 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, theirdata 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 of countries
is Mizutani and Uranishi (2013). They used data for 30 railway companies from 23 OECD countries for theyears 1994 to 2007, giving 420 observations. Whilst most of the observations were from Europe, they

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included the vertically integrated passenger railways of Japan, as well as South Korea and Turkey. Where
vertical separation had been implemented, they added together the infrastructure manager and thetrain operating company to form a single observation. The basic source of data was the 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, length of haul and number of freight cars per train to reflect differences in thecharacteristics, and therefore costs, of the train-kilometres. Factor prices for labour, track, rolling stockand other materials were estimated. Finally route-kilometres, train-kilometres per route-kilometre andthe percentage of electrified line 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 actuallytakes 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 transaction costs caused by vertical separation will be muchgreater in densely trafficked networks than in less densely trafficked ones.
Van de Velde et al. (2012) take this work further by updating and improving the data set and introducing
separate dummy variables for holding companies and complete vertical separation (this work has also
been published later in the academic literature (see Nash et al., forthcoming; and Mizutani et al., 2014).
They also include Britain, the country in which the most radical reforms had taken place, but which 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, vertical separation
reduces costs at low levels of density but increases them at high levels: at mean European density levels,
costs are not affected by the change. This effect is not likely to be one of pure transaction costs(negotiating and enforcing contracts), which have been shown to be a relatively small proportion of total
systems costs (Merkert, Smith and Nash, 2012) but is more likely a problem of misalignment of
incentives leading to poor integration of infrastructure and operations in circumstances (dense traffic)
where this is 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
the holding 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 forprivatisation), whilst a high proportion of revenue coming from freight rather than passengers tends toincrease the costs of vertical separation (perhaps because planning freight services efficiently requires
closer day-to-day working than for passengers, 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 shows 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 that verticalseparation 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 isfurther disconcerting that it has not been possible to find clear competition effects in the data. To date,no research has yet been published to consider the impact of regulation on costs, though research is
ongoing at the 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.

Conclusion
There are various measures of efficiency available, and all have their uses. Partial productivity measures
may have their value in shedding light on the utilisation of particular resources, but they are inadequate
as measures of overall efficiency, such as are needed in econometric studies of the influences ofalternative policies on the efficiency of the rail industry as a whole. For this there are a number ofpossibilities, 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 of Railtrack
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. There appear to be various reasons forthat, but the use of management contracts to deal with cases of financial 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 be factors in this (though it must be noted thatsplitting franchises to reduce their size increases the prospect of franchise overlaps). Most continental
franchises are smaller and subject to less overlap of homogeneous services, and they appear to be more
successful. However, it must still be noted that the studies carried out other than in Britain are based ondata on subsidies and not on cost, and it may be that costs have been reallocated elsewhere in thesystem.
Studies of European-wide reforms suffer even more data problems than studies in a single country, but arecent study which sought to overcome these was van de Velde et al. (2012), using partly published dataand partly 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.
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 forgood-quality, consistent data on the variables we are seeking to measure (in cost analysis, a key area
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here is the costs of depreciation and interest). Better measures of policy variables are needed, including
in particular the quality of regulation and the degree of competition. At the same time, themeasurement of service quality is an important factor – as noted above, no sensible operator minimises
costs wholly at 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 and commitment,
and sometimes companies may start involvement in a benchmarking project, only to withdraw later.
Long-term commitment to developing a framework and data collection exercise is required. Ultimately, it
may be that data issues cannot be fully addressed without a concerted, European-wide effort by multiple
policy makers and regulators to require data to be collected to a common set of definitions, withappropriate 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 among companies, methodological advances mean that, with enough data points (andwith panel data), there are ways of disentangling inefficiency from unobserved factors that cause costs
to vary among railways. Thus, we consider that it is both possible and sensible to continue to develop ourunderstanding 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 and reducing carbon as well as responding to the increased challenges posed byclimate change. All of these raise new data and methodological challenges which researchers and policy
makers will need to grapple with in the coming years.

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Notes

1 Source: European Commission Transport in Figures.
2 This is published data as distinct from the confidential data from the LICB project described earlier.

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ANNEX A. LIST OF ROUNDTABLE PARTICIPANTS

Annex A. List of Roundtable participants
John THOMAS (Chair), Regulatory Affairs Advisor, Etihad Rail PJSC, United Arab Emirates
Yahya AL AMEERI, Senior Coordinator GCC Relations, Etihad Rail, United Arab Emirates
Miguel AMARAL, Senior Economist, Autorité de Régulation des Activités ferroviaires, France
Vincent BENEZECH, Transport Analyst, International Transport Forum (ITF)
Heiner BENTE, Chairman of the Advisory Board, Civity Management Consultants GmbH & Co., Germany
Alain BONNAFOUS, Professeur émérite, Université de Lyon, France
Richard BULLOCK, Director, Bullpin Pty Ltd., Australia
Yves CROZET, Université de Lyon, France
Pablo GUTIERREZ, Permanent Delegation of Mexico to the OECD
Torben HOLVAD, Economic Advisor, European Railway Agency, France
Marc IVALDI, Toulouse School of Economics, France
Jakob KARLSHØJ, Head of Rail Division, Ministry of Transport, Denmark
Heike LINK, German Institute for Economic Research, Germany
Daniel LOSCHACOFF, Head of Rail Division, KPMG Global Infrastructure, Portugal
Airy MAGNIEN, Head of Unit Data, Statistics and Economics, Union Internationale des Chemins de Fer,
FranceDejan MAKOVŠEK, Economist, ITF
Christopher NASH, University of Leeds, United Kingdom
Jan-Eric NILSSON, VTI, Sweden
Stephen PERKINS, Head of Research, ITF
Anna PISARKIEWICZ, Policy Analyst, OECD
Henry POSNER III, Chairman, Railroad Development Corporation, USA
Andrew S. J. SMITH, Senior Lecturer in Transport Regulation and Economics, University of Leeds, United
Kingdom
Bernard SWARTENBROEKX, Attaché for Economic Affairs, Federal Ministry of Mobility and Transport,
Belgium
Jan SWIER, Strategic Advisor AM, ProRail, The Netherlands
Louis THOMPSON, Principal, Thompson, Galenson & Associates, LLC, USA
Mike TRETHEWAY, Chief Economist and Chief Strategy Officer, InterVISTAS Consulting Group, Canada

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Efficiency in Railway Operations andInfrastructure Management

International Transport Forum
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contact@itf-oecd.org
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Published: 07/2019 | Photo credit: Liu Liming/123RT

This report provides a framework for making reliable comparisonsof the efficiency of rail systems. Efficiency is of concern to all
governments but measuring it for railways is challenging. This isbecause of the complexity of providing rail services and the diversityof business models adopted to deliver them. Restructuring of thesector in many countries makes trends in costs and efficiency
particularly difficult to track. There are also trade-offs to be madein how detailed the analysis needs to be and the availability ofconsistent data over time. The report proposes a balanced scorecard
for characterising performance.