Institute for Transport Studies
FACULTY OF ENVIRONMENTRail efficiency: cost research and its
implications for policy
International Transport Forum (ITF) Roundtable on Efficiency inRailway Operations and Infrastructure Management
Chris Nash and Andrew Smith

Institute for Transport Studies, University of Leeds
November 18th 2014 - ParisOutline
1. Background and introduction

2. Efficiency measurement methodology
3. Studies of the British rail reforms
4. Studies of European rail policy

5. Conclusions

Outline
1. Background and introduction

2. Efficiency measurement methodology
3. Studies of the British rail reforms
4. Studies of European rail policy

5. Conclusions

Policy context- EC wish to see rail as the main mode of medium distancepassenger and long distance freight transport- Requires a big improvement in service quality and capacity- Only affordable if costs greatly reduced- EC is relying on the impact of reforms to increase within-railcompetition to achieve this- So it is very important to:
Identify efficient railways and learn from them
Identify what reforms work in what circumstances

Particular problems inmeasuring railway efficiency I
1. Continued monopoly in many areas
2. Government intervention on outputs and prices

Together these mean that methods which rely on competitivemarkets (e.g. measures of profitability) may be misleading.
What is generally in the hands of railway management is cost
efficiency (although there may be political interventions evenhere)

Particular problems inmeasuring railway efficiency III
Measuring inputs

Railways use a variety of inputs – staff, fuel, locomotives,
passenger cars, freight wagons, infrastructure etc
All vary in capability
Also huge variations in subcontracting (maintenance of trackand rolling stock, cleaning etc)
Cost measures may be more comparable
But still some problems especially regarding consistency ofdepreciation and interest

Particular problems inmeasuring railway efficiency II
Railways produce a host of different outputs:

Carrying different types of passengers and freight between
different origins and destinations with different levels ofservice at different times of day
A number of summary measures may be used:
Passenger km and freight tonne km
Train km by type of train
Vehicle km by type of vehicle
Ideally we would use them all as part of our measurement ofoutputs, along with indicators of service quality and operatingenvironment.

Outline
1. Background and introduction

2. Efficiency measurement methodology
3. Studies of the British rail reforms
4. Studies of European rail policy

5. Conclusions

Some definitions
Technical efficiency: production function: Y = f (inputs)- Are inputs ,minimised for the level of output required?
Allocative efficiency- Is the combination of inputs used the minimum cost one?
Cost efficiency: cost function: C = f (outputs; input prices)- The product of technical and allocative efficiency
Efficiency is a relative measure: productivity measures ratio of
outputs to inputs (similar concept but important differences)

A starting point for measuring
efficiency• Unit cost measures widely used as a starting point – but only
partial measures (which denominator to use?)

Cost pertrack km

Cost pertrain km

• Relative efficiency scores simultaneously take account ofvariation in track km and train km (and other cost drivers)• So potentially gives a single, more definitive measure of
relative performance (if a robust model can be achieved)• An added benefit of statistical models: we can determine
what the data is telling us about the impact of key
variables on cost (elasticities; scale / density economies)Why a statistical / econometricmodel?

Cost

Firm A has
high unit costs– is itinefficient?

AO

Efficiency
frontier

OutputWhy a statistical / econometricmodel?

Cost
AO

Efficiency
frontier

OutputWhy a statistical / econometricmodel?

Cost• Allow flexibility on the shape of thecost-output relationship (e.g. allow
economies of scale)• Allow multiple outputs / other costdrivers (e.g. train and track-km)

AO

Efficiency
frontier

Train-km

Why a statistical / econometricmodel?

Cost• Allow flexibility on the shape of thecost-output relationship (e.g. allow
economies of scale)• Allow multiple outputs / other cost
drivers (e.g. train and track-km)

AO

Efficiency
frontier

Track-km

Why a statistical / econometricmodel?

Cost• Allow flexibility on the shape of thecost-output relationship (e.g. allow
economies of scale)• Allow multiple outputs / other cost
drivers (e.g. train and track-km)

AEfficiency
frontier• So we can explain costs in terms ofa set of explanatory factors, e.g.
– Network size; traffic density andtype; other (e.g. electrification;multiple track); potentially, others…

OOutput• Having accounted for these factors,
and random noise, produce anoverall measure of efficiency

Statistical approach versus
DEA
1. DEA does not distinguish random noise (e.g. random
events affecting costs; errors in data) from inefficiency
2. DEA does not give information on how costs vary with costdrivers – in statistical approaches this information is a
useful piece of information in judging the robustness of themodel
3. DEA sensitive to outliers and hard to incorporate a wide
range of variables – except through a second stage
approach, which is then a statistical model anyway

Links between methodology
and data

Input data:
PhysicalProduction Function / Frontier
Distance Function / Frontier
DEA

Input data:
Financial

Cost Function / FrontierOutline
1. Policy context and uses of efficiency analysis

2. Efficiency measurement methodology
3. Studies of the British rail reforms4. Studies of European rail policy

5. Conclusions

British experience• In 1994, a separate infrastructure company – Railtrack –was set up and subsequently privatised• Over 1994-7 all train operations were privatised as:- 25 passenger franchises- 2 freight companies plus open access (2 main new entrants)- What happened to costs and why?

Summary of findings on train
operating company costs• Costs up from £4bn in 1998 to around £6bn (or more) in 2012:– A rise of at least 15% per train-km or 9% per vehicle-km– Contrasts with savings of 20-30% elsewhere in Europe– Cost-plus contracts in cases of franchise failure very damaging– No clear signs that costs are coming down substantially yet• On scale and density:– British franchises may be too large– However, splitting up franchises might lead to more franchise overlaps –loss of economies of density– That said, service heterogeneity mean that economies of density foundfrom earlier studies might be over-stated• See Smith and Wheat (2012) and Wheat and Smith(2014)

Rail infrastructure cost trendsin Britain£m 2012 prices
MaintenanceOperating Costs
Renewals
Enhancements

1998
1,055
1,004
1,605
281
3,946

2013 Growth968
-8%
1,390
39%
2,672
66%
2,318
723%
7,349
86%

• Total unit costs up by 45% per train-km• OM&R unit costs up 7% per train-km• Though, don’t forget, substantial economies of density

International benchmarking study:
national data – frontier parameters
Preferred model
Comparator model
Comparator model
Dependent variable:
Dependent variable:
Dependent variable:
Total costs (steady-state adjusted)
Total costs (unadjusted)
Maintenance costs
Coeff.
Coeff.
Coeff.
Frontier parameters
CONSTANT6.2453 *** CONSTANT
6.2382 *** CONSTANT
5.4770 ***ROUTE
1.0743 *** ROUTE
1.0913 *** ROUTE
0.8430 ***PASSDR
0.3345 *** PASSDR
0.3115 *** PASSDR
0.1362 **FRDR
0.1792 *** FRDR
0.1472 *** FRDR
0.1567 ***SING-0.9181 *** SING
-0.9681 *** SING
-0.7146 ***ELEC-0.0370
ELEC-0.0690ELEC
0.0733TIME
0.0556 *** TIME
0.0561 *** TIME
0.0469 ***TIME2-0.0048 *** TIME2-0.0048 *** TIME2-0.0027 **Efficiency parameters1

4.0541 *** 4.1810 *** u0.4560 ***  u0.4694 ***  u
 R1

 R1
0.0585-4.5467
R1
 N1
 N10.2252
0.2031 **  N 1
N2-0.0570 **  N 2-0.0513 **  N 2*** (**, *) indicates parameter significance at the 1% (5%, 10%) level
Other firm specific  parameters are included in the model but not shownfor confidentiality reasons. λ = σ u/σv
1

• Source: Smith (2012)

3.6678 ***0.3374 ***0.1634 **0.2689 **-0.0520 ***Efficiency estimates forNetwork Rail (PR08)
Profile of Network Rail Efficiency Scores: Flexible Cuesta00 Model1

0.9

40%gap0.8

Score against frontier

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

1996

1997

1998

1999

2000

2001

2002

2003

Implies a gap against the frontier of 40% in 2006

2004

2005

2006

PR13 results• Range 13-24%• Ignoring the extremes
would suggest a gapof 23% (ORR)• As an aside: overall
assessment based
mainly on bottom up
studies:– 16% for maintenance– 20% for renewals• Source: Office of Rail Regulation (2013)

Another approach: Dual Level
Inefficiency Model

Infrastructure
MangerRegion (subcompany)

IM2IM1

R11R21 … RS1• Source: Smith and Wheat (2012)

R12…

R22 … RS2

Inefficiency due
to systematicdifferences
between firms– external
inefficiency
Inefficiency due
variation in
performance atregional level –internalinefficiency

Challenges• Data quality / number of data points?
• Time consuming to collect your own data set – requires
commitment over many years from the industry• How to deal with lumpy / cyclical capital costs?

• Modelling fundamental differences in characteristics andquality of railways – there are new methods that can better
control for unobserved heterogeneity than in the past
• Understanding uncertainty in efficiency modelling - See
Wheat, Greene and Smith (2014)

Outline
1. Policy context and uses of efficiency analysis

2. Efficiency measurement methodology
3. Studies of the British rail reforms
4. Studies of European rail policy

5. Conclusions

EU rail policy• EU Policy of introducing competition within mode for
freight and passenger• Requires non discriminatory access to infrastructure forcompetitors• Current legislation requires separate accounts andseparation of ‘key powers’, but holding company model
permitted• Big issue: should complete vertical separation berequired?

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EVES-Rail Study28

Results of past studies• US studies (e.g. Bitzan, 2003) – vertical separation raises costs• 4 European studies. E.g. Growitsch and Wetzel (2009): verticalseparation raises costs• Friebel et. al. (2010). Reforms improve efficiency but only where they
are sequential and not in a package• Cantos et. al. (2010). Vertical separation with horizontal separationand new entry in freight improves efficiency. Cantos et. al. (2011).
Vertical separation effect not statistically significant; passenger
tendering found to improve efficiency• Merkert, Smith and Nash (2011). Transaction costs around 2-3% of
total costs• Mizutani and Uranishi (2013). Impact of vertical separation depends
on traffic density

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EVES-Rail Study29

Contribution of EVES-rail costmodelling• Data– Adding the British data to the sample.
– Updating in time from 2007 to 2010.
– Uses UIC data but verified, improved and extended by CER
members via questionnaire• Develop structural dummies to better answer the research questions– In particular: holding versus vertical separation• Improve modelling of market opening dummies– Actual rather than potential freight entry– Passenger competition index that reflects degree of entry• Improve accuracy of timing of structural and market opening reforms

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EVES-Rail Study30

Cost regression model•

26 OECD countries – 1994-2010•

Total rail industry cost = f (control variables, test variables)

Control variables

Test variables(cost drivers not related to policy)(policy variables that may affect costs)



Passenger output

Vertical separation dummy variable

Freight output

Vertical separation dummy variable * train densityRoute lengthVertical separation dummy variable * freight revenue proportionTechnology index



Holding company dummy variable

Wage rateHolding company dummy variable * train densityEnergy priceHolding company dummy variable * freight revenue proportionMaterials priceHorizontal separation dummy variable

Capital price

Passenger competition measureFreight competition dummy variable

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EVES-Rail Study

31

Cost regression – key findings• Passenger and freight market opening had no significant impact• Horizontal separation of freight has reduced costs• At higher traffic densities, vertical separation increases costs– At mean traffic densities, vertical separation does not significantly change
costs– Whereas a holding company model reduces them, compared withcomplete vertical integration (weakly significant)• A higher share of freight in total revenues increases the costs ofvertical separation– Freight traffic may cause more coordination problems in a separated
environment than passenger traffic

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EVES-Rail Study32

Concluding remarks• Econometric approach to efficiency measurement has strong
advantages in:- Allowing for multiple outputs: but singe measure of efficiency- Controlling for other differences between railways- Allowing for random noise in the model• Main challenge is data:– Number of data points (companies; time; regions)– Comparability of data over time and between countries– Needs to incorporate quality and other factors in the model– Collecting good quality data takes time and commitment – ideallyeconomic regulators / Ministries need to co-ordinate

Concluding remarks- The evidence on the impact of reforms is mixed – they
seem to have worked in some circumstances but not inothers- There is a need for a pragmatic approach that looks atwhat works in what circumstances rather than a ‘one size
fits all’ approach

Thank you for your attention
Chris Nash and Andrew Smith