2010-30
Effects of Regulatory and Competition Policy on Performance:
An Empirical Analysis of OECD Members’ Rail Industries
Fumitoshi Mizutani Shuji Uranishi

Effects of Regulatory and Competition Policy on Performance:
An Empirical Analysis of OECD Members’ Rail Industries*

Fumitoshi Mizutani (Kobe University)
Shuji Uranishi (Fukuyama Heisei University)[Abstract]: The main purpose of this study is to analyze structural separation policies,
especially vertical (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 not vertical separation and/or functional separation can reduce costs.

For thisanalysis, we select 25 railway organizations from 23 OECD countries over the 11 years from1997 to 2007.

Our findings show that because the functional separation dummy has a negative

sign with statistical significance, functional separation can reduce the cost of a railway.

The

vertical separation dummy generally shows a negative sign, indicating that vertical separation
tends to reduce rail costs, but some results show that the vertical separation dummy is notstatistically significant.
[JEL Classification]: L23, L33, L51, L92, R48[Key Words]: Vertical Separation, Functional Separation, Total Cost Function, Railway

1 Introduction
Since the Japan National Railway (JNR) was privatized and subdivided into 6
passenger JR companies and 1 nationwide freight JR company in 1987, the privatization andregulation of railways have been carried out in many countries, each according to its own
railway regulation and competition policies.

For example, while vertical separation (i.e.

*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 Research Centre of the OECD and the International Transport Forum), Siebe
Riedstra (Dutch Ministry of Infrastructure and Transport), and Bastin Zibrandtsen (Danish Ministry ofTransport).

1

operation-infrastructure separation) is a common policy in the European Union, vertical
integration is still the structure of choice in the Japanese rail industry1.

Massive horizontal

separation of the former state railway was adopted in the UK and Japan, but in some countries
the descendent organization of the former state railway has a vital role in the market.

As forthe ownership structure, a commercial 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 the model in an empirical study of the Swedish railway sector, finds that external
competitive pressure is strong in most supply segments.
different ways in different countries.

However, competition is handled in

For example, direct competition in the railway market, inwhich 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.

There are a wide variety of railway regulations and competition policies, withmuch empirical and descriptive research having been carried out on individual countries’
railway regulations.

Although some studies such as Lodge (2002) explore the notion ofregulatory failure in the railway domain by taking an analytical and a comparative perspective,
there are few studies analyzing regulatory and competition policies across the internationalboard.

While some studies such as Oum and Yu (1994) and Lan and Lin (2006) analyze a railorganization’s performance by using a cross-sectional data set, these studies do not focus on
regulatory policy.

Oum and Yu (1994) undertake an international 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.
This study focuses mainly on the structural separation policy, the most controversial
among various regulatory policies in the rail industry, with our main purpose being to analyze
how vertical separation policy affects each individual rail operator’s performance.
performance measures, we pay special attention to cost structure changes.

Among

There already exist

many empirical studies on vertical separation’s effect on cost, but their results vary, with somestudies supporting the idea that vertical separation improves efficiency (e.g. Shires et al., Kim
and Kim (2001), Ivaldi and McCullough (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 suchdiffering, inconclusive results, the separation issue needs further analysis.
1

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

2

This paper consists of five sections after the introduction.

In the first section, wesummarize the previous literature, including both theoretical and empirical studies related to thevertical separation policy. In the second section, we explain the empirical cost model for thisanalysis.

The cost model is specified as translog total cost function.

In the cost model, bothvertical separation dummy and functional separation dummy variables are included.
Furthermore, in order to control the output qualities, hedonic specification for output measures
is used.

In the third section, we explain the sample selection and data.

Rail operators andinfrastructure managers in the OECD countries for the years 1997 to 2007 are chosen.

Themain data source is a compilation of railway statistics issued by the International Union ofRailways (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.

In the fourth

section, by using empirical results, we evaluate whether or not vertical separation and functional
separation can reduce the cost of railways.

Last, we outline important points garnered fromthis analysis.

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

In this section, we will give an overview of studies about the theoreticaleffects of vertical separation, such as Nash (1997), Preston (2002), Ksoll (2004) and Drew
(2009).

Other studies, such as Nash and Rivera-Trujillo (2004) and Di Pietrantonio andPelkmans (2004), summarize and discuss regulatory reforms, including vertical separation, in
the EU railway industry.
developing countries.

Pittman (2003, 2005) discusses structural separation with a focus on

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 avariety of operators; (2) clarification of intra-industry relationships; (3) specialization in both
operation and infrastructure.

On the other hand, he notes that separation has adverse effects on(1) pricing and performance; (2) timetabling and slot allocation; (3) investment; (4) safety; and2

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 theprivatized railways in Britain. Their results suggest that unbundling and competition for franchises
combined with commercial objectives can provide strong incentives towards better performance, as is thecase for investment 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 suitablenon-linear access prices investment incentives under separation become identical to those underintegration.

3

integrated information and ticketing.
Preston (2002) applies Oliver Williamson’s framework of transaction economics to therailway industry, in which he considers this issue as a trade-off between market governance,
which implies vertical separation, and bureaucratic internal governance, which implies vertical
integration. He evaluates the advantages and disadvantages of the operation-infrastructure
separation.
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 infrastructure provision within an integrated firm ensures higher productivity levels andmarket driven allocation; (4) integration yields cost savings and synergies in shared facilities
and services; (5) co-existence of integration and competition drives technological and productinnovation; (6) embracing staff identification and responsibility increases quality and safety; (7)partial avoidance of double marginalization increases consumer welfare; (8) strategic behaviorof the integrated firm can counteract excessive entry.

On the other hand, there are four

disadvantages for vertical integrations: (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 alongwith lower and/or misguided performance incentives in internal compared to fully externaltransactions.
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
other operators with open access to the network.

He concludes that vertical separation benefits

freight customers more than just open access does.
There exists no definitive theoretical study of vertical separation in the railway
industry.

However, if we review existing literature from a theoretical point of view, we mustsay that it is uncertain as to 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.

Somestudies such as Shires et al., Kim and Kim (2001), and Ivaldi and McCullough (2001) show thatvertical separation 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 the4gradual introduction of tendering (Preston, 2002, p.12).
Kim and Kim (2001) analyze the cost structure of Seoul’s subway systems by using
the stochastic frontier cost function.

Their calculation results show that the total cost of thevertically separated 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.
between operations and infrastructure.

They find there are no cost complementarities

This result implies that at all levels of output

characterizing freight rail operations in the U. S., there may be no inherent technologicaladvantages 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 inorder to analyze 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 tovertical integration.
Cantos Sánchez (2001) analyzes the vertical relationship by applying translog total
cost function for a data set of 12 European state railways for the period 1973 – 1990.

He

obtains the result that there are complementary effects between the costs deriving from freighttransport and infrastructure, while the effects between the costs deriving from passenger
transport and the infrastructure are substitute.

He concludes that infrastructure and operations

must be coordinated both in order to maintain the coordination effect and to avoid possible
inefficiencies.
Bitzan (2003) examines the cost implications of competition over existing US freightrail lines by testing for the condition of cost subadditivity.

He finds that there are economies

associated with vertically integrated roadway maintenance and transport.

This result suggeststhat vertical separation 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 effectof vertical 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 from2000 to 2004.

From their analysis, they conclude that for a majority of European railways,
5

there exist economies of scope.
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 reducecost.

The results indicate that vertically separated systems might not be significantly different

from vertically integrated ones.
Thus, previous empirical analysis has produced inconsistent results.

It remains

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

3 Empirical Cost ModelIn this study we employ a translog total cost function,3 in which we include two kindsof institutional variables: a vertical separation dummy (Dvs) and a functional separation dummy(Dfs).

The translog cost model used here is shown as follows:2

lnTC =0 +Y lnY + jjlnwj + NlnN + T T + (1/2)YY (lnY) + jYj (lnY)(lnwj) +YN (lnY)(lnN) + YT (lnY)(T) + (1/2) kjjk (lnwj) (lnwk) +jjN (lnwj) (lnN) + jjT (lnwj) (T) + (1/2) NN (lnN)2+ NT (lnN)(T) +2(1/2)TT T + VSDVS + FSDFS
lnY = lnQ + f f ln Hf,
where

(1)
(2)

TC: total cost
Y: output measure
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),
DVS: vertical separation dummy (vertical separation =1, otherwise = 0),
DFS: functional separation dummy (functional separation = 1, otherwise = 0).

3

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.
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).

6

In this model, we impose the restriction on input factor prices such that jj = 1, kjk =0, jjN = 0, jjT = 0, jYj = 0, jk = kj, jN = Nj, jT = Tj. Furthermore, we apply Shephard’s
Lemma to the total cost function.

Then we can obtain the input share equations as follows:

Sj = j + Yj (lnY) + kjk (lnwk) + jN (lnN) + jT (T),
where

(3)

Sj: input j’s share of total cost.

As for the estimation technique, we apply the full information maximum likelihood(FIML) 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.

4 Sample Selection and Data
4.1 Sample Selection
The main purpose of this study is to examine how differences in structural reform
affect cost structure; that is, we evaluate how differences in unbundling methods, such asvertical separation and functional separation, affect cost difference.

In order to evaluate thestructural factor only, we selected railway organizations with relatively similar conditions.

Asa sample selection, we chose railway organizations from OECD countries, excluding those ofOECD 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, forexample that of Smith (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, wecollected data on 25 railway 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 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 since1997.

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 an integrated system.
7

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~
-

4.2 Main Data Source and Definition of Variable
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
SourceCosts, Output measures, Wage, (1) International Railway Statistics by the UIC
Number of employees, Rolling (2) Jane’s World Railways
stock, Route length etc.
(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
Exchange rate
Eurostat
GDP deflator(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.
8

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 aslabor and rolling stock are also combined in cases where organizations are combined.
Table 3 Total Costs in the Structurally Separated Organization
StructureTypeofrailway Definition of total costs
Structuralorganization
variable
VerticalVertical integration
Vertically integrated company’s total DVS =0
structure
cost
Vertical separation
Operation company’s total cost + DVS =1
Infrastructure company’s total cost
Functional Functional integration Functionally integrated company’s DFS =0
structure
total cost
Functional separation
Passenger company’s total cost + DFS =1
Freight company’s total cost
(Note):(1) DVS : vertical separation dummy, DFS : functional separation dummy

dummy

Table 4 shows the definition of all variables used for the estimation of total costfunction.

First, total costs (TC) in this study are defined as the sum of labor, energy, material

costs and capital costs.

Service costs for the rail organization whose infrastructure service isseparated from rail operation are included in the total costs.
As for output measure, we use the total number of train kilometers (Q) for bothpassenger 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).
First, 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 ofpassengers per train to the designated capacity of a passenger vehicle.

The designated capacityof a passenger vehicle is calculated by multiplying 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 ofpassengers.

As we explained before, these output measures and output characteristics

measures are specified as a hedonic function.
There are two kinds of input factor prices.

First, labor price (wL) is obtained by

dividing labor costs by the total number of employees.

Material and capital price (wK) is

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

In this study,

we assume that the rolling stock’s weight is 28% and the route length’s weight is 72%.
9The

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
Q
thousand
Total train-km(1)167,031 227,878
1,747
936,714(Output)
km
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)
HPRS
Share of passenger revenue to0.58120.23990.0541
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
DVS
Vertical separation dummy
0.33090.47140.00001.0000(Vertical separation)(Vertical separation = 1)
DFSFunctional separation dummy
0.14180.34950.00001.0000(Functional separation = 1)
(Functional separation)SL
Share of labor
0.39370.13200.11550.7434(Share of labor)
input expenditure
SK
Share of material and
0.60630.13200.25660.8845(Share of material etc)
capital expenditure(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

As for the network variable, we include the total route length (N).

We consider two

kinds of technology (T). First, in determining which variables to use, we considered possible
proxy variables that would show technological progress, such as the percentage of ATS or ATC,electrified line length.
(T1).

In this study, we define technology as the percentage of electrified lines

Although we considered using the ratio of ATS or ATC as variables, we were forced toforgo their use due to a lack of data availability. Alternatively, technology is used as a measureof time trends (T2), in which the year 1997 is equal to one.

In this specification, all railway

organizations can progress technologically in a linear fashion and can obtain technology on an
equal basis.
Finally, two kinds of structural dummy variables are defined.

First, the verticalseparation dummy (DVS) is defined as a binary measure, in which the vertically separated
10

railway company is equal to one but otherwise is zero.
(DVS) is also defined as a binary measure.

The functional separation dummy

If a railway company’s passenger and freight

services are separated, this measure is equal to one but otherwise is zero.

5 Empirical Results
We estimate the total cost function shown in equation-(1) and (2) with equation-(3).
For our estimation, we use the full information maximum likelihood (FIML) method by the total
cost function and input share equations.

The estimation results of the total cost function aresummarized in Table 5.
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 costfunction be a non-monotone decreasing function in both output and input factor prices.
Whether or not the monotonicity conditions are satisfied was evaluated by checking that thepartial derivative of the cost function with respect to output and input factor prices is notnegative (i.e. lnC/lnY > 0 , lnC/lnwj > 0).
satisfied.

Around the sample mean, these conditions are

Determining whether or not the Hessian matrix holds negative semi-definite can test

for the concavity condition in input factor prices.
observations satisfy the concavity condition.

In our test results, 94.55 to 96.73% of

Therefore, we conclude that it is acceptable touse this cost model.
We evaluate the effects of structural reform based on empirical results.

First, because

the coefficients of the functional separation dummy (DFS) in any case of our analysis show thenegative sign with a statistical significance of 1%, it seems clear that companies can reduce costwhen they functionally 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, afunctional separation policy is advisable.
Second, as for vertical separation, in general, vertical separation tends to reduce the costsof railways, as the coefficient of the vertical separation dummy (DVS) shows the negative sign.
However, this does not always hold.

In a case where we take time trend as technology variable,

the coefficient of the vertical separation dummy is not statistically significant at 10%, although
the sign of the coefficient is negative.

Therefore, vertical separation would be slightly better

than vertical integration in terms of cost reduction.
Last, as Case 3 and Case 4 show, the coefficient of the cross effect of these separations(DVS ･ DFS) is positive.

Therefore, the cost reducing effect by each separation gradually
11becomes smaller because of the other separation policy.
Our results support that vertical separation contributes to cost reduction in the railway
industry, which is consistent with studies by Shires et al., Kim and Kim (2001), Ivaldi andMcCullough (2001) and Friebel et al. (2010) but inconsitent with studies such as Cantos
Sanchez (2001), Bitzan (2003), Jensen and Stelling (2007), and Growitsch and Wetzel (2009).
Unlike many previous studies, ours includes observations from former eastern European
countries’ state railways, whose less densely operated train systems may have made it costadvantageous for them to separate infrastructure management from train operation.
Table 5 Estimation Results of the Total Cost Function
Variable
Case 1Case 2
Case 3
Case 4
Variable
Case 1Case 2
Case 3
Case 4-0.0029
Y･T2
Y
0.5866*** 0.6345*** 0.5341*** 0.5463***-0.0082(0.0108)(0.0826) (0.1143) (0.0657) (0.1033)(0.0112)HPRS -0.5622*** -0.7847*** -0.5612*** -0.7716*** wL･N -0.1310*** -0.0909*** -0.1201*** -0.0817***(0.0680) (0.0643) (0.0663) (0.0673)(0.0171) (0.0199) (0.0159) (0.0189)
HLF-0.2590*** -0.1911* -0.3689*** -0.3097** wL･T1 -0.0355***-0.0373***(0.0938) (0.1135) (0.0851) (0.1219)(0.0050)(0.0052)-0.0075***
HPTL
0.2276*** 0.3032*** 0.1331** 0.2356*** wL･T2-0.0079***(0.0022)(0.0650) (0.0842) (0.0677) (0.0898)(0.0022)wL
0.3686*** 0.4186*** 0.3674*** 0.4165*** wK･N 0.1310*** 0.0909*** 0.1201*** 0.0817***(0.0085) (0.0197) (0.0084) (0.0195)(0.0171) (0.0199) (0.0159) (0.0189)
wK
0.6314*** 0.5814*** 0.6326*** 0.5835*** wK･T1 0.0355***
0.0373***(0.0085) (0.0197) (0.0084) (0.0195)(0.0050)(0.0052)
0.0075***N
0.3647*** 0.3237*** 0.4429*** 0.4201*** wK･T2
0.0079***(0.0022)(0.0766) (0.1038) (0.0575) (0.0890)(0.0022)
N･T1
T1-0.2659***-0.2732***-0.2524**-0.1728(0.0919)(0.0905)(0.1267)(0.1131)-0.0187-0.0014
N･T2
T2-0.0161
0.0038(0.0184)(0.0098)(0.0189)(0.0104)-0.1070
Y･Y
T1･T1-0.0160-0.0210-0.3086-0.1626-0.1998(0.0217)(0.0199)(0.2128) (0.1566) (0.2030) (0.1385)
0.0005N･N
T2･T2
0.0000-0.4641** -0.3494*** -0.1677 -0.1978*(0.0028)(0.0029)(0.1973) (0.1310) (0.1976) (0.1186)
wL･wL 0.1481*** 0.1435*** 0.1446*** 0.1390***
DVS-0.1713** -0.0973 -0.2031** -0.1269(0.0125) (0.0141) (0.0121) (0.0139)(0.0859) (0.1000) (0.0900) (0.0925)
wL･wK -0.1481*** -0.1435*** -0.1446*** -0.1390***DFS-0.2201*** -0.2310*** -0.5866*** -0.4623***(0.0125) (0.0141) (0.0121) (0.0139)(0.0581) (0.0678) (0.01087) (0.1188)
wK･wK 0.1481*** 0.1435*** 0.1446*** 0.1390*** DVS･DFS
0.4552*** 0.2962***(0.0125) (0.0141) (0.0121) (0.0139)(0.1310) (0.1358)
Y･wL 0.1220*** 0.0738*** 0.1140*** 0.0654*** Constant 8.6059*** 8.6450*** 8.5807*** 8.6421***(0.0395) (0.0664) (0.0360) (0.0624)(0.0153) (0.0175) (0.0142) (0.0163)
40.8750 16.4281 47.1361 15.1197
Y･wK -0.1220*** -0.0738*** -0.1140*** -0.0654*** Log of(0.0153) (0.0175) (0.0142) (0.0163) likelihood
0.1352 Pseudo R 0.9779
0.9733
Y･N
0.3456* 0.2400*
0.1496
0.97350.9789(0.1901) (0.1338) (0.1875) (0.1179) squared
Y･T1
0.06470.0324
Satisfied
96.73%
96.00%
96.73%(0.1012)(0.1005)
concavity 94.55%
condition(Note):(1) *** Significant at 1 percent, ** 5 percent, * 10 percent.
(2) Number of observations: 275

12

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 shows the negative sign with a statisticalsignificance of 1%, functional separation appears to lower a railway’s costs.

Because of

diseconomies of scope between passenger service and freight service, functional separation is abetter policy than the alternative.

Second, in general, vertical separation tends to reduce thecosts of railways, but this does not always hold. When we take time trend as a technology
variable, the coefficient of the vertical separation dummy is not statistically significant at 10%,although the sign of the coefficient shows the negative.

Therefore, vertical separation would

be slightly better than vertical integration in terms of cost reduction.

Last, the relationshipbetween vertical separation and functional separation is complementary, because the coefficientof the cross-effect of these variables shows the positive sign.
In conclusion, our results regarding vertical separation support studies such as Shires
et al., Kim and Kim (2001), Ivaldi and McCullough (2001), but the effect is still weak.

In afuture study, in which we hope to obtain more conclusive results, we must distinguish noise
factors from structural factors (i.e. vertical separation).
[2010.4.30 980]
References
Affuso, L. and D. Newbery (2002) “The Impact of Structural and Contractual Arrangements on
a Vertically Separated Railway,” The Economic and Social Review, Vol.33, No.1, pp.83-92.
Bitzan, J. D. (2003) “Railroad Costs and Competition: The Implications of Introducing
Competition to Railroad Networks,” Journal of Transport Economics and Policy, Vol.37,
No.2, pp.201-225.
Buehler, S., A. Schmutzler and M. Benz (2004) “Infrastructure Quality in Deregulated
13

Industries: Is There an Underinvestment Problem?” International Journal of Industrial
Organization, Vol.22, No.2, pp.253-267.
Cantos Sánchez, P. (2001) “Vertical Relationships for the European Railway Industry,”
Transport Policy, Vol.8, No.2, pp.77-83.
Di Pietrantonio, L. and J. Pelkmans (2004) The Economics of EU Railway Reform, Bruges
European Economic Policy Briefings, College of Europe, The Hague, Holland.
Drew, J. (2009) “The Benefits for Rail Freight Customers of Vertical Separation and Open
Access,” Transport Reviews, Vol.29, No.2, pp.223-237.
ECMT (1998) Rail Restructuring in Europe, OECD, Paris, France.
ECMT (2001) Railway Reform: Regulation of Freight Transport Markets, OECD, Paris, France.
ECMT (2005) Railway Reform and Charges for the Use of Infrastructure, OECD, Paris, France.
Friebel, G., M. Ivaldi and C. Vibes (2010) “Railway (De)Regulation: A European Efficiency
Comparison,” Economica, Vol.77, No.305, pp.77-99.
Growitsch, C. and H. Wetzel (2009) “Testing for Economies of Scope in European Railways:
An Efficiency Analysis,” Journal of Transport Economics and Policy, Vol.43, No.1,
pp.1-24.
Ivaldi, M. and G. J. McCullough (2001) “Density and Integration Effects on Class I U.S. Freight
Railroads,” Journal of Regulatory Economics, Vol.19, No.2, pp.161-182.
Jensen, A. (1998) “Competition in Railway Monopolies,” Transportation Research-E, Vol.34,
No.4, pp.267-287.
Jensen, A. and P. Stelling (2007) “Economic Impacts of Swedish Railway Deregulation: A
Longitudinal Study,” Transportation Research-E, Vol.43, No. 5, pp.516-534.
Kim, H. Y. (1987) “Economies of Scale and Scope in Multiproduct Firms: Evidence from US
Railroads,” Applied Economics, Vol.19, pp.733-741.
Kim, M. and S. Kim (2001) “An Analysis of the Cost Structure of a Seoul’s Rail Transit
Property: Stochastic Frontier Approach,” A Paper Presented to Ninth World Conference on
Transport Research, Seoul, Korea, July 22-27, 2001.
Ksoll, M. (2004) “Integration of Infrastructure and Transport: An Assessment from Industrial
Economics and Railway Perspectives,” Paper Presented at the 2nd Conference on Railroad
Industry Structure, Competition and Investment, October 8-9, 2004, Evanston, IL, USA.
Lan, L. W. and E. T. J. Lin (2006) “Performance Measurement for Railway Transport:
Stochastic Distance Functions with Inefficiency and Ineffectiveness Effects,” Journal ofTransport Economics and Policy, Vol.40, No.3, pp.383-408.
Lodge, M. (2002) “The Wrong Type of Regulation?

Regulatory Failure and the Railways in

Britain and Germany,” Journal of Public Policy, Vol.22, No.3, pp.271-297.
Mizutani, F. (2004) “Privately Owned Railways’ Cost Function, Organization Size and14

Ownership,” Journal of Regulatory Economics, Vol.25, No.3, pp.297-322.
Mizutani, F. and K. Shoji (2004) “Rail Operation-Infrastructure Separation: the Case of Kobe
Rapid Transit Railway,” Transport Policy, Vol.11, pp.251-263.
Mizutani, F. and S. Uranishi (2007) “The Effects of Privatization on Productivity and Capital
Adjustment,” International Journal of Transport Economics, Vol.34, No.2, pp.197-224.
Nash, C. (1997) “The Separation of Operation from Infrastructure in the Provision of Railway
Services: The British Experience,” in ECMT (ed.) The Separation of Operation from
Infrastructure in the Provision of Railway Service, OECD, Paris, France, pp.53-89.
Nash, C. and C. Rivera-Trujillo (2004) “Rail Regulatory Reform in Europe: Principles andPractice,” Paper Presented at the Conference on Competition in the Rail Industry,
September 2004, Madrid, Spain.
Oum, T. H. and C. Yu (1994) “Economic Efficiency of Railways and Implications for Public
Policy: A Comparative Study of the OECD Countries’ Railways,” Journal of Transport
Economics and Policy, Vol.28, No.2, pp.121-138.
Pittman, R. (2003) “Vertical Restructuring (or Not) of the Infrastructure Sectors of Transition
Economies,” Journal of Industry, Competition and Trade, Vol.3, No.1/2, pp.5-26.
Pittman, R. (2005) “Structural Separation to Create Competition? The Case of Freight
Railways,” Review of Network Economics, Vol.4, No.3, pp.181-196.
Preston, J. (2002) “The Transaction Cost Economics of Railways,” Trasporti Europei, Vol.8,
No.20/21, pp.6-15.
Savage, I. (1997) “Scale Economies in United States Rail Transit Systems,” Transportation
Research: Part A, Vol.31, No.6, pp.459-473.
Smith, A. S. (2006) “Are Britain’s Railways Costing Too Much?: Perspectives Based on TFP
Comparisons with British Rail 1963-2002,” Journal of Transport Economics and Policy,
Vol.40, No.1, pp.1-44.

15