road vs rail

7/29/2019 Road vs Rail

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In the 1950s, the rail mode occupied a dominant position in transport within India. Since then,

however, the transport sector in the country has been characterized by a secular decline in the share

of rail mode. Internalization of the external costs of transport may not be sufficient for the

achievement of a socially optimal modal split unless account is taken of the factors behind thecurrent modal split. This paper attempts an investigation of these issues on the basis of data relating

to eight representative sections in the country where the two modes are in competition.

India became a decidedly road-dominant economy in the beginning of the eighties with the

railways losing out in respect of freight traffic in addition to its already declining share in passenger

traffic. The dominance of road over rail has since continued unabated till the present and is likely

to continue into the future.

This paper reviews the trends in transport and modal split in India from the fifties onwards and

looks at the factors likely to influence modal choice. In the literature, an individuals choice of mode

is divided into two main categories:

personal characteristics of the individual (income, tastes, auto ownership, competing family

needs for the car)

characteristics of transportation alternatives available (relative time, cost, and comfort).

Based on time-series including user costs, per capita domestic product, and consumption

expenditure, an econometric analysis of inter-modal competition in the eight sections selected for

the current study reveals the following:

In the case of passenger traffic, increases in the user cost difference and the user cost ratio

between road and rail have an upward impact on the relative traffic volume of rail.

Income (as represented by per capita gross state domestic product) seems to play a part in

determining choice between travel by car on road and first-class/air-conditioned travel on

rail.

The relationship between modal split and user cost difference/cost ratio in the case of

competition between bus on road and second-class/sleeper-class travel on rail appears to

be a non-linear one. In the case of freight competition, the modal share of rail does not go up with increase in

the user cost difference or cost ratio between road and rail.

It is the income variable that appears to influence modal choice in freight transport in the

expected manner with shippers patronizing the qualitatively superior road mode when per

capita state domestic product goes up.

To arrive at a socially optimal modal split, therefore, it is necessary to concentrate on

improvements in the quality of service on rail while at the same time devising measures to

internalize the external costs of transport.

Modal Split betweenRail and Road Modesof Transport in India

Prosenjit Dey Chaudhury

ExecutiveSummary

R E S E A R C H

includes research articles thatfocus on the analysis and

resolution of managerial andacademic issues based on

analytical and empirical orcase research

KEY WORDS

Modal Split

User Cost

Vehicle Operating Costs

Value of Passenger Time

Feasible GeneralizedLeast Squares

VIKALPA VOLUME 30 NO 1 JANUARY - MARCH 2005 17

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The rail and road modes are worldwide the

dominant modes of transport. The origin and

rapid growth of railways in the nineteenth

century meant, in some cases, the displacement of the

road mode, both for passenger and freight movement.

This was especially the case for long-distance travel

before and in the early days of the internal combustion

engine. In the early years of the twentieth century,however, the era of motorization set in and travel by road

became more popular. After the Second World War,

rapid industrial development was accompanied by

acceleration in the growth of motorized transport.

Volumes of traffic on the rail and road modes grew

significantly with the latter often showing a greater

increase than the former as it could more readily meet

the demand for transport among different sections of the

population.

In the 1950s, the rail mode occupied a dominant

position in transport within India. Since then, however,

the transport sector in the country has been characterized

by a secular decline in the share of rail in the total traffic

carried by both road and rail, although, in absolute

terms, traffic on both modes has increased significantly.

The decline in the rail share has been pronounced for

both passenger and goods traffic. This phenomenon

gives rise to a number of issues that must engage the

attention of the policy-maker. While it is true that the

road mode has inherent advantages of convenience,

flexibility, and adaptability and may in many cases bequalitatively superior to the rail mode, nonetheless, its

dominance may not imply a socially desirable modal

split. A number of studies has found that the external

costs of rail transport are lower than those for road

transport (Button, 1993; Government of Australia, 1994,

1995, and 1996; Government of New Zealand, 1996; Ren-

nings et al., 1999; Savelli and Domergue, 1998;

Wiederkehr, 1998). The market-determined split between

rail and road may be corrected through the internalization

of the external costs of transport such as resulting from

pollution and accidents. Accordingly, the policy-ordecision-maker must find ways to internalize these

external costs in order to ensure desirable modal choice

in transport. However, a proper inquiry into the subject

should begin with an understanding of the factors that

determine the current modal split in transport.

Internalization of external costs may not be sufficient for

the achievement of a socially optimal modal split unless

an account is taken of these factors.

We shall, in this paper, attempt an investigation of

the factors behind the choice between rail and road in

India on the basis of data relating to eight representative

sections in the country where the two modes are in

competition. Since the data is aggregative in nature and

does not cover all the variables that should ideally be

included in such an exercise, the findings are meant to

provide preliminary, general ideas about the factorsbehind modal choice in passenger travel and freight

shipment. We first review the trends in transport and

modal split in India concentrating on the two principal

modes and the findings of the important committees

such as the National Transport Policy Committee. Next,

we look at the factors likely to influence modal choice

in transport and, finally, describe our own exercise in

understanding modal choice between rail and road in

the country.

TRENDS IN TRANSPORT AND MODALSHARES IN INDIA

The Committee on Transport Planning and Coordination

(Planning Commission, 1966), set up in 1959, noted that

the burden of the increase in internal traffic since the

First Plan had fallen mainly on the railways and on the

road transport. Over the period 1950-51 to 1964-65, rail

freight traffic increased nearly two and a half times and

the same category of traffic on road went up almost four

times. During the same period, the share of rail in the

total freight traffic carried by rail and road came downfrom 79 per cent to 73 per cent. The Committee noted

that railways accounted for close to 77 per cent of the

movement of bulk commodities such as coal, iron ore,

limestone, cement, and petroleum products. Passenger

traffic by rail increased by nearly 40 per cent over the

period 1950-51 to 1964-65, while passenger traffic by

road went up more than three times.

During the period 1950-51 to 1963-64, freight traffic

increased at a rate distinctly faster than either the rate

of growth of national income or the expansion of output

in the industrial, mineral, and agricultural sectors. While

national income went up almost 60 per cent over the

period, the total freight tonne-kilometres of rail and road

showed a more than two-fold increase. Passenger traffic

also tended to rise somewhat faster than growth in

national income, showing an almost two-fold increase.

The Committee attributed the faster growth of transport

output to the emphasis given to the development of

industries, especially heavy industries, since the Second

18 MODAL SPLIT BETWEEN RAIL AND ROAD MODES OF TRANSPORT IN INDIA

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Plan. However, the higher rate of growth of the transport

sector did not indicate that the supply of transport services

had always been able to keep up with demand.

During the period under review, it was seen that

in the years when there was a slackening in economic

growth, excess capacity was present in the transport

sector, especially on the railways. However, in the other

years, when the tempo of economic activity was pickingup, the transport sector could not cope with the demand

and there were severe bottlenecks. The emphasis of the

Five Year Plans on heavy and basic industries tended

to increase the proportion of traffic in bulk commodities

carried by the railways. On the other hand, the growth

of consumer goods industries and industries requiring

special facilities had led to increased demand for road

transport facilities.

The report of the National Transport Policy

Committee (NTPC) noted a marked decline in the rail

share in total traffic carried during the period 1950-51

to 1977-78 (Planning Commission, 1980). However, in

absolute terms, the volume of traffic carried by rail had

undergone a sharp increase. Thus, while the freight

traffic carried by rail in 1950-51 was 44 billion tonne

kilometres (btkms), the number had gone up to 163 in

1977-78, an almost four-fold increase. Not only had the

volume of originating traffic carried by rail undergone

a sharp increase, but the average lead of freight shipment

on rail had increased one and a half times over the same

period standing at 686 kms in 1977-78. The bulk of freighttraffic carried by rail comprised of goods like coal, iron

and steel, cement, fertilisers, and petroleum products.

The proportion of such goods increased from 55 per cent

to around 80 per cent of the total rail freight shipment

between 1950-51 and 1977-78. The traffic in general goods

remained more or less stationary between 1960-61 and

1977-78, confined to a range of 45-50 million tonnes. In

keeping with the trend observed by the Committee on

Transport Planning and Coordination, an increasing

proportion of traffic in manufactured or high-value

products had gone over to road transport which hadbeen carrying such freight over progressively longer

distances.

Freight traffic carried by both the rail and road

modes increased almost five times between 1950-51 and

1977-78. While in the fifties and the sixties, the rate of

growth of freight traffic was nearly twice as much as that

of national income, in the seventies, freight traffic on

road and rail had slowed down, growing at the same

rate as national income. The rate of growth of passenger

traffic had, however, been much higher than the growth

rate of population and national income. Passengers

travelling in second-class constituted over 95 per cent

of the total passenger traffic of the Indian Railways.

Although in respect of long-distance travel, rail was the

cheapest and quickest mode of transport, especially for

second-class passengers, nevertheless, roadwaysprovided a significantly better service for short-distance

travel.

The NTPC Report observed that, in the seventies,

the growth of transport capacity lagged considerably

behind the requirements of the national economy. The

railways came under considerable pressure to meet the

burden of transport without commensurate investment

in rolling stock or line haul capacity, resulting in

bottlenecks. Unforeseen shifts in the pattern of traffic

placed additional strain on the railway system which

from time to time had to also cope with dislocations

caused by floods and other natural calamities. At times,

even road transport could not meet the increasing demand

for freight shipments. Although there had been a steady

growth in the number of commercial vehicles, there was,

at times, an acute shortage of trucks.

Since the primary objectives of the NTPC were to

recommend an optimal inter-modal mix of different

modes of transport and to suggest organizational,

administrative, fiscal, and legal measures for giving

effect to recommended national transport policy, it dealtextensively with traffic forecasts and the optimal

allocation between modes. Estimates were worked out

for a time horizon extending till the end of the twentieth

century. According to the NTPCs projections, the

railways were expected to carry 468 btkms in the year

2000 as against the 155 btkms carried in 1978-79. The

major part of the projected traffic would be moving over

long distances. Road transport was expected to carry 182

btkms of traffic at the end of the twentieth century. Of

this, nearly 130 btkms would be intra-regional and 52

btkms inter-regional. Accordingly, the percentage sharesof rail and road in freight traffic worked out to 72 per

cent and 28 per cent respectively. The inter-modal

allocation was based on calculations of resource costs

and took into account the shadow price of scarce inputs

like energy. The NTPC took into account an expected

rise in the price of diesel and its consequential impact

on break-even levels (i.e., distances of traffic where the

costs of transport across different modes are equalized),


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and assumed a shift to rail of at least 50 per cent of traffic

moving by road beyond these break-even levels.

However, the Committee stated that the increase in rail

share would not materialize unless appropriate

investment and pricing policies were pursued to ensure

the suggested modal split. It was of the view that if

minimum resource cost was to be the guiding principle

for determining inter-modal mix, the railways shouldplay a larger role in the nations transport system. The

NTPCs expectations of future modal split in this regard

have not been fulfilled: the modal shares of rail and road

in the total freight traffic carried in 2000-01 were 26 per

cent and 74 per cent respectively (Ministry of Railways,

2002; Ministry of Surface Transport, 2001).

The last major committee to look into the

development of the transport sector as a whole in India

was the Steering Committee on Perspective Planning for

Transport Development (Planning Commission, 1988).

The report of the Committee contains projections of

transport demand based on a study by RITES which

makes projections of traffic volumes and average leads

for the years 1994-95 and 1999-2000. Rail freight traffic

was projected at 462 btkms in 1999-2000 with an average

lead of 852 kms. The corresponding road traffic was

projected at 157 btkms, the average lead being 397 kms.

The break-up between rail and road is accordingly in

the ratio 75:25. Assuming that recent trends of transport

coefficients and average leads would continue for the

future, the Committee projected freight traffic on rail inthe year 2000 at 516 btkms. By the trend growth rate

approach, rail freight traffic was projected at 374 btkms

and road freight traffic at 686 btkms in 1999-2000, giving

a modal split of 35:65 between rail and road respectively.

For passenger traffic in the same year, the rail volume

was projected at 256 billion passenger kilometres (bpkms)

and the road traffic at 2916 bpkms, giving a modal split

of 8:92 between rail and road. While no transport

committees with such broad objectives as the ones

discussed here have since been set up, nevertheless,from time to time, the Government of India has constituted

expert groups to look into the aspects of one or more

transport modes in the country.

Table 1 gives a summary of the historical trend in

traffic volumes and modal shares of the rail and road

modes of transport (Planning Commission, 1988, 2001;

Ministry of Surface Transport, 1996, 1999, 2001; and the

Annual Stat istical Statements of the Indian Railways for

various years). India became a decidedly road-dominant

economy in the beginning of the eighties with the railways

losing out in respect of freight traffic in addition to its

already declining share in passenger traffic. The

dominance of road over rail has since continued unabated

till the present and is almost certain to continue into the

future. The share of rail in the total freight traffic carried

by both rail and road declined from 61 per cent in 1970-

71 to 47 per cent in 1980-81, 30 per cent in 1990-91, and

26 per cent in 2000-01. The decline in the share of rail

passenger traffic is almost equally dramatic: the rail

mode had a much reduced share of 31 per cent in 1970-

71 which declined to 24 per cent in 1980-81, 15 per centin 1990-91, and seems to have risen slightly to 18 per

cent in 2000-01.

Table 1: Rail and Road Traffic Volumes and Modal Shares

Year Rail Freight Rail Pass. Road Freight Road Pass. Total Freight Total Pass. Rail Modal Rail ModalTraffic Traffic Traffic Traffic Traffic Traffic Share in Share

Freight in Pass.Transport Transport

(BTKM) (BPKM) (BTKM) (BPKM) (BTKM) (BPKM) (%) (%)

1950-51 44 67 12.09 44.80 56.09 111.80 78.45 59.93

1960-61 88 78 32.53 105.04 120.53 183.04 73.01 42.61

1963-64 107 89 41.05 139.70 148.05 228.70 72.27 38.921964-65 107 93 45.46 162.13 152.46 255.13 70.18 36.45

1970-71 127 118 82.36 263.09 209.36 381.09 60.66 30.96

1977-78 163 177 114.97 484.98 277.97 661.98 58.64 26.74

1980-81 159 209 178.36 664.83 337.36 873.83 47.13 23.92

1985-86 206 241 307.03 1038.56 513.03 1279.56 40.15 18.83

1990-91 243 296 566.66 1615.20 809.66 1911.20 30.01 15.49

1995-96 274 342 762.00 2238.00 1036.00 2580.00 26.45 13.26

2000-01 312 457 899.26 2127.96 1211.26 2584.96 25.76 17.68

Note: pass. passenger, BTKM billion tonne kilometres, BPKM billion passenger kilometres.


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During the period 1950-51 to 2000-01, the elasticity

of total freight transport carried by road and rail in India

with respect to GDP was 1.48 while the same for total

passenger transport was 1.70. Thus, the total transport

output in India with respect to both freight and passenger

service has grown faster than the national income. The

total freight traffic increased from 56 btkms in 1950-51

to 337 btkms in 1980-81 and then to 1,211 btkms in 2000-01, a more than twenty-fold increase since 1950-51. The

total passenger traffic grew from 112 bpkms in 1950-51

to 874 bpkms in 1980-81 and subsequently to 2,580 bpkms

in 2000-01, a twenty three-fold increase since 1950-51.

Between 1950-51 and 2000-01, the elasticity of rail

passenger transport service in India with respect to GDP

was 1.01. Passenger service on rail, therefore, appears

to have kept pace with GDP. From a figure of 67 bpkms

in 1950-51, rail carried 209 bpkms in 1980-81 and

subsequently 457 bpkms in 2000-01, an almost seven-

fold increase since 1950-51. Suburban traffic accounted

for about 19.4 per cent of the total bpkms in 2000-01.

The elasticity of rail freight transport with respect to

GDP turns out to be 0.86. The number of net freight tonne

kilometres (revenue-earning) went up from 44 billion in

1950-51 to 159 billion in 1980-81 and then to 312 billion

in 2000-01, an increase of seven times since the beginning.

All forms of road transport have shown spectacular

increase in volume since independence. Road passenger

traffic and road freight traffic grew at annual rates of

8.02 per cent and 9 per cent respectively during theperiod 1950-51 to 2000-01. The elasticity of road freight

transport with respect to GDP was 2.15 while for road

passenger transport, it was 2.00. These elasticities are

substantially higher than those of rail transport. The

passenger kilometres of road transport went up from 45

billion in 1950-51 to 665 bill ion in 1980-81 and then to

2,128 billion in 2000-01, an almost fifty-fold increase

since the initial period. In terms of net tonne kilometres,

freight movement by road transport rose from 12 billion

in 1950-51 to 178 billion in 1980-81 and subsequently to

899 billion in 2000-01, an increase of seventy-five timessince 1950-51.*

Transport volumes have actually grown to levels

greater than those predicted in the work of the committees

described above. The pattern of economic development

with increasing dispersion of industries and markets,

the nature of modern production with requirements of

efficient delivery of factors and products, and, to a certain

extent, the spurt in passenger movement on account of

higher incomes all mean that the demand for transport

has been growing at a faster rate than the growth in

national product. These developments have also meant

a lower share for the rail mode than predicted in earlier

studies. Road transport has been more flexible than rail

transport in adapting to the needs of the economy,specializing in the transport of high-value, non-bulk

products. There has been spectacular development of

motorized road transport both for passenger and freight

movement. While the greater share of the road mode in

transport demand may be explained by inherent

advantages in terms of accessibility, convenience, and

door-to-door delivery, factors such as underinvestment

in rolling stock and line haul capacity on the rail mode,

along with the lack of a customer-oriented approach,

have led to an increasing shift in patronage towards the

road mode.

FACTORS LIKELY TO INFLUENCE MODALCHOICE

A number of variables might be included in a study of

the factors behind a choice of transport mode. Intuitively,

the relative cost of alternative transport modes should

have an influence on the decision-maker: the cheaper

mode ought to be the preferred mode. However, it is not

immediately known which form of the cost variable is

most relevant. Some studies have used the cost ratiobetween alternative modes while others have used the

cost difference. Under the difference formulation, the

consumer gives the same amount of consideration to

choosing between a Rs 1.05 and Re 1.00 pair of alternative

as between a Re 0.10 and a Re 0.05 pair. On the other

hand, if the cost ratio between alternative modes is of

importance, then the first pair of alternatives would have

to be, say, Rs 2.00 and Re 1.00 if the consumer is to be

indifferent between this pair and the Re 0.10/Re 0.05

pair. Lave (1969) is of the opinion that neither the diffe-

rence nor the ratio formulation seems to be absolutely

correct but that the truth would seem to lie close to the

former.

The variable of relative time of travel of alternative

transport modes also presents the same problem of choice

as that between the difference and the ratio specification.

In support of the difference formulation for the relative

time variable, Lave (1969) cites contemporary writings

concerning the value of time (Becker, 1965; Moses and* Calculated from data in the above sources.


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Williamson, 1965). Once it is assumed that time has

monetary value, then a time differential can be expressed

in currency units, and the relative time and relative cost

information for a given fair of alternatives may be

combined into a single relative cost figure to describe

the difference between the two modes. Among other

things, this makes it possible to say that, other things

being equal, the passenger or consignor will choose thatmode which has the lowest combined cost. The mea-

surement of the value of time is important because time

savings amount to most of the potential benefit from

improvements in transport, typically about 80 per cent.

The next important influencing variable on modal

choice is relative comfort and convenience. Lave (1969)

argues that deficiency in both comfort and convenience

of public transport in the US had been the most important

factor in the post-war decline in the patronage of mass

transit. However, hardly any study had made an attempt

to quantify the comfort variable and use it in a modal

split model. Ideally, the analyst should have a scale of

subjective valuations of comfort and a corresponding list

of objective characteristics such as seating dimensions,

crowding, booking procedure for shipments, etc. It would

then be possible to develop on objectively measurable

set of indices of comfort.

The above three variables relative costs, relative

time, and relative comfort are instrumental variables

in the sense that they could be useful for implementing

some normative goal on the part of the decision-maker.The next important variable is personal income. It is a

difficult variable to handle since its influence may be

felt in other directions and it may interact in a complex

manner with other variables influencing modal choice.

On account of its collinearity with many other variables,

a number of aggregate modal split models have derived

the major part of their explanatory power from the income

variable alone. If one is analysing the modal choice

between, say, road and rail in respect of first-class

passenger travel, then the income variable is important

since rising income levels might help to explain why incertain situations car travel, which is more expensive,

is being preferred by the consumer of transport services

to travel on first-class rail. One may also look upon the

income variable as operating a constraint on choice of

mode. It is reasonable to assume that if incomes fall

below a certain critical level, then the commuter will not

be able to afford car travel.

Other variables that might be included in a detailed

study of modal split are purpose of trip, family size and

composition, sex and age of the commuter, and distance

of travel.

GENERATION OF DATASETS

In India, there is no detailed or extensive database on

modal split between transport modes on important routes

along with the costs of transport operations and otherimportant variables such as user perception of travel or

shipment on alternative modes. Studies in the past have

looked at the important trends in transport in the country

and sometimes discussed specific modal costs. However,

the information contained in these studies does not permit

the construction of a sufficient database for econometric

analysis. On the one hand, the data on modal splits is

either at an aggregate level or confined to a few selected

routes, and on the other hand, in many cases, the data

on modal splits is not accompanied by corresponding

data on user costs on the part of the passenger or shipper.

In addition, there is no time series of modal split and

accompanying factors such as user costs and perceptions

of the quality of service.

For this study, relevant data on modal split between

rail and road, user costs, and per capita income could

be obtained either direct ly or estimated for eight

representative sections in the country: New Delhi-Mughal

Sarai, Jalandhar-Jammu, Jabalpur-Allahabad, Lucknow-

Gorakhpur, Secunderabad-Wadi, Gudur-Renigunta,

Bhopal-Ujjain, and Ratlam-Godhra. In all these sections,the rail and road modes are in competition with each

other, both in passenger and freight traffic, the rail track

(whether single-line or double-line) being contiguous

with a national or state highway (mostly two-lane). The

first four of the above sections have national highways

while the remainder have state highways. Besides, the

selected sections vary in respect of terrain and length.

The lengths for railways were worked out by looking

at the zonal working time-tables giving distances between

successive stations for the concerned sections. The lengths

of national highways were obtained from the Ministry

of Road Transport and Highways (formerly Ministry of

Surface Transport), while for state highways, they were

calculated on the basis of state road maps. The longest

ection, namely New Delhi-Mughal Sarai, has a rail route

length of 780 kms and a road length of 825 kms, while

the shortest section, Gudur-Renigunta, has a rail route

length of 83 kms and a road length of 75 kms.

Our objective is to relate the modal split between


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rail and road to important explanatory variables such

as relative user costs and per capita income, given that

suitable data on other factors, such as user perception

of quality of service on alternative modes, is not available.

The main hypothesis of this study is that as the difference

between road and rail user costs, or the ratio between

the two goes up, then, other things being equal, the share

of the rail mode in the total traffic should increase. Inaddition, we wish to bring out the role of measures of

personal income in our analysis. Since the road mode

has such advantages as greater convenience and

accessibility, the hypothesis to be tested with regard to

the income and consumption expenditure variables is

that as these go up, the share of the rail mode should

decline as consumers are enabled to choose the

(qualitatively better but probably more expensive) road

mode. These hypotheses are examined under three

scenarios of inter-modal competition.

It should be borne in mind that all traffic within our

selected sections, which are a part of the larger network,

does not necessarily begin and end at the terminal points.

The origin and destination of traffic movements may lie

outside or inside the sections. Thus, a certain sectional

distance is not a criterion for competition between rail

and road. However, these traffic volumes are related to

explanatory variables such as user costs within the

sections; it seems reasonable to assume that the latter

variables, in so far as they have any influencing power,

can explain more or less the pattern of traffic movingwithin the selected sections. The role of network effects,

therefore, may not be fully captured in the user cost

estimates derived for the current analysis, especially in

so far as the component of operating cost reflected in

the fares and charges paid to the transport operator is

concerned.

We shall now describe how time-series data on each

of the variables is constructed.

Traffic Volumes

The time series of traffic volumes relating to bothpassenger and freight transport for both the rail and road

modes were first derived in each of the selected sections.

Table 2 summarizes the steps in the generation of data

on traffic volumes for each case of competition between

rail and road. We have looked at three cases of inter-

modal competition: (i) between car on road and first-

class/air-conditioned (AC) travel on rail (such categories

as AC chair and AC sleeper classes being included in

the latter); (ii) between bus on road and second-class/

sleeper-class travel on rail; and (iii) between freight

services on rail and road. On the basis of the data

contained in the given sources, calculated growth rates

and assumptions and estimates of the average daily

numbers of cars, buses, and trucks in each of the selected

road sections were first made for the period 1986-87 to

2000-01, and then the passenger kilometres and net tonnekilometres represented by these vehicle numbers were

worked out.

In the case of rail traffic volumes, the total daily

passenger kilometres (pkms) for a particular section

were calculated for the year 1998-99 by multiplying the

occupancy of each train1 by the lead and frequency, and

then summing up across all passenger trains. For the

years before and after 1998-99, the rates of change of

passenger kilometres for the previous and successive

years for the regional railways covering the selected

section of interest were used to estimate the daily pkms

of rail traffic. The following classes of rail passenger

travel were taken to be in competition with travel by car:

general air-conditioned, general first-class, air-

conditioned chair, air-conditioned first class, air-

conditioned sleeper, air-conditioned three tier, first-class

rail, and first-class ordinary classes. The classes of rail

travel that are taken to be competitive with travel by

bus on road are second-class mail, second-class ordinary,

sleeper-class mail, and ordinary sleeper classes. Coming

to the derivation of freight transport volumes on rail,we made use of the statements of line capacity utilization

in 1998-99 and the tonnage of a four-wheeler wagon to

arrive at daily net tonne kilometres (ntkms) for a particular

section in 1998-99. Rates of change of ntkms were

calculated on the basis of data in the Annual Statist ical

Statements in order to derive the ntkms of other years

for the particular section.

User Costs

Our next objective is to estimate the user costs of travel

or shipment on each of the rail and road modes for theperiod in order later to analyse the relationship, if any,

between cost differentials or cost ratios between the

modes and the share of traffic of one mode in total traffic.

The total user cost for transport service consists of a

number of components. There is, first, the financial

payment to the supplier (which may depend on tax and

subsidy elements). This payment is reflected in bus fares,

train fares, and shipment rates. Apart from this basic


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payment, the user of transport service has to incur costs

for services other than those provided by the transport

supplier. This category of cost includes the following:(i) cost of porterage and local transport (in the case of

passenger service), and (ii) cost of packing, handling,

and local cartage (in the case of freight service). The cost

of porterage and local transport is relevant in the cases

of travel by bus on road and travel or shipment by rail.

The cost of packing, handling, and local cartage is incurred

when the shipper avails of freight service on either the

rail or road mode. A special category of costs in the case

of freight service comprises illegal rental payments or

unofficial fees that are charged to the user by various

parties directly or indirectly connected with the provision

of transport service.

Besides these categories of costs, the passenger or

shipper incurs special costs that are not reflected in

transactions with other parties. When a passenger is

travelling, a value is attached to the time he or she spends

in transit, depending upon the opportunity cost of travel.

This opportunity cost is the loss in earnings which is

reflected in the hourly wage rate. The value of passenger

time (VOPT) is included as a component of the total user

cost of passenger travel. Similarly, when a shipper

undertakes to avail of freight service by a particularmode, he or she incurs a special cost while the commodity

is in transit. This is again an opportunity cost which is

measured by the interest the shipper would earn on the

total value of the shipped commodity. The opportunity

cost of freight shipment is termed the cost of commodity

in transit and is a component of the total user cost of

freight shipment.

The total user cost, therefore, includes the financial

cost of transport service charged by the supplier, costs

that are incurred apart from the payment to the supplier,

and opportunity costs. The three cases of competition

and the categories of user cost applicable to each case

are presented in Table 3.

We now discuss the derivation of the components

of user cost for each of the three cases of inter-modal

competition. Exhibit 1 illustrates the different steps in

the generation of data on user costs for both modes. In

the case of road transport, the manual of the Indian

Roads Congress (IRC) provided the means of making

Table 2: Generation of Data on Traffic Volumes

Nature of Inter- Variable Sources of Data Assumptions Values Obtainedmodal Competition

Car on road vs Road passenger Road surveys, discussions, Car occupancy Time-series from 1987-88first-class/AC traffic volume motor transport statistics, factor = 4 to 1999-2000travel on rail statistics of the ASRTU

Rail passenger H.Q. of zonal railways of Average daily occupancy do traffic volume interest for the year 1998-99, of train for intercity

Indian Railways ASST for travel = 80% of stated

other years carrying capacity

Bus on road vs Road passenger Same as for the road traffic Bus occupancy Time-series from 1986-87second-class/sleeper- traffic volume volume in the previous factor = 40 to 1999-2000class travel on rail scenario of competition

Rail passenger Same as for the rail traffic Average daily occupancy do traffic volume volume in the previous of train as above

scenario of competition

Freight shipment by Road freight Road surveys, discussions, Proportion of LCVs = 15%, Time-series from 1986-87road vs freight traffic volume motor transport statistics, proportion of MAVs = 10% in to 1999-2000shipment by rail road user cost study north India and 8% in south

India, LCV payload = 5 tonnes,HCV payload = 9 tonnes, MAVAv. comp. payload = 18 tonnes,load factor for LCV, HCV andMAV = 100%, 100% and 90%

respectively on NH, and 90%,90% and 80% on SH, 20%empty trucks on NH and 30%empties on SH

Rail freight Line capacity utilization Payload of four-wheeler do traffic volume statements for 1998-99, Indian wagon = 24 tonnes

Railways ASST for other years

Notes: ASRTU Association of State Road Transport Undertakings, H.Q. headquarters, ASST Annual Statistical Statements, LCV light commercial vehicle, MAV multi-axle vehicle, HCV heavy commercial vehicle, Av. Comp. average composite, NH nationalhighways, SH state highways; the reference for the road user cost study is Ministry of Surface Transport/CES (1989).


24


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estimates of road operating cost for all types of vehicles

in different types of terrain, road surface, and traffic

congestion (Indian Roads Congress, 1993). The data

contained in the manual was based on a road user cost

study of 1982 and further results were obtained in the

Study for Updating Road User Cost Data (Kadiyali andAssociates, 1992). Adjusting the data for congestion and

making use of assumptions relating to the composition

of traffic, we have derived the estimated costs of travel

by car per passenger kilometre incorporating both the

higher cost of travel by taxi and VOPT. 2

In respect of the rail mode, the Annual Statist ical

Statements were used to derive estimates of the average

rail fares per passenger kilometre for the air-conditioned

and first classes during the period of interest in all the

selected eight rail sections. The estimates were then

expressed in 1997-98 prices by use of the GDP deflator.The costs of porterage and local transport for passenger

travel by rail, as given in Planning Commission/RITES

(1987-88), were adjusted for inflation to express them in

terms of prices of the year 1997-98. The same source was

used to estimate VOPT on rail. The sum of rail fare, costs

of porterage, local transport, and VOPT on rail gives us

the total user cost per passenger kilometre for travel by

first-class/air-conditioned rail classes in each of our

selected horizons across the given time period.

The average fares per passenger kilometre for travel

by bus were estimated by making use of the performance

statistics of public bus companies published by the

Association of State Road Transport Undertakings

(ASRTU). As we are concerned with intercity traffic, in

making these estimates, we have excluded the data

relating to operations in metropolitan areas. The cost of

porterage and local transport for bus travel was taken

from the same source as mentioned above for rail travel.

The VOPT pertaining to travel by bus was next worked

out by using the data in the IRC manual which allowed

us to estimate different values of VOPT in different

conditions of traffic density.

To estimate the corresponding user costs of travel

by rail , the Annual Statistical Statements of the Indian

Railways were used for the period under considerationand the average rail fare per passenger kilometre was

worked out. To these estimates of rail fares, the cost of

porterage and local transport and the values of passenger

time per passenger kilometre (as worked out earlier)

were added to arrive at the total user costs of travel in

second-class/sleeper-class on rail.

To estimate the road freight bill, we first estimated

the costs of operation of trucks per net tonne kilometre

in the same way as was done for car in the first scenario

of inter-modal competition. Applying a mark-up to these

operating costs, estimates of freight bills paid by theshipper on road were derived. An average cost of packing,

handling, and local cartage for road shipment was worked

out by taking seven principal commodities for which

data was available for both road and rail. Other expenses

incurred en route on the part of the truck operator included

check-post expenses, charges paid to transport officials

and the police, loading and unloading charges and others.

Using information in the report of the Steering Committee

on Trucking Operations in India, we worked out the

unofficial expenses of freight shipment on road per tonne

kilometre and applied them to each of our selected

sections in accordance with geographical proximity. An

average cost of commodity in transit on road was worked

out by making use of the VOC tables referred to earlier

duly adjusted for congestion as reflected in the prevailing

density of traffic.

In the case of rail mode, rail freight rates were

estimated by using data on earnings from revenue-

earning freight traffic in theAnnual Stat istical Statements .

Table 3: Cases of Inter-modal Competition and Components of Rail and Road User Costs Considered in the CurrentStudy

Nature of Inter-modal Fare/Charge Cost of Cost of Packing, Unofficial Fees/ Value of Cost ofCompetition Paid to Supplier of Porterage and Handling, and Illegal Rents Passenger Commodity

Transport Service Local Transport Local Cartage Time in Transit

Car on road vsfirst-class/AC travel on rail XX X* XX

Bus on road vs second-class/

sleeper-class travel on rail XX XX XXFreight shipment by road vsfreight shipment by rail XX XX XX XX

* This component of user cost is included in the case of travel by air.X User cost is included in the user cost of only one mode.XX Particular category of user cost is included in total user cost for both the modes.


25


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An average cost of packing, handling, and local cartage

was next worked out by taking into account the same

important commodities as in the case of road mode. The

next item of user cost for freight shipment by rail

considered in our analysis was the cost of extra-official

fees and charges relating to documentation, claims

settlement, etc. Data on commissions and extra-official

fees in the Indian Railways was difficult to obtain. Inworking out the value of illegal rental payments in India

for 1964, Krueger reproduces data of a study done in

1966 in which some information is given on this subject

(Krueger, 1974). For this study, the data was adjusted

in line with inflation, increasing corruption, documen-

tation, and other charges unique to the railways to arrive

at a figure representing the sum of extra-official fees and

other charges to be paid by the user of rail freight service.

Finally, the cost of commodity in transit for rail shipment

was derived by using data for the seven specific

commodities mentioned earlier.

Measures of Income/Expenditure

The next important explanatory variable that we included

in our datasets was per capita income. Unfortunately,

the available data did not permit us to construct a time

series of personal income for each of the scenarios of

competition in the sections selected for this study.

Accordingly, we had to devise some proxy measures of

this variable. In the case of two of the scenarios of

competitionbetween sleeper-class/second-class railand bus and between rail and road freight service we

used the series on gross state domestic product (GSDP)

published by the Central Statistical Organization and

population figures of each state to work out values of

per capita GSDP for each of the selected sections in all

the years considered. The per capita GSDP values thus

derived served as proxies for the income variable in our

analysis of the two scenarios of competition mentioned

above.

While considering the competition between air-

conditioned, first-class rail, and car, we need to have

estimates of income of the upper-income bracket of the

population who are most likely to use these transport

services. For this particular case of competition, we

estimated the levels of minimum consumption expen-

diture of the richest 10 per cent of households in each

of the states covering our selected sections. For the period

covered by this study, there are three quinquennial

surveys of consumer expenditure published by the

National Sample Survey Organization (NSSO): 1987-88,

1993-94, and 1999-2000 (termed the 43rd, 50th and 55th

rounds respectively). In each of these surveys, we have

looked at the distribution of a thousand sampled

households in the urban sector of each concerned state

over classes of monthly per capita consumer expenditure

(MPCE). A lognormal distribution was fitted to the given

data in order to make estimates of consumptionexpenditure of the richest 10 per cent of the population.

Given these estimates of upper-level consumption

expenditure in the years 1987-88, 1993-94, and 1999-

2000, the values for the intervening years were filled in

by looking at the changes in the ratio of estimated

consumption expenditure to the per capita GSDP. It was

assumed that the ratio would change according to a

geometric progression.

We thus constructed three datasets in which were

included relative traffic volumes of the rail mode (or the

ratios of rail to road traffic volumes), user cost differences,

user cost ratios, per capita GSDP, and upper income level

consumption expenditures. Our next task is to analyse

whether there are any statistically significant relation-

ships between the share of rail and the explanatory

variables.

THE MODEL

We have taken the relative traffic volume of rail (the ratio

of the rail traffic volume to the road volume) as the

dependent variable while the independent variables(depending on the particular case of inter-modal

competition) include: (i) the difference between the user

cost on road and the same on rail, (ii) the ratio of the

user cost on road to the same on rail, (iii) per capita

monthly consumption expenditure of the upper-income

class, and (iv) per capita yearly gross state domestic

product. The inclusion of more variables in our analysis

could have resulted in a greater probability of finding

significant relationships under the options of various

econometric models. However, because of paucity of

data, we had to limit ourselves to the above explanatory

variables in explaining modal spilt between rail and

road. The restricted nature of the choice of explanatory

variables may influence the nature of the model in which

statistically significant relationships are ultimately

derived.

The econometric analysis of the trends in traffic in

all the three cases of competition was carried out with

the help of Stata 6.0. The datasets were arranged in


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panels, each of which is composed of the data of a

particular transport section from 1986-87/1987-88 to 1999-

2000. Heteroscedasticity was detected in the data in all

the three cases. The datasets were, therefore, analysed

under generalized least squares (GLS) regression

involving both random effects and fixed effects. In all

the datasets, the Hausman test did not indicate any

systematic differences in coefficients between the twomodels. The coefficients of the explanatory variables

turned out to be insignificant at the 10 per cent level in

most cases. Subsequently, cross-sectional time-series

feasible generalized least squares (FGLS) was carried out

on the datasets. The models tested were: (i) generalized

least squares with heteroscedastic panels, (ii) GLS with

heteroscedastic panels and within-panel correlation in

the form of a common AR(1) coefficient for all panels,

(iii) GLS with heteroscedastic panels and panel-specific

AR(1) correlation. The model that finally yielded

statistically significant relationships was found to be the

cross-sectional time-series FGLS model involving

heteroscedastic panels with cross-sectional correlation

and panel-specific AR(1) correlation. This model is

described in the Appendix.

RESULTS AND INTERPRETATION

The econometric exercise was aimed at seeing whether

there is change in the relative traffic volume of rail in

relation to the user cost difference or ratio between road

and rail and measures of personal income and expen-

diture. The share of rail was expected to rise with increasein the values of the cost variables as the least expensive

mode is preferred. Conversely, it was expected to fall

with increase in income or expenditure since customers

and shippers were likely to favour the road mode with

its attendant qualitative advantages.

It should be mentioned that the effects of a change

in the inter-modal user cost difference (user cost ratio)

cannot be isolated from the accompanying change in the

inter-modal cost ratio (cost difference). Furthermore, if

one of the variables changes, the other need not change

in a fixed manner. Hence, the statistical relationships

that we have been able to establish are conditional on

the structure of changes, in the given datasets, in one

cost variable and accompanying movements in the other.

The main results are presented in Table 4. While

sectional and year dummy variables are included in the

analysis, we concentrate on the impact of the main

explanatory variables of cost and income on the relative

traffic volume of rail.3

Passenger Transport

In the case of inter-modal competition involving car onroad and first-class/air-conditioned rail, we find that

linear relationships hold between relative traffic volume

on the one hand and user cost difference/ratio and

consumption expenditure on the other. The elasticity of

the relative traffic volume of rail with respect to user

cost difference between road and rail is only 0.022.

However, a 10 per cent rise in monthly per capita

consumption expenditure leads to a 9.9 per cent decrease

in the relative traffic volume, with the cost difference

variable being held constant. Coming next to the effect

of the cost ratio between road and rail, we find that the

elasticity of relative traffic volume of rail with respect

to user cost ratio is 0.53. If the cost ratio is unchanged,

a 10 per cent increase in consumption expenditure leads

to a 11.6 per cent fall in the relative traffic volume.

Equiproportional increases in the cost difference

and cost ratio variables, therefore, lead to a greater

upward impact on the relative traffic volume in the case

of the latter than for the former variable. The relationship

Table 4: Cross-sectional Time-series FGLS Regressionsof Relative Traffic Volume of Rail on Cost andOther Variables

Form of Equation Explanatory EstimatedVariables Coefficient

Passenger transport: Competition between car on road and first-class/air-conditioned rail

Log-linear Cost difference 0.022

Consumption expenditure -0.985Log-linear Cost ratio 0.533

Consumption expenditure -1.157

Passenger transport: Competition between bus on road andsecond-class/sleeper-class rail

Linear Cost difference 0.162

Linear Cost ratio 0.171

Cubic Cost difference 0.475

Cost difference squared -1.088

Cost difference cubed 1.009

Cubic Cost ratio 7.399

Cost ratio squared -5.501

Cost ratio cubed 1.386

Freight transport: Competition in freight traffic between road andrail

Log-linear Cost difference -1.253

Per capita SDP -0.352

Log-linear Cost ratio -0.88


Quadratic Cost difference 9.438

Cost difference squared -3.697


Note: All the coefficients are significant at 5 per cent level.


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between consumption expenditure and relative traffic

volume suggests that as incomes rise for the upper-

income bracket of the population, there is a tendency

to switch over to the more expensive car mode affording

greater comfort and convenience.

Next we turn to the case of competition between bus

on road and second-class/sleeper-class travel on rail.

The results of the cross-sectional time-series FGLSregression of relative traffic volume of rail on cost

variables and per capita state domestic product (SDP)

yielded coefficients of the SDP variable that suggested

the lack of a statistically significant impact of this factor

on relative traffic volume. Inter-modal competition with

bus on road does not seem to be influenced by the income

factor, which in this study is represented by per capita

GSDP. Hence, only the cost factors were used in the

subsequent analysis. For the vast majority of the travelling

population, user cost differentials or ratios appear to be

a more important factor in determining modal choice.

Taking the cost difference between rail and road first,

we find that linear regression yields a relationship in

which a unit increase in this variable is related to an

increase in the relative traffic volume of rail by an amount

0.162. If we come to the cost ratio, a unit rise in this

variable leads to an increase in the traffic volume by an

amount 0.171. Since the semi-log and log transformations

of the regression yielded insignificant coefficients, we

decided to examine the results of non-linear regression

of relative traffic volume on either of the cost variables.Quadratic regression yielded statistically insignificant

coefficients in both the cost difference and cost variable

cases. However, regression using a cubic form yielded

statistically significant coefficients in both cases. For a

uniform change in cost difference or the cost ratio between

road and rail, the relative traffic volume of rail increases

in a fluctuating manner with declining percentage

increases for an initial range of cost difference or cost

ratio values and increasing rates of increase for sub-

sequent values.

Ignoring the coefficients of the sectional and year

dummies, we have used, as an illustrative case, the equa-

tion of the cubic regression of relative traffic volume of

rail on the cost difference between road and rail to

examine the behaviour of the latter if the value of the

cost variable is increased uniformly in the range given

by the dataset of this scenario of inter-modal competition

(Table 5).

For a uniform change in the cost difference between

road and rail, the relative traffic volume of rail increases

in a fluctuating manner with declining percentage in-

creases between the cost difference values 0.01 and 0.37,

and thereafter increasing rates of increase for the

subsequent values. The same behaviour is exhibited in

the case of the cost ratio variable. Given the structure

of cost differences and ratios in the given dataset, when

either the user cost difference or cost ratio between roadand rail rises to certain critical levels, the modal share

of rail (as indicated by the relative traffic volume), which

has been rising at a diminishing rate, begins to increase

in an exponential manner.

Freight Transport

We finally come to the analysis of competition in freight

transport between the two modes. Linear regression

under the cross-sectional time-series FGLS model was

variables carried out separately for cost difference and

cost ratio variables. The elasticity of relative traffic volume

of rail with respect to user cost difference is -1.25, while

the same with respect to user cost ratio is -0.88. The

modal share of rail does not go up with increase in the

user cost difference or cost ratio between road and rail

unlike the earlier cases of competition in passenger traffic.

The elasticity of the traffic volume with respect to per

capita SDP is about -0.35 in both cost difference and cost

ratio relationships. It is per capita SDP, therefore, that

Table 5: Inter-modal Cost Difference and Relative Traffic

Volume (competition between bus and rail)

Cost Difference Relative Percentage Increasebetween Road and Traffic Volume of Volume withRail (Re) of Rail* Respect to Previous

Value

0.01 0.749 -

0.05 0.765 2.20

0.09 0.779 1.76

0.13 0.789 1.40

0.17 0.798 1.10

0.21 0.805 0.86

0.25 0.810 0.67

0.29 0.815 0.53

0.33 0.818 0.45

0.37 0.822 0.41

0.41 0.825 0.42

0.45 0.829 0.48

0.49 0.834 0.59

0.53 0.840 0.73

0.57 0.848 0.93

0.61 0.858 1.16

0.65 0.870 1.43

0.69 0.885 1.73

* The ratio of rail volume to road volume.


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appears to influence modal split in the expected manner,

suggesting that users of rail switch to the road mode as

incomes rise, the latter offering a range of facilities which

the rail mode cannot provide. A quadratic regression

yields statistically significant coefficients for the cost

difference variable and insignificant coefficients for the

cost ratio variable. We used the quadratic relationship

between traffic volume, cost difference, and per capitaSDP to examine how relative traffic volume of rail varies

with uniform increase in the values of cost difference

in the range given by the dataset, while per capita yearly

SDP is fixed at an average level (Rs 11,500) and the

coefficients of the section and year dummies in the

equation are ignored (Table 6).

The table shows that the modal share of rail increases

at a diminishing rate when the cost difference between

road and rail is increased from 1 to 1.28. Beyond this

point, however, the modal share declines continuously

at an increasing rate. We may surmise that the cost factor

is not very helpful in explaining modal split in the case

of competition in freight service. Qualitative factors seem

to play a more important part. It is probable that increases

in the user cost for shipment by road are accompanied

by a rise in the perceived quality of service prompting

users to switch over to this mode.

It may be noted that generally, the relationship,

where it exists, between the cost variable and modal

share is a weak one. Our datasets show that the user

cost difference between road and rail (as well as the user

cost ratio) for both passenger and freight competition

has moved upward although with significant fluctua-

tions. The results on the competition between car on road

and first-class/AC travel on rail implies that if this trend

continues into the future, there will be an increasing

share of the rail mode that will, however, be counter-balanced by a movement towards road as personal

incomes go up.

The relevant dataset shows that the rail share has

indeed been rising for this set of competition. For the

competition between bus on road and second-class/

sleeper-class travel on rail, the concerned dataset shows

a declining share of rail although with significant fluc-

tuation from year to year. Our results indicate a weak

relationship between these fluctuations and movements

in the user cost variable, other factors influencing modal

split not being taken into account. The national trend

of increasingly lower modal share of rail in passenger

transport is almost certain to continue on account of

these other factors, chief among them probably being

determinants of the quality of service such as availability

and comfort. Finally, as far as competition in freight

transport is concerned, we have seen that increasing

differentials in user costs do not explain modal split

while future rise in incomes can only mean a lower share

of the rail mode.

Some idea on future aggregate modal splits in Indiamay be obtained from recent studies (Expert Group on

Indian Railways, 2001; Ministry of Surface Transport,

2001). As far as passenger traffic is concerned, modal

shares of rail and road are expected to be 14 per cent

and 86 per cent respectively in 2005-06 under the

assumption of unchanged structure of rail fares. These

shares are expected to change slightly in favour of road

if there is adjustment in upper- and second-class rail

fares. The modal shares of rail and road in freight traffic

are projected at 21 per cent and 79 per cent for 2015-

16 under the assumption of uniform growth rate in trafficof all commodities on rail. With the assumption of

commodity-specific growth rates, this rail share is

expected to go down slightly.

CONCLUSION

In the light of these results, it may be stated that efforts

at reduction of high tariffs for shipment by rail should

be accompanied by improvements in the quality of

Table 6: Inter-modal Cost Difference and Relative Traffic

Volume (inter-modal competition in freightservice)

Cost Difference Relative Traffic Percentage Change ofbetween Road Volume of Rail Volume with Respectand Rail to Previous Value

1.00 3.324 -

1.04 3.400 2.28

1.08 3.464 1.88

1.12 3.517 1.51

1.16 3.557 1.15

1.20 3.586 0.80

1.24 3.602 0.47

1.28 3.607 0.14

1.32 3.600 -0.19

1.36 3.582 -0.521.40 3.551 -0.85

1.44 3.509 -1.19

1.48 3.454 -1.55

1.52 3.388 -1.91

1.56 3.310 -2.30

1.60 3.221 -2.71

1.64 3.119 -3.15

1.68 3.006 -3.64

1.72 2.880 -4.17

1.76 2.743 -4.76

1.80 2.594 -5.43


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service. Shippers perception of the quality of service is

influenced by factors such as connectivity, availability,

reliability, transit time, ease of payment, negotiability,

adaptability, product suitability for mode, claim-

processing time, access to decision-makers, suitability

of price, customer-friendly attitude, and customer infor-

mation. For all these parameters, a survey among shippers

in India found that the rail mode is ranked significantlylower than the road mode (A F Ferguson & Co., 1999).

The issue of quality of service is crucial in the segment

of freight transport more so because it is one area in

which the railways seem to have a greater environmental

and financial advantage over roadways than in passenger

transport (Dey Chaudhury, 2003). It is, therefore, impera-

tive that the task of redressing the current distortion in

freight modal shares be addressed on the part of the

policy-maker in the interest of bringing about a socially

desirable modal split. While our analysis shows that user

cost factors appear to play a negligible role, attention

should nevertheless be given to ways of internalizing

the external costs of freight transport so that each mode

of transport is made to bear the social costs of transport.

In particular, heavy goods vehicles on road that cause

damage to the pavement should be made to defray the

cost of road repair and maintenance.

Much of the profit generated by the railways in

freight movement goes towards the subsidization of

passenger transport. Because of social commitment, there

is little scope for increase in passenger fares. If theobjective is to divert passenger traffic from road, then

measures aimed at the internalization of the external

costs of transport, which studies show are generally

lower for rail, need to be considered. Road users should

be made to pay for the cost of infrastructure provision

and maintenance through toll charges. The pricing of

transport services should take into account the costs of

such factors as pollution and congestion. The external

costs of accident need to be covered through compulsory

subscription to an insurance regime. An appropriate

legal and supervisory framework should be put in place

to facilitate the settlement of claims. Again, as in the case

of freight transport, improvement in the quality of serviceon rail must not be neglected. Since supply has often

lagged behind demand, enhancement of capacity in

passenger services on rail is all the more important in

any policy initiative aimed at attracting customers from

the road mode.

The issue of modal choice in transport should be

given more prominence by policy-makers and analysts.

The loss of rail dominance to road in India is in line with

the experience of many countries, but has not occupied

the central position in policy discussions on the transport

sector (World Bank, 1995). While the present study yields

some general, preliminary results, more detailed exercises

need to be carried out in order to understand better the

factors behind modal choice. Studies concentrating on

modal split in important transport sections would bring

out the section-specific role played by various factors

and possibly help to establish critical fare and tariff

levels.

While rail and road are the principal modes of

transport in the country and the competition between

the two is of clear relevance to policy-makers, it shouldbe remembered that waterways and pipelines have a

significant share in freight movement. Hence, studies on

modal choice should also take into account these other

modes and examine their relation to the rail and road

modes.

ENDNOTES

1. In the discussions with railway officials, it was suggestedthat the average daily occupancy of trains for intercitytravel could be assumed to be 80 per cent of the statedcarrying capacity.

2. The VOPT for travel on road varies with the level ofcongestion whereas for rail it is fixed. This may be

justified on the ground that the speed of intercity transitby rail has not varied significantly over the period.

3. The coefficients of the sectional and year dummiesindicate that spatial and temporal effects are in somecases significant across all the scenarios of competitionstudied.


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Exhibit 1: Generation of Data on User Costs

Nature of Variable Component Sources of Data Assumptions Values ObtainedInter-modalCompetition

Car on road vsfirst-class/ AC Road user cost Operating cost Indian Roads Congress Ratio of old-tech to Time-seriestravel on rail (1993), Kadiyali and new-tech cars=1:3 from 1987-88 to

Associates (1992) for 1986-87 to 1991-92 1999-2000

and 1:4 thereafter,proportion of taxis=15%,

cost of taxi travel 25%higher than cost of travelby personal car

VOPT do do Rail user cost Passenger fare Indian Railways ASST do

Cost of porterage

and local transport Planning Commission/ Case of travel between Rs 29.41 per passengerRITES (1987-88) met. city and mof. town for travel between met.

may be applied to all city and mof. town andbut two of the selected Rs. 12.00 for travelsections between two mof. towns

(in economic terms at1997-98 prices for the50 km distance slab)

VOPT do do Re. 0.913 per passengerkilometre for travelbetween met. city andmof. town and Rs. 1.122for travel between two

mof. towns (at 1997-98prices)

Bus on road vs Road user cost Passenger fare Statistics of the ASRTU - Time-series from 1986-second-class/ 87 to 1999-2000sleeper-classtravel on rail

Cost of porterage Planning Commission/ Same as for this Rs. 7.92 per passengerand local transport RITES (1987-88) component in rail user for travel between met.

cost above city and mof. town and

Rs. 13.18 for travelbetween two mof. towns(in economic terms at1997-98 prices for the50 km distance slab)

VOPT Indian Roads Congress (1993) Time-series from 1986-87 to 1999-2000

Rail user cost Passenger fare Indian Railways ASST do

Cost of porterage Planning Commission/ Same as for this Same as for thisand local transport RITES (1987-88) component in rail user component in rail user

cost in above scenario cost in above scenarioof competition of competition

VOPT do do do

Freight shipment Road user cost Road freight bill Indian Roads Congress (1993), Mark-up of 13% on Time-series from 1986-by road vs freight Ministry of Railways/CES (1993) operating cost in order to 87 to 1999-2000shipment by rail cover brokers commission

and truckers profitCost of packing, Planning Commission/ Weighted average based Rs. 100.54 per tonnehandling, and local RITES (1987-88), on commodity shares moved as the averagecartage Ministry of Railways/ applicable for the period cost (in 1997-98

RITES (1996) under study prices)Unofficial expenses Ministry of Surface Transport/ Costs pertaining to Re. 0.139 per tonne km

AITD (1999) important routes for Mumbai-Delhi, Re.applicable to selected 0.044 for Kolkata-Delhi,sec tions by geographical Re. 0.038 for Kolkata-proximity Chennai

Transit cost of Indian Roads Congress (1993) Time-series from 1986-commodity 87 to 1999-2000

Rail user cost Freight rate Indian Railways ASST do Cost of packing, Planning Commission/ Same as for this Rs. 117.41 per tonnehandling, and local RITES (1987-88), component in road user moved as the averagecartage latest commodity shares cost cost (at 1997-98

prices)Unofficial expenses Krueger (1974) i) Figure given for each Rs. 15.09 per tonne

wagon loaded applicable loaded ( inclusive o fto average rail shipper documentation charges

ii) Increase in corrupt ion and in 1997-98 prices)since 1966.

Transit cost of Planning Commission/ Weighted average based Rs. 7.27 per tonne for acommodity RITES (1987-88) on seven principal distance slab of 650 km

commodities applicable (in 1997-98 prices)for the period under study

Notes: old-tech old-technology, new-tech new technology, met. city metropolitan city, mof. town mofussil town, ASST Annual Statistical Statements, ASRTU Association of State Road Transport Undertakings.


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Prosenjit Dey Chaudhury is currently an Economist with

Consulting Engineering Services (India) Private Limited, New

Delhi. He has been involved in studies on the environmental

and social sustainability of transport modes in India, overloading

of commercial vehicles, proposed expressways in the National

Capital Region, etc. In 2004, he received his doctoral degree

for his thesis titled Environmental Sustainability of Transport:

Issues in the Case of Rail and Road, submitted to the

Jawaharlal Nehru University, New Delhi. His areas of interest

include economic development, environment, infrastructure,

and energy.

e-mail: [email protected]


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