2010-48
Does Vertical Separation Reduce Cost?
An Empirical Analysis of the Rail Industry
in OECD Countries
Fumitoshi Mizutani Shuji Uranishi

Does Vertical Separation Reduce Cost?
An Empirical Analysis of the Rail Industry in OECD Countries*
byFumitoshi Mizutani (Kobe University)
Shuji Uranishi (Fukuyama Heisei University)

Correspondent Author: Fumitoshi Mizutani
Kobe University, Graduate School of Business Administration
2-1 Rokkodai, Nada-ku, Kobe 657-8501(Tel): +81-78-803-6905(Fax):+81-78-803-6977(E-mail): toshi@kobe-u.ac.jp[Abstract]: The main purpose of this study is to analyze structural separation policies, especiallyvertical (i.e. operation-infrastructure) separation and functional (i.e. passenger-freight service)separation. Using the total cost function of a railway organization, we evaluate whether or notvertical separation and/or functional separation can reduce costs. For this analysis, we select 25
railway organizations from 23 OECD countries over 11 years, from 1997 to 2007.

Our findings

show that because the functional separation dummy has a negative sign with statistical significance,
functional separation can be seen to reduce the cost of a railway.

The effect of the verticalseparation changes according to the train density of a railway organization.

With lower train

density, vertical separation tends to reduce cost, while with higher train density vertical separation
increases cost.

[JEL Classification]: L23, L33, L51, L92, R48[Key Words]: Vertical Separation, Functional Separation, Total Cost Function, Railway*

An earlier version of this paper was presented at the 12th World Conference on Transport Research in Lisbon,
Portugal, July 11-15, 2010. We would like to thank participants for valuable comments and suggestions. For
providing valuable information and materials, we would especially like to thank Simon Fletcher (International Union
of Railways: UIC), Vincent Vu (International Union of Railways: UIC), Stephen Perkins (Joint Transport ResearchCentre of the OECD and the International Transport Forum), Siebe Riedstra (Dutch Ministry of Infrastructure andTransport), and Bastin Zibrandtsen (Danish Ministry of Transport).

1

Does Vertical Separation Reduce Cost?
An Empirical Analysis of the Rail Industry in OECD Countries
1 Introduction
Since the Japan National Railway (JNR) was privatized and subdivided into six
passenger JR companies and one nationwide freight JR company in 1987, privatization andregulation of railways have been effected in many countries, each according to its own railway
regulation

andcompetition

policies.

For

example,whileverticalseparation(i.e.

operation-infrastructure separation) is a common policy in the European Union, vertical integrationis still the structure of choice in the Japanese rail industry1.
there are many variations.

Among vertical separation options,

For example, accounting might be separated from other functions, orthere might be organizational separation of rail operations and infrastructure, or there might beorganizational separation involving a holding company.

Massive horizontal separation of former

state railways was adopted in the UK and Japan, but in some countries the descendent organizationof the former state railway has a vital role in the market. As for the ownership structure, acommercial organization with private ownership is common in the UK and Japan, in contrast to
Denmark, where Danish Railway is still a publicly owned organization.
Competition policy is a vitally important issue in regulatory reform.

Jensen (1998), by

using a model in an empirical study of the Swedish railway sector, finds that external competitive
pressure is strong in most supply segments.
different countries.

However, competition is handled in different ways in

For example, direct competition in the railway market, in which the rail

operator is selected by competitive tendering, is favored in many European countries while in Japan,
rather than allowing direct market competition to occur, regulators apply yardstick regulation (i.e.
the benchmark competition policy) to existing railway organizations (see for example, Mizutani et al(2009)).

There are various railway regulations and competition policies, with much empirical anddescriptive research having been carried out on individual countries’ railway regulations.

Althoughsome studies such as Lodge (2002) explore the notion of regulatory failure in the railway domain bytaking an analytical and a comparative perspective, there are few studies analyzing regulatory andcompetition policies across the international board. While some studies such as Oum and Yu(1994) and Lan and Lin (2006) analyze a rail organization’s performance by using a cross-sectional
data set, these studies do not focus on regulatory policy.

Oum and Yu (1994) undertake aninternational comparison of economic efficiency among OECD countries’ railways.

Lan and Lin(2006) present an international comparison of performance measurements for railways by using
stochastic distance functions.
1

A series of regulatory reforms and regulation policies of each country is summarized, for example, by
the ECMT (1998, 2001, 2005).

2

This study focuses mainly on structural separation policy, the most controversial among
various regulatory policies in the rail industry, with our main purpose being to analyze how verticalseparation policy affects each individual rail operator’s performance.
measures, we pay special attention to cost structure changes.

Among performance

There already exist many empiricalstudies on vertical separation’s effect on cost, but their results vary, with some studies supporting theidea that vertical separation improves efficiency (e.g. Shires et al., Kim and Kim (2001), Ivaldi andMcCullough (2001)); some suggesting the opposite (e.g. Cantos Sanchez (2001), Bitzan (2003),
Jensen and Stelling (2007) and Growitsch and Wetzel (2009)); and one showing no effect (e.g.
Mizutani and Shoji (2004)).

Because studies have shown such differing, inconclusive results, theseparation issue needs further analysis.

In this study, we explain the effect of vertical separation

policy on cost, by considering the differences in the degree of train density in the empirical costmodel.
This paper consists of five sections after the introduction.

In the second section, wesummarize the previous literature, including both theoretical and empirical studies related to thevertical separation policy.

In the third section, we explain theoretical background by applying

Williamson’s transaction cost economics.
Preston (2002) and Hori (1996).
analysis.

For the conceptual argument here, we are indebted to

In the fourth section, we explain the empirical cost model for thisThe cost model is specified as translog total cost function.

In the cost model, bothvertical separation dummy and functional separation dummy variables are included.

The cost

model is specified as to what extent vertical separation’s effects are changed by the degree of train
density of a rail organization.

Furthermore, in order to control the output qualities, hedonic

specification for output measures is used.

In the fifth section, the empirical results are shown, afterand explanation of the sample selection and data. Rail operators and infrastructure managers inOECD countries for the years from 1997 to 2007 are chosen.

The main data source is acompilation of railway statistics issued by the International Union of Railways (UIC).

The

observations in the analysis cover, for example, JRs in Japan, SNCF in France, NS in Holland, SJ in
Sweden, KORAIL in South Korea, and so on.

By using empirical results, we evaluate whether or

not vertical separation and functional separation can reduce the cost of railways.
important points garnered from this analysis.
The main results of this study are as follows.

Last, we outline

First, because the functional separation (i.e.

passenger and freight service) dummy has a negative sign with statistical significance, functional
separation can be seen to reduce the cost of a railway. Second, the effect of the vertical separation
changes according to the train density of a railway organization.

With lower train density, verticalseparation tends to reduce costs, while with higher train density, it would increase costs.

3

2 Previous Studies
There are many studies regarding structural separation in the rail industry, most aiming todiscuss the advantages and disadvantages of vertical separation based on both theory and empiricalevidence.

In this section, we will give an overview of studies regarding the theoretical effects ofvertical separation, such as Nash (1997), Hori (1996), Preston (2002), Ksoll (2004) and Drew (2009).
Other studies, such as Nash and Rivera-Trujillo (2004) and Di Pietrantonio and Pelkmans (2004),
summarize and discuss regulatory reforms, including vertical separation, in the EU railway industry.
Pittman (2003, 2005) discusses structural separation with a focus on developing countries.

There

are studies on the unbundling issue by Affuso and Newbery (2004) and infrastructure quality by
Buehler et al. (2004)2.
First, Nash (1997) cites the potential advantages of separation: (1) promotion of a varietyof operators; (2) clarification of intra-industry relationships; (3) specialization in both operation andinfrastructure.

On the other hand, he notes that separation has adverse effects on (1) pricing andperformance; (2) timetabling and slot allocation; (3) investment; (4) safety; and integrated
information and ticketing.

These advantages and disadvantages are summarized in order toformulate a practical policy for the rail industry.
Hori (1996) and Preston (2002) apply Oliver Williamson’s framework of transaction costeconomics to the railway industry.
example, model building.

These studies are not theoretical and do not involve, forHowever, they consider this issue as a trade-off between market

governance, which implies vertical separation, and bureaucratic internal governance, which implies
vertical integration.

For example, Preston (2002) evaluates the advantages and disadvantages ofthe operation-infrastructure separation. He mentions that the rail industry may well be thought ofas one in which there is site-specific transfer of intermediate product across successive stages andhence unified governance (i.e. vertical integration) might be preferred, but this would need to bevalidated by empirical results.

Although Hori (1996) mentions in his study that the degree of asset

specificity increases in urban rapid rail systems compared with local rail systems, he does not
explain why the degree of asset specification increases.
Like Nash (1997), Ksoll (2004) explores the arguments both in favor of and against
vertical integration in railways.

There are eight advantages: (1) lower complexity of interfaces

simplifies operational co-ordination and conflict settlement; (2) comprehensive investment
incentives and avoidance of holdups strengthen capacity, quality, safety and innovation; (3) private
2

Affuso and Newbery (2004) investigate whether or not the investment pattern of the rail passenger
franchisees responds to structural and contractual characteristics using a unique panel data on privatizedrailways in Britain. Their results suggest that unbundling and competition for franchises combined withcommercial objectives can provide strong incentives towards better performance, as is the case forinvestment behavior. Buehler et al. (2004) investigate how various institutional settings affect a network
provider’s incentives to invest in infrastructure quality. In their analysis, with suitable non-linear accessprices, investment incentives under separation become identical to those under integration.

4

infrastructure provision within an integrated firm ensures higher productivity levels and market
driven allocation; (4) integration yields cost savings and synergies in shared facilities and services;(5) co-existence of integration and competition drives technological and product innovation; (6)encouragement of staff identification and responsibility increases quality and safety; (7) partial
avoidance of double marginalization increases consumer welfare; (8) strategic behavior of theintegrated firm can counteract excessive entry. On the other hand, there are four disadvantages ofvertical integration: (1) integration involves the risk of discriminatory behavior by the infrastructure
provider against downstream competition; (2) integration complicates regulation of the infrastructure
monopoly; (3) there is a conflict between public infrastructure obligations and private infrastructure
management; (4) integration may go along with lower and/or misguided performance incentives ininternal compared to fully external transactions.
Kurosaki (2008, 2009) classify and evaluate the vertical separation policies which havebeen effected in the world’s rail industries.

He classifies vertical separation options into fourgroups: (1) vertical separation without within-rail competition (e.g. Vietnam, Indonesia and Tunisia),(2) vertical separation with competition among operators (e.g. Sweden, U.K., Germany, France andAustralia), (3) vertical separation for passenger or freight traffic (e.g. Japan (JR Freight), US(Amtrack)), (4) concession of long-run access to infrastructure (e.g. Mexico). He summarizes
appropriate forms of vertical separation policy based on different market structure (e.g. with/without
within-rail competition, the market share of passenger and freight service.

Kurosaki’s contribution

is his recognition that there is more than one feasible form of vertical separation.

However, theempirical evidence by which he reaches his conclusions remain weak.
Drew (2009) reviews and analyses the benefits for rail freight customers of the two
principal models for introducing competition in main line railway networks: (1) the verticalseparation of infrastructure from operations; and (2) the introduction of competition providing otheroperators with open access to the network.

He concludes that vertical separation benefits freightcustomers more than open access does alone.
Thus, there exists no definitive theoretical study of vertical separation in the railway
industry.

If we review existing literature from a theoretical point of view, we must say it is notclear whether or not a separation policy (i.e. vertical separation or vertical integration) is desirable.
As for empirical studies, there are many, but their results are not consistent.

Some studies

such as Shires et al., Kim and Kim (2001), and Ivaldi and McCullough (2001) show that verticalseparation is better than vertical integration in terms of efficiency.
First, a study by Shires et al. shows that rail operating costs in Sweden are reduced byabout 10% after vertical separation, although this separation was also accompanied by the gradualintroduction of tendering (Preston, 2002, p.12).
Kim and Kim (2001) analyze the cost structure of Seoul’s subway systems by using the5stochastic frontier cost function.

Their calculation results show that the total cost of the verticallyseparated system was about 3.6% lower than that of the vertically integrated system in 1998.
Ivaldi and McCullough (2001) apply the cost function to Class I U.S. railways andevaluate the effects of vertical separation.
operations and infrastructure.

They find there are no cost complementarities between

This result implies that at all levels of output characterizing freightrail operations in the U. S., there may be no inherent technological advantages from vertical
integration.
Although their study does not focus only on structural reforms, Friebel et al. (2010) apply
a production frontier model for railways in EU countries over a period of 20 years in order toanalyze the effect of regulatory reforms such as vertical separation, the introduction of third-party
access, and the creation of independent regulatory institutions. They find that regulatory reforms
increase efficiency.
On the other hand, some studies such as Cantos Sánchez (2001), Bitzan (2003), Jensen and
Stelling (2007) and Growitsch and Wetzel (2009) show that vertical separation is inferior to vertical
integration.
Cantos Sánchez (2001) analyzes the vertical relationship by applying translog total costfunction for a data set of 12 European state railways for the period 1973 – 1990.

He obtains theresult that there are complementary effects between the costs deriving from freight transport andinfrastructure, while the effects between the costs deriving from passenger transport and theinfrastructure are substitute.

He concludes that infrastructure and operations must be coordinatedboth in order to maintain the coordination effect and to avoid possible inefficiencies.
Bitzan (2003) examines the cost implications of competition over existing US freight raillines by testing for the condition of cost subadditivity.

He finds that there are economies associatedwith vertically integrated roadway maintenance and transport.

This result suggests that verticalseparation of infrastructure from operations increases costs.
Jensen and Stelling (2007), by using data from the railway industry in Sweden, explore
how deregulation has affected cost efficiency.

In their cost estimation, they evaluate the effect ofvertical separation and conclude that it increases costs.
Growitsch and Wetzel (2009) investigate the performance of European railways with aparticular focus on economies of vertical integration. They apply data envelopment analysis (DEA)
to a data set of 54 railway companies from 27 European countries over 5 years from 2000 to 2004.
From their analysis, they conclude that for a majority of European railways, there exist economies ofscope.
One study shows no difference in efficiency.

Mizutani and Shoji (2004) apply thetranslog cost function to maintenance activities in the Japanese rail industry and evaluate whether or
not the vertical separation of operation and infrastructure activities would reduce cost.
6

The resultsindicate that vertically separated systems might not be significantly different from verticallyintegrated ones.
Thus, previous empirical analysis has produced inconsistent results.

It remains unclear

whether vertical separation yields efficiency or inefficiency with regard to cost.

3 Theoretical Background of Vertical Separation in the Rail Industry
In this study, we assume that vertical separation can be explained by both the frameworkof transaction cost economics by Williamson (1985) and the application to the rail industry by Hori
(1996) and Preston (2002). We also assume that the degree of asset specificity is changed by train
density.
Based on Hori (1996) and Preston (2002), the application of Williamson’s transaction costeconomics to the rail industry is as follows.

The key concept is the trade-off between the costassociated with market governance, which implies vertical separation, and internal governance,
which implies vertical integration.

We define ΔC as the difference between the production costs ofinternal governance (CB(k)) and the production costs of market governance (CM(k)).

The

production costs of market governance, CM(k) are always lower than the production costs of internal
governance, CB(k) because the specialized company can expect economies of scale in infrastructure
management at any degree of asset specificity, k.

However, if the degree of asset specificity is too

high, the production cost difference becomes small because the specialized company cannot enjoy
scale economies.

Therefore, the production cost difference ΔC is positive but declines as thedegree of asset specificity increases.
Furthermore, governance costs, ΔG, are defined as the difference between internal
governance costs (GB(k)) and market governance costs (GM(k)).

If the degree of asset specificity isvery low, the railway organization can purchase infrastructure management services from anybody inthe market.

Therefore, internal governance costs are much higher than market governance costs.

On the other hand, if the degree of asset specificity is very high, market governance costs becomelarger than internal governance costs because the railway company cannot find a safe infrastructure
management company.
When we consider the sum of ΔC and ΔG, we can see the crossover point at k**. To theleft of point k**, market governance will be cost effective because ΔC and ΔG are positive. Thismeans that vertical separation could be reasonable. On the other hand, to the right of point k**,bureaucratic internal governance will be cost effective.
reasonable.

7

Therefore, vertical integration could beCost Difference
ΔG : Governance Cost Difference
ΔC : Production Cost Difference
ΔC

ΔG

ΔG + ΔCk*k

Asset Specificity (k)(train density)**

Vertical integration is desirable.

Vertical separation is desirable.

Figure 1 Concept of Production and Governance Costs and Asset Specificity
Finally, we will explain how the degree of asset specificity of the rail infrastructure isrelated to the train density of a rail organization.

First, as for production costs, the maintenance ofrail infrastructure is divided into two components: maintenance activity related to track length andmaintenance activity related to train density.

As the maintenance activities of first component are

fixed regardless of the degree of train density, we can expect a railway to make use of resources
available on the market.

However, as for the second component of maintenance activities (e.g. railand electric wire inspection), when train density is very high, daily maintenance activities become
more frequent and demanding.

Under these circumstances, it is better that maintenance staff andmachinery be owned by the internal organization.

Such ownership would reduce the difference inproduction costs between internal governance and market use.

Therefore, the production costdifference decreases as the train density increases.
A more important point is governance cost.
train density increases.

Cost differences decrease more rapidly as

Governance costs by market governance are for example, labor cost related

to coordination divisions between two organizations, material costs such as documents related tomaintenance activity plans, legal costs, and insurance costs.

These costs are clearly huge under thecondition of heavy train density because safe train operations are necessary.

Of course, withinternal governance, these costs are to some degree necessary but coordination and legal costs
become much smaller because the top manager can decide who has more priority and management8

power among different internal divisions.

Therefore, as the train density becomes larger, the degreeof the asset specification becomes larger.

As a result, governance costs by market governancebecome much larger with heavy train density.
From these arguments, the plausible conclusion is that vertical integration is more costeffective than vertical separation, if the rail organization’s train density is very heavy.

On the other

hand, vertical separation is more cost effective than vertical integration under low train density.
This happens because the degree of asset specification in the rail infrastructure is highly related tothe train density of the rail organization.

Although there is systematic empirical evidence, inparticular, the claims by JR Freight company to JR passenger companies after the privatization of
JNR shows support for our argument (see for example, Mizutani and Nakamura (2004)).

4 Empirical Cost Model
The most important characteristic of our cost model is specified such that the effect of thevertical separation varies by train density.

As explained above, we expected the effect of verticalseparation on cost to be positive because the coordination costs between two organizations would behigher than the cost saved by specializing in different activities (i.e. rail operation and infrastructure
management).
In this study we employ a translog total cost function,3 in which we include two kinds ofinstitutional variables: a vertical separation dummy (Dvs) and a functional separation dummy (Dfs).
The cost model is specified such that the effect of vertical separation on the cost varies by the degreeof train density.

The output measure is specified as a hedonic function of output characteristics inorder to control the differences in the output conditions.
The translog cost model used here is shown as follows:2

lnTC =α0 +αY lnY + Σjβjlnwj + γNlnN + τT T + (1/2)αYY (lnY) + ΣjαYj (lnY)(lnwj) +αYN (lnY)(lnN) + αYT (lnY)(T) + (1/2) ΣkΣjβjk (lnwj) (lnwk) +ΣjβjN (lnwj) (lnN) + ΣjβjT (lnwj) (T) + (1/2) γNN (lnN)2+ γNT (lnN)(T) +2(1/2)τTT T + (δVS1 + δVS2lnV)DVS + δFSDFS
lnY = lnQ + Σf ηf ln Hf,
where

(1)
(2)

TC: total cost
Y: output measure3

Several studies (e.g. Savage (1997), Mizutani (2004), Mizutani and Uranishi (2007)) use the variable
cost function. However, as the main purpose of this study is to evaluate the effect of infrastructure
management on cost, we use the total cost function.

9

Q: quantity of output
Hf: characteristics of output ( f = PR (passenger revenue share),
LF (load factor of passenger service), PTL (passenger travel length))
wj: input factor price (j (or k) = L (labor), K (material and capital)),
N: total route length,
T: technology 4 (T1: percentage of electrified length, or T2 : time trend),
V: train density,
DVS: vertical separation dummy (vertical separation =1, otherwise = 0),
DFS: functional separation dummy (functional separation = 1, otherwise = 0).
In this model, we impose the restriction on input factor prices such that Σjβj = 1, Σkβjk = 0,ΣjβjN = 0, ΣjβjT = 0, ΣjαYj = 0, βjk = βkj, βjN = βNj, βjT = βTj.
Lemma to the total cost function.

Furthermore, we apply Shephard’s

Then we can obtain the input share equations as follows:

Sj = βj + αYj (lnY) + Σkβjk (lnwk) + βjN (lnN) + βjT (T),
where

(3)

Sj: input j’s share of total cost.

As for the estimation technique, we apply the seemingly unrelated regression (SUR)
method by the total cost function and the input share equations.

For the estimation, we will divide

all observations of each variable by the sample mean, except for time trend.

5 Empirical Analysis
5.1 Sample Selection
The main purpose of this study is to examine how differences in structural reform affectcost structure; that is, we evaluate how differences in unbundling methods, such as verticalseparation and functional separation, affect cost difference.

In order to evaluate the structural

factor only, we selected railway organizations with relatively similar conditions.

As a sample

selection, we chose railway organizations from OECD countries, excluding those of OECD railway
organizations in the US, Canada and Australia, however, because their network conditions are
generally different (e.g. long line hauls). And while there exist cost studies, for example that ofSmith (2006), which do use data from the UK rail industry, we unfortunately have to forgo including
the UK because of the overall lack of data.

As Table 1 shows, we collected data on 25 railway

4

In this study, as for technology variable (T), we take the natural logarithm for percentage of electrified
length (i.e. ln T1) but do not take it for time trend (i.e. T2).

10

organizations from 23 OECD countries for the 11 years from 1997 to 2007, giving us 275
observations (i.e. 25 railways times 11 years).
We follow the definition of structural reform 5 of the UIC, which classifies railway
organizations into five categories: (1) integrated company, (2) railway undertaking, (3) passenger
operator, (4) freight operator, and (5) infrastructure manager.

For example, as for

operation-infrastructure management, DSB in Denmark was separated from its infrastructureorganization (BDK) in 1997, so that DSB is classified as having had vertical separation since 1997.
However, the freight service of DSB, which became Railion DK, was separated in 2001, so that DSB
is also classified as having had functional separation since 2001.

KORAIL in Korea was neither

vertically nor functionally separated between 1997 and 2007, so that KORAIL is classified as anintegrated system.

Therefore, the vertical separation here means that the activities of rail operations

and infrastructure management are provided by completely separate organizations.

Two different

organizations under the same holding company are not separated bodies.
Table 1 Railway Operators for Our StudyNo.

Railway Operator

Country

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

ÖBB (Österreichische Bundesbahnen)
SNCB/NMBS (Société Nationale des Chemins de fer Belges)
BLS (BLS AG)SBB CFF FFS (Schweizerische Bundesbahnen)
CD (České Dráhy)
DB AG (Deutsche Bahn AG)
DSB (Danske Statsbaner)
RENFE (Red Nacional de los Ferrocarriles Españoles)
VR (VR-Group Ltd)SNCF (Société Nationale des Chemins de fer Français)OSE (Hellenic Railway Organization)
GySEV/RÖEE (Györ-Sopron-Ebenfurti Vasút Részvénytarsasag)
MAV (Magyar Államvasutak Rt.)
CIE (Coras Iompair Éireann)FS (Ferrovie dello Stato SpA)JR (JR Group)
KOREAIL (Korean National Railroad)
CFL (Société Nationale des Chemins de fer Luxembourgeois)
NS (N. V. Nederlandse Spoorwegen)
NSB (Norges Statsbaner AS)
PKP (Polskie Koleje Państwowe S. A.)
CP (Caminhos de Ferro Portugueses, E. P)
SJ (Statens Jämvägar AB)
ZSSK (Slovak Rail)TCDD (Türkiye Cumhuriyeti Deviet Demiryollari Isletmesi)

Austria
Belgium
Switzerland
Switzerland
Czech Rep.
Germany
Denmark
Spain
FinlandFranceGreeceHungary
Hungary
Ireland
Italy
Japan
South Korea
Luxembourg
Netherlands
NorwayPolandPortugal
SwedenSlovakia
TurkeyVertical
Separation
1997~
2005~
1995~
1997~
2007~
1998~
1996~
1997~
1988~
2002~
-

Functional
Separation
2003~
2003~
2001~
2006~
1987~
2007~
2000~
2002~
2002~
2005~
-

5.2 Main Data Source and Definition of Variable
5

There are some studies on classifications of structural reforms (e.g. ECMT (1998, 2001) and Kurosaki(2008, 2009). In this study, we focus on the effect of the organizational separation. And the standard
data are well organized from the data set by the UIC. Therefore, we follow the definition of the UIC.

11

The main data source for this study is International Railway Statistics, annually issued by
the UIC, in which, however, some railway organizations’ data is incomplete, so that we werecompelled to supply missing data from several other sources.

Table 2 shows our main data sources.

Table 2 Major Data Sources for Our Study
Items
Costs, Output measures, Wage,
Number of employees, Rollingstock, Route length etc.

Exchange rate
GDP deflator

Source(1) International Railway Statistics by the UIC(2) Jane’s World Railways(3) Annual reports by each individual railway organization(4) Danish Ministry of Transport for missing data of DSB and
BDK(5) Annual Railway Statistics for JR
Eurostat(1) World Development Indicators by the World Bank
(2) Economic Outlook 83 Database by OECD

Before we explain the definition of variables, we must explain the treatment of total cost in
the structurally separated organization.

In this study, we analyze the structural separation effect on

the cost structure.

In the case of structurally separated companies, we combine these organizations,as Table 3 shows.

It is worth noting that input factors such as labor and rolling stock are also

combined in cases where organizations are combined.
Table 3 Total Costs in the Structurally Separated Organization
StructureTypeofrailwayorganization
Vertical integration

Verticalstructure

Vertical separation
Functional
structure

Functional integration
Functional separation

Definition of total costs
Vertically integrated company’s total
cost
Operation company’s total cost +Infrastructure company’s total cost
Functionally integrated company’s total
cost
Passenger company’s total cost + Freightcompany’s total cost

Structuralvariable
DVS =0dummy

DVS =1DFS =0
DFS =1(Note):(1) DVS : vertical separation dummy, DFS : functional separation dummy

Table 4 shows the definition of all variables used for the estimation of total cost function.
First, total costs (TC) in this study are defined as the sum of labor, energy, material costs and capitalcosts.

Service costs for the rail organization whose infrastructure service is separated from rail

operation are included in the total costs.
As for output measure, we use the total number of train kilometers (Q) for both passenger
services and freight services.

In order to avoid estimation bias based on different kinds of output,

we also include three kinds of variables of output characteristics: passenger revenue share (HPR),load factor of passenger service (HLF) and passenger travel length (HPTL).
12First, passenger revenue

share is defined as the ratio of passenger service revenue to total rail service revenues.

Second,passenger load factor is defined as the ratio of the number of passengers per train to the designatedcapacity of a passenger vehicle.

The designated capacity of a passenger vehicle is calculated bymultiplying the number of vehicles per train by the number of seats per passenger vehicle. The
number of passengers per train is obtained by dividing revenue passenger kilometers by passenger
train kilometers.

Third, passenger travel length is measured as the ratio of revenue passenger

kilometers to the total number of passengers.

As we explained before, these output measures andoutput characteristics measures are specified as a hedonic function.
Table 4 Definition of Variables Used for the Estimation of Cost Function
Variable

Definition

Unit

Mean

Standard
Minimum
Deviation

MaximumTC
Sum of labor, energy
million
4,572
7,637
78
37,148(Total cost)energy and capital costeuro
thousandQ
Total train-km(1)167,031 227,878
1,747
936,714km(Output)wL
Labor costs per employee
euro35,277
20,193
4,203
100,731(Wage)
wK
Material and capital costs per
euro
213,689 190,915
11,295
919,693(Material and capital price) composite material index(2)N
Total route km
km
8,524
9,197
220
38,450(Total route length)
T1
Percentage of electrified line
%
53.91
27.12
0.01
100.00(Technology index 1)
T2
Time trend (Year 1997=1)
6.000
3.168
1.000
11.000(Technology index 2)
Share of passenger revenue to
HPRS
0.58120.23990.0541
0.9677(Passenger revenue share)
total revenue(3)HLF
Passenger per train
0.36610.14240.12640.9355to capacity(4)(Load factor of passenger)
HPTL
Revenue passenger-km per
km
54.46
31.45
14.64
190.21(Passenger travel length)
passenger
Train-km per route length perV
Train/day 55.4475 32.3542
12.2569
142.6948(Train density)
day
DVS
Vertical separation dummy
0.33090.47140.00001.0000(Vertical separation = 1)
(Vertical separation)
DFS
Functional separation dummy
0.14180.34950.00001.0000(Functional separation)(Functional separation = 1)
SL
Share of labor
0.39370.13200.11550.7434(Share of labor)
input expenditure
SK
Share of material and
0.60630.13200.25660.8845
capital expenditure(Share of material etc)
(Note):(1) Total train-km (Q) = passenger train-km + freight train-km(2) Composite material index (M) = 0.28 * rolling stock + 0.72* total route lengths(3) Passenger revenue share(HPRS) = Passenger service turnover / Passenger and freight service turnover(4) Load factor of passenger (HLF) = Passengers per train / Capacity
Where Capacity = Number of wagons per train * Number of seats per passenger wagon
Number of wagons per train = Passenger gross hauled ton-km / Passenger train-km / 50 ton *1000
Passengers per train = Revenue passenger-km / Passenger train-km * 1000
(5) Train density (V) = train-km/route-km/365

13

There are two kinds of input factor prices.
labor costs by the total number of employees.

First, labor price (wL) is obtained by dividing

Material and capital price (wK) is obtained by

dividing material and capital costs by the composite material index. The composite material index
is the weighted share of rolling stock and route length.

In this study, we assume that the rollingstock’s weight is 28% and the route length’s weight is 72%.
data we are unable to include energy prices.

We should note that due to a lack ofThese are included in the price of material and capital.

As for the network variable, we include the total route length (N). We consider two kindsof technology (T).

First, in determining which variables to use, we considered possible proxyvariables that would show technological progress, such as the percentage of ATS or ATC, electrified
line length.

In this study, we define technology as the percentage of electrified lines (T1).

Although we considered using the ratio of ATS or ATC as variables, we were forced to forgo theiruse due to a lack of data availability.

Alternatively, technology is used as a measure of time trends(T2), in which the year 1997 is equal to one.

In this specification, all railway organizations canprogress technologically in a linear fashion and can obtain technology on an equal basis.
Train density (V) is obtained by dividing the number of train-km by the total route length ofa railway. This variable is measured as per-day so that it is divided by 365.

This variable is usedwith the vertical separation dummy in order to measure the multiple effects of vertical separation andtrain density, as explained above.
Finally, two kinds of structural dummy variables are defined.

First, the vertical separation

dummy (DVS) is defined as a binary measure, in which the vertically separated railway company isequal to one but is otherwise zero.
binary measure.

The functional separation dummy (DVS) is also defined as a

If a railway company’s passenger and freight services are separated, this measureis equal to one but is otherwise zero.

5.3 Empirical Results
We estimate the total cost function shown in equation-(1) and (2) with equation-(3).

Forour estimation, we use the seeming unrelated regression (SUR) method by the total cost function andinput share equations.

The estimation results of the total cost function are summarized in Table 5.

We show four cases in Table 5: two kinds of technology variable (i.e. percentage of electrified line ortime trend) and whether the train density is considered in the vertical separation dummy (i.e.
with/without the cross-term of train density and vertical separation dummy).
The goodness-of-fit in the regressions is acceptably high for these models because pseudo R2
are very high. As for the required properties in the cost function, first, symmetry and homogeneity
conditions in input factor prices are satisfied, because we imposed restrictions on the cost model.
Second, as for monotonicity conditions, it is necessary that the cost function be a non-monotone
decreasing function in both output and input factor prices.
14

Whether or not the monotonicity

conditions are satisfied was evaluated by checking that the partial derivative of the cost function
with respect to output and input factor prices is not negative (i.e. ∂lnC/∂lnY > 0 , ∂lnC/∂lnwj > 0).
Around the sample mean, these conditions are satisfied.

Determining whether or not the Hessian

matrix holds negative semi-definite can test for the concavity condition in input factor prices.

In

our test results, 93.45% of observations satisfy the concavity condition. Among these cases, Case 3
is the best based on the log likelihood statistics. Therefore, we conclude that these cases are all
acceptable but Case 3 is the best.
Table 5 Estimation Results of the Total Cost Function
Variable
Y
HPRS
HLF
HPTL
wL
wK
N
T1
T2
Y･Y
N･N
wL･wL
wL･wK
wK･wK
Y･wL
Y･wK
Y･N
Y･T1

Case 1Case 2
Case 3
Case 4
Variable
Case 1Case 2
Case 3
Case 4***-0.0041
Y･T2
0.00000.5743*** 0.6072*** 0.5541*** 0.5253(0.0063)(0.0028)(0.0461) (0.0637) (0.0493) (0.0587)
***
***
wL･N
-0.5556*** -0.7487*** -0.5860*** -0.7973-0.1338*** -0.0988*** -0.1332*** -0.0946(0.0131) (0.0132) (0.0137) (0.0128)(0.0531) (0.0516) (0.0556) (0.0579)***
wL･T1 -0.0347***-0.2701*** -0.2173** -0.2989*** -0.3194-0.0330***(0.0776) (0.0905) (0.0855) (0.0997)(0.0054)(0.0051)***-0.0077***
wL･T2
0.2289*** 0.3141*** 0.2817*** 0.3291-0.0079***(0.0019)(0.0478) (0.0496) (0.0515) (0.0529)(0.0019)
***
***
wK･N
0.3681*** 0.4161*** 0.3684*** 0.4152
0.1338*** 0.0988*** 0.1332*** 0.0946(0.0065) (0.0134) (0.0068) (0.0136)(0.0131) (0.0132) (0.0137) (0.0128)
***
wK･T1
0.6319*** 0.5839*** 0.6316*** 0.5848
0.0347***0.0330***(0.0065) (0.0134) (0.0068) (0.0136)(0.0054)(0.0051)***
0.0077***
wK･T2
0.3791*** 0.3508*** 0.3981*** 0.4404
0.0079***(0.0019)(0.0464) (0.0630) (0.0492) (0.0590)(0.0019)
N･T1-0.2551***-0.2794***-0.1237***-0.2436***(0.0521)(0.0868)(0.0529)(0.0127)0.0003-0.0145
N･T2-0.0138-0.0792***(0.0065)(0.0164)(0.0125)(0.0172)*
T1･T1
0.4090***-0.0090-0.3680** -0.1738* -0.2127 -0.1270(0.1478) (0.0895) (0.1470) (0.0762)(0.1467)(0.0089)***0.0006
T2･T2
0.2570***0.1237*** 0.0792*** -0.4278*** -0.2895(0.0026)(0.0127) (0.0125) (0.1608) (0.0872)(0.0896)
***
***
DVS
0.1528*** 0.1556*** 0.1514*** 0.1550-0.2051*** -0.2013*** -0.1849*** -0.1644(0.0104) (0.0109) (0.0109) (0.0112)(0.0375) (0.0359) (0.0377) (0.0370)
***
***
V･DVS
-0.1528*** -0.1556*** -0.1514*** -0.1550
0.1378*** 0.2199(0.0104) (0.0109) (0.0109) (0.0112)(0.0523) (0.0451)
***
***
DFS
0.1528*** 0.1556*** 0.1514*** 0.1550-0.2246*** -0.2454*** -0.2654*** -0.3009(0.0104) (0.0109) (0.0109) (0.0112)(0.0435) (0.0445) (0.0438) (0.0437)******
Constant 8.6248*** 8.6724*** 8.6208*** 8.6701
0.0948-0.0058 0.1204*** 0.0740(0.0597) (0.0070) (0.0132) (0.0121)(0.0260) (0.0500) (0.0249) (0.0476)***
Log of-0.5362*** -0.3785*** -0.1204*** -0.0740
44.8827 31.2916 50.8841 48.5389(0.1629) (0.0996) (0.0132) (0.0121) likelihood****0.1895-0.30290.00170.28080.97630.97950.9791
Pseudo R2 0.9786(0.0856) (0.0072) (0.1455) (0.0773)**-0.0205
0.0428Satisfied
93.45%
93.45%
93.45%(0.0087)(0.0607)
concavity 93.45%
condition(Note):(1) *** Significant at 1 percent, ** 5 percent, * 10 percent.
(2) Number of observations: 275

15

We evaluate the effects of structural reform based on empirical results.

First, because thecoefficients of the functional separation dummy (DFS) in any case of our analysis show the negative
sign with a statistical significance of 1%, it seems clear that companies can reduce cost when theyfunctionally separate passenger and freight services.

In fact, Kim (1987) finds that there are

diseconomies of scope between passenger and freight service.

If this is true, a functional separation

policy is advisable.
Second, as for vertical separation, in general, vertical separation tends to reduce the costs ofrailways, as the coefficient of the vertical separation dummy (DVS) shows only the negative sign.
These have a statistical significance of 1%.

However, the cross-tem with train density (V・DVS)

shows the positive sign with a significance of 1%. From these results, it can be seen that thevertical separation effects with lower train density tend to reduce the total costs of a railway
organization.

But as train density increases, vertical separation causes an increase in a railway’s

total costs.
Our results could explain why previous studies have produced differing results.

In the caseof lower train density, our result supports that vertical separation contributes to cost reduction in therailway industry, which is consistent with studies by Shires et al., Kim and Kim (2001), Ivaldi andMcCullough (2001) and Friebel et al. (2010).

However, in the case of higher train density, ourresult shows that the vertical separation causes an increase in costs, which is consistent with studies
such as Cantos Sanchez (2001), Bitzan (2003), Jensen and Stelling (2007), and Growitsch andWetzel (2009).
Why does this kind of result appear?
Williamson (1985) and Preston (2002).

We can explain by using the framework of

In the case of lower train density, as trains are operated on

tracks, the coordination cost is low between the operating company and the infrastructure company.
Therefore, production costs can be saved by specializing in activities (i.e. train operation andinfrastructure management).

On the other hand, in the case of higher train density, coordination

between the train operating company and the infrastructure management company is expensive
because there are necessarily a lot of costs for such things as meeting for maintenance scheduling,
maintaining safety under busy train operation, and so on.

Therefore, any costs saved by verticalseparation specialization would be canceled out by high coordination costs between two different
organizations.
In our calculation, if train density reaches about 2.11 to 3.83 times the sample mean, verticalseparation starts to increase the costs, as Figure 2 shows. Among our observations in 2007, railway
organizations with the higher train density are BLS (Switzerland), SBB CFF FFS (Switzerland), JR(Japan), KOREAIL (South Korea) and NS (Netherlands).6
6

Except for NS, a vertical separation

The railway organization with the higher train density is a railway organization whose value of train
density (V) is 1.7 times the value of the sample mean.

16

policy has not been taken in these rail organizations.

Figure 2 Cost Reduction Rate by The Degree of Train Density

6 Conclusion
Regulatory reforms, including privatization and deregulation in the rail industry, havebeen carried out in many countries, each with its own regulation and competition policies.
Especially noticeable is that while vertical separation is common in Western Europe, vertical
integration is still standard in East Asia and Eastern Europe.

The main purpose of this study has

been to analyze structural separation policy, especially vertical (i.e. operation-infrastructure)
separation and functional (i.e. passenger-freight service) separation.

By using the total costfunction of a railway organization, we evaluate whether or not vertical separation and/or functional
separation could reduce its costs.

We selected 25 railway organizations from 23 OECD countries

for the 11 years between 1997 and 2007.
Our main findings are as follows.

First, because the coefficients of the functional

separation dummy in any case of our analysis show the negative sign with a statistical significanceof 1%, functional separation appears to lower a railway’s costs.

Because of diseconomies of scope

between passenger service and freight service, functional separation is a better policy than thealternative. Second, as for vertical separation, in general, vertical separation tends to reduce thecosts of railways, as the coefficient of the vertical separation dummy shows only the negative sign.
17However, the cross-term with train density (V・DVS) shows the positive sign with significance of 1%.
From these results, the overall vertical separation effects with lower train density tend to reduce thetotal cost of a railway organization.

But as train density increases, the vertical separation causes

total costs to increase.
In conclusion, our results regarding vertical separation show that the effects on cost
reduction depend on the magnitude of train density. If a rail organization has lower train density,
the vertical separation policy is reasonable.

However, a rail organization with higher train density

should take a vertical integration policy.

[2010.9.21 998]
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