GraSPP-DP-E-08-003 and ITPU-DP-E-08-001

Funding System and Road Transport;
International Comparative Analysis

Katsuhiro Yamaguchi
November 2008

GraSPP Discussion Paper E-08-003

ITPU Discussion Paper E-08-001

GraSPP-DP-E-08-003 and ITPU-DP-E-08-001

Funding System and Road Transport;
International Comparative Analysis
Katsuhiro Yamaguchi
November 2008

Professor, Graduate School of Public Policy
The University of Tokyo
7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
Phone: +81-3-5841-1710
Fax: +81-3-5841-7877
yamaguchi@pp.u-tokyo.ac.jp

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Discussion Papers are a series of manuscripts in their draft form. They are not intended
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the author.

Funding System and Road Transport;
International Comparative Analysis

Katsuhiro Yamaguchi1
Graduate School of Public Policy, The University of Tokyo, Japan
Abstract
Tension between necessity for infrastructure investment and scarcity of public
capital is inevitable in modern motorized nations. When fiscal policy needs to betightened public investment may face deceleration. On the other hand lax funding
scheme may give way to over-investment. After an overview of recent trend in funding
system of road investment this paper highlights institutional differences in five major
nations, USA, UK, France, Germany and Japan, and examines how actual investment isaffected. Between 1990 and 2004, we find that in nations such as USA and Japan, whereroad fund is established, the level of hypothecated fuel tax revenue affects roadinvestment while in other three nations such a relationship is not observed. In USA,
Germany and Japan general fiscal condition of the government, indexed by primarybalance, has significant effect on road investment with time lags ranging from 1.5 to 4.6
years. In UK and France there is no evidence of such an effect.
Given the funding system, we then assess efficiency of road transport in these
five nations during the same period from two perspectives; efficiency in terms of 1) how
road network has impacted macro-economic MFP growth and 2) how the road transportsystem as a whole is performing. For the former, this paper reveals that impact of road
capital improvement on macro-economic MFP growth is positive only to the extent thatphysical network had expanded. In Japan and USA and more recently in Germany
disproportionate growth in nominal value of road capital has negatively affected MFP
growth. For the latter, road transport system composed of road network, automobiles
and fuel consumption has been improving with the exception of Japan. Redundancy oftransport networks and sparse land-use are major sources of inefficiency.

Keywords: Funding system, road investment, MFP, efficiency.

1Tel. : +81-3-5841-1710; fax : +81-3-5841-7877

E-mail address: yamaguchi@pp.u-tokyo.ac.jp (K. Yamaguchi)

1. Introduction
20th Century could be characterized as an era of automobiles. Faced withmotorization each nation has aligned its funding system to cope with development ofnecessary infrastructure. Evolution of funding system depends not only on genuine needfor infrastructure, but also on public policy framework and political manifestation.
For infrastructure authorities the way in which the funding system is structured
is a major concern. There are a number of key institutional factors that shape the scheme.
First is the institutional difference in government fund and taxation. Some nations havespecial fund dedicated to road investment while some do not. Some special funds covertransit as well as roads while some only deal with the latter. Taxation of automobile
ownership and usage has become one of major ways to raise government revenue. Someare pooled to the special fund while some are sourced to the general government
account. Reasons of taxation may differ accordingly. When a special fund is formed,
pressure from the treasury is often alleviated and the government institution in charge ofinfrastructure attains more power. Such a scheme is attractive for infrastructure
authorities while the other side of the coin is that for the treasury flexibility of crosssectoral funding is reduced.
Second is the extent to which concession is used. In most nations,infrastructure such as turn-pikes, highways, bridges and tunnels have been more or lessfinanced by such a vehicle. Mega-projects that require large lump-sum capital up-front
are often spun-off to concessions that finance through government secured bonds. In
some major concessions, actual traffic is way below the estimate, resulting in poor
return on investment. Risk of inefficient investment is always a concern for the tax
payers. Private capital involvement has thus become increasingly common in securing
Value for Money.
Third, government facing financial difficulty does not only look for
expenditures to cut but also assets to sell. Today, transport infrastructures are on thetable list of many governments in need of fiscal reform. Privatization of governmental
concessions and leasing out toll roads could tighten the tap as well as provide ways forgovernments to clean the balance sheet.
In sum, tension between need for infrastructure investment and scarcity ofcapital resource is inevitable in modern motorized nations. When the fiscal policy needs
to be tightened deceleration of infrastructure improvement may occur, resulting in lower
potential economic growth. Soft funding scheme, on the other hand, may give way toover-investment.
Another major concern for the infrastructure authorities is the efficiency ofinvestment. How would difference in funding system affect the performance of roadinvestment? What difference would it make to the productivity and efficiency of thenational economy and the road transport system?
Since 1990’s there has been an outburst of analysis regarding macro-economic
productivity of public investment. Pioneering work by Aschauer (1989) attributed poor
economic performance to the deteriorating public infrastructure. Although a number ofsubsequent studies2 contended that empirical evidence of its contribution is at best
fragile, recent analysis on road investment tends to yielded positive effects. Fernald(1999) identified relatively high causal relationship between road investment andproductivity growth in industries with high automobile intensity in USA. Kopp (2007)
confirms similar effects in Europe. Nakazato (2005) applied growth regression approachon highway investment in Japan through 1960 to 1999 and reported positive effects
until 1970’s after which it has diminished. Another area of interest is the efficiency of
the road transport system as a whole. Various productivity measurement methodologies
have been applied to transport infrastructure such as airports, sea ports and railways.
Roads, however, has not been the object of analysis. Efficiency of road transport
depends not only on infrastructure but also on automobile and its usage. At the national
level, it may also be affected by the level of other transport modes and urbanization.
This paper tries to highlight some of these contemporary issues in roadinvestment and road transport system. In particular we take a close look at roadinvestment in five major nations; USA, UK, France, Germany and Japan. Empiricalanalyses focus on period between 1990 and 2004 to identify the impacts of recent policychanges in road investment.
The rest of the paper is organized as follows. Next section gives a short
description of the evolution of funding system for road investment. Funding systems in
the five nations are elaborated. In section three, effect of the funding system on roadinvestment is analyzed. Quantitative analysis of how road investment is affected byrevenue level of special fund and the general condition of the treasury is presented. In
section four, we conduct a regression analysis to identify road investment’s impact onmacro-economic multi factor productivity growth (MFP) and argue that physicalimprovement of the road capital stock has positive effect while nominal monetary valueof the stock causes distortion in productivity assessment. Section five utilizes a numberof methodologies to assess efficiency of road transport systems and identify factors
behind the discrepancy between the five nations. Section six gives concluding remarks.

2

See for instance Holtz-Eakin (1994) and Strum (1998).

2. Evolution of funding system for road investment
2.1 General trend
Pre-automobile history of road network developed in two different ways. One
was the authentic construction by the public sector. Urban road networks were usually
under the responsibility of local governments. Two was the turnpikes. These long route
structures, often developed by trusts and semi-private enterprises, were tolled to cover
maintenance cost and in some instances to finance new construction. Advent of railway
in the 19th century caused most turnpikes to be consolidated. In the 20th century road
construction entered a new era with the rise of the automobiles. Need for dramatic
improvement of the network was accelerated by national defense objectives. The sheer
size of the investment and the difficulty of collecting user-charges lead governments toengage themselves directly in trunk route development. As government’s role grew invarious functions scarcity of public capital became severe. Fuel and automobile
ownership became one of major objects of taxation.
Fiscal policy needs the flexibility of resource allocation in response to changes
in policy agenda. Infrastructure development, on the other hand, requires a stable fund.
In UK, France, USA and then in Japan special government funds or accounts were
established to hypothecate automobile related tax for road expenditure. Some of themineral oil tax in Germany was also earmarked to road investment absent a special fund.
As witnessed in abolition of the UK Road Fund in 1995, and also in subsequent history
in France with Le Fonds Special d’Investissement Routier (FSIR) established in 1951
and ceased in 1981, special fund is neither universal nor eternal. In Germany portion of
the fuel tax and the recent introduced autobahn charge on large commercial vehicles(HGV) are dedicated in part to road investment but less than a quarter of total
motor-transport levies is appropriated to expenditure on roads3. Characteristics of thefunding system and the level of automobile related tax are illustrated in Figure 1 andFigure 2.
(Figure 1 and Figure 2 about here)“Pay as you go“ has been the common principle in many governments. In order
to circumvent the pay-go rule public agency with a special purpose of financing andconstructing infrastructure was often established. These agencies issued bonds backed
by the government to raise funds. Since they cannot rely on taxation as a revenue stream
3

VDA (2007)

tolling was used to pay back the loans. Future projection of road transport demand wasoften tempted to be overestimated resulting in lack of credible investments. The idea of
PFI/ PPP is to involve the private sector that is more parsimonious and better at project
management. There has been repercussion, however, after the initial offspring of
PFI/PPP projects. Potential benefits and limitations are still to be judged.
In the following section, a brief history of road investment funding systems infive major nations, USA, UK, France, Germany and Japan, is introduced. Table 1
underpins the basic characteristics of these five nations.
(Table 1 about here)
2.2 Funding system in major nations
2.2.1 USA
Highway Account of Highway Trust Fund was established in 1956 to pool fueltax revenue for financing transport infrastructure investments including federal
inter-state highways. Basic rule was that capital outlay for a given fiscal year would be
limited to the resources available to the states in that year. Also, tolling was prohibitedon interstates. In the 1960’s mass transit was added to its responsibility. Under ISTEA(1991-97) tolling on non-Interstates was made possible and under TEA-21 (1998-2003)
three new financial tools were introduced; State Infrastructure Banks (SIB),
Transportation Infrastructure Finance and Innovation Act (TIFIA) credit assistance andGrant Anticipation Revenue Vehicles (GARVEEs). Also, conversion of free highways
into tolled roads has been made possible. Under the current SAFETEA-LU (2005- ) anumber of tolling-related programs are continued and initiated. Concession is not onlyused for new constructions. In recent years local governments have started to pursue theconcession model for existing infrastructures. Chicago Skyway and Indiana Toll Roads
are recent examples of take-over of existing structure by long-term concessions.
2.2.2 UK
Turnpikes that were developed before the advent of railways and automobiles
were consolidated by the public sector in the early 20th century. Road Fund establishedin 1909 pooled fuel tax subsequently converted into registration fees. Earmarking,however, was generally loose. In the 1960’s and 70’s central support to the local
governments were reinforced by Rate Support Grant and Transport Supplementary
Grant. Since the 1980’s a series of fiscal consolidation did not leave road infrastructure
without change. Road Fund was terminated in 1995. PFI application on road waspromoted through Design-Build-Finance-Operate (DBFO). According to the UK
Highway Agency the estimated total capital value of the road schemes within the DBFO

highway program is close to £1.3 billion. Another distinct feature is the integrated
transport policy orientation. Since the late 1990’s transport policy has placed strongemphasis on sustainable transport through railway reform, transport-land-use planning,road-user charge, etc. Congestion pricing introduced in London as of 2003 is amilestone in contemporary urban transport policy.
2.2.3 France
In France road was considered as strategic infrastructure by the reigns since thepre-automobile era. Heritage was as much as 40,000 km by 1930. Initially 22% of fueltax was appropriated to FSIR, a special fund for road investment established in 1951. As
the government deficit accumulated, more and more resources were diverted to thegeneral account and FSIR was eventually terminated in 1981. Loi d’Orientation desTransports Interierus (LOTI) enacted in 1982, promoted decentralization of government
agencies and utilization of private initiatives. Semi-public corporations, Societed’Economie Mixte, in charge of motorways were established. During 1982 – 86 Special
fund for mega-projects, Le Fond Special de Grands Travaux, funded large investments
in road, public transport and energy saving projects. The fund depended on increased
fuel tax, but as for roads, general funds were reduced so that the total expenditure did
not experience significant increase. Since 1990 a fund for the capital city of Paris, leFond pour l’Amenagement de l’lle de Franc, is serving social infrastructure investments.
In 2004 Agence de Financement des Infrastructures de Transport de Franc wasestablished to invest in mega-projects.
2.2.4 Germany
Roads in Germany have historically been developed without tolls. Until levy
on trucks over 12 metric tons (HGV) was introduced in 2005 the highway system,
Bundesautobahn, had been developed as 12,000km of toll-free system. In addition to
general government fund, mineral oil tax, incremental portion of which pooled fortransport infrastructure since 1955, has been the major source of road investment.
German transport policy has a strong emphasis on railway and the mineral oil tax
revenue has been utilized for trunk and transit railroads. Since reunification need fortransport infrastructure investment was accelerated. EU fiscal policy on government
deficit has put a cap on public spending. In 1994 legislation was passed to allow BOTconcession for bridges and tunnels. However, only two of these projects are operational
as of 2007. Introduction of HGV toll has opened a window of opportunity for PPP intrunk roads. Verkehrsinfrastrukturfinanzierungsgesellschaft (VIFG), a state owned
agencey established in 2003, is supporting lane-expansion projects funded by HGV toll
revenue. First of these A-model projects was awarded by VIFG in 2007.

2.2.5 Japan
In Japan a series of fuel tax was introduced in the 1950’s and pooled into theRoad Improvement Special Account (RISA), established in 1958. Additional tax onownership of automobile was introduced in 1971. Quarter of the revenue wasdistributed to local governments for road expenditure and 80% of the rest wasapportioned to the RISA through the general account. Roads are classified into national,
prefecture and local roads. Tax level was increased to match the five year investment
plan. Abundant resource in RISA sometimes cause overflow. Public transport is outside
the scope of RISA4. Basic rule under the legislation is that usage of roads must be freeof charge except for tolled roads5. The enterprise tasked to develop nation-wide highway,
Japan Highway Public Corporation (JH) established in 1956, was a public corporation
which raised most of its capital through government backed loans payable by future toll
revenue6. Since 1985, when Japanese economy embarked on a mission to turn theexport-oriented economy into a system that depended more on endogenous growth, thelevel of public investment was lifted. During the economic turmoil in the 1990’s,
additional fund from the general account was poured into new road construction tocounter recession. With only 1/25 the size and half the population, road investment inJapan had exceeded that of US during the 1990’s. Since the turn of the century, theprime-ministership has put an end to the soft budget. After a political vanquish, JH, apowerful entity in highway development, was privatized into one highway holding
company and three regional highway corporations in 2005. In 2008 five special funds
on public investment including RISA are planned to be integrated.

3. Effect of the Funding System on Road Investment
In this section we investigate how the funding system affects road investment.
In nations with specific fund institution the level of road investment is expected to be
influenced by the level of revenue. There may be a time lag due to account settlement,
although it is expected to be short. Another factor affecting road investment is thegeneral condition of the fiscal policy. Using panel data fro 22 OECD countries for theperiod 1980-92 Strum (1998) found that government capital spending is reduced during
fiscal stringency. We test the hypothesis with primary balance. When primary balance is
4Treasury has been eyeing on RDSA funds to expend for general purposes. This, of course, is faced by opposition
from the construction and automobile industry backed by politicians. Some argue that at least reimbursement of thegovernment debts incurred for road investment should be born by the automobile users.
5 Toll road needs to be turned into free road once debt service is completed.
6 There are a number of toll rods operated by local public agencies as well as a small number of private turnpikes
which are regulated as a business enterprise.

negative and increasing necessity to restore the situation tightens government budget forcapital outlay. This effect, if it exists, is expected to yield time lag since adjustment by
the treasury often involves political decision making which takes time. Figures 3-1 to
Figure 3-3 illustrate recent trends of relevant data in USA, UK, France, Germany andJapan.
(Figures 3-1, 3-2 and 3-2 about here)
We analyze these two effects through level of fuel taxation revenue7 (REV)
which is the major source of road investment and primary balance (PB) both in terms ofshare in GDP and standardized. Gross road investment in percentage share of GDP (RI)
is regressed against these variables with lags8. The result is depicted in Table 2.
(Table 2 about here)
In both USA and Japan, where special fund is institutionalized, fuel tax revenue
has positive and statistically significant effect on road investment. Average time lags are
2.3 years and 1.0 year respectively. As for primary balance USA, Germany and Japan
have positive and significant effect on road investment. Average time lags are 4.6 years,
3.3 years and 1.5 years respectively. Neither fuel tax revenue nor primary balance isaffecting road investment in UK and France.

4. Road investment and Economic Growth
Efficiency of transport infrastructures has a number of different perspectives.
Here we are concerned with the economic impact of infrastructure investment on macro
economic performance. We are interested in identifying how public capital stock, alongwith labor and private capital stock, enters into the macro production function andaffects productivity growth of an economy as a whole.
We use a simplified version of specification presented by Fernald (1999) andextended by Kopp (2007). National economy could be modeled as follows:

Yi = U i F i ( K i , Li , Gi )78

For USA and Japan vehicle tax revenue is included.
Almon lag is used for time lag specification.

iwhere, Yi is output, U i is Hicks-neutral technology level, F (J ) is production

function with three arguments; K i as private capital, Li as labor and Gi as road
capital of country i . Cost minimization implies that elasticity of each input equals
input’s share s Ji . Thus Solow’s MFP growth residual dpi could be expressed by
growth rate of inputs, as follows:

dpi ≡ dyi − s Ki dk i − s Li dli − sGi dg i
Let du i be the technology improvement. Then the above equation could be rewritten
as follows:

dpi = φsGi dg i + dui
We estimate the following regression model with panel data of five nations, USA, UK,
France, Germany and Japan for years between 1992 and 2004, thus transcript t is added.

dpit = cit + φsGit dg it + dpi ,t −1 + ε itduit is represented by the constant term cit . dpi ,t −1 is the lagged variable. ε it .is theerror term. Data sources are listed in the Appendix. All monetary figures are converted
into PPP adjusted international constant9 US Dollars. Hausman specification test withrespect to the fixed effect model supported random effect model. GMM is also run toenhance robustness.
(Table 3-1 and Table 3-2

about here)

Result of the panel regression is listed in Table 3-1 and GMM in Table 3-2.
Three different data are used for road investment. First, road capital growth in physicalunit (kilometers) is used. Coefficient is 0.1782. The order is in line with previous studies.
Next we used nominal road capital value. The coefficient is negative so we adjust thevalue by deflator calculated by setting nominal road capital value per kilometer in year
2000 as 100.

9Constant at year 2000.

(Figure 4 about here)
From Figure 4 we can see that Japan and to a less extent USA have experienced“inflation.” Adding an incremental kilometer of road has become more and more
expensive. It has pushed up the nominal road capital value such that its effect on MFP
growth is negative. The third row indicates the result of the regression analysis using thedeflated value. Coefficient of road capital value growth is 0.1847, a similar value to thatof road capital growth in physical unit. This implies that MFP growth responds to thephysical improvement of the road network and not to the nominal capital value invested.
GMM yields slightly lower coefficients but they are statistically significant.

5. Efficiency of Road Transport
We now focus on the efficiency of the road transport sector. Road transport as aservice could be expressed in the form of a production function. Ingram and Zhi (1999)
take the production function approach but take the number of motor vehicles per
kilometer of road as a proxy of input ratio. Here, we take the direct approach inmeasuring productivity. Productivity, the ratio of output and input, could be calculated
relatively simply if there is only one output and input. If we have multiple outputs
and/or inputs, however, we need to aggregate these multiple factors into an index toderive the ratio. In this section we consider a case with one output; vehicle kilometer
performed, and three inputs; road, automobile and fuel.
The following three methodologies are used to derive efficiency indices.
Econometric production function model assumes that each country’s road transportsystem is efficient. This assumption is unlikely to hold so DEA and Tornqvist index arealso deployed. It is followed by an analysis of multilateral superlative Tornqvist index tocompare efficiency change among the five countries and identify factors that explain the
difference10.
1) Production function model
2) Data envelopment analysis (DEA)
3) Tornqvist index
5.1 Production function model
Production function model has an advantage in that parametric estimation of a10

Another efficiency measurement methodology, stochastic frontier, is used to check the results from multilateralsuperlative Tornqvist index analysis.

production function form could be conducted and statistically verified. The other side of
the coin is that specification of a production function is necessary. With respect to theestimated efficiency level, trend of each observation over time could be identified.
Methodology using constant rate of substitution (CRS) production function
proposed by Yoshida (2005) is modified to Cobb=Douglas production function
specification. Following equation was estimated using panel data 2SLS fixed effectmodel11.

ln vkmit = c + α 1 ln road it + α 2 ln fuelit + α 3 autoit + α 4 ln vkmi ,t −1 + μ i + vt + uit
where,

ln vkmit : natural log of vehicle kilometers performed (standardized values) in country iin year t.

ln vkmi ,t −1 : lag (1) value of ln vkmit
ln road it : natural log of total road length (standardized) in country i in year t.
ln fuelit : natural log of total fuel consumed (standardized) in country i in year t.
ln autoit : natural log of total number of automobiles in use (standardized) in country iin year t.
c : time invariant constant term across all observations

μ i : time invariant fixed effect for country ivt : dummy variable for year t
u it : error term
Productivity eit = exp(ln vkmit ) / exp(ln vkˆmit )Productivity growth is derived by;

ei ,t +1 / ei ,t11

Natural log of number of automobiles, fuel price, GDP, population (standardized values).

ln vkˆmit : estimated value of ln vkmit from the above equation.
Result of the estimate for the production function is listed on Table 4. As
expected, coefficients of total road length and fuel consumption are positive andstatistically significant. Result of the second equation shows that coefficient of numberof automobiles is not statistical significance. This could be explained by the
supplemental nature of automobiles to road infrastructure. Productivity growth isdepicted together with results of other methodologies in Figure 5-1 to 5-5. Although
there is a general tendency of positive growth, fluctuation makes it difficult to inferspecific conclusion.
(Table 4, Figures 5-1 to 5-5 about here)
5.2 Data Envelopment Analysis
Data envelopment analysis (DEA) measures efficiency by distance function
relative to production possibility curve using linear-programming techniques. It is a
flexible approach in that production possibility curve is derived from non-parametricmethods without specifying a functional form and also without input price data. Its
disadvantage is that the derivation of production possibility curve is affected by outliers.
Malmquist index given as follows by Coelli et al (2005) is frequently used for
productivity comparison between two time periods s and t.

mo ( q s , x s , q t , x t ) = [d os (qt , xt ) d ot (qt , xt ) 1 / 2×] ,d os (q s , x s ) d ot (q s , x s )λwhere, d o (qλ , xλ ) is the distance of pair of output and input vectors (qλ , xλ ) withrespect to production possibility curve by technology λ =s, t. Malmquist index iscomputed by DEAP Version 2.1 provided by Coelli, T.J.
Again, the result of the productivity growth measurement is depicted together
with results of other methodologies in Figure 5-1 to 5-5. DEA Malmquist index is inparallel form and slightly exceeding that of the production function model.
5.3 Tornqvist Index
TFP growth expressed as Tornqvist index is defined as follows:ln

TFPi ,t +1
TFPit

Y
X
= ln i ,t +1 − ln i ,t +1 , where Yit : output, X it : input
Yit
X it

G
A
F
G
A
For inputs we consider road X it , automobile X it and fuel X it , with sit , sit andsitF as cost share of road, automobile and fuel respectively. The equation then becomes;ln

TFPi ,t +1
TFPit

= ln

Yi ,t +1
Yit

X iA,t +1 1 F
X iF,t +1
X iG.t +1 1 A
1 G
G
A
F
− ( si ,t +1 + sit ) ln G − ( si ,t +1 + sit ) ln A − ( si ,t +1 + sit ) ln F
2
2
2
X it
X it
X it

As illustrated by Figure 5-1 to Figure 5-5 the three different methodologies of efficiency
measures yield similar results. Since these indices are not comparable across nations,
we calculate the superlative index to observe difference between them.
5.4 Multilateral superlative Tornqvist Index
Multilateral superlative Tornqvist index developed by Caves, Christensen andDiewert (1982) could be applied to the road transport system as follows:)

Let Yt be geometric mean of Yt , Yt be arithmetic mean of Yt , and similarly for))
X tk X tk stk stk where k=G, A, F.
Then, with average level of output and inputs in year one as the benchmark, multilateralsuperlative Tornqvist index growth for country i in year t would be as follows:ln

TFPi ,t +1
TFPit

Yi ,t +1 1 G
X iG,t +1 1 A
X iA,t +1 1 F
X iF,t +1
GAF= ln ) − ( si ,t +1 + st ) ln ) G − ( si ,t +1 + st ) ln ) A − ( si ,t +1 + st ) ln ) F
2
2
2
Yt
Xt
Xt
Xt

)
)
)
)
Ys +1 1 G
X sG+1 1 A
X sA+1 1 F
X sF+1GAF+ ∑ [ ln ) − ( s s +1 + s s ) ln ) G − ( s s +1 + s s ) ln ) A − ( s s +1 + s s ) ln ) F ]
2
2
Ys 2
Xs
Xs
Xs
s =1
t −1

Multilateral superlative Tornqvist index plotted in Figure 6 shows that UK
and USA followed by Germany and France are efficient and Japan is in a dismally poor
position. Stochastic frontier analysis yields similar results12. Here again efficiency inJapan lags behind others.

12

Stochastic frontier analysis was conducted by open program FRONTIER ver. 4.1 provided by Coelli,
T.J. One output (vehicle kilometers) and three inputs (road length, number of automobiles and fuelconsumed) are used.

(Figure 6 and Table 5 about here)

In order to identify factors behind the difference, multilateral superlative
Tornqvist index was regressed against the following seven variables; highway length
per land area ( highwayit ), ratio of new automobile sales to the stock of vehicles( newautoit ), Population density ( popit ), urban population ratio ( urbanit ), annualchange in percentage of population aged over 65 ( over 65 it ), railway length per landarea ( railit )and internet users per population ( int ernetit ).
The first two variables try to capture the technical improvement in roadinfrastructure and automobiles. Positive effects would be expected if there are technicalimprovements in infrastructure by enhanced throughput of vehicles. Also, speed
improvement, fuel efficiency etc. in automobiles could increase the total efficiency of
the road transport system. Population density reflects the level of congestion, thus it isexpected to have a negative coefficient. Aging society makes society more immobile so
it is expected to have a negative effect on efficiency. Higher railway intensity is
expected to result in lower efficiency of road transport since the redundancy is higher.
Level of urbanization is expected to have a positive coefficient since the road transport
assets are used more intensively. Higher internet usage may have a negative effect on
toad transport efficiency if its substituting effect overrides the spillover effect ofenhanced communication on spatial activities.
In Table 5 result of the panel regression is listed. Hausman test supported thefixed effect model. The first two variables turned out to be insignificant, meaning thattechnical improvement in road infrastructure and automobiles does not affect theefficiency of the road system. The other variables were statistically significant at the 1%or the 5% level. As expected, popit , over 65 it and railit have negative effect on
efficiency of road transport while urbanit and int ernetit have positive impacts.

6. Concluding Remarks

Tension between necessity for infrastructure investment and scarcity of capital
resource is inevitable in modern motorized nations. When the fiscal policy is
consolidated lack of investment in infrastructure may lead to deceleration of economic
growth. On the other side lax funding scheme may give way to over-investment. Afteran overview of recent trend in funding system of road investment this paper identified
institutional differences in five major nations, USA, UK, France, Germany and Japan.
In nations such as USA and Japan, where special fund is established, the level of fueltax that is hypothecated to road investment affects the level of road investment. Other
three nations do not exhibit such relationship. In USA, Germany and Japan generalfiscal condition of the government, indexed by the level of primary balance, hassignificant effect on road investment with time lags ranging from 1.5 to 4.6 years.
Impact of road capital improvement on macro-economic productivity growthis positive only to the extent that physical network had expanded. Japan and USA in the
1990’s and more recently in Germany have experienced excessive growth in nominal
value of road capital affecting negatively on MFP growth. In the five nations, roadtransport system composed of road capital, automobiles and fuel consumption has been
improving with the exception of Japan in which multilateral superlative Tornqvist index
is lagging far behind other four nations. Redundancy of transport network with othertransport mode such as railway and sparse land-use are some of the major sources of
inefficiency.
In sum, funding system could make a difference in road investment. It is not,however, a sufficient condition. General fiscal consolidation may or may not affect roadinvestment. Earmarking may provide stable resources but could lead to inefficientinvestment in the sense that contribution to macro-economic productivity growth iswatered down by costly capital outlay per incremental increase in physical road asset.
Efficiency of the transport system as a whole may also be affected. In order to realize an
efficient road transport system integrated transport policy with multi-modal andtransport/land-use consideration should be pursued.

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Appendix: Data sources

Gross road investment: USA; Federal Highway Administration, UK; Department forTransport, France; Ministère de l'Écologie, du Développement et de l'Aménagement
durables, Germany ; Verkehr in Zahlen, Japan ; Annual Road Statistics 2007.
Primary balance: Organization for Economic Development and Co-operation (OECD)
Fuel tax revenue: product of fuel consumption and tax rate provided by International
Energy Agency (IEA)
GDP, Marginal factor of productivity: OECDNet road capital stock value: calculated or supplemented by the author with theperpetual inventory method as follows;
USA: Productive Highway Capital Stock Measures, Fraumeni (1999)
supplemented by the author,
UK: calculated by the author using data from Department for Transport,
France: ; calculated by the author using data from Ministère de l'Écologie, duDéveloppement et de l'Aménagement durables,
Germany: Verkehr in Zahlen,
Japan: Social Capital of Japan, Cabinet Office.
Total road length, vehicle kilometer, total number of automobiles in use: World Road
Statistics, International Road Federation; World Motor Vehicle Statistics, Japan
Automobile Manufacturers Association.
Net automobile capital stock value: calculated by the author with the perpetualinventory method using the following data; World Motor Vehicle Statistics, Japan
Automobile Manufacturers Association; STAN database, OECD.

Fuel consumption, fuel price: IEA

Taxation on automobile
ownership and usage

UK
France*Germany
Japan*
Special funds
or accounts

General funds

USA*relatively receptive to highway
concessions

Figure 1: Characteristics of funding system for road investment in the five nations

US$
2500

2000

1500
Vehicle tax
Fuel tax
1000

500

*
0
USA

*

UK

France**GermanyJapanof special fund for road investment* :: source
appropriated for road investment**

Source: Annals of Road Statistics (2006) MLIT, Japan, converted by PPP exchange rate (2004)
Annual automobile tax burden estimated with a standard automobile weighing 1.5 tons, 6-year vintage,
annual fuel consumption of 1,200 liters. Tax rate as of Jan. 2005.

Figure 2: Level of annual automobile related tax (2004)

0.0250.02
USA
UK
FranceGermany
Japan

0.015

0.01

0.005

20
04

20
02

20
00

19
98

19
96

19
94

19
92

19
90

0

Figure 3-1: Gross road investment’s share of GDP
0.035
0.03
0.025

USA
UK
FranceGermany
Japan

0.02
0.015
0.01
0.005

20
04

20
02

20
00

19
98

19
96

19
94

19
92

19
90

0

Figure 3-2: Fuel tax revenue’s share of GDP
0.08
0.06
0.04
0.02

-0.04
-0.06
-0.08

Figure 3-3: Primary balance’s share of GDP

20
04

20
02

20
00

19
98

19
96

19
94

-0.02

19
92

19
90

0

USA
UK
FranceGermanyJapan

Road capital value deflator (2000=100)

20
04

20
02

20
00

19
98

19
96

19
94

USA
UK
FranceGermany
Japan

19
92

19
90

140
120
100
80
60
40
20
0

Figure 4: Road capital value deflator (2000=100)

0.03
0.02
0.01

2004

2003

2001
2002

2000

1998
1999

1997

1996

1994
1995

1993

-0.01

1992

0

Production
function
DEA
Tornqvist

-0.02
-0.03
-0.04

Figure 5-1: Road transport TFP change in USA

0.06
0.04
0.02

-0.04
-0.06
-0.08

Figure 5-2: Road transport TFP change in UK

2004

2003

2001
2002

1999
2000

1998

1996
1997

1994
1995

1993

-0.02

1992

0

Production
function
DEA
Tornqvist

0.05
0.04
0.03
Production
funcetion
DEA

0.02
0.01
2004

2003

2001
2002

2000

1998
1999

1997

1996

1994
1995

1993

-0.01

1992

0
Tornqvist

-0.02
-0.03
-0.04

Figure 5-3: Road transport TFP change in France

0.1
0.08
0.06

Production
function
DEA

0.04
0.02
0

-0.04
-0.06

Figure 5-4: Road transport TFP change in Germany

2004

2003

2001
2002

2000

1998
1999

1997

1996

1994
1995

1993

1992

-0.02

Tornqvist

0.03
0.02
0.01

-0.02
-0.03
-0.04

Figure 5-5: Road transport TFP change in Japan

2004

2003

2002

2000
2001

1999

1998

1997

1995
1996

1994

1993

-0.01

1992

0

Production
function
DEA
Tornqvist

0.2
0.15
0.1
0.05

-0.15
-0.2
-0.25

Figure 6: Multilateral superlative TFP index

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

-0.1

1993

-0.05

1992

0

USA
UK
FranceGermanyJapan

Table 1: Summary of statistics for the five nations
Total
road
length*thousandkm

NumberAnnualof

fuel

Populatautomob

consumption*

iles*†

ion**††

billionmillionmillionlitersGDP**

AnnualAnnualAnnualLand

Inhabita

road

road

road

size

ble landinvestm

maintenexpenditent**

ance**

ure**

AnnualAnnualAnnual road

road

road

investment

maintenan

expenditur

per GDP**ce pere per**billion

1000

1000

billionbillionbillion

US$

km2

km2

US$

US$

US$

-

GDP

GDP**

-

-

USA

6,408

243

653

294

10,574

9,629

6,199

64

33

97

0.61%

0.31%

0.92%

UK

388

34

51

60

1,722

243

217

4

6

10

0.23%

0.35%

0.58%

France

1,002

36

55

60

1,645

552

400

11

5

16

0.67%

0.30%

0.97%

Germany

657

49

68

83

2,191

357

242

18

3

21

0.82%

0.14%

0.96%

75

98

128

3,456

378

118

55

16

71

1.59%

0.46%

2.05%

Japan

1,188
*

Note: as of Jan 1st 2004,†**

year of 2004,

Cars, buses and trucks

†††

††

Monetary figures are in PPP exchange rate converted 2000 constant US Dollars. See Appendix for data sources.

Automobile fuel including gasoline, diesel and LPG are converted into unleaded gasoline equivalent units

tax on automobile ownership and usage hypothecated to special fund on road investment.

Table 2: Results for regression analysis of gross road investment and fiscal conditions

USA

UK

FranceGermany

Dep.var.=RI

C

REV

REV(-1)REV(-2)REV(-3)REV(-4)REV(-5)PB

PB(-1)

PB(-2)

PB(-3)

PB(-4)

PB(-5)RHO

Ad. R2Estimate-0.002

0.039

0.294

0.312

0.208

0.099

0.103

-0.00003

0.00006

0.00003-0.00003-0.00002

0.00016-0.9690.995

t-statistics-3.9561.723

18.04822.968

8.437

2.898

3.261

-17.865

18.23914.221-13.267-4.89645.961-22.218

p-value[.000][.085][.000][.000][.000][.004][.001][.000][.000][.000][.000][.000][.000][.000]

sum of lag

1.055[14.480]0.00017[63.940]

average lag

2.326

4.561

Estimate

0.014-0.131-0.096

0.012

0.085

0.018

-0.2940.00001-0.00005-0.00004

0.00001

0.00004

0.00000-0.965t-statistics

3.657-8.459-1.744

0.253

4.965

1.557

-12.867

0.327-1.482-1.298

0.499

5.847

-0.134-19.480p-value[.000][.000][.081][.800][.000][.120][.000][.744][.138][.194][.618][.000][.893][.000]

sum of lag-0.406[-2.905]-0.00004[-0.3368]

average lag

2.996

0.988

-0.948Estimate-0.0940.701-0.784-0.230

1.070

1.823

0.735

0.00008-0.00107-0.00140-0.00103-0.00009

0.00130-0.890t-statistics-1.6324.855-1.850-2.153

1.938

1.820

1.027

0.698

-2.466-1.939-1.487-0.3162.282-8.310p-value[.103][.000][.064][.031][.053][.069][.304][.485][.014][.053][.137][.752][.022][.000]

sum of lag

3.315[1.774]-0.00221[-1.517]

average lag

3.901

0.982

0.368

Estimate

0.030-0.220-0.216-0.197-0.163-0.113-0.0470.000160.000140.000160.000240.000360.00052-0.960t-statistics

8.069-4.952-5.762-5.969-5.978-6.078-3.771

4.986

4.544

4.259

4.316

4.512

4.710

-16.756

p-value[.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000][.000]

sum of lag-0.957[-5.836]0.00157[4.614]average lag

1.869

0.001540.001290.00057

0.861

3.310
-0.0006

Japan

Estimate-0.2555.326

13.232

9.041

0.560

-4.4011.965

0.000830.00137-0.994
6t-statistics-72.70924.644265.360975.1477

2.6268-17.1169.841

26.55660.318137.567

127.161

25.554-11.918-111.565

p-value[.000][.000][.000][.000][.009][.000][.000][.000][.000][.000][.000][.000][.000][.000]

sum of lag

25.720[75.33]0.00494[52.97]average lag

0.980

1.482

0.987

Table 3-1: Results for panel data analysis of TFP growth (random effect model)
n=65
Dependent variable: dpi
Independent variables: dg iclag (1) of dpi

R2

Physical unit (km)0.1782(2.62**)

0.0157(4.53**)-0.0420(-0.31)0.34764

Nominal road capital-0.0746(-2.08*)0.0186(4.98**)-0.0588(-0.41)0.27414

Real road capital

0.1847(2.76**)

0.0157(4.54**)-0.0382(-0.28)0.36204

Notes:
Figures in parenthesis show z-statistic and significance level.
** significance, p< .01, *. significance, p< .05
1

Instrumental variables: natural log of number of automobiles, fuel price, GDP, population (standardized

values).
2

Hausman specification test with respect to the fixed effect model supported random effect model.

3 Coefficients for year dummies are omitted for brevity.
4 R2 for within variables.

Table 3-2: Results for panel data analysis of TFP growth (GMM)
n=65
Dependent variable: dpi
Independent variables: dg ilag (1) of dpi

Physical unit (km)0.1254(1.92)

0.0037(1.34)-0.1402(-1.08)Nominal road capital-0.0355(-0.52)0.0002(0.82)-0.1112(-0.84)

Real road capital

0.1369(2.12*)

0.0004(1.43)-0.1418(-1.10)Notes:
Figures in parenthesis show z-statistic and significance level.
** significance, p< .01, *. significance, p< .05
1cInstrumental variables: Primary balance percentage of GDP in t-2 and t-5.

Table 4: Results for estimate of road transport production function model
n=75

Independent variables 3

Dependent
variableln road itln fuelitln autoitln vkmi ,t −10.3404(2.98**)

0.2669(3.69**)-

0.3110(2.44**)

0.2435(2.86**)

0.0854(0.53)

ln vkmit

1, 2

ln vkmit

1, 2

c

R2

0.5707(8.37**)-0.0253(-1.43)

0.97340.5570(7.61**)-0.0164(-0.67)

0.9734

Notes:
Figures in parenthesis show z-statistic and significance level.
** significance, p< .01.
1

Instrumental variables: natural log of number of automobiles, fuel price, GDP, population (standardized

values).
2

Hausman specification test with respect to the random effect model supported fixed effect model.

3 Coefficients for year dummies are omitted for brevity.
4 R2 for within variables.

Table 5: Results for regression analysis of multilateral superlative TFP index( mltfpit )Dependent variable: mltfpit

Independent variables

highwayit

newautoit

popit

urbanitover65itrailitint ernetit

1.550(0.36)-0.185(-0.49)-0.008(-3.02*)0.134(2.24*)-0.130(-3.28**)-2.390(-2.35*)0.0001(3.12**)

n=65
Notes:

R2(within variable): 0.664
Figures in parenthesis show z-statistic and significance level.
** significance, p< .01, * significance, p< .05.
2

Hausman specification test with respect to the random effect model supported fixed effect model.

c0.7244(1.07)