Transport Investment and Economic Development

Transport Investment and Economic Development

A major concern of all decision makers has been to ensure that there are clear benefits from transport investment proposals. The travel time savings are clear, but the wider economic developments have presented enormous difficulty in terms of both theoretical arguments and empirical evidence. This book reviews the history of the debate and argues that the agenda has changed. Concerns about economic development need to be placed in the new economy and a much wider social and environmental context. These issues are presented together with a major analytical investigation of macro economic models, evaluation in transport and micro economic approaches. The final part of the book presents a series of case studies for road, rail and airport investment schemes, particularly focusing on the economic development aspects. This book makes a major contribution to the debate and is directed at researchers, decision makers and students who are interested in the wider economic development impacts of transport investment decisions. David Banister is Professor of Transport Planning at University College London. He has produced sixteen books on all aspects of transport policy and planning analysis. Joseph Berechman is a Professor and Chairman of the Public Policy Department at Tel Aviv University, Israel. His major academic interests include Transportation Economics, Transportation and Land Use Systems Planning and Policy Analysis. He has published extensively in these fields and was a faculty member and visiting scholar in a number of American and European universities and research institutes. Presently he is also a Senior Research Associate at the University Transportation Research Center in New York.

Also available: Towards an Urban Renaissance Urban Task Force 1–851121–65–X Transport Policy and the Environment Edited by David Banister 0–419–23140–4 Transport and Urban Development Edited by David Banister 0–419–20390–7 Transport Planning: An International Perspective David Banister 0–419–18930–0 Transport, the Environment and Sustainable Development David Banister and Kenneth Button 0–419–17870–8

Transport Investment and Economic Development

David Banister and Joseph Berechman

First published 2000 in the UK and the USA by UCL Press 11 New Fetter Lane, London EC4P 4EE The name of University College London (UCL) is a registered trade mark used by UCL Press with the consent of the owner. This edition published in the Taylor & Francis e-Library, 2003. UCL Press is an imprint of the Taylor & Francis Group © 2000 David Banister and Joseph Berechman The right of David Banister and Joseph Berechman to be identified as the Authors of this work has been asserted by them in accordance with the Copyright, Designs and Patents Act 1988 All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data A catalogue record for this book has been requested ISBN 0-203-22087-0 Master e-book ISBN

ISBN 0-203-27560-8 (Adobe eReader Format) ISBN 0419-25590-7 (hbk) ISBN 0419-25600-8 (pbk)

This book is dedicated to my late father, Jacob Berechman, whose love for learning has guided me throughout my life.

The cathedrals would never have been built if people had considered only the short term. It is the difference between a society that plants conifers and poplars and one that plants oak trees. Bertrand de Jouvenel The sovereign has the duty of erecting and maintaining certain public works and certain public institutions, which it can never be for the interest of any individual, or small number of individuals, to erect and maintain because the profit could never repay the expense to any individual or small number of individuals though it may frequently do much more than repay it to a great society. Adam Smith, Wealth of Nations (1967 edition) Only if overall fixed-asset investment (e.g., highways, bridges and power grid) grows by 15 to 18 percent, can we reach 8 percent economic growth. Zeng Peiyan, minister in charge of the State Development Planning Commission, The New York Times (‘China Plans to Spend $1 Trillion on Big Public Projects’), 24 September 1998

Contents

List of figures Acknowledgements

ix xi

PART I

Objectives and scope

1

1

Background and objectives

3

2

Scope of analysis: definitions, approach and methodological framework

33

PART II

Contemporary issues

57

3

Transport infrastructure investment

59

4

The evolving economy

83

5

Social, spatial and environmental effects

107

PART III

Methodology: analytical approaches and modelling 6

127

Modelling the growth effects of transport capital investments: a macro level analysis

131

7

Economic evaluation of transportation projects

161

8

A model of transport infrastructure development and local economic growth

211

viii

Contents

PART IV

Empirical case studies 9

237

The economic impacts of roads

239

10

The economic impacts of rail

257

11

The economic impacts of airports

287

12

Interpretation of impacts and policy conclusions

317

References Index

335 363

List of figures

1.1 2.1

Links between GDP and infrastructure stock, 1990. The basic causality paradigm of the relationships between transport infrastructure investment and economic development. Part III The complementarity of approaches. 7.1 Traditional view of the effects of transportation infrastructure investment. 7.2 The new scheme for the evaluation of economic growth benefits from transport investment. A7.1.1 Measurement of benefits. 8.1 Schematic view of the relationships between the production, household and transportation sectors. 8.2 Equilibrium solutions before and after increase in transportation infrastructure capacity. 8.3 Equilibrium labour in the economy as a function of transportation infrastructure capacity. 9.1 The M25 London orbital motorway. 9.2 Map of the A71 route in France. 9.3 Map of the Amsterdam agglomeration. 10.1 Methodological framework of LRRT analysis. 10.2 The Buffalo rail transit and development patterns in the metropolitan area. 10.3 The Japanese Shinkansen high-speed rail system. 10.4 The French TGV high-speed rail system. 10.5 The San Franscisco BART transit system. 11.1 Employment impact model. 12.1 Illustration of the necessary sets of conditions. 12.2 Transport and economic development at the regional level. 12.3 The role of policymaking in achieving economic growth.

20

41 128 164 173 203 224 228 229 241 249 252 260 264 279 281 284 291 319 325 333

Acknowledgements

The idea for this book arose from our common concern over the lack of research and understanding of the links between transport investment and economic development. It seems a long way from those early discussions at the Tinbergen Institute in 1994 to this completed text. We now understand why no other author(s) has really tried to tackle the complexity of these links. We would like to thank the staff and our colleagues at the Tinbergen Institute in Amsterdam for their continuous support and understanding in accommodating us at Kaisergracht, in particular Elfie Bonke and Marian Duppon, but also many others. Prof. Peter Nijkamp (Department of Economics, The Free University of Amsterdam), who managed to lure both of us to the Tinbergen at the same time as VSB Visiting Professors (1994–7), facilitated the whole enterprise. This opportunity to work together more than anything provided the necessary space, time and support to really initiate the long discussions and debates, now reflected in the book. Jody Kersten from the Free University was also instrumental in getting us to work together by organizing the local arrangements in Amsterdam. Several of our academic colleagues helped with particular chapters: Prof. Robert (Buzz) Paaswell (Director, University Transportation Research Center, The City College, New York) on the Buffalo Case Study (Chapter 10); Alan McLellan (University College London) on the Economic Development Effects of Airports (Chapter 11); Prof. José Hulguin-Veras (University Transportation Research Center, the City College, New York), on the Benefit Cost Analysis (Chapter 7); Prof. Jan Brueckner, (Department of Economics, University of Illinois) on the microeconomic modelling (Chapter 8). Many others have commented on different chapters, or on particular sections of the book that have been present at conferences and workshops and have at various stages encouraged us to continue with the writing of the book. Our thanks are warmly extended to all these colleagues. David Banister and Joseph Berechman June 1999 London

Part I

Objectives and scope

Main issues and structure It has always been assumed that a high quality transport infrastructure is an essential prerequisite for economic development, yet this assumption has never really been investigated in depth. Over time it has become axiomatic and part of the underlying rationale for transport investment decisions. The basic purpose of this book is to explore in depth the arguments for and against this assumption and to come to conclusions on the key relationships between transport and infrastructure investment and economic development. We also accept that there are other important influences on economic development. In particular, we argue that political and institutional factors provide the broader context within which decisions are made. They also influence the means by which finance for investment can be raised and provide the organizational and legal framework for action. In this book we primarily concentrate on the links between transport investment and economic development, but at the end we return to the political and organizational context through the case studies and concentrate on this in the concluding chapter. The first part of the book outlines the main issues and arguments in the debate, taking a series of different perspectives. First, we examine the historical evidence, then the extensive literature on the productivity of infrastructure investments and the links between infrastructure and development. Most of the book is addressed at infrastructure investment in developed economies, but the very different impacts in developing countries and cities are covered in the overview. We also outline the importance of the subject matter and a series of key questions to be addressed in any analysis of transport infrastructure investment and economic development. The range of the subject matter is vast and of key concern to all national and local governments. To limit the scope of this book we outline a conceptual approach in Chapter 2, together with a methodological framework within which analysis can take place. A principal element in this framework is the scale at which analysis should be undertaken. Most of the recent research

2

Objectives and scope

has concentrated on the links between national economic indicators and infrastructure investment, but causality and time are difficult elements to deal with at this scale. The regional analysis focuses mainly on accessibility changes within networks rather than economic impacts. Our analysis is primarily at the microeconomic level where causality can be implied, but there are still limitations with data. Part I sets the scene and limits the scope of our investigations. It also acts as the framework within which the other three parts of the book are placed. We then progress on to the changing contemporary world in which decisions are made (Part II), the review of methods and the development of a micro analytic model (Part III), and the presentation of the case study material (Part IV).

Chapter 1

Background and objectives

1.1 Introduction The belief that public investment in infrastructure will generate economic growth has often been used as a justification for the allocation of resources to the transport sector. Much of the road-building programme in developed and developing countries has been promoted on these grounds, yet the arguments seem far from clear. In the USA, for example, the Clinton administration has proposed a substantial investment in infrastructure, as it has public support (Gillen, 1993), even though Congress is against it because of the implications for the public budgets. It is popular among users of the road as they can see a higher quality infrastructure that will allow them to maintain their very mobile car-based lifestyles. Industry has also traditionally been a strong supporter of infrastructure investment, arguing that it will make it more competitive, help get the country out of recession and create jobs in the short term. These are all strong arguments but, as we shall see later in this book, the evidence is equivocal, particularly in countries and cities where there is already a high quality infrastructure. Similar sentiments were being expressed in the UK with the publication of Roads for Prosperity (UK Department of Transport 1989). An expanded programme of investment in motorways and trunk roads to relieve congestion was announced. The programme effectively doubled the existing investment plans and was seen as a commitment to the provision of infrastructure ‘suited to the single market and other competitive challenges of the 1990s and beyond’ (para. 2). It was also argued that the investment was necessary for industry and to improve the country’s economic geography, through increasing opportunities for less favoured regions, assisting urban regeneration and helping more prosperous areas to cope with growth. The programme had resulted from a substantial increase in road traffic (+35 per cent) in the 1980s and the prospects of a doubling of road traffic from 1988 to 2025. Something had to be done and the government decided that road building was the main alternative to be pursued. One of the fundamental objectives of the road

4

Objectives and scope

programme is to assist economic growth by reducing transport costs. Yet even in the following year when the review of the road programme was made (UK Department of Transport 1990), there was little mention of the economic growth arguments, only those on environment, safety and urban regeneration. Similar arguments have been presented in Europe. A review of the problems carried out by an international team of experts (Group Transport 2000 Plus 1990) concluded that the overcrowding and saturation of new facilities even at the moment of commission, the need for new links and the choice of layouts, gauges and operating methods were all issues to be addressed. But above all the people consulted by the team of experts focused on infrastructure costs and many of them talked in terms of passing them directly to the users. The costs of constructing new infrastructure and replacing existing infrastructure are considerable and the massive investments in the 1950s and 1960s have been followed by lower levels of investment. Since 1975, investment on inland transport has fallen in west Europe by 20 per cent in real terms and it has halved as a proportion of gross domestic product (GDP) to 0.8 per cent. This reduction in infrastructure investment reflected general reductions in public expenditure, the world recession in the 1970s resulting from high oil prices and the generally lower levels of increase in transport demand. Non-investment in transport infrastructure takes time to show an effect and, given the shortterm time horizons of politicians, any delay in commitments to expensive projects meant savings in public budgets and lower taxes. Investment decisions were delayed, particularly expensive new links between countries and those that involved tunnelling. With the economic upturn in the 1980s there was substantial new growth in transport demand, but it also became apparent that growth in traffic had continued throughout the 1970s as well. Investment had not been reduced because of reductions in demand for mobility, but for other macroeconomic reasons such as pressures on public budgets, high interest rates and industrial recession. Underlying these arguments is the premise that there is a fundamental link between growth in transport investment and economic growth. It has been consistently argued that there is a clear relationship over time between GDP growth (a measure of economic growth) and a range of measures of transport and transport-related investments. Such comparisons have generally been made over the last fifty years since the advent of the car, and more recently air travel. It has also been a period of economic growth and stability, as there have been no major wars to reduce or redirect output. Similarly, there has been a substantial increase in trade within and between countries. It is not surprising that the demand for travel has increased in parallel with economic growth. Many other measures of wealth or well-being have also increased in a similar way: for example, the growth in income levels, the purchase of consumer goods, the numbers of people in schools and higher or continuing education, life expectancy, etc.

Background and objectives

5

However, as efficiency and productivity increase, the linear links with GDP may be reduced as there is no a priori reason why transport demand should rise with GDP. Production and distribution processes (and individual passenger travel) could become either less transport intensive or more transport intensive. Conversely, if prices rise substantially or there is a concerted international action, then again the simple linear relationship may be broken. This has already happened with energy consumption as price rises and greater efficiency in production and consumption have resulted in growth levels far less than those in GDP. These trends and relationships are important, but they are not set in tabloids of stone—they are not immovable. Sustainable growth and development has the basic objective of maintaining growth in the national and international economies, but with the use of less resources, particularly non-renewable resources. This means that we would expect a continued growth in GDP but with fewer resources used in transport. This does not necessarily mean that there will be less transport. But it does mean that we have to become more efficient in our use of resources. In addition, the current technological revolution in information and communications may also weaken the links between transport growth and economic growth. The concentration on physical measures ignores the other forms of transactions that take place, such as movement of information, finance, commerce and document handling by informatics and technologicalmeans. This is where real substitution is taking place. Apart from the arguments at the national level, there have also been strong urban and regional arguments for investment in transport infrastructure. The regional development policies in the European Union (EU) are powerful and substantial investment has been transferred from the centre to the periphery. This means that the larger countries of Germany, France and the UK have supported infrastructure projects in the poorer European Union countries of Spain, Greece, Portugal and Italy. The arguments used by the EU are that regional development policy strengthens integration and cohesion of the EU as a whole, while at the same time reducing the disadvantages in peripheral or poorly connected locations. In the longer term it will maintain and enhance the competitiveness of Europe. In other words, it is asserted that expanded transport infrastructure will provide overall economic development in the longer term. Even here there are many unresolved questions. It is not clear whether such a policy actually provides the greatest benefit to these peripheral regions. Little empirical evidence is available on whether infrastructure investment in the periphery actually strengthens the centre, as it extends market area and permits migration of labour to the centre where opportunities are perceived to be greater. It is unclear whether the local economy in the peripheral region benefits over the longer term. If competitiveness of the EU or the individual country in world markets is being discussed, then infrastructure investment should be in those locations where the greatest return is expected. This is likely to be in the regions with the most buoyant economic conditions or

6

Objectives and scope

where particular circumstances are likely to result in high returns, for example, where there is a particular skill or a natural resource available. The issue here is whether a high quality transport infrastructure is a necessary condition to bring about economic growth in depressed or emerging regions. Traditional arguments (e.g. Botham 1980) and more recent reviews (e.g. Hart 1993) have all suggested that road building is not the key determinant for growth (Section 1.2). The Merseyside situation in the UK is informative here. In Liverpool (the main city in the Merseyside conurbation), substantial road programmes were promoted in the 1960s and 1970s as the expected increases in population and employment, together with rising productivity and income, would all lead to substantial increases in the volume of goods and passenger travel, particularly by car. It was clear even before the studies were completed that the Merseyside conurbation was losing population and employment and that the whole of the local economy needed to be restructured. Inadequate road networks were not a key component of that restructuring process and investment was required in retraining, new industries and a regeneration of the local economy. Nevertheless, the road investment programme was still kept as an integral part of the strategy, only the argument changed. Roads were originally justified on the basis of the expected growth in traffic, and the necessity to accommodate and direct this growth. Subsequently, the same roads were being defended as a means to regenerate the local economy (Banister 1994). From a firm’s perspective, similar conclusions can be drawn. There are many ways in which firms can use the transport system to their own advantage so that costs can be minimized. If a road network is improved, then the firm is likely to make longer and more frequent journeys, which may minimize their own costs, but raise substantial environmental costs (McKinnon and Woodburn 1994). Conversely, firms could make more use of logistics systems and new forms of management and organization to minimize transport costs (Weijers 1995). Similarly, firms might consolidate on one site or disperse to several sites to maintain competitiveness. In all cases, there seem to be a range of alternatives available so that profit levels are maintained or increased. Transport costs are only one part of that decision, yet there are many ways in which each firm can maintain its competitive position. The evidence cited here gives a flavour of the main issues and problems to be raised in this book. At one level the traditionally held view that there is a strong link between transport infrastructure growth and economic growth does seems to be supported, particularly if national statistics on trends over the last fifty years are used. However, this aggregate view simplifies the more interesting political and economic arguments for investment, the regional variations and the actions of individual firms and people in their own decisions. Some governments are less convinced by the strengths of these arguments. In Canada there has been a recent recommendation (cited in Gillen 1993) against any large public investment in transport infrastructure. Part of the

Background and objectives

7

justification for this recommendation was the lack of any clear understanding of how such investment would lead to long-term economic growth and development. The recommendation from the Royal Commission on National Passenger Transportation was first to get the pricing of the infrastructure right. This chapter aims to set the scene for the book by outlining some of the main debates in greater detail. The development of the argument is presented through an historical perspective on the debate, particularly the huge interest in the subject in the 1980s and its current revival. This is followed by a review of the triggers for this renewed interest, principally through the debate on the productivity of infrastructure investments from the macroeconomic literature. We then turn to the development literature to explore the seemingly clearer relationships in developing countries. All of these debates have taken place at the macro level, while most of the remainder of the book concentrates on the regional and local scales. 1.2 The debate: a historical perspective 1.2.1 Introduction The debate over the links between transport infrastructure investment and economic development is not new. Ever since roads and railways were built, one of the main arguments has been the impact that the infrastructure would have on production costs. Initially, as there were few links in the network, the impacts would be clearly identified and causal relationships could be inferred. Transport investment would help open up new areas for agricultural production, create new markets for goods and link in isolated areas with the main towns and cities. Essentially, this is the development argument that has been applied more recently to countries passing through the development stage (see Section 1.4). We have no fundamental disagreement with these arguments. Our contention is to establish whether the same arguments are still relevant in advanced economies where the infrastructure is already well developed, where more complex market systems are in operation and where transport costs play a less important role in the total production costs. We are also addressing the new forms of production based on post-industrial and technological developments, with high levels of car ownership and mobility, and high levels of employment in service industries. In this section we ask whether the arguments used nearly two hundred years ago are still relevant today. 1.2.2 The early days 1800–1970: location theory Early studies by economists opened up a debate as to whether a reduction in transport costs brought new areas and products into the market. Rostow

8

Objectives and scope

(1960a) argued that this was the case and that transport investment also contributed to a major new export sector and was instrumental in the development of the modern coal, iron and engineering industries. Conversely, Mitchell (1964) in his extensive economic history of the UK railway system concluded that these necessary conditions stated by Rostow were already met in the UK before the railways were built. In the UK the railways were effectively completed in 1852 and did not have a great immediate effect on the economy. There were substantial direct effects in the construction phase through the employment of unskilled labourers and stimulation of the iron and steel industries, but their major effect was in the development of the capital market and the levels of savings. They encouraged investment in profitable (and unprofitable) enterprises. A fascinating study of the development of the horse-drawn barge (trekschuit) and the canal network (trekvaart) and its impact on the Dutch economy also illustrates this historical debate (De Vries 1981). The growth in the canal network was phenomenal, with some 658 km constructed (1632– 1839), linking thirty cities so that people (and freight) could travel around the country. The passenger services were used for both business and pleasure, with charges being made to travel or to walk (the charging points for walkers were the bridges). Demand peaked in the 1670s, but decline followed which was attributed to poor maintenance, dishonesty by skippers, competition from unregulated carriers and poor economic conditions. There was a revival in the 1800s, but the canals were then being replaced by railways and roads. At the end of his investigations, De Vries (1981) concluded that the economic rationale for the canal network was unclear, as it may have only affected the level of economic performance, not the actual rate of economic growth. But the canal system may have contributed more to gross regional production (in 1670) than the railways did two hundred years later (in 1850). This debate was complicated by the more sophisticated economic arguments of Fogel (1964), who conducted an historical study of the impact of railroad development on the American economic growth during the nineteenth century. He concluded that railways had a primary impact on the costs of transport and that there were social savings resulting from the movement of agricultural output by rail. The social saving was defined as the difference between transport by rail and the second best alternative, mainly waterways. His analysis covered the four commodities (wheat, corn, pork and beef) which accounted for over 90 per cent of agricultural regional movements in the USA in the nineteenth century. Fogel also examined the derived effects or those consequences that followed from the savings in transport costs. These were divided into those which would have been induced by any innovation that lowered transport costs (disembodied) and those that specifically related to railways (embodied). His conclusions were very different to those reached by Rostow. Fogel thought that ‘no single innovation was vital for economic growth during the 19th century’. Economic growth was a

Background and objectives

9

consequence of the knowledge acquired in the course of scientific revolution and this was the basis for a multiplicity of innovations. Rail development in the USA has helped shaping growth in a particular direction but was not a prerequisite for it. In the UK the industrial revolution was completed before the railways were built. The railways were part of that process, not a precondition. They emerged out of an effort to apply scientific and technological knowledge to the improvement of products and the reduction of costs. Cheap inland transport is a necessary condition for economic growth, but the satisfaction of this condition did not entail a specific form of transport. The land use transport links were explicitly included in Von Thünen’s classic (1826) study on the impact that transport has had on patterns of agricultural development. As the quality of transport improves, the land devoted to agricultural production is extended. This in turn allows land values and land uses to be reflected in the relative location advantages which the transport system provides. Many other studies, all classics, developed from this starting point (e.g. Isard 1956; Wingo 1963; Alonso 1964). They were based on concepts of urban economics, land economics and rents using methods that assumed optimality and equilibrium in land allocation, a single market and no uncertainties. As distance from the centre increased, the total costs of transport also increased, and these factors determined the highest use value of any particular location. As distance from the centre increased, land values decreased. This theory simplifies reality and promotes transport as the main determinant of land value and hence uses. Changes in the costs of transport will influence the distribution of activities through the land market. As cities become more complex with high quality transport, they will increase in size and residential densities will reduce. The lower transport costs and lower peripheral land costs mean that with a fixed budget more can be spent on housing. These theories have had great intuitive appeal over the last two hundred years as their logic is simple and the mechanisms driving them are transparent. The research of Christaller (1933) in southern Germany was the most influential as he demonstrated the links between transport costs and the spatial distribution of economic activity. He proposed an urban hierarchy of a number of market towns, each with different transport costs, specializations and differential product values. As the towns went up the hierarchy, the range of products increased and the quality of transport improved. The larger centres were able to increase their share of the total economic activity and this in turn led to concentration of economic activity with a few centres dominating the region. Improvements in transport infrastructure strengthened the accessibility and dominance of the central city—central place theory. The economic base to these early theories was complemented by other studies in the USA which focused more on historical and social factors and

10

Objectives and scope

on cycles of growth and decline (e.g. Hurd 1924; Burgess 1925; Hoyt 1939). Rather than assuming that all activities could locate anywhere within the ‘ideal’ city, additional constraints were placed on the requirements of particular types of activities. For example, certain industries require waterside facilities and their workforce would locate nearby, thus limiting other types of activities. Similarly, high quality housing might locate near the city centre, but over time high income people would move out to the city fringe and this ‘old’ housing would be cascaded to lower income households. Simple concepts of distance were replaced by evolutionary approaches to location. Harris and Ullman (1945) concentrated on specialization, agglomeration economies, clustering and class segregation as reasons why ideal patterns do not emerge in real cities. Instead of one centre emerging, several sub-centres would develop. This in turn has led to concepts of hierarchies of development (Berry 1967). These studies have their limitations, but they have formed the basis of much current thinking. They were essentially descriptive and used simplifications designed to establish causal relationships to help understand the development of urban areas. Monocentricity of employment is perhaps the most widely criticized assumption (Deakin 1991). But other factors, such as the standard household structure with one worker, the dominance of transport, the power of market forces and the limited treatment of time, space, political, institutional, legal and social factors, all mean that new approaches are needed (Alcaly 1976). The critique of these early approaches was both technical and conceptual. In the 1970s and 1980s questions were asked about the methods being used and the underlying logic of the links between transport and economic development. Initial scepticism turned into full-scale criticism. For example, Wilson (1978) stated: When we turn to transportation, the roles that improved transportation are supposed to play are numerous. For example, transportation improvements have been cited as having important positive effects on political unity, social cohesion, economic growth, specialisation, and price stability as well as on attitudinal change. Yet…precisely opposite effects are equally plausible. (Wilson 1978:102) The developmental tradition (Hart 1983) has involved a strong belief that transport made a vital contribution to economic growth. His historical analysis traces both public and private involvement in transport infrastructure investment, with assumed high rates of return and substantial multiplier effects. Yet, many of the early companies, in particular those involved in rail investments, went bankrupt. But this may have been due to other factors such as macroeconomic factors and conditions for competition. The conclusion

Background and objectives

11

from much of the economic history has been to downgrade the impact of transport innovation on aggregate performance. Hart (1983:15) concludes that transport was one aspect of productivity improvement, but that the changes in agriculture and manufacturing stimulated growth in incomes and began to generate substantial volumes of traffic: ‘improved transport was a luxury afforded out of economic growth’. Location theory argues for the strengthening of the centre with a concentration of economic activity. Yet, much of the historical evidence, even in the early period, suggested that there was substantial variation between different cities and a weakening of the influence of the city. Regional development policies also assumed that investment in transport would help alleviate depressed industrial regions and open up rural areas by increasing their share of economic activity. Even here the concentration arguments were being replaced by those promoting the spread of growth from the more prosperous regions. 1.2.3 The 1970s and 1980s: urban and regional modelling The next twenty years have seen the development of a series of more sophisticated models for land use and transport analysis at both the urban and regional scales. This has allowed the addition of more complex social structures for households, information on land availability and housing stock, as well as more complex notions of transport costs (i.e. generalized costs of travel). More important, though, has been the recognition that cities exist, so ideal forms are no longer taken as the starting point. A vast literature has emerged (for a review see Berechman and Small 1988; Anas et al. 1998). This new generation of urban models allocates housing and jobs within urban regions on the basis of their relative accessibility, land availability, income levels, population and employment by category and other social and physical characteristics. These methods have had some success in modelling the existing urban structure and predicting changes in locations given investment options (e.g. roads), new developments (e.g. housing) or changes in travel costs (e.g. pricing). Again, most of the models focus on changes in transport costs as being the principal driving mechanism. Other important location factors such as quality of housing, lifestyle considerations, family constraints, quality of the neighbourhood, which come out as important in social surveys, do not feature as dominant issues in the analysis. Urban modelling approaches have also been matched by econometric analyses, input-output models and regional analyses. In many of these studies, the focus has been on the employment impacts, not on understanding how cities or regions work. Botham (1983) tried to estimate the contribution of the roads programme in Great Britain to regional development. The methodology adopted requires the counterfactual situation to be set up to answer the question as to the nature of the spatial structure of the British

12

Objectives and scope

economy in the absence of the roads programme (1957–76). Using a set of regression equations, Botham estimates the changes in the spatial distribution of employment brought about by the changes in accessibility. The tentative conclusion reached is that the impact is marginal, if (in the absence of road investment) it is assumed that transport costs remain constant over time. If congestion is assumed to increase costs, the impact is increased, but it is difficult to put a value on it. This means that the roads programme in Great Britain has encouraged spatial concentration. This conclusion is qualified by the recognition that other policies may have had a greater impact than infrastructure investment. Pricing and taxation policy, restrictions on drivers’ working hours and the value of work time may all have had a greater impact on accessibility. Similarly, spatial specialization, labour supply, wage rates and migration patterns may all have influenced employment patterns more substantially than transport infrastructure investment. At the major link level, similar problems have been encountered. In their analysis of the traffic generation effects of the M62 motorway between Leeds and Manchester, Gwilliam and Judge (1978) conclude that large-scale traffic growth may indicate that a motorway has stimulated economic growth. Growth in traffic on the M62 route was 34.3 per cent (1970–77), and this compared with a national growth rate on rural roads (excluding motorways) of 24.8 per cent over the same period. The difference between the two figures gives an estimate of the generated traffic but, as noted by Botham (1983:25), the results are very sensitive to the assumptions made, namely the counterfactual situation. Dodgson (1974) has calculated that the greatest reduction in the total costs of manufacturing and distribution in any area brought about by the construction of the M62 motorway was about 0.33 per cent. For most areas, it was substantially less. Any attempt to measure the effects on local employment failed as the scale of the impact was so small. A third study (Mackie and Simon 1986) takes an individual link to determine whether road investment benefits industry. The Humber Bridge forms a major new link across an estuary in Yorkshire (Great Britain), and its direct impact seems to have been in extending the market area of companies rather than their location. Some firms felt that the tolls had cancelled out operating cost savings, but that timesavings could be used to increase vehicle and driver productivity (Mackie and Simon 1986:383). This conclusion raises an important issue in the debate. In cost benefit analysis, a clear distinction is drawn between user and non-user benefits. User benefits result from travel time savings, operating cost reductions and reductions in the numbers and severity of accidents. Non-user benefits accrue to other people and are often associated with the economic development benefits. Traditionally, economists have excluded these non-user benefits from the cost benefit analysis, because of the difficulties in distinguishing generation effects from redistribution effects and the associated problems of double-counting

Background and objectives

13

benefits (Friedlander 1975; Dodgson 1973. See also Chapter 7). Rephann (1993) concludes, ‘benefit-cost analysis only includes development benefits to the extent that it results in user benefits for existing and generated traffic’ (p.439). It would seem that the crucial issue of benefits and costs redistribution also needs to be considered in an overall assessment of the economic development effects of transport investment. In addition to the methodological questions, there are important policy considerations. Within government there is a common assumption that new roads encourage economic growth by inducing industries to relocate to those regions with better quality infrastructure. Two hypotheses underlie this view (SACTRA 1977): The first is that the direct road user benefits which result from a scheme substantially understate the total benefit to the economy. The second is that trunk road construction is an efficient method of attracting economic growth to selected areas. Thus the regional distribution of benefits, even if the total is accurately measured, may be important. Yet, despite the strength of the assertions about the importance of these effects, little actual evidence has been presented to us. (SACTRA 1977: para 20.18). The conclusion from this influential report is that the restructuring effects of road construction on economic growth in developed countries are weak and at best not proven. The main benefits to industry are in travel timesavings, but its importance in terms of relocation has been overstated. The SACTRA committee supported the government in its current practice of excluding the indirect effects from the evaluation. Yet the arguments for new roads continues as it produces ‘important efficiency gains for commerce and industry’ (UK Department of Transport 1993) and makes industry more competitive. The standard argument promoted by the Confederation of British Industry and other road organizations is that improved roads reduce transport costs (primary effect), while the lowering of distribution costs relative to production cost changes firms’ optimum production and distribution strategies (secondary effect) (Mackie and Simon 1986). The strong argument on the development effects of roads has become diluted as roads are now seen as being ‘necessary’ but ‘not sufficient’ for economic development (Huddleston and Pangotra 1990). The weaker development argument suggests that if a region has all the economic factors present for growth, then its full potential will not be realized without further transport investment. This type of argument has been central to regional development theory (Hirschman 1958; Hansen 1965). Hirschman promoted unbalanced growth with social overhead capital being concentrated in regions and industries where it can maximize development. Although this strategy increases inequalities, it was claimed that they would

14

Objectives and scope

only occur in the short run as the private sector would ensure stable growth overall. It was unclear as to what mechanisms would be used to ensure stability. The approach adopted by Hansen (1965) differs as public capital is divided into economic overhead capital (roads, sewerage, water, utilities) and social overhead capital (education, health, nutrition). He also divides regions into those that are congested, intermediate and underdeveloped. His argument is that economic overhead capital should be concentrated in the intermediate regions and that social overhead capital should go into the underdeveloped regions. The debate is over whether, through concentration or dispersal, regional development objectives can be achieved and whether economic growth at a regional level can be reconciled with the distribution of that growth. The spillover effects of economic growth will affect all parts of the region. The question is whether the effects are greatest if directed towards the location of greatest potential (i.e. the centre) or whether it is dispersed to all parts of the region. There seems to be little agreement here. As Rephann (1993) concludes, Hirschman would promote road investment in the developing urbanized regions; Hansen would target the intermediate regions; while growth pole proponents (e.g. Allen and MacLennan 1970; Hansen 1971) would argue for concentrating highways in urban areas which exhibit prior dynamism. Rephann (1993) comes to five conclusions on the empirical evidence from the USA: • •

• • •

Road investment appears to have a greater effect on economic activity in the less industrialized regions such as the Sunbelt. Extremely underdeveloped regions are less promising candidates for road development than regions in an intermediate stage of development which are experiencing low growth. The effects are positively correlated with urbanization levels and metropolitan proximity. Other types of infrastructure (e.g. airports) may contribute to the effectiveness of highway investments. Additional roads may result in diminishing marginal returns.

These conclusions are consistent with those proposed by Hansen (1965). The basic implication for regional development theory is that road investment will be most effective in promoting development ‘when certain “indicators”, “signals”, or “triggering forces” are present’ (Rephann 1993:447). Much of the debate during the 1960s, the 1970s and 1980s revolved around freight transport. It was consistently argued that a reduction in freight transport costs would lead to the exploitation of scale economies, and this in turn would lead to reductions in commodity prices. The underlying argument was that these benefits exceeded the direct reductions in transport costs (Tinbergen 1957; Bos and Kocyk 1961). However, this argument depends on

Background and objectives

15

the assumption that increased use of factor inputs (such as labour, capital and natural resources) would be attracted by the rise in factor prices which resulted (Friedlander 1975). However, if the factor inputs were assumed to be fixed, the reduction in transport costs would be an accurate measure of the total effect of the scheme, provided that the benefits to any generated traffic could also be captured (Dodgson 1973). These indirect benefits would be greatest in locations where the factors of production were currently unemployed. In addition, the increased demand resulting from the road itself may have multiplier effects on regional and national income. Their effects would result in an increase in aggregate demand and should not be regarded as benefits specific to road construction (SACTRA 1977). There are several weaknesses in the argument presented here, particularly as it relates to depressed areas: •

•

•

•

•

A reduction in transport costs to a depressed area may make it easier to supply other areas from the area in question, but at the same time it will make it easier to supply that area from elsewhere. It is directly analogous to the international trade theory argument of the impacts of a reduction in the tariff. Regions that will benefit from investment are those that already have nontransport advantages, such as a buoyant local economy, new industries, a heavy inflow of investment, available sites and a high quality labour market. The problems of depressed areas can be traced to non-transport factors (e.g. the need for industrial restructuring and increases in labour productivity). Improved communications may do more harm than good. The same applies to the effect of improved transport to and from ports and the impacts that this may have on the country’s balance of trade (SACTRA 1977). Where transport cost savings have led to an economic benefit, they have not necessarily led to a parallel increase in employment. Where savings occur, restructuring can take place allowing more capital intensive production, further savings in labour costs and an increased profit margin (Hopkins 1986 quoted in Grieco 1994). These arguments ignore the issue of transport financing. If taxes are used, for example, it would reduce income and hence demand.

The empirical evidence to support the arguments also reflects clearly on the thinking during the 1970s and 1980s. Some has already been mentioned with respect to particular networks or links, but the majority of the evidence comes from surveys of firms about their intentions to expand or relocate as a result of a new transport link (Table 1.1). The same conclusions can be drawn from the surveys of firms as from the modelling and review material presented in this section. The general conclusion from these surveys has been that transport is a second order consideration

16

Objectives and scope

Table 1.1 Surveys of firms’ location decisions

for location or relocation decisions, provided that there is a good quality road network available. Transport is a background variable that has to be present, but comes behind factors that more directly affect the efficiency, productivity and profitability of a firm. It is also considered a factor that is beyond the control of an individual firm. The scale of any impact is small whether perceived by the firms or translated into savings in transports costs. However, regional policy in the UK still favoured regions with high unemployment. The first priority was for improved trunk roads linking these regions with those of greater prosperity; the second being for higher levels of intra-regional road spending to promote local mobility and growth (Hart 1993). Continental Europe showed less of a transport bias towards disadvantaged industrial regions and most investment at this time went to road and rail infrastructure in the rural areas. 1.2.4 The 1990s: macroeconomic approaches The debate seemed to diminish in its intensity over the latter part of the 1980s and even into the 1990s, but it now seems to have re-emerged. Much of the discussion follows the main conclusions from the 1970s’ and 1980s’ interest in the subject, but there are several new dimensions. In this section

Background and objectives

17

we shall concentrate on some of the main new themes which have emerged, most of which are taken up later in the book in more detail (Chapter 6). 1

2

3

4

5

The lack of investment in the infrastructure of all types (e.g. schools, water and public buildings) has had an adverse effect on efficiency and productivity. The difference here is examining the implications of not investing in the infrastructure rather than identifying the impacts of additional investment. This debate is outlined in Sections 1.3 and 6.5 and concerns much of the recent research in the USA and elsewhere (e.g. Aschauer 1989a, b; Munnell 1990a; Berechman 1995; Helling 1997). The land development effects of roads have a long history, but recent debates have tried to identify under what conditions measurable development effects can be identified. If particular situations can be isolated where rises in land values, rent levels and house prices can be measured, then there is a strong case to recapture some of that increased value, through taxation or through contributions of developers to the financing of the transport infrastructure (Anas et al., 1998). The crucial question of the financing of transport infrastructure through traditional public expenditure routes or through new forms of publicprivate partnership has become a central issue in the UK, Europe and USA (Chapter 3). Environmental arguments have become a more important consideration in the political agenda, particularly if it can be demonstrated that new roads lead to more and longer trips. Low density development has been facilitated by high quality infrastructure and the availability of the car. This in turn leads to greater land consumption, more energy consumption and greater levels of environmental pollution. But even here the debate continues over the effects that new roads have on employment, new traffic, destination choice and the use of different modes of transport (SACTRA 1994 and Chapter 5). The view from industry (CBI 1995) is that competitiveness is being jeopardized by a lack of investment in the infrastructure. With a congested network, distribution is becoming increasingly unreliable and a competitive time to market is not being provided. This contrasts with the heavier levels of investment being made in Europe and, as with the earlier arguments, the relative competitiveness of region has been extended to the international level. The Confederation of British Industry argues that from 1975–90 UK transport investment was little more than half the European average. This has narrowed recently, but is now widening again (1998). Their case for greater investment revolves around strategy and consensus. In France, for example, a key priority is to ‘assert the position of France in European and world-wide competition’, so investment takes place. Elsewhere (e.g. in Germany and the Netherlands), long-term infrastructure plans are agreed and implemented (Chapter 4).

18

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6

A further new element in the debate has been the concept of induced traffic (SACTRA 1994), which includes all forms of new traffic generated by new road construction. The economic values of a road investment scheme can be overestimated by the omission of induced traffic, particularly where a network is operating close to capacity; where traveller response to changes in travel time or costs is high; or where the implementation of a scheme causes large changes in travel costs. The SACTRA report recommended that ‘scheme appraisal must be carried out within the context of economic and environmental appraisals at the strategic area-wide level which take account of induced traffic through variable demand methods. Much greater emphasis needs to be placed on the strategic assessment of trunk routes within a corridor or regional or urban context’ (1994:iv, para 17). These questions are returned to in Chapter 3; in particular the links between transport investments and land use or development changes.

All of these more recent elements, together with the longer established arguments from the 1960s, 1970s and 1980s, crystallize the importance of the issue. Both the methodological arguments and the empirical evidence are inconclusive, yet major investment decisions are made on the basis of inadequate theory, data and arguments concerning causality. Two possible explanations might be that either we know the answers but are not prepared to discuss them, or that we really do not know the nature, strength and direction of these links. Our starting point is the latter explanation. Table 1.2 summarizes how priorities for transport infrastructure investment have changed over the last fifty years, both in Europe and the UK. In reviewing Table 1.2 we need also to recognize the political dimension which must be seen as an integral part of the rationale, as transport and economic development links are still maintained almost as an act of faith. Unless there are clear and measurable economic benefits from investment in transport infrastructure in the poorer areas of Europe, what other justification can be used? There is a history of infrastructure investment in both congested and peripheral regions, yet the rationale is still not clear. 1.3 The debate: productivity of infrastructure investments Many studies in both developed and developing countries have tried to establish a statistical link between aggregate infrastructure investment and growth in GDP. The findings are staggering with rates of return of up to 60 per cent. In turn, there has been an extensive debate over the validity of the analysis and the claimed causality in the relationship. Two main criticisms have been raised. The first question is whether the simple relationship between output increase (GDP) and input (rate of investment in infrastructure) is not influenced by other factors not included in the analysis. The second is the

Background and objectives

19

Table 1.2 Changes in policy on transport infrastructure investment

nature of the causality—whether growth leads to additional infrastructure investment, or whether investment leads to growth, or whether there is an interaction effect. An example of a simple statistical causation is shown by Figure 1.1. A more sophisticated development in the debate has been the use of time series data for the USA and other countries (Aschauer 1989a, b; Munnell 1990a). Aschauer tests two hypotheses in trying to demonstrate whether public capital ‘crowds out’ private capital. The first hypothesis argues that higher public investment raises the national rate of capital accumulation above the levels chosen by private sector agents. The second argues that public capital, particularly infrastructure capital (including roads, water, sewers and airports), is likely to bear a complementary relationship with private capital in the private production technology (Aschauer 1989b). His main conclusions are that there is a link between non-military public capital stock and measures of private sector productivity (Aschauer 1989c) and that the public sector inputs are complementary (Hypothesis 2). He further concluded that the decline in US productivity in the 1970s had been precipitated by declining rates of public capital investment. This important part of the debate is given extensive

20

Objectives and scope

Figure 1.1 Links Between GDP and infrastructure stock per capita, 1990. Source: World Bank 1994 Note: Axes are logarithmic; infrastructure includes roads, rail, power, irrigation and telephones. a Purchasing power parity (PPP) dollars are valued in Summers and Heston 1985 international prices

coverage in Chapter 6, where the theoretical, analytical and empirical evidence from the USA and elsewhere is presented. The general conclusions reached are that public capital has an impact on economic growth, on private capital and on labour productivity, but the magnitude and significance of these effects are not clear. The key issue in any analysis of complex interrelationships is the unravelling of these linkages, so that it is clear what can be concluded as predictable correlations and what is still unconnected or remains as uncertain relationships. Certainly, the results from the production function analysis may overstate the scale of the expected impacts of public infrastructure investment, but the links between public investment in infrastructure and economic growth and private capital productivity are important concerns for analysis (Munnell 1993).

Background and objectives

21

1.4 The debate: infrastructures for development Although most of the debate and material presented in this book concentrates on infrastructure investment in developed countries, there is a substantial amount of literature on developing countries. Indeed, much of the early research carried out in the 1960s by geographers takes examples from the developing world, particularly in terms of development of transport networks from ports into the hinterland, the opening up of new areas for development and the development of dominance (e.g. Taaffe et al. 1963). In developing countries, infrastructure includes all forms of services, not only transport. Over $200bn a year is invested in new infrastructure, some 4 per cent of the national output and 50 per cent of all investment (World Bank 1994). This includes public utilities (power, telecommunications, piped water supply, sanitation and sewerage, solid waste collection and disposal and piped gas), public works (roads and major dam and canal works for irrigation and drainage) and other transport projects (urban and interurban railways, urban transport, ports and waterways, and airports). The quality of this infrastructure is alleged to determine the relative success of each country in expanding and diversifying its economic base, in accommodating population growth, in reducing poverty and in improving the environment. The development argument is that good infrastructure (in a general sense) raises productivity and lowers production costs, but that it has to expand fast enough both to accommodate growth and to open up new areas for development. Although the precise nature of the link is unclear (Section 1.5), at the cross-section there does appear to be a correspondence between the increase in the infrastructure stock and the level of GDP per capital (Figure 1.1). A 1 per cent increase in the total infrastructure stock is said to be matched by a 1 per cent increase in GDP across all countries (World Bank 1994). But this general conclusion may give different results if specific types of infrastructure are examined individually.1 Similarly, the balance between the different forms of infrastructure changes with the level of development. High-income economies invest more in power, roads and telecommunications infrastructure, while low-income economies are more concerned with the basic infrastructure such as water and irrigation. The World Bank concludes that there is a high potential return from investment in infrastructure provided that the overall policy conditions are good. Returns are lower by 50 per cent or more in countries with restrictive trade policies. These conclusions were confirmed by the Brookings Institute (Kresge and Roberts 1971) some twenty-five years ago, when they concluded that although infrastructure investments had good rates of return, success largely depended upon general economic policy: ‘Infrastructure is a necessary, although not sufficient, precondition for growth—adequate complements of other resources must be present as well’ (World Bank 1994:17).

22

Objectives and scope

The growth impacts also depend on other factors such as the timing, location of the additional capacity, levels of actual and forecast demand and the existing structure of the network. Most investment is promoted by the public sector and is seen as a means to promote economic development and to provide employment, particularly in recessions. It is seen as an investment in the future. Many of the investment techniques used are labour intensive and this provides employment with intensive economic growth. However, despite these clear intentions, even in developing countries, it is often capital expenditure on infrastructure that is reduced as the impacts are not immediately apparent. In this respect, there is little difference between developed and developing countries as the political short-term priorities and pressures on public finances take precedence over the longer term economic development objectives. An examination of the rates of return from projects funded by the World Bank gives an average return of 16 per cent (1980–90). These rates of return have been re-estimated after the project has been completed (including loan disbursement—Table 1.3). As can be seen from the table, returns have been lowest (and declining) for irrigation and drainage projects. Transport investments have a consistently high rate of return, except for airports (small sample) and railways. The main reason for the higher or lower than expected rates of return is the actual growth in demand. Overestimation of demand in some cases exceeded 20 per cent, but in the railway sector traffic failed to reach its projected level in 29 out of 31 cases. In 10 cases, traffic actually declined. The highest rates of return were in the projects that had high growth levels in demand, and this was most evident in roads and ports (Table 1.3). Table 1.3 Average economic rates of return on World Bank supported projects 1974–92

Source: World Bank (1994: Table 1.2) Note: 1 Rates of return are financial, not economic.

Background and objectives

23

At the cross-section, it does seem that there is a clear relationship between growth in the infrastructure stock and GDP. This conclusion is not unsurprising as development on a worldwide scale must show variation as income levels vary. One of the main determinants must be the level of infrastructure provision as this again varies substantially between countries, in proportion to their wealth levels. The more fundamental conclusion on whether this link is a causal one has not been proved, nor has the direction of that link. The World Bank evidence also seems to suggest that the relationship is a one-to-one ratio between infrastructure and GDP growth. It would be interesting to see whether this relationship is stable over time. Perhaps growth in GDP in highincome economies is no longer related to the increase in infrastructure provision in the same ratio. However, it should be remembered that the World Bank definition of infrastructure includes all the social overhead capital, not only transport infrastructure that is the main concern here.2 Similarly, the high rates of return on infrastructure projects in developing countries are to be expected, particularly where the infrastructure is opening up new areas for development or where the existing network is limited. New investment will make a substantial impact on accessibility, but more importantly will open up new markets and areas for development. The rates of return sought by the World Bank are about twice the levels expected in transport projects in developed countries—about 15 per cent as compared with 8 per cent—and there are numerous projects that exceed this threshold. The main conclusion from this review of the evidence in developing countries is that infrastructure (in the general sense of social overhead capital) can produce major benefits in economic growth, poverty alleviation and environmental sustainability. But this only occurs when it provides services that respond to effective demand and it does so efficiently. The World Bank (1994) places the responsibility for poor performance on the individual national governments. Improvements are required in the management of projects so that they are run like businesses, and in the introduction of competition. The discipline of the market is required in infrastructure provision, together with a greater involvement of the private and community sectors. Too often in the past has the provision of infrastructure been seen solely as the prerogative of the public sector. Investment per se is not sufficient, since poor management of projects can cancel out any potential benefits. The measurement of the quality of infrastructure management is rather complex and largely inaccurate. In this book, we do not attempt to measure the quality of the management in any quantitative analysis, so the problem of predicting future effects remains unsolved. But in a more general sense, the World Bank’s view on transport and development is very clear: Transport is central to development. Without physical access to jobs, health, education, and other amenities, the quality of life suffers;

24

Objectives and scope

without physical access to resources and markets, growth stagnates, and poverty reduction cannot be sustained. Inappropriately designed transport strategies and programs, however, can result in networks and services that aggravate the condition of the poor, harm the environment, ignore the changing needs of users, and exceed the capacity of public finances. Macroeconomic studies by the World Bank show that investing in transport promotes growth by increasing the social return to private investment without crowding out other productive investment. Microeconomic analysis confirms the high social value of transport. The estimated economic rate of return on transport projects at completion is 22 per cent, half as high again as the Bank average. Improvements in rural transport have lowered the costs of agricultural production directly, by increasing access to markets and credit, and indirectly, by facilitating the development of the non-agricultural rural economy. Improvements in urban transport have increased labour market efficiency and access to amenities, making changes in the scale and form of urban agglomerations possible. Improvements in interurban transport have facilitated domestic and international trade and sped the movement of freight, as well as of people. (World Bank 1996:1) There are parallels between experiences in developed and developing countries, but the clear focus of this book is on developed economies. It is here that the debates are less clear, yet the arguments for the links between transport infrastructure investment and economic development are still made. In developing countries, where the transport network is sparse and of a lower quality, the development arguments seem clearer. But as all economies globally move towards being more developed, the links between transport infrastructure investment and economic development will become less clear in all countries. 1.5 The need for the book 1.5.1 Rationale and novelty Transport investment and economic development have in the past been closely linked, with substantial pressure being placed on central government to finance major infrastructure projects on the understanding that this will lead to increased competitiveness and economic development. From the review of the historical evidence cited in this chapter, the case is far from clear, particularly in developed economies where additional investment has little impact on the overall accessibility within the transport system. The first aim of the book is therefore to bring together the wide-ranging

Background and objectives

25

historical, economic, geographical and development literature which has been produced over the last thirty years on the subject. The purpose of this review is to present a synthesis of knowledge and to identify the main debates that will be elaborated on in the main parts of the book. The second aim is to examine new methodological approaches for the analysis of the relationships between transportation network development and economic growth. These are essentially microeconomic models that differ from the more traditional macroeconomic approaches to economic development (see further in Chapter 2). The thinking behind the argument for new approaches is that only at the local level will the impacts of transport investment actually be identified. Macro level analysis does not allow the subtlety of change and impacts of specific infrastructure projects to be measured, nor does it allow the causality of relationships to be established, nor does it control adequately for the full range of impacts to be measured. Related to this second aim is the realization that new priorities are present in the economy and that many of the traditional arguments relating to both location theory and economic base theory no longer apply. People and firms are much more flexible in their location decisions and patterns of interactions and travel are more complex. Similarly, there are new priorities which influence decisions and this book confronts some of them. There are new economic forces at work as the economy moves from one based on manufacturing to one based on service and information industries. Just as the industrial revolution caused a fundamental shift in employment from agriculture to industry, so is the new technological revolution causing a major change in the employment base of most developed economies. Parallel with this change is the new demographic profile of the population, with an increased life expectancy, early retirement and new household structures. Traditional patterns of families and a dependent elderly population are now being replaced by a much more heterogeneous family structure with a large new active elderly population. The economic forces, which have been dependent upon traditional patterns of population structure, are now being transformed by these new patterns. Similarly, where transport investment has traditionally been seen as a role for only the public sector, new forms of financing are being promoted through private sector investments and joint ventures involving both the public and private sectors. Projects which have been mainly financed from national budgets are now being supported by international agencies such as the European Union (e.g. through the Regional Development Funds), the European Investment Bank and a wide range of private sector financial institutions (e.g. banks and pension funds). Finally, much analysis of infrastructure investment has concentrated solely on the impact of the new link being proposed in terms of its specific contribution to improving travel, principally through travel time reductions.

26

Objectives and scope

In terms of the economic development impacts and the contribution of a single link to the network as a whole, there is much less knowledge. The major analytical and empirical parts of the book provide a range of contrasting approaches. We tackle these through analysis at different levels and with examples from different modes. We also interpret the economic development impacts of the extensive literature of benefit-cost analysis, which lies at the heart of current debates on investment, particularly as it relates to externalities and network effects. We develop a microeconomic modelling approach to explore the linkages between firms, employees and the transport network. All these elements provide new insights and interpretations to the complex relationships being investigated. The case studies of road, rail and airport investment decisions are based on secondary data and exhaustive investigations into how development effects can be isolated and measured. This has proved quite a frustrating task as the information available is never perfect, but even then some clear conclusions can be drawn. Again, this complementary analysis, which carefully assembles and reviews the available material, is new and provides more qualitative analysis to match the quantitative and theoretical analysis in Parts II and III. As such, we aim to provide a major synthetic and original input to the great debate on the links between transport infrastructure investment and economic development. 1.5.2 The ten key questions Having presented the general rationale for the book and a basic review of the key elements in the debate, it is now appropriate to address some of the major unresolved issues that will be taken up later. A major contribution to the literature will have been achieved if even some of these questions can be answered. The ten key questions that have emerged so far are listed below in two groups. The first group (1–6) we examine in detail. The second group (7–10) we either touch upon briefly or not at all, although the questions are still quite important. 1 Is the growth impact of any new transport link in developed countries likely to be significant? Most networks in developed countries are already well connected, so the impact of any new link is likely to be small individually—yet the combined effects of new links may be substantial. Accessibility can be assessed as an absolute and as a relative concept. How important is accessibility in regional competitiveness, both in terms of its contribution to the overall buoyancy of a regional economy and in terms of its redistributional effects from one region to another? Travel timesavings resulting from new links in most developed countries are likely to be small. In many cases investment is in upgrading existing links and in marginal additions to networks (e.g. bypasses).

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27

Does investment have to be of a sufficient (unspecified) scale before any impact can be identified, let alone measured? Chapters 8, 9 and 10 examine aspects of this issue. 2 Are transport costs a small part of total production and labour costs? Transport costs are often a small part of the total production costs of firms. Even where they form a high proportion of the total activity/participation costs of individuals, they are relatively insensitive to marginal changes in costs or accessibility. Firms’ and individuals’ activity patterns are more likely to be influenced by the uncertainties and unreliability of the transport system. The time and monetary costs of these factors have already been taken into the decision to travel and have effectively been discounted. Will changes in these factors be of a sufficient scale or importance to have any real impact on travel decisions? The effect of investment in a new link, given the network, is discussed in Chapters 3, 7, 9 and 10. 3 Are buoyant local economic conditions more important than transport infrastructure improvements in generating growth? Buoyant local economies attract more investment and in-migration of labour (perhaps at the expense of less buoyant economies). The return on infrastructure investments may be higher in these buoyant economies, but the construction costs may also be higher (due to higher labour costs, land acquisition costs or environmental costs). The classical debate in the regional development literature is between those who argue for more investments in the buoyant areas, which will then have spin-off or cascading benefits to other areas (Myrdal 1957), and those who suggest that investments should be targeted directly to the less well-off regions (Rostow 1960a, b). The impacts will be felt in both the short term and the longer term and they will be: • • •

direct—transport related; indirect—land use and development related; new—jobs, activities, etc. attracted to the region.

Measurement in each of these categories is not easy, in particular the new effects—it is also important to consider the scale or level at which the impact can be measured and identified. The modelling part of the book (Part III) examines transportation investment within the framework of the national or local economy. Chapters 10 and 11 briefly discuss other aspects of local economic conditions when evaluating the impacts of rail and airport investment. 4 Are the unique characteristics of the area and the spatial extent over which the growth impacts are to be felt considered? Even with a clearly defined before and after survey, it is necessary to control for other changes which are taking place in the economy. Hence a control area is often used to isolate changes due to other effects rather than the transport investment. The difficulty here is to find an appropriate corridor or area

28

Objectives and scope

for comparative purposes. This problem is particularly acute where the investment is large (and unique) or where the expected impacts are highly localized (e.g. in a rail investment where the impacts are expected around the stations). Chapters 5, 8, 9 and 10 address the local and unique characteristics, as well as the spatial extent, over which the investment impacts are likely to occur. 5 Can one generalize about the results from specific case studies? There is a substantial amount of information available from individual studies, but as yet little consensus or theory deriving from it. It has been difficult to generalize the results as case studies have been focused at the micro level, and the macro level studies have been subject to much debate. This question is addressed in all of the three major parts of the book. 6 What is the role of technological change in affecting the relationships between transport investment and economic growth? Technology is permitting new forms of production as processes are sped up, through increased specialization and through short production runs. The role of transport and infrastructure in these processes is becoming more complex. For example, there has been a substantial increase in the amount of traffic in transit at any one point in time so that the warehousing and stock level requirements can be reduced. Freight distribution networks have been radically reorganized to take account of the availability of new technology and high quality (road) infrastructures. It is also possible that changes in transport infrastructure can promote technological change, particularly in the way in which particular activities are carried out. The best example here is the possibility of teleworking which requires both a good quality transport infrastructure and an appropriate telecommunications infrastructure. Chapter 4, 5 and 6 discuss the impact of technology on the consequences from a transportation infrastructure investment. 7 How slow, long term and complex are the adjustment and readjustment processes within the regional economy following a transport investment? It is very difficult to establish cause and effect, and the effects may operate in different directions. For example, the relative effects in one location may be positive, but it may have negative effects elsewhere—a competition effect. It is important to carry out a range of analyses to cover ex ante and ex post situations so that our understanding of the basic processes at work can be strengthened. Improvements in the relative position of one part of the regional economy might be at the expense of another part of the economy (this is the pie theory—namely that the total size is fixed, but the debate is over shares of the pie). The relative positions change, but there may also be some net increase in production in the economy as a whole (national or international), particularly if interregional trade is promoted. Through these actions it is argued that the competitive position of one region will improve (perhaps at the expense of others)—with lower

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29

production costs and better access to markets. This in turn will generate more output and profitability, which will lead to higher income, levels, more expenditure and increases in employment. This question is briefly touched upon in Chapter 4. 8 Does a good transport infrastructure raise the image and the perceptions of an area, thereby attracting additional private investment? A new transport investment raises the profile of one area and development is often promoted on the quality of the new transport infrastructure. For example, this has been a major argument by some cities for new investment in metro and tram systems (e.g. Atlanta and Sheffield). Similarly, a lack of infrastructure may work against the attractiveness of a particular location. These arguments have been extensively used in justifying developer contributions to the costs of infrastructure as developers are seen as one of the main beneficiaries from the investment. The intention is to recapture some of the benefits from infrastructure investment rather than allowing the benefits to remain with the developer-value capture. The increased opportunity for high rent levels often accrues only to the developer, not the provider (often the public sector) of the infrastructure. Chapter 9 and 10 partly discuss this issue. 9 How can one assess the course of economic development in an area if the transport investment was not made? This counterfactual situation has always presented difficulties. Although it is difficult to measure what has actually happened, it is even harder to measure what would have happened with no transport investment, or with an investment in an alternative transport project, or an investment in a different type of project altogether (e.g. a training project). The scale of the impact is likely to be small and there will be substantial ‘noise’ in any analysis. Does this make the problem too difficult for analysis, so any theoretical advance will be in the form of a general theory (e.g. Myrdal), and any micro level advance will result from individual case studies from which it will be difficult to generalize? Chapter 11 examines an investment option at an airport with and without a new major facility. 10 What is the role of expectations, regarding the impacts of investment, in achieving growth? This question has two main aspects. First, as time passes it becomes harder to measure the impacts of any particular investment with any degree of certainty. Second, it also affects the expectations of investors about whether their returns will be realized over a short or long period and this, in turn, will affect their propensity to invest. We do not really address this question within the book apart from a minor reference in Chapter 3. All of these questions suggest that we need to make a careful analysis of the main issues to clarify the relationships conceptually, theoretically and empirically. The basic question addressed in this book, namely the link between

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transport infrastructure investment and economic development, is likely to become increasingly important as the user benefits of additional investment in a well-connected network diminish and as other non-user benefits become more important. 1.6 Objectives and structure of the book The basic question addressed in this book is whether there is (or should be) a clear link between investment in transport infrastructure and economic development. If a link can be established, it is then important to determine whether there is any causality inferred. It is also necessary to establish whether the relationship is linear or whether there are decreasing marginal productivities, especially in regions that are already well connected. This again raises important questions as to what are the necessary conditions for causal links to be established. The book is divided into four main parts. In Part I (Chapters 1 and 2), the context of the debate is set out through a short historical review, discussion of the productivity of infrastructure investment and the infrastructure for development argument. It also outlines the rationale for the book, and covers the definitions and approaches adopted and methodological framework used. Part II provides a perspective on some of the major changes taking place in society. Even if links can (historically) be found, it is argued that these contextual changes must now form a central part of the debate. The primary concern over economic growth must be extended to include the means by which transport infrastructure is funded, the changing nature of the economy itself and the new priority being given to distributional and environmental concerns. In the past, apparently simple relationships may have been appropriate, but the new complexity of change means that new methods are required. In Part III three major analytical approaches are presented. A macro level analysis of the means by which the growth effects of modelling capital investments in transport is presented in Chapter 6, together with some empirical results. This is followed by a comprehensive coverage of benefit cost analysis in transportation. Key elements for our debate are highlighted, including values of time, discount rates, the treatment of time, risk and uncertainty. The importance of network accessibility forms a key concern here and, where possible, the analytical evidence is complemented by empirical data. The third major chapter at the heart of Part III is a microeconomic modelling approach to transport infrastructure development and economic growth. It includes the theoretical framework, the modelling approach and some results from a series of simulations and empirical studies. As a complement to the analytical chapters, Part IV presents a series of case studies derived from secondary data. In each case, major examples are supplemented by minor cases, with chapters on road investment, rail

Background and objectives

31

investment and airport investment. The transport impacts are described, but most emphasis is placed on the economic development impacts in the terms most suitable to the project (e.g. development, employment, income generation, etc.). The concluding chapter returns to the questions raised in this chapter with a summary of whether they have been answered or not. It also presents a new conceptual framework within which transport and economic development can be placed. Other avenues for research are also highlighted as the debate is continuing. This book provides the initial catalyst to finding the answers to one of the great unresolved questions in transport. We do not pretend to be able to answer all the questions, but at least we will have satisfied our own curiosity on the true nature of these links. Notes 1

2

The same study also reports that a 1 per cent increase in per capita GDP results in a 0.3 per cent increase in household access to safe water, a 0.8 per cent increase in paved roads, a 1.5 per cent increase in power and a 1.7 per cent increase in telecommunications (Data for 1990, World Bank 1994). This, however, is the reverse causality to that examined in this book. Most definitions of infrastructure concentrate on the economic infrastructure which includes services such as: public utilities: power, telecommunications, piped water supply, sanitation, sewerage, solid waste collection and disposal, and piped gas; public works: roads and major dam and canal works for irrigation and drainage; other transport sectors: urban and interurban railways, urban transport, ports and waterways, and airports. It includes the social overhead capital, which comprises those basic services without which all forms of productive services cannot function. It encompasses activities that share technical features (e.g. economies of scale) and economic features (e.g. spillovers from users to nonusers). Transport infrastructure comprises large-scale capital intensive natural monopolies that form a common element in the total costs of production and travel. They form the basic network used for the movement of people and goods (based on World Bank 1994).

Chapter 2

Scope of analysis Definitions, approach and methodological framework

2.1 Introduction The literature on the relationships between transport infrastructure investment and economic development provides ample evidence to suggest positive correlation between these variables. Anas (1995), for example, has shown how reductions in commuting time, resulting from subway headways improvements in New York City, will positively affect commercial and residential real estate properties. Rephann (1993) has found a positive effect of road development on economic growth at the regional level. Aschauer (1989a, 1990) provides statistical evidence showing the effect of aggregate investment in infrastructure capital (including transport) on macroeconomic measures like GDP. While these and similar evidence has been criticized on analytical and empirical grounds (Gramlich 1994), the fact remains that many researchers and policymakers strongly believe that transport infrastructure development will have a significant impact on economic growth at the urban, regional and countrywide level. Some of these arguments have been presented in Chapter 1. While we accept this view as a basis for our discussion we contend that there are a number of unsettled theoretical, methodological and empirical questions that need to be properly studied before one can conclude with certainty that a given transport infrastructure has measurable and significant effects on economic development. Key examples of such unsettled issues are: •

• •

How to correctly define and measure infrastructure development, given that the level of transport services rendered by infrastructure facilities, is affected by a number of supply-side factors such as physical, managerial (e.g. transportation systems management), economic and urban planning policies. The definition and measurement of economic development that is affected by a large number of transport and non-transport factors. The nature of the causality mechanism by which infrastructure investment is transformed into economic growth. Is there only one mechanism that

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Objectives and scope

•

can explain growth at the urban, regional and national levels? How does it work and how does it evolve over time and space? Even if there are such mechanisms, are some investments more productive, in terms of economic growth, than others (e.g. rail vs. road), and if so how can they be identified?

In this chapter we argue that a useful way to gain a better understanding of these issues is first to provide a conceptual and methodological framework which will enable us to examine these and related questions in a coherent and systematic way. Thus, the main objective of this chapter is to suggest such a framework and define and explore its various components. Then, we will show how each of the questions like those presented above can fit into this framework, thereby enabling a better and more productive analysis and measurement of the impact of transport infrastructure investment on economic development. A major conclusion emanating from the introduction (Chapter 1) and the development of this methodological framework (Chapter 2) is the understanding that particular models or methods of impact measurement are only adequate for specific problems and not for others. For example, we will argue that production function type models, used in the literature to measure the relationships between the country’s level of infrastructure supply and GDP growth, are largely inadequate for evaluating the effect of infrastructure development on the economic growth of cities and regions. Similarly, models that do not account for continuous changes in urban demography, the urban economy and production processes cannot be used to predict the impact of a new infrastructure facility on urban economic development. The structure of the chapter is as follows. In Section 2.2 we provide definitions of the basic concepts and terms. Section 2.3 describes the key tenets of the conceptual approach which underlies the analysis in this book. In Section 2.4 we present the overall methodological framework proposed for this book, including a detailed examination of the principal components of this framework relative to its structure and linkages with other components. Section 2.5 discusses the key methodological and analytical questions which need to be addressed in improving our understanding of the links between transport infrastructure investment and economic development. We end with an overview of the linkages between the contextual (Part II) and the analytical parts of the book (Part III). 2.2 Basic definitions The objective of this section is to clarify at the outset some of the basic concepts and terms that will be used throughout this book. The terms economic growth and economic development are used interchangeably in the literature and in the ensuing discussion when deemed

Scope of analysis

35

relevant; we will follow that practice. However, for analytical purposes it is useful to distinguish between the two concepts with respect to their use in modelling and measurement. The concept of economic growth is mainly applicable when examining the effect of the expansion of public capital on the national economy. In this context it measures changes in the level of GDP (or GDP per capita) resulting from additional (gross) investment in the total stock of infrastructure such as the roadway system, ports and airports, health and educational facilities. In contrast, the concept of economic development is used primarily when examining the effect of additional investment in specific types of infrastructure on the urban and regional economy. Moreover, this concept also encompasses some non-growth objectives such as changes in urban form, equity effects and reductions in environmental quality. Thus, changes in regional employment (by type), adjusted for changes in location of firms and households, are used as a proper measure for assessing the effect of transport infrastructure investment on local economic development. In general, we regard the change in economic opportunity resulting from accessibility improvements, which is capitalized in the form of a greater use of input factors, expanded output or enhanced welfare, as economic development. Another key concept that needs to be clarified is transport infrastructure. Infrastructure is the durable capital of the city, region and the country and its location is fixed. In the transport sector it includes roads, railways, canals, ports, airports, communications (e.g. air traffic control) and terminals (or other interchanges). A key characteristic of the services rendered by these infrastructure facilities is their spatial dimension which is manifested by their network structure (e.g. road or rail networks). On the other hand, benefits from these services may not be spatially ubiquitous and, in many cases, benefits significantly attenuate from the point of supply (i.e. the need to access a bus or railway station). The spatial dimension of transport infrastructure facilities also implies that they are natural monopolies over a large territory in that the per unit cost declines with increasing output (to a given capacity). Another characteristic is their public good nature (again given capacity), since market exclusion is not feasible in any practical sense for cost, social and legal reasons. Transport infrastructure facilities are durable, which implies significant sunk costs as they often remain in place long after households and businesses have relocated. Their indivisibility means that the capital investment costs are often very high and the costs of additional capacity and maintenance are also considerable. Finally, there is a propensity for infrastructure facilities to generate social and environmental externalities either through their particular location or through the services that they produce. In summary, transport infrastructures have the following characteristics: they are networks; they form an indispensable part of the total production costs of goods; they have substantial elements of natural monopoly; their

36

Objectives and scope

capital costs are high, but their running costs are low and the sunk costs necessary to establish an infrastructure are substantial (Kay 1993). 2.3 Conceptual approach The modelling and measurement of the impact of transport infrastructure investment on economic development requires a specification of the theoretical framework which underlies the potential causality links between these two phenomena. Here we outline the conceptual background, followed by a more detailed methodological framework for the modelling of these links (Section 2.4). The most fundamental outcome of an investment in transport infrastructure is the changes in the relative prices of accessibility of various locations. Since the network structure of transport systems makes accessibility spatially nonuniform, an investment in a new facility, or the improvement of an existing one, necessarily alters the present equilibrium structure of accessibility prices. This price change, in turn, implies changes in the relative advantage of spatially located activities and the economic opportunities both for the production and consumption sectors. The main reason being that the costs of inputs (e.g. labour) and the prices of outputs (e.g. housing) at alternative locations change as a function of costs of accessibility to these locations. Furthermore, the extent and strength of various scale, scope and network economies, which affect the location decisions of firms, may become less pronounced as relative accessibility improves. It should be understood that the degree to which an improvement in transport infrastructure affects economic development is not independent of the economic and demographic characteristics of the region where the improvement takes place. A given change in accessibility will have a different effect on the location and consumption decisions (e.g. mode choice and use) of two-employee households than it will have on single-employee households. Similarly, retailers will react differently to accessibility improvements than industrial firms with regard to their location and use of labour. Hence, from a theoretical standpoint, the analysis of the impact of infrastructure investment of economic development must consider the nature of the local economy and the different actors that make decisions. This argument is based on three fundamental premises. First, the investment is an effective investment. This means that the investment has tangible effects on the performance of transport networks. Thus, investments whose composition, magnitude, type or location do not alter considerably the performance of transport networks are assumed to be non-effective ones and, as a consequence, not to generate economic development. On the other hand, an investment that improves the organization and provision of transport services in an area, even if it does not involve the construction of new facilities, is regarded as effective if it generates measurable effects.

Scope of analysis

37

Second, the causal linkage between infrastructure investment and economic growth must be manifest in changes in transport-economic behaviour. This premise implies that economic development ensues only if economic agents such as households and firms, as well as markets, react to the changes in the performance of the transport network. In the short to medium term, this reaction is confined to travel variables such as trip generation rates, travel volumes or choice of routes. In the longer term, this reaction must also be manifested in location decisions of households and firms and in changes in land and property prices. In total, changes in accessibility from infrastructure improvements need to be accompanied by changes in economic behaviour and prices in order to constitute economic development. Third, transport improvements which influence travel behaviour and transport markets must eventually be transformed into measurable economic benefits. These benefits include improved factor productivity, larger output, increased demand for inputs, increased property values and greater demand for consumer goods. Having stated these three important premises, it must also be stated that the degree to which infrastructure improvements will affect economic development is obviously not independent of the level and performance of the inplace capital infrastructure. Thus, in areas where the stock of the transport infrastructure (e.g. roads, access roads, rail systems) is highly developed, even a sizeable infrastructure investment is unlikely to affect travel behaviour and markets significantly and, as a consequence, economic development. In general, therefore, we can expect, ceteris paribus, a declining marginal ‘economic development effect’ from additional infrastructure investment. At the extreme, when the region’s transport infrastructure is fully developed so that any additional investment will not improve accessibility, no economic gains from the investment (save for the multiplier effects) will result. In summary, what the discussion in this section suggests is that the transport system can be viewed as a constraint on the attainment of economic opportunities within a region by households, commercial and industrial activities. An additional investment in transport infrastructure facilities lessens this constraint, thereby enabling the attainment of higher economic development1. As the region’s infrastructure becomes more developed, in terms of cost and ease of travel, the less is its binding effect on economic development. Hence, in analysing the effect of an additional investment on the local economy it is necessary first to assess its relative contribution to the region’s accessibility, through its effect on travel behaviour and markets. An important theoretical question arising from the above discussion is whether transport development constitutes necessary and sufficient conditions for local economic development. If transport can be regarded as a constraint on the attainment of economic opportunities in an area, then it can be regarded as a necessary condition. One of the main objectives of this book is to determine whether and when it becomes a sufficient condition for economic development.

38

Objectives and scope

We now present the three major elements of this question: method of financing, equilibrium analysis and dynamic process. Method of f inancing First, transport investments require the use of real resources, such as labour, capital and land. If not financed from sources outside this region, it will be necessary to divert regional resources from other uses such as consumption, production or other forms of investments. Thus, if the region’s level of transport infrastructure is well developed so that it does not impose a serious binding constraint on the local economy, any additional investment, in fact, may reduce the region’s economic well-being. For this reason, it is necessary to account for the effect on the local economy of the way resources are raised (i.e. the method financing) for additional infrastructure investment. The method of financing infrastructure expansion has some further ramifications for local economic development. Consider two alternatives: first, the use of general revenues raised through local income or consumption taxes. Either tax will reduce households’ disposable income thereby affecting, inter alia, individuals’ propensity to travel. It will further affect the economy, as households’ demand for output will decline. Alternatively, inefficiency in the form of dead-weight loss associated with such tax schemes can also be expected. In addition, income tax may also affect labour to capital ratios since it raises the cost of using a unit of labour. All of these effects have a negative impact on local economic development. A second alternative is to finance new infrastructure investment by taxing travel (e.g. hiking up petrol taxes or imposing road tolls). Such taxes, inevitably, affect the use of specific modes of travel (e.g. roads) thereby lowering the level of accessibility associated with the use of this mode. Equilibrium analysis In response to improvements in accessibility from infrastructure investment, firms and households can increase their demand for infrastructure facilities (the trip generation effect). They may also change their trip pattern (the trip distribution effect); their choice of travel mode and their travel route (the modal split and trip assignment effects). They may relocate (the spatial location effect), or they may adopt all of these options. In turn, each of these effects will influence the degree of use (and the quality level) of transport which is in existence or which is being newly constructed (as well as accessibility). This discussion implies that, in modelling the effects of transport improvements on the local economy, it is necessary to carry it out within an equilibrium framework so that changes in the total demand for infrastructure facilities, resulting from changes in travel patterns and rates and in spatial location, are equilibrated with network supply.

Scope of analysis

39

Important factors affecting the equilibrium results from infrastructure improvements are the interrelationships between economic development and travel behaviour. If economic development increases per capita regional income and if travel demand is income elastic, then more travel will ensue from the economic development, induced by infrastructure investment. As a consequence, we can expect a different state of equilibrium in comparison to the case where travel behaviour is unaffected by economic development. Furthermore, the additional demand for travel may, in turn, generate a subsequent need for new infrastructure facilities. Dynamic process An important aspect of these interrelationships is that they are not instantaneous and, in general, require considerable periods of time to transpire. The main reasons for this are the long periods necessary for investment implementation as well as the time needed for the demand side adjustment. Underlying these time lags are market imperfections including incomplete information concerning infrastructure development, uncertainty regarding the behaviour of public authorities and private entities, high transaction costs emanating from imperfect land market and general market externalities. All of these make the transformation of transport improvements into economic benefits highly time dependent. The overall result is a dynamic process whose evolution depends on the initial conditions of local transport and activity systems and on the local transport and economic policies. 2.4 Methodological framework On the basis of the above discussion, Figure 2.1 presents a schematic paradigm showing the causal relationships between infrastructure investment and economic development. From this figure it is apparent that we conceive infrastructure investment as affecting network performance, transport markets and externalities (e.g. pollution). It is, of course, a question of analytical modelling to show exactly the mechanism by which these effects come about. This issue is discussed at a later stage (Chapter 8). Whatever the mechanism is, the combined results (termed here as ‘accessibility effects’) are changes in relative accessibility in terms of mode, network links, spatial location and time of day. Accessibility effects, in turn, stimulate the so-called ‘real effects’. These include changes in factor productivity, changes in the location of households and firms, changes in production and in consumption decisions, and changes in agglomeration economies. As explained below (Figure 2.1), the accessibility effects are further capitalized in the form of land rent and consumer surplus. Thus, we can measure economic development (from infrastructure investment) either through the ‘real effects’ or through the ‘capitalized effects’. It is also seen that the investment generates the so-called,

40

Objectives and scope

‘multiplier effect’ which results from the infusion of a large sum of capital into the local economy. We do not regard this effect as part of the economic development from the transport investment. Lastly, in the longer run, economic development is likely to encourage further demand for travel that, in turn, will give rise to further infrastructure development. The use of this paradigm for the analytical representation and empirical measurement of the linkages between transport investment and economic development requires the inclusion of two additional factors: the geographic unit and the time span of analysis. That is, while the basic relationships shown in Figure 2.1 are assumed to hold at any geographic level and at any time scale, their actual analysis, modelling and measurement requires a different definition and specification for each of the geographic levels and time periods. Conceptually, we consider three geographical scales and two time periods for the analysis. These are: the urban level, the regional level and the country (or whole economy) level, and the short to medium run effects (up to 10 years2) and the medium to long term effects (over ten years). This basic matrix of analysis is a 3×2 matrix of the geographic and time units, where each of the matrix’s cells contains the relationships expressed in Figure 2.1. One major result from the adoption of this approach is that each of the four main components in Figure 2.1 requires a different definition and a different model or method of analysis at each cell of the matrix. The analysis of the effect of infrastructure expansion on economic development at each time period and (in particular) at each geographic level calls for a specific definition of infrastructure investment and of economic growth. Similarly, the measurement of network performance and the modelling of transporteconomic behaviour will vary as a function of the geographic unit and time scale. For example, at the urban level the addition of a new road or rail link to the existing network in a specific area represents ‘infrastructure investment’ while its short-to-medium run effect on the volume of retail business can be regarded as local ‘economic growth’. To measure such an effect, one needs accessibility type models that link retail trade with travel times. Such definitions and modelling approaches would, of course, largely be inadequate in the long term at the regional or country level. The distinction between the urban and regional levels of analysis is somewhat problematic, as often it is very difficult (if not outright impossible) analytically to separate the city from the region where it is located relative to the incidence of accessibility and economic development benefits. We nevertheless make this distinction since, in some cases, transport investments are being made with the explicit objective of benefiting a particular local area such as a city or a county. Such an example is the Buffalo, New York subway which was constructed with the declared intent to revitalize Buffalo’s declining downtown at the expense of outlying suburban areas (Berechman and Paaswell 1983; Chapter 10). The international level of analysis refers to the cases where an investment

Scope of analysis

41

Figure 2.1 The basic causality paradigm of the relationships between transport infrastructure investment and economic development.

links the transport networks of two (or more) countries. The Channel Tunnel is a prominent example of such an investment. While the economic development benefits from such an investment can be quite significant, it is not a major concern of this book where the focus is primarily on the three more local level impacts—the country, regional and local levels. The above methodological framework permits a systematic typology of various definitions, models and empirical results which have been reported in the literature and relate to the effect that transport infrastructure investment has on economic growth. However, before we can proceed to discuss this in more detail, it is necessary to elaborate further on the major factors which underlie the above framework, in particular, the suggested relationships between

42

Objectives and scope

transport infrastructure investment and economic development. Figure 2.1 depicts the general structure of the methodological framework used in this book, together with the interrelationships between each of the main components. Next we discuss the nature of each of these four components. 2.4.1 The investment component A fundamental concept in transport supply analysis is the network structure of all transport systems. Land transport networks can be viewed as the durable capital component of delivery systems that connect spatially diffused activities. The formation of such durable capital facilities requires very high fixed costs (a substantial part of which is sunk costs). In the absence of significant congestion this capital is a local public good. The provision of these capital facilities is normally carried out under conditions of scale economies (thus implying the possibility of a natural monopoly) whereas their consumption is associated with network, scope and density economies. Externalities are normally associated with the consumption of services emanating from these facilities. Adopting this view it becomes obvious that in assessing the effects of an investment in transport infrastructure on economic growth, several principal factors have to be considered with regard to the in-place network. These are: the type of the investment, its relative size and its efficient provision and consumption. The type of the investment has two dimensions: its particular technology (e.g. rail vs. road) and its purpose. To see this within the concept of a network, consider the case of a rail investment that provides a missing link between two previously disjointed rail networks. Such a project is likely to have a markedly different (most likely a greater) effect on mobility and economic growth than a similar size investment in a new link in either of these two separate networks would have. A similar argument can be made for road projects or for a project that makes rail and road networks easily accessible to each other. Therefore, it is the type of the transport technology (e.g. rail, road) and the nature of the investment (e.g. linking separated networks, a maintenance project or the addition of a new link) that matters in relation to its expected effects. The magnitude of the investment is also crucially related to the concept of a transport network. In general, even a large size investment (in monetary or physical terms) usually represents only a modest expansion of the in-place network and, as a consequence, will have small mobility and economic effects. Therefore, in general, when defining an investment in transport infrastructure it is necessary to assess its relative size rather than its absolute size. Defined in this way, the effect of a modest relative-size investment is likely to be quite small and localized. That is, in areas where the in-place network is well developed, the economic growth effects from even a large investment are

Scope of analysis

43

likely to be confined to the urban level. On the other hand, the cumulative effects of such investments will be associated with regional or country level economic development. The efficiency of an investment is another major element that needs to be regarded when considering the effect of a particular infrastructure investment on economic growth. The importance of this attribute of an infrastructure investment stems from the fact that it would be totally wrong to regard the economic benefits from an inefficient investment as the correct ones. An illustrative example is the case when the direct transport benefits from the investment are rather small (present value of users’ time and operating costs savings are below the present value of the investment costs) and the only way to justify the investment is to ascribe to it high economic development benefits. Under these circumstances it is correct to ask whether this particular investment should be carried out even though it can generate high economic development benefits. In defining the efficiency of infrastructure investment we need to emphasize that, since we speak of a public capital good, it is necessary to regard efficiency from the point of view of the economy or the public sector rather than from that of the private sector. Thus, profit maximization is not an objective in the provision of infrastructure whereas social welfare should be3. Given this view we can define the efficient provision of infrastructure facility in terms of technical efficiency, allocative efficiency or social optimum. The first definition implies an investment whose output (capacity) meets the demand for this facility, e.g. in terms of travel volume. The second implies optimal output at minimum costs. The third, implies that all social costs and benefits have been accounted for and a social discount rate has been used to discount future benefits and costs. With respect to the network concept, social optimum requires that the benefits and costs of the entire relevant network be regarded, following the provision of the new investment. An infrastructure investment can generate various production and consumption externalities that need to be accounted for if social optimum is to be attained. A textbook definition of externalities refers to non-priced changes in the utility of a third party caused by production or consumption activities of direct users. Changes in the congestion, pollution or noise levels from additional traffic, following an infrastructure investment, which accrue to non-users or to other trip-makers on other links of the network, are obvious examples. On the other hand, economic development benefits emanating from an investment would not qualify as externalities since, under normal conditions, they carry a market price (e.g. the wage rate of additional labour or land rents from land development). In general, it would be erroneous to assess the economic effects from an investment without first considering the non-priced externalities. For example, the optimal level of an investment should be computed only after the expected traffic in the expanded network (following the addition of the new capacity), has been subjected to congestion

44

Objectives and scope

tolls or to pollution charges which, in turn, will affect the actual level of this traffic thus the optimal size of the investment. In summary, it is a mistake to examine the effects of a particular infrastructure investment irrespective of its type, relative size, its social efficient level and the externalities it may generate. All of these factors should be considered when viewing the investment within the network of which it is a part. Having defined the investment component of the paradigm depicted in Figure 2.1 (trigger mechanism), we now turn to examine the network performance component. 2.4.2 Network per formance component Four principal determinants characterize the network performance module of the analytical paradigm depicted in Figure 2.1 (performance and accessibility). These are: accessibility and travel flows; savings of vehicle operating costs; network effectiveness and intermodality (i.e. the interaction with other transport facilities and technologies). Beginning with the accessibility and travel flows determinant it is obvious that, given the particular infrastructure investment, the performance of the network is measured primarily by such factors as travel time savings between any pair of locations, by the resultant volume of traffic on each link and by changes in the relative accessibility of locations i and j (i, j = 1,....., N). Similarly, users’ savings in vehicle operating costs such as in fuel and maintenance costs are also measures of network performance. While the magnitude of changes in these factors cannot be divorced from the attributes of the investment defined above, they constitute key indicators of network performance. Since in this chapter we regard the ‘network’ as our ‘basic unit of analysis’, it is necessary to consider further the overall effectiveness of this unit. Three factors define network effectiveness: positive network externalities; network connectivity; network efficiency. The first factor relates to the fact that as more links are added to the network additional locations become accessible from all other locations thereby making all potential users better off. In this regard, the layout of the network plays a vital role in enhancing the network’s effectiveness. Network connectivity is defined as the number of alternative routes available to users who wish to reach a given destination location given their origin location. The larger is this number, the greater is network connectivity. Network efficiency is defined as the capability of a transport network to process the area’s volume of daily traffic in terms of the relative length of the peak period, or the percentage of total traffic that is processed during the peak period. A new investment in a transport infrastructure facility usually affects the use of other infrastructure facilities, either because of cross demand elasticities or because two transport technologies are complementary or because there are scope and network economies so that the expansion of one network reduces

Scope of analysis

45

the costs of using another one. The generic name used to describe these effects is intermodality. To illustrate, the construction of a new road diverts traffic from other links, thereby reducing travel times in these links, but in the longer run it generates more traffic in the entire network. The construction of a transport centre where rail, bus and auto systems meet can reduce connection times, but in the longer run it may produce more traffic in each of these systems, thereby increasing travel times. The development of truck freight facilities (e.g. truck parks) is likely to increase truck freight movement at the expense of rail freight traffic, whereas the development of facilities which make rail and truck technologies complementary may increase overall freight movement by truck and rail due to network economies. Of particular interest in this regard is the alleged complementarity between transport and telecommunication technologies. Yet present knowledge in this area does not permit an unequivocal prediction of the effect of an investment in one technology on the development and use of the other. Nevertheless, the ability to use telecommunications to affect travel needs careful consideration. At the urban level and in the short-to-medium run, accessibility factors are the main network performance measurers. If, for example, accessibility is measured in terms of travel times on specific routes by alternative modes, then changes in relative accessibility between spatially distributed activities will define the performance of the network following the investment. In the longer run, when transport and land use markets can be assumed to be in equilibrium, changes in the relative generalized prices for commuting and for freight hauling are the appropriate indicators of network performance. 2.4.3 Transpor t-economic behaviour This is made up of two principal elements (location and real effects, Figure 2.1). First, there is the structure and performance of transport markets. Second, there is the response of land-use activity systems (mainly, households and firms) to changes in transport markets relative to their spatial location and consumption and production decisions. The essence of the transport-economic behaviour component is thus the analysis of the interrelationships between the transport and land-use markets. As alluded to above, the distinction between short-to-medium and medium-to-long run periods is predicated primarily on the basis of the timespan required for markets to achieve a state of equilibrium. Since we argued above that infrastructure investments affect the relative prices of travel between locations, the equilibrium number of commuter trips or the equilibrium volume of goods hauled between locations, by mode type and by time of day, will also change as a result. However, these may not be the only changes. For example, the introduction of a new freight rail link is bound to affect not only relative prices of freight movement in a given area (city or a region), but also the degree of competitiveness in freight markets.

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Depending on demand and supply conditions, these markets can evolve into becoming more competitive or more monopolistic. For example, when large-scale economies are associated with rail operations. In either case, in the long run, this effect will determine equilibrium prices and quantities in transport markets. Since economic entities like households and firms make their decisions on the basis of these factors, economic growth (which is the ultimate manifestation of these decisions) is also influenced by these equilibrium conditions. Almost all of the theoretical models developed for analysing the local economic results from transport development have regarded the costs of travel as a key input factor in private production processes. Thus, a major premise underlying the voluminous literature on urban and regional development is that changes in the equilibrium prices and quantities of commuting and haulage will affect the spatial locations of households and firms (e.g. Henderson 1977; Muth, 1985). In particular, if the development of the transport infrastructure leads to lower generalized costs of travel then, holding all other factors constant, these models predict decentralization of households and firms into the hinterland. The response of land use activity systems in the form of classic location theory argues that land, labour and capital are the primary inputs to the production process, and that the use of land is determined by the relative importance of each of the inputs. Transport infrastructure determines the relative accessibility of places and hence has a major impact on the location of industry through the implicit trade off between accessibility and land values. Some industries need to locate in the more accessible sites and so are prepared to pay more for that competitive advantage. Others can locate more peripherally as accessibility is less important. All the models developed are based on equilibrium theories of land allocation and optimality in decisionmaking (Isard, 1956; Alonso, 1964). These arguments have been developed with respect to traditional forms of manufacturing where agglomeration economies and economies of scale are important. They may be less appropriate for the new forms of manufacturing which have developed over the last twenty years where industry has become much more selective in where it locates. Access to suppliers and markets have become less important than the availability of a skilled labour force, suitable land for development, a high quality environment and car based accessibility. All these factors have led to a growing demand for development at sites on the periphery of major urban areas. These are now the most accessible locations. Conversely, low-skilled production tasks can now be carried in inaccessible peripheral locations or in low-income economies where the costs of labour are much cheaper. The post-industrial organization and the structural changes taking place in industry, together with technological change, make it difficult to isolate the impact of transport infrastructure on location decisions of firms and people. Economies of scope have become

Scope of analysis

47

more important in the new manufacturing and service processes than traditional economies of scale (Banister, 1994). Investment decisions in transport infrastructure will also affect the consumption and production decisions by households and firms. These can be seen in a number of ways. First, as explained earlier, infrastructure facilities are regarded by private firms as a free of charge public capital. In their production process, this public capital may be a substitute or a complementary factor for private capital as well as for other input factors like labour. The expansion of this public capital will affect both the level of output and the use of other input factors including labour and private capital. A second way by which transport infrastructure capital development affect private firms’ output is through its effect on agglomeration economies. Improved accessibility causes firms to relocate which, in turn, affects agglomeration economies, thereby their level of output. Third, better accessibility also affects the propensity of households to supply labour, used by firms as a key input factor. Theory maintains that households equilibrate marginal utility (earnings) from additional hour of labour including home-work-home time of commute, with the marginal utility of leisure time consumption, given a 24 hours time constraint. Reducing commute time, by investing in a transport infrastructure, will enable households to achieve another equilibrium with more hours of work supplied (Berechman 1994). As with the other components, the exact modelling of changes in transport markets from infrastructure investment and the interaction of these markets with land-use activity markets will vary as a function of the geographical and time units considered. At the urban level and in the short-to-medium run, lower costs of commuting will encourage households to suburbanize while largely not affecting the location (and output) of industrial and commercial firms. Hence, partial equilibrium models and costbenefit analyses type models are used to examine the effect of infrastructure expansion on urban development (e.g. Nijkamp, 1986; Forkenbrock and Foster 1990; Huddleston and Pangotra 1990). At the country and long-run level of analysis, expansion of the transport infrastructure can be expected to affect equilibrium prices and quantities in transport markets and, as a result, the equilibrium use of inputs and level of output by firms, as well as the supply of labour by households. Spatial changes are not regarded at this level of geographical analysis. It is for these reasons that economy-wide aggregate production function models, in which the stock of public capital is an input factor, are used to explore the effect of infrastructure investment on economic growth (Aschauer 1991; Munnell 1992). 2.4.4 Economic development component Given the theoretical and empirical models used for the analysis of these relationships at each level of analysis (the transport-economic behaviour

48

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module), the primary issues dealt with here are the proper definition and measurement of economic growth (Figure 2.1). It is possible to argue that the use of a model like an aggregate production function, where change in total output (e.g. GDP) is the explained variable, also defines economic growth. While under certain conditions economic growth can be defined and measured as changes in total output, this is certainly not the case for the urban and regional level of analysis in the short-to-medium and in the longer run. This is so because of the need to separate efficiency gains from transfer effects and spatial effects from production and consumption effects4. A significant feature of all infrastructure projects is their investment multiplier effect which stimulates local use of factors and demand for final goods and is a function of the size of the investment but not of its type. That is, a capital investment in an infrastructure facility of any kind (e.g. health, education or recreation), at the urban and regional level, necessarily infuses the local economy with a substantial amount of funds. These funds, in turn, stimulate this economy in terms of firms’ demand for labour and to other inputs and in terms of consumers’ demand for goods and services. These increased demands, further stimulate economic activity—the multiplier effect. Since we are interested in the efficiency gains solely from the transport improvements, it is necessary to differentiate these gains from the multiplier effect which, in essence, are transfer payments. Given these observations, economic growth can be defined and measured in a number of alternative ways, depending on the geographic and time levels of analysis. Thus, at the urban level in short-to-medium run economic growth can be defined as increased employment, by type (e.g. Dodgson 1974). At the medium-to-long run this change in employment should be adjusted to firms’ and households’ locations to reflect changes in spatial equilibrium (Berechman and Paaswell 1994). Econometric cost and production models were used to explore effects of investment in public infrastructure, though changes in spatial equilibrium patterns were not accounted for (Munnell 1990b). Multiregional input-output models are also used to gauge the economic effects of additional capital expenditures, where regional output is the main indicator of economic growth (Rephann 1993). At the country level, long-run level of analysis, partial and full factor productivity are common growth indicators (e.g. Bajo-Rubio and Sosvilla-Rivero 1993). Social rate of return is another measurer of economic growth at this level (e.g. Garcia-Milà and McGuire 1992). A further complication is the fact that while most, if not all, of the studies of economic growth from transport infrastructure analysis are static ones, the phenomena of infrastructure expansion and economic growth are essentially dynamic in nature. Thus, from a policy analysis viewpoint, rather than question what the economic development effects from a given infrastructure policy are, it is more useful to ask what the dynamic evolution or economic growth path of this policy is. Very few studies actually tried to tackle this question (see review by Gramlich 1994) and it remains as a

Scope of analysis

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significant research question to delineate growth path from continuous infrastructure development. Two important elements of the debate will be mentioned here. It is very difficult to generalize about the importance of transport infrastructure, as a causal factor in bringing about economic growth in depressed or peripheral regions. Although much of the evidence is not supported by clear analysis, it seems that transport is only one of the ingredients necessary to generate growth (Botham 1983), particularly if that growth is to be sustained over a period of time. Directing transport investment towards disadvantaged regions may improve their relative economic position provided that other policies are also in place to promote greater efficiency and productivity (Hart 1993). Past studies have attempted to assess the impact of infrastructure on the cost structures and hence the competitiveness of local plants and companies. However, there is little evidence that new transport infrastructure significantly lowers the costs of production (Diamond and Spence 1989). When a firm relocates, transport is normally only a second order consideration as these costs are a small part of total production costs. However, these costs may be important in sectors such as retailing and certain services where accessibility to customers may influence a firm’s performance. A second impact may result from the reduction of transport costs and its effect in reducing overall production costs, which in turn increases profit and output. However, if transport costs are a small part of total production costs, the impact here is, again, minimal. Most of the research (Parkinson 1981) has related to the road sector, but similar conclusions could be drawn from rail and urban transport investments (e.g. metro and trams). The second important issue is the current debate over the impact of transport investment on levels of traffic demand. The traditional argument has been to build new infrastructure to promote development and to relieve congestion as both factors will, in turn, lead to economic growth. However, counter-arguments (SACTRA 1999) now acknowledge that in many situations roads induce more traffic. This induced traffic includes generated trips, existing trips to new destinations and longer journeys. The question here is whether that induced traffic actually increases economic growth, or whether its impact is neutral or even negative. If new investment in road only leads to more traffic without any material increase in output or productivity from the economy, then can it be justified? Such debates have important implications for investment and pricing strategies, given that in many high-income economies the elasticities of demand for travel are low and that improvements in accessibility may only result in a new congested equilibrium being reached. Short-term relief from congestion results in new patterns of activity being undertaken, which in turn result in more travel and further congestion. In summary, in addition to the quite complex modelling and data issues involved in economic growth analysis, the key questions in evaluating the economic consequences from transport infrastructure investment are: how

50

Objectives and scope

to separate the measured growth effects from transfer and spatial effects and how to do so within a framework of equilibrium analysis. 2.5 Discussion and overview Before moving into the substance of the book, it is important to emphasize certain arguments, which seem to have become ‘accepted’ by not really being challenged. It is often assumed that those countries, regions or cities which attract a high proportion of transport infrastructure investment will have a competitive advantage over those locations which have been less successful in obtaining investment. This argument started with the Rostow (1960a) and Fogel (1964) exchange about whether transport is a prerequisite for economic growth or part of the process (discussed in Chapter 1). Transport growth has been linked directly to growth in gross domestic product (GDP) and one of the major determinants of future levels of transport demand is the assumed increase in GDP. Historically, a 1 per cent growth in GDP has led to a 0.93 per cent growth in freight (including a 1.74 per cent growth in road freight) and a 1.24 per cent growth in passenger traffic (including a 1.40 per cent growth in car traffic). Although the link between energy consumption and economic growth was broken in the 1970s, the link between transport and economic growth still remains (Short 1993). The current environmental imperative (Chapter 5) questions whether this link exists in practice and, if so, whether it should be broken. If global warming reduction targets are to be achieved, then there is a strong case for reducing the transport intensity of the economy (decoupling, Chapter 12). Transport costs as a proportion of total production costs in many industries are relatively small, and other factors such as availability of skilled labour, suitable sites, government grants and a quality environment may all be more important than transport. Any decrease in transport costs may not be reflected by cheaper prices to consumers. Savings may be absorbed by entrepreneurs or landowners through higher profits and rents, or they may be absorbed by employees in higher wages (Chapters 4, 7, 8). Again, there are no simple answers. A combination of these factors is likely, but it then becomes a very difficult measurement problem. Even with large-scale transport infrastructure investment, one is expecting measurable changes across a wide range of industries, but to isolate the effects on profits, rents, wages and cheaper prices is almost impossible, particularly given transport’s small size in total production costs. Most of the literature mentioned in the earlier part of this chapter (and Chapter 1) relates to traditional manufacturing industry, not to the new servicebased economies. Here again, the transport dimension of total production costs becomes obscured as many transactions are carried out remotely and there is a high level of complementarity, even between competing networks. Transport and communications networks do not compete with each other, as

Scope of analysis

51

the most accessible locations are those where several of the networks actually come together, as can be seen, for example, in Lille (France) in rail networks (Chapters 4, 10). The same types of development at a multi-network level can be seen at Charles de Gaulle Airport near Paris where the road, rail and air networks all work together to promote a vast new commercial and business centre at Roissy. There are synergetic effects between and within networks, so the relationships are not necessarily linear. Measurement must also be broader than just the use of the network as access it also relates to the opportunity to use it and the possibility of being linked to a potential customer. The value of telecommunications and transport networks relates both to your own access and the number of other people who also have access to it. In uncongested conditions, their use of the networks is not relevant, but in congested conditions the levels of use and price for access to the networks both become more important (Chapter 8). There are many arguments from traditional location theory and more recent trade theory (Krugman 1991a) about where new development is likely to take place. Geographic concentration relies on the interaction between increasing returns, transport costs and demand. With sufficiently strong returns, each manufacturer will serve a national market from a single location. Krugman (1991a:41) argues that it is the interaction of increasing returns and uncertainty which ‘makes sense of Marshall’s labour pooling argument for localization’. Labour market pooling, together with the supply of intermediate goods and knowledge spillovers, ensures economies of localization at the regional and city levels. People can change jobs without moving houses. In a more general sense, Krugman (1991a) also argues that patterns of development reflect the culture of Europe, not the geography. The richer regions are closer to large markets that are themselves the richer locations. There is a circularity here which may reinforce the dominance of the centre, even if access is improved to the low wage peripheral regions in Europe through road and rail investment. A reduction in transport costs allows firms to locate where it is cheapest, but it also facilitates concentration of production in one location to realize economies of scale. As Krugman (1991a:46) suggests, ‘it may pay to concentrate (production) at the location with higher costs but better access’. Low transport costs result in production at peripheral low cost labour markets, and high transport costs mean production in many locations serving local markets. Intermediate transport costs result in production being centred where labour costs are highest. Economic theory suggests that, in urban areas, all benefits from transport improvements are capitalized as ‘consumer surplus’ and as ‘producer surplus’, the latter referred to as rents or, in the case or urban land market, as ‘land rents’ (Anas 1984). Thus, in theory it is possible to regard the sum of consumer surplus and land rent as the total benefits from a transport project and compare them with its costs in order to assess the projects profitability for the economy.

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However, the actual measurement of these effects is quite problematic both on theoretical and empirical grounds and in many cases we can only measure land rents which vastly underestimate total benefits from the project (Mohring 1994). In a study of the effect of reducing New York subway rush-hour headway from ten to five minutes Anas (1995), found that over 97 per cent of total benefits from this change is increased in consumer surplus, while less than 3 per cent is capitalized into the rent of housing and commercial properties. For this reason transport analysts typically enumerate all possible internal and external changes generated by a specific project and subsequently define them as benefits or costs from the project. This approach leaves open two cardinal questions: Does the list of changes exhaust total benefits from the improvements? Does the division of the internal and external changes into benefits and costs give correct solutions? Underlying much of the argument over increased local development resulting from transport investment is the change in accessibility, or the ease with which people can travel to and from a particular location. Traditionally, improvements in accessibility have been viewed as a benefit to the local area as it becomes more attractive as a location to live and work in, and as property and land values rise. However, the empirical evidence now suggests that the accessibility changes are relatively small, particularly in a dense network of routes, and that the impacts are highly localized around the new facility (e.g. rail station or airport). A new investment may give a short-term relief to urban congestion, but the additional capacity resulting from the reallocation of travel from existing congested links will quickly be absorbed as a new congested equilibrium is reached. The changes in accessibility resulting from new investment in an already dense and congested network will not be of a sufficient scale to have a major long-term impact on the local economy. It is unlikely to be of a sufficient scale to attract major new employment into the city. The impact may be to encourage longer distance travel out of the city as the new investment will make other locations more accessible. Accessibility works in both directions. There seems to be a scale element here, as the investment must be of a sufficient scale and located in an area with particularly poor accessibility to have a measurable impact. It must have a demonstration effect as well as an accessibility effect. In this way it may increase one location’s accessibility, relative to another area’s accessibility. But such cases may be rare in the developed world where transport infrastructure is fairly ubiquitous. It is only in the developing countries that the changes in accessibility resulting from investment in new infrastructure will have a major impact on regional and local development. More important is the complementarity found within networks. Accessibility tends to be viewed as the impact of one new link on the network as a whole. However, many investments are strongly complementary and do not need to be consumed in fixed proportions as they form systems.

Scope of analysis

53

Competition is really taking place between systems and not between individual products. So accessibility should not only be viewed as the changes in one particular system (e.g. rail), but the new competitive position of that system in relation to other systems (e.g. road). Lille again provides a good example, as Euralille provides an interchange between international Eurostar rail services, national TGV rail, regional rail and local VAL and tramway systems. The real value of improvements in the quality of the network is that it provides the opportunity for people and businesses to take part in the network, even if they choose not to. There is an optionality benefit. The value of membership to one user is positively affected when another user joins and enlarges the network (Katz and Schapiro 1994). New concepts of networks and accessibility are required to determine under what conditions the competitive position of one network will be changed as compared with another on at least three dimensions—to influence expectations, to facilitate co-ordination and to ensure compatibility. Locations with poor quality transport will be significantly disadvantaged when compared with locations with high quality transport infrastructure. Yet, much of the evidence cited here suggests that investment which does take place only marginally affects accessibility. It seems that changes at the margin may not be sufficiently large to result in a location change or in that location becoming uncompetitive. Accessibility may be a surrogate for a broader more important issue, namely the image of the area. It is not the impact of investment on the production costs and the competitiveness of a firm that is important. The changes will not show themselves directly in the balance sheet. Change and location is a much more subtle process. We would argue that the impact of investment (or lack of it) is important in establishing the image of an area and hence its attractiveness to new development. This in turn will have an impact on the local labour market so that high quality (and high income) labour will be attracted. Transport investment may act as the trigger mechanism to this process. The alternative explanation seems to lead to the conclusion that only existing locations will ever be attractive as they have first mover advantages and will always be more accessible than other new or peripheral locations. There is a basic uncertainty (and perhaps confusion) over whether changes in accessibility lead to new development or a reallocation of development from other areas. There may be no absolute gain, as a fixed total of activity is reallocated as a result of the transport infrastructure investment. If this is the case, then there may be a regional development argument for transport infrastructure investment in peripheral regions or in local economies where industrial restructuring is taking place, provided that it can also be demonstrated empirically and theoretically that industry then moves to these investment locations. These first two chapters have given a broad overview of the main debating points within the rich research area of transport investment decisions and

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economic development. There are no clear or simple answers, just a series of complex questions. We have tried to structure the debate and give a flavour of the uncertainties and controversies that exist. We have also tried to develop a conceptual framework within which analysis can take place. This has meant a simplification of the processes at work, together with a clear differentiation between the various scales of analysis and the role of time in those processes. The next part of the book (Part II) elaborates on the contextual issues that also influence the analysis. We would argue that these additional factors, not normally considered in analysis, have altered the main arguments, in some cases fundamentally. So even if the traditional arguments were appropriate to the situation ten or fifteen years ago, they are not so relevant today. New forms of investment mean that the traditional roles for the public and private sectors is changing. The move from the industrial to the post-industrial society means that new forms of production and consumption are evolving. There is new concern over the spatial, social and distributional issues, together with the environmental imperative. These changes all mean that decisions are now influenced by both economic and non-economic factors. Previously, the economic factors had been the primary concern in the analysis of transport investment and economic development. These contextual issues form the basis from which the analytical part of the book is developed (Part III). Here, the importance of scale is primary as we move from the macroeconomic analysis and the assessment of transport projects in the context of economic development to micro level analysis where this book makes its major contribution. Table 2.1 summarizes the linkages between the contextual and analytical parts of the book. Notes 1

2 3

4

If travel costs were zero everywhere, attempts by firms and households to optimize their location in order to maximize profits or utility would become nonsensical since it would be possible to carry out necessary interactions (e.g. getting from place of residence to place of employment) at zero cost. We consider ten years as the time it takes for land use and travel markets to converge to a state of equilibrium following an external change. Facilities like roads where revenues from road charges or from development rights are used by these entrepreneurs to pay for the capital costs. Under such arrangements, and given a profit maximization objective, a social welfare maximizing road is unlikely to be built. A correct approach would be to define an efficient road, from the economy viewpoint, and then recruit private firms to build and operate it (Banister et al. 1995; Gomez-Ibanez and Meyer 1995). Even at the aggregate country level, in many cases, it is important to separate these effects. First, such models can be sector based models so that positive growth impacts in one sector need to be balanced against changes in others (Deno 1991). Second, transfer effects occur at the country level as well. Third, when the country’s economy is strongly affected by international trade, infrastructure development can encourage cross-border relocation of firms as the many USA-Mexico crossborder studies have shown.

Table 2.1 Linkages between the contextual and analytical parts of the book

Part II

Contemporary issues

Main issues and structure Part of our main argument for revisiting the key debates over transport investment and economic development is to incorporate some of the major changes that have taken place in society over the last thirty years. We have divided these changes into three. The first relates to the nature of transport investment and how that has changed, particularly in western economies where there are already extensive high quality networks. Much of the debate is now over replacement of infrastructure, rather than new investment, and most recently the traditional role that the public sector has played in providing infrastructure has been questioned. All governments are concerned about the increasing levels of public expenditure and the proportion of public expenditure in relation to total expenditure. The public goods arguments relating to transport are being questioned, as is the right to free use of the infrastructure at the point of delivery. More fundamental is the issue of who actually owns the transport infrastructure, what rights do individuals and companies have over access to them, and what are the associated property rights. Secondly, the debates go much wider than the actual infrastructure and include the changes that are taking place in the economy itself. We are now in a transition phase from a manufacturing based economy with a major service sector to one that is information based. Technological change will have a fundamental impact on the rationale for work, the location of workplaces, the time available for leisure activities and the way in which people organize their lives. This in turn will affect the structure of cities, the balance between rural and urban areas and the ability of firms and industry to locate wherever they wish. Traditional locational constraints have been broken. On a national and international scale, there are two complementary forces at work. Economic activity is becoming global in its scale and location as multinational companies increase their market share and as production processes are changed with ‘flatter’ organizations and a substantial amount

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of outsourcing. Manufacturing and assembly are carried out where appropriate skills are available at the lowest cost (including transport costs). Companies are becoming more fragmented both structurally and spatially as the new technological infrastructure, together with a high quality transport infrastructure, permit global production processes. The second force is that of demographic change as the growth in total population within western countries stabilizes. However, the structure of that population is fundamentally altering as people live longer and traditional family structures become weaker. The implications of these external factors on the demand for travel, on location and on economic development are profound. This, in itself, would justify a review and re-analysis of the debates on the links between transport investment and economic development. The third change has been the much greater priority given to distributional and environmental issues. Although governments are primarily concerned with economic growth and maintaining or increasing competitiveness, there is also a need to make greater efforts to improve levels of equity (both social equity and spatial equity), and to reduce the environmental impacts of transport. Regional development and concerns over the problems of particular groups within society have always featured highly in national (and international) policy statements. But with the reduction in the role of the state in investment and the aim of reducing levels of public subsidy, a lower priority has been given to these activities. Macroeconomic policies of reducing levels of taxation have taken preference over the broader welfare policies followed in the past. In addition, the environmental debate has become more important as the global dimensions have been added to the local pollution concerns, and as concerns have risen over the possible links between levels of car-based mobility and health. These debates have been widened as governments have given a high priority to sustainability and sustainable development. To achieve objectives related to the environment and sustainability, actions need to be taken to increase the efficiency of travel through using less energy and producing less emissions. This can be achieved through a combination of pricing, technology, regulation, planning and raising levels of public awareness and support for action. All these issues will be discussed in detail to give a clear picture of the changes that have taken place, together with the implications for the links between transport investment and economic growth. This forms the context within which the more detailed methodological, modelling and empirical research can be placed. It also provides the rationale for a ‘new look’ at these important relationships.

Chapter 3

Transport infrastructure investment

3.1 Introduction In Chapter 2.2 we introduced the basic definitions of transport infrastructure. Here we elaborate on these concepts, again starting with the broad definitions given by Kay (1993) who states that transport and other infrastructures have the following characteristics: 1 2

3

4 5

They are networks involving delivery systems and there are substantial interactions in the provision of services to individual customers. They form a small but indispensable part of the total costs of a wide range of products in which they are used. Thus, the losses that result from service failure are often very large relative to the basic cost of service provision. They have substantial elements of natural monopoly. Competitive provision of the infrastructure is costly, often prohibitively so. This need not exclude competition in the use of infrastructure. Capital costs of infrastructure are generally large relative to their running costs. The sunk costs of establishing an infrastructure are substantial. A high proportion of the total cost of a service has already been irrevocably incurred before that service is offered.

Several examples of infrastructure can be identified, some of which have all five characteristics, while others only have a few elements. The distribution networks of public utilities and the development of road and rail systems generally meet all five conditions. Activities that meet some of the criteria are sometimes not categorized as infrastructure. For example, postal services and payment systems in financial services have several features in common with utility distribution systems. They involve networks, have substantial sunk costs, are strategic, indispensable and have a relatively low unit cost (De Rus et al. 1997). The success of cities and regions has always been based on the quality of their

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infrastructure. This in turn requires a commitment over a long period of time to continued new investment and replacement of existing stock. Infrastructure is the durable capital of the city and a country, and its location is fixed: in the transport sector it includes roads, railways, communications (e.g. air traffic control) and terminals. As the World Bank (1994) suggests, one should take a very broadbased approach to infrastructure as it covers all the social overhead capital necessary for development (including education, health and nutrition), rather than the narrower definition of economic overhead capital (including roads, sewerage, water and utilities). Often the services that are obtained from the infrastructure have a spatial dimension (e.g. the distribution of the rail network), with the benefit from that service declining as distance from the supply point increases (e.g. stations). Their key characteristics are: many people benefit from a single infrastructure; they can be used over and over again; the infrastructure remains when people and businesses move in and out of an area and it provides the means for integration and co-ordination of activities over time and space. The infrastructure forms the arteries of cities and nations and the communications systems are the nerves. The health and prosperity of cities and nations depends on the quality of these networks (Banister 1993b). Infrastructure is a capital good for which users do not pay the full market price and is often perceived as a source of external economies (Youngson 1967; Lakshmanan 1989). Provision of infrastructure leads to a high cost for the first user and a small marginal cost for additional users (Diewert 1986). Improved transport infrastructure influences production and consumption patterns as it reduces transport costs and travel times. As such it can redistribute benefits among economic groups and between regions. It is interesting to note here that it is extremely difficult to build any new transport infrastructure in many countries as people do not think that there are any benefits to them, either directly or indirectly. This relates to both road and rail investment. Public perceptions of the benefits seem to differ to the claimed economic benefits. Infrastructure investment may reduce the need for capital and labour, as productivity is improved. These impacts are felt at both the firm level (can be investigated through microeconomic analysis) and at the regional and national levels (can be investigated through production function models). Infrastructure can also influence employment and private capital through changes in accessibility and marginal transport costs and the possibility of private investment in infrastructure. These issues can be investigated through regional economic models and through surveys among entrepreneurs on their location decisions (Rietveld and Nijkamp 1993). In their conclusions, Rietveld and Nijkamp (1993) make some general remarks about infrastructure: 1

Infrastructure is a generic term that needs to be carefully qualified to make it suitable for focused policy analysis. Additional infrastructure in

Transport infrastructure investment

2

3

4

5

6

7

8

61

regions where there are already good quality transport systems does not have the same impact as where the existing network is sparse or of a poor quality. The links between transport and land use need to be clarified: for example, large-scale concentrations of public/private activities (e.g. offices or warehouses) may be a response to existing transport infrastructures, but it may equally be a result of patterns which would have occurred in any case. This is a matter of cause and effect. Infrastructure is subject to decreasing marginal productivities. When a region has good links, any addition to the network will have a proportionally reduced impact. An extensive high quality network may just make more industry footloose and this in turn reduces the importance of location as a decision factor by firms and individuals. New types of high quality infrastructure may have a significant impact: for example, the new European high-speed rail network may revolutionize travel around the continent as happened when the first generation of railways were built over 150 years ago. Improvements in transport infrastructure may not be a sufficient reason for regional development as many intermediary factors also play a role. The impacts may also be redistributive as well as generative. The gains in one region or city may be at the expense of another, so the overall effect may be neutral or even negative. Improvements in transport infrastructure lead to a decrease in transport costs. This advantage may be absorbed by entrepreneurs or land owners in the form of profits or rents, or it may be absorbed by employees in higher wages. It could also be passed on to consumers as lower prices. A combination of the above alternatives is likely, but there is no method to establish the most appropriate distribution. Infrastructure is a multidimensional phenomenon and there may be synergies between various types of infrastructure. This has been recognized at a theoretical level, but little is said on exactly what these synergies might be. Infrastructure analysis has focused mainly on firms, but not on households. These two are interrelated, but little analysis has looked at the combined interactions between firm’s and household’s location decisions.

From these different perspectives, the wide-ranging features of transport infrastructure are clear, but in practice the operational definitions mainly relate to their form and function. In this book, we take the broad definition of transport infrastructure to include the networks and terminal facilities used for the movement of people and goods. Their characteristics include: • •

large scale capital intensive natural monopolies; common element in the total costs of products or travel;

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Contemporary issues

•

road, rail and air infrastructure, but many of the points made would be equally applicable to other forms of transport infrastructure; all scales of infrastructure.

•

The scope of the analysis here explicitly deals with the national/international, regional and local scales and the analysis and case studies presented in the rest of the book have been selected to explore the full range of infrastructure types. As can be seen from the introductory section in this book (Part I), there has been a history of interest and debate over the links between transport and economic development. The importance of the issue cannot be questioned as it underlies much of the rationale for investment in the transport infrastructure. We also take as given that a minimum quantity and quality of infrastructure is essential as twentieth-century production, communication, employment and wealth patterns depend upon mobility and transport. The evidence cited in Part I from the World Bank and other sources is clear on this. Our interest lies in revisiting these old debates to establish whether they are still valid in advanced economies and to investigate the new agenda. Consequently, most of the remaining analysis and the case studies focus on the theoretical and empirical evidence from the developed world. In each case there is a variety of measures and so the impact of transport infrastructure investment is unlikely to be consistent either in its scale or direction. This complexity is inevitable and makes it inherently difficult to come to simple clear conclusions. It also makes causality difficult to assess. But it does allow a clearer realism within the analysis and avoids over-simple conclusions. 3.2 Type of investment It is often argued that most transport infrastructure investment has been traditionally the responsibility of the public sector, with only limited contributions from the private sector (Chapter 3.1). This is true if the restrictive definition of the network is used as the basis for analysis. But even here, much of the original transport network has been built with private capital. Public involvement has mainly been seen during the twentieth century. However, many other types of investment in transport infrastructure are still in the private domain. Many transport interchanges and terminal facilities (e.g. rail stations, car parks and airports) have been private sector investments, as have some transport vehicles and communications systems. The question of finance and the arguments about the particular role of the public and private sectors are covered in Chapter 3.3. Again, it is clear that some of the traditional criteria for the involvement of the public sector as the prime agency for investment finance are weakening and the possibilities of partnership funding are increasing. Second, in many western countries and cities there is now little new

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investment in the transport infrastructure as few new roads and railways are being built. Small-scale additions to the network are taking place (e.g. bypasses), but the 1980s saw the end of the first great period of motorway construction in developed countries. In the UK the symbolic opening of the M25 orbital motorway around London in October 1986 marked the end of new construction (Chapter 9.2). The rationale for this conclusion to motorway building was threefold. First, the network had been completed and the main centres had been joined by a high quality restricted access road network that in many cases followed the original rail network. It is now being duplicated in some countries by the high-speed rail network. Second, the funding for new roads was being substantially reduced as national governments cut back on public expenditure, as the road-building programme had always been unpopular and as public budgets have come under increasing pressure. Third, with the new arguments on environment and sustainability (Chapter 5), it was no longer seen as desirable to build more new roads, particularly as recent evidence (SACTRA 1994) suggested that they induced new traffic. It is unclear how long this period of ‘no new construction’ will last. Most investment is now being channelled into replacing and upgrading existing infrastructure. Many of the road and rail networks were built in the last century and even the more recent motorway networks built in the last fifty years require substantial investment to replace worn-out sections. This process is both expensive and disruptive. Similarly, capacity on existing networks is being increased through adding lanes to roads and through the use of new technology on both roads and railways. Advanced information and communications systems, together with new signalling and control systems, substantially increase the capacity of existing transport infrastructure. The new debate is over whether there is a case for the extension of privatization of road and rail infrastructure. The US experience (GomezIbanez and Meyer 1995) suggests that ‘toll roads are unlikely to be a very promising area for privatization’ as there are few new possibilities for viable investment opportunities. All the potentially profitable roads have already been built. There is greater potential for the private sector in roads built for development reasons as there is likely to be less opposition. The costs are lower and the potential for development is higher—the less difficult category. Roads built to relieve congestion require more complicated packages as costs are higher and there is likely to be greater opposition. Consequently, private sector interest is lower—the more difficult category. The main role for the private sector may be as innovator; the benchmark against which the performance of public authorities can be measured and stimulated. Examples here would include new methods of charging by time of day or levels of congestion, or by maximizing capacity through charging single occupancy vehicles for using high occupancy vehicle lanes. The US experience provides an informative reference point against which to judge road investment decisions in Europe.

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Table 3.1 Tolled motorways in Europe

Source: Munro-Lafon and Mussett (1994). Note: These are not congestion tolls; other European countries have extensive motorway networks which are not tolled, for example, Germany 11,100 km, UK 3,300 km, Netherlands 2,200 km, Belgium 1,700 km, Sweden 1,100 km.

In western Europe as a whole there are some 40,000 km of motorways, of which 13,500 km are tolled (Table 3.1). About 90 per cent of the tolled motorways are in France, Italy and Spain. In 1991, the annual revenue from tolls ranged from £500 million in Spain to over £1bn in Italy and around £2bn in France (UK Department of Transport 1993). France has the best developed system of toll motorways, constructed through letting concessions to semipublic bodies (Société d’Economie Mixte—SEM) which are contracted to build particular sections of road. Legislation has also been passed to allow private companies to build certain roads. In all cases, the state has implicitly supported the private sector by providing financial support through low or zero interest cash advances, guaranteed loans, or the provision of related infrastructure. At present, France has seven autoroute SEMs, one private autoroute concessionaire (COFIROUTE), and two tunnel SEMs (UK Department of Transport 1993). The SEMs and COFIROUTE keep the revenues from the tolls that can then be used for road maintenance and the construction of new autoroutes. Elsewhere, private sector funding has mainly been directed at very specific, often relatively small-scale links, such as bridges and airport roads. These new links often duplicate existing routes where capacity is limited. They are constructed for congestion relief reasons. This places them in Gomez-Ibanez and Meyer’s more difficult category. The main difference to the US situation

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Table 3.2 The Dartford River Crossing on the M25 London orbital motorway

is that in Europe the public sector sometimes allows the private sector to take over the operation of the existing congested link and the new privately funded parallel link. This ‘deal’ places the private sector in a virtual monopoly position, particularly where there are no alternative routes. If growth in demand is expected and the scale of investment modest, then the interests of the private sector are substantial. In the UK, the two best examples are the Queen Elizabeth II Bridge across the river Thames, which duplicates the existing Dartford tunnels on the M25 London orbital motorway, and the second Severn Bridge across the river Severn between Bristol in England and Newport and Cardiff in Wales. In both cases the private sector construction company has taken over the existing tunnel or bridge (Table 3.2). The private sector may not be interested in funding new links within an already dense network, but it is interested in taking over existing networks and running them. The privatization of the rail networks has already taken place in the USA, Japan, Sweden and the UK with the new owners taking the responsibility for replacement investment, new capital investment and upgrading existing facilities. Users are charged according to the route, time and other criteria (e.g. length or weight of train). The possibilities for toll roads have already been mentioned here, but a debate is beginning on whether other roads should also be privatized with shadow tolls being charged. The arguments here are not clear-cut. Supporters of private roads would argue that private sector management practices are more cost effective and that paying for roads by the user makes the cost allocation issue clear. Opponents would say that roads should be seen as public goods and paid for through the

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public sector as funding can be obtained more cheaply by governments than private companies. We now develop these arguments further. 3.3 Investment financing One of the basic political questions being addressed by decision makers is the amount of infrastructure which should be provided—whether it should meet unconstrained demand or whether demand should be constrained—and how it should be financed. Most transport infrastructure projects are funded by the public sector as they are large scale, involve a high risk and have long payback periods. As noted above it is only where the private sector has some degree of a monopoly position (e.g. in road bridges across fjords or estuaries) that real interest for some alternative forms of financing has been raised. Transport projects are notorious for their cost overruns, for technical deficiencies in construction and consequent high maintenance costs and for optimism in their estimates of future demand. The Channel Tunnel project illustrates all of these problems, as does the Oresund link between Denmark and Sweden and the Great Belt project between Zealand and Funen (The Netherlands). 3.3.1 The historical arguments The loss of interest in transport infrastructure investment by private investors may be explained by the switch in the financing of railway investment from international private sector capital to national public financing and the increasing share of road transport investment from public funds, at the expense of rail transport. In the aviation sector, in many countries both airlines and airport infrastructure have until recently been financed through the public sector. This contrasts with the railways of the last century where private sector investment was dominant. The growth in road transport has meant that many firms in manufacturing operated their own transport fleets rather than buying in services from railway companies. The growth in own fleet operations was in part due to restrictive licensing in road transport. In addition to these regulatory issues, there is the question of property rights and ownership of the railways (Foster 1992) and more recently the ownership of the roads. Any uncertainty over the ownership of the (new and existing) infrastructure and time involved would increase the risk to the private sector. The private sector has only really shown interest in terminals and interchanges as these are seen as being closer to the other sectors where private capital has been directed (e.g. office and commercial sectors). Transport nodes allow the development of associated buildings that can be sold or rented out and also allow the internal space to be franchised. Airport terminals demonstrate this potential as some of the highest rents are charged for this prime retail space. In the UK the fifteen largest airports will extend their retail floor space by 40 per cent to 600,000 sq ft over the next seven years.

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However, the interest of the private sector in the links on the transport infrastructure network has been less enthusiastic. The historical evidence outlined above emphasizes the decline of private sector railways together with the legislative and administrative controls as the main reasons for the fall in market investment in transport. The infrastructure school of thought emphasizes certain features of the transport infrastructure, which contrast with the directly productive activities financed through the market economy. Hirschman (1958) contrasted ‘social overhead capital’ and directly productive activities. He defines social overhead capital as comprising those basic services without which primary, secondary and tertiary productive services cannot function. In the widest sense it includes all public services from law and order through education and public health to transport, communications, power and water supply and agricultural overhead capital (e.g. irrigation and drainage systems). The central part of the concept can be restricted to transport and power. Hirschman sees infrastructure projects as having the following characteristics: 1 2 3 4 5 6

They are an input to directly productive activities. They are typically provided by public agencies or by private agencies under public control. The products are supplied free or at regulated prices. The products are not subject to competing imports. Production is characterized by ‘lumpiness’ (technical indivisibilities) and a high capital output ratio. Output may not be measurable.

In the Hirschman classification above, (3) would represent a major obstacle to market provision. The non-operation of the exclusion principal and the presence of free riders preclude market investment in this sector. The other characteristics such as capital lumpiness and non-measurable outputs would present less formidable obstacles to market provision. The Hirschman arguments would all favour continued involvement of the public sector in transport infrastructure provision. Economic and technological changes since Hirschman defined infrastructure in the terms set out above have blurred the distinction between infrastructure and directly productive activity. Thus infrastructure can increasingly be provided in the market sector and a return to the high levels of private sector investment in transport, typical of the last century, can again occur. There are five changes that allow this to happen: 1

Pricing mechanisms are available for roads, seaports and airports and the exclusion principle may be applied. Smart card technology has reduced the transaction costs of road pricing. Second-best alternatives such as shadow tolls can also be used.

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2

Capital intensity and lumpiness are less of a barrier to private sector investment now than when Hirschman wrote. For example, the development of airports in the immediate post-war period coincided with the belief that only the state had the resources to undertake such large investments. This contrasts with the present situation when many governments experience severe constraints on public expenditures whereas private sector financial institutions have experienced a large increase in their supply of available funds for investment. Administrative reforms have been instituted to establish agencies charged with devising pricing formulae for privatized utilities such as gas, water, electricity, telecommunications and airports. Cross-border interconnectors for gas and electricity have made these products internationally traded goods. Reverse charges have brought competition in international telephone calls. Output measurement techniques have been improved by research in areas such as programme budgeting, cost benefit analysis, cost effectiveness analysis and research on factor productivity.

3

4

5

The ability to charge prices for transport infrastructure, the constraints imposed by the public sector borrowing requirement (PSBR) when private finance is more readily available and the development of output and productivity measures have reduced the distinction between social overhead capital and directly productive investment. 3.3.2 The privatization arguments Privatization has an important demonstration effect and its perceived success is likely to lead to further actions. The arguments have included wider share ownership, income redistribution and reductions in trade union power. The empirical evidence is less convincing and privatization may not be the most suitable policy instrument for achieving each of these goals (Vickers and Yarrow 1988). However, the economic arguments may be more powerful. Stevens (1992) concludes: The property rights theory of the firm suggests that public enterprises should perform less efficiently and less profitably than private enterprises. In a private enterprise, both internal control—via the shareholders—and external control—through the discipline of the capital market—provide incentives to avoid inefficiencies. By contrast, public enterprises are not subject to the discipline of the capital markets, and internal monitoring is conducted by politicians who do not necessarily see their role as supervising the efficiency with which managers allocate resources. (Stevens 1992:12)

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Much of the evidence is inconclusive on whether the private sector is necessarily more efficient than the public sector. Many cost savings can and have been made in the public sector prior to privatization and the empirical evidence has not led to clear conclusions (Kay, et al. 1986; Millward 1986). Several different strategies seem to have been developed to reduce levels of public expenditure in transport. Privatization of transport companies is one means to reduce pressures on public sector budgets and reduce the public sector borrowing requirement. However, when the transport infrastructure is considered, the options available are less clear. In the past, transport networks have been seen as a public good and a long list of problems have been presented as to why it would be difficult to use private sector funding, except in certain situations or where very clear guarantees were given. The main concern to the private sector is the risk of investing large amounts of capital in projects where there are: • • • • • •

long periods between the start of the investment and the financial returns to investors; irreversibility of investment or where there are substantial sunk costs, it is costly to start a project and even more costly to withdraw; financial returns do not flow until the whole infrastructure is completed; political influences on the production of goods and services; long amortization periods when loan repayment periods are often over a much shorter term; uncertain impacts of concurrent investments on demand in a network setting.

Quite naturally, the conservatism of developers would mean that investment would be concentrated on buildings and transport terminals/interchanges rather than in the infrastructure itself. There are lower risk projects available with shorter payback periods and greater certainty (until recently) over the rates of return. In addition to the general questions of risk, transport projects have in the past proved difficult to assess. They often have: • • • •

substantial cost overruns; levels of demand which are difficult to predict accurately; been considered as a free good at the point of delivery; aroused considerable public opposition to their construction and this has often delayed their implementation, sometimes after substantial investment has been made in scoping studies (e.g. environmental impact assessments).

High levels of risk and uncertainty mean that the private sector has been reluctant to get involved, despite having the resources available. Investment

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funds currently have large sums of capital available and it would seem natural to direct some of this cash into transport infrastructure. 3.3.3 Role of the public and private sectors and joint projects The key question facing governments in many countries is to establish the means to bridge this funding gap. Both the public and private sectors have important roles to play in the construction, renewal and maintenance of the road, rail and air infrastructure. Over the next decade it is likely that new means of financing will be established, yet the public sector cannot withdraw completely. Much of the funding will still remain the responsibility of the public sector, but the role for transport planning must be to facilitate private sector and joint ventures through advice, predictions, land assembly and accelerated public inquiry procedures. The public sector still has a key role to play: • • •

•

where the market fails and intervention takes place for accessibility, distributional and equity reasons, and where transaction costs are high; where there are significant externalities involving the use of non-renewable resources, land acquisition, safety and environmental concerns; where transport interacts with other sectors, such as the generation effects of new developments and priorities given for regional or local development objectives; where transport has national and international implications, such as promoting a capital city (e.g. London or Paris) as a world city or maintaining high quality international air and rail links.

However, all these roles are essentially passive and the more important position for the public sector must be to promote a partnership between the public and private sectors. Public sector actions There are a series of positive actions that can be taken by the public sector to facilitate interest from the private sector. As most transport infrastructure investment is long term, there must be a stable policy framework and a set of planning procedures within which the private sector can operate. In addition, the public sector could raise capital through special funds that are seen as ancillary budgets remaining under parliamentary control. Only the balance would be shown in the general budget. Access to these funds would take the form of a competition between the various government departments or there could be a competition within departments for the available budget.

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Alternatively, earmarked charges could be raised from toll roads (as in Norway) to finance further investment. Some economists (e.g. Buchanan 1963) have favoured earmarking as a means to compartmentalize fiscal decisions and to allow individuals to participate in public expenditure decisions. A new impetus has been given to the hypothecation arguments with the realization that greater transparency and public acceptability of increased taxation is essential if substantial sums of public expenditure are to be allocated to transport infrastructure. The acceptability of environmentally efficient taxes on motorists will depend upon part of the revenues raised being used to fund less environmentally damaging modes of transport. If long-run changes in demand are also desired, then the decisions concerning hypothecation must also be transparent, otherwise most motorists will continue to use the car and pay a higher price. A second major contribution that can be made by the public sector is to facilitate the complex processes of raising the substantial sums of capital required for transport infrastructure projects. These include raising capital: •

• •

• •

in Europe through loans from the European Investment Bank and the European Union’s European Coal and Steel Community, the Regional Development Funds and the new European Investment Fund; through transport bonds and other long-term investments (such as pension funds) which are extensively used in Japan; through tax incentives to the private sector by making their capital contributions tax deductible. This might allow pension funds and financial institutions to become involved in road construction as an investment opportunity (suggested for road building in Norway with funds set aside in development areas. This proposal was not accepted by the government); through employment taxes (as in Paris and other French cities) or a tax on petrol (as in Germany and the USA); through user charges from tolls and road pricing.

More controversially, the public sector could guarantee loans to the public or the private sectors, thus accepting a substantial part of the risk. Considerable debate is currently taking place on this issue as the balance of the risk is still with the public sector and not the operator of the system, for example, where the government (public sector) underwrites the loans to the railways (public sector). Much of the French TGV routes have been funded in this way. Under French transport policy, the French Railways (SNCF) are responsible for developing the railways and they have had a high credit rating as the government guarantees the loans. This means that interest rates are lower than commercial rates (typically by 1 per cent) and high rates of return are not essential (as they would be on equity risk capital). This allows SNCF to run at a loss and pay no corporation tax. A private sector package would have taken much longer to set up (Gérardin 1990). In the longer term, the

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TGV system is expected to make a profit and may repay a substantial part of the amortized debt, but in the short term SNCF has substantial debts. A similar company in the private sector with similar levels of debts (FFrl00bn, 1987) would have been placed in liquidation. However, these semi-public undertakings in both the road and rail sectors in France have allowed revenue funding to switch between projects. The cash flows generated by the infrastructure for which the loans have been completely repaid have been used to finance further extensions to the network: what Gérardin (1990) calls the overspill principle, similar to that used in the last century to finance the last great expansion of the rail network. Private sector projects There are particular types of infrastructure projects which the private sector are prepared to finance and operate with minimum levels of guarantees (e.g. the Queen Elizabeth II Bridge on the M25 around London, Table 3.2). These low risk projects are comparatively small in scale and place the private sector operator in a monopoly position. Interest is particularly high where there are congested conditions on the existing infrastructure and where anticipated growth in demand is high. If a reasonably lengthy ownership period can be negotiated, then the expected payback is substantial. In this case the private sector plans, designs, builds, operates, owns and finances the project. The role of the public sector is secondary and limited to the promotion of supporting actions (e.g. enabling legislation) and negotiations on the length of the ownership period. This needs to be balanced against the expected growth in traffic and the levels of charges to be paid by the users. Successful negotiations on time, traffic growth and user charges mean that the project will go ahead. In most cases, one or more of these conditions are not met and as a consequence the private sector has been reluctant about making any firm commitment. A further complication is the ownership of the infrastructure after the period guaranteed to the private sector. Reversion to the public sector may mean that the route no longer has a toll on it, but maintenance costs and reconstruction costs are likely to be substantial (particularly on tunnel infrastructure). This means that the public sector may incur substantial costs at some time in the future. The most notable exception to all the above is the Channel Tunnel project which has been funded by the private sector. This is Europe’s largest transport construction project, costing some £10bn and the length of the ownership period by the private sector is fifty-five years. It has been completely designed, constructed, owned, operated and financed by the private sector. The substantial risks are with the private sector over a very long period of time, yet the rewards may also be substantial if predicted levels of demand are met.

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Joint projects It is in partnership between the private and public sectors where most potential lies. The public sector can anticipate the growth in demand that results from the growth in the economy, from rising income levels and from new developments. It can also assist in the land assembly process and in the public inquiry so that the time from project inception to completion is minimized. In certain situations, it can also help with the costs of construction of the actual infrastructure, but it is the private sector which will manage and run the facility, including setting the levels of tolls or fares. There are several different approaches that can be used. The land could remain in public ownership with a contract between the public and private sectors. This concession would be granted through a tender or franchise and funding would be the responsibility of the private sector with some public contribution in the form of loans and loan guarantee. The private sector operator would set the charges, but constraints on the quality of service would be set by the public authority granting the concession (e.g. minimum levels of service and safety standards). Maintenance of the infrastructure would also remain in the public sector, and public money would only be used to ensure that the project actually takes place. This means that the risk to the private sector has to be equivalent to other investment opportunities. One possibility here is to restrict the public sector contribution to those factors which reduce the negative externalities (i.e. the public sector would pay for environmental improvements), but even this is controversial if the polluter pays principle is used. It is often cheaper to guarantee loans than to contribute directly. Rather than have a toll for the use of the facility, the public sector could pay a fee or shadow toll to the private sector for every vehicle using the road (Button and Rietveld 1993). Such an arrangement would allow a scheme proposed by the public sector to be built by the private sector and operated as a free (at the point of use) facility. The risk to the contractor is in the demand forecasts as they would only be paid a fixed rate for the actual numbers of vehicles using the facility. At the end of the contract period, the public sector might have the option to buy the road. Development gains can also provide an important incentive to the private sector. In the past, planning permission has often been granted subject to certain conditions being met. These conditions have involved road construction, particularly in locations where development pressures are substantial. A different option would be to give the developers the rights to develop land around the road or rail infrastructure that the developer had financed. Land values at new accessible locations, principally at road interchanges or rail terminals/stations, rise substantially (Stopher 1993) and the potential for development is considerable. In this case the public authority would acquire more land than is actually required for the construction of the infrastructure. This land could either be

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sold to the developer with the profits being used to finance the construction of the infrastructure by the public sector, or an agreement could be reached with the private sector that it takes the responsibility for construction and acquires the land. In this second alternative, the private sector has the associated land development rights that are similar to the air rights being granted in urban areas over new station developments. The private sector could either carry out the full development or build the infrastructure and sell on the associated development rights. Another approach is the auctioning of a prepared project to reduce the uncertainty regarding time and costs as construction can begin immediately. The private sector has to make a bid to complete the final design and to finance, construct and operate the project. Fielding and Klein (1993) argue that competition in the bidding would be enhanced and the post-contractual administration costs would be reduced. The clearing-before-awarding approach should ensure that: • •

•

the risk to the private sector is reduced and thus bids would be expected to have a lower rate of return; the costs to the public sector for inquiries, environmental impact assessment and land acquisition may be lower than they would be to the private sector, again reducing risks and sunk costs; the whole process involves both the public and private sectors in appropriate roles. Public sector involvement in the approving and awarding phases means that the proposals may be less vulnerable to political tampering and all the necessary procedures are followed in full.

There do seem to be substantial opportunities for the private sector to become more involved in the process of planning, designing, building, financing and operating the links on the transport network, as well as investing in and operating the nodes on the network. It is likely that progress must be made in partnership between the public and the private sectors. The combination of both sectors substantially reduces the front-end risk and the likelihood of final cost overrun. It also clarifies the difficulties of estimating the payback to the private sector and the necessary period over which it would accrue. The financial rates of return could be substantial and revenue flows in the short term are possible. The public sector also needs to take a much more active role in promoting the project and steering it through the planning and design stages. The main difficulties have been overcome, but other factors must also be resolved: •

Risk sharing between the public and private sectors is necessary. This is fine in principle, but in practice the private sector has been adept in making sure that most of the risk remains in the public sector.

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•

•

•

75

The free rider problem has to be resolved. Again, companies and individuals have been adept at not contributing to the benefits (direct and indirect) that transport infrastructure investment brings to them through, for example, larger market areas or increases in property values. New transport infrastructure needs to be seen as part of the development process, not separate from it. Implicit in the analysis in this book is that transport infrastructure investment is an integral part of the wider development process, but it is often thought of and analysed as independent of this. Consistency and stability in financing needs to be maintained as political and commercial horizons are often short term, while major infrastructure decisions are long term.

With joint projects, the private sector would recover their costs through user charges, but a balance needs to be sought between the private and public sectors in terms of their risks and rewards. An interesting example of a joint project is the Cross-Israel Highway project, now under construction. The main component of this undertaking is an 84 km, limited access highway, running South-North to bypass major metropolitan areas in the centre of the country. The cost of construction (net of land acquisition) is estimated at $1 billion. Following an international bidding process (in which the required level of service was prespecified) the project is financed and built by a private consortium. Revenues will come from users fees. The contract signed by the government and the private consortium places the majority of the risk with the public sector. It includes elements such as guaranteed minimum level of demand and rate of currency exchange. The discussion here has attempted to open up the debate and it reflects the investment component (Figure 2.1) in the conceptual framework developed. We have not come up with a magic solution to the problem of financing and pricing transport infrastructure, but it has raised some of the principal issues that must be resolved. The private sector has a strong tradition of commercial, office and residential development and has successfully moved into the transport sector to take over the development of terminals and interchanges. The challenge set here is to determine whether private sector portfolios can be further extended to include transport links. The transport infrastructure needs substantial investment in new and upgraded roads and railways. Public budgets are limited and there must be a commercial opportunity for the private sector to enter the market. The question then moves to the identification of how commercially provided infrastructure (in its broadest sense) impacts economic development. 3.4 Implications for economic development In this chapter we have tried to illustrate the case that the traditional arguments between transport infrastructure investment and economic

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development have been weakened, if not broken. Four main reasons lead us to that conclusion. First, transport is now seen as part of the wider process of economic integration that has strong political, social and environmental implications, as well as the better known economic implications. It is also part of the new agenda of competitiveness and a renewed focus on measures of productivity. Second, there has been a substantial decline in the levels of investment in new infrastructure. Available funds are being channelled into maintenance and upgrading the existing infrastructure. Third, the role of the private sector and new partnerships have given an added importance to the alternative sources of investment capital. Finally, there is the question of the impact of investment on transport efficiency. Here there seems to be a fundamental difference between the project evaluation which gives high levels of consumer surplus from each alternative (from cost benefit analysis, see Chapter 7) and the macroeconomic modelling impacts which suggest rather more limited effects on GDP growth and factor productivity (Chapter 6). We examine each of these four points in more detail in this concluding section. Transport and economic integration This has broadened the debate from the narrower focus of economic development. Development is concerned with raising the economic performance and condition of all parts of the state, region or locality, through a combination of policy interventions to raise growth levels and the distribution of that growth. Economic integration is concerned with achieving convergence in the economic performance by removing restrictions on the movement of goods, services and factors of production. As well as the removal of the physical barriers, it covers the harmonization of social, fiscal and environmental barriers and in Europe it may lead to economic and monetary union. Equally important to the aims of integration is the means to achieve it. Here, the processes of regulation and liberalization act as powerful opposing or complementary forces to achieve change through microeconomic policy decisions. Yet the ability to move people and goods around countries and regions does not in itself guarantee integration. Indeed, it could be argued (Vickerman 1994; Vickerman et al. 1999) that the richer regions become richer and the poorer regions poorer, irrespective of whether investment takes place in the transport infrastructure or not. An essential component in our understanding of the links between transport and economic integration must be the microeconomic conceptualization of the processes at work in the location decisions of firms, the impact of external factors on integration and their relative competitiveness over time (Chapter 8). The ingredients of any new approach to analysis must incorporate the focus on competitiveness of industry and productivity rather than the traditional concerns with development. The argument here is that transport

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may have an instrumental role in the efficient production of goods and services; it can be substituted for other factors of production; there may be economies of scale; and increasing returns to scale, and conditions of imperfect competition, knowledge and technology may all exist. In the SACTRA report (1994: para 9.30–9.32), two situations have been identified where real economic benefits arise in imperfect markets and where prices do not reflect marginal resource costs. The first situation occurs where there is widespread underemployment of labour (i.e. the wage rate does not equal the opportunity value), and infrastructure investment could bring about new economic activity at the national level. The key element here is that of additionality—there is a net gain. The second situation occurs where the road diverted economic activity from a low unemployment location to a high unemployment location. The net result here is that the economy as a whole can operate at a higher level of employment than would otherwise be the case. It seems that for both underemployment and the unemployment, the cases argued are cautious. Even if there were change, causality would be difficult to infer. Road investment decisions in advanced economies are seldom of sufficient scale to encourage major change in levels of underemployment or unemployment. Far more likely is the situation where individual firms and people make savings through relocation in closer proximity to the new investment. The spatial arguments may be more important than the economic arguments, yet the spatial arguments have important economic implications. Businesses may reduce their distribution costs through taking advantage of a high quality road network, thereby increasing productivity. This in turn may result in fewer distribution points so that travel distances are increased, but savings accrue to the individual company and indirectly to the final consumer through cheaper prices. In a well-connected network of high quality roads, companies will act to minimize their total distribution costs, which in turn may lead to more transport intensive operations. Economies of scale in warehousing are exploited by firms so that, although the total vehicle miles are increased, the number of manufacturing and distribution points can be reduced (a full discussion is presented in Chapter 4). Current rationale has switched to the need to maintain and improve competitiveness, particularly with respect to international competitors (UK Department of Transport 1996: para 4.1–4.19). Here it is argued that transport investment can form an important component of business operating costs and that congestion and unreliability of trips add to costs, ‘particularly for those companies in the service sector or those businesses essentially serving urban areas’ (ibid.: para 4.12(a)). However, the same study (carried out for the UK Department of Transport by Ernst and Young) stated that ‘the impact of new transport infrastructure on business costs is much less clear than is often perceived, though over 60 per cent of respondents to the survey reported benefiting from particular transport infrastructure improvements completed

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within the last five years’ (ibid.: para 4.12(c)). The conclusions reached from this assessment of contemporary issues in transport investment are: • • • •

•

•

The argument has moved from concern over development to one of competitiveness. The role of government has become less interventionist with lower levels of commitment to financing new infrastructure. The role of government is increasingly seen as setting the right market framework within which others can invest. The private sector has been reluctant to fill the gap as the risks are high, the returns accrue only over a long time period and there are better market opportunities for investment. The possibilities of a new generation of road investment is unlikely and even where there is a commitment in Europe to invest in trans-European networks the problem of finance may prevent action taking place. Uncertainties and unreliability within transport networks are key concerns to firms and individuals. Congestion is usually cited as a major problem. Coping strategies include relocation decisions and improvements in the internal efficiency of firms and individuals, through the use of logistics, technology and trip chains.

New investment and maintenance These concerns feature highly in national priorities. In many countries, the levels of investment in new infrastructure have been reduced as public budgets have been squeezed and as public concerns over environmental issues have increased. The option of building new infrastructure to meet expected levels of demand is seen as being financially and politically unacceptable. In addition, much of the existing infrastructure is in need of renewal and upgrading as it is ‘worn out’. It is both expensive and disruptive to rebuild existing roads as they still have to be used during the reconstruction. Increasingly, public budgets will be directed towards maintenance of existing roads rather than investment in new roads. Expansion of the existing network is quicker (although expensive) through adding lanes to the highways as land acquisition has already taken place and as the time-consuming planning procedures do not have to take place. As public funds are reduced, and increasingly allocated to maintenance and expansion of the existing network, new sources of funding are required for new infrastructure. Private sector and new partnerships These now form an essential component for expanding the network. Two important points need to be elaborated on: financing and economic benefits. The greatest potential for real progress in developing new forms of financing

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for transport investment is through joint funding opportunities where the risks and returns are enjoyed by both the private and the public sectors. The principal role for the public sector is in the planning and design stages of the road. These lengthy procedures, including public inquiries, land acquisition and compulsory purchase rights, can best be undertaken by the public sector. The private sector would then undertake the construction and operation of the road, after a competitive tendering stage. Finance may come from the private sector or from both the public and private sectors. If the road is to relieve congestion, then most of the revenues would come from tolls. But if the road was built for development reasons, then returns would come primarily from the development rights associated with the land adjacent to the road. Another alternative is the use of shadow tolls on new (and existing) motorways. The private sector would finance, construct and operate the motorway and they would be paid a charge according to the numbers and mix of vehicles using the route. It seems that there are many possible arrangements for public and private sector partnership, but few seem to have been adopted in either the USA or Europe. Yet it is through joint funding opportunities that most major new road projects will be funded, particularly where expected levels of demand are modest or where development objectives are paramount. Both the theoretical arguments and the practical experience from Europe and the USA seem to raise the main questions, but not to resolve the issues. Private sector investment is possible for well-defined, small-scale projects where the risks are low and the returns guaranteed. For larger scale, higher risk projects with longer payback periods, the public sector is still the main agent, perhaps with greater assistance from the private sector to promote innovation. More generally, the crucial questions facing governments are over the appropriate transport policy for the end of the millennium and the role that road investment might have in that strategy. There is considerable pressure to reduce levels of public expenditure in road infrastructure and the belief that investment can be linked with economic growth is still unproven. Other arguments, such as environmental impacts of new roads and whether the pricing of the infrastructure is appropriate, seem to dominate. Similarly, the claims for public sector budget constraints and inefficiency and higher levels of private sector productivity are also unclear. These complex issues may never be completely resolved, but the question of how to raise new sources of finance for roads needs clear answers now. Joint ventures between the public and private sectors with a sharing of the risks and the rewards must be seen as a major opportunity for releasing more capital for the funding of road infrastructure projects. Impact of investment on transport eff iciency In general, transport investment alters the transport costs of users and the subsequent patterns of location and trade. Yet the presence of good transport

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Table 3.3 Expenditure on new road construction,1988

Source: International Road Federation (1992).

links does not guarantee prosperity and the absence of good transport links does not necessarily act as a constraint on the economy (Vickerman 1998). This potential non-symmetry needs some explanation. Across most developed countries, the expenditure on new road construction has declined as a percentage of GDP to very low levels (Table 3.3). Over the last twenty years, these figures have been reduced by between 25 per cent and 50 per cent. The irony here is that growth in demand for transport is assumed to be strongly related to growth in GDP, yet the proportion of GDP allocated to transport infrastructure investment has fallen. This might suggest that there is overcapacity in the transport system (unlikely, at least at certain times of the day), or it might suggest that the transport intensity (the relationship between the volume of use and the level of economic activity) is independent of investment in the infrastructure. When cost benefit analysis is carried out on new and upgraded roads, the benefit cost ratios are high at between 2:1 for national roads and 5:1 for local roads (Roy 1994). The social rates of return on the European highspeed rail network have also been high (a minimum of 8 per cent, but as high as 25 per cent), but they have recently been questioned as being too optimistic for the regional development effects. What is clear is that the financial rate of return is substantially less than the social rate of return. This has meant that transport projects have proved less attractive to governments and the private sector than other investment projects. The key question then is whether a direct link can be established between non-investment in transport infrastructure and economic efficiency and productivity. It is here that the macroeconomic modelling work of Aschauer (1989a) and Munnell (1990a) have had a major impact on the debate (Chapters 1 and 6). In the UK, Lynde and Richmond (1993) concluded that ‘a higher rate of infrastructure investment, sufficient to maintain the public capital contribution at its pre-1980 average level, could have brought about an increase in the rate of growth of labour productivity in UK manufacturing

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from around 4.0 per cent to about 4.5 per cent per annum’. The estimated productivity effects of transport infrastructure derived from production function models is high and considerably higher than those derived from an analysis of firms’ transport costs. Rietveld (1994) has suggested that, although the effect of improved infrastructure on transport costs may be limited, the impact on profit margins might be substantial. This line of argument leads Roy (1994) to conclude that there is a causation between infrastructure investment and productivity resulting from the macroeconomic research and high returns from cost benefit analysis. There are welfare gains to leisure users of the transport infrastructure and gains ‘that rebound to the benefit of the economy-wide productivity and of the national interests of competitiveness’. In Chapter 6 we examine these issues in depth. The basic parameters of the arguments for public sector investment in the transport infrastructure for economic development gains have fundamentally changed. For the reasons outlined in this chapter, new financial arrangements are now taking over from the traditional public sector provision in all developed countries. This is not a short-term reaction to reductions in public expenditure, but a fundamental reassessment of government priorities and a switching towards other sectors of the economy (e.g. education, health and employment). New sources of finance are necessary and this must involve the private sector working in partnership with the public sector. Similarly, the rather narrow economic development arguments have been broadened to explore the processes of economic integration and competitiveness as the markets have become regional and international. Investment is now increasing efficiency and productivity and the new agenda concerns the means as to how these gains to companies and individuals can be recouped, at least in part, through pricing the transport infrastructure. So there are two key new dimensions to the debate, namely how the infrastructure should be financed and how it should be priced.

4

The evolving economy

4.1 Introduction Much of the debate has focused on the links between transport infrastructure investment and economic development within a traditional context of work, location and travel. As we have discussed in the previous chapter on investment, new forms of financing are required as public budgets become less available and as the role of the private sector is encouraged through possibilities such as the private finance initiative in the UK and public-private partnerships. In this chapter we give a complementary view of the evolving economy. Four main changes are highlighted, each of which will have a substantial effect on the demand for travel. The underlying argument is that novel methodological frameworks and forms of analysis are required to investigate the links between transport infrastructure investment and economic development. The changes in society brought about by information technology are likely to be as great as those that caused the shift from an agrarian to an industrial economy in the last century. The best estimate is that nearly 40 per cent of the workforce in advanced countries is engaged in information industries (Hall 1996). This is the knowledge-based society. Yet some would argue that this is not a revolution, but an extension of manufacturing as all new jobs can be traced back to production processes (Cohen and Zysman 1987). Technological innovation can be traced back 150 years to the invention of photography and the electric telegraph (Table 4.1). The interesting point here Table 4.1 Short waves of communications and information technology

Source: Hall (1996).

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is the increasing speed with which innovation is occurring in the communications and information industries. At the current rate, a further major break-through will take place within the next five years. The location of manufacturing is being separated from the information centres. Manufacturing production is being dispersed globally to locations where suitable labour can be obtained at a low cost. Transport has become an important facilitator of that dispersal as it allows flexibility and dispersed production processes. Conversely, the information centres are being concentrated at the few ‘global cities’ (London, Paris, Tokyo, New York), together with the second tier ‘world cities’. These 20–25 cities account for most of the headquarters of financial services, the major production companies and the national governments (Friedmann 1986; King 1990). As Sassens (1991) has concluded, ‘weight of economic activity over the last fifteen years has shifted from production places…to centers of finance and highly specialized services’. 4.2 Changes in work patterns Society is changing from one based on work to one based more on leisure and recreation. This does not mean that we will no longer work, but that the need to work will be reduced as the rewards are higher and as people inherit more wealth. As standards of living rise, the imperative to earn is reduced and other activities become more attractive. The transport implications of these changes are profound. Within the expanding market for travel, the importance of work-related activities is being reduced (Table 4.2). In Great Britain, four main trip purposes now account (equally) for about 75 per cent of all trips. Twenty years ago these same four purposes accounted for only 65 per cent of trips, with the work purpose being the dominant partner. The greatest increase has been in leisure activities, including personal business trips and social activities. The same pattern can be observed elsewhere in Europe (Banister 1994) and in the USA (Table 4.2). With the growth in non-work related activities, it is likely that the time spent travelling will increase. Traditionally, it has been argued (e.g. Zahavi 1982; Stokes 1994) that people have a fixed travel time budget of about 60 minutes per day. As modes of transport get faster, travel distances have increased, but the overall time budget has remained constant. The evidence on changes in travel time budgets is difficult to obtain as it is not collected in most surveys and as time perception is often not accurate. For example, in the former West Germany, the number of activities undertaken each day per mobile person was 2.1 in 1982—this involved 3.8 trips. The travel time per day was 63 minutes and the distance travelled was 26 kms (Brög 1992). In Great Britain, there has been a remarkable consistency in daily travel time over the last eight years (1989–96), averaging at almost exactly 60 minutes per day.

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Table 4.2 Trip purposes from national travel survey data in Great Britain and the USA

Sources: Department of Transport (various), US Department of Transportation (1996). Note: Not all columns sum to 100 per cent and some of the definitions have changed.The Great Britain and US definitions are not comparable—so these figures are indicative. The starred values include day trips.

This situation may be changing. As journey lengths increase, as new activities take place and as congestion increases, it is likely that travel time will also exceed the 60-minute threshold. In addition, with the increase in long distance travel (particularly by air), the average is again likely to increase as it is difficult to see situations in which people actually choose to travel less. Certainly, the distribution around the mean value will change with increasing numbers of people spending larger amounts of time travelling. However, there is considerable evidence of change in distance travelled for different social groups (Tables 4.3 and 4.4). The greatest percentage increase over the 30–year period has come from the elderly and young, but the absolute increases still show the greatest growth in distance travelled for men (+130.1 km) and women (+106.6 km), with the elderly (+81.7 km) and children (+53.3 km) lagging behind (Table 4.4). Part of the explanation for the increase in travel distance is the growth in car ownership (Table 4.4). This is consistent with the travel time budget argument where extra distance traveled by car has no time penalty. If seven hours per week are available for travel, then average speeds have increased from 16 km/hr to 30 km/hr over the 30–year period. The average travel speed calculated here for Great Britain (1985–6) is similar to that for the former West Germany of 25 km/hr (1982). Although the overall patterns of change are similar in Great Britain and the USA, there are significant differences (Table 4.5). Trip rates are similar, but there are substantial differences in the average trip lengths with the US figures being 50 per cent higher than those in Great Britain. This is not surprising, given the geography of the two countries. The net result is that US travel per person per year is much higher than in Great Britain, and the increasing average trip lengths in Great Britain (+33 per cent) have not been

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Table 4.3 Change in distance travelled per person per week in Great Britain

Source: Department of Transport (various). Table 4.4 Travel distance per person per week by car ownership in Great Britain

Source: Department of Transport (various). Note: Distances in kilometers and percentage of households in parentheses. * These two distances were not separated in the 1994–6 survey. Table 4.5 Comparison of travel patterns in Great Britain and the USA

Sources: Department of Transport (various) and US Department of Transportation (1996). Note: US figures exclude commercial driving—distance driven by taxi drivers, truck drivers and delivery services.

recorded in the USA (-4 per cent). The growth in travel (1970–94) relates to real income increases (+50 per cent in real terms), increasing car ownership levels (+78 per cent in USA and +86 per cent in GB), as well as population increases (+27 per cent in US and +9 per cent in GB). In both countries, the labour force has expanded as a result of the post-war baby boom and the growth in female participation rates. Women now make up nearly half the labour force.

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However, if we examine the national travel survey data a little more closely, we find that for our same four social groups the growth has taken place in non-work travel (Table 4.6). The growth in travel for non-work related activities has been 51 per cent, 26 per cent, 26 per cent and 29 per cent respectively in total distance travelled. This increase is explained by a combination of growth in journeys and average journey lengths. The changing nature of society is already beginning to manifest itself in terms of new patterns of mobility with work-related activities (commuting and business) only accounting for 23 per cent of trips in the expanding market. Table 4.6 Non-work travel per person per week in Great Britain

Source: Department of Transport (various). Note: Distances are in kilometers.

The second major change that is affecting work travel is the reduced importance of the city centre as a source of employment and the development of much more dispersed patterns of employment. The pattern of work journeys is becoming more varied, both spatially and temporally. Commuting patterns have become more complex, with cross-commuting becoming more important than commuting to city centres. Households with an established residential base are likely to meet their career needs by longer distance commuting, particularly if there is more than one person employed within the household. There is likely to be a longer term adjustment process as jobs also decentralize and move closer to where employees live, but these locational changes take place infrequently and have a differential impact on employees. A firm moving closer to one group of employees is likely to become more remote from other employees at the same time. Gordon et al. (1991) argue that there is a dynamic process at work and that auto commuting trip times for the twenty largest US metropolitan areas suggests that average trip times have remained constant (1980–85), or have reduced by a statistically significant amount. Their explanation is simply that the market operates spontaneously through the relocation of firms and households to achieve the balance of keeping commuting times within tolerable limits. This spontaneity is optimistic, as the cycles for household and firm relocation decisions takes place over a much longer period than the five years

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covered by this study, and that longitudinal data are essential to follow the individual movers and non-movers. Work is only one determinant of the location decision and we would argue that it is difficult to capture this process through cross-sectional analysis. Other factors, such as quality of the environment, local schools and other facilities and proximity to friends are all important social factors in the decision, as is the financial imperative of the availability of housing at an affordable price. Life-cycle changes may also influence both the mobility patterns and location decisions of households (Table 4.7). Here, it is suggested that there is a dynamic process which is related to more factors than just work. Location decisions of households reflect stages in the life cycle and the range of necessary and desired activities in which a particular household needs to participate. With the increase in non-conventional households or groupings of people, this picture may again be complicated (Chapter 5). There are substantial push and pull factors at work as the balance is sought between what a city centre can offer in terms of employment and other opportunities and the requirements of families and others at different stages in the life cycle. Table 4.7 Life cycle effects on travel and location

Source: Banister (1994).

The reasons for residential mobility may have less to do with proximity to work than with finding the right type of accommodation. In times of recession, high interest rates or job insecurity, fewer residential moves will take place. This has been the situation in the UK (in the early 1990s) where the housing market was static and prices had been falling as people had to sell at a loss (the problem of negative equity). Even with cheaper prices, there seems to be a reluctance to move. In Paris, there are large differences in rents between sitting tenants and new arrivals and this has resulted in housing immobility

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(Orfeuil 1992). The attractions of moving to new suburban residential estates as owner occupiers is attractive, particularly if the developer is prepared to buy your existing property and to give attractive repayment terms, at least in the short term. The location of work may only be one consideration in both of the decisions of whether to move and where to move. The third dimension of change is the nature of work itself. The traditional concept of regular employment over a lifetime with fixed hours, often with just one employer, is changing. There are now several different types of employment, even within one organization—what Handy (1995) calls the shamrock organization. The first leaf of the shamrock represents the core workers, made up of qualified professionals, technicians and managers. These are the full-time workers who give 100 per cent to the company and are wellpaid employees. But these core workers have been reduced in number as a result of downsizing or restructuring. The second leaf is the contractual fringe where non-essential work is contracted out to specialists who do a better job more cheaply. Technology and just-in-time delivery means that the subcontractor carries the stock needed by the core company. Failure to deliver means that the work will be subcontracted elsewhere. Payment is made strictly on results and these subcontracted firms would supply several different companies with their expertise. This is part of the specialization process. The third leaf is the flexible labour force that is essentially the underclass as they are hired and fired according to need. Most of them are part-time workers or temporary workers, often women or others returning to the labour force, some with skills and others without. These people are employed, often at low wage rates with limited rights, to help overcome periods of peak demand or shortage of full-time workers. Few companies are prepared to invest in this flexible labour force and they have been labelled the ‘contingent worker’ in the USA (Belous 1989; Section 4.4). A fourth leaf of the shamrock is the role that the customer plays in reducing the costs of the organization. We now do our own shopping and make deliveries through the use of the car, fill our own cars with petrol and use self-service restaurants. A service is removed and can then be reinvented and charged as a new form of personal service. The fourth dimension in the changing pattern of work travel is the effect that technology, mainly telecommunications, might have on commuting (Nilles 1991). The original optimistic estimates have not been met and the limited empirical evidence suggests that total travel (for work and other activities) is not reduced. However, there is no evidence that telecommuting generates greater separation of home and work (Pendyala et al. 1991). Regular telecommuting is uncommon and the likely impact is more subtle as increased flexibility is given to time management. Most telecommuters work at home for one or two days each week and in the USA it is estimated (Handy and Mohktarian 1995) that about 1.5 per cent of the workforce (a maximum figure) telecommute on any given day. With the significant changes in economic factors and the changing nature of work, it is likely that the scale of homeworking will increase. Ironically, it may be a revisiting

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of the homeworking which characterized manufacturing over a century ago—this was a highly exploitative relationship. Homework associated with technology and a contingent workforce may mean that commuting distances increase with uncertainty, given multiple employment locations. The probability of changing jobs decreases the value of access to the present job, while moving costs determine the opportunity cost of moving (Crane 1996a). Both factors lead to longer average commutes, flatter rent gradients and greater residential decentralization. But as Giuliano (1996b) concludes, it is difficult to isolate location patterns between the different elements of the labour, from work patterns that are explained largely by socio-economic characteristics. She concludes that more precise measures of job uncertainty and flexibility are required before clear conclusions on whether existing trends of decentralization and growth in the popularity of high amenity areas can be reinforced. Underlying these changes is the increased employment in service and technologically based industries. These new forms of employment allow organizations to employ people in more flexible ways. Apart from changes in the actual nature of work, there are changes in the conditions of work, payments, pensions, security of jobs and individual rights. The traditional notion of work has changed fundamentally over the last twenty years. 4.3 Economic changes Integrally linked to the changing patterns of travel induced by work-related changes are those resulting from the restructuring of the economy in postindustrial society. Recent theoretical arguments (e.g. Dosi et al. 1988) suggest that technical change is forcing a transformation of the economy and that new processes of dynamic adjustment pose radically different challenges to those allocative mechanisms postulated by traditional theory. Innovation is now considered to be fundamental to economic growth as competition is based on quality, not only price—this is the basic argument within neoSchumpeterarian theory (Nelson and Winter 1982, summarized in Table 4.8). Although most of this analysis has been aspatial, networking (including face to face contact) and location decisions are both significant determinants of success (Lundvall 1992; Sako 1992; Sabel 1993). The spatial dimensions form the focus of recent research (Simmie and Kirby 1996) where the historical considerations of industrial change are matched to those relating to new technology (Table 4.9). Many of the alternatives listed here are concerned with both the organization form and the spatial distribution of innovations and high technology industry as they in turn drive the economy. In particular the new role of the powerful multinational companies may be crucial in determining the spatial agglomeration (or dispersal) of new forms of economic activity. There are the proponents of the new global economy (e.g. Henderson and Castells 1987),

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Table 4.8 Long waves of development

Sources: Berry (1991); Hall (1996); Kondratieff (1935); Kuznets (1966); Mensch (1979); Schumpeter (1939).

Table 4.9 Historical and technological explanations of current industrial change

Source: Based on Simmie and Kirby (1996) Note: These choices are not mutually exclusive.

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who argue that production is increasingly dominated by these multinational corporations with large research and development budgets who can determine how the market will operate. On the other hand, there are those who maintain that local places are equally important and that innovations take place in small niche products (e.g. Storper and Christopherson 1987; Lundvall 1992). It is likely as with much innovation that the reality is variable and involves all scales of operation and types of organizations. Whatever the explanation, it is clear that fundamental change is taking place. At the macroeconomic level, globalization of the world economy has taken place with capital centralizing and concentrating at the international level. The power of the multinational corporations bears witness to this process. The large enterprise can maximize profit levels by localizing its activities where the labour rates are cheapest for the level of skill required. Regions and cities become the pivotal nodes in the global network and control both where goods are produced and how their dominant position in the market can be maintained. However, location does still seem to be important and not all places are homogeneous as assumed here. The concept of flexible specialization (Piore and Sabel 1984) argues that firms (particularly industries) are saturating the markets with a standard range of mass-produced goods. The demands of consumers are more differentiated and as they become more sophisticated new products are required. These more specialized products cannot be produced on the production line, so firms have to adopt flexible strategies. This is a strategy of permanent innovation where firms accommodate continuous change rather than trying to control for it. Piore and Sabel (1984) conclude that flexible specialization constitutes a shift of technological paradigm with the re-emergence of craft based industries. These industries then develop their own networking with subcontracting, thus reducing their complexity as organizations and allowing the diffusion of innovation throughout the regional economy. There also seem to be agglomeration economies from firms grouping together in specific locations. However, much of the flexible specialization seems to relate to existing firms trying to survive rather than innovative new firms entering the market. Many new high technology industries do not follow this pattern as they are capital intensive, requiring a high level of skill and support infrastructure. Their location constraints would not allow them to develop as craft industries. More important though are the links between flexible specialization and the macroeconomic globalization effects. Far from allowing local-based craft industries, these economic factors are moving the world economy towards global integration and centralization of command and control. Where smallscale enterprises do exist, they are dependent upon the multinational companies as these large-scale enterprise control the networks on which flexible specialization depends. The concept of Marshallian districts attempts to address the reasons why

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spatial concentration takes place (Becattini 1990). Here, the argument used is that industrial districts re-emerge, often as a result of small-scale initiatives coming out of the local university, or the local ethnic community, or large firms adopting a survival strategy of vertical disintegration.1 Similar criticisms have been made here as those directed at flexible specialization. There are few example of new Marshallian districts, the globalization issues are not accommodated and it is difficult to identify the conditions under which such districts might develop. The third approach at an explanation of the global-local economic debate is to examine the competitive advantages of particular locations (Porter and Van der Linde 1995). Porter (1990) argues: competitive advantage is created and sustained through a highly localized process. Differences in national economic structures, values, cultures, institutions, and histories contribute profoundly to competitive success. The role of the home nation seems to be as strong as ever. While globalization of competition might appear to make the nation less important, instead it seems to make it more so. With fewer impediments to trade to shelter uncompetitive firms and industries, the home nation takes on a growing significance because it is the source of the skills and technology that underpin competitive advantage. (Porter 1990:19) Although the multinational corporations are powerful, they do not have to operate within national boundaries, with governments having a major influence on whether their competitive advantage is maintained or enhanced. Within countries, the same arguments can be applied at the regional and local levels, particularly as they relate to factor conditions, demand, supporting industries and a firm’s strategy, structure and rivalry. The spatial restructuring of the economy resulting from new patterns of working and technological innovation is a complex process and not easily encapsulated in one theory. Within the globalization process, it does seem that location is important, but there are many factors which might influence that choice. Traditional theories based on neo-classical economics are no longer relevant (Chapter 1), but there is a range of new spatial imperatives. One such review of technopole development has been carried out by Castells and Hall (1994). They identify four types of new high-tech developments: • • •

complexes of high technology industries occupying new industrial spaces or regenerating old ones; sciences cities—Akademgorodok (Russia) and Tsukuba (outside Tokyo); planned high technology business areas—Hinshu (Japan), SophiaAntipolis (South of France), Cambridge (UK);

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•

innovative metropoles—London, Paris, Tokyo, Munich, southern California;

Yet, even from the largest scale synthesis of the evidence, it is difficult to explain why investment and development take place in one particular location rather than another. Traditional views on location decisions and the potential for agglomeration economies suggest that there are economic, behavioural and technological factors (Dosi et al. 1988). Economic factors include cost minimization, externalities and economies of scale. Behavioural factors cover qualitative measures of transactions and learning. Technological factors balance flexibility against being locked into the production process. More recent contributions (e.g. Giersch 1995; McCann 1995) seek to draw a distinction between those incurred in overcoming distance and the costs incurred from being located at one point in space. McCann (1995) identifies four elements that are key to microeconomic theories of location. Distance-transaction costs mean that firms will locate together assuming that they buy from the same supplier and sell to the same markets. The same argument is true of his second element, namely the location-specific factor efficiency costs. In both cases there are agglomeration economies of proximity and factor efficiency. But he qualifies this second argument for clustering by the statement that this occurs: ‘only where it is clear that the existing level of agglomeration is the cause of the existing factor-efficiency prices can we rightly talk about agglomeration factor efficiencies’ (McCann 1995:573). The two other elements of possible agglomeration economies are hierarchy co-ordination costs, which relate to the nature and stability of the production and consumption hierarchy, and the hierarchy coincidence opportunity costs, which relate to levels of sales (the sales maximization principle). These factors relate to the existing numbers of firms and households and involve agglomerations of scale in the traditional sense. McCann (1995) argues that underlying spatial economic questions are issues of the nature of production hierarchies that help to explain why spatial clustering takes place. It is only when factor prices are pushed up through higher wages that this clustering process might break down. Even though firms may locate in close proximity to one another, this does not necessarily mean that they will have links with other firms in the same industrial sector (localization economies) or with other firms or households in the same area (urbanization economies). The linkages are increasingly important as telecommunications and networking, together with business related travel, form important components in service and technological based industries. However, they need not be local links. They could be regional, national or international links, depending on the activity in which the firm is engaged. McCann (1995) reaches the conclusion:

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Many clustering situations are wrongly characterized as localization or urbanization economies, when the cost reason for clustering has little or nothing to do with the location of other firms, but rather is due to the relationship between local factor efficiency prices and the cost considerations dependent on the location of suppliers and customers in totally different regions. The result is that authors (unspecified) then wrongly attempt to account for this observed spatial clustering in terms of hypothesized information economies, in situations in which this is simply not appropriate. (McCann 1995:573). Yet the evidence seems to be accumulating to suggest that there are still agglomeration economies, even in high technology economy, and that the new factors of production are instrumental in bringing this about. At the theoretical level Kutay (1988a, b) has demonstrated that with two different costs (one for commuting and one for information), location depends on the relative balance between the two. When information costs are sufficiently low that all workers work at home, the land-rent gradient becomes convex at the centre. This reflects diseconomies of agglomeration and employers would seek to disperse to the periphery. However, such simple explanations may not be entirely appropriate as vertical disintegration means that the clustering of suppliers would lead to agglomeration economies (Storper and Christopherson 1987). Outsourcing and subcontractors’ work can be best maintained through close proximity and continuous contact. Indirectly, the clustering of employment may also facilitate the growth of short-term, temporary and flexible work patterns, as job accessibility is important to those in employment or seeking employment (Sections 4.2, 4.4). The traditional view (Vickrey 1977) that activities cluster geographically provided that the agglomeration benefits outweigh the congestion costs needs to be reviewed, as congestion is only one part of the location decision. The new structure of the labour market and the changing structure of businesses, together with the role of technology, all need to be included in the new understanding of agglomeration. For example, the work of Romer (1996) suggests that the congestion cost curve flattens as technology improves, so agglomeration economies may be felt over a wider area, and this in turn would lead to footloose location. Others (e.g. Simmie 1998) argue that local factor production costs and qualities are critical innovation inputs. These are not the traditional factors of infrastructure, telecommunications, land and buildings, but the new ingredients of the knowledge-based economy, such as skilled labour, venture and risk capital, new technology and new knowledge and information.

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4.4 Technological change The main source of profit and power in the late twentieth century is knowledge and information, and conflicts are likely to occur over the distribution of and access to that knowledge. Even money is becoming less important and tangible as transactions are carried out electronically, only to be seen in a symbolic form on a screen. This global view of the shifts in power presented by Toffler (1991) and summarized above will transcend all activities at all levels and will create a radically different society. The technological revolution is the third great change in modern society, following on from the agricultural and industrial revolutions. Apart from the transport effects which are dealt with in this section and the more general spatial impacts (Section 4.5), technology impacts on the organization of work. Technology has resulted in convergence within and between industries, with vertical disintegration, flexible production methods and specialization (Capello 1994). It has also had important implications for employment as it can make use of low-cost labour or specialized horizontal integration. Flexibility means that production must be sensitive to changing markets. It also implies a flexible labour force with less job security and a high turnover of staff, with the consequent wage polarization (Appelbaum and Alpin 1990). The numbers of core workers have been minimized, workforce is being outsourced to cheaper labour, contingent workers are typically part time, working for lower wages and fewer non-wage benefits (Golden and Appelbaum 1992), as with Handy’s shamrock (Section 4.2). They have a weak affiliation with their employer. Normally they have no contract and have only been employed in the recent past. Belous (1989) includes the self-employed, temporary workers, part-time workers and those in the business service sector as they have only weak employer affiliation. In the USA these workers account for between 30 per cent and 37 per cent of the total labour force (1988) and this level has increased by 25 per cent since 1980. More recently, the impacts of information and communications technology have pervaded much of our everyday life, but they have not been uniform; nor is there a strict cause and effect relationship. There is tremendous potential for further developments in technology that can affect all aspects of people’s lifestyles and the way in which business and industry is carried out. Yet there is also a reluctance among users to accept the new technology without question. Given both the potential and the constraints, there are at least four ways in which the availability of knowledge and information will radically change transport demand. First, the production and distribution processes introduced by Henry Ford at his Highland Park assembly plant in 1913 are now being extended and replaced. The conveyor belt now extends beyond the manufacturing system to the distribution system. Technology and information allows a complete

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service from the assembly of materials through the production of the car to the testing and distribution processes, and the delivery to the final consumer. These concepts do not only apply to the manufacture and distribution of vehicles, but to all commodities. Freight distribution systems have been restructured on regional and metropolitan warehousing depots, often at accessible motorway intersections. Road transport informatics (RTI) impact on all parts of freight transport operations as well as location decisions. With the trend in Europe towards longer distance trucking and the increased use of multimodal combinations of vehicles and carriers, integrated approaches to freight transport are essential to ensure the optimal use of information and the new flexibility in both production and distribution processes. The potential is available to develop a Europe-wide integrated freight transport network, but old barriers still remain, namely who should pay the costs of pollution, the increased resource costs caused by the growth in international road freight and the compensation of individual member countries for transit traffic— ‘the territoriality issue’. The EU in its recent Transport Policy White Paper (CEC 1998) is trying to harmonize the user pays approach to replace the patchwork of charging arrangements. These different charging systems create competitive distortions between and within the different modes of transport and between EU member states. In the road sector, five EU countries levy road tolls, six use the Eurovignette scheme, others run different systems (e.g. for bridges and tunnels), or do not charge directly for road space (Section 3.3.3). Second, there is the belief that technology (in particular RTI) can help in delaying the inevitable gridlock when the city comes to a complete stop through congestion. Traffic management schemes have been very effective in squeezing more capacity out of a given road network and the expectation here is that technology through intelligent highways and smart cars can continue that process. Increased flexibility in work and leisure patterns together with the possibility of telecommuting have all provided the opportunity for change. Again, it should be noted that both information and knowledge have been instrumental in creating the conditions for this opportunity. However, each revolution in the past has resulted in increases in travel and average trip lengths and there is no reason to expect a change as a result of the current revolution. Road users will be affected in three different ways: •

•

Information services to the traveller that will allow decisions to be made on the basis of the best real time information. These services would apply equally to public transport services and to route guidance information given to the car driver. This is likely to increase intermodality in trip making and allow extra flexibility in response to real time changes. Control systems within the vehicle. By the year 2000, it is estimated that 10–15 per cent of the costs of new cars will relate to RTI services (Lex Motoring, 1992), being provided within the car.

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•

Control over the transport network, including demand management and traffic control systems. These are already in common use.

Third, the infrastructure network forms one major key to an integrated transport system. Investment in new roads and in upgrading and expanding existing roads (e.g. through additional lanes) will be complemented by the new high-speed rail networks (the TENs—trans European networks—in Europe) and telecommunications networks, including the new value added networks (VANs) and the local area networks (LANs). It is this combination of networks that will facilitate the most fundamental changes brought about by knowledge advances and information technology. These include logistics planning, electronic data interchange, electronic route guidance, emergency transport planning, information systems and databases for environmental monitoring (Hepworth and Ducatel 1992). The fourth element is the more general impact that teleactivities will have on the demand for travel and location decisions. The new infonetworks (e.g. VANs and LANs) allow activities to be carried out remotely without the need to travel. Much of the current debate has been over the impact on commuting patterns, but even greater opportunities lie in teleconferencing, teleshopping, telebanking and forms of teleleisure (including participatory game shows). In principle, there is less reason to travel in a physical sense (Section 4.3). But the simple substitution arguments have been widely discredited (Mokhtarian 1996). Adaptations to the availability of technology are much more subtle than the simple substitution argument suggests. Opportunities are increased and a new flexibility in travel arises. This means that the impact of the new technology is much more varied as different responses are made by individuals and firms to accommodate the opportunities within the context of their own requirements. It should also be noted that not all individuals and firms are competing under these new market conditions. As with all innovations, particularly those involving technology, there are knowledge and cost constraints which limit the market, at least in the short term. The new flexibility described here impacts primarily on the wealthy, well-educated people who have the knowledge and resources to use the technology. There are strong distributional implications resulting from both the availability and use of the new technology. As a consequence, the spatial imperative may no longer apply as cities will become much looser spatial organizations, as the costs of urban centrality and high land prices will be balanced against the benefits of dispersal. The movement out of cities will continue with only front office functions remaining. Growth will be concentrated in corridors of good communications and at peripheral urban locations where it is cost effective to link in with both the transport and information networks. Peripheral areas may still remain isolated and separate from the new infrastructure as access costs and capacity

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requirements may make the installation costs of the new networks uneconomic and the costs of using the system too high. The most attractive locations will be those where the transport and information networks link in with other factors such as a skilled labour force, a high quality environment and the availability of low cost land. Interchanges may provide particularly suitable locations for logistical platforms. International airports, high-speed rail (e.g. TGV) stations, and major motorway intersections could all provide sites of maximum accessibility which would minimize location and transport costs, and also be on the international information network. There is no question that very significant changes are taking place on both the political and technological fronts in Europe, North America and elsewhere, and technology is likely to have a profound effect on transport at all levels. However, the exact nature and scale of that impact is far from clear and it seems that implementation will not be equal across all transport sectors, but will be selective and will take a considerable time for the full effects to become apparent. More generally, the technological revolution is impacting on all forms of transport, on location decisions and, more fundamentally, on the way in which we do things. The new informatics and telecommunications networks have caused a revolution in data handling, processing and transmission. This in turn impacts on all households and firms and has substantial effects on the relative competitive advantage of particular regions and its importance is likely to increase (Capello 1994), as is its impact on the form and function of cities. 4.5 Global cities and spatial change As with the economic and technological factors, cities are also changing as there is intense competition for global status. There is perhaps room for three (or four) cities in this group, distributed around the globe so that at least two of them are functioning at any one point in time. Hall (1996) names London, New York and Tokyo as his three. Their unique position is based on the production of specialized services (e.g. financial services, media services, education and health services) which control the global movement of capital and information. They also carry out important continental and national functions (e.g. government, culture, tourism, services and manufacturing). The key question at the turn of the century is whether they can maintain their dominant position as other cities compete for these lucrative functions. This competition is particularly evident in Europe where the traditional dominant position of London is being challenged. The two cities in Japan and the USA are less vulnerable as these countries have a major share in the global markets, both in terms of production and consumption. But the UK is being challenged by other major economies within Europe as there is no single dominant partner

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here. Individual cities are challenging for parts of the information services traditionally located in London (e.g. Paris and Frankfurt). The EU is a major player here as the tradition is to allocate key European functions around the member states. The recent decision to locate the European Central Bank in Frankfurt has significantly weakened London’s banking and financial services dominance, as has the previous decision to locate the European Investment Bank in Luxembourg. Europe has a rich array of the second tier world cities—Amsterdam, Brussels, Copenhagen, Berlin, Vienna, Prague, Rome, Dublin, Lisbon, Madrid, Stockholm, Helsinki, Athens, Paris. These are the capital cities of Europe with a rich history of government, culture, education, tourist and commercial activity. In addition, there are major regional capitals that have particular expertise (e.g. Zurich, and Frankfurt for banking; Barcelona and Milan for commerce; Geneva for international agencies). Similar examples can be identified in the USA (e.g. Washington for government; Chicago and San Francisco for financial services; Los Angeles for culture and entertainment) and Japan (e.g. Osaka for trade). This global and world hierarchy is connected by the main international airlines, the new telecommunications systems and high-speed railway networks. In the future, rather than one city within a particular location having dominance in all functions, it would seem likely that cities will become networked so that even the specialized information services become devolved. The great unknown in the pattern of urban development is the effect that the phenomenal growth in the emerging countries will have. The rate of economic growth in the Pacific Rim countries and Brazil is such that these rapidly growing economies with newly emerging mega cities will also be seeking a larger share of the economic benefits arising from the globalization of economic activity and the technological revolution. Similarly, the transition of the countries of central and eastern Europe, together with the CIS countries (Commonwealth of Independent States, the old Soviet Union), from command economies to market economies will eventually lead to a new balance of power between the global cities. One of the major limiting factors in the redistribution of power is the quality of the transport and communications infrastructure. Despite recent investment, the cities that are not in the global or world category are less well connected than the key centres. Even though they may have well-educated and technical competent workers, the opportunities available to them are more limited. This in turn encourages the mobility of labour to the higher order centres which increases the dominance of the centre. Even though much recent investment has been made in improving the transport and communications infrastructure between the major countries of the world and between the established centres and more peripheral regions (particularly in Europe, Japan and the USA), it is unclear whether this has increased the

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attractiveness of the centre or has made the peripheral regions more competitive (Chapter 5). Apart from the competition between global and world cities, the nature of the cities themselves is also changing. This has been commented upon (Section 4.2) with respect to commuting patterns, but the spatial structure of cities has become much looser as residential suburbanization took place. This was followed by the decentralization of employment to more spacious and cheaper sites, often close to the road network. As manufacturing and assembly plants were replaced by research and development opportunities associated with high technology production, further decentralization took place. Most recently, the non-essential functions of many organizations have been relocated to peripheral sites, often not even in the city. With the advent of cheap and reliable technology, these ‘back office’ functions can be carried out in locations where office space and suitably trained labour is cheap. They can even be carried out within the home or at a local telecentre where the overhead costs are minimal. One of the major disadvantages of the global cities is that rent levels and labour costs are very high. It is often difficult to attract suitable labour, particularly if the housing and environmental costs of living in the city are included (e.g. congestion, air pollution, safety and security). The net result is that the global and world cities have vast labour market areas as long-distance commuting becomes attractive. The time taken (and the costs involved) of commuting by rail or car are balanced against the substantial benefits of lower housing costs, better educational opportunities and a higher quality of life. Again, the high-quality road and rail network has allowed new patterns of commuting to develop. The advent of the mobile office and mobile communications has allowed commuting time to be used more profitably and has also permitted more flexible work patterns (Section 4.2). In Japan and Europe, the new high-speed rail networks have ‘shrunk’ space so that it is often faster to reach city centres from more distant locations on the high-speed network than it is to reach the city centre from nearer (suburban) locations which are not on the high speed network. There is a time-distance inversion effect. We seem to be moving towards what Webber has called the non-urban realm where communities share activities and exchange information (Webber, 1963). This communication could take place locally, at the city scale, worldwide or through mixed contacts. Although people might live in cities, there was no inherent reason why this should be so. The necessity for continuous face-to-face contact has passed and dispersed cities operate just as well as concentrated cities. It is not a spatial problem, except in that larger cities tend to have lower communication costs, but even these are declining.

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4.6 Implications for travel demand and economic development These four major trends in the economy will have enormous implications for travel demand over the next twenty years. In each individual case, there could be substantial growth in demand, but when the effects are taken together the impacts are likely to be even greater. There is a synergy between these workrelated, socio-economic, technological and spatial factors, which in turn will have dramatic effects on network performance and transport behaviour. An examination of the trends in mobility over the last ten years (1986–96) gives a feel for the potential exponential growth over the next ten to twenty years. In the fifteen countries of the EU, there has been a 30 per cent growth in the numbers of cars and taxis, with car ownership now averaging over 400 vehicles per 1,000 population. Travel measured in vehicle km and passenger km has also increased by a similar amount with the total amount of passenger travel per head of population increasing to about 9,000 km per annum. However, the overall pattern has considerable variation which cannot only be explained by population, size, area density or economic factors (e.g. GDP) alone. The greatest increase in car ownership has been in the peripheral EU countries with low levels of GDP per capita (Portugal, Greece and Spain). Growth has also been high in Luxembourg, Italy, the UK, Finland and Austria, but from a higher base. Denmark has had the lowest levels of increase, followed by France, Germany, Sweden, the Netherlands, Ireland and Belgium. Even in the USA, the growth in car ownership has been over 32 per cent and the ‘assumed’ saturation level of 650 cars per 1,000 population has now been reached. The increase in car ownership in the USA over the decade has been greater than that of many EU countries. Over the next fifteen years, there will be a significant further increase in car drivers and the number of cars in many European countries, so that the overall level will reach about 550 cars per 1,000 population in 2010. This level is similar to that in the USA in 1985. There are likely to be 60 million new cars in the fifteen EU countries (+38 per cent) and much of the growth will take place in households where there is already one car. It is unlikely that the road capacity will increase by anywhere near the same amount, so congestion will increase. Substantial growth has taken place in car use with the dominant position of the car being reinforced as the main mode of transport. Only in Japan, where there are long journey distances, high densities and substantial investment in the Shinkansen high-speed railway, has the proportion of travel by rail been increased (to 37 per cent of passenger km). Even here, it could be argued that the particular circumstances of megalopolis along the eastern coast of Japan has made such action essential. There is no urban development on the same scale, density or intensity anywhere in the EU or the USA.

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Stability is often assumed in the relationship between car ownership and car use. Evidence from Britain and other European countries suggests that trip lengths have increased significantly and that the lower mileage recorded by second cars in car-owning households is outweighed by the increased mileage recorded by households obtaining their first car. In countries where car ownership is still increasing and where structural changes in the economy are taking place, both the numbers of trips made and the distances travelled will continue to increase leading to greater congestion. Two sets of conclusions can be drawn from this chapter. It seems that the combination of all these four major economic changes will result in an increase in travel demand, perhaps of an even greater scale than has been seen in the recent past. That growth will not be in the work purpose, but in the leisure and other purposes. Globalization effects in terms of the economic arguments and the spatial arguments means that economies of scale and agglomeration both continue to exist, but there is a high level of transport intensity as new forms of production and specialization take place. Both the labour market characteristics (flexibility and fluidity), and the production characteristics (flexibility and specialization) are changing radically. The great unknown is the role that technology will actually play apart from facilitating change and the distribution of power. In most of these macroeconomic, spatial and technological changes, transport has been seen as an essential given. It has been assumed that there will be the means to transport goods and people, locally, nationally and internationally, at a speed, quality and price that can be accommodated within these changes. In other words, the evolving economy is not transport dependent. The conclusion must be that this new agenda is independent of transport or that there is a sufficient supply of transport to allow it to happen. This conclusion has been in part tested through the Centre for Economics and Business Research (CEBR 1994) model of the UK economy where different levels of road expenditure were tested for their impact on congestion and employment (Table 4.10). Two basic options were tested in the model, a 50 per cent increase in roads expenditure and a 50 per cent decrease in roads expenditure for motorways and trunk roads (i.e. those roads principally constructed and maintained by the national government through the Highways Agency). The figures given here are interesting as the argument that road investment has little effect on traffic growth is clearly supported. There seems to be little variation around the expected increase in traffic (57 per cent to 2010). However, congestion is expected to rise and speeds fall irrespective of whether investment is kept at current levels or increased or decreased. Congestion will increase by 7.6 per cent under the increased investment option (13.4%-5.8%) and by 21.6 per cent under the reduced investment option (13.4%+8.2%). Translated in monetary costs of lost time, the numbers are substantial and even larger when related to the effect on GDP. The full economic impact takes time to reveal itself, as the effect on GDP in the first five years

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Table 4.10 Road investment, congestion and employment in the UK for alternative infrastructure investment levels in 2010

Source: Based on CEBR (1994). Notes: Target year (2010) changes are given in this table. Current levels of expenditure is about £2bn per annum. To stabilize congestion over the period to 2010 would mean raising petrol prices to £15 per gallon (1994 prices or £3.33 per liter) or a 5.50 times increase in real terms. Real costs of fuel to rise by 3.54% per annum in the figures given above. All values given in 1993 prices.

with the 50 per cent reduction target is only 0.24 per cent (1998), but the effect on growth and employment then increases to 1.1 per cent (2010). According to the CEBR report (1994), reductions in road investment would directly affect the construction sector in the first instance, and then its suppliers. Over time, the costs imposed by rising congestion would have an additional impact on consumer expenditure and the costs to industry and business resulting from higher transport costs. This in turn would lead to lower levels of investment and in the longer term have a negative impact on competitiveness. The increased investment would have the reverse effects and both sets of figures would be moderated by interest rates as public expenditure would be reduced or increased. This macroeconomic modelling approach suggests that investment levels in the road infrastructure are strongly related to economic performance, measured by transport indicators (congestion and speed), by economic indicators (GDP and employment) and by wider indicators (inflation rates, balance of payments, levels of public expenditure). If the assumptions used in the CEBR model are accepted, then the arguments used in this book are not supported. The evolving economy is very transport dependent and the existing levels of supply are not sufficient for the maintenance of the competitiveness of the UK economy. It seems that the evidence from the wider literature cited here is at odds with the more specific macro modelling approaches applied directly to the assessment of the impact of road investment options on employment. Perhaps, the reality is somewhere in between. But one also needs clearly to establish the explanation in terms of whether it is due to the methods being used or the actual situation. Cost benefit analysis has been the main method used for the evaluation of public sector investment decisions in the transport sector (Chapter 7). It was developed in a time when unemployment was low

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and takes no account of the employment effects. The methodological argument was that transport investment decisions have high sunk costs and low marginal costs, so that the prices charged should be close to zero to maximize use. Although revenues were low, the consumer surplus was large and this could be recycled into productive activities, principally private sector growth. This may in turn result in new jobs being created, but more often it led to higher productivity (and profits). Financial rates of return from public projects could be increased if government tax revenue from accelerated growth leads to higher levels of employment and company taxes being paid. The multiplier effects are substantial, depending on the output elasticities used and the implied rates of return. However, all these additional benefits are dependent on the assumption that the consumer surplus is recycled into productive activities, in particular higher levels of employment, rather than higher levels of capitalization and profits. A broader based macroeconomic approach to evaluation of transport infrastructure investment, such as that used by the CEBR (1994), attempts to include these multiplier effects through a variety of methods. Feedback is explicit as investment brings lower labour costs resulting from a wider (more accessible) labour catchment area and a closer matching of skills to jobs. This in turn may also lead to higher productivity. These first round effects may be accessible locations and raised labour costs (unless there is a surplus of labour). Again, assumptions are made about the link between investment and employment rather than substitution effects resulting in higher productivity and output without any additional labour. Other effects are also important, particularly at the broader programme or regional level as new investment may result in induced travel as the propensity to travel may increase with the improved infrastructure quality. Similarly, barrier conditions need to be considered as actions taking place in one location may affect other locations, particularly in peripheral locations or through the means by which projects might be financed (geographical and functional barriers). The financing and taxation issues are also important. The taxation component in publicly funded projects is often substantial, but it can be reduced if construction workers would have been unemployed, leading to short-term welfare savings. It can also be reduced if economic growth is stimulated. CEBR calculations on London Underground suggest that ‘a dynamic approach to fiscal analysis of projects can show them to have a positive effect on the Public Sector Borrowing Requirement within a relatively short period of time, largely because of their contributions to faster economic growth’ (quoted in EUROCASE 1996:56). The net cost to the economy of a project is therefore unclear as it increases demand and this may in turn affect costs. On the supply side, higher government spending on public projects may lead to increases in borrowing and interest rates in the short term and levels of taxation in the longer term.

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On the demand side, this may result in price and wage inflation that reduces the real value of output gains and this in turn is inflationary. These monetary (supply) and resource (demand) effects are known as ‘crowding out’ (in a deregulated or ‘free’ market the effects maybe reduced as capital is more mobile). In all of this discussion, the two critical factors are the links between transport infrastructure investment and economic growth, together with the multiplier effects on employment. Most of the modelling and evaluation approaches make the assumption that there is a link and then establish the size of that relationship. The other effects on taxation and public finances all flow on from that relationship. The arguments presented in this chapter on the evolving economy would suggest that the link is weak and becoming weaker as labour markets become more flexible and fluid and as technology has a greater role in the production process. In addition is the network argument which suggests that the changes in accessibility and reduced transport costs brought about by new investment are small in most developed countries. These changes are not likely to affect the production processes of companies, particularly where the transport supply chain forms a relatively small part of the total production costs. All of these questions are crucial both to our understanding of these fundamental relationships and in developing appropriate methods for analysing them. Note 1

Vertical disintegration takes place when a company restructures itself and contracts out much of its activity to outside organisations.

Chapter 5

Social, spatial and environmental effects

5.1 Introduction Although economic factors and competitiveness have been the driving forces for most national governments, there is a growing realization that other priorities are also important in balancing the dominance of the growth arguments. Principal among these complementary priorities is the changing structure of the population itself, both in terms of its age profile and new forms of family structure. In addition, there is the fact that the current generation is the first to have had almost unlimited access to the car and the opportunity for high levels of mobility. The second great social issue is the concern over equity. This covers both social equity and spatial equity. Not all society has equal access to transport and the growth arguments have to be balanced against the distribution of the benefits. Provision has to be made for those without access to a car or public transport and governments have always tried to provide opportunities in remote and peripheral areas, often through transport investment. Projects with lower levels of economic return have to be balanced against the regional development benefits and reductions in levels of isolation. The most recent addition to the debate is the growing importance of environment, particularly as it relates to sustainability. Most governments have made a firm commitment to stabilize levels of CO2 emissions at their 1990 levels in the year 2000. This commitment was made under the Framework Convention on Climate Change (Rio Summit 1992). In the transport sector, the level of CO2 emissions is directly related to the amount of fossil fuels used. Stabilizing emissions requires a combination of more efficient use of fuel and less travel, but the trends in most countries are in the opposite direction. Further investment in transport may help achieve more fuel efficiency, at least in the short term, as relief of congestion allows the transport system to operate more efficiently. But, in the longer term those benefits may be offset by the growth in traffic. In addition to the CO2 stabilization targets there are many other environmental costs associated with transport. In many cases, the objectives of transport investment must be to

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identify situations where the economic, equity and environmental factors all point in the same direction. It is not a simple trade-off between these factors, but a clear policy challenge. We want investment in opportunities that lead to economic development, with a more equitable distribution and environmental benefits—this is the ‘win-win-win’ situation. In terms of the more specific discussions relevant to this book, there are important considerations about urban form and structure and the new means by which investment decisions should be made. The economic and development benefits have to be supplemented by distributional and environmental benefits. Here we discuss the issues and principles and in Chapter 7 we present the analytical evidence. 5.2 Demographic changes The patterns of mobility are similar in all fifteen European Union countries and those in the wider European economic area (EU countries and EFTA). The levels of mobility are somewhat lower than those in the USA, but higher than in Japan. In all cases, except for Japan, there is an overriding dependence on the car for travel (Table 5.1). The growth in population is slow, with the total population being stable at around 370 million in the EU15, 260 million in the USA and 125 million in Japan. The current low levels of fertility will be maintained, at least into the next century. 5.2.1 A geing and changing family structures However, within this relatively stable population, two major changes can be detected. The most significant growth in population will take place in the elderly and non-working population as a joint effect of the increases in life expectancy and the tendency to retire earlier. The proportion of elderly people in western Europe (over 65 years) will rise from 13 per cent (1985) to 20 per cent (2020). This means that for the OECD countries (Organization for Economic Co-operation and Development 1985), the number of elderly people will increase over that same period from 85 million to 147 million. The population of most advanced economies is becoming greyer. In addition to the ageing of the population, there are other important changes taking place in demographic terms: •

Average household size is expected to continue to fall from current levels of 2.7 persons per household to 2.4 persons per household (2010). Household size reflects the lower fertility rates and births outside marriage. In the 1970s, divorce rates doubled in Belgium and France and tripled in the Netherlands. By 1986, births outside marriage accounted for nearly half the total births in Denmark and Sweden (Masser, et al. 1992). The concept of a traditional household with two adults (married) with children is no longer valid. Indeed,

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Table 5.1 International comparisons, 1984–94

Source: Transport statistics Great Britain (UK Department of Transport 1997). Notes: Car includes cars and taxis. EU15 had 158 million cars and taxis in 1994 and 116 million in 1984.

•

it may never have been true. The structure of households is no longer based on the family unit as there are now so many variants. Perhaps, at the micro level, this means that analysis should be based on the individual rather than the household as a unit. Similarly, much of trip generation is based on the assumption that certain activities relate to household size. There are common activities in which all households have to participate. However, if the unit of study, namely the household, no longer exists or exists in many different forms, then it becomes difficult to establish a clear methodological framework for analyses. Perhaps each person should be treated as an individual with particular characteristics, not as part of a household unit, but this weakens the concept of family or joint activities. The question of whether the most appropriate scale for analysis should be at the individual level or in some grouping (family or other) is unresolved, as activities are undertaken individually, as part of a family, as part of a group or as part of some other arrangement.

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•

The increased participation of women in the labour force is also apparent in all European countries, particularly the growth in part-time working. There are still substantial differences between European countries in the level of female participation with a particular growth in the Mediterranean countries (e.g. Italy). This southern growth is from a lower starting point, but the national differences may also reflect the different cultures and traditions The implications for work-related travel are likely to be substantial as many households have two wage earners, so the location decision may not be optimal for even one of the workers. It has been argued (e.g. Boddy and Thrift 1990; Banister and Bayliss 1992) that households now establish a residential base and career needs are met by (long-distance) commuting. The broader implications of the new patterns of work have been discussed in Chapter 4.

•

5.2.2 The motorization effect The second major change has been the motorization effect. The current cohort of elderly people in the EU are the first to have experienced the use of the car all their lives and they will not want to give it up. To expect that today’s elderly population will adopt the travel patterns of the elderly of yesterday is unrealistic. The implication of this argument is that dynamic approaches must be developed to account for the desire of individuals to maintain the ability to drive as long as possible. A demographic analysis of car ownership and use patterns takes as its starting point the growth in licence ownership and car ownership for different age groups of men and women (Madre and Lambert 1989; Banister 1994). In the USA nearly 90 per cent of the adult population have driving licences and there are, on average, nearly two vehicles per household. The distances travelled by residents averages at over 29,000 km per household. These levels are about one-third higher than those in the EU. The latest evidence from the GB national travel surveys demonstrates the increasing dependence on the car over the last twenty years and the gradual growth in mobility (Table 5.2). Over that period total travel per person has increased by 37 per cent with the car’s share also increasing from 68 per cent to 78 per cent. The proportion of travel by all other modes has declined (except van/truck) both in absolute terms and as a proportion of the total travel. The growth in travel is explained in equal parts by the increase in trips made and the increase in the average journey length. The picture painted here is of substantial further increases in travel over the next fifteen years, resulting from demographic changes. Two possible influences might limit this growth. First, broader environmental concerns might lead to the realization that unconstrained growth is not desirable and that alternatives to travel by car and air must be sought. To achieve such a change may not be possible in the short term, but the birth of a new ‘green

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Table 5.2 Distance travelled per person per year in Great Britain (km)

Source: UK Department of Transport (various). Note: Other includes London Underground, motorcycle, taxi/minibus, bicycle, other private and other public. 1 The top figure includes short walks and the bottom figure includes only trips over 1.6 km in length. 2 The top figure is the average journey length for all trips and the bottom figure is the average journey length for all trips over 1.6 km. Figures in brackets are percentages.

generation’ who are prepared not to travel so much, particularly by environmentally damaging modes of transport, might trigger such a change. At present there seems to be no evidence of such a movement. The second is the argument that in Europe distances between cities are relatively short and that the road infrastructure is not so well developed as that in the USA. Consequently, the need to own a car and the ability to use it is not so dominant as in the USA and therefore saturation levels of ownership may be lower in Europe than in the USA. Again, the evidence is limited. This saturation level may just be wishful thinking, rather than one based on a true understanding of consumer choice and marketing by the vehicle manufacturers. In terms of identifying future trends and the impacts on travel demand, certain conclusions can be drawn. Within the overall patterns it seems that particular sections of the population may travel more by car. Women and the elderly are two groups that have traditionally driven less than other people. Fundamental changes have taken place in women’s participation rates in the labour force, their greater independence and the increase in ‘non-standard’ households. These changes would all suggest that increases in their travel patterns (including the increases in numbers of trips made, trip lengths, complexity of trips and use of the car) would be greater than average. Similarly, with the growth in life expectancy, health, aspirations and affluence of the elderly, one would expect that they would keep the car for as long as possible and make greater use of it in their extended retirement. It is unrealistic to

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expect that elderly people in the future will have the same travel patterns by mode as a similar elderly group today. In summary, there are at least three compelling arguments that would strongly suggest that trip rates by mode for particular groups would not remain stable in time: 1

2

3

Present-day expectations and travel patterns will influence aspirations in the future. This cohort effect will be most apparent with the elderly who are the first generation to have experienced mass car ownership and so can be expected to continue to use that mode as long as possible. The growth in leisure time and the high value now being placed on the quality of life, and the importance of stage in life cycle: life-cycle changes refer not only to the four basic conventional groups (i.e. married couples with no children; families with young children; families of adults; retired), but to the wide range of unconventional groups (e.g. single-parent families). Changes in lifestyle and life-cycle effects have had fundamental impacts on the range of activities that people require, the increasing complexity of travel patterns and the increase in travel distances. Complementary changes have also taken place with the structural changes in the economy and changes in the distribution of industry, commerce and retailing which have tended to follow the decentralization of population. The increase in levels of affluence and the unprecedented growth in car ownership levels: some of this affluence has resulted from the growth in western economies, but the greater part has been the growth in savings and wealth from property value increases. That new wealth is likely to be used by the newly retired elderly or passed on to their next generation.

It seems that the demand for travel will continue to increase but the nature of that demand may change as a result of demographic factors. Although the changes in population structure are important, other changes (such as the industrial structure, technological innovation, levels of affluence and leisure time) will also influence demand. The problem here is in unravelling the complexity of issues so that the effects of one group of factors can be isolated. Similarly, there is a range of policy instruments that can be used to influence levels of demand and mediate between the different interests. 5.3 Spatial and social equity effects These general changes brought about by the aging and motorization effects conceal other fundamental changes within the population. Transport as with other commodities will never be available to all people equally, nor will it be distributed equally over space. As we have already seen, there are agglomeration economies (Section 4.3), and income and age constraints will

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mean that not all people will have equal access to facilities and services. Even if it were available to all equally, it would not be ideal as different people (and businesses) have different requirements. Many of these requirements have already been mentioned in the changing patterns of work and leisure, the changes in family structure and the changes in business organization. In addition to the changes in patterns of demand, there have been significant changes in the distribution of services and facilities (the spatial factors are considered in Section 5.5). The basic issue here is that of accessibility to facilities, both at the aggregate level and for particular groups of people. Accessibility relates both to the physical distribution of land uses within the urban areas and the availability of transport, and to the needs of the people to use the services provided. Access is a function of both travel times and the number and quality of nearby destinations (Handy 1993) and the value different people place on access to different destinations also varies. The regional dimension is important as investment in infrastructure is often justified on the basis of improvements in accessibility and an increase in economic performance. The arguments, particularly on causality, have never been clear (Vickerman 1995) and the fundamental process of regional economic development leading to convergence or divergence is still intensely debated (e.g. Romer 1986; Krugman 1991a; Vickerman 1994). Traditional arguments of regional economic growth are aspatial. Neo-classical theories suggest the free movement of resources are seeking higher marginal returns. Keynesian theories view regional variations in aggregate aspatial analysis and these are both at odds with microeconomic approaches to the understanding of location. Recent arguments have stressed the importance of space and the existence of increasing returns as the basis for understanding the spatial economy. The rationale here is that increasing returns explain the separation of production and the spatial concentration of industry. However, the assumptions used in these models have also been questioned (Krugman 1991a) as they are based on perfect competition assumptions of free entry and common levels of technology. In particular, the importance allocated to transport costs in these models is too great. The question here is the degree to which competitiveness will improve from transport cost reduction. As noted throughout this book, transport costs are a small part of total production costs, yet they seem to have been given a disproportionately large role in explaining competitive advantage and location decisions of firms. With the advent of a high technology, service-based society, with flexible labour markets and high levels of skills, transport costs alone cannot explain which locations are most attractive. The ‘new growth economies’ (Romer 1986) emphasizes economic growth as endogenous to an economic system, rather than as the result of outside forces. It is the differential quality of factors of production, including the skills and knowledge of the labour force, which are internal to the economic

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system that explains the differential growth. Yet even here it is difficult to draw tight boundaries around systems as much of the development takes place within a national, international or global context. Regional boundaries are not geographically based. The ‘new economic geography’ (Krugman 1991a) argues that imperfect competition models, together with economies of scale, can best explain location decisions. The Krugman (1991a) argument is that a tension exists between convergence factors, such as market size (agglomeration economies) which leads to concentration and divergence factors, such as the competitive elements which allow disadvantaged regions to maintain production (a deconcentrating effect). From the analytical research, it seems that changes in transport costs can lead to concentration or deconcentration, depending on existing cost structures, the elasticity of substitution and the initial quality of the transport infrastructure (Krugman and Venables 1990). If transport costs are very high, there will be a decentralizing effect unless the local markets are small or the extent of scale economies substantial enough to outweigh the transport costs (Vickerman 1995). If transport costs are low, concentration will take place, unless there are substantial local markets and low-scale economies that would justify a larger number of locations. The difficulty comes then in trying to relate location and land use factors to the regional development arguments, particularly under conditions of increasing returns to scale. Firms may move to locations where new scale economies can be achieved through cheap raw material (and labour) inputs. But the transport costs are not only the distance-related costs, as they should include all the other quality factors related to the production process—the integrated logistic chain (Chapter 4). As Vickerman (1995) clearly argues, the main problem is one of aggregation. It is not appropriate to have a single approach, as the infrastructure is important for the individual firm, but the aggregation of individual benefits, does not necessarily lead to regional benefits. There are many other factors at work (Gramlich 1994). Two basic economic arguments determine the role that transport investment decisions have on the spatial distribution of economic development. The non-spatial arguments examine the aggregate level of economic activity in terms of productivity and competitiveness. Infrastructure is seen as a public good which enhances the productivity of the private factors of production, or it combines with private capital in an optimism ratio to raise productive potential. Spare capacity leads to new opportunities and internal scale economies or external agglomeration economies. Conversely, bottlenecks limit growth potential. The alternative set of spatial arguments emphasizes the differential performance of the different locations, together with the tension between forces of convergence and divergence. The actual effects are much more disaggregate in nature and depend on the individual firm, its competitive position, economies of scale and imperfect competition. These distributional questions need to be balanced against the actual

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infrastructure investment decisions made over the last twenty years and the implied government thinking on causality. Transport infrastructure investment has fallen as a proportion of GDP and it is not clear that firms have responded in particular ways in particular locations. New forms of operation have been introduced to maintain competitive position and reduce production costs (e.g. technology, logistics and new organizational structures, Chapter 4). These may be included as second or third round multiplier effects. Industry has to rely upon new forms of operation to increase productivity and efficiency if new investment in transport infrastructure does not take place. 5.4 Environmental and sustainability effects In 1996 the transport sector was responsible for over 25 per cent of world primary energy use and 23 per cent of CO2 emissions from fossil fuel use. It forms the most rapidly growing sector with energy use in 1996 at about 70 EJ1. Without action, this figure will double to 140 EJ in 2025. Industrialized countries will contribute the majority of this figure until 2025. After that date, the majority of transport related emissions would come from those countries that are currently developing rapidly or have economies in transition. Transport activity increases with rising economic activity, disposable income, access to motorized transport and falling real vehicle and fuel costs. Projections of transport greenhouse gas emissions follow the historic trends as CO2 emissions are directly related to energy use in the transport sector. The assumptions made are that the relationships between transport fuel consumption and variables such as gross domestic product (GDP), fuel prices and vehicle energy efficiency will remain stable, at least until 2025 (Grübler et al. 1993). More recent research (e.g. Acutt and Dodgson 1998) suggests that the relationship between energy use and economic factors is not stable and that, in Europe, car ownership and use may saturate at lower per capita levels than those found in the USA and Canada. In addition, technological innovation may result in greater levels of mobility being achieved with lower levels of energy input. There are also strong political and economic arguments for breaking the historic links between transport demand, energy use and economic factors as has happened in the energy sector (Peake 1994; Banister 1996). Despite the many advantages brought about by the car and other transport, there are also serious negative consequences for society as a whole (Table 5.3 ). The environmental costs of transport have been grouped under four main headings—pollution, resources, environment and development (Table 5.3). Decisions taken to improve benefits along one dimension may be likely to increase costs along another dimension or in another sector. The complexity of decision making in environmental policy cannot be underestimated, but all governments must now face difficult choices. More detailed discussions on the elements of the environment, the role of transport, and policy measures

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can be found in Johansson (1987), Banister and Button (1993), Whitelegg (1993), Maddison et al (1996), Banister (1998). Many of the environmental costs of transport are non-linear in their effects (e.g. health effects and congestion). The crucial issue becomes not how to measure, but how to avoid reaching critical levels where the environmental costs become too high (e.g. lethal doses of pollution). Still, the measurement difficulties are substantial and placing values (or money costs) on environmental factors tends to be subjective (Button 1994). It is only recently that these issues have become central concerns in evaluation (Chapter 7) and in decisions on infrastructure investment. The challenge for environmental policy in transport is to improve as many elements of this complex interrelated list of environmental costs as possible (Table 5.3) without increasing those elsewhere, or at least being aware of them and making an informed choice. It should also be remembered that transport is only one (albeit important) part of the economy and so the environmental choices in the transport sector need to be balanced against other priorities. It is argued that transport infrastructure investment has social benefits (e.g. bypasses of congested town centres) but that it destroys the environment (e.g. through the generation of more car travel). More recently, the environmental arguments have been linked to those of sustainability. This more sophisticated view links environmental concerns with those of economic development and equity. To achieve an objective of sustainable development, at least five different sets of objectives need to be addressed. In this section we have been concerned with the environmental objectives. The second objective is to maintain competitiveness through economic growth and development objectives (Section 5.3). Where possible, the environmental and development objectives should be working in the same direction—this is the ‘win-win’ situation, and many transport investment decisions have tried to achieve these benefits. For example, as noted above, bypass schemes have been justified both by the economic benefits from reduced travel times and by opening up new areas for development. But they have also brought environmental benefits to town centres. In addition to these two fundamental objectives, the concerns over sustainability present three new objectives. The equity objectives (Section 5.3) are concerned with the distribution of costs and benefits to society, both socially and spatially. These intragenerational effects are contrasted with the intergenerational objectives (futurity), highlighted by the most often quoted definition of sustainable development, namely—‘development that meets the needs of the present without compromising the ability of future generations to meet their own needs’ (WCED 1987). The final objective is participation in its widest form. Too often in the past, decisions have been made without the support of the affected parties. To achieve the objectives stated for sustainable development, we have to carry out our daily activities in different ways, using resources more efficiently. Similarly, industry and the new post-industrial economy need

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Table 5.3 The environmental costs of transport

Sources: Based on Banister (1993b) and Whitelegg (1993). Note: SSSI—Areas of special scientific interest, which can only be developed in very special situations.

to be more sustainable in their operations and organisation. This requires clear policy directions through pricing, regulation and control, but the scale of change necessary to achieve sustainability objectives also requires political support from all affected parties. Unless this support is forthcoming, little progress will be made. Underlying much of the debate over the environment and sustainability is

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the crucial link between the environment and competitiveness. Much of the literature discusses the balance between these two dimensions. We would argue that the most productive way to actually achieve sustainability objectives is through these two dimensions operating in the same direction—the ‘winwin’ situation. Porter and Van der Linde (1995:97) have argued that the ‘struggle between ecology and the economy grows out of a static view of environmental regulation, in which technology, products, processes and customer needs are all fixed’. In the static world, firms have made costminimizing choices. Environmental regulation raises costs and reduces market share of domestic companies on global markets. They go on to develop a dynamic paradigm, based on innovation and the capacity to improve competitiveness through shifting the constraints. Properly designed environmental standards can trigger innovation, which may more than offset the costs of compliance. We would go further and suggest that environmental incentives should be used to promote greater efficiency and innovation. A positive promotion of environmental incentives is one way to achieve sustainability objectives and gain public support through the demonstration effects of policy actions. Transport policymakers have always accepted that transport imposes environmental costs. However, the scale of the problem and its nature are now much greater. There is much more transport today than there has been in the past and that trend is likely to increase, particularly with the emerging economies of central and eastern Europe and those of the Pacific Rim. In the longer term the greatest growth is likely to be in China and India. In addition, the continuous growth in air transport has added a further new element of travel. But it is the rapid growth in car ownership and use that forms the most important factor in assessing the environmental costs of transport. It is clear that the simple growth in transport poses environmental de gradation, but there are also substantial qualitative factors (Table 5.3). For many years transport policy has been primarily concerned with the local problems of transport, principally congestion, accidents and noise. The debate in the last twenty years has become more sophisticated and complex as the broader impacts of transport have embraced both global and international effects, as can be seen from the following example. Concern over the damaging effects of ‘acid rain’ on forests and water life grew in the 1970s and 1980s. The importance of NOX and other gaseous emissions from cars were recognized as major contributing factors. Concern also emerged in the 1980s over high-level ozone depletion and its impact on the long-term incidence of skin cancer. Transport’s role was relatively small, as it was confined to CFCs in air-conditioning units. In the 1990s global warming has become the key issue with its impact on raising average temperatures and the consequences for climate change and sea level rises. The new agenda requires the collective action of national governments and international agencies in limiting the growth of CO2 emissions.

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The scientific evidence is powerful, but the essential catalyst for change has been public concern over environmental issues. In part, this interest is a reflection of increased affluence and being able to afford to take action on environmental issues. More fundamentally, there is a growing concern over the long-term future of the planet and a commitment to sustainability. Transport has become a key element in that debate and one that is beginning to attract a disproportionate amount of attention. Transport contributes at all levels to environmental degradation (local, global and transboundary). It has a high profile, it is perceived as being a major intruder and it also interacts with other activities which can be seen as being environmentally harmful (e.g. tourism). Transport is also seen as an area where governments can and have intervened through fiscal measures, regulation and the planning system. Many of the environmental costs imposed by transport are the consequences of policy decisions made for other reasons (e.g. regional development). Action to improve the environmental quality should also rest with governments, but governments will only act if there is a direct political benefit, and/or if there is sufficient public support, and/or if there is some international agreement. This is the irony of the debate and it is reflected in the inconsistencies in people’s actions and the inability of governments to take effective steps. People are aware of the environmental costs of transport and are supportive of actions by governments to improve environmental quality, provided that they result in no change to their lifestyles and they can continue their use of the car, and provided that it does not increase costs. This is a problem with no solution. It accounts for the general resistance against higher prices in transport so that some of the environmental externalities can be internalized. It accounts for the belief that technological solutions will solve the problem through more efficient engines, alternative fuels (e.g. electricity or hydrogen) and add-on technologies (e.g. catalytic converters). It accounts for the focus on positive-planning policies to reduce journey lengths through higher densities and concentration of development in larger settlements. It accounts for the use of public awareness campaigns and raising the social consciousness to gain public support for actions that are often politically unpopular. In short, even if we could establish clear links between health quality and amount of motorized travel, this might not be a necessary condition to radically change policy direction. There will always be strong reasons to continue to keep the external costs of transport as externalities and to resist the strong environmental case for internalizing them. 5.5 Urban form and structure As with the debate over whether road infrastructure reduces congestion and vehicle emissions or leads to a more dispersed and inefficient pattern of land development (Downs 1992), there is also uncertainty over the most efficient

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urban form. Urban form covers the spatial configuration of fixed elements within a metropolitan area, including the pattern of land use and density and the supporting transport and communications infrastructure. There is some agreement over the different urban form types (UK Department of the Environment 1993). 1

2 3

4

5

Urban infill. Here the aim is to make maximum use of urban sites to accommodate development. Increasingly, the terms urban compaction and intensification are used. Urban extensions. This is the suburbanization solution that has been popular, but rarely questioned. Multiple village extensions. This has resulted from pragmatic approaches to planning rather than a selected solution. It is often unpopular, particularly with residents in the villages. Key villages. This concept was popular, but has not been used recently. It was argued that key villages would help maintain rural services and public transport. New settlements. In the 1980s there were many schemes for privately promoted new settlements, but few have actually been started. There is now a renewed interest in the concept as the housing market is stronger than in the last ten years.

In addition to urban form types, settlements need to be considered in relation to one another, not solely in isolation. The compact city results from higher population sizes and densities in the city, with high quality accessible public transport. The edge city encourages development at selected peripheral points together with increased investment in orbital roads to link the edge cities (Newton 1997). The corridor city focuses growth along linear corridors where high quality public transport is available, while the fringe city encourages general suburban development along the road network. All these possibilities apply to individual cities and also to city regions (Banister et al. 1997). Most urban areas do not conform to any one type as patterns of development are continually changing. Overall, it is clear that there has been a flattening out of density gradients, but there is still no consensus over what is the most desirable urban form in terms of energy efficiency and environmental quality. Even if there was some agreement it may not be possible to achieve that pattern of development. If there is little agreement over the ideal urban form which is both energy efficient and environmentally attractive, there is even less consensus on the role that transport plays. The well-publicized debate in the American literature between the ‘Stalinist views’ of Newman and Kenworthy (1989) and the ‘Friedman views’ of Gordon and Richardson (1997) highlighted the substantial differences. The much-cited Newman and Kenworthy (1989)

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review of thirty-two world cities by urban density and energy use in transport acted as the focus of the debate. Questions were raised about the quality of the data, the importance of particular cities in shaping the curve (e.g. Hong Kong and Moscow), the relatively small differences between cities in the same part of the world and the policy conclusions, particularly about the role of public transport. The contrast between Newman and Kenworthy’s conclusions and those of Gordon and Richardson (1997) could not be more stark.Gordon and Richardson concluded ‘that urban sprawl is a transportation solution, not a problem’. The argument was that there is a dynamic process which is continuously at work. As urban sprawl takes place, jobs follow people so that the journey to work length remains relatively constant over time. Gordon and Richardson base their analysis on US journey to work data. But the journey to work is becoming less important. Households often have more than one worker and the growth in travel is taking place for other trip purposes (Ewing 1997). Most commentators do not take these extreme positions, but are content to focus on the intermediate issues of reducing trip lengths, encouraging moderate concentration, specialization and mixed use (Banister 1997). Transport seems to play an ever-decreasing role in the location decisions of households and businesses (Giuliano 1996a), but there still seems to be an identifiable localized link with journey lengths (Cervero and Landis 1995a), even for the journey to work. These are shorter in ‘balanced than unbalanced areas’ (Frank and Pivo 1994). Even if development took place in transitoriented development (TOD) or in more traditional neighbourhoods, some commentators (e.g. Calthorpe 1993; Crane 1996b) suggest that the cost of travel by all modes would increase. Others again argue that shorter journeys mean more journeys, as travel time budgets are fixed (Gordon and Richardson 1997). One popular element of the debate is the role that telecommuting and other forms of technological substitution might have on travel. The original optimistic views that we would all stay at home and communicate have been replaced by more sophisticated arguments (Mokhtarian 1996). In California, it is estimated that 6.1 per cent of the workforce telecommutes on average for 1.2 days a week. This means that about 1.5 per cent of the workforce telecommutes on any given day and this accounts for about 1.1 per cent of vehicle miles travelled. When considered with total household travel it amounts of 0.7 per cent of all travel. The reductions in the future may be less as commute distances of telecommuters fall closer to average, and as the stimulation effect grows. Mokhtarian’s conclusion is that the aggregate travel impact ‘will remain relatively flat well into the future’, even if the amount of telecommuting increases considerably. Perhaps there is a greater potential in other activities as firms downsize, leaving traditional city locations and have a dispersed workforce distributed in locations (even homes) where the labour and overhead costs are much

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lower (Section 4.4). Similarly, telephone banking, teleshopping, catalogue marketing and other services may offer a greater travel reduction potential. These types of transactions are not dependent on a personal contact, as with many work-related activities, but on a purely impersonal relationship. The evidence of the impact of telecommuting and other forms of ‘teleactivities’ on residential location is not conclusive. They may encourage more dispersed locational patterns (Hopkins et al. 1994; Lund and Mokhtarian 1994), or they may have little direct impact (Nijkamp and Salomon 1989; Nilles 1991). As with many other innovations, ‘teleactions’ allow a wider variety of actions and an increased flexibility in what types of actions can be carried out. The links between land use, urban form, sustainability and transport are complex, and the role that infrastructure investment can have in this process is unclear. Some would argue for a balance between jobs and housing (Cervero 1989) to minimize trip lengths. Others urge neo-traditional neighbourhood design (Calthorpe 1993) to bring the small scale back to cities. Others look towards transit-oriented development (Cervero 1994) to influence mode choice. Yet the outcomes are still unproven as the variety and scale of responses have been substantial. In particular, it seems that it is difficult to get the car user to leave the vehicle at home and use other forms of transport. Similarly, the complexity of the labour market and the distribution of facilities means that journey lengths have also become longer (Boarnet and Sarmiento 1996). New investment in transport infrastructure will always facilitate more travel, particularly by car. Even if the investment is in rail or public transport, mode switchers (to public transport) make it easier for non-mode switchers (car drivers) to use their vehicles. Similarly, the new opportunities provided by technology make it easier to carry out work, shopping and business-related activities from home or a local telecentre. Again this new flexibility provides freedom to organize everyday activities on a useroriented basis, reducing the intended effects of land use policy interventions. 5.6 Implications for economic development The new agenda relating to the social, spatial and environmental effects of infrastructure investment has been outlined in this chapter. The apparently simple causal relationships between investment and economic development do not hold and may never have been appropriate. There is no agreement within the literature and there are sufficient new elements to question the validity of any relationships established. It is impossible to unravel the full complexity of the arguments, yet it seems that many actions will have unexpected results. For example, the construction or expansion of the existing infrastructure may affect the emissions levels both directly and indirectly. By reducing levels of congestion (at least in the short term), new roads would allow traffic to flow more smoothly at a faster speed. This means that there will be less pollution per unit distance up to optimal speeds (about 50 mph), with the

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exception of NOX that increases with speed. The indirect effects reflect growing car dependence, longer journeys and changes in land use, all resulting from additional road capacity. The net result is that total travel increases (Mackie 1996), with additional fuel being used and higher pollution levels. In cities, these effects are likely to be even more pronounced, provided that congestion is reduced. Vehicles operate least efficiently under congested conditions with a high proportion of stop-start driving. In addition, many city journeys are short and vehicles operating under cold start conditions also use more fuel and create more emissions. Catalytic converters are ineffective when cold. It is here that most VOCs, CO2 and NOX emissions are made. To get round the problem of conflicting emissions factors, the US Clean Air Amendment Act (1990) required an analysis of the net impact of all infrastructure projects in non-attainment areas. These non-attainment areas are designated in metropolitan areas in which national air quality standards are not met. Actions are required in all non-attainment areas to reduce emissions levels and reach the preset targets within a given time frame that is set according to the severity of the problem. Ironically, the greater the severity, the longer the time given—one extreme ozone non-attainment area (Los Angeles) has been given twenty years (US Department of Transportation (DOT), Bureau of Transportation Statistics 1996:195). Similar air quality standards are being introduced in Europe, with local authorities having new responsibilities to measure emissions levels. If limits are exceeded, the air quality management areas will be designated and strategies evolved to improve the environmental quality, including the possibility of banning cars at certain times and under certain conditions. This new emphasis on the environment and sustainability has also resulted in a reassessment of the road building alternative. It is now seen as being ineffective in the longer term and having undesirable environmental consequences in the short term. New roads generate more travel. The evidence on induced demand in the USA is less clear than in the UK and Europe. It may reflect the existing levels of congestion within the transport network. In Europe much of the strategic network, together with the urban road network, is congested at certain times of the day. New infrastructure only gives short-term relief as existing traffic is redistributed across the network. But it also seems that there is a substantial latent demand which is released (Mackie 1996). In the USA, the growth in traffic is usually less than the capacity added, even over a longer period of time (Downs 1992). Again, there is considerable doubt over the true nature of latent demand—whether the demand curve has actually changed, or whether it reflects a movement along the demand curve. The former case would be considered new demand (e.g. changing tastes and preferences of individuals), but the latter case is really only a response to changing economic conditions (e.g. lower generalized travel costs). Current methods are not sufficiently flexible to relate emissions to traffic flow and the impacts of induced travel. So it is impossible to determine whether

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a new highway has a positive or negative effect on emissions (Transportation Research Board 1995). Once a road has been built, the negative environmental effects in terms of emissions, land take, noise, intrusion and accidents (Table 5.3) are likely to be felt for many years. It is both difficult to establish sound methodologies for the measurement and assessment of the environmental effects and to establish an appropriate time span over which those effects should be considered. Road infrastructure investment decisions mean that the new link will be present for many generations. The environmental and sustainability issues have also resulted in a similar questioning of traditional concepts of physical accessibility. New technology and life-styles that are increasingly leisure based have changed the importance of certain activities, particularly work-related. Much of this change has resulted from the recent increases in living standards, higher real income levels and universal access to technology. The service and manufacturing base of the economy is being replaced by a knowledge—and skills-based economy. New forms of lifestyle and production have made it possible to create much greater flexibility in travel behaviour and in the location of industrial, commercial and residential activities. This new dynamic is continuous and creates changes in travel demand patterns, greater complexity with circumferential movements around cities and reverse commuting. It has also created new demands for social and leisure based activities both in the urban area and nationally and internationally. Isolating transport’s role in the land development process is difficult, particularly in urban settings. In the USA, much of the low-density residential development has been facilitated by the availability of cheap land, the low costs of motoring and high quality of the road network. Employment has also shifted from the congested and high-rent central business districts to the suburbs and the metropolitan areas have now expanded to become multicentred cities. Between 1982 and 1992, built-up and urban land in the USA has increased by 14 million acres. As a result, developed land in the USA totals 95 million acres or about 5 per cent of the total land area (excluding Alaska) (US Department of Transportation, Bureau of Transportation Statistics 1996:164). New forms of economic development have replaced the traditional notions as we pass through a transition phase from a work-based to a leisure-based society (Handy 1995), and as the new forms of production take place with associated changes in the organization and functioning of companies. It is impossible to present the full range of the changes that are taking place. One example illustrates this well. The economic effects of developing the transEuropean transport network (TENs) may not be as great as originally predicted. The construction of the Ecu 90Bn ECU (£75 Billion) fourteen priority projects agreed at the Essen European Council could create between 130,000 and 230,000 new permanent jobs, but it now emerges that this estimate is based on two strong assumptions: that there is no significant

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economic overcapacity; and that these priority projects would not affect other related projects (a crowding out effect). The net effect on jobs may even be negative if investment is simply diverted from other projects (EU DGII 1997). The conclusion is that new opportunities may be created, but it is not clear whether they will be taken up. Economic benefits will accrue from increased output and from social cohesion and environmental benefits, as most of the projects are rail investment in core and peripheral locations (Banister et al. 1998). Note 1

EJ=Exa Joules. Joule is a measure of energy (kg m2 s-2) and Exa is 1018.

Part III

Methodology: analytical approaches and modelling

Main issues and structure In the review of approaches to the analysis of the links between transport infrastructure and economic growth, we concluded that most research has concentrated on the macroeconomic level. We also concluded, that although statistical relationships can be established at this level, it is difficult to construct causal relationships that support the data as the effects of external factors, time and stage in development will all influence the direction and strength of those relationships. In this part we develop the analytical aspects of this book in three related ways. Chapter 6 explores the production and consumption effects of investments through a review of the macroeconomic literature on modelling public infrastructure and growth. Two basic types of models are reviewed, the production function models and the cost function-based models, and impacts are assessed in terms of productivity gains and economic growth. These approaches are essentially positivistic and their success (or failure) is based on the strength of the empirical relationships developed. Chapter 7 examines the economic evaluation of transportation projects where decisions have to be made about investments in the infrastructure with the expected outcomes being measured in terms of economic development. This is a more normative approach which takes a rational perspective of the network characteristics and examines the economic impacts of investment alternatives. Issues such as accessibility, value of time, discount rates, the time span of projects, risk and uncertainty, as well as multiplier effects and new forms of financing are all included here. Crucial new questions are raised about the method of financing infrastructure investments (see also Chapter 4) and the allocation of risks between the different agencies involved with the construction of new transport infrastructure. However, the primary objective of this part of the book is to propose an alternative analytical framework capable of showing how development of the transport infrastructure can affect economic growth at the micro level. While the concept of economic growth was explained earlier it should be

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The complementarity of approaches.

re-emphasized that our focus is on local economic growth, namely that of cities and regions. Therefore, in Chapter 8 we construct a simple model of local economy consisting of production (firms), consumption and labour supply (households) and transport sectors. By changing a key parameter in this system (i.e. the capacity of the transportation system) we can observe how it affects growth, mainly the equilibrium level of employment in this economy (see diagram above). Under market conditions investment in the transportation system will necessarily affect land use and, as a consequence, a host of other variables such as consumption, labour demand and supply and travel behaviour. However, from a normative economics viewpoint the objective of an investment in transportation infrastructure is not to enhance the performance of the transportation system per se but rather the enhancement of social welfare. Quite obviously it is much easier to define and measure system performance than social welfare. It follows that not every investment in infrastructure should be undertaken, even if it improves system’s conditions such as traffic flow, unless it can also improve economic welfare conditions, such as the equilibrium level of employment or consumption.1 Another important observation is that it is possible to affect the behaviour of consumers by changing relative prices rather than by a direct investment. For example, it is possible to mitigate congestion levels by increasing gasoline tax without any change in the system’s capacity. Indeed, in many cases transportation planners will obtain better results if they can set correctly the price of travel rather than directly regulating traffic quantities and use of modes.2 However, for certain infrastructure facilities (including

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transportation), investment by the state can be highly warranted, provided it is possible to show social welfare improvement. It has been observed that a key factor shaping urban land use patterns is the dominant mode of use during the initial stage of development. Cities that have adopted fixed rail (above ground and underground) have developed a quite different urban form than cities that use buses, trams and private cars as their predominant modes. However, this modal effect on urban growth pattern tends to abate as cities reach maturity and rate of suburbanization growth far exceeds the rate of development of this mode. In the analysis in Chapter 8, we assume an in-place transportation system and focus on the effect of an incremental development of this system on local economic growth. This system can be a highway network or fixed rail provided its capacity serves as a constraint on travel times and flow. The types of analysis presented in this part of the book are complementary, as they examine the links between transport infrastructure investment and economic development at three separate contexts and scales, using different analytical methods and approaches. In essence, they are examining the same problem at the policy and project levels. The difference is that the arguments used are based on different premises. We argue that in developed economies where the transport networks are already well established, any new link is likely to have a very small impact (probably immeasurable) on GDP growth and on regional accessibility. Actually to identify effects, the scale of analysis must be at the local level where impacts can be measured by location decisions of firms, labour supply and measures of output, such as productivity. Notes 1

2

We disregard here equity issues which can arise when a transportation investment is made. These equity effects are transportation related as well as economic growth related. For example, in older cities the suburbanization effect of infrastructure development has led to the immigration of skilled labour to suburbs, thereby leaving behind unskilled poor households. See Chapters 5 and 8 for a discussion. It has been estimated that a 10% increase in gasoline price will retard excess commuting by 15%.

6

Modelling the growth effects of transport capital investments A macro level analysis

The sovereign has the duty of erecting and maintaining certain public works and certain public institutions, which it can never be for the interest of any individual, or small number of individuals, to erect and maintain because the profit could never repay the expense to any individual or small number of individuals though it may frequently do much more than repay it to a great society. Adam Smith, Wealth of Nations, 1967 edition

6.1 Introduction In Part IV we examine several case studies of the effect of transportation infrastructure investment on urban and regional development.1 A major conclusion from these studies is that such developments are difficult to measure and to substantiate. The main reasons for this are the scale of the analysis, unaccounted for spillover effects and the presence of many other intervening variables, mainly counterproductive local policies. But what about macro level (national or state) analysis where these effects are either inconsequential or can be controlled for, as can the effects of other key aggregate variables? In this chapter we examine this issue with regard to two key questions. First, does the level of the infrastructure stock (primarily transportation infrastructure) affect national or state economy growth? Second, if it does what is the marginal contribution, from additional investment in public capital, on factor productivity? Beginning with the seminal paper by Aschauer (1989a), many empirical studies have established a statistical link between the level of the public infrastructure stock and between economic growth and productivity. The basic argument, as put forward by Aschauer, is that rather than crowding out private investment, public investment stimulates it by increasing the rate of return to private capital (quoted in US, DOT 1992). Yet, from a theoretical perspective we might ask why, in the first place, should there be such a linkage? And if it exists, what is the economic mechanism that underlies these relationships? The importance of answering these questions emanates from

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the scientific tradition of requiring empirical models, here used to measure and estimate economic phenomena, to be founded on theoretical considerations regarding the cause and effect and functional relationships between principal variables. In addition, for policy purposes, it is rather important to understand the conditions under which an additional investment will engender growth. For this reason Section 6.2 examines theoretical arguments in modelling the impact of public infrastructure on growth and productivity. Section 6.3 surveys several types of analytical models found in the literature that link infrastructure capital with economic growth. Major empirical results from these models are presented and evaluated in Sections 6.4 and 6.5. Principal conclusions are presented in Section 6.6. At the outset we must emphasize that only relatively few studies, found in the literature, have examined the effect of transportation capital accumulation on economic growth. Most studies have examined the impact of aggregate public capital outlay or a subset of it as, for example, all capital dedicated to highways, sanitation and sewage, electric and water utilities (e.g. Munnell 1990a; Holtz-Eakin 1993), or highways, water and sewers (Morrison and Schwartz 1996). Nonetheless, we survey these studies since transportation is a major component in the country’s or state’s total public capital stock (about 15–20 per cent) so that expansion of the transportation infrastructure capital is likely to affect growth in the same way as the expansion of other capital outlays. Furthermore, it is conceivable that the same causality mechanism which links changes in total capital stock with economic growth can also explain the relationships between transportation capital and growth. Hence the importance of reviewing these studies. The studies reviewed in this chapter focus mainly on economic growth and productivity gains from government spending on infrastructure capital.2 It goes without saying that other types of government spending, such as on research and education programs, can also influence economic growth. Furthermore, the studies reviewed here, by and large, considered economic gains accrued to the private sector only, in particular to manufacturing, thereby disregarding possible gains to other sectors. Consumers, for example, will realize welfare gains from government spending on infrastructure facilities, which act to abate negative environmental externalities. Hence, the results from the studies reviewed here do not cover the full scope of effects from public capital investments. 6.2 Theoretical considerations in modelling public infrastructure and growth As the name implies, public infrastructure capital is provided by a public agency (whether national, regional or local) which, for brevity, we refer to as ‘the government’. Two fundamental questions underlie the modelling of the effect on economic growth of public infrastructure provision. First, why should

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the government be engaged in this activity for which it has to set complex and costly institutions whose role is to fund, build, operate and maintain infrastructure facilities? Second, what is the structural mechanism by which infrastructure development affects economic growth? These two questions are interrelated as the analysis of the functional relationships between infrastructure and growth requires first that we understand the rationale for the government involvement in infrastructure supply. 6.2.1 Rationale of government provision of infrastructure3 Arguments for public supply of infrastructure facilities have a long history in economic thought (see the quotation from Adam Smith on p. 131). Put in modern terms, a standard textbook argument for the provision of transportation infrastructure by the public sector is that, left to the private sector, these facilities would be produced at a substantially sub-optimal social level or not at all. Nevertheless, these facilities contribute positively and significantly to the national economy and to social welfare. Briefly stated, the generic name given in the economic literature to this phenomenon is market failure. It refers to such phenomena as public good and scale economies in facility construction, in the assembly of massive units of land necessary for building and connecting large transport networks, and in securing right of ways. The generation of externalities, positive and negative, by the provision and use of transport facilities is another market failure argument put forward to explain public supply of such infrastructure facilities. Accordingly, the internalization of these externalities, which is necessary for the optimization of social welfare, can be achieved only if the public sector owns and control the capacity and level of utilization of these infrastructure systems.4 Private provision of such facilities would be suboptimal, as private investors would not be able to internalize these externalities as the government can. To illustrate, consider the case of a public good where exclusion of individuals from its consumption is rather costly or unfeasible while the longrun marginal costs of servicing an additional user, given capacity constraints, are negligible. Inter-city and inter-state highways, local streets and feeder roads, as well as forms of mass transit, are typical examples. Under these conditions, individuals have an incentive not to reveal their true preferences regarding their desired level of consumption of these goods, thereby benefiting from their provision without having to bear the associated costs (the free rider phenomenon). Consequently, private enterprises would not be able to earn sufficient revenues to recover the capital and maintenance costs of building, operating and maintaining these infrastructure facilities. Therefore, these facilities would not be produced at all or will be produced at a level, which is substantially below the optimal social level. Indeed, historical records

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show that the provision of transport facilities like local roads, turnpikes, canals and bridges in the long run could not be supported by the private sector. The main reasons for this are heavy losses induced by the inability to enforce excludability, recover capital costs and competition from substitutable facilities and modes (Taylor 1951). It might be asked, however, to what degree does the government attain the objectives implied by the rationale outlined above when undertaking capital infrastructure investment projects? Given the case, providing a satisfactory answer to this question is a formidable task which requires detailed information (in many cases, unattainable) and careful welfare analysis. However, it should be observed that, by and large, the above rationale is regarded by many as a ‘maxim’ that does not require analytical examination or empirical verification. The numerous documented cases of the so-called ‘government failure’ cast a doubt on this perspective. We next turn to evaluate theoretical arguments linking infrastructure investment with economic growth. 6.2.2 Interrelationship between infrastructure investment and economic growth: some theoretical arguments Conceptually, economic growth can be defined as enhanced individuals’ utility from increase in the aggregate quantity of goods and services they consume and from a larger variety of these goods and services available in the economy (Quigley 1998).5 For measurement purposes, economic growth can be defined in terms of the annual rate of increase in the per capita level of output. Alternatively, it can be defined in terms of enhanced productivity of input factors. Quigley (1998) notes that these consumption and production definitions are analogous relative to the underlying conditions necessary for increased utility or aggregate output.6 Given these definitions, the question is how to model the relationships between public infrastructure development and economic growth in order to ascertain empirically the degree to which the former affects the latter. The causality interrelationships between infrastructure investment and economic growth, embedded in the models reviewed shortly, are based on two fundamental premises: • •

that infrastructure capital expansion increases the efficiency and profitability of the business sector; that this increase stimulates business investment in private capital (Aschauer 1989a, b).

It does so by enabling firms to exploit scale and agglomeration economies, by increasing their efficiency through market expansion and competition, by better utilization of inputs, by linking disconnected markets and by making firms and markets more receptive to innovation leading to further growth.

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What are the conditions necessary for the validity of these premises? Below we examine some key functions, focusing on transportation infrastructure as our major illustration of public capital and its effect on economic growth.7 In order to establish causality relationships between infrastructure expansion and economic growth we must assume that infrastructure, such as transportation, is indeed a consequential intermediate input in private production processes. Its ample supply at low costs to users8 is therefore conjectured to have a positive impact on economic growth by stimulating the production of a large number of final goods and services that use public capital as a significant input factor. A corollary condition is that transportation capital stock must be a complementary factor to some private input factors in order to spur further investment in private capital. If public transportation capital is a substitute to all private input factors and if it is correctly priced, the use of these factors may decline if additional investment in transportation infrastructure is made. To illustrate, it has been alleged that increased telecommuting, through higher private firms’ investment in telecommunication facilities will enable employees to benefit from flexible working hours and locations (e.g. work at home), thereby increasing their productivity. If, however, telecommunication and transportation facilities are substitute factors, added investment in transportation infrastructure may discourage private investment in telecommunications, thereby reducing potential labour productivity gains from telecommuting. It follows that in specifying an empirical model for the estimation of the effect of transportation infrastructure expansion on growth, the model’s structure should allow for the estimation of substitution and complementarity relationships between private inputs and public capital. As already explained, in order to evaluate the effect of public capital on growth it is necessary to specify a causality mechanism and direction between these variables. The models reviewed below implicitly or explicitly conjecture that public capital positively affects the rate of return of private capital, hence private capital accumulation. Given the technical substitution between private capital and labour inputs, labour productivity rates improve as a function of the growth rate of the stock of private capital. These effects, in turn, spur greater total output, thus, growth (Deno 1988; Aschauer 1989a, b, 1991; Munnell, 1990b). We call this causality linkage ‘public infrastructure accumulation induced growth’. But what about cases where highly productive countries, states or regions, with high growth rates, attract private capital and productive labour which, in turn, demand higher levels of infrastructure investment? In such cases the causality direction is reversed as the present state of high rate of growth stimulates infrastructure investment rather than the reverse. Disregarding such possibilities might result in problems of simultaneity in the empirical analysis which, in turn, will generate incorrect estimates. Assuming public infrastructure accumulation induced growth type causality, economic growth further stimulates the demand for public capital

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services (like transportation) which, subsequently, provide the pretext for further infrastructure investment. Hence, infrastructure investment decisions should be regarded as endogenous to the economy. For this reason the analysis of the impact of public capital on the economy should ideally be carried out within a general equilibrium model. This characterization of the causality linkage further requires a dynamic model framework. Two alternative approaches can be found in the literature. One which embeds the aggregate output-infrastructure functional relationships within a growth model that specifies a time dependent growth path for exogenous variables such as technical efficiency, size of the labour force and public capital depreciation (see, for example, Aschauer 1991; Holtz-Eakin and Schwartz 1995). Alternatively, it is possible to link recursively changes in the economy, caused by infrastructure investment, with the level of infrastructure services generated by this investment. That is, we require that capital stock investment made in period ‘t’ will affect private input factor flow, thus economic growth, in subsequent periods (t+1, t+2, or t+3, etc.,). The modelling objective then is jointly to estimate longitudinal changes in output, in private capital formation and in infrastructure accumulation (see for example, Deno and Eberts 1991; Morrison and Schwartz 1996). For reasons like public funds availability and lengthy time periods required to construct public infrastructure facilities, we can expect the level of public capital stock in the economy, in general, to be suboptimal. As is the case with the relaxation of any real constraint on the economy, when the amount of an input factor (such as transportation capital) is below its optimal level, its marginal productivity is positive and probably high relative to that of other input factors. Therefore, a well-specified empirical model of, say, the effect of transportation infrastructure development on the economy, is likely to reveal a positive and most likely high rate of economic growth from the investment. The theoretical question then is, are the benefits from public capital accumulation primarily the result of public capital scarcity or does public capital have genuine economic growth impacts on the economy? The problem of optimal public capital stock is further acerbated when considering the fact that the efficient management of capital infrastructure can have a profound effect on the actual amount of public capital services available in the economy. For example, the use of improved traffic management technologies such as intelligent transportation systems (ITS) can effectively enhance the performance of existing transportation infrastructure facilities. Moreover, the level and quality of services available to users from public infrastructure are affected by myriad factors. The type of the infrastructure facility (e.g. rail vs. highway), its location, particular design, level of maintenance and form of governance are some examples. Therefore, the lumping together of all forms of public infrastructure facilities to compute a

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single aggregate measure of public capital stock while disregarding the particular attributes of specific types of capital is likely to generate biased estimates of the rate of economic growth from this capital. Incorrect estimates of economic growth from public infrastructure development can also occur if the measure of economic growth, e.g. the annual rate of change in per capita GNP, does not account for externalities (both positive and negative), resulting from the expansion and use of such capital. Air and noise pollution, environmental degradation and traffic accidents are examples of such externalities, which are unaccounted for in most aggregate measures of economic growth. Another important dimension that needs to be considered is the spatial arrangements of public capital infrastructure. That is, the assumption that the benefits from public capital accumulation can be captured in an aggregate production model does not comply with our knowledge of the spatial organization and spatial equilibrium of activities (Haughwout 1996). Investment in transportation infrastructure, for example, affects the relative attractiveness of regions. This, in turn, affects the economic and locational behaviour of households and firms as well as the use of technology. Some models reviewed below do regard the impact of space and spatial spillover effects. They show that once spatial impacts are accounted for, the effect of public capital on economic growth is rather small or even insignificant. The last relevant issue to be mentioned here is the method of funding used by the government for the provision of public infrastructure. The use of taxation as a means to raise public funds, in general, tends to distort resource allocation as well affect optimal rates of private consumption and investment. If a government (national or local) borrows money through capital markets it faces the possibility of ‘crowding out’ private investors. In general, the social price of public infrastructure funding, including the relevant deadweight loss, should be considered as it affects the net contribution of infrastructure expansion to economic growth (Morrison and Schwartz 1996). Alternative methods of financing public capital are not inconsequential with respect to social welfare gains and optimal use of resources. 6.3 Analytical models In this section we review two principal model type approaches used to assess the effect of infrastructure development on economic growth. These are: production function models (Section 6.3.1) and cost function models (Section 6.3.2). Subsequently, in Section 6.3.3, we examine some analytical and measurement difficulties associated with the use of these models. The fundamental question that these model types attempt to answer is: What is the elasticity of aggregate output with respect to public capital?

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6.3.1 Production function based models A prototype production function model, found in many productivity analysis studies (e.g. Aschauer 1989a; Munnell 1990a; Durkin and Wassmer 1994; Holtz-Eakin and Schwartz 1995) has the following Cobb-Douglas structure: (6.1)

Where Y is aggregate output (e.g. GDP),9 MFP is a measure of multi-factor productivity (i.e. the level of technology), and L, KP, KG are, respectively, labour (e.g. aggregate hours worked by the labour force in the private sector), private capital and non-military public capital.10 The subscript ‘t’ denotes time. Some models (Jones 1998, Chapter 6) further augment the labour component, L, by a factor h, which indicates the labour skill level of a individuals, i.e., (hL) t . Given the estimated parameters, a major objective of the analysis is to compute the elasticity of output with respect to the public infrastructure stock, e , i.e. G

Typically a log form of equation (6.1) is estimated (Munnell 1993). ln where

(6.2)

is the output elasticity with respect to public capital (i.e. ). To test for capital productivity Aschauer (1989a) used the following procedure. He has divided equation (6.2) through by KP,t, introduced a constant term, C0, a trend variable (as a proxy for MFP) and a measure of capacity utilization by the private sector at time t.11 The objective of this last term is to control for the effect of business cycle. Therefore: ln

g

(6.3)

where the term, lt, is a trend variable, CUt measures capacity utilization and the parameter g, is the elasticity of output (per unit of private capital) with respect to public capital (per unit of private capital).12 To test for the possibility of constant returns to scale Egbert and de Haan (1995) introduced an additional restriction: ln (6.4)

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In estimating (6.4) if the term (a+ß +g–1) is statistically not different than 0, we cannot reject the hypothesis of constant returns to scale.13 One major drawback of this type of analysis is that models like (6.4) do not directly link longitudinal changes in the stock of public capital with changes in private investments. That is, the model implicitly assumes that the effect of infrastructure investment on economic growth is conditional upon the level of private capital in the economy and that both investments take place during the same time period. In other words, a model such as (6.4) assumes that the effects of public and private capital on output occur simultaneously. It further assumes that these changes take place irrespective of time-related changes in other intervening variables such as the size of the labour force or technical efficiency.14 To account for these deficiencies several extensions of the basic model have been proposed. One approach is to embed the model within a macroeconomic growth model by explicitly making the relationships between public infrastructure accumulation and economic growth dependent on the growth path of key exogenous variables (Holtz-Eakin and Schwartz 1995). These authors have specified a model in which technical efficiency (transforming physical units into units of labour) and the size of the labour force grow over time at constant rates J and h, respectively. In addition, public capital stock accumulates at annual fixed proportion q (which is the propensity to invest by the government) and it depreciates at a geometric rate d. Hence, the steady state form of their model (in which variables are expressed in per employee units) is given by: ln

(6.5)

where yi, is the per employee level of output of observation i, which is the gross state product (GSP) and KP,i is the stock of private capital (of that state). The basic concept of growth implies periodical changes in output from periodical changes in inputs. Therefore, it may be desirable to define the basic model (6.2), as a first-order difference equation:15 (6.6)

Durkin and Wassmer (1994) have empirically estimated such a model with the added lag effect of public capital on output. The general structure of their lagged model is: (6.7)

Model (6.7) indicates that public capital investment at time ‘t’ affects the level of private capital and labour at subsequent time periods. If the stock of public capital at time ‘t’, KG,t, is assumed to affect output at that time period

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but to affect private capital and labour at time period ‘t+1’, a simultaneous equation model such as (6.8)–(6.9) or (6.8)–(6.10) can be used: ln

(6.8)

ln

(6.9)

ln

(6.10)

or:

An important question in economic growth theory is what is the level of investment in the economy necessary to achieve a steady-state growth path, defined as that growth rate at which the amount of capital per employee remains constant.16 Thus, we may ask what should be the level of public investment necessary to achieve a steady-state growth path? Aschauer (1991) proposed such a model whose empirical objective is to assess the effect of transportation infrastructure investment on growth of different states (in the USA). He begins by asserting that the rate of growth of the ratio of private capital to labour,

is a positive function of the capacity of the regional

transportation network,17 KG, and of a set of other factors affecting private capital accumulation Z. That is: (6.11)

Given the differences between states relative to their labour productivity rates, he introduces a ‘catch up function’ which defines how the rate of growth of labour productivity in less productive states will converge to that of the highly productive ones.18 Thus, at period t the level of labour productivity in a low productivity state is defined as: ln

(6.12)

where f represents the rate of growth of labour productivity in the high-level productivity states; is the stock of private capital at time t, qt is the catch up function of the low productivity rate state, and h is the base rate of growth. From (6.12) the annual growth rate of labour productivity is: (6.13)

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Equation (6.13) implies that the annual change in labour productivity (the catch up function) at time t is negatively related to the level of labour productivity at time t-1. It is a first order difference equation which can be solved for the average rate of labour productivity growth over the interval [0, T] where 0 and T are the base and final periods, respectively. Let,

, and

is labour productivity at the base

year.19 From (6.12), (6.14)

Jointly, equations (6.12) and (6.14) constitute a non-linear (in the parameters) dynamic model of labour productivity changes from infrastructure capital accumulation.20 (Empirical results are discussed in Sections 6.4 and 6.5 below.) One major statistical problem with this approach is that taking first order differences can amplify measurement errors. For example, the amount of public capital is largely measured with errors caused by the use of alternative definitions of public capital and inaccurate assessment of the actual conditions of the public capital stock. Taking first order differences will exacerbate such errors, as the variance of the errors will double, resulting in biased estimates.21 6.3.2 Cost function based models Against the analytical and statistical limitations of the production function approach, several studies adopted a cost function model approach. This section introduces the general structure of this model and shows its use in the assessment of returns to infrastructure investment. For cost function studies see, for example, Berndt and Hannson (1992); Lynde and Richmond (1992); Nadiri and Mamuneas (1994); Morrison and Schwartz (1996). As with the production function model the objective of this model is to investigate the effect of public capital formation on national or state’s economic growth and productivity. However, whereas the production function model produces marginal product measures (e.g. the marginal productivity of an additional unit of public capital investment) the cost function model produces shadow value parameters that indicate the cost savings from an additional public capital investment. Therefore, to explain growth the cost function model must show how the expansion of public infrastructure enables private firms to reduce their average costs by lessening the use of private inputs or by increasing their productivity. These cost savings are regarded as returns to public investment (Morrison and Schwartz 1996). Most of the cost function studies reviewed here make the implicit

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assumption that the cost function indeed represents the behaviour of private firms with respect to their demand and use of inputs, as manifested by their cost minimization behaviour.22 The general structure of the cost function model is given by (6.15): (6.15)

– is a vector of input where C is the private sectors’ variable cost function, w factor prices and, as before, KP is private capital, KG is public capital, t is a time index (representing technological change) and Y is output. In general, the input price vector includes the prices of labour (production and nonproduction labour), private capital, public capital, materials and energy. The question of how properly to define and measure the price of public capital is, however, quite a convoluted one (see shortly). Nevertheless, the inclusion of public capital allows for the evaluation of its impact on the shape of the long-run private cost function. Thus, the key element in the analysis is the computation of the shadow price of public capital or its cost elasticity, i.e. –¶ ln C/¶ ln KG.23 Several studies have attempted to provide a proper measure for the price of public capital (e.g. Morrison and Schwartz 1996). While it is possible to argue that for private firms the price of public infrastructure such as transportation is zero, it is certainly not so from a social viewpoint. A further question is how to compute the price of public infrastructure in the presence of spillover effects across metropolitan areas or even state borders. If such effects are, in fact, extensive they represent real alternative costs for firms that do not locate where they can most benefit from them.24 The computation of a measure for the price of public capital involves (at least) three main factors: (a) the rate at which a government raises funds (e.g. through borrowing); (b) the rate of depreciation of public capital; (c) the deadweight loss (excess burden) of taxation. Morrison and Schwartz (1996), for example, use government’s bond yields as the cost of government’s fund raising. They further use private sector rate of depreciation of capital as an upper bound of the rate of depreciation of public capital (assuming that the latter has, on the average, a longer life span than private capital has). Their measure of the price of public capital is: (1\l)(rG + dG), where l is investment price deflator and rG and dG are bonds’ yields and rate of depreciation respectively.25 They further augment this measure by adding an index of the deadweight loss of taxation. For the USA, they take this loss to be between $1.00 and $1.50 for each dollar raised through taxes.26 The cost function model enables the derivation of several measures of productivity change and economic growth from public infrastructure development. The total change in costs with respect to time, hC,t, which measures overall changes in productivity, can be decomposed as follows (Appelbaum and Berechman 1991):

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(6.16)

where Si is the share of input i in total costs and hC,Y is the elasticity of costs with respect to output. Note that hC,Y can be less, equal to or greater than 1, depending on whether the economy operates under conditions of scale economies and whether the effect of public capital is to reduce total variable costs. Morrison and Schwartz (1996) further decompose (6.16) explicitly to allow for the effects of technical change and for the level of private and public infrastructure capital on total variable costs.27 Two principal types of cost functions models were used in the literature. These are the translogarithmic (e.g. Nadiri and Mamuneas 1994) and generalized Leontief (e.g. Seitz 1993). Both functions belong to a family of functions known as a ‘flexible form’ type. That is, these functions place very few a priori restrictions on the attributes of the underlying production technology such as the degree of factor substitution or its homogeneity, thereby permitting substitution and complementary relationships between inputs.28 Specifying (6.15) as a flexible form function, we can further compute the effect of public capital on the demand for private capital, i.e. ¶KP /¶KG. Before we turn to discuss some of the difficulties involved in using the production and cost function models, we should point out a third approach used in the literature to estimate the relationships between public capital and economic growth—the profit function model approach. This model, which is rather closely related to the cost function model, assumes that the decision unit (e.g. the state) can choose the optimal level of output e.g., gross state product, by selecting the optimal level of input factors, given their market prices. Deno (1988) used this approach to estimate the elasticity of output with respect to transportation capital (see Section 6.4, Table 6.1). 6.3.3 Analytical and measurement problems The production and cost function based models presented above constitute the major analytical mechanism used to study the macro-level linkage between public infrastructure development and economic growth. Aside from some statistical problems (see shortly), they all share some common deficiencies relative to the theoretical requirements outlined above (Section 6.2). Therefore, before we present the myriad empirical results from the estimation of these models, it is useful briefly to examine some of these issues. One fundamental problem with these models is that they implicitly assume an efficient level of public capital stock in terms of the level of service that this stock provides. In other words, it is assumed that available public capital is rather efficient so that better management or improved maintenance could not increase the level of services and, therefore, only additional investment will spawn farther growth. Even the dynamic models, which are formulated

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in a way that ensures convergence into a steady-state growth path, implicitly assume the presence of efficient capital stock in the economy. Furthermore, the models examined above presuppose efficient and competitive markets for private inputs which, under variable cost minimization behaviour, will result in their efficient (and optimal) use. How valid are these assumptions for the analysis of public capital impact on growth? It is rather doubtful whether public agencies, which operate in political environment and face uncertain fiscal constraints, are capable of supplying and maintaining an efficient level of public infrastructure capital. As a consequence, the estimation of positive growth rates from additional investments, in fact, may constitute a faulty policy guide as better management of available infrastructure facilities may yield the same economic growth effects. A second major problem with the macroeconomic models presented above is the actual direction of the causality. That is, do increases in investment in public capital stock lead to greater output, as is assumed by these models or, alternatively, does increased aggregate output propel further public investment (Eisner 1991). The empirical evidence on this question is somewhat mixed. Some researchers argue that the causation is, in fact, reversed: rising productivity in the private sector effectuates infrastructure capital outlays (Tatom 1993; Gramlich 1994); whereas others (Aschauer 1991) argue the opposite (see further in Section 6.4). In this regard it also questionable whether production and cost function models constitute a suitable analytical basis for establishing causal relationships. The use of cost functions in general has been justified on the grounds that production functions omit input prices and a priori place restrictions on the firms’ technology and behaviour (Friedlander 1990). As shown above (6.16), cost functions can also be used to disentangle the effects of technology changes, scale economies, effect of input shares and the links between costs and outputs. Yet they leave open the question of what is the actual price of public infrastructure that private businesses face. Public infrastructure, such as the roadway system, is regarded by the private sector as a public good, which is provided to users at zero marginal costs.29 Hence, should the private sector’s cost function reflect the costs associated with public infrastructure provision? To the economy as a whole these costs are real and should be computed and accounted for (as was done by Morrison and Schwartz 1996). The theoretical question still remains, however. To what extent does the cost function model reflect the costs of public capital development even though private sector decision-makers do not normally regard these costs as costs to their firms? In general, the contribution of public capital to productivity cannot be equated with that of private capital using, as a basis, market prices that are generated from market transactions. Hence, variations in the estimated elasticities of costs (and output) to public capital expansion, in part, are due to alternative approaches to the pricing of this capital (Jorgenson 1991).

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From the empirical results presented below it can be concluded that, in many cases, the correlation between trends in output and public infrastructure investment is spurious. This can emanate from a number of factors, a key one being the probable lagged relationships between the public investment and the consequent economic growth. Investment in a particular year might lead to an increase in output three or even five years later. Statistically the problem arises from the existence of a common trend in the time series data which then leads to false correlation estimates (Jorgenson 1991). The firstorder difference equation approach, described above, presumably controls for non-stationary variables. In general, however, if the underlying common trends are not accounted for properly, what the analysis merely demonstrates is that the patterns of productivity and public investment growth are similar (Schultze 1990). In summary, when using econometric models to estimate the macroeconomic effects of public infrastructure development on cycles of economic growth and productivity changes, a number of factors may bring about inaccurate estimates. Key ones are the mis-specification of models relative to causality and time lags, unaccounted for scale effects, incorrect measurement and weighting of inputs and outputs, unaccounted for true technological changes, and the presence of externalities including spatial spillovers. In the next two sections we survey empirical results obtained from various studies on the effects of aggregate public infrastructure capital, including transportation, on productivity and economic growth. 6.4 Ef fects of transportation infrastructure development: empirical results Most empirical studies have estimated the effects of total public capital on economic growth and productivity. Only some of these have included as a separate category transportation capital. Very few studies, however, have directly examined the impacts of transportation infrastructure development on economic growth. Given the aim of this book we begin by examining in some detail two such studies. Subsequently we examine studies which have decomposed total public capital into transportation infrastructure capital and others. Aschauer (1991) has analysed the relationships between transportation infrastructure spending and economic growth and labour productivity using a production function based growth model such as in equation (6.11) to (6.14)30 Annual change in output per employee was used as the dependent variable (the left-hand side of equation 6.14). His database was composed of observations on highway and transit spending in 48 contiguous USA states, for the period 1969–86. The principal finding from this study is that the effect of total transportation expenditures on the growth rate of the ratio of private capital to labour is rather high (f=0.166, in equation 6.13), and is

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significant (R2»0.44). He interpreted this result to contemplate that a $10b increase in transportation outlay at year ‘t’, spread over the 48 states, would result in $2.05 billion increase in private capital in the same year, a quite hefty effect. Based on the estimated elasticity of output with respect to private capital (a=0.486, in equation 6.14), he calculated that this increase in private capital will further stimulate total output by $980mn during that year, and output per employee by $8. Furthermore, since his estimated parameters show a slow rate of convergence towards a steady state, the ultimate increase in output is estimated to be 28.1 times over the initial increase in output amounting to $27b ($980mn times 28.1). By further decomposing total transportation spending between transit and highway expenditures, Aschauer found that the impact of transit investment alone on the rate of growth of capital to labour ratio is 2.3 times that of total transportation spending (f=0.384). With highway expenditures only, the estimated parameter was significantly less than that of transit (f=0.231).31 It should be remembered though that expenditures on the physical infrastructure stock of highways and roads constitute the largest share of total transportation capital investment, and that a good part of it represents maintenance, repair and reconstruction costs. Hence, despite the larger effect of transit spending on productivity, from a policy perspective, the opportunities to significantly increase transit investment at the expense of highway are rather limited. Lastly, Aschauer addressed the issue of the direction of causality, mentioned above. Using state budget as an instrumental variable in this simultaneous equations growth model, he concluded that transport expenditures induce productivity growth rather than the reverse. Seitz (1993) investigated the effect on productivity changes from the expansion of the highway network in Germany (formerly FRG), using a general Leontief cost function model. The database was composed of panel data of 31 industries in the manufacturing sector and of the annual capital stock of the FRG’s road network, for the years 1970–89. The database also distinguished between capital expenditures on motorways only and on the total road network. Industry output is measured in terms of net value added. Labour inputs are in units of total number of hours worked and the cost of labour was obtained by dividing total wages paid by the number of hours worked. Private capital is measured in terms of net capital stock and the price of capital is an adjusted Jorgenson measure of users cost of capital.32 The stock of transportation infrastructure is measured in two alternative ways: as the length of the motorway network (in km) and as the monetary value of the real net capital stock of roads and bridges (in 1980 prices in billions of DM). The estimated system of equations contains the cost function and the labour and capital share equations, with industry-fixed effect using industry-specific dummy variable.33 Under the two measures of output the estimated parameters were quite similar so that the same conclusions were drawn. First, these parameters

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have indicated complementary relationships between private capital and public transport infrastructure (¶KP/¶KG >0), whereas labour and public capital are substitute inputs (¶L/¶KG <0). In other words, additional investment in transportation infrastructure is likely to raise marginal productivity of private capital. This result was also obtained from studies of the effect of aggregate public capital on private capital (e.g. Tatom 1991) discussed below. The substitution relationships between public capital and labour imply a reduction in the use of labour following additional infrastructure investment. What is the marginal benefit from the expansion of the transportation capital stock? To answer this question, Seitz computed the shadow price of transportation infrastructure investment, Ei,G, (Ei,G=¶Ci/¶KG). It is defined as the change in average cost for each of industry i in the sample caused by a 1 km increase in the size of the motorway or by DM1bn expansion of the net capital stock of roads and bridges. For all industries the average decline in average costs, when the motorway is expanded by 1 km, is about DM32 (for every DM1mn increase in output). For the net capital stock output measure average costs declined by about DM500 (which is about 5 per cent of average private costs). However, these average cost declines vary considerably by industry type. Estimates for some industries were statistically insignificant (19 out of 31 in the case, where net capital stock was used as the output measure).34 The results from these two studies are not directly comparable due to differences in the model approaches and databases used. However, both conclude that investment in transportation infrastructure stock enhances private capital profitability, thereby stimulating private capital investment and hence economic growth. Moreover, on the average investment in transportation infrastructure stock produces a high rate of return for the economy with considerable marginal net benefits from an additional investment. On the other hand, both studies report substitution relationships between labour and transportation infrastructure investment implying that further investment may reduce employment. This conclusion should be taken cautiously, however, since the estimated results were derived from partial equilibrium models. That is, if additional infrastructure investment leads to private capital growth (due to the complementary relationships) and the latter effect induces output expansion, more labour may ultimately be needed, despite the substitution between public capital and labour, depending on the relative magnitude of these effects. What can be learnt about these issues from studies that regarded transportation capital as part of total public capital? To answer this question we next turn to studies which have used total public capital stock categorized into transportation capital and other types of public capital. They then discerned the effect of transportation infrastructure on employment and output. Garcia-Milà and McGuire (1992) have estimated a production function

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model with public capital input composed of highway capital and human capital (measured as education expenditures). A third input is private capital and output is measured as gross state product (GSP). The estimated elasticity of GSP with respect to highway capital is 0.04, whereas that for human capital is 0.15 (for private structures and private capital equipment the elasticities were 0.10 and 0.37, respectively). A similar result regarding GSP elasticity with respect to transportation infrastructure (highway stock) was obtained by Munnell (1990b). Using data from 48 USA states for the years 1970–86, she found GSP elasticity with respect to highways to be 0.06, whereas for water and sewer infrastructure to be 0.12. For the category labelled ‘other public stock’ the elasticity was 0.01. (For labour and for private capital the corresponding output elasticities were 0.55 and 0.31 respectively). Eisner (1991) further examined Munnell’s results and the data from which they were derived. He found that when the database is arranged as a crosssection, public capital indeed has a small but significant effect on states’ growth. However, when the data are arranged to reflect longitudinal variations, the effect of public capital is insignificant. He also found that states with high public capital per capita also have high output per capita, and that investment in public capital in a given year does not increase output that year. What seems to be in doubt is the causality relationships between public infrastructure and growth, or the specification of the empirical model (e.g. the use of lagged variables), or both. McGuire (1992), who studied the sensitivity and robustness of the results obtained by Munnell (1990b) and Garcia-Milà and McGuire (1992), obtained somewhat different results, mainly in order of magnitude.35 To that end, he used the same data sets as these researchers but categorized public capital into highways, water and sewers and others. Using a Cobb-Douglas production function, he estimated output elasticity with respect to highway capital to be in the range of 0.121 to 0.370. When controlling for state effects 36 he estimated the elasticity of output (measured as GSP) with respect to highway infrastructure capital to be within the range of 0.121 to 0.127 (and the elasticity of output with respect to water and sewer capital to be in the range of 0.043 to 0.064). A major problem with production function models that use state-based data (GSP) is that they fail to consider inter-state factor mobility, mainly labour and capital. That is, the prime effect of transportation infrastructure investment is to change the relative accessibility and attractiveness of specific regions. This effect, in turn, results in the relocation of firms and labour (and households) across jurisdictions. Failure to account for these spatial changes is bound to produce biased elasticity estimates (see also below the study by Moreno et al. 1997). Some empirical studies found that when using a lagged model (like equation 6.7) the effect of public infrastructure on growth was statistically significant

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(Bajo-Rubio and Sosvilla-Rivero 1993; Durkin and Wassmer 1994). Kelejian and Robinson (1997) have estimated a production function model in which private output at time ‘t’ is a function of labour at ‘t’ and private and public capital stock at ‘t-1’, where public capital is composed of highway capital stock, water and sewer, and others. To account for spillover effects they have also included in their model output to labour ratio in neighbouring states. Based on their estimates they conclude that previous inferences of positive marginal productivity of labour with respect to infrastructure are not supported. Keeler and Ying (1988) have used a cost function approach to assess the impact of highway investment on the cost and productivity of private trucking. Their study examined the use of Class 1 regional trucking firms since 1950 in the USA. Their results indicate that the expansion of highways between 1950 and 1973 had a significant impact on the productivity growth of the private trucking sector. Deno (1988) has used a profit function model in which profits of the private sector are specified as a function of the prices and quantities of private capital of labour and of the stock of public capital. The latter is disaggregated into highway capital, and sewer and water capital. Using a state level database, he estimated output elasticity with respect to highway capital to be 0.31 (with respect to sewer capital to be 0.30 and with respect to water capital to be 0.07). Haughwout (1996) has studied the effect of the highway stock on state’s economy within the framework of a spatial equilibrium model. To that end he estimated a two stage least square model in which the first equation explains output (in GSP units) as a function of the state’s land area, private capital, labour and population density. In the second equation population density is defined as a function of the public highway stock, of other public capital and state’s debt. Using data on 48 contiguous states for the years 1977–92, he concluded that highway investment will induce higher population density which, in turn, will lead to higher output but the latter effect is rather small. Table 6.1 summarizes the main results from these studies. As Table 6.1 shows, the range of output elasticity with respect to transportation capital is quite large. Apparently, the specific model used, the estimation method as well as the nature of the database all affect the estimated results. While there are still major doubts regarding the actual magnitude of the elasticity of output with respect to changes in transportation capital, the above results suggest the following. First, transportation infrastructure capital seems to have a small but significant impact on economic growth in terms of output elasticity. However, other results (not shown here) also indicate that other public inputs, such as education, have a substantially higher impact on growth than transportation capital. The second major implication is that the nature of the causality between transportation infrastructure development with economic growth is rather

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Table 6.1 Selected results from studies of the impact of transportation infrastructure investment on economic growth

equivocal with respect to direction, functional relationships and effect of intervening variables. While the above results seem to suggest that transportation capital expansion (as well as the expansion of other public capital inputs) propels economic growth and enhances productivity, it is far from clear what conditions must be met for this result to transpire. For example, high rate of population growth coupled with increasing population densities that, in turn, create high demand for transportation services, might be a prerequisite for transportation infrastructure investment to have a significant effect on growth. Furthermore, economic growth tends to lag behind transportation investment, as the capitalization of the investment’s effects is time dependent. It may also depend on the behaviour of the labour and private capital markets. That is, for transportation capital expansion to have a consequential impact on economic growth it should affect labour and

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private capital inputs, relative to their price, their actual use and productivity. Similarly, the impact of transportation investment on technological change should also be expected as, for example, is the case for the development and use of telecommunications technology. 6.5 Ef fects of aggregate public infrastructure development We have noted above that the majority of studies have estimated the effect of changes in total public capital stock on state or national economic growth and productivity changes. Since the main focus of this chapter is the effect of expanding the transportation infrastructure stock on growth and productivity, in this section we briefly summarize major results from studies that have used aggregate public capital and subsequently use these results for comparative purposes. We categorize these results into productivity gains (Section 6.5.1) and economic growth results (Section 6.5.2). 6.5.1 Productivity gains Munnell (1990a, 1993) has estimated a log-linear production function (such as equation 6.2), using USA data, which covered non-military federal, state and local public capital for selected years (1948–87). She reported the elasticity of labour productivity with respect to public capital to range from 0.31 to 0.39 (i.e. a 10 per cent increase in public capital would raise labour productivity by 3.1 to 3.9 per cent). From additional calculations of multi factor productivity (MFP), Munnell concluded that much of the increase in MFP during the early part of the sampled period was, in fact, due to the build-up of public capital vis-à-vis its effect on output. Holtz-Eakin and Schwartz (1994) have computed the effect on productivity gains from raising the propensity to invest in public capital in each state by 10 per cent of new spending in 1986 (about $10bn in 1982 prices). Using the maximum level of the underlying structural parameters (see equation 6.5), the results show only modest effects. Across the sampled 48 USA states the average increase in productivity was about 1.02 per cent for the entire sampled period (1970–86), implying an insignificant annual increase in the productivity rate of each state’s economy. Morrison and Schwartz (1996) have used a cost function model to estimate the direct effect of public capital on productivity changes. They used the state’s GSP as their database. The range of impacts was 0.192 per cent for northern states and 0.622 per cent for southern states. When including negative productivity growth effects, the total impact of infrastructure investment on productivity growth was (mean values): 0.245 per cent (east), 0.153 per cent (north), 0.622 per cent (south) and -0.971 per cent (west). They concluded that the direct cost-savings impact of public capital investment is generally

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significant but it is rather small. The introduction of the social price of public infrastructure further lowered these estimates. Nadiri and Mamuneas (1994) have used a translogarithmic cost function model with three private inputs: labour, intermediate and private capital and two public inputs, infrastructure and R&D capital. They have applied this model to USA data describing manufacturing industries at the two digits Standard Industrial Classification (SIC) level. Major results from this study indicate that the elasticity of total costs with respect to public capital expansion ranges from -0.11 to -0.22, which is lower than previous studies have determined. With respect to the demand for private inputs, they found that an increase in infrastructure capital leads to a decline in the demand for labour and private capital in each industry and to an increase in the demand for intermediate inputs. These results by Nadiri and Mamuneas (1991, 1994) essentially indicate a non-constant degree of substitution between public infrastructure and private inputs. Further analysis shows that the level and changes in labour productivity during the sampled period were mildly affected by the public sector’s provision of capital. That is, changes in the demand for labour are the result of two major effects: a downward shift in the cost function of the analysed industries induced by investment in public capital stock and the substitution of private inputs (i.e. labour) with public ones. Overall, the results from these studies indicate that additional public capital investment has a positive impact on productivity, mainly labour, but that this effect is rather small and is affected by a number of intervening factors. In particular, this is the case when we consider the ‘output’ and ‘substitution’ effects of public capital increase. That is, if public capital is indeed a substitute input factor for labour, as the results from several studies show, additional infrastructure investment may have a counter effect on job creation and use. Since the output effect tends to foster greater aggregate demand for labour, while the substitution effect operates in the opposite direction, the overall impact on labour of further investment in public infrastructure is a priori not certain. The empirical results presented above seem to suggest that, on average, the output effect surpasses the substitution effect. What the above results seem to propose is that, following public capital development, private output grew proportionally faster than labour, hence the increase in labour productivity. It should be emphasized, however, that these results are from a macro-level analysis and may not hold for an individual investment or for a particular region or industry. For this reason it is rather important to assess the contribution to employment and labour productivity of specific infrastructure projects in specific geographical areas. 6.5.2 Economic growth Aschauer (1989a, b) has attempted to estimate what would have been the impact on growth of an additional investment of $60bn in public capital in 1985 (a 6

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per cent increase in the infrastructure stock). Assuming elasticity estimate of 0.24 for the change in productivity from public capital investment, he concluded that aggregate private output would have increased by 1.4 per cent, a gain of $70bn in the first year. With conservative assumptions, he further estimated total gains to be $600bn, which translate to benefit cost ratio of 10:1. Munnell (1990a) has re-estimated the original aggregate time-series parameters of Aschauer and came to similar conclusions. Yet, Munnell concluded that these impacts were much too large to be fully credible. Her studies (1990a, 1993) found that a 1 per cent increase in the stock of public capital would raise private output by 0.34 per cent, still a quite large estimate. In another study she found that while public capital has a positive impact on several measures of state-level economic activity (such as GSP, investment and employment growth), the order of magnitude was much less than that reported by Aschauer (see Munnell 1990b). For example, the elasticity of output with respect to public capital was estimated to be 0.15. This lower figure is consistent with those found by other researchers in the USA. Table 6.2 summarizes principal results from studies that estimated the effect of total public capital stock on growth. The figures shown in this table again highlight how diversified are the empirical results, depending on model specification, the database and measures of output. Table 6.2 Some estimates of the effect of total public capital expansiona (US data)

Source: Based, in part, US DOT (1992). Notes: a All the national and state level analyses used Cobb-Douglas production function models except where noted; b GSP=gross state product; c This is the direct impact on productivity growth rate. Mean total impact for the western region of the US is: -0.971.

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6.5.3 Results from non-USA studies The above results pertain mainly to USA data and experience. It is, therefore, illuminating to examine the degree to which results from other countries support the USA findings. Berechman (1995) has applied a simultaneous equation model (such as in 6.8 and 6.9) to Israeli macroeconomic data for the years 1964–89. His results show that a 10 per cent increase in public capital in year t will increase output at this year by 3.5 per cent and will increase private capital by 5 per cent at t+1. Estimation of the second set of equations (6.8 and 6.10) shows a much smaller effect of public capital on output. Specifically, a 10 per cent increase in public capital at year t will increase labour at year t+1 by 0.33 per cent and total output by 0.73 per cent.37 In general, these results imply that investment in public capital affects total output both directly (by increasing total factor productivity) and indirectly vis-à-vis its effect on partial productivity of labour and private capital. Ford and Poret (1991) have used a database composed of observations from 11 OECD countries. Their results vary greatly by country and results that are compatible with USA findings were obtained only for Germany, Canada, Belgium and Sweden. Easterly and Rebelo (1993), in their study of 100 developing countries (1970–88), found that transportation and communication investments are positively correlated with growth (in the range 0.59 to 0.66) and uncorrelated with levels of private investment. Their conclusion was that there were supernormal returns on public investment in infrastructure and that ‘it raises growth by increasing the social return to private investment but not by raising private investment itself. Berndt and Hansson (1992) used a cost function approach to test the links between public infrastructure capital and private sector’s costs in Sweden (1960–88). Their main conclusion was that public capital expansion improves private sector productivity by reducing its costs. However, they have also concluded that total public capital stock exceeds that which can be justified on the basis of reducing private sector costs alone, and that this excess is being lessened over time as less public infrastructure investment is now taking place. Aschauer’s (1989c) study of the Group of 7 Countries (G7) suggested a positive statistical link between the ratio of public capital investment to GDP and the growth in output per worker. Moreno et al. (1997) have studied the effect of public capital development with transportation infrastructure included on the growth of Spanish regions (1964–91). In their study they used a production function model with public capital which was divided between the monetary value of stock of roads, highways, railways, harbours, airports, water and sewage facilities and urban structures, and between what they labelled the ‘social stock’ of health and education. They found a slight impact of public capital on industrial productivity in Spanish regions. However, when a spatial dimension was introduced into their model to account for inter-regional spillover effects they

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found that public capital development has no direct effect on regional growth. Table 6.3 compares elasticity estimates from various countries. International comparison difficulties notwithstanding, the elasticity estimates reported in Table 6.3 suggest that, in general, output elasticities for non-USA countries and regions are far smaller than those within the USA. The explanation for this phenomenon is not quite clear, particularly for developing countries. In those countries the stock of public capital per capita is much lower than in the USA and, therefore, one could expect that the impact of public sector infrastructure investment on output would be larger. Table 6.3 Estimates of public capital growth effects worldwide

Source: Based on World Bank (1994: Box 1.1).

6.6 Conclusions No doubt the question as to what degree does public capital, in particular transportation infrastructure, affect economic growth and factor productivity is rather complex and subject to a myriad of intervening factors. This chapter has concentrated on the recent flood of econometric studies carried out in the USA and elsewhere attempting empirically to assess these relationships. If one needs to draw a single conclusion from this literature it is that public capital development has some positive impacts on economic growth and private factors productivity, but the magnitude and significance of the estimated effects are far from being conclusive. Furthermore, when considering the models and the databases used, it is quite difficult unequivocally to determine how robust these estimates are. As generally is the case, complex economic phenomena lie somewhere between orderly patterns of economic

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development and random processes, between predictable correlation of trends and between unrelated outcomes. The widespread use of production function models, as in the original seminal research by Aschauer, has been criticized analytically and econometrically, as well as in terms of the scale of the expected impacts. One major problem cited with Aschauer’s modelling approach is that when using longitudinal data on output, private and public input factors and on proxy variables representing technological changes, there is likely to be an underlying common non-stationary trend. Unless properly treated, the statistical estimates of the elasticities of aggregate output with respect to public capital are implausible and probably wrong. In fact, it has been shown that the interval estimated for the elasticity of output with respect to public capital contains both negative and positive values so that further investment in public infrastructure stock may adversely affect economic growth. A related attack against Aschauer’s type of analysis is that it merely demonstrates that the patterns of productivity and public investment growth are similar and that this is what the correlation shows. In other words, this model does not demonstrate causality, rather, it presupposes it. There were rising patterns of productivity and public investment in the USA in the 1950s and 1960s and falling patterns in the 1970s and 1980s. Consequently, the correlation is likely to generate grossly inflated estimates of the returns from public infrastructure investment (Schultze 1990). In the words of Krugman, Aschauer’s findings are ‘more a matter of correlation than causation’ (Krugman 1994: Chapter 4). The treatment of transportation capital in the models reviewed in this chapter is a good example of the causality problem. By and large, transportation infrastructure is an unpriced input factor into private production processes. As a result, do private enterprises at all regard the availability of this factor in their decisions concerning optimal level of output and use of private input factors? If they do not, generic production function models, such as equation 6.1, may be inappropriate.38 If they do, to what degree? Only few macroeconomic studies have attempted to deal with this question by assessing the shadow price of transportation capital and including it in their model (e.g. Morrison and Schwartz 1996). But even these attempts largely disregard the true social costs of using transportation infrastructure. The latter costs are the product of congestion costs, the costs of infrastructure deterioration (e.g. highway pavement, bridges or tunnels, structural decay, caused by different vehicles, mainly trucks, Small et al. 1989) and the costs from other non-market externalities such as air and noise pollution. Given that the empirical models examined above did not account for these issues, it is plausible to contend that their elasticity estimates are rather biased. It is hard to discern the direction of the estimation bias, but it does cast a doubt on the use of these estimates for policy design. Furthermore, the models reviewed in this chapter failed to account for the changing economic

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environment examined in Part II. In particular, the kinds of measures of efficiency and productivity they used may be inadequate when considering the increasing size and role of the service sector in the economy. The above criticism notwithstanding, it has been suggested that the main contribution of Aschauer’s type of studies has been to draw attention to the importance of public infrastructure in promoting economic growth and private capital productivity (see Aschauer 1993a; Munnell 1993). Moreover, the analysis also indicates that with respect to the growth effect of output from public capital expansion, what matters is not the size of the investment in public capital stock but rather the annual per cent increase of this stock. That is, a large size investment in public infrastructure is bound to have an insignificant impact on economic growth if it constitutes a negligible addition to the in-place public infrastructure stock. For example, a massive investment in a new transportation link may yield insignificant growth effects if this link constitutes but a small portion of a well-developed network On a more general level, the main motivation for undertaking the modelling efforts explored above is to show the need for further investment in public infrastructure. This, however, may be superfluous since it is plausible to regard the present level of public infrastructure in the economy, including transportation, as suboptimal. Following Jorgensen (1991) and Gramlich (1994), the main reasons cited are: 1

2

3

Engineering Needs Assessment (ENA) determines the mismatch between what is available in terms of the infrastructure stock (including both quantity and quality) and what should be available, given some normatively set standards. However, ENA does not make a strong case for an overall shortfall in local and national infrastructure capital, except where there is substantial congestion. No attempt is made to link the ENA with economic growth. Contemporary political processes and institutions make new investments in public capital rather difficult to implement. Peterson (1991) suggests that infrastructure may be under-supplied relative to people’s preferences, but here again nothing was made of the potential economic benefits. Studies of public investments’ rate of return involve a benefit-cost analysis to establish whether the rate of return on a particular project exceeds market rates or treasury set thresholds. Rarely these methods involve estimates of non-market benefits, in particular, the project’s contribution to national or regional growth.

Two contrasting general conclusions can be drawn from the discussion in this chapter. First, commonly used methods at the macro level to assess the effect of public capital expansion on economic growth suffer from significant analytical and measurement shortcomings. This would suggest that a micro level analysis is more appropriate for the task at hand as this is also the scale

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at which most forms of benefit cost analysis are carried out. Second, despite the criticisms developed in this chapter, there does seem to be a positive statistical link between public capital investment and output growth. If nonmarket benefits are also added, the case for public investment may be quite strong. Notes 1 We regard the infrastructure of the economy as the physical stock of public facilities, such as roads and highways and water and sewer systems, and not as the rules, regulations and institutions that run the economy (see Jones 1998: Chapter 7, who regard the latter as national infrastructure). 2 According to (US, DOT 1992) total public infrastructure stock includes: highways, streets, educational and hospital buildings, sewer and water facilities, conservation and development facilities, gas, electric and transit facilities and other miscellaneous but non-military structures and equipment. 3 In part, this brief discussion of government’s provision of transport infrastructure facilities is based on Berechman (1994). 4 There is also the argument of equity, which essentially implies that spatial mobility, provided by transport infrastructure facilities, is a merit good that should be available at a reasonable level and price to all citizens, irrespective of their ability to pay for it. In particular, people residing in rural areas, where population is sparse, cannot pay for infrastructure facilities needed to make them accessible to activity centres. Hence the need for government’s involvement in the provision of transport infrastructure. See Chapter 3 for a further discussion. 5 Fujita (1988) has pointed out that if individuals’ utility function is Cobb-Douglas in traded goods, housing and local consumption goods, and assuming constant elasticity of substitution in the utility function between the consumption goods, then utility will increase with the number and type of the locally produced consumption goods. 6 If the aggregate production function is a Cobb-Douglas in private inputs (e.g. labour, capital and land) and has a constant elasticity of substitution in some specialized input factors (e.g. shared public capital), aggregate output will increase with the amount and with the number of types of input factors. 7 In recent years there is a growing literature on general purpose technologies (GPT) which are technologies that spawn economic growth by enabling the development of new opportunities relative to improved productivity and efficiency. The introduction of the electric motor, which took advantage of electric power, is a noticeable example of GPT (see, for example, Bresnahan and Trajtenberg 1995). Public capital, like transportation facilities, falls within the category of infrastructure goods, which enter as an input factor most consumption and production activities, and as such cannot be regarded as GPT. The main reasons being that the growth effect from such facilities is less continuous and less dynamic and does not necessarily generate compatible innovations as GPT does. 8 We do not claim here that infrastructure should be priced to users at below its real social costs. Our contention is that since infrastructure is regarded as a public good, its supply by the government will ensure its ubiquitous availability at a price which is below the price that the free market would have set. 9 Some authors (e.g., Durkin and Wassmer, 1994), define Y to be private output only. Others (e.g., Aschauer 1989a) use total output which includes private and public outputs. 10 In this model output is assumed to be affected by the amount of public capital.

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But public infrastructure facilities such as city streets and roads are in fact public goods (at least to the extent that their usage does not exceed their capacity), available at zero costs to users. It is therefore legitimate to ask whether such formulation is appropriate. Holtz-Eakin and Schwartz (1995) have divided equation (6.2) by labour quantity to yield the production function per effective labour unit. Aschauer (1989a) estimated g to equal 0.39, i.e. a 10 percent increase in the ratio of public capital to private capital would raise productivity (output per unit of private capital) by 3.9 percent—quite a substantial effect. Several authors (e.g. Young 1992; Holtz-Eakin 1994) have concluded constant returns to scale, whereas others (e.g. Moreno et al. 1997) have estimated slight economies of scale for total public capital. The model also assumes constant elasticity of substitution between public and private capital and between public capital and labour. As explained shortly, the use of a first order difference equation model is also for the purpose of controlling for non-stationary variables. The most well known early steady-state growth model is that by Solow (1956). Aschauer measures the capacity of the highway network in units of “miles paved per square mile of land area”—a somewhat unconventional measure of network capacity. The concept of a “catch up function” implies that labour productivity rates in different countries or regions tend to converge, over time, to the same rate. The reason being that with free trade, factor mobility and technology transfer, the productivity rates of regions with initial low levels of productivity tend to catch up with those of the high level productivity states. The value of ß must be between 0 and 2, for a stable steady-state solution. In the empirical estimation Aschauer uses Zellner’s SURE procedure to estimate the model’s parameters. Tatom (1991) and McGuire (1992) have used the annual growth rate instead of the actual value of a particular variable, in order to avoid error problems associated with the use of a first-order difference equation model. One important analytical implication of this assumption is that the cost function approach allows the derivation of input demand functions with endogenous dependent variables. It shows the percent change in costs from a 1 percent change in public capital. Under proper conditions it can be shown to be the dual of the output elasticity measure from the production function model, ¶ ln Y/¶ ln KG. Several studies (e.g. Holtz-Eakin and Schwartz 1994) of spillover effects of state infrastructure, mainly highways, did not find such effects to be either sizeable or statistically significant. See Munnell (1990a) for a definition of price deflator of public capital. This is the marginal cost of tax dollars. For example, assuming the cost to be $1.50 for every $1 of public spending, it includes the cost of $1.0 of tax revenue and additional $0.50 of efficiency loss to the economy. They point out that if hC,Y=1, equation (6.16) is equivalent to the common productivity index hY,t, where .

28 For econometric analysis and exposition see, for example, McFadden (1978), Johnston (1984) and Berndt (1991).

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29 We disregard highway congestion pricing which, at present, is quite uncommon. 30 Equations (6.12) and (6.14) were estimated jointly using the SURE technique. 31 One would expect that the parameter q for the total transportation outlay would fall between that of transit and that of highway. While Aschauer does not explain this apparent inconsistency, one plausible explanation is that the two longitudinal sets of expenditure data are negatively correlated. 32 The formula is:

33

34

35 36 37 38

where rt is the cost of capital for industry i, b is the interest rate on a 10-year government bond, li, is the rate of depreciation and PIi, is price index of investment capital (Jorgenson 1963). The estimation method used imposes the constraint that the parameters of the three equations are identical across all industries in the sample, except for the industry’s specific constant (see Hsiao 1986). Consequently, industry specific fixed effects can be tested. Berechman and Paaswell (1996) have studied the effect of transportation infrastructure investment on the labour force participation behaviour in lowincome regions. They found that accessibility improvements from transportation investments affected the rate of labour participation in some employment sectors but not in others. See further in Chapter 8. Based on 1992 review by the Federal Highway Administration (US, DOT, FHA). McGuire controlled for state fixed effects by including state dummy variables and for state random effects by letting one component of the error term to vary systematically by state. All estimated parameters were significantly different than zero at the 5% level. The Durbin-Watson statistic showed a positive serial correlation. In Chapter 8 we deal with the impact of transportation on firms’ location decisions.

Chapter 7

Economic evaluation of transportation projects

7.1 Introduction In Chapter 6 we examined models aimed at capturing the impacts of changes in the aggregate capital stock on national and regional growth. We have shown that at the present level of knowledge it is impossible to determine unequivocally that expansion of the existing public capital stock (including transportation infrastructure) will encourage economic growth. Even if we could conclude with a reasonable degree of certainty, that further investment in the total capital stock promotes growth, it is necessary to recognize that such investments are carried out incrementally, as individual projects are planned, approved and executed one at a time. Underlying this process of project implementation is a project evaluation process whose main objective is to determine the economic and social contribution of a particular project to societal welfare, including economic growth. In this chapter we adopt the general definition of economic growth from a transportation improvement as ‘the continuous increase in economic activity, in the impacted area, that can be attributed to this investment’. As we shall argue later, if we consider the network aspect of transportation improvements, this definition might be dubious relative to the delineation of the area of incidence of the transportation effects. The main objectives of this chapter are: First, to provide a theoretical explanation for the potential linkage between the prime benefits from a transportation investment and local economic growth. Second, to examine in some detail key elements of the evaluation process that affect the measurement of benefits from a transportation infrastructure project. Finally, to investigate the extent of the potential economic growth effects. The dominant approach used in project evaluation is benefit-cost analysis (BCA).1 The essence of this approach is a systematic quantification and comparison of the various benefits and costs generated by a project. To that end, the various effects from the project are first enumerated and classified as benefits or costs. Subsequently, an attempt is made to quantify each effect and express it in monetary terms using appropriate conversion factors. Since

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the benefits and costs effects accrue at different future points in time, it is necessary to discount them in order to establish commensurability. The derived present values of the streams of future benefits and costs provide the criterion for the evaluation decision. Even this succinct description of the BCA approach raises many fundamental questions regarding the actual identification, quantitative assessment and aggregation (into a single value) of the multidimensional effects from a given transportation project. For example, infrastructure projects generate benefits and costs whose distribution is non-uniform relative to different populations. As a result, we face the problem of how to make judgements between the increase in the welfare of those who stand to gain from the project and the decline in welfare of those who stand to lose from it. The BCA literature on the single benefit-cost index and similar issues is voluminous and it is beyond our scope here to review it in any degree of detail.2 Our overall objective is to examine the potential effect of transportation infrastructure investment on economic growth. In the following discussion, we will focus primarily on those aspects of BCA that are relevant to this objective. Within the framework of BCA of transportation investment projects, we need to determine which benefit effects should be included in the analysis. A major argument is that only direct travel costs savings, namely, travel times and monetary operating costs, should be regarded as benefits in the evaluation of transportation projects (Mohring and Harwitz 1962; Mohring 1993). As a result, the inclusion of other effects like economic growth as additional benefits amounts to the double counting of benefits, since economic growth effects are in essence a manifestation of the capitalized travel costs savings. Only under very special conditions can such effects be considered as additional benefits. The production function analysis (Chapter 6) implicitly presupposes this to be the case. In response to this argument, it is possible to claim that infrastructure investment is associated with myriad of scale effects and externalities which produce more than just travel costs savings. By measuring economic growth effects we do not necessarily double count investment benefits. Examples of scale effects and externalities are network economies, land assembly economies, congestion reduction effects and pollution generation or abatement. Furthermore, as Mohring (1993) has pointed out, in cases where the economy cannot be regarded as a closed one (e.g. regional economies), the import-export effects of transportation costs reduction is not directly caused by the capitalization of the primary transportation benefits. Therefore, this growth should be counted as part of the project’s stream of benefits. As we have seen in Chapter 6, a major objective of the macro level production and cost functions literature is to determine the marginal productivity of the infrastructure stock in terms of economic growth and improved productivity. What can this information tell us about a new investment project? If we can assume that the political-economic system will

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continue to allocate infrastructure projects as in the past, the information on the marginal productivity of capital can be used as an additional criterion to further screen projects after each has been subjected to a benefit-cost analysis. For example, in Section 6.4 we reported that McGuire (1992) estimated output elasticity with respect to highway capital to be in the range of 0.121 to 0.370. Assuming this estimate to be credible, we can demand that each transportation project with a positive net present value (NPV) will also yield at least that productivity level.3 Another relevant aspect of project appraisal is the nature of growth. It can be argued that an increase in labour productivity (from infrastructure development) can lead to employment decline as less workers would be needed to produce a given level of output. If economic growth is measured in terms of employment then productivity gains may lessen growth. Following this reasoning, and assuming that transportation projects indeed can raise labour productivity in some industries in some locations, we might ask whether further investment in transportation infrastructure will lead to job losses in these industries and to a growth decline throughout the economy. Krugman (1997) assails this kind of reasoning and labels it ‘fallacy of composition’. Briefly stated, productivity gains in one industry lower output prices from that industry. As a result, consumers’ disposable incomes rise and more money is spent on buying other products, thereby creating jobs elsewhere. Hence what we need to observe are changes in employment in the entire economy and not only in the industries where productivity gains were made. The same logic should also apply to cases where infrastructure investment in a given region improves productivity and lowers regional employment. In reality, there are many instances when the decision to implement a transportation infrastructure project in a given area is predicated on the purported ability of this project to raise the level of employment in this specific area (see Section 8.5 for a case study). Mainly for political and funding reasons, employment increases in adjacent areas are regarded by the political decisionmakers as incidental. Consequently, it is important to look into the nature of the expected benefits from an infrastructure improvement. In this chapter we examine two major issues, namely travel time savings and activity relocation. Travel time savings from an infrastructure project can produce multiple effects. However, the measurement of these effects in addition to travel time savings is likely to amount to the double-counting of benefits. Only when we can show value added type effects, which are over and above the ordinary capitalization of travel time saving benefits can we argue that growth benefits may have occurred. Thus, one major objective of this chapter is to explore the conditions under which such growth effects from an infrastructure investment project can legitimately be added to the travel time savings effects without performing double-counting of benefits (Section 7.2). A second major objective of this chapter is to examine key factors that affect the measured benefits from a transportation infrastructure project and

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which, therefore, affect the measurement of potential economic growth effects. Sections 7.3 and 7.4 critically examine these factors and their treatment in a BCA framework. In Section 7.5 we briefly survey other evaluation methods of transportation projects.4 7.2 Fundamentals of transportation benefit-cost analysis The most fundamental effect of transportation investment is to improve travel conditions, which, in turn, alter individuals’ behaviour such as mode choice, route choice, time of travel choice and destination choice. The aggregate results of these changes in individuals’ behaviour are manifested, at the network level, in traffic volumes and patterns, in travel times and costs by facility type and in the relative accessibility of locations. These impacts further affect location decisions of households and firms, thus land rent and urban form. They also affect consumers’ behaviour (e.g. the allocation of time between work and leisure), and firms’ production and business decisions. In addition, the investment in infrastructure induces other non-direct effects of which the main ones are the investment multiplier and various forms of externalities (e.g. environmental degradation). These relationships between infrastructure investment and the direct and indirect effects are traditionally depicted as in Figure 7.1. We claim that this view is rather incorrect and present an alternative approach (Section 7.2.2). The various effects portrayed in Figure 7.1 raise

Figure 7.1 Traditional view of the effects of transportation infrastructure investment.

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two questions relative to the correct evaluation of the overall benefits and costs from a project. First, what should be regarded as the overall objective of the project and how should we define the evaluation criteria? Second, should the benefits be regarded (and measured) only as changes in network’s travel time and accessibility, or should changes in land rent, firms’ costs and effects from various externalities also be included? The answer to the first question is rather straightforward in economic theory. In normative terms, the objective of any public project is the maximization of social welfare, given a host of financial and legal constraints and taking into account income redistribution effects. There is a rich literature, mainly in the field of public finance, on the proper interpretation of welfare improvements and on the conditions which are necessary for their attainment (see, for example, Musgrave and Musgrave 1989). The answer to the second question depends primarily on the presence (or absence) of two basic conditions (see Section 7.2.1 and Chapter 8). First, can we identify market imperfections leading to effects such as network and agglomeration economies? Second, to what degree are markets competitive enough to distinguish between price changes from increased consumption or from pure transfers between individuals or groups? Given these conditions, how should benefits from a transportation project be measured? 7.2.1 The measurement of benef its from a transpor tation investment project The most commonly used measure to assess the welfare gains from a public investment such as in transportation infrastructure, is the change in consumer surplus (see, for example, Boardman et al. 1996: Chapter 3). Analytically, it is the area under the aggregate private demand curve bounded by the prices of the transportation service before and after the project was implemented. Underlying this measure is the concept of willingness to pay for travel improvements. It indicates the monetary sum that an informed consumer is ready to pay for a particular improvement produced by a transportation project. Under reasonable conditions, changes in consumer surplus from an improvement correctly measure the willingness to pay for this improvement (Bradford and Hildebrandt 1977). Since a transportation project may also generate negative impacts such as environmental nuisances, the willingness to pay principle can also be applied to indicate the amount of money consumers are willing to pay to avoid such harmful effects. These values can then be regarded as part of the project’s costs and be added, after proper quantification, to its capital and operating costs. Before we proceed with our main subject of assessing the growth benefits from a transportation project, the reader should be aware of the fact that the rationale and use of consumer surplus is quite controversial in the economic

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literature. There are a host of difficult questions concerning the aggregation of the demand functions of individuals with diverse preferences and incomes in order to produce an aggregate demand function from which changes in consumer surplus can be calculated. The key issue is that any aggregation procedure requires the performance of interpersonal comparisons relative to preferences and marginal utility of income. This raises a further question regarding the plausibility of using consumer surplus to assess welfare gains from price changes.5 Here, we are making use of changes in the price and volume of travel before and after the implementation of a transportation investment project to assess its primary benefits. This approach reflects the rationale of using consumer surplus as a measure of welfare gains, where travel times and traffic volumes serve as the key variables that define the travel demand function. Technically, the computations are as follows. By and large a transportation infrastructure project implies capacity expansion of one or several links of the existing network. Given the origin-destination (O-D) matrix, and using a traffic assignment procedure, link travel times are computed assuming the new capacity. These, in turn, are aggregated into interzonal travel times. The change in total regional travel time is then computed as: where Di,j, Ci,j, are the interzonal O-D demand matrix and the interzonal travel time (costs) matrix, respectively, (i,j) are indexes of zones. The superscripts A, B denote the ‘after’ and ‘before’ the project. Notice that for it is necessary to assume inelastic demand and no change in O-D locations, i.e. If travel demand is elastic, or if some travellers switch to different destinations or use alternative modes, the sign and magnitude of can take any value including The latter case may imply that the network is as congested after the project as it was before, since travel demand has increased sufficiently to outweigh the effect of the reduction in travel time from the capacity improvement. In this case, other measures of accessibility improvements are needed (see later). Next we highlight some issues relevant to the use of consumer surplus in the context of transportation investment projects and economic growth. As already indicated, under normal conditions (see shortly), a change in consumer surplus from an infrastructure improvement project is used as the principal measure of total benefits from this project. Counting other potential effects as additional benefits, therefore, amounts to double counting of benefits. In this regard, we need to determine how to treat benefits to new users. Examples are: users that previously did not use a certain mode or a facility (e.g. a congested public mode or a highway), users that did not travel at a certain time period (e.g. at peak time), or that did not travel to a certain destination (e.g. to city centre). All these types of users may have latent demand for a certain trip type or a service. Prior to the transportation improvement, they choose not to participate as they perceived the price to be too high. As explained by Layard and Glaister (1994:6) and Small (1999), the measured

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changes in consumer surplus already include the benefits accrued to these new users as well. Hence, adding the benefits of new users to the measured total change in consumer surplus is erroneous. However, summing benefits that accrue to new users separately from the benefits gained by existing users is a valid approach.6 The conditions under which the measurement of the prime benefits from a transportation project (namely accessibility) would not suffice to account for the total benefits from the project need to be established. We maintain that this would be the case in presence of externalities which are significantly high to affect resource allocation by individuals and firms. Underlying this discussion is our conjecture that transportation changes are a major cause for the generation of externalities at the urban and regional level (Section 7.2.2). We have already alluded to the fact that transportation services are input factors into a host of production, consumption and locational activities. Therefore, a reduction in transportation costs, from an infrastructure improvement, is likely to affect firms’ and individuals’ behaviour in other markets, some of which may be outside the price system. We will refer to these effects as allocative externalities.7 Essentially, these effects entail two underlying conditions. First, that actions by one party will impact the utility or the production function of another party (whether an individual or a firm), thereby influencing their consumption or production levels. Second, that the responsible party would not compensate the affected party (or would not be compensated by them) for the changes in utility or profit levels. The result is an inefficient allocation of resources in society as a non-optimal amount of resources would be utilized for the production of goods and services in these markets. One noticeable example is traffic congestion which affects drivers’ travel times and volumes, which in turn influences their allocation of time between leisure and work activities and their use of infrastructure facilities. One firm’s cost reduction, caused by changes in its proximity to another firm, is another example (see Chapter 8). Another form of externalities are pecuniary externalities. They arise when a reduction in transportation costs from a project alters relative prices in other markets. Such price changes, in turn, will create benefits and costs to third parties in these markets. Several researchers (e.g. Small 1999) noted that if these markets were sufficiently competitive then benefits to one group would be fully offset by costs to another. Put alternatively, in competitive markets pecuniary externalities amount to transfers among economic entities. Therefore, there is no need to consider them in evaluating the project as long as we are not concerned with income distribution analysis. It also does not hold in the presence of market imperfections such as scale or agglomeration effects. Under such conditions we need to consider benefits and costs accrued in other markets. The land market provides a good example. Following changes in accessibility from a transportation improvement, firms and households are likely to relocate, thereby raising land rents in some locations.

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Landowners in those sites stand to gain at the expense of the alternative locations. In general, transportation improvements affect the relative accessibility of different locations resulting in some locations becoming more attractive for residential, employment and commercial activities that previously located elsewhere in the region. In the absence of allocative externalities, these shifts in location also represent transfer of benefits rather than the creation of new ones. Mohring and Williamson (1969) have identified another important form of benefits transfer and label it industrial reorganization benefits. Accordingly, reduced travel times from a transportation project affect production logistics and shipments. Reduced inventories, just-in-time production, consolidation of car or truck loads and overnight guaranteed delivery are examples of these benefits. As important as these effects might be, it is necessary to demonstrate how they are capitalized into the firm’s decision-making process relative to the use of input factors in order to consider them as additional benefits not already captured by the reduction in transportation costs. A per unit real cost reduction is a major indication of such an effect. For additional benefits to transpire it is necessary that allocative externalities will be present, for example, scale economies at the firm level or agglomeration economies at the sector’s level. Otherwise, industrial reorganization benefits are similar in nature to benefits accrued to new users and hence are already captured by the change in total consumer surplus. The principal conclusion from the above discussion is that, in general, all the benefits from a transportation project are those already represented by changes in the consumer surplus, caused by accessibility changes. Additional benefits should be included if and only if it is possible to demonstrate the existence of allocative externalities. Adding other effects, such as transfers between parties, will result in over-estimation of benefits from the project. 7.2.2 Allocative externalities and growth effects In this analysis we consider four major categories of allocative externalities that arise from or are affected by transportation infrastructure investment projects. These are industrial agglomeration, labour market imperfections, transportation network economies and environmental effects.8 We then show schematically how these effects may influence the level of economic growth from a transportation project. Agglomeration economies Agglomeration economies are benefits accrued to firms resulting from their geographical proximity to other firms. These economies can arise from intrafirm scale and scope economies at a location, or from inter-firm externalities (Chinitz 1961). In the latter case, agglomeration externalities

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can ensue from accessibility to local public goods, from the use of shared input factors, from information spillovers, or from access to a common local pool of trained labour. Whatever the reason, the key element in the realization of agglomeration benefits is that their level is an increasing function of spatial closeness (see Chapter 8 for a microeconomic model with agglomeration externalities). Labour market imperfections The second type of allocative externalities are labour market imperfections. Following Cogan (1980) the total supply of labour by individuals, measured in units of ‘actual hours worked’, is a function of two main choices: individuals’ willingness to participate in the labour force and their allocation of time between work and non-work activities. In turn, these choices are a function of three main components: individuals’ preference and attributes including households’ constraints (e.g. the number of pre-school age children), institutional arrangements (e.g. work rules) and market entry barriers (e.g. lack of adequate information on the location of employment opportunities or spatially inaccessible job sites). The last two components represent labour market imperfections in that they introduce discontinuities into the labour supply function and cause sub-optimal labour market participation decisions. Travel time improvements can reduce these imperfections as, for example, there might be travel costs thresholds above which some individuals might be unwilling to enter the workforce. (In Chapter 8 we present some empirical evidence that support these arguments.)9 Network economies The third type of allocative externalities are network economies. A new transportation facility, like a road or a rail link, is typically part of a larger network. Due to intrinsic non-linearities in network traffic flow, the addition of such a link can result in increased traffic flow over the entire network that is larger than just the additional traffic over the new facility. Bottleneck and scheduling models of travel essentially make use of this network characteristic (Small 1992: Chapter 3).10 A related form of allocative externalities, resulting from network economies, is the case where two disjoint networks are linked by a newly constructed facility. The effect of such an investment might be similar to the case where trade began between two markets or countries that previously were closed off to each other.11 The overall trade activity that will ensue from the transportation investment might be greater than that indicated by the travel volume over the new facility. Related possible benefits can also emerge when, prior to the infrastructure investment, a monopolist serves each of these markets. As shown by Jara-Díaz (1986), the result of

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introducing a new link that reduces travel costs between these formerly disjoint regions is the lowering of monopoly rents and deadweight losses by having greater competition. It can further lower prices of final products in each of the individual regional economies. Hence, total users9 surplus from the transportation project may not capture the total benefits to the economy.12 Environmental effects Environmental effects caused by traffic and auto use constitute the fourth type of allocative externalities. As most are unpriced or otherwise unregulated, they can cause significant misallocations of resources in the economy. In general, environmental effects spur negative benefits and are regarded as indirect costs of transportation infrastructure investment that may inhibit growth. Table 7.1 illustrates the magnitude of these externalities. Table 7.1 External effects from transportation in 17 West European countries ($US per 100 users/ km, or per 100 tons/km)

Source: Small and Kazimi (1995).

A major caveat to the above discussion is that a correct assessment of benefits needs to be carried out at the point of social equilibrium, where the price paid by users reflects the full marginal costs of supplying transportation services. By full marginal costs we mean private costs of use plus the costs of all allocative externalities including congestion and environmental costs.13 Failure to do so is likely to result in an erroneous estimate of the true benefits from a transportation project (see example in 7.2.3). In conclusion, if the aggregate travel demand function has been properly estimated, allocative externalities have been accounted for and the point of equilibrium is correctly defined, the benefits generated by a project can be accurately measured.

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7.2.3 Spatial changes from transpor tation improvements and economic growth Improved accessibility from transportation infrastructure improvements, ceteris paribus, can affect the location decisions of households and firms in the impacted region. These spatial rearrangements are bound to produce welfare gains to consumers and producers, stemming from their capacity to relocate to where utility level and profit-making capabilities are enhanced. The question then is does this activity relocation also constitute economic growth? The common answer to this question is that improved transportation stimulates efficient spatial patterns of households and business and that this increased spatial efficiency will spur economic growth. This inference is supported by a vast amount of theoretical analysis, the main conclusion of which is that reduced travel costs (from transportation investments) are likely to encourage further activity decentralization and, at the same time, intensify agglomeration and urbanization economies (see a recent review by Anas et al. 1998). Yet to argue that increased accessibility (from reduced transportation costs) improves spatial efficiency and generates growth is dependent on two conditions. First, that reduced travel times will indeed result in consequential activity relocation and, second, that we agree on what constitutes efficient urban activity patterns. For an activity to relocate following generalized travel cost reductions, it is necessary that the value of these reduced costs will exceed the costs of relocation. In a well-developed network, like those of most contemporary metropolitan areas, improved link capacity (even a major one) is unlikely to bring about significant travel cost reductions. On the other hand, relocation costs, which include monetary and non-monetary household’s or firm’s costs, can be quite substantial. Moreover, as shown in Chapter 4, two-employee households presently make up a sizeable portion of all households. For such households, a directional travel time improvement is unlikely to warrant a move. In general, travel time and costs are only one factor among many that influence household’s decision to relocate. Even a ubiquitous change in metropolitan travel costs,14 may not result in spatial relocation. Arnott (1998), for example, has shown that when considering trip timing decisions and assuming no toll revenue redistribution (hence no income effect), optimal congestion tolls have no effect on urban spatial form. Similar arguments also apply to firms’ relocation decisions. Typically, transportation costs constitute but a small fraction of total firms’ production costs. Firms’ relocation transpires over long time periods and is often spurred by non-transportation related factors such as tax opportunities or direct subsidies.15 As explained in Chapter 4, the location of firms in the service sector is usually more responsive to consumers’ demand than to travel costs per se. High-tech firms, which in many cases locate in areas where land costs are already low, are largely insensitive to improved accessibility. Empirically,

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there is little evidence to indicate that noticeable firm relocation can be unequivocally attributed to a particular transportation improvement. (The case studies in Part IV document this claim). Turning now to the second condition, the crucial question here is when can decentralized spatial activity patterns (induced by travel costs reductions) be regarded as efficient. In a simple monocentric urban model it is rather easy to ascertain an efficient urban form. But this is certainly not the case for a more realistic multi-centre (including the CBD and many sub-centres) contemporary urban area. Moreover, the durability of structures and of transportation and non-transportation facilities make observed market spatial equilibrium patterns unlikely to be efficient. Put alternatively, under common market conditions, several spatial equilibrium patterns can emerge, some being more efficient than others. History often determines the prevailing (though not necessarily the efficient) one (Anas et al. 1998). Still another key factor affecting the efficiency of urban forms is the fact that rarely do households and firms actually pay the full social costs of relocation, including the increased costs of public services provision and the numerous pervasive externalities (e.g. traffic congestion). One often hears arguments against activity diffusion, which are based on the enormous social externalities such as housing and job market segregation, that dispersed spatial patterns entail. Given the durability of urban facilities and structures there are real costs of reduced activity level at the CBD resulting from further suburbanization. To summarize, in the long run accessibility improvements from transportation infrastructure investments tend to result in activity relocation and more dispersed urban patterns. However, considering the predominant multi-centre urban form, the underpricing of congestion costs and the many other unpriced urban externalities whose impact is exacerbated by further activity dispersion, the reduction of travel costs via facility expansion is unlikely to result in more efficient urban spatial patterns. Therefore, it is not quite clear how economic growth benefits can emerge (let alone be measured) from spatial relocation following transportation infrastructure improvements. But even if spatial efficiency does improve, following a particular transportation investment, one must establish the presence of underlying allocative externalities, like external scale economies, in order to ascertain economic growth benefits. 7.2.4 A proposed scheme for the evaluation of economic growth benef its from a transpor t infrastructure investment Based on the above discussion, we now present a modified version of Figure 7.1 to demonstrate our conjectures regarding the relationships between allocative externalities and spatial redistribution, following a transportation

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Figure 7.2 The new scheme for the evaluation of economic growth benefits from transportation investment

infrastructure project, and between economic growth. These relationships are shown schematically in Figure 7.2. Prior to that, we need to point out that in both Figures 7.1. and 7.2 we have included investment multiplier effects. They have been placed as indirect effects (Figure 7.1) or outside the process (Figure 7.2). There are two reasons for this. First, multiplier effects are not unique to transport investments. Multipliers are important in all investments involving the creation of additional income, consumption and employment, as there are linkages which lead to second and further rounds of effects. Second, there are many other (non-investment) ways to achieve similar multiplier effects, such as through the taxation system. The importance

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of multipliers should not be underestimated, but we have not included them as part of the economic growth component as they are considered to occur at one point in time rather than continuing to develop in the future, as implied by the concept of growth. Figure 7.2 underscores our contention that economic growth from infrastructure improvement is predicated on the presence of allocative externalities. Spatial redistribution of activities can also lead to economic growth, as prior to the project, high transportation costs inhibited some activities from locating where their marginal productivity (or marginal utility) exceeded the cost of relocation. The key point to observe about Figure 7.2 is that if allocative externalities are not present in the local economy, then all of the benefits from an investment project are confined to travel or accessibility related benefits. These benefits are fully captured by the measured change in consumer surplus and displayed in Figure 7.2 as ‘welfare gains’. In that case, growth effects cannot be expected, and attempts to regard some benefits as economic growth amounts to double counting. In Appendix 7.1, we give an example to demonstrate the possibility of assessing benefits from a transportation project while taking into account allocative externalities. 7.3 Primary benefits from transportation improvements: network accessibility Figure 7.2 highlights our assertion that changes in travel conditions, which are the primary direct benefits from a transportation investment, are also the fundamental generators of potential growth benefits from the project if allocative externalities are present. While we do not claim that the relationships between travel conditions and growth effects are linear, it is our contention that the former has a positive impact on the latter. In general, a greater improvement in travel conditions will bring about a larger economic growth effect via its impact on the various allocative externalities but this effect may abate quite rapidly (see simulation results in Chapter 8). Given this characterization of the causality linkage between transportation improvements and economic growth, it is pertinent to examine the following two questions: first, how to properly define and measure changes in travel conditions; second, how to specify the functional relationships between changes in travel conditions and potential growth effects. In this section we focus on the first question while in Chapter 8 we examine the second. Overall, changes in travel conditions are commonly summarized by the generic concept of accessibility, which can be defined as ‘the ease of access between spatial opportunities’. This definition implies that variables like travel time and costs, associated with a trip to a location, are the key components that determine accessibility. Alternatively, accessibility can be defined as ‘the potential attainment of a set of transportation choices’. This definition implies a subjective judgement that a user must makes among a number of travel

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options in order to select one, based on the perceived attributes of each option, his own characteristics and assuming a utility maximization behaviour. The result of adopting either definition leads to a different quantitative measure of accessibility. Under the first travel time (or distance) and the monetary costs of a trip, weighted by the level of activities at the origin and destination locations, are the main variables that define accessibility. For example, in conventional trip distribution models travel time and monetary costs are combined to produce a single measure of accessibility known as ‘generalized costs’. Formally, generalized cost of using mode, k, Ck is computed as Ck = Pk + v . Tk, where Pk is the monetary cost, v is the value of time, and Tk is travel time by mode k (including access time, wait time, in vehicle time and modal penalties). Notice that travel time is also a function of travel volume where the latter is computed for a specific link of the network. Hence, it is possible to compute a measure of ‘link accessibility’. Alternatively, it is possible to aggregate link’s generalized costs over all links to arrive at a measure of ‘network accessibility’. In either case, the travel benefits from a transportation project, which aims at expanding the capacity of a certain highway or of a public transit mode, are measured as changes in accessibility. As explained above such changes are equivalent to changes in consumer surplus. The second definition of accessibility asserts that accessibility should be measured at the individual level since that is the kind of accessibility which users consider in making travel choices. It follows, therefore, that in addition to the modal or link attributes (e.g. speed and money costs), given the origin and destination locations, the computation of individual accessibility should also account for users’ attributes (e.g. income and demographic variables). Viewing accessibility this way leads to accessibility measures which are based on random utility models such as multinomial logit. The accessibility of individual i denoted by ACCESSi, can then be measured as: (7.1)

where Vik are observed transportation attributes of a choice alternative k, from the choice set R by individual i.16 The connection between this accessibility measure and consumer surplus can be expressed as the difference between accessibility level before the project is implemented (i.e. current level of the attributes) and afterwards. Letting 1 and 2 denote the before and after levels, a change in consumer surplus is defined as: (7.2)

Ben-Akiva and Lerman (1985) have noted that the use of this expression to measure accessibility creates a major difficulty, as different specifications of the multinomial logit model will produce accessibility terms that are expressed in different units.

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In a seminal paper, Small and Rosen (1981) have shown that for the multinomial logit model, in the absence of income effects, the expected change in consumer surplus can be computed as: (7.3)

where l is the marginal utility of income (¶Vi /¶I) and Vk is the systematic utility of an individual.17 For example, in a discrete choice analysis and assuming a compensating variation type demand function, (7.3) can be written as: (7.4)

where v1, v2 are the mean of the indirect utility for travel scenarios 1 and 2, respectively. Niemeier (1997) provides an interesting example of the use of DCS as an accessibility measure. In her study she empirically calculates the monetary value of mode-destination accessibility for the morning journey to work. This measure, when computed for the entire sample, is shown to be quite different than when computed for various market segments. Another example of the use of a discrete choice-based accessibility measure is in Levine (1998). His accessibility measure is used to assess the impact on residential location decisions of commute time, relative to other factors such as suburban housing regulation. He found that while at the regional scale accessibility matters, the match between housing and work places is much more responsive to housing policies than to accessibility. At this junction, it is important to emphasize that, whatever the accessibility measure adopted, its formulation and use should reflect the network attributes and performance. As alluded to earlier, this carries consequential implications for BCA of transportation projects. That is, travel time improvement on a specific link, even if significant, does not necessarily imply a measurable change in travel behaviour on the entire network. In fact, commercially available network models are unable to properly distinguish between various temporal and spatial effects, newly generated trips (assuming travel demand elasticity, which is greater than zero in absolute terms) and between diversion effects.18 Under such conditions, in a well-developed metropolitan network even a major investment in few links may generate only small welfare gains to warrant the investment, as a conventional BCA would indicate. In part this reality provides added support to the need also to examine potential growth benefits from the project. A final issue to be discussed here that also bears impact on the magnitude of the measured changes in accessibility is the non-incremental aspect of transportation investments. In reality transportation infrastructure investments

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are indivisible and lumpy while benefit-cost rules are specified in terms of marginal changes. For example, the benefit-cost rule derived in Appendix 1 states that under the first-best social equilibrium the marginal dollar invested in road capacity should equal the reduction in total congestion costs from that capacity investment. With a non-incremental lumpy investment this rule may not hold. Under such conditions the magnitude of the overall computed consumer surplus depends then on the size of the capacity improvement, the change in the generalized travel costs, the initial level of demand and demand elasticity (Williams and Moore 1990). As implied by Figure 7.2, economic growth transpires in the impacted area from agglomeration effects, labour market choices, network economies and from environmental improvements. How can these impacts be quantified and measured in monetary values to be used in BCA? In general, when we deal with impacts for which market prices exist, these prices should be used to compute the true economic value of these effects. For example, increased output from agglomeration can be evaluated at the output’s market price. Similarly, if the labour market’s response to travel time reduction is increased employment, the market wage rate can be used to evaluate these changes. In the case of network economies, infrastructure improvements can reduce more than proportionally travel time saving, traffic accidents and increase mode use. For example, for highway networks Kraus (1981) has estimated increasing returns to scale with respect to highway length expansion and with respect to capacity expansion of intersections.19 Similarly, upgrading the railroad track to allow greater travel speed and comfort is bound to prompt greater use of rail by passengers and freight and decreased highway travel time and accidents. Unfortunately, these effects do not have direct market price. Yet, it is possible to use imputed prices to measure their economic values. Thus, through various methods such as revealed preferences in choice situations, it is possible to assess the amount of money that potential users are willing to pay to save travel time, to ship freight at greater speed or reduce the risk of car accidents. In environmental analysis, stated preference techniques are used to derive willingness to pay to reduce noxious externalities. The value to people of abating these effects can then be computed using the stated values as the conversion factors. 7.4 Key factors affecting the measurement of benefits We have stressed the point that growth effects from transportation infrastructure expansion are a function of the primary direct benefits from the project, namely accessibility improvements, hence the importance of examining key factors which bear the largest impact on the measured direct benefits. In this section we examine three such factors. These are: the value of time; the discount rate of future benefits and costs and the time span of

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projects and risk and uncertainty associated with project selection and with the assessment of future benefits and costs. 7.4.1 Value of time savings By far the largest category of direct benefits from any given transportation project is ‘travel time savings’ expressed in monetary values. These benefits are a combination of the actual amount of time saved and the value of a unit of time saved, generally known as the ‘value of time’ (VOT).20 Since the latter component is not directly observable, it needs to be empirically estimated, mainly from choice situations. In this section we examine theories proposed for assessing the VOT and then present some empirical results on VOT estimates. The reader is again referred to Figure 7.2 which shows our basic assertion that the fundamental generator of potential economic growth effects are travel time savings. Hence our focus on VOT which is the factor that converts units of travel time saved into monetary benefits. For persons travelling to or from a location, travel creates three major types of costs: money costs; opportunity cost of time and disutility of travelling. The first type refers to out-of-pocket direct costs of travel, e.g. bus fare, tolls or the costs of car use. The second refers to the alternative use of the amount of time spent in travel in carrying out a utility producing activity such as work. Obviously, the degree to which time can be used productively to accomplish other activities varies considerably between people and trip purposes. In principal, its value lies between 0 (time saved cannot be used productively elsewhere) and 1 (time saved can be fully utilized). The third cost component refers to the inconvenience that travelling creates and which individuals are willing to pay in order to forgo. A full analytical explanation of the derivation of values of time is given in Appendix 7.2. A major problem with the model framework presented in Appendix 7.2 is that, in reality, time allocation decisions are usually carried out jointly within the household as part of the household’s allocation of time to activities. The utility maximization problem may, therefore, be an unsuitable framework for such joint production decisions (Pollak and Wachter 1975). Another problem that arises when assessing the value of time-savings is trip chaining. A significant proportion of all trips are chained trips where the decision to perform a given trip (e.g. shopping) is not independent of the decision to undertake another one (e.g. work trip). In this case the value of time savings for the second trip cannot be assessed as if it was a stand-alone trip which is what the model (Appendix 7.2:7.17) indicates. These and other conceptual difficulties notwithstanding, the basic logic of benefit-cost analysis framework necessitates the use of time-savings values. A large number of studies have been conducted in order to compute such values for various modes and under various conditions pertaining to travel purposes and household’s attributes. What is common to all of these studies

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is that they derive the values of time savings from choice situations where trip makers are faced with several mutually exclusive alternatives each conferring a specific travel time or travel time components (e.g., wait time, in-vehicle time and access and egress time). The choice between these alternatives, while accounting for all other differences, seemingly reveals the value that individuals attach to the amount of travel time they can save by choosing a specific alternative. In general, we can distinguish between several categories of choice situations. The main ones are choice of speed, route, mode, time of departure and safety. Given these choice situations, the literature distinguishes between three major modelling approaches to the estimation of VOT. These are analyses of specific choice situations, models that consider the determinants of VOT savings for travel purposes and discrete choice model approaches. An interesting example of a specific choice situation analysis, was conducted as a ‘real world’ experiment by Hauer and Greenough (1982). In this experiment, subway riders in Toronto were offered cash rewards in exchange for the loss of a specified amount of time (e.g. not getting on the next arriving train). From the results of this experiment the distribution of the value of time was obtained as a function of the time of day, duration of the delay, trip purpose and socio-economic attributes. Horowitz (1978) used a regression analysis on a stated preference database to study the effect on travel time of trip length, travel mode, time of day trip purpose and road conditions. Hensher (1989) proposed a model which considers the determinants of VOT savings for business travel and used it on stated preference data to assess the choice between a tolled and a free urban road. Given that the VOT is estimated from choice situations it is just natural that discrete choice modelling, based on random utility theory, is the prevalent method used for estimating VOT.21 The objective is to define the systematic utility component V in a way that enables empirical estimation in terms of observed socio-economic characteristics of users and trip attributes. For example, V can be defined as a linear function of travel costs and travel time, i.e. where the indices i, n indicate an individual and a travel mode, respectively. From this definition, the marginal value of time saved, which is the marginal rate of substitution between time and money, is ß1/ß2. Small (1992:19), specified Vin as a non-linear function of travel costs, travel time and the wage rate of an individual. He further introduces mode dummy variables that interact with travel time to reflect choice alternatives. As a result, the estimated value of time saved also varies across modes.22 A key observation from the review of the numerous available travel time studies is that VOT varies considerably among individuals, locations, market segments and time of travel, and that these variations are a function of a large number of variables. For practical application, therefore, it is common to use VOT that represents an ‘average value’ for a group of trip makers with

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similar socio-economic and travel profiles. For example, it is conceivable that the VOT for private auto users travelling to work at peak time is significantly higher than that in non-work travel (Guttman 1975). Small (1992) and Waters II (1992) estimated VOT for peak-time users to be 50 per cent of the hourly pre-tax wage rate.23 Several general conclusions regarding actual VOTs can be drawn from the empirical literature on the subject. First, having higher flexibility, relative to the various time constraints, tends to increase VOT. For example, the study by MVA (1987) has shown that people with variable work hours have VOT which is 15–20 per cent higher than that of other workers. Similarly, the ability to schedule activities tends to increase estimated VOT values (Small 1982). Higher tax rates have an opposite effect (Forsyth 1980). A second major conclusion is that non-linear relationships exist between income and VOT. Thus, studies have shown that VOT for in-vehicle time is less than the hourly gross wage rate but it is an increasing function of income since rising income implies larger opportunity costs of time saved. Waters II (1992) has estimated the elasticity of VOT with respect to income to be about 0.8, though this general elasticity value depends on trip length, mode use and trip purpose. Gunn (1991) reports smaller effects of income on VOT, except for business travel. A further conclusion is that users place a higher weight on wait and walk time relative to in-vehicle time. Usually, the value of these time component is two to three times that of in-vehicle time. In this regard, inter-modal transfers, which implies additional wait time, entail heavy opportunity costs of time and therefore should be weighted accordingly (Small 1992). Finally, the common practice is to use the same VOT for small and large blocks of time saved. This practice has been challenged on the grounds that small time blocks saved (e.g. 5–10 minutes) are valued less by trip makers than larger ones (e.g. 20–25 minutes). Following this rationale it might be argued that the total value of time saved for a large group of people where each saves only 3–5 minutes is negligible. Obviously, it is possible to introduce counter-arguments, for example, that for some activities small time savings are a sufficient perquisite for undertaking them. In general, the use of average VOT across people probably accounts for this problem mainly because a small but a significant number of people place a very high value even on small time blocks. Time savings for commercial traff ic A major proportion of all highway traffic is commercial vehicle movement, mainly of trucks. Therefore, in assessing total time savings from an infrastructure project we need to compute separately time savings for truck traffic as well as the VOT factors that are applicable to trucks of different types. Total time savings for commercial vehicles are composed of three

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components: driver’s time, vehicle’s time and time savings of the cargo carried. Typically, the value of the third component is not calculated and is much smaller than the first two.24 Waters et al. (1995) list four alternative approaches for estimating the value of time for commercial traffic. The main two are first, the ‘cost savings’ approach, which regards the costs that trucking firms can save from reduced travel times while hauling the same level of freight. The second is the ‘incremental revenue’ approach, which assesses the value of extra output that can be produced from reduced travel time.25 The cost saving approach essentially is equivalent to the measurement of producer surplus when the cost of travel declines though, under this approach, only a portion of the surplus is actually being regarded as ‘cost savings’. That is, given the down sloping aggregate demand function for shippers’ services and given that drivers’ and vehicles’ time are real inputs into the shippers’ cost function, the same output can now be produced at lower costs to the trucking firm. Yet, at lower costs (i.e. a downward movement of the supply curve) and in a competitive market, at equilibrium, more output will be demanded and produced so that total producer (and consumer) surplus is rather larger than the measured cost savings. The incremental revenue approach is the computation of the value of additional output at previous equilibrium price. This value depends on the elasticities of the aggregate demand and supply functions. It measures gross revenue, which is subject to taxes and perhaps other non-related costs (e.g. costs of scheduling of additional traffic to haul the additional output). In terms of BCA this approach better reflects the value to society which is engendered by the infrastructure project than does the cost saving approach. The reason being that it shows the economic value of the additional output that the freed resources (drivers’ and vehicles’ time) can produce, where this additional output is evaluated at the gross market prices. Blauwens and Van de Voorde (1988) have used a mode choice approach to determine the value of time savings in commodity transport. Their approach, which is similar to that used in commuting markets, is based on comparing the costs of hauling freight by truck versus inland waterways. Using a regression analysis on data from 43 districts in Belgium for 15 commodity groups, they conclude that VOT in freight is 74.3 per cent of the value of the cargo (see Table 7.2 for Belgium). As with small blocks of time saved for private car traffic one needs to ask whether small time savings are valuable to shippers. An infrastructure project, even if large, that produces small time savings in a network setting would show relative little benefits if small time savings would not have an economic value. Furthermore, the cumulative effect of a number of infrastructure improvements that have a significant impact on travel time cannot be correctly measured if small time savings would not be valuable to firms and consumers. Taking this to be their working assumption and following the incremental revenue approach, Waters et al. (1995) have estimated the maximum value

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of time savings for trucks in British Columbia, Canada. Their estimated values were CDN$25.51 per hour for two-axle diesel truck hauling bulk commodity, to CDN$31.12 per hour for 7–8 axle trucks. For general freight, the figures were CDN$31.02 and CDN$35.82, respectively. They also computed the value of time for small trucks and carried out an extensive sensitivity analysis to test for the effect of various assumptions (e.g. the proportion of drivers’ hourly wage rate that should be regarded) on the estimated values. Table 7.2 shows VOT values by trip purpose in various countries. The values for business travel were computed using the opportunity cost approach. All values are expressed in 1997 European currency unit (Ecu) per vehicle hour, except where noted. Some countries do not differentiate VOT values by trip purpose. Table 7.2 Value of travel time savings by trip purpose used in different countries (in 1997 Ecu per vehicle hour) and for commercial vehicles (in 1993 US $)

Sources: Waters et al. (1995); Haaland and Odeck (1997). Notes: a Figures are from EURET 385/1994 report and are shown 1990 Ecu. b Vehicle occupancy rate is assumed to be 2.4.Value of time per person is calculated by dividing per capita GDP by the number of annual hours. c Values are derived from stated preferences studies. d Mean values from various states. e Five-axle diesel vehicles. f American Association of State Highway and Transportation Officials (AASHTO) 1977 Manual on User Benefit Analysis of Highway and Bus Transit Improvements (Washington DC). g For Ontario. h State highways.

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7.4.2 Discount rate and time span of projects26 As emphasized earlier, the essence of BCA is to determine the degree to which capital and operational costs of a project are recovered over its lifetime. The common evaluation criteria are net present value (NPV), internal rate of return (IRR) and, to a lesser extent, multicriteria analysis (MCA). The use of these criteria requires the specification of a discount factor of future streams of benefits and costs as well as the first and last year of the project. In this section we focus on the choice of the discount rate and its impact on the measured direct and growth benefits. The importance of the discount factor cannot be exaggerated. International data show that by and large they are determined at the state or national level and are obligatory for all projects initiated and implemented by public local, regional and national agencies. There is ample literature on the ‘correct’ discount factor that ought to be used in the evaluation of public projects. The major issues examined in this literature are the theoretical underpinnings of the proper discount rate, how to actually calculate it and whether the same rate should be applied to all types of public projects. As highlighted by Figure 7.2, total benefits from a transportation project include direct travel benefits and externalities effects that, in turn, include growth benefits. For the purpose of discounting future benefits we need to decide whether these benefit types should be lumped together into a single measure of total benefits, or whether each should be evaluated separately with respect to discounting and time horizon. Using conventional criteria of evaluation (see shortly), this decision can be rather consequential for the acceptance or rejection of the project. The key idea behind much of the literature on the correct specification of the discount factor is that it should reflect the marginal rate of time preference, sometime also referred to as the rate of substitution of time preference. Succinctly stated, the overtime consumption pattern of consumers indicates their preferences about how to allocate their income between consumption and investment (or savings), where investment in this period implies postponement of consumption to later periods. Since governments raise capital for public projects by taxing consumers or by borrowing from them, it effectively affects their temporal consumption and investment decisions. In addition, investment in risk-free assets (e.g. government’s risk-free bonds or the sale of such assets) is a typical mechanism available to consumers to change their consumption pattern between periods, hence, the use of the interest rate of government’s risk-free bonds to determine consumers’ marginal rate of time preference.27 Investment funds raised by the government become unavailable for investment by firms that could have earned a market rate of return.28 Therefore, one plausible approach for obtaining an approximation for the ‘correct’ discount factor is to compute the weighted average of a risk-free rate asset and the

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market rate on private investments. The weights are the proportions of the public funds obtained from consumption and from investment respectively. There are a number of technical and theoretical problems with this approach, including the fact that capital funds invested in any specific public project generally come from a large pool of funds (e.g. the general budget). As a result, frequently it is impossible to ascertain what proportion of this project’s capital has been drawn from consumption or from private and corporate investment. Moreover, since the weights depend on the project’s financial flows that are attributed to consumption or investment, and since these flows need to be discounted first, there is a problem of simultaneity in computing the weights. If funding of a project is primarily a combination of bonds issued specifically for this project, bank loans and dedicated taxes (e.g. gasoline tax for roadworks), we further face the issue that each source is subject to a different interest or tax rate. This fact again affects the computation of the ‘correct’ discount factor for this project. Other problems in computing the proper discount factor relate to the validity of underlying assumptions, including that of a competitive financial market, the perfect insight of consumers with respect to future income and consumption,29 and the neutrality of taxation relative to financial markets. The initial distribution of income and wealth presents another difficulty since it affects in the marginal rate of time preference of individuals. The question as to whether all project types should be discounted using the same factor also needs to be examined. This becomes an important issue when evaluating projects whose output is a public good (e.g. adding capacity to the road network) as compared with projects whose output is a private good (e.g. subsidized transit trips). As pointed out by Stiglitz (1982), the marginal rate of substitution of public goods between time periods is systematically different from that of private goods. Following these arguments it is of no surprise that there is no consensus in the literature regarding the computation of a ‘correct’ discount rate. Both Baumol (1964) and Stiglitz (1982) maintain that the proper discount rate for public projects should be somewhere between consumers’ rate of substitution of time preference and the social marginal rate of productivity of private capital (the shadow price of capital). Sandomo and Dreze (1971) have developed a model which takes into account the inflow of foreign capital, government borrowing power and government’s budget constraint. They showed that in an open economy the correct discount rate of public projects is a function of the risk-free interest rate, the price of capital used by private firms and the marginal rate of return on foreign capital. The above theoretical constructs notwithstanding, in many countries the common practice is to use a discount factor which is dictated by an overseeing agency (e.g. the ministry of finance), and which is not necessarily anchored in a well-founded economic theory. The rate imposed reflects other objectives such as the wish to promote a given type of project. For example, in Germany the common practice in evaluating transportation projects is to employ a low

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discount rate (presently 3 per cent). It depends, inter-alia, on the market interest rates, future interest rates and the client’s (the state or the region) time horizon. Underlying this low discount is the expressed wish of the German federal government (FRG) to encourage long-term projects whose benefits will be received in distant future times. In the Netherlands, the official rate of return for low risk projects is 4 per cent. For projects with higher risk levels a different rate is applied, which is based on the long-run net interest rate of government bonds. In Greece a project is evaluated on the basis of the first year rate of return only and, as a result, in road projects no discounting of future benefits and costs is performed at all. Table 7.3 provides data on Table 7.3 Evaluation method, evaluation period, discount rate and use of residual capital values of transportation projects,1995

Source: EURET/385/94 report commissioned by the European Commission DG VII. Notes: a The project’s benefits are estimated for 30 years and assumed constant thereafter. Benefits and costs are discounted over infinite lifetime. b Future discounted maintenance costs are added to the first year total project’s costs. c Various trial discount rates are used as a form of sensitivity analysis. d This is the official rate of return for risk-free projects. For risky projects a higher discount factor is applied. This level is based on the long-run net interest rate of government bonds. e Austroads (1996). f US Office of Management and Budget (OMB) 1992.

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evaluation methods, evaluation periods, discount rates and use of residual capital value in different countries. 7.4.3 Risk and uncer tainty in project evaluation In the project evaluation literature the terms risk and uncertainty are used to imply several different meanings. In general, the term risk is used to indicate the likelihood of selecting the wrong project or a project which is economically non-viable. Since public projects displace private ones, and since private investors are risk averse and allow for risk in their choice of private investments, BCA should also account for individuals’ risk aversion. If not, the public sector might accept projects that have been rejected by the private sector (Webb 1973). However, it might be argued that the whole purpose of BCA is to weed out unwarranted projects by a careful examination of their expected costs and benefits which, in essence, is an attempt to minimize the risk of selecting inadequate projects. Sometimes the term risk is used to indicate the effect of a given project on the welfare of taxpayers, relative to the distribution of the project’s indirect costs (negative externalities). Arrow and Lind (1970) have argued that, in the case of public projects, the costs (and benefits) of a project are dispersed among a very large number of consumers and are inconsequential in assessing the project’s value. Yet projects (e.g. a road expansion) expose a specific and rather small segment of the population to the risk of reduced welfare from the potential adverse effects of this project. In such cases, we need to include the consumers’ costs of bearing this risk in the BCA, which in turn is likely to affect the ensuing potential growth effects, if any. The term uncertainty is sometimes used to indicate the degree of inaccuracy associated with the forecast of the project’s future benefits and costs. We will return shortly to this issue. Still another aspect of uncertainty is that of the project’s degree of inflexibility. This term is used to imply that irreversibility costs are rather prohibitive. That is, since there is always an intrinsic level of uncertainty regarding the future state of the economy, a project that is largely irreversible, or that cannot be stopped without rendering its costs sunk, should be ranked inferior to one with same NPV but which is flexible to a reasonable degree. Inaccurate estimate of benefits and costs can still result in the selection of a ‘correct’ project. Here, we focus primarily on uncertainty, while implicitly assuming that the reduction of uncertainty by means of better models and data will also lessen the risk of selecting a wrong project, or a project that confers unacceptable risk on specific segments of the population. Underlying this view is the notion that the whole objective of BCA is to diminish the level of uncertainty and risk associated with a particular project. As with the other factors (e.g. the value of time and appropriate discount factor), the level of economic growth that can be expected from a given

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infrastructure project is a function of the degree of uncertainty associated with this project’s estimated impacts. That is, underestimation of the project’s costs or overestimation of its primary benefits (i.e. travel effects) may result in significantly distorted economic growth estimates. As indicated by Figure 7.2, the functional relationships between the level of the primary benefits and the economic growth benefits from a given project, are non-linear. Hence, a specific level of uncertainty associated with the estimated direct transportation benefits can be amplified by the forces (allocative externalities) that generate growth benefits, thereby rendering them too excessive to be credible. How severe are these problems in actual benefit-cost analysis? A number of studies have attempted to ascertain how accurate benefits and costs projections were, done as part of project evaluation, prior to implementation. To that end, they have carried out ex ante and ex post comparisons of particular infrastructure investments. Boardman et al. (1994) applied this approach to a 303 km highway project in British Columbia and concluded that major differences in net benefits were not due to (what might have been expected) differences in estimates of benefits, but rather to underestimated actual construction costs. Skamris and Flyvbjerg (1997) have concluded that in the case of Danish bridge and tunnel projects, on the average, construction costs were consistently 50 per cent to 100 per cent undervalued, whereas traffic forecasts were about 60 per cent overestimated. In a much cited study, Pickrell (1989) has surveyed ten US rail projects. He found that in all cases actual ridership was away below the ex-ante estimates, while actual capital and operating costs surpassed the projected ones by about 50 per cent. In some cases average cost per rail passenger exceeded the estimated costs by as much as 188 per cent. Kain (1990) has studied the exante land use and ridership projections for a $2.6bn rail transit investment in Dallas, Texas (DART) and has demonstrated how inflated and even misleading these projections have actually been. To balance the picture, other studies have found that in some cases the projected benefits and costs were quite accurate (Walmsley and Pickett 1992). However, the majority of studies on this subject have concluded that, on the average, proposed transport systems cost 50 per cent more than their ex-ante estimates, while the ex-post demand is about 50 per cent below the estimated demand. This conclusion seems to underlie an established maxim in transportation BCA, which says that in order to arrive at the correct benefits and costs values of a transport infrastructure project one should halve the project’s predicted benefits and double its estimated costs. The reasons for the prevalent erroneous estimates of benefits and costs from a given project vary considerably. They range from unsubstantiated working assumptions, misspecification of models, inadequate data and the pursuant of incongruous objectives to careless or even deceitful analysis. In a very large study at the World Bank, a large gap has been discovered between the economic rate of return of over 1,000 projects approved by the bank

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during the period 1968–80 and the rate of return of these same projects which were reestimated some years later (Little and Mirrlees 1990). A significant proportion of projects whose economic rate of return was judged satisfactory, when the original cost-benefit analysis was carried out, turned out not to be so in the follow-up analysis. Increasingly, transportation infrastructure projects are financed through capital market funding which, in turn, implies financial risk. One major source of risk is volatile interest rates or exchange rates in the case of international funding (Haynes and Krmenec 1989). Another source is due to the fact that user charges are increasingly becoming the main source of income to support privately or even publicly financed capital projects (see, for example, Hirschman et al. 1995). However, in many cases the projection of future revenues from user charges is rather dubious. The main reasons are: the inherent uncertainty regarding the level of future demand and the corresponding demand elasticities and the effect of other infrastructure projects on the temporal and spatial distribution of demand.30 A further source of uncertainty is associated with technological changes in the transportation sector. The use of intelligent transportation systems, electronic toll systems and automated busways, as well as the introduction of a new generation of high-speed trains and guided vehicles, is likely to make present projection of future benefits and costs quite unreliable. What can be done to account for risk and uncertainty in the evaluation of infrastructure transportation projects? Above, we have mentioned the argument by Arrow and Lind (1970) that for public projects the risk margins of costs (or negative benefits) are quite small as they are distributed among a very large number of individuals. Thus, they can be regarded as certain sums and no special allowance for the project’s risk is necessary. However, in cases where a significant portion of the project’s costs (or negative externalities) are borne by a small and identifiable group of individuals (e.g. noise pollution or traffic nuisance in a certain locale), their monetary value should be added to the project’s costs. In the case of externalities, their shadow prices should be calculated as additional costs associated with their removal. Alternatively, the risk premium that the affected individuals would need to assume in order to avoid the expected decline in their welfare can be regarded as an additional cost of the project. With regard to uncertainty, Little and Mirrlees (1974) have shown that in order to account for uncertainty relative to the actual value of the social net value (SNV) of a project it should be adjusted according to the following formula: (7.6)

where E(NPV), E(GNP) are the expected values of the project’s net present

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value and the expected value of gross national product, respectively. The term cov(NPV, GNP) is the covariance of these two variables and ß is the coefficient of relative risk aversion. The decision rule then is, if SNV is positive, the project should be undertaken. It was mentioned above that a project’s inflexibility or its degree of irreversibility is a major source of uncertainty. It is due to uncertain future demand, on the one hand, and the sunk costs property of the investment on the other. One possible way to reduce this type of uncertainty is to defer the implementation of the project by a certain period (say, one year). Such a delay may involve the loss of benefits for a period of that duration which, in turn, needs to be weighted against the benefits of acquiring new information about future demand, plus the saved opportunity costs of capital. Pindyck (1991) and others (e.g. Brennan and Schwartz 1985) have argued that in the presence of sunk costs these benefits can be quite large and ignoring them can lead to incorrect investment rules such as the standard NPV. These rules of appraisal must, therefore, be modified to account for the opportunity costs of delaying the project. In transportation infrastructure investments, a significant proportion of the capital costs is irreversible, which in essence makes them sunk costs. The irreversibility of transportation infrastructure projects stems fundamentally from two sources: the use of land and structures and legal and political commitments. Transportation investments such as rail, highways or ports are the result of long-range planning processes that involve the acquisition and legal enactment of rights of way. If, subsequent to these processes implementation does not follow, the costs involved with these activities cannot be recovered. Similarly, once construction begins, land and structures can be converted into alternative uses only at prohibitive costs. Moreover, in anticipation for the implementation of the announced project, development along the project’s planned route or site is likely to take place, thereby confining the alternative use of land. In addition, in the context of urban and regional planning, there might be significant political sunk costs if the project is not completed as planned. At present, in many countries there is the political resolution and even public support to undertake transportation projects in which tolls (whether or not congestion tolls) are used as a means to mitigate traffic and to recover capital costs. What is not known with certainty are the demand elasticities, hence the future revenues from the project. The reasons for this uncertainty can vary, ranging from the unknown effect on demand of concurrent investments in complementary or substitutable transportation facilities, to the relocation of residential, commercial or employment activities. Whatever the reason might be, if the recovering of capital costs is a necessary condition for the investment, faced with uncertain future revenues and assuming sunk costs, the possible impacts of project delay should be examined. What then should be the economic decision rule whether to invest now or

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postpone the investment to a future period? Following Dixit (1989) and Pindyck (1991), capital sunk costs should be spent if the monetary value of the project’s output, P, is greater than the project’s variable costs, c, and its discounted (sunk) capital costs, k, and if the variance of future values of P, (denoted by s ) is zero. That is, the decision should be to invest today if where r is the discount rate. However, if s > 0, (i.e. there exists uncertainty over future demands of the project’s output), there are opportunity costs for delaying the investment since P can go up. In that case it may pay to delay the investment by one year and then invest only if P increases.31 A known characteristic of transportation infrastructure investments is their lumpiness. When given an origin and destination location it is not possible to construct a variable portion of a highway or railroad. Similarly, it is not possible to construct varying sizes of airport runway or seaport dock. By and large, technology, geography and intermodality requirements dictate minimum size investments that can be augmented only by discrete units of additional capacity. As a result, the investment function is discrete in capacity. Therefore, at the time of the initial investment it is necessary to determine how much to invest and how much capacity will be added on in the future by incremental investments. The rule for an optimal investment assuming a continuous investment function, demand certainty and no sunk costs is shown in Appendix 7.1. However, if demand is uncertain and transportation infrastructure investments are irreversible, it may be optimal to implement only an investment of a minimum level capacity, postponing additional investments to future periods. In this case, it is necessary to include the opportunity costs of a delay in computing the NPV of each incremental investment. What then would be the optimal investment in incremental capacity? Because of the lumpiness of infrastructure investment, analytical rules (i.e. those in Appendix 7.1) are difficult to derive. However, to the degree that the literature on firms’ investment decisions under conditions of irreversibility and uncertainty is germane to the transport investment problems, we can conclude that uncertainty over future demand and irreversibility may increase the value of a marginal unit of capital.32 Pindyck (1988) has shown that demand uncertainty will result in delayed incremental investments, but it will make these investments larger when they are finally made. From a transportation policymaking viewpoint, there might be political, institutional or even legal costs for a delay. If the costs of a postponement as a function of the delay time become increasingly prohibitive (e.g. due to objections by opposition to the project or if it may result in lengthy litigation), it is less likely that the delay option will actually be exercised. Similarly, if the alternative costs of land, presently held by the state, are an increasing function of elapsed time (e.g. due to residential development in the vicinity of the project), it may become uneconomical to exploit the delay option. When examining the timing impacts of transport infrastructure

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investments, an interesting question arises relative to the actual period when local economic growth effects begin to transpire. That is, accessibility changes, which are the prime generators of growth benefits from the project (Figure 7.2), might accrue only at the conclusion of the project (or at least of a substantial part of it). Yet it is quite plausible that in anticipation of these accessibility improvements, some development might begin even prior to the project’s implementation. In that case it becomes necessary to account for these benefits as if they accrue at the time the initial investment is made, rather than at later periods. Of course, such considerations will affect the project’s computed NPV. Presently, within the framework of BCA, there is no established analytical procedure to predict the time when growth benefits from a project actually materialize. The best we can do is to learn from case studies about the revealed response of households and firms to planned transportation projects. In the empirical part of the book (Part IV) we examine some aspects of this issue. Several lessons can be drawn from the above discussion. First, risk and uncertainty factors are intrinsic to benefit and cost assessments of transportation infrastructure projects. As such they can significantly affect the actual travel and accessibility benefits from a project which, in turn, affect the magnitude of potential economic growth effects that are attributed to the project, hence the importance of properly appraising the levels of risk and uncertainty of a given infrastructure investment. Second, there is the question of who actually makes the benefits and costs projections. That is, since the uncertainty associated with benefits and costs forecasts is central to the overall risk profile of a project, one has to consider with great care projections prepared by bodies that are directly linked to the project or that stand to benefit from its implementation. Rather, the use of an independent organization is greatly recommended. Third, it is highly desirable that the final estimation of benefits and costs will be carried out later in the process when detailed information becomes available. This estimation can complement earlier forecasts prepared at the initial stage of alternatives analysis (Walmsley and Pickett 1992). Finally, it is desirable to assess the value of postponing the project’s implementation by a given period of, say, one year. The annual savings in capital costs should then be compared with the foregone first year benefits properly discounted and adjusted for the uncertainty. 7.5 Alternative evaluation approaches As is evident from Tables 7.3 and 7.4, the most common method used for the evaluation of highway and rail projects is traditional benefit cost analysis which includes benefits to costs ratios, NPV and IRR. In some countries (e.g. the Netherlands and Belgium), other methods are also used, mainly to complement BCA. The major differences between BCA and these other approaches are in their purpose and scope. Whereas BCA uses measures that

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relate mainly to the specific objectives of the proposed projects (e.g. travel time reduction), other methods use a broader range of measures that relate to social and environmental objectives. Moreover, whereas BCA uses as its evaluation criteria quantitative objective measures (e.g. increased traffic volume), other methods also use qualitative-subjective measures like social congruity. It is convenient to classify all evaluation methods into four main categories: 1

2

3

4

Benefit cost comparisons: • cost-effectiveness analysis (CEA). • benefit-cost ratios. • benefit-cost analysis (BCA). • risk-benefit analysis. Multi-criteria analysis (MCA): This is really a large family of methods designed specifically for particular applications (e.g. regime analysis, flag methods, discrete and continuous methods). Impact statements (IS): • social impact statement. • environmental impact statement. Others: • total cost analysis. • full costs and benefits analysis. • project’s life cycle analysis.

Given our focus on economic growth, it is pertinent to ask which of these approaches is most suitable for the evaluation of economic development effects from an infrastructure project. A related issue is whether it is desirable to enforce the use of a standardized evaluation approach for all projects by all public decision-making bodies. While the use of a single method avoids disagreement among partners to the same project (e.g. various municipalities) and enables a true comparisons of results, the use of an inadequate appraisal method can foster the selection of the wrong projects everywhere. A 1994 study by the European Commission has categorized all impacts from BCA into nine categories. Six are mandatory impacts that should be assessed in every case and three are discretionary. The mandatory impacts include construction costs, maintenance costs, vehicle operating costs, time saving costs, safety and local environment. The discretionary impacts include strategic environment, strategic planning and economic development, and strategic policy. Thus, economic growth was relegated only a secondary weight in the overall evaluation of a project. On the other hand, recent ISTEA legislation in the US has placed economic development effects at the top of the list of impacts that a local planning organization (MPO) must consider.33 How do these ‘other approaches’ handle economic development impacts?

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As already explained, a key difficulty with the application of BCA is its inherent prerequisite to quantify all benefit and cost elements and then convert them into monetary values. Obviously, in many cases such efforts can turn into highly speculative and subjective activities. For this reason the idea behind Cost Effectiveness Analysis (CEA) is to select a policy alternative which is assessed to generate the greatest amount of ‘output’, given the investment. We use the term ‘output’ to denote that outcomes from a policy are not converted into monetary value and aggregated to produce total benefits as in conventional BCA. Rather, the results from a policy are measured in real units. The CEA approach then produces an index of this output over the available budget. Application of this index to different policy alternatives is subsequently used to select the ‘best’ policy option. For example, output from a policy whose objective is traffic calming can be ‘the change in the number of vehicles that cross a certain intersection’. Notice that this approach does not guarantee that the project’s total benefits would exceed its total costs, as in the case of BCA, nor does it concurrently consider multiple types of outcomes from the project. In contrast, the Multi Criteria Analysis (MCA) family of approaches tackles the problem of multiple outputs from a project that cannot be measured in monetary values and then aggregated into a single index of total benefits. It does so by placing weights on the different benefit and cost effects (or objectives and criteria) which reflect the importance that a decision-maker attaches to each effect. Thus, total benefits from the project, TB, are measured as where wi is the weight given to effect type i, (whose magnitude is Xi), and

The weights can be derived by pooling the opinion of

experts or simply by asking decision-makers to define them. However, in reality when it comes to deciding on the proper weight to assign to each category of benefits and costs, there is no agreement on how to derive a fair and systematic weighting scheme—the weighting systems and values obtained are subjective. In this regard, MCA is not any better than BCA, which requires the quantification of all variables in monetary units. For example, if economic growth is an important criterion for the undertaking of an infrastructure investment, a policymaker will attach a larger weight factor for growth effects, thereby affecting the selection of projects. Impact statement (IS) methods typically require the enumeration and quantification of all impacts, including the positive and negative ones. Subsequently, an impact matrix showing the effects from each policy alternative is produced. A decision regarding the choice of the ‘best’ alternative is then made on the basis of inspecting the impact matrix. Under IS, there is no attempt made to distinguish between effects that should be regarded as benefits and those that are costs. Thus, in terms of BCA, double counting is possible. Moreover, since there is no aggregation of effects to compute total benefits and total costs, the final choice of an alternative is largely arbitrary.

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In terms of economic growth effects, the IS method is similar to MCA in identifying such possible effects except that no weights are attached to them. It is possible to find in the literature many variations of the above key approaches, in particular of BCA. We group these methods under the header ‘others’ and notice that basically this category includes methods that can be regarded as an expanded list of costs and benefits. DeCorla-Souza et al. (1997) have proposed total cost analysis (TCA) as an alternative to BCA in the evaluation of transportation projects. Under their approach the full costs of each alternative or of each mode (including direct and indirect costs) are accounted for. Given the above discussion, which method is the most well used for the evaluation of transportation infrastructure projects? For illustration purposes, Table 7.4 summarizes the principal characteristics of the evaluation methods used in the in European Union (EU) for the appraisal of rail projects. Table 7.4 Summary of rail investment appraisal attributes in EU member countries

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Source: Commission of the European Communities (1992). Notes: NPV=net present value; IRR=internal rate of return; B/C=benefit/cost; N/A=not available.

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It is evident from Table 7.4 that most EU countries use the traditional BCA approach with net present value (NPV) and internal rate of return (IRR) as their key evaluation criteria. It should be understood that in many European countries the rail system is regarded as the backbone of the entire transportation system. As a result, special efforts are made by these countries to implement long-term and large-scale rail projects. For example, small countries like Denmark and the Netherlands, in carrying out BCA, place special emphasis on international rail links. In Germany a very low discount rate is used to encourage long-term rail projects. 7.6 Conclusions The principal objectives of this chapter were: first, to examine the underlying rationale that can be used to explain the linkage between the prime benefits from a transportation infrastructure investment and local economic growth; second, to examine at some detail, key elements of the evaluation process that affect the measurement of benefits from a project and, consequently, the extent of the potential economic growth effect; third, to provide evidence from various countries relative to their use of evaluation methods and criteria. Since benefit-cost analysis is the leading practical approach to the assessment of transportation infrastructure projects, we cast the analysis within the framework of this methodology (Figure 7.2). We have defined local economic growth from a transportation improvement as ‘the continuous increase in economic activity, in the impacted area, that can be attributed to this investment’. Given this definition the most important conclusion from this chapter is that transportation capital investments do not necessarily generate economic growth benefits. In fact, under regular conditions they generate almost exclusively accessibility improvement benefits with, perhaps, some environmental effects. Thus, attempts to measure additional benefits, such as economic development, will amount to double counting of benefits. It is only when the analyst can demonstrate the existence of certain allocative externalities that additional economic growth benefits can rightly be ascribed to the investment. Such externalities include spatial agglomeration, labour market imperfections, transportation network economies and environmental improvements. Correspondingly, spatial relocation of land-use activities following an infrastructure investment, even if shown to be engendered by the investment, cannot be regarded as economic growth. For such an effect to transpire there must be a consequent increase in economic activity in the impacted area which can stem from agglomeration or labour market economies. A related conclusion from the discussion in this chapter is that the prime benefits from a transportation infrastructure investment are accessibility improvements and that all other potential benefits, mainly economic growth, emanate from these principal benefits. Hence, all factors that can affect the

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measured level of accessibility benefits from a project will also affect the measured level of economic growth benefits. These factors include the value of travel time, the discount rate used to compute the net present value of the project, and risk and uncertainty elements which affect the magnitude of future streams of benefits and costs. Without properly accounting for these factors, the measured accessibility benefits from a given project will be biased and so will the measured economic growth effects. Furthermore, since the relationships between the prime benefits and potential growth effects are non-linear, the erroneous measurement of accessibility benefits might produce exceedingly inflated growth benefits. Finally, benefits from a transportation project can be correctly measured only if evaluated at the point of social equilibrium. For this to take place it is necessary that the total social costs of the project will be borne by the users. Otherwise, without accurate knowledge of demand and cost function elasticities it is not possible to ascertain whether the actually measured benefits over—or underestimate the optimal ones. Notes 1 The literature also uses the terms cost-benefit analysis (CBA or COBA) or social benefit cost analysis (SBCA). 2 For classical reviews of BCA the reader is referred to Prest and Turvey (1965) and Mishan (1969). For a treatment of BCA of transportation projects see Nash (1993) and Small (1999). 3 McGuire’s estimate is an average value for a large number of projects, some of which may have generated output elasticity values that were well below or above this figure. 4 The Intermodal Surface Transportation Efficiency Act (ISTEA) in the USA requires that such analysis be carried out before projects can be approved for funding and implementation. 5 It is beyond our scope here to examine these and related issues at any degree of rigour. Suffice it to point out that beginning with Marshall (1920:811), Hicks (1943) and Friedman (1949) to more contemporary writers like Willig (1976) and Mishan (1988), the theoretical underpinnings of consumer surplus have been thoroughly examined. It should also be pointed out that interpersonal utility comparisons are done routinely at all levels including political decision-making, as a matter of carrying out social and economic public policy. For approaches dealing with income and price effects in the computation of consumer surplus see Freidman (1984: Chapter 5). 6 In many highway improvement cases, which aim at alleviating tight congestion, latent demand is high enough to make the facility after the improvement as congested as it was prior to the expansion. Downs (1962) has pointed out that, this phenomenon notwithstanding, the project has generated real benefits to society in the form of more people performing trips that they wish to undertake. 7 Small (1999) calls them ‘technological externalities’. 8 In a recent review Anas et at. (1998) have examined types of externalities that affect urban spatial structures. They have categorized them as spatial nonhomogeneities (i.e. factors affecting the uniqueness of locations), firms’ internal

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9 10

11

12

13 14 15

16 17

18

19 20 21

Methodology

scale economies, external or inter-firm scale economies (regarded above as agglomeration economies), imperfect competition (e.g. spatial oligopoly). Such imperfections may also prevail at the demand side where firms’ demand for labour is constrained by the available pool of skilled labour which, in turn, is affected by spatial accessibility. This network effect can also work in the opposite direction. Known as the ‘Braess’ paradox’, in some instances the addition of a new link to an existing network may actually increase total travel time. This phenomenon is mainly due to the fact that users minimize their private travel time rather than the system’s average or system’s total travel time. If congestion is unpriced, the result of introducing a new link may be the diversion of traffic from a longer but uncongested link to this new link with the overall result of slower traffic everywhere. Interesting examples are the Scandinavian link and the Nordic link. The Scandinavian link is a transport corridor connecting Oslo, Gothenburg and Stockholm with Malmo, Copenhagen and Hamburg by means of a four-lane highway and a dual-track railway. The Nordic link is a transport corridor from Hamburg through the Danish peninsula to southern Norway. See Kristiansen (1993) for details. Jara-Díaz (1986) and Jara-Díaz and Farah (1988) have shown that if the demand curve for final goods in each region is linear and the two monopoly firms operate under conditions of constant marginal costs, total transportation benefits, from an infrastructure expansion, are approximately half the size of the change in total consumer surplus. The other half is attributable to gains in trade and reduction in prices of final goods. See, for example, Small and Kazimi (1995) on air pollution costs and Viscusi (1993) on the economic costs of road accidents. A quite unlikely result when considering metropolitan modal availability, network layout, as well as political factors. In fact, Gordon and Richardson (1994) have shown empirically that, as a result of firms relocation to suburban areas and the formation of sub-centres over the last two decades, average travel times and congestion levels have declined, even though no substantial investments in infrastructure facilites were made. Using a multinomial logit formulation this expression, can be derived from the expected value of utility maximization by an individual, i.e. E(Max.Uk), ᭙k Î R, where Uk is total utility from alternative k (McFadden 1981). Expression (7.3) is identical to the expected compensating variation (the amount of income a consumer must receive to leave his utility unaffected by the price change), or the equivalent variation (the amount of income a consumer would be willing to forgo to avoid the price change). For example, a widely used commercial network assignment model is ‘EMME2’. This model, once calibrated relative to parameters of the link’s volume-capacity function, computes minimum travel time over the network between all origindestination pairs, given the demand matrix. Following a capacity improvement of a given link and using the previously calibrated parameters, the model cannot separate between the additional traffic on the improved link from traffic caused by users switching between peak and off-peak periods, from newly generated traffic, or from traffic diverted from other destinations. His estimate of overall network returns to scale is 0.84. It implies that if congestion tolls were imposed on all links of the network, revenue would recover 84 per cent of total capital costs. Sometimes the term value of travel time savings (VTTS) is also used. For a rigorous exposition the reader is referred to Ben-Akiva and Lerman (1985).

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22 MVA report (1987:90–2), points out that the differences in VOT savings across modes, in fact, may reflect self-selection by trip makers. Those with high value of time will tend to select fast modes. The inclusion in the econometric model variables that can control for such effects may help overcome this problem. 23 For car passengers, Waters II (1992) found VOT to be only 35 per cent of the wage rate. He further recommended that VOT should increase with the traffic conditions and it would reach 100 per cent of the hourly wage rate for stalled traffic. 24 Presumably, the equilibrium price of hauling cargo reflects cargo’s travel time. In that case the value of time savings to the cargo carried is already included in the time savings to the driver and vehicle which are reflected in the price charged by the shipper. 25 The other two approaches are, first, inference of VOT time from previous public projects that affect truck movement (e.g. willingness to pay tolls to travel at higher speeds); second, assessment of VOT from stated preference studies of carriers. 26 ‘The long run is a misleading guide to current affairs. In the long run we are all dead’ (John Maynard Keynes). 27 Since consumers are also the owners of firms, their investment in firms, whether private or public, also reflects their substitution between consumption and investment. 28 The use of these rates assumes that competitive markets rates reflect the real net social return. See, for example, Boardman et al. (1996) for calculation of rate of return of private investments. 29 Pigou maintained that consumers suffer from ‘defective telescopic faculty’, implying that they tend to over-emphasize present and near future periods. 30 See Engel et al. (1996) for data on the volatile distribution of the percentage of vehicles paying tolls in selected cities in Chile between 1987 and 1995. 31 To illustrate and following Pindyck (1991), consider an example of a public investment in a toll road. Assuming no operating costs, we denote the capital outlay by I. The investment is expected to generate annual revenues of B1 over the project’s life span of N years. Assuming sunk capital costs, the decision to invest today (t=0) is irreversible. We further assume that investment made this year will generate revenues the following year (t=1). Due to lack of information regarding the impact of simultaneous investments in parallel and connecting roads there is uncertainty about future demand elasticities, hence over revenues in subsequent years (t=2, 3, …, N). With probability p, revenues can rise to B2, (B2 > B1), but with probability of (1-p) they can fall to B3, (B3 < B1). Let r be the project’s discount rate. What is the best course of action between the decision to invest this year and the decision to delay the investment by one year? Obviously, if the difference between the NPV of the project postponed by one year and its NPV of investing this year is positive, the delay is economically warranted. Remembering that we invest next year only if revenues go up (which will happen in probability p), the rule is:

The term DNPV is the monetary value of the flexibility to invest at a future year rather than at present. Let I=$1,000; B1=$200; B2=$300; B3=$100; N=10; p=0.5; r=10 per cent. It can easily be seen that for this DNPV=$154.4, illustration hence, it pays to delay the project by one year. Notice that this result will hold for every

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32 Assuming the marginal revenue function from the investment is convex in price to users. 33 The new Transportation Equity Act (TEA-21), signed by President Clinton in late 1998, includes the following as a key MPO factor: ‘Support the economic vitality of the metropolitan area, especially by enabling global competitiveness, productivity, and efficiency’ (Progress, 1998).

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APPENDIX 7.1 Example: evaluation of transportation capacity improvement benefits in the presence of an allocative externality The objective of this example is to demonstrate the possibility of assessing benefits from a transportation project while taking into account allocative externalities. To that end, consider a transportation infrastructure project aimed at improving travel times and flow over a certain segment of the road network by expanding its capacity. From a social welfare viewpoint we ask: What is the benefit-cost rule which should be used in order to determine the level of optimal benefits and investment in the presence of congestion externalities?1 Analytically, our objective is to determine optimal traffic volume and capacity at the point where users pay the full marginal social costs of travel, namely actual travel time and money costs plus the costs of congestion externality. To facilitate the analysis we assume identical users with a fixed demand function. We use the following notation. Let, V be traffic volume; K be road capacity; P(V) is users’ demand function (travel price as a function of traffic volume); C(V, K) is users’ average cost function of travel; and F(K) is the capacity investment function. All variables are defined per one unit of road (1 km of lane road). The objective is to maximize a social welfare function W, with respect to V and K. Notice that in this analysis we do not assume latent demand so that P(V) is known with certainty. The social welfare function is defined as: (A7.1.1)

First order conditions are2 (A7.1.2)

(A7.1.3)

The expression: whereas

is the social marginal cost curve is total congestion costs as a function of K and V.

From condition (A7.1.2) we obtain the known result that at equilibrium user

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price P(V), should equal total social marginal costs which are composed of average user’s costs and congestion costs, i.e. (A7.1.4)

where P*, is the social optimal price that users need to pay in order to internalize the congestion externality. From condition (A7.1.3) we obtain the cost-benefit rule for the investment. That is, the marginal dollar invested in road capacity expansion,

should generate benefits (measured in units

of travel time) that exactly equal the reduction in total congestion costs from that investment, – These conditions are graphically shown in Figure A7.1.1, where point E* represents social equilibrium and V* is optimal level of traffic, given optimal road capacity. The reader should notice that the attainment of maximum social welfare requires these two conditions to hold simultaneously. Thus, the benefit-cost rule for capacity expansion applies when calculated for the optimal volume of traffic V*. The full price that users need to cover which equals social marginal costs at point V* is composed of the congestion externality, private users’ costs and the investment costs. To compute it, from (A7.1.2) and (A7.1.3) we obtain

(A7.1.5)

where l is the degree of homogeneity of the travel cost function in V and K.3 From (A7.1.5), to achieve a Pareto Optimum solution total payment by all users, V*.P(V*) should equal the social costs (composed of total private travel costs, plus congestion costs), V·C(V, K), plus total investment costs, F(K). Therefore, for this result to hold we must assume that l=1, and that there are no scale economies in capacity investment.4 The primary objective of the above discussion is to highlight the notion that, from a theoretical viewpoint, a correct assessment of benefits from a transportation infrastructure improvement requires that the analysis be carried out at the point of social equilibrium (point E* in Figure A7.1.1). The reader should notice that for equilibrium to be at point E* the value of the congestion externality

should be priced to users as a congestion toll whose

value is P*-PG per user (Figure A7.1.1). However, from a practical viewpoint, highway tolls, even if imposed, are rarely optimal relative to the time of day

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Figure A7.1.1 Measurement of benefits.

and the congestion level at that period. Consequently, benefits from capacity expansion are normally measured at a point of sub-optimal tolls or, which is the more prevalent case, at market equilibrium where the demand function P(V), intersects the users’ cost function C(V, K), (point V0 in Figure A7.1.1). The question of interest then is whether benefits from a given capacity expansion, associated with market equilibrium solution are systematically larger than those associated with the first best (point E*)? Some authors (e.g. Wheaton 1978) argued that the lack of road pricing would lead to over-investment in highway capacity. The main reason being that without pricing more users will use the highways than is socially optimal, thus requiring greater capacity. Others, however, have shown that this conclusion is rather contingent on the value of a number of key parameters. Wilson (1983) has shown that for discrete toll changes and under certain values of users’ demand elasticity with respect to traffic volume, Wheaton’s general conclusion may not hold. The study by d’Ouville and McDonald (1990) has highlighted the role of the elasticity of substitution between the travel cost function C(V, K) and capacity (denoted by s). They generally conclude that the smaller is s, the larger are the cost savings (benefits) from a given capacity expansion. An additional factor noted above is the elasticity of the investment function with respect to capacity (denoted by y), which can influence the results. Given these factors, in general it is not possible to provide a definite answer to the above question (see Grunau 1994 for analytical exposition). However,

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if we can assume that travel demand is price inelastic, that (as estimated by d’Ouville and McDonald 1990), that l =1 and y =0, then we can conclude that when moving from a regime of no tolls to that of sub-optimal ones to that of optimal tolls, smaller capacity expansion investments will be required. If we further consider the fact that tolls are usually imposed on very few links of a network, thereby causing traffic diversion to untolled links, overinvestment in capacity can be expected, as was originally claimed by Wheaton (1978). It follows, therefore, that benefits from a transportation infrastructure project are probably exaggerated if estimated under users’ market conditions. Notes 1

See Small (1992: Chapter 2) for a similar analysis. For the derivation of such rules at the network level see Yang and Huang (1998).

2

Second order conditions are assumed to hold, i.e.

3

The degree of homogeneity of a function f(x, y) is defined as, If, l =1, the function is said to be homogeneous of degree one. If the average cost function C is homogeneous of degree 1, then the total cost function is homogeneous of degree 2.

4

Analytically, y

where y is the elasticity of the capacity investment

function with respect to the investment.

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APPENDIX 7.2 Derivation of the value of time: a review and analysis Most theoretical approaches which underlie the derivation of the value of time are descendants of Becker’s formulation (Becker 1965). In his model the utility of a representative consumer is maximized subject to time and budget constraints. Subsequent developments by DeSerpa (1971, 1973), Mohring (1976), Bruzelius (1979) and Hensher and Truoung (1984) have introduced the time spent in various activities directly into the utility function. To derive analytical definitions of the value of time we present a time allocation model which contains two time categories: time spent at work and time spent in leisure activities (see also MVA 1987). Subsequently, we will introduce a third category, namely, time spent in travel. Formally, the time allocation problem faced by a representative consumer can be modelled as follows (DeSerpa 1971, 1973; MVA 1987; Small 1992): max.U(tw, tl, z) tw, tl, z subject to:

(A7.2.1)

In this formulation zi is a consumption good whose price is pzi and z is a vector of consumption goods. The term tw is time spent at work, and w is the wage rate (net of taxes). Hence the term: w·tw is earned income1 and y denotes unearned income. Let tl be the time spent on non-work activities. We further denote by the total time endowment of individuals (normally, 24 hours, net of essential requirements like sleeping and eating),2 and by and and the minimum amount of time allotted to work and leisure activities, respectively. In this analysis we assume that the level of consumption goods z is independent of the cost of travel and that it is exogenously defined. The parameters. l , µ, q, h are the shadow prices of the constraints. The first constraint in (A7.2.1) is the monetary budget whereas the second is the time budget. The constraints: and imply that each time component requires a minimum time duration3 (which may, of course, be zero).4 Essentially, in this model a consumer makes three simultaneous decisions. He decides about the allocation of income between consumption goods; the utility maximization quantity of each good; and the allocation of time between work and leisure activities. Setting up the Lagrangian of model A7.2.1 we get:

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(A7.2.2)

The Lagrangian multipliers l, µ, q, and h reflect, respectively, the marginal utility of income (earned and unearned), the marginal utility of total time available , the marginal utility of changing the minimum working hours constraint and the marginal utility of additional leisure time. The first order conditions are: (A7.2.3)

(A7.2.4)

(A7.2.5)

By using the consumption good as a numeraire (and setting its price to equal 1), we get: (A7.2.6)

The left-hand-side of (A7.2.6) is the value of time (in undertaking a leisure activity). If the leisure time constraint is not binding then this marginal valuation of time

, equals

time (MVA 1987). If it is binding,

which is known as the resource value of , the difference,

is referred to as

the value of time savings in an activity (DeSerpa 1971). It is also commonly referred to as the value of time, which is used to appraise transportation projects. From (A7.2.6), the value of time-saving equals the resource value of time

, minus the marginal valuation of time spent for leisure activity

Dividing A7.2.3 by A7.2.5 and assuming that the work hours constraint is not binding (q=0), we obtain the marginal utility of time used for work. That is:

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(A7.2.7)

Expression A7.2.7 implies that when an individual is free to work as many hours as he wishes, he will do so until the last unit of time spent at work approximately equals his wage rate, minus the resource value of time.5 From the above first order conditions we get: (A7.2.8)

If the minimum work hours and the minimum leisure time constraints are not binding (i.e. q = 0, h = 0), then the left-hand side of equation (A7.2.8) is the total reward for an additional unit of work time which, at equilibrium, equals the utility value from the last unit of leisure time foregone (the righthand side). This value is, essentially, the opportunity costs of work time. Notice that the above formulation does not include travel time as a separate element of time. On the one hand it is possible to claim that travel time is just another type of time spent in undertaking an activity similar to, say, leisure. The derivation of travel time value will then be identical to that of leisure . On the other hand it might be argued that travel time differs from any other type of time in a number of important ways. First, it confers a disutility, whereas the utility from time spent in all other activities is positive. Second, to a large extent, travel time is not fully controllable by individuals as it is affected by exogenous conditions like road congestion, route layout, and frequency of public transit modes and availability. Third, individuals can affect their travel time by such transportation means as mode choice, time of departure or choice of a route. Relocation is perhaps the most effective way to control for travel time, though for many individuals it is prohibitively costly and in the short run non-feasible. Thus, given location, the question is how to treat travel time in a utility maximization framework. In the random utility literature Train and McFadden (1978) first took up this question. Their objective was to examine how price and income can enter the direct utility function of mode choice. Truong and Hensher (1985) further investigated this issue within a framework of DeSerpa’s model. Their objective was to derive a simple form of the deterministic element of the random utility model for the choice between travel alternatives (see also MVA 1987). The main result is that the ratio of the shadow prices of the income constraint, which include travel costs, to that of the constraint of travel time associated with a transportation alternative (e.g. a mode or a route) is the value of time saving spent in using that alternative. It is possible to reformulate problem A7.2.1, to investigate the impact of

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change in accessibility on travel time where accessibility is a combination of travel time and costs. Thus:

max . U (tw, tl, z, t(d)) tw, tl, z subject to:

(A7.2.9)

In this model t(d) and F(d) are the time and costs of travelling distance d, respectively. Notice that the money cost of travel, F(d), is introduced directly into the budget constraint (see also Mohring 1976). Following the same analysis as above we obtain: (A7.2.10)

This condition indicates that the marginal utility of travel time equals the (negative) ratio of the shadow price of the income constraint l weighted, to the shadow price of the time constraint µ. The weight is the change in the marginal cost of travel from a marginal change in travel time, which we have regarded as accessibility change. Assuming that the minimum leisure time constraint is not binding, we get: (A7.2.11)

The left-hand side of A7.2.11 represents the total value gained from an additional travel or distance. It is composed of the change in utility from travel time plus the accessibility change during that time period. As in A7.2.6, the total value of the last unit of travel equals the value of the last unit of leisure time foregone. Notice that conditions which are internal or external to the individual can affect his subjective valuation of time. Examples of internal conditions are preferences structure, level of earned and unearned income and/or proximity to work location. On the other hand, the wage rate, the money cost of travel, the price of the consumption good and traffic conditions are external factors. Hence, the same individual might change his valuation of a unit of travel time saved, depending on the work and travel environment he faces.

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Notes 1

2 3 4

5

Notice that we do not assume here that the hourly wage rate (w) depends on the amount of time spent at work (tw) as some studies do (see, for example, Ramjerdi 1993). Similarly, we do not assume that the wage rate is location dependent, i.e. w(d); (see Gunn 1991). Adding such assumptions would not alter the main conclusions arrived here in any fundamental way. The choice of 24 hours implies that other variables in the system, such as earned and unearned income, should be proportionate to this time period. In reality, these time constraints might be a function of other factors. Here we assume them to be constants and exogenous. This characterization of the model is fundamentally different from Becker’s (1965) household activity production model mainly in that here we explicitly distinguish between time as a commodity (which enters the utility function) and time as a resource which is subject to a resource constraint (see DeSerpa 1971). By definition, the unit of time of 24 hours is always binding so that strictly speaking µ> 0.

8

A model of transport infrastructure development and local economic growth

8.1 Introduction The fundamental rationale of economic growth from transportation infrastructure developments is that the primary effects from these investments are improved travel times and volumes over specific links of the transport network (Chapter 7). These factors then affect the relative accessibility of locations within the impacted area. These accessibility changes can potentially encourage economic growth, provided that market externalities, mainly production and labour market economies, are present. We have already examined the effect of capital accumulation on national and state growth (Chapter 6). In this chapter we focus on local economic growth emanating from transportation improvements, mainly with regard to the use of labour and labour productivity. In general, changes in the relative accessibility from facility development in a particular area can generate two potential effects. They can bring about activity relocation and they can cause firms and households in the impacted area to modify their production and consumption schemes. We regard these changes as ‘local economic growth’, indicating the increase in local output or in output per capita, and in the use of input factors (mainly labour) and in factor productivity1. Empirically, in assessing local economic growth arising from a particular transport infrastructure investment, it is rather a complicated task to separate changes in activity location from changes in the production and consumption schemes of firms and households. Theoretically, however, we may wish to identify the conditions for economic growth spurred by activity relocation and those generated by changes in the production and consumption behaviour of firms and households. In either case we need to define the mechanism by which changes in accessibility are transformed into local economic growth. Underlying this analysis is our contention that growth arises primarily from the ability of firms and households to exploit positive externalities, such as agglomeration economies, or lessened negative ones, such as alleviating traffic congestion. Thus, we explicitly assert that the presence of such non-internalized

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externalities constitutes a necessary (though not a sufficient) condition for local economic growth emanating from infrastructure development. The model presented in this chapter is based on this assertion. The structure of this chapter is as follows. In Section 8.2 we present a theoretical framework within which we investigate key structural elements that underlie the relationships between infrastructure investment and local economic growth. Subsequently, in Section 8.3, we formulate a simple model of the spatial economy in which firms operate under conditions of agglomeration economies, and households that supply labour input, are subject to travel congestion. Through simulations (Section 8.4), we examine the impacts of transportation network capacity improvements on local economic growth in terms of the equilibrium level of employment and labour productivity. In Section 8.5 we briefly present results from an empirical analysis of the relationships between accessibility improvements and labour force participation, using real data from the Bronx in New York. Major conclusions are presented in Section 8.6. 8.2 Theoretical framework 8.2.1 Determinants of transpor t development and local economic growth in a spatial economy As noted in Section 7.2.2, major explanation advanced in the literature for observed differences in urban spatial patterns, the level of economic growth and labour productivity across cities, is agglomeration economies. The concept of agglomeration economies and its use in the analysis of firms’ location, industrial clustering and the formation of urban activity centres, has a long tradition in regional science and urban economics (see, for example, Isard 1956; Weber 1956, Chinitz 1961; Beeson 1992; Selting et al. 1994; Anas et al. 1998). In contemporary urban economies, industrial agglomeration economies are manifested in the forms of forward linkages (firms interact with customers), backward linkages (firms interact with suppliers) and sideways linkages (firms interact with each other).2 For the present analysis we regard agglomeration economies as the case when average costs decline as more production takes place within a specific geographical area. It basically arises from positive technological and pecuniary externalities that emerge when economic agents (firms) locate in close spatial proximity (Anas et al. 1998). In general, such externalities include a Smithian labour specialization, technological innovation and diffusion and human capital accumulation. Possible factors that underlie their emergence are information spillovers, the use of a common infrastructure facilities (such as energy, communication and transportation), and access to a common pool of specialized labour force. Whatever the exact reason, these effects stimulate productivity gains and bring about a reduction in production costs.3

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In attempting to explain the extent and impacts of agglomeration economies, some researchers have resorted to the concept of positive feedback effects in the local economy that give rise to spatial clustering of activities such as employment and shopping centres (see, for example, Arthur 1991; Krugman 1991b). The presence of such feedbacks can amplify the impact of changes in the activity level of one economic entity on the production and the location decisions of others. These effects, in turn, induce economic activities to cluster in groups of varying sizes, in order to reduce interaction and production costs. The sizes of such clusters are affected by limiting forces, mainly land scarcity and transportation congestion.4 Four key factors need to be recognized in analysing the effect of agglomeration economics on growth. First, that agglomeration economies, as production externalities, can be external to the economic entity (the firm) that produces it. Thus, agglomeration economies reduce the industry’s marginal costs at a given level of output, even if each firm operates at decreasing returns. This is a rather important observation as it implies that the analysis is not confined to the case of declining cost industries such as public utilities. Second, that in order to determine the benefits from agglomeration economies, they must be expressed in measurable units. Changes in activity density, in per capita income, in the unit cost of production, in output-to-input ratio, or in the variety of goods and services produced in the region are examples of such quantifiable units. The third factor to recognize in assessing the effect of agglomeration economies is that their exploitation implies some spatial proximity between firms, and therefore their level attenuates over space. Thus, the correct analysis of agglomeration should be cast within a framework of a spatial economy. Finally, the derivation of benefits from agglomeration economies requires some spatial interaction, which in turn implies costs of interaction (e.g. travel costs). If these costs are rather extensive relative to the agglomeration effect, spatial clustering and economic growth may, in fact, not transpire. Given this perspective, how can we model the impact of accessibility changes on the local economy? Borukhov and Hochman (1977) have formulated a model in which all locating activities wish to interact spatially with each other. This behaviour gives rise to agglomeration economies and the creation of activity centres. In general, the formation of activity sub-centres is explained on the basis of the interrelationships between the positive externalities emanating from the concentration of production and service activities and the negative externalities associated with the costs of spatial interaction. Harris and Wilson (1978) have shown that the trade-off between scale economies in location and transportation costs can result in the emergence of multiple activity centres whose number depends on the relative strength of these forces. Papageorgiu and Smith (1983) have further shown that the distribution of activity concentration over space will become non-uniform when agglomeration

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economics (defined as positive externalities from co-location) are strong relative to the costs of spatial interaction. It should be recognized that the pattern and volume of inter-nodal travel, carried out over a transportation network, is the physical manifestation of spatial interaction. As a consequence, the actual costs of spatial interaction are affected, inter alia, by the capacity and other attributes of this network. It follows that observed activity concentration, given the degree of agglomeration economies, can be affected by network development. Furthermore, since total output in the economy (as well as the demand for labour) is affected by activity location and concentration patterns (e.g. by sub-centring), the expansion of the transportation network can affect the level of economic activity through its effect on travel costs. Since travel costs also affect households’ disposable incomes and their allocation of time between labour and leisure, infrastructure expansion can further affect households’ supply of labour, thereby the equilibrium amount of labour used in the economy. In summary, when formulating a model to show the effects of infrastructure development on local economic growth, it is necessary to take account of all these factors. To reiterate, the key ones are agglomeration economies, network structure (including the costs of spatial interaction) and the response of firms and households to the presence of production, labour and transportation externalities. 8.2.2 Measures of economic growth Before presenting our modelling approach, it is useful to examine alternative measures of growth. We cluster them into four main categories: 1

2

3

Conventional firm-related real growth measures5 : these include changes in output-to-input ratio, changes in partial and full factor productivity, changes in the amount of input factors employed (mainly labour), and changes in the firm’s technical and cost efficiency. Individual or household related measures of economic growth: these entail an increase in individuals’ utility relative to their consumption and opportunity space. They include changes in the size of the job market area, changes in the number of non-work related spatial opportunities (e.g. shopping outlets) and changes in the amount of time allocated to leisure activities. Under certain conditions (see below), the willingness of households to increase their supply of labour is another indicator of local economic growth. Increases in consumption, following an infrastructure improvement, ceteris paribus, can be used as another indicator of growth. This latter effect results from the capitalization of the investment’s benefits in the form of enhanced consumer surplus (Anas 1995). Technology-related growth measures: basically these reflect the increase in the use of technologies, which are complementary to

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215

traditional trans- portation effects, following infrastructure improvements. Changes in business production strategy, such as just-in-time production, increased intermodality in freight movement and improved access to major regional facilities like airports, are all examples of such measures. In this regard, if telecommunications are indeed a complementary technology to conventional transportation as some studies argue (Plaut 1997), then the rate of proliferation of such technologies can also be used as a measure of local economic growth from transport infrastructure improvement. Market-related growth measures: these are actually a combination of the above measures. They include indicators such as the level of equilibrium employment, income per capita, the product range and the number of new firms coming into the market (the region or the city).

The main growth indicators used in the present analysis are the equilibrium employment level and labour productivity. Other measures include the allocation of time between work and leisure and changes in the demand for the consumption good. In terms of Figure 8.1 (p. 226), our primary growth variables are changes in firms’ demand for labour, in households’ supply of labour, and changes in their consumption patterns, following improved accessibility from an infrastructure capacity investment. 8.3 A model of a spatial production economy with transportation infrastructure effects In this section, we formulate a model of a spatial economy with households, production firms and transportation infrastructure as its main components. Beginning from a state of equilibrium, by changing the capacity of the transportation system, we can observe changes in the equilibrium levels of the economic growth measures outlined above. 8.3.1 Structure of model We assume an urban economy which is comprised of three principal sectors: • •

•

a households’ sector, which supplies labour units and consumes leisure and consumer goods; a production sector represented by locating firms, which produce nonhomogeneous output for external markets and use labour and private capital as their main inputs; a transportation infrastructure sector, which is represented by an interzonal transportation network.

The physical capacity of this network affects the level of congestion and, as explained shortly, the amount of labour, which households are willing to supply. Space is divided conventionally into locational zones.

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Beginning with the production sector, we assume non-homogeneous firms with respect to their output type (i.e. they produce differentiable goods). Labour, on the other hand, is assumed to be a homogeneous input factor composed of units of time. We further assume that outputs are sold in export markets so that the price of each is determined exogenously. The production and location decisions of firms, as well as their demand for labour and the amount of labour that households supply, are governed by three primary forces—agglomeration economies, leisure and work time substitution and travel congestion. It is explicitly being assumed that the level of output of each firm and their average costs are positively affected by agglomeration economies, which induce these firms to locate in close proximity. However, as firms move closer, the level of travel congestion also grows, as a result of more trips being concentrated in a smaller area. As a result, households will alter their allocation of time by reducing the amount of time spent on all activities, including labour. In this model we disregard the costs of firms’ relocation. The main role of the households sector is to supply labour for production purposes and consume leisure and non-leisure type commodities. Therefore, we assume homogeneous consumers who trade off leisure with work time. We further assume a fixed residential location relative to employment location, though in this model firms are free to move in order to optimize their location relative to agglomeration economies. Individuals travel between their residential and employment location zones and purchase a consumption good for which they pay using their income from work. The third component in the model is the transportation sector, which represents the product of government’s decision-making regarding infrastructure provision, financing and regulation. In Parts I and II we discussed in detail the role of the public sector in the supply of transportation infrastructure. Here, we merely point out the fact that by increasing the capacity level of the fixed infrastructure through transportation investments, congestion is alleviated and a new spatial and production equilibrium is achieved. While this public supply is not cost free, we disregard investment costs, but we assume that the addition of new infrastructure facilities or the expansion of existing ones, is welfare improving. We also disregard the money costs of travel that commuters pay which (in part) may cover the investment’s capital and operating costs. Before presenting the analytical structure of the model, it is worth noting its limitations. First, we disregard the actual time it takes the land use and transportation systems to achieve equilibrium. The model essentially carries out static equilibrium comparisons, where the equilibrium level of employment and productivity is compared with the previous level before a capacityincreasing investment took place. In reality, the effect of infrastructure expansion on growth usually takes several years to evolve. In several places in this book (e.g. Chapters 5 and 7, and the various case studies presented in Part IV), we examine this issue.

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A second limitation of the model is that it assumes an exogenous demand for output produced by local firms, which is independent of local consumption. This may not hold if firms produce primarily for local markets and are strongly affected by local demand. We treat demand and price of output as completely determined by external markets. The underlying assumption is that the local economy is open and small enough relative to the national economy. Making firms’ output dependent on local demand will mainly complicate the analysis without adding any significant insight to the qualitative results. A third caveat is that in this analysis we basically disregard the actual use of land by firms, but more importantly by the household sector. We do not consider residential location changes following changes in accessibility. What these assumptions imply is that residential location choices, land consumption and travel to non-work activities (e.g. shopping) are all unaffected by changes in travel times following infrastructure improvements. In a general equilibrium framework, changes in accessibility from transportation infrastructure improvements should also influence residential location, land consumption by households and firms and travel to non-work activities. Hence, this model produces only partial equilibrium results. However, given the scope of this analysis these results are judged sufficient to explain the effect of infrastructure improvements on local economic growth.6 Finally, we do not account for the method of financing infrastructure investments, which can undoubtedly affect the model’s equilibrium solution and the measured economic growth effects. If, for example, infrastructure is financed through consumption taxes (e.g. a sale tax), it may alter households’ allocation of time between leisure and consumption. On the other hand, if taxes are levied on firms (e.g. a payroll tax), it may affect their demand for labour. The implicit assumption made here is that infrastructure investments are financed from external resources (e.g. through grants) provided by the national government. 8.3.2 Model formulation We consider an economy with three main sectors. A production sector, a households sector and a transportation sector. In this section we formulate the structure and behaviour of each of these sectors, their spatial interaction and the organization of the economy. The production sector Let the index r or r’ (r, r’=1, 2), denote a firm type and the indexes i and j (i, j=1, …, N) denote spatial locations. Thus, for example, yr,i , denotes the output of firm r in location i. For simplicity in this analysis we assume a two firm economy, i.e. r=1, 2. Let y1, y2 be non-homogeneous outputs of these firms.

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Methodology

We assume that they are sold in competitive external markets so that the two firms face horizontal demand functions with prices p1, p2, respectively, which are independent of each firm’s location and output level. The production of these outputs requires the use of three inputs: labour (l) supplied by households, private capital (k), and land (x). We assume the firms to be price takers in factor markets. Given the prices and level of output demanded, the real wage rate and capital costs, and the land rent (at each location), the firms decide on their level of output and use of labour and capital inputs. Their objective function is profit maximization subject to their production technology. In this model all activities, including labour, are measured in units of time (i.e. daily man hours - worked per worker). Total labour (in time units) available in the economy, l , is divided between three main uses: labour used by firms 1, labour used by firm 2, (l1 and l2, respectively), and labour used for consumption and leisure purposes, le (i.e. the time that households allocate for these activities). To simplify the analysis, we assume land rent and lot size to be constant, and independent of firms’ location. That is, in the simulation analysis we assign each land unit at each zone i, a fixed land price (px), which a firm takes as a given. One obvious extension of the model is the inclusion of a land market in which equilibrium land prices are a function of location decisions by firms and households. We assume production to be affected by inter-firm agglomeration economies, so that the level of output of the firm in site i affects production by the firm in j. This agglomeration effect is further assumed to be spatially dependent in that a closer proximity between the firms confers a positive effect on each firm’s level of output. As we will see shortly, the ability of one firm to benefit from locating closer to the other is a major force that drives the model. Denoting by f1 and f2 the production functions of the two firms, it is assumed that ;

, which is interpreted as agglomeration economies,

given the firms’ location sites in i and j. Let (yi¹j, dij), be the degree of agglomeration between the two firms, which also depend on their relative proximity, di,j (see shortly). Thus, the output level of each firm is given by the following production function: (8.1) (8.2)

The constants A1, A2 represent the technology used. Notice that unlike the production function formulations described in Chapter 6, here the firms are not assumed to include public infrastructure as a specific input factor in their production function. Rather, the effect of infrastructure expansion enters the

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analysis through its impact on the level of the inter-firm agglomeration economies and on households’ home-to-work travel time, which is shown to affect their willingness to supply labour. The overall agglomeration effect (yi¹j, dij) is comprised of two components. First, a ‘fixed component, denoted by “g”’, which represents the particular type of inter-firm agglomeration economies, due to the firms’ specific product mix, and which is independent of their location. We assume

0 and

that g is symmetric.7 The second component is the spatial separation (e.g. the distance) between the two firms, dij. Hence, the overall agglomeration effect attenuates nonlinearly, as spatial separation between the firms, increases. Thus, total agglomeration economies are written as: (8.3)

where g is the spatial decay factor. Using Equation 8.3, each firm’s production functions is:

(8.4)

where a, ß, s are, respectively, the labour, capital and land elasticity parameters. Since we do, assume no scale economies in production, a + ß =1. Notice that if g=o, (i.e. no product-mix specific agglomeration economies), the overall agglomeration effect is completely ineffective. On the other hand, if the spatial friction factor(g) is very large (and g > 0), the agglomeration effect declines. Each firm’s profit functions is given by: (8.5)

Each firm is assumed to maximize its profits (Equation 8.5) subject to its production function (Equation 8.4), thereby deriving the optimal output level. As already explained, in this analysis we disregard the impact of land market on production by assuming an inelastic demand for land and fixed lot sizes, irrespective of land price, which are exogenously determined. Thus, in what follows we set the land elasticity parameter, sr (Equation 8.4) to zero. Still another important assumption in this model is the Cournot-Nash behaviour of the production firms. That is, we assume that each firm takes the location and output level of the other as fixed and, given the market demand for its output and the costs of input factors, chooses its location and output level accordingly. In general, the labour demand function, of each firm, is:

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Methodology

(8.6)

More specifically, maximization of Equation 8.5 with respect to the amount of labour input employed and subject to the production function (Equation 8.4) yields the following demand function for labour: (8.7)

From Equation 8.7, as expected, more labour demanded is associated with greater output, i.e.: . More interesting, however, is to observe the effect of agglomeration8. Thus, labour demanded by firm r is an increasing function of the level of output of firm’s r’. That is, Similarly, the demand for capital,kdr is a function of the output level, the price of capital and the agglomeration effect. Specifically: (8.8)

The household sector In this model each household located in each zone i is assumed to have one worker who provides labour inputs, defined as units of time spent at the work place. Hence, total labour supply, ls, is lr=1 + lr=2 = ls In addition, households consume a consumption good, z, and non-work time. The latter is composed of leisure time, denoted by le, and time used for home-to-work travel, lT. Total time available in the economy, denoted by is: (8.9)

Consumers, are assumed to maximize utility defined over z and le. For simplicity, the utility function is defined as Cobb-Douglas and it is maximized subject to budget and time availability constraints.9 That is:

(8.10)

where pz is the price of the consumption good and w is the wage rate (assumed uniform across all households). The taste coefficients µ1 and µ2 are assumed

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identical across individuals and they measure the (constant) proportion of net income spent on le and z. Also, we assume a utility function, which is homogeneous uij of degree one, i.e. µ1+µ2=1. The term U0 = e implies constant idiosyncratic taste of each home-work location pair {ij}. The monetary budget constraint (in Equation 8.9) indicates that leisure time and travel time are purchased at the opportunity costs of time which, in the present case, is the wage rate. The solution to Equation 8.10 yields the demand functions for leisure time (le) and for the consumption good (z), expressed as functions of the wage rate the price of z, and the shadow price of time. These functions have the attributes of unitary own price and income elasticity and zero cross-price elasticity. That is: (8.11)

(8.12)

where U is the present utility level, and l and j are the shadow prices of the budget and time constraints, respectively. From Equation 8.11, given the utility level and the constraints, the amount of leisure time demanded is an increasing function of the propensity to consume leisure (µ1) and a decreasing function of the wage rate (which is the alternative costs of work time) and the (shadow) price of time. Similarly, the amount of the consumption good demanded is an increasing function of the its taste coefficient (µ2) and an inverse function of its price. From 8.11, given the amount of leisure time households wish to consume, we can compute from Equation 8.9 the amount of labour (in time units) they are willing to supply, as a function of the amount of time used for home-towork travel. As travel time increases (see shortly), households will substitute leisure time for labour, where the key substitution factors are the alternative cost of work time (the wage rate) and the leisure time elasticity parameter, µ1. In general, given the wage rate and the amount of leisure time demanded, the amount of labour supplied by households will decline as more time is used for travel to work. Conversely, as accessibility improves, households will be predisposed to supply more labour. Moreover, additional benefits from improved accessibility might be capitalized in the consumer surplus and manifested in increased consumption of the consumption good. The relative size of µ1 and µ2 will determine the magnitude of this result. The transportation sector As already noted in this urban economy, the production of outputs (y1 and y2) entail travel of employees between their zones of residence and zones of

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employment where the firms locate. To simplify the analysis, we assume that the level of commute is a fixed proportion of the level of labour used for production (e.g. two trips per day per employee). We further assume a generic mode of transport (e.g. private car)10 and an in-place transportation network with fixed capacity. As firms locate at closer proximity so that they can benefit from enhanced agglomeration economies, more congestion will ensue. That is so for two main reasons. First, more commuters now travel to the same area (e.g. the CBD), resulting in higher traffic volumes sharing the same fixed transport infrastructure facilities. Second, because increased agglomeration results in higher levels of outputs that are then translated into increased demand for labour and travel. Of course, both effects can transpire concurrently and, as noted above, the final result might be less time available for work activities. These relationships between total travel time for work purposes between locations i and j, the level of employment used by firm r(r=1, 2), the capacity of the transport infrastructure facilities and the distance between sites i and j, are given by Equation 8.13: (8.13)

where tij measures total travel time between a residential site in i, and employment site j. The amount of labour in site in j (j ¹ i) actually employed by firm r, is (lr)j. The fixed carrying capacity of the transportation infrastructure between each ij pair is denoted by Kij. From Equation 8.13, the effect of expanding the capacity of the transport infrastructure is to reduce travel time, i.e.

On the other hand, travel time increases as the number of trips

increases. As explained earlier, the number of trips is a constant multiple of the employment level, which, in turn, is a positive function of output level. Hence, the effect of increased output on home-to-work travel time is positive, i.e. (r=1, 2). For each firm r (r=1, 2), an explicit form of Equation 8.13 is:

(8.14)

where r1 and r2 are parameters of the volume-capacity function (the term in the square parenthesis). The variable lr,ij is the actual amount of labour used by firm r(r=1, 2) located in site j, which travels from residential location in site i. We assume two trips per employee per time unit (e.g. a day). This volume-capacity function is expressed in units of travel time per unit distance (e.g. 1 km). Hence, to compute total travel time we multiply this function by the actual distance travelled between i and j, dij. The reader should notice that Equation 8.14 might measure travel times between an ij pair inaccurately. If some trips made by workers employed by

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firms 1 and 2 share the same infrastructure facilities (e.g. use the same links), this may occur even if the two firms do not locate at the same site. To correctly measure tij we need to introduce a separate network sub-model, which in the present model framework is not done, mainly in order to avoid needless computational intricacies that a network modelling entails. For this reason in the actual computations of travel times to work we make the assumption that both trip types exactly share the same infrastructure facilities as if both firms are located at the same site j.11 That is:12 (8.15)

We define lT (equation 8.9) as: Sijtij Given this qualification, Equation 8.15 is central to the model. Travel times affect the allocation of time between leisure and work and, as a result, the equilibrium amount of labour and the wage rate. Travel times, on the other hand, are affected by the distance workers need to travel from place of residence to place of employment, by the number of work trips and by road capacity. The latter is an exogenous variable that can therefore be used to affect the equilibrium labour (and output) in the economy. Operation of the Model The overall structure and operation of the model is depicted by Figure 8.1. We begin from an assumed initial state of known output prices (which are assumed exogenous to this economy), quantity of output demanded, thus demand for input factors, and input prices depicted in Figure 8.1 as the production sector (I). We also assume a known distribution of households relative to location and number of employees (the initial supply of labour in the economy, l ), titled as the household/labour sector (II). Lastly, we assume a given transportation infrastructure sector (III), which generates an initial OD accessibility matrix. Given these components the model’s operation is described by the following steps. Step 1: Compute optimal firms’ location. From the above initial conditions, the model first determines each firm’s best location, defined as the one that maximizes agglomeration effect (Equation 8.3). Notice that at this initial stage we assume dij > 0 , so that given the value of the friction factor g the two firms do not necessarily locate at the same zone. Step 2: Compute optimal firms’ output. Given the firms’ location and level of agglomeration, the model now computes each firm’s optimum output by maximizing the profit function subject to the production function (Equation 8.5 subject to Equation 8.4). Step 3: Compute demand for labour. Given each firm’s optimum level of

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Figure 8.1 Schematic view of the relationships between the production, household and transportation sectors.

output, next the model derives their demand for labour, (Equation 8.7), by maximizing their profit functions (Equation 8.5), subject to their production function (Equation 8.4), with respect to the input labour whose initial price is assumed. Step 4: Compute equilibrium amount of labour. In this step the model computes the equilibrium amount and price (wage rate) of labour (w), by equilibrating the amount of labour demand by firms with the supply of labour, which in the first run of the model is the initial availability of labour in the economy

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Step 5: Compute interzonal travel times (level of congestion). Having the labour equilibrium quantity, the model determines travel times tij that this amount of labour will generate (Equation 8.15), given the transportation infrastructure. Step 6: Compute the optimal amount of leisure time. Having computed the wage rate and the amount of time used for travel purposes (lT), subsequently from the consumers’ utility function (Equation 8.10), the model obtains the optimum amount of leisure households desire to consume, le, and the consumption good (Equations 8.11 and 8.12). Step 7: Compute general equilibrium in the economy. Rewriting Equation 8.9, we obtain the labour supply function ls, expressed as a function of total time available, leisure time and congestion. That is: (8.16) where l = Sijtij. Both the labour demand function (Equation 8.7), and the labour supply function (Equation 8.16) are functions of labour units (l) and the wage rate (w). Hence, we can solve for the labour market equilibrium. Let l* and w* be the solution variables. If the value of a particular {l*, w*} pair maximizes each firm’s profit function (Equation 8.6), then we regard the economy to be in a state of equilibrium. Otherwise, the values of l* and w* become the new initial values and the process repeats itself from Step 1 to Step 7, till equilibrium is reached. (For a proof of existence of an equilibrium solution, see Berechman 1994).13 The key question that this book sets out to examine is: Can the expansion of the transportation infrastructure influence local economic growth? Within the framework of the above model economic growth is expressed primarily in terms of changes in the equilibrium amount of labour used in the economy. This amount is a function of the demand for labour inputs by firms and the supply of labour by households, where both labour demand and supply are affected by transportation costs. The former is affected through the impact of these costs on firms’ location and the latter is affected through the impact of travel costs on individuals’ decisions regarding their allocation of time between work and non-work activities. By comparing the equilibrium level of employment before and after an infrastructure capacity improvement we can ascertain the degree to which it has indeed enhanced employment. We can thus examine these relationships for successive transportation capacity improvements to identify the consequent pattern of changes in employment. Changes in labour productivity, defined as the equilibrium level of output to labour ratio, is another measure of growth used here. Next we describe numerical simulations with this model. T

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8.4 Simulating transport infrastructure expansion and economic growth The principal objective of the simulation exercise is to assess the impact of transportation infrastructure expansion on the equilibrium level of employment. We begin with a description of the various components of the simulated system. 8.4.1 Input data Spatial organization We consider an urban area, which is a corridor or a line segment, divided into discrete i, j zones (i, j=1, …, N; N=5) as in Table 8.1. Households are located in i=1, while firms 1 and 2 (also denoted by their output type y1, and y2), are initially located at j=3 and j=5, respectively. Table 8.1 Location of households and firms in the simulated area

The production sector

The production function used in the simulation is similar to that given by Equation (8.4). That is:

(8.17)

The parameter values used for these simulations are as follows: A1 = A2 = 0.5; a + b = 1; a =0.7; ar=2 = 0.6 The land elasticity parameter, sr, assumed r r r=1 zero.14 The initial value of the agglomeration effect, g, is set to 2.5. The distance decay parameter g is set to 2.0. The exogenous prices of unit output p1=1.2 and p2=1.3. The cost of capital is also assumed to be exogenously set, pk,r=1= 1.5, and pk,r=2 = 1.3. Given these input data, the firms first determine their best locations. Afterwards, they maximize their profits with respect to output (as in Equation 8.6) subject to its production function. Subsequently, we derive the demand for input factors, labour and capital. The household sector We assume five households, located in zone 1, each- endowed with 24 units of time. Hence, total time available in the economy l = 120. The value of time

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($/hour) is set to be 60 per cent of the wage rate (v = 0.6w) which initially is set to $10.0/hour. Thus, v=6. The utility function (Equation 8.10) contains two taste coefficients m1, m2. For the simulations we set them to equal 0.4 and 0.6, respectively (the utility function is assumed homogeneous of degree one). The initial amount of the consumption good is: z=9, and its price is pz=10. The transportation sector In this spatial configuration the transportation system is a linear network where distances are measured between zonal centroids. The size of each cell is assumed to be 1×1 km. Thus, the distance from households’ location in i=1 to the firms’ location in i=3 and i=5 are 2 km and 4 km respectively. Thus, the interzonal travel distance matrix is symmetric and is as follows: Table 8.2 Interzonal travel distance matrix (di,j)

As already explained, in this model we do not have a formal network. Therefore, we consider the capacity of the route connecting any two zonal centroids, Kij. We set it to equal 10 for all ij pairs and increased it incrementally afterwards. To compute travel times we use a volume capacity function like in Equation 8.15 with the parameters r1, r2 set to 0.15 and 2.5, respectively. Thus, the explicit travel time function used for the simulations is: (8.18)

From the data given above and from Equation 8.18 the initial travel time matrix can be computed. In order to convert travel times to travel costs we use: 8.4.2 Simulation results Using the above input data and parameters, we have performed a large number of simulations to assess the effect of infrastructure developments on the equilibrium of labour in this economy. As in most simulation analyses, the actual numerical results are largely susceptible to the parameters’ values adopted and to the initial conditions (see for example, Anas and Kim 1996).

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Figure 8.2 Equilibrium solutions (E , E ), before and after increase in transportation infrastructure 1 2 capacity (K, K’; K’>K).

Figure 8.2 shows the impact of an incremental increase in the capacity of the transportation infrastructure on the equilibrium level of employment and output. In Figure 8.2 point E1 represents the intersection of labour supply, defined also as a function of the travel time, with the demand for labour by firms, which is defined as a function of output and inter-firm accessibility. Since home-to-work travel time and inter-firm accessibility are both a function of the capacity of the interzonal transportation system (Kij), we can express the equilibrium amount of labour in the economy also as a function of this capacity. Point E2 represents a new equilibrium level following the expansion of the transportation capacity, from Kij to It can be seen that E2 is associated with higher levels of labour l* and output yr* than the levels represented by point E1. The next question is how will continuous increase in transportation capacity affect the equilibrium amounts of employment? To answer this question we have systematically increased the capacity of the interzonal transportation system by steps of 0.1 (i.e. Kij=10, 10.1, 10.2, …14.9, 15) up to 50 percent. By rerunning the simulation model for each level, we have computed the consequent equilibrium level of employment. The numerical results are graphically shown by Figure 8.3. The results of these simulations indicate that as capacity increases so does

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Figure 8.3 Equilbrium labour in the economy (l*) as a function of transportation infrastructure capacity (K).

equilibrium employment. However, this effect abates quite rapidly.15 Beyond a certain level further expansion of the transportation infrastructure capacity will have no measurable effect on equilibrium use of labour. To test for the model’s sensitivity to basic conditions we run a simulation with no agglomeration economies and reduced labour market effects. That is, we neutralized the agglomeration effect (i.e. g=0 in Equation 8.17), and the substitution effect between leisure time and other activities including work time (i.e.µ1=0 in Equation 8.10). Under these conditions the effect of transportation capacity increase was rather negligible as the only effect of this capacity expansion was to somewhat increase labour supply ls (Equation 8.16). How do transportation capacity improvements affect labour productivity? Labour productivity was defined as output per unit of labour input. In this analysis both variables tend to increase in response to an increase in transportation capacity. The results from the simulations, however, show that there is no constant pattern of labour productivity changes. In some cases the relative increase in output exceeds the increase in labour (hence, productivity rises) whereas in other cases the opposite happens (productivity declines). Apparently, it is the non-linear effects of the various parameters in the model that determine productivity results. It is interesting to compare the above results with those obtained by Kilkenny (1998) who has examined the effect of reduced transport costs on

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rural development, using an equilibrium model of firms’ and workers’ location, with wage rates and output (industrial and agriculture) being the major variables. The results from this study show that as transport costs decrease, welfare rises in a pattern similar to that of Figure 8.3, first a sizeable increase, which then quickly lessens as transport costs further decrease. Given these results from simulation studies that use hypothetical data we next ask whether the use of real-world data will also support the above findings? Therefore, we present results from a study which has examined the potential effects of transportation improvements in an economically distressed region on the propensity of individuals to participate the labour force.16 8.5 Results from an empirical study Berechman and Paaswell (1996) have estimated the impact of transportation improvements on labour force participation in an economically depressed area, the South Bronx, New York, where major transportation infrastructure projects were planned, mainly in order to promote employment. In this study a 2SLS regression model was developed to assess the effect of accessibility changes on labour participation. Thus, the model included an accessibility function and an employment function. Using real-world data, the model estimated the effect of travel time reductions on the propensity of individuals to enter the labour market for different job categories. In doing so the model took into account mode type, time of departure, car ownership, socio-economic attributes (e.g. income, education and age of children) and the wage rate by industry type. The model also accounted for employment and residential locations within and outside the South Bronx, though it did not account for possible relocation by households and employment, following the accessibility improvements. Main regression results are presented in Table 8.3. The results in Table 8.3 show parameter estimates for the accessibility and employment equations for four selected job types, by various variables (empty cells in the table imply insignificant parameters). To illustrate, in Table 8.3 in the accessibility function the parameter estimate for transit use is 0.806815, indicating that a 10 per cent decline in transit travel time will increase transit use by 8.06 per cent. 10 per cent increase in peak time departures (7:30– 7:59) will increase travel time by 2.86 per cent. Three main results emerge from this analysis. First, in terms of travel time, accessibility improvements indeed tend to encourage higher levels of labour market participation. Second, these effects are job specific as some job types are more susceptible to accessibility improvements than others. For example, in the employment equation, improved accessibility (i.e. travel time reduction) of 10 per cent, will raise executive type employment by 2.37 per cent, technician by 1.87 per cent, administrative by 0.96 per cent and transport by

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only 0.79 per cent. Third, other factors, most noticeably the wage rate in an industry, have considerable impacts on employment decisions, which in many cases are much larger than the accessibility improvement effects. For example, a 10 per cent increase in the wage rate in the finance, insurance and real estate (FIRE) sector will boost executive type jobs by 4.85 per cent, technicians by 4.47 per cent and administrative by 4.22 per cent. These levels are more than twice the employment effect from accessibility improvements. In general, when considering all other intervening factors, accessibility enhancement has a limited impact on the propensity of individuals to enter the labour force. These findings seem to support the results from the simulation analysis, mainly with regard to the limited ability of accessibility improvements from infrastructure development to promote employment. 8.6 Conclusions In this chapter we have developed a microeconomic model to examine the impact of increased capacity of the transportation infrastructure on local economic growth. The equilibrium level of employment is used as the principal growth measure. We have hypothesized that firm’s agglomeration and individuals’ trade-off between leisure and work determine the level of output and employment in the modelled economy. We further hypothesized that as travel time declines, following capacity expansion, and accounting for firms’ location, the additional free time will in part result in increased supply of labour. If the demand for labour inputs also increases, and accounting for the wage rate, employment may also increase in the economy. The key conclusion that can be drawn from the simulation results of this model is that accessibility costs, which are a function of the capacity of the transportation system, do matter in affecting labour market decisions. However, other key factors such as firms’ location decisions, external demand for their final output, individuals preference structure and the shape of the transportation network also play a major role in determining growth effect. Accounting for these factors the impact of transportation infrastructure expansion on the economy tends to lessen quite rapidly as more capacity is added, hence, the results of major case studies described in Part IV, which show that even a major infrastructure project can have a negligible effect if the additional capacity is only a small proportion of the in-place transportation capacity.

Table 8.3 A 2SLS estimation of accessibility and employment functions

Source: Berechman and Paaswell (1996). Note: Parameters shown are adjusted coefficients (see text), and significant at 0.05 level or better.

Table 8.3 Continued.

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Notes 1 More accurately, we need to examine changes in consumer surplus. 2 In a large empirical study Schwartz (1992) has demonstrated how firms located over the entire metropolitan area of New York, Los Angeles and Chicago interact mainly with firms located in the respective central cities. 3 As noted in Chapter 6, most of the theoretical and empirical work that links productivity gains with scale effects was carried out at the national level. See for example Romer (1986). At the urban level most of the work associates productivity improvements with city size. See, for example, Fujita and Ogawa (1982), Henderson (1986) and Moomaw (1981). 4 Other forces affecting the extent of activity clusters include transaction costs, incomplete contracts and information, and flexibility in interaction. McCann (1995) attributes locational clustering to various costs and revenues associated with the location of firms. These include distance-transaction cost, locationspecific factor efficiency costs, hierarchy-co-ordination costs and hierarchyopportunity costs. 5 To be distinguished from financial measures such as increase in the value of firms’ stock. While financial measures of firms’ performance may also reflect real ones, external factors such as the market’s rate of interest or rate of inflation have a critical impact on financial variables. 6 For a study which explicitly models residential behaviour relative to location and non-work (i.e. shopping) trips see Anas (1995) and Anas and Kim (1996). The effects of infrastructure policy on location were studies by Haughwout (1996) who formulated a model which simultaneously considered equilibrium labour and land markets, though it does not consider changes in the transportation market and their effect on location. 7 That is, the effect of y1, on y2, is the same as the effect of y2 on y1. 8 We also assume decreasing marginal productivity of labur, i.e. 9 Notice that in this economy no non-labour income is available. 10 Travel time is, of course, not independent of the mode of travel used. Car use requires parking whereas travel by a bus requires access time, egress time, wait time and in-vehicle time. Commuters on a fixed route rail transit further need to reach rail stations either by walk or by the use of a car, which needs to be parked. 11 However, when we compute agglomeration economies (Equation 8.3), at least initially, we regard both firms as if they are located in separate sites. 12 Analytically it is not possible to ascertain whether this approach overestimates or underestimates travel times between all ij pairs, which would have been estimated, if we had included a formal network sub-model. 13 In the general formulation of the model (Berechman 1994), a social welfare However, here function was also included. It has the form we do not conceive of a social planner who is capable of manipulating the system in order to produce socially optimal values for firms’ output, the consumption good and leisure time. Therefore, in this analysis we use the firms’ profit function as the criteria for the model convergence into equilibrium solution. At the point of convergence we obtain the equilibrium (but not necessarily socially optimal) values for output and labour. 14 As explained above, in this analysis we do not consider the use of land by households and firms and therefore, the effect of changes in land rent on the location of firms and households.

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15 The simulation results show that after 20 per cent capacity increase, employment increases by a merely 2–3 per cent. 16 While not directly related to transportation improvements, Durkin Jr. and Wassmer (1994) have obtained similar results. Using a production function model they found that under certain conditions the positive marginal effect of increase in public capital spending on urban growth tends to increase first and then abate and become negative with further spending, given average city size.

Part IV

Empirical case studies

Main issues and structure Part IV completes the analytical section by presenting a series of case studies. The intention here is to use carefully selected complementary case studies that illustrate the range of impacts which might be expected from transport infrastructure investments at the local level. As can be seen from the table below, these case studies cover a range of different modes of transport at the local and national levels. They are all taken from developed countries and cities. Some are analytical in nature, using high quality primary data, while others entail an extensive review of available secondary data and supporting information. The selection of case studies depends on the availability of source material for the range of projects to be considered. The best source material has been obtained on rail investment projects at both the programme level for international and regional links and for individual projects at the city level. This has allowed detailed analysis on the ex post impacts, particularly as they relate to the labour markets and employment. The airport material is also available. The impacts here are felt both at the local level through direct and induced employment and at the regional/national level through multiplier Part IV scheme for case study selection

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effects. Ironically, the most difficult case study has been the assembly of information relating to new road construction at the regional and local levels. This is partly because little new construction is taking place, but is also explained by the complex phasing and time taken for many road investments. Both the construction and the impacts are phased over a considerable period of time, which in turn complicates data collection and the analysis of impacts.

9

The economic impacts of roads

9.1 Introduction Roads play a fundamental part in the development of cities and regions, as they affect accessibility and the relative attractiveness of all locations. Recent investments have mainly taken place outside cities as new construction in cities is often seen as reducing the attractiveness of city centres. This apparent contradiction between the benefits of roads (e.g. better access) and the negative aspects (e.g. environmental quality) has never really been addressed in analysis (see Part III) which has tended to concentrate on the physical quantitative aspects rather than the social and environmental qualitative aspects. Most road investment has taken place in specific corridors where development has been encouraged, or in locations between and around cities to establish the inter-urban network. The consequence of investment in these locations would suggest that development pressures have also responded by moving to these corridors and to the network outside cities. These, so-called ‘greenfield sites’ are also attractive to developers as land prices are lower than in the city centre; land assembly is easier; the development costs are lower; the sites are car accessible and the quality of the environment is perceived as being high. It is not surprising that investment in new roads out of city centres has generated substantial pressures (Headicar 1996). This chapter examines the debate over the economic impacts of roads through a case study of three major motorways: the M25 London orbital motorway, the French A71 motorway and the Amsterdam orbital motorway. Mainly for reasons of data availability, the first of these case studies will be examined in greater detail than the other two. The basic motorway network in Europe has been constructed over the last fifty years and there are now some 46,000 km in the fifteen European Union (EU15) countries. Two-thirds of this network has been constructed since 1970 and one of the principal justifications for investment has been the beneficial impacts on accessibility, economic development and employment (see, for example, Blum 1982). Although the focus is primarily on one case study, the conclusion widens the

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discussion to examine impacts of roads on regional and urban development in other European countries.

9.2 The London M25 motorway 9.2.1 M25 history The M25 was completed on 29 October 1986 and provides a high quality motorway orbital route around London. Its total length is 188 km and it runs mainly in greenbelt land1 at between 20 and 35 km from the centre of London, carrying over 700,000 vehicles a day (see Figure 9.1). It is operated at near to or above capacity over particular sections for much of the working day. The idea of an orbital highway around London dates back to 1905 when a Royal Commission on London’s traffic suggested a ring about 20 km from the city centre (Greater London Council, 1970). The M25 concept predates the motor age. The motorway became the M25 in November 1975, when two London orbital relief roads were combined to form one route. Previously, plans were being developed separately for an outer orbital in the north (M16) and another in the south (M25). Sections were opened over a 10–year period from the first section (near Potters Bar in 1975) to the final section (from Micklefield to South Mimms in October 1986). The total cost of the motorway was £1,000mn (in 1985 prices) and some 200,000 vehicles a day use the busiest sections. Since 1986, over a quarter of the motorway has been widened from three to four lanes in each direction to accommodate the ever-increasing levels of demand (Highways Agency 1996). Many sections of the motorway are operating at or above capacity and studies have been carried out on the introduction of traffic management measures to maximize the operational efficiency of the existing M25 (e.g. Rendel et al. 1989). Road widening and junction improvements have taken place, with other complementary measures to improve alternative roads, particularly for local traffic. Speed restrictions have been introduced on some busy sections to reduce speed variation and lane switching and increase utilization of existing capacity. Information signs are used to warn motorists of delays on the M25 so that other routes can be taken. Studies are taking place on the possibilities of reducing lane widths to 3.45m for all vehicles, 3.25m for non-Heavy Goods Vehicles (HGV) and 2.0m for the hard shoulder, and for limiting access to the motorway. The M25 has reached its capacity within three years of opening and most of this growth came from traffic diverting to the new road, newly generated traffic and the use of new destinations that had become accessible. There was very little mention in any study of the new traffic generated as a result of firms locating near to the M25.

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Figure 9.1 The M25 London orbital motorway.

9.2.2 M25 development pressures The justification for the M25 included congestion relief in central London as some 30 per cent of traffic using the motorway is bypassing the city, but it also provides links between the region’s four main airports (Heathrow, Gatwick, Stansted, Luton). The motorway provided relief to the local network, but it has generated longer journeys as it provides a quicker route. Reactions to M25 have been mixed. The official view is that the motorway provides an outstanding example of a road ‘that would aid economic recovery and development’ (UK Department of Transport 1980). The unofficial view is encapsulated in Chris Rea’s hit single ‘Road to Hell’ which was inspired by the M25. In between these two extremes, the Standing Conference on London and Southeast Regional Planning (1982) is more cautious. In its comprehensive impact study of the M25, completed before the motorway was opened, they identified two main consequences of the road on the regional economy. There may be a conflict between the motorway’s transport function, the economic objectives of assisting industrial and commercial development as part of the

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nation’s recovery from recession on the one hand, and the maintenance of the green belt on the other. Economic objectives were in conflict with open space preservation. Second, they recognized that the main development pressures which the motorway is likely to accentuate will occur in the western sectors, while the main opportunities and needs for new investment lie in inner London and the eastern sectors. As a result of this, the regional strategy may fail in these respects, particularly if the counties surrounding London actively competed for new development. The Greater London Development Plan identifies the preferred locations for industry and office development in London, together with the strategic centres for major new retailing locations. The structure plans produced by each of the surrounding counties make provision for further development in their own areas, usually specifying preferred locations, maximum acceptable increases in floor space and land take necessary for each type of development. Development plans (e.g. structure plans) in the UK are not in the form of zoning ordinances detailing location, density and building heights, but more general policies and proposals for the use and development of land. They provide guidance to developers and a framework within which development control can be exercised. Individual proposals are treated on their own merits, but should take notice of the provisions of the development plan (Headicar 1996). To counter the preference for locating in the western corridor from London which has a more buoyant economy and better levels of accessibility, the regional strategy was designed to promote the eastern sector with future transport investment to spread the M25 benefits in towards central London. The strategy also identified individual well-located existing centres for further development and explored the potential for ‘green development’ (science parks and recreation centres) in the western sector. The process of road construction and development was seen at this time (early 1980s) as an integral part of strategic planning. New development opportunities brought about by increased road accessibility could be used to create jobs and local economic benefits in declining areas, particularly in the eastern sector of the M25. More specific studies were also carried out to identify which sectors of the economy would benefit most from location in close proximity to the motorway (Nathaniel Lichfield and Partners and Goldstein Leigh Associates 1981). Here it was suggested that five types of development would compete for motorway accessible locations. 1

2

Warehousing activities serving national and regional markets, where transport costs were a significant element of total costs. For example, Dagenham, Dartford, Grays and Redbridge. High technology growth industries would locate in towns just beyond the green belt.

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4 5

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Offices that did not require central London locations would move out of the city where costs were high and environmental quality was low. But good accessibility to specialized and local clerical staff is an essential component of office location decisions. For example, Romford, Croydon, Orpington, Hounslow, Uxbridge and Barnett. Hypermarket and superstore developments would take place near M25 junctions, but this conflicts with green belt policies. Discount shopping stores would locate along the minor orbital routes around London (e.g. North Circular Road) and along the main arterial routes.

It was argued that accessibility was maximized at the junctions where the M25 met with the main radial routes into London. At these sites there were fragmented parcels of land (broken up by the M25) where existing uses were no longer viable and these locations were appropriate for new uses. 9.2.3 M25 accessibility impacts One of the principal debates over the impact of the M25 is its effect on regional accessibility, and on the patterns of employment change that it might cause. Early research (Jones 1982) using the Regional Highway Traffic Model suggested that under uncongested conditions the M25 would have a major impact on accessibility, particularly in certain sectors. Central London already has high levels of accessibility both with and without the M25, so the effects of the M25 on the centre of London are marginal.2 Significant improvements were found in the peripheral areas (20–30 km from central London), with a greater impact in the east than in the west (increases of 10–20 per cent). However, the greatest improvements (up 50 per cent increases in accessibility) were found in particular corridors around London where the M25 met the main arterial routes (e.g. Redbridge and Harlow along the M11, Guildford and Leatherhead along the A3). There were substantial savings in travel time averaging about 30 minutes in the morning peak and 20 minutes in the offpeak period. Jones (1982) concluded that because of the increases in accessibility conferred by the M25, locations within easy reach of an M25 junction (10 minutes travel time) are likely to be subject to considerable pressures for development. A more recent study (Linneker and Spence 1992) has used a wider range of accessibility measures to assess the impact of the M25 on Britain as a whole, in particular the southeastern region. Market potential measures were calculated for the ‘with-road’ (1987) and ‘without-road’ (1981) cases using exogenously determined procedure of route minimization between regional zones (179 in all). Time, distance and cost functions were calculated for HGVs and cars, at two points in time (1981 and 1987). They both had the form of: where, MPi is the market potential of zone i; Pj is a measure

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of market potential in zone j (employment in 1981 and 1987); Cij is a measure of transport cost from i to j (e.g. time, generalized cost or distance); and a is an exponent (assumed to be 1 in this study). The results demonstrate that significant accessibility changes have taken place, but not always in the direction anticipated. For HGVs there have been clear increases in accessibility in all zones with the exception of Inner London where the M25 has, in fact, increased travel times. The greatest change for HGVs takes place in the Inner South East (Kent) areas for travel time savings (reduction of 9.6 per cent), while for the other measures of accessibility (distance and cost) there were increases in all areas, particularly the Inner South West area (Surrey, Kent, Berkshire). The M25 meant that the distances travelled increased here by 4.7 per cent and the generalized travel cost increase was 2.5 per cent. For cars, the results of Linneker and Spence study were even more pronounced with travel time reductions of between 8.4 per cent and 12.7 per cent in all areas around central London. Travel distances increased with the greatest change (+6.2 per cent) in the Inner South West area, but generalized travel costs were reduced in all areas apart from Inner and Outer London. The greatest reduction was found in the Inner South Area (-4.5 per cent). The implications of these results are not easy to interpret. The M25 is a major investment that would be expected to have an impact on the accessibility at the regional and national level. Given the assumptions on speeds, values of time and employment, the scale of the accessibility impacts is small and the increase in travel distance and generalized cost (for HGV) suggest that the effects of the M25 are negative. It is only travel time that has been reduced as the M25 provides a higher speed route, but given the levels of congestion and the capacity limitations, even this saving may be diminished. One possible test would be to estimate the speed of using the M25 that would negate any savings. What level of congestion is needed on the M25 to cancel out all savings (time, distance and cost)? Turning now to the economic growth effects of the M25, in a follow-up piece of research Linneker and Spence (1996) used a multiple regression analysis to examine the M25’s impact on employment growth as this relates to changes in relative accessibility and transport cost advantages. Dodgson (1974) used a similar analysis to explore the employment implications of the M62 Trans-Pennine Motorway on 30 local areas (1960–66). Botham (1980) used the same approach to test the employment effects of the post-war road programme between 1957 and 1972 on an intersectoral and inter-regional basis. The Linneker and Spence (1996) analysis uses employment criteria to indicate the regional economic development change (1981–7). Two measures were used to examine regional economic development: 1

Differential employment shift (1981–7) is used to measure local economic performance. This is the competitive element in shift-share analysis, which

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measures the employment change in an area, given a particular industrial structure. Demand for labour index (1981–7), defined as the difference between actual employment change and the expected change, which arises from the natural increase in the local population. Positive values of this index indicate in-migration of residence, in-commuting by trip makers, higher productivity, increased levels of activity and lower levels of unemployment.

These measures were analysed by regression and correlation analysis with a range of independent variables to reflect: 1 2 3 4 5

Industrial structure index (1981) which provides an industrial profile of an area in relative terms. Congestion based on population density. Employment density (jobs per unit area—hectare). Labour availability based on the ratio between total employment in an area and the resident working population. Accessibility measured by market potential as explained earlier.

The results are counter-intuitive and differ from those produced by Dodgson (1974) and Botham (1980). It was found that areas with a high accessibility relative to other areas are losing employment. The market potential accessibility measures are negatively related to the demand for labour index and the differential employment shift. This effect is modified if the dynamic accessibility change is isolated (i.e. the pure M25 road effect on accessibility). Those areas that have shown the highest percentage increases in accessibility (only measured by time) have shown higher employment growth or reduced employment loss. Linneker and Spence (1996) were cautious in their explanation. Proximity to centres of high population densities (the large conurbations) produces the poorest employment performance. The regional dimension seems to emphasize this difference as the London region performs better than other locations (see also Frost and Spence 1991). Accessibility from new road construction facilitates the ability of local firms to expand market area and hence create more employment. But it may also allow expansion of firms outside the region into the newly accessible locations. The resultant competitive position is the combination of the two sets of factors. Physical accessibility, as measured in the Linneker and Spence (1996) study, is only one part of the competitive position and it affects different activities in a variety of ways. Economic potential may need to be redefined (Newman and Vickerman 1993), particularly as it is often given the impression of smooth, continuous change in accessibility over the whole region (or country). The implication

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has been that the improvements in infrastructure will be concentrated in the core area and that new infrastructure can never promote regional development or cohesion except where it overcomes a physical barrier (Vickerman 1998). Improvements in transport infrastructure can change initial levels of accessibility, but they will reinforce locations with good accessibility. Relative gains may be greater in peripheral locations, but absolute gains are highest in the locations with the highest starting points. As Vickerman (1995) concludes, accessibility is a relative concept and once certain minimum thresholds are reached we require further understanding of the way that accessibility is used to further the interests of a region’s enterprises to achieve real comprehension of the economic impact. 9.2.4 M25 retail development One of the main changes anticipated in property market analysis (e.g. Nathaniel Lichfield and Partners and Goldstein Leigh Associates 1981), was the pressure for retail developments at accessible locations on the M25. These retail developments would include superstores and hypermarkets as well as retail warehousing. Prior to the completion of the M25 in 1986, the arguments conflicted. Damesick (1986:157) concluded that ‘the role of the M25, by itself, in creating wholly new opportunities for economic growth in the SouthEast on an inter-regional basis is likely to be small, relative to the region’s other existing attributes’. The M25 only represents a marginal addition to the region’s locational advantages and so is likely to enhance existing trends rather than create new ones. The contrary view was put forward by Simmons (1985), who argued that government has only belatedly accepted the M25’s ability to create development opportunities. He was critical of the absence of any strategic view on where development should take place. Even if there were a strategy, there is considerable doubt as to whether development pressure could be directed in practice. Gould (1987) has carried out one of the few empirical studies with his investigation of planning applications for retail developments. The survey (1986) covered all seven county authorities, including the Greater London Council, through which the M25 passes or is in close proximity.3 A total of 40 districts were affected by the M25 and 30 of these responded to the survey. In addition, a further 22 districts were surveyed which were not affected by the M25, but were part of the same regional economy. There were 144 retail applications in the ‘M25 belt’ (4.8 per authority) and 73 in the ‘control area’ (3.3 per authority). These figures are in line with national trends and the upturn in retailing taking place in the 1980s. The proximity to the M25 was not seen to be important in over three-quarters of the cases, and this was confirmed with interviews with the retailers. Most of the retailers were not influenced by the policies of the local authorities and were concerned about their own network of stores and their market share. As 75 per cent of their

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turnover came from customers within a 10-minute offpeak drive time, it was unlikely that many would use the motorway. However, in terms of floor space the impact of the M25 has been substantial. For the larger developments (> 18,000 sq m) involving regional shopping centres and retail warehouses, proximity to the M25 was important. The 14 applications that stated that proximity to the M25 was very important accounted for 821,226 sq m of floor space, which constitute 10 per cent of the applications with 45 per cent of the floor space. Smaller scale applications were not influenced by the M25. Details of 10 of these 14 applications are given in Table A9.1 in Appendix 9.1. These 10 account for 96 per cent of the floor space of these 14 applications, and the other four are very small development proposals (which included Iver, Colnbrook (Richings Park) and Elstree Aldenham retail park). Moreover, these 10 key applications are located in just six sites. In 1986, land values soared as a result of this increased demand. For industrial use it was £500,000 per acre. For technological parks and retail warehousing it was £750,000 per acre. For food retailing it rose to about £2 million per acre (Procter 1988). The clear conclusion reached from this empirical analysis was that the M25 has an important role in enlarging the catchment areas for regional shopping and warehouses, but not for those smaller supermarkets used more frequently. Consumers making regular convenience or low value bulky goods purchases are not prepared to travel for more than 10 minutes by car to the shop. As M25 access is limited, there are few locations that would benefit from the ‘10 minute’ rule. The M25 has a very limited impact on this scale of retail development. Despite the small impact of the M25 on local retail facilities, it has a substantial impact on the regional scale of retail developments. For this level of development to transpire, strong agglomeration economies must be present. This observation supports our basic contention (see Chapter 7) regarding the necessary functional relationships between accessibility improvements and agglomeration economies, in order to engender economic growth. At the beginning of the process (in 1986), planning applications were mainly speculative, but the planning regime under the Conservative government was supportive of prestige large-scale developments. Many applications were received (see Table A9.1), but gradually that number was reduced as the property market situation became less buoyant in the late 1980s and early 1990s. The property market business was also restructuring itself (Table A9.1). A second reason for the change in approach has been the strong public desire to maintain the green belt. Even the strong Thatcherite free market economics was not able to shift public opinion, so many of the applications in the green belt were withdrawn at an early stage as it was unlikely that they would be approved after a local inquiry, or even after statutory appeal to the minister. Consequently, most development has taken place further away from the M25, outside the green belt. The districts adjacent to the M25 have increased

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Table 9.1 Case study: Lakeside retail development in Thurrock, Essex

Sources: Based on Capital Shopping Centers (1997) and research by Stamp (1997).

office space by 2.4 million sq m, resulting in some 160,000 new jobs, (1989– 91; OECD/ECMT, 1995). The Blue Water Park development (Table A9.1) is the largest retail and leisure centre in Europe (1999). It is anchored by key retailers such as John Lewis, Marks and Spencer and the House of Fraser, with 275 other retailers. There are three leisure villages, each with a separate theme, together with a 12–screen cinema. The 100 hectares site also has 150 acres of parkland and 23 acres of lakes.4 It seems that once the ‘anchor’ retailers are established, there is considerable leverage to win over

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other retailers so that agglomeration economies can take place. The Lakeside development has expanded by some 30 per cent as related developments have been added to the original development, mainly through linkages between firms with similar or complementary markets. A key conclusion from this review is that political and policy decision making have a rather consequential impact on the ability of a major transport infrastructure investment such as the M25 to generate economic growth. An interesting example of this impact is given by Table 9.1, which shows the evolution of the development of the largest (in 1995) retail shopping centre in the South East of England—140,000 sq m. The evolution of this particular retail project as outlined in Table 9.1 clearly demonstrates how crucial were the impacts of the political and planning processes on the ability of the M25 to bring about economic growth effects. We shall return to this conclusion in subsequent chapters and provide a summary statement in Chapter 12. 9.3 The impact of the French A71 The A71 motorway in central France links the cities of Orleans and Clermont Ferrand via the city of Bourges (see Figure 9.2). An empirical case study of this project was undertaken by Zembri-Mary (1996) and had two main aims. The first was to establish whether there were any anticipatory reactions, in the form of real estate transactions, to the A71 in the corridor through a comparative study of three locations close to the autoroute (Montmarault, Gannat, Riom), as compared with one ‘control’ location (Lapalisse) some 40 km away from it (see Figure 9.2). The second objective was to comment on the quantitative measures, as compared with

Figure 9.2 Map of the A71 route in France.

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more qualitative explanations. Before we report the findings of this study it is worth reviewing the history of the A71, which is summarized in Table 9.2. Table 9.2 History of the A71

Source: Based on Zembri-Mary (1996).

The study obtained the price of all 730 transactions over 20 years in the four study locations (the three study locations and one control location). This information included area, the designation on use (e.g. agricultural), the plot location and the present and future usage of the site. A series of adjustments were carried out on the data so that comparison could take place between the four locations. Explanations were sought after discussions with experts on the quantitative estimation of change. Two clear types of impacts were observed, one related to the actions taken by the towns and the other related to proximity to the interchange with the A71. The volume of transactions was high during the 1970s and the beginning of the 1980s, mainly resulting from the rationalization of the land distribution and the attempt to consolidate small unused or enclosed plots. The controlling administrative agency, SAFER,5 had a key role here in land assembly. This consolidation was matched by a slowing down of transactions between individuals as landowners delayed selling to capitalize on the expected increases in land prices. The land market (1985–7) increased in value as a result of the transactions necessary for the implementation and development of the A71. In the three locations close to the A71 prices rose by about three times, with 1987 being the peak year (when the A71 was opened). These increases were even greater (more than ten times) when the land was changed from agricultural to improvement land (zoned for business use). Around

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Montmarault, for example, agricultural land was valued at about Ffr 1.5 per sq m (1980), but this figure increase to Ffr5 per sq m in 1987 without reclassification, and Ffr22–44 per sq m with reclassification as business use (also in 1987). The conclusions reached in this study (Zembri-Mary 1996) reflect the importance of actions being taken at the appropriate time and the involvement of the three main actors—the land owners, the town council and SAFER (the rural development agency). Land is the major issue, with the links between the owners and the town council important both in terms of the agreed price and the timing of the acquisition. The plots of land in one case were valued by the land registry at an average price of Ffr 13 per sq m (about $2.2), but the owners went to the expropriation court and finally sold for Ffr22 per sq m (all compensation included). This meant that the franchising company of the area zoned for business had to pay nearly Ffr500,000 more than expected. Local authorities need to anticipate when to purchase and to have the resources available. SAFER has an important role to play in putting land together at a lower price and then selling on to the local authority, as it does not have a profit maximization motive. The land registry has to ratify all transactions made by the local authority so that the use of public money can be monitored. However, their role is both as guardian of the public interest and as facilitator, as the land registry can also pay above market price for a plot of land which is essential to the progress of the road. Several conclusions can be reached from this study. First, land values rise as a result of the road, mainly due to the construction process, but also due to the zoning of business activities at interchanges. This increase in value takes place in anticipation of the new road and continues after the road is opened. Second, the planning agencies such as the administration for rural land development play an important role in effecting the economic consequences of road development. The third conclusion is that these relationships are dynamic in that they can be identified before, during and after the road was constructed. 9.4 The Amsterdam orbital motorway This orbital motorway around Amsterdam was completed in 1990. The route is only about 5 km from the city centre, but serves to divert through traffic from city streets (see Figure 9.3). The travel impacts of the completion of the orbital motorway around Amsterdam have been substantial in terms of route choice and timing of trips, but they are less noticeable in terms of modal choice and trip frequencies. Despite its close proximity to the city centre, it has led to a substantial (20 per cent) reduction in congestion in the Amsterdam area. With regard to economic development effects, the completion of the road has reinforced the position of locations that were already in a strong competitive position. Little

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Figure 9.3 Map of the Amsterdam agglomeration.

change was observed in office rents and those offices nearest to the motorway junctions demonstrated a negative relationship between rent levels and distance. Yet the businesses interviewed claimed that proximity to the orbital motorway is important for transport related activities. A survey of office rentals in locations where distance by road to the orbital motorway did and did not change provided counter-intuitive results (Bruinsma et al. 1996). Offices located in areas that should have benefitted from improved accessibility showed an insignificant increase in rent levels (1987–91). These increases were much lower than increases in those areas not affected by the motorway. As the motorway was only opened in 1990, the after survey (1991) may not have picked up those changes which may require longer periods to be fully capitalized in the real-estate market. Alternatively, the market may have already anticipated the impact and adjustments have already taken place. But this explanation is not supported by trends in office rent levels prior to 1987. Bruinsma et al (1996) concluded that no impact on office rent levels is attributable to the Amsterdam orbital

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motorway. Yet, when this qualitative analysis is compared with a quantitative analysis (using a regression model) the orbital motorway does appear as a significant explanatory variable. It was found that prices are some Ffr23 per sq m (about $12.1) higher at locations near to the motorway junctions, as compared with those locations 1 km away from the junctions (amounting to about a 10 per cent premium). Other location variables were also significant in the regression analysis, as was the ‘newness’ of the office building, with modern offices attracting a premium value. The conclusion reached here was that although the effects are not directly visible in locations where the new motorway sections were constructed, the orbital motorway is an important location factor for office firms. A survey of three groups of employers with businesses near to the existing orbital motorway sections, near to the new orbital motorway sections and further away from the motorway provided additional supporting evidence. Firms experienced traffic delays prior to the completion of the orbital motorway (90 per cent) and this affected business efficiency. The new route improved accessibility and this had in turn resulted in increased turnover and productivity (10–20 per cent of firms). So the motorway had an important impact on competitiveness, particularly as it related to transport activities (e.g. the movement of goods, commuting and access to clients or customers). From a comprehensive survey of road investment schemes in the Netherlands, it has been concluded (Rienstra et al. 1998; Rietveld and Bruinsma 1998) that there is no evidence of a clear impact on overall employment in the regions resulting from the new patterns of accessibility. Peripheral accessibility has increased faster than central accessibility, but the two methodologies used (based on reference regions and comparison and on a labour market model) failed to give any consistent results. The conclusion reached was that more sensitive analysis is required at a lower level of spatial disaggregation. (The model framework in Chapter 8 is an example of such an analysis.) This study has implications for the M25 and other major road investments. It seems that the impacts are not just in terms of reductions in delays and improvements in accessibility, but also in general business confidence. The means by which these factors are then internalized (if at all) in the form of competitive advantage, productivity gains, higher rent levels, etc. all need careful examination through comparative analysis (with control areas to isolate change) over the appropriate length of time. Response to change or investment is a process which does not necessarily take place at the same time and firms or individuals may respond in a variety of ways according to their own constraints or requirements. The subtle nature of many of these processes makes the issue of causality more difficult to explain. Certainly, more formal quantitative methods such as the model framework in Chapter 8 need to be supplemented with more sensitive qualitative analysis.

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9.5 Conclusions The road case studies reviewed in this chapter seem to suggest a link between transport investment, economic activity and development. Yet in each of these cases, those factors were not investigated within a unifying framework and, as a result, it is quite difficult to assess whether these investments have indeed led to any new economic development and, if so, by how much. In addition, the critical role of policymaking in affecting these variables was also not clearly identified though, as we have underscored many times in this book, it is a key factor in understanding the relationships between transport investments and economic development. In general, road investment decisions such as those reviewed here do not attempt to quantify the impact on economic activity and development. They mainly examine the transport costs and benefits, principally through changes in travel times. But as we have expounded in Chapter 7 (see Figure 7.2), any economic development benefits are a function (though a non-linear) of accessibility benefits. These benefits are crucially dependent upon the assumptions made on traffic speeds through the network often assuming that there will be no new traffic generated (a fixed trip matrix). The absence of the explicit inclusion of redistributed traffic (to new accessible destinations) and newly generated traffic (resulting from latent demand and from new activity) is a major limitation. The M25 amply illustrates these problems and the high levels of demand, congestion and capacity limitations mean that the benefits of travel time savings are substantially less than originally estimated. This does not mean that the road should not have been built. Rather it means that the methods used should be able to estimate both the travel time savings to existing traffic (already done) and the travel time savings to a substantially increased level of demand mainly at much lower speeds (not done). Only then can potential economic development benefits be correctly assessed. Notes 1 2 3 4 5

The green belt is an area of tight planning constraint designed to contain the urban sprawl of major urban areas such as London. This finding supports our assertion throughout this book that in well-developed economies where existing transportation networks provide high level accessibility, even a major project like the M25 will improve accessibility only marginally. Defined as being within ten minutes offpeak drive time from the M25. One hectare contains 10,000 sq m or 2.471 acres. Each acre is 4,840 sq yd. Société d’Aménagement Foncier et d’Etablissement Rural (SAFER) (the administration for rural land development).

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APPENDIX 9.1 Table A9.1 Applications for major new retail development around the M25

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Table A9.1 Continued

Source: Gould (1987) and updated.

10

The economic impacts of rail

10.1 Introduction Investment in rail systems has often been justified not only on the basis of strict benefit cost analysis, but also by the broader employment and development benefits. In fact, many investment decisions would not have been made if only the transport benefits had been considered. For example, the Jubilee Line extension in London had a transport benefit-cost ratio of less than one (0.95:1 in 1991 prices assuming an 8 per cent discount rate over 30 years), when benefit-cost ratios would be over 1.3 to 1 for investment to take place. This means that over 34 per cent of the benefits would be non-transport based and related to new employment and local inward investment (London Transport 1993). These additional benefits are very difficult to measure prior to the investment decision and it is only through careful modelling or retrospective analysis that we can attempt a systematic review. In this extensive chapter, we carry out analysis from two different perspectives. The first is a detailed modelling study of the Buffalo Light Rail Rapid Transit (LRRT) system carried out before the investment was made. This ex ante analysis raises important methodological issues which have to be resolved, particularly if the investment is to encourage local economic growth (as in the Buffalo case). As this investment has already been made it is now possible to carry out an ex post analysis to see whether the transport and local economic benefits have been achieved. The second perspective is to review material from a wider range of sources in which empirical studies have been carried out to assess the impact of rail investments. This evidence has been divided into the large-scale interregional high-speed rail investments and the smaller, more local-scale urban investments. The focus at the urban scale is the BART system in San Francisco where there are now twenty years of data available to assess the economic impacts of the rail system. The bulk of this chapter focuses on the Buffalo LRRT study relative to its methodology and major test results. Therefore, the design of the chapter is as follows. Section 10.2 describes the methodology and specific models used for analysing the impacts of the LRRT project on the CBD area as well as major

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regional economic and demographic long-run trends, which are inputs to these models. Principal tests and results are described in Section 10.3. Section 10.4 provides a postscript of the Buffalo LRRT project. Key conclusions from the Buffalo case are presented in Section 10.5. Section 10.6 discusses ex post results from three other major rail projects, the Japanese Shinkansen, the French TGV high-speed rail systems, and the BART system in the Bay area in California. Main conclusions are in Section 10.7. 10.2 The Buffalo Light Rapid Rail Transit system As explained in Parts I and II, transportation projects, by and large, are designed and implemented primarily in order to solve transportation problems such as alleviating traffic congestion or improving road safety. Transportation investments are seldom made with the explicit objective of achieving nontransportation objectives such as local economic growth. It is no wonder, therefore, that most studies of the non-transportation effects of transportation investments were based on projects whose main objective was an accessibility improvement (e.g. Boyce et al. 1972). In contrast, here we examine a transportation project whose principal goal was to encourage local economic growth, in particular to revitalize the declining CBD area. Studies done in the past on the capability of transportation investments to bolster the growth of a declining CBD were based on projects whose main objective was an improved transportation system (e.g. Mackett 1980; Poulton 1980; Cervero and Landis 1997). Our main underlying objective here is to ascertain the degree to which a project designed first and foremost to encourage CBD growth is able to accomplish its stated economic goal. The project under study is a 6.4 mile (about 10.6 km) Light Rail Rapid Transit system (LRRT) in Buffalo, New York. Construction began in the mid-1979 and lasted for about six years. The project was located within a declining central city area and represented a large ($450mn, in 1978 prices) public investment for which user or transportation benefits were not the sole or even a major consideration. Rather, economic and land development and the creation of jobs primarily in the CBD area were the major factors in the funding decision. These growth objectives were to be obtained by providing adequate capacity to transfer fast and conveniently a large number of passengers from the city edge to the heart of the downtown. The availability of such capacity was seen as a major impetus for engendering retail, commercial, cultural and entertainment activities in the CBD (the downtown) area. In brief, the Buffalo LRRT project was viewed as a necessary condition to stop the decline of the depressed urban core and encourage revitalization by stimulating additional public and private investments. This rationale became the official raison d’être for undertaking this investment. This analysis is based on an ex ante study carried out during the project’s implementation period in order to ascertain the potential ability of the LRRT

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investment to reverse the current trend of decline of the CBD area and promote its revitalization (Paaswell and Berechman 1981; Berechman and Paaswell 1983). We also report some general observations of the present-day status of the project. They strongly support the main findings of this study. 10.2.1 The Buffalo LRRT: background and methodology The Buffalo LRRT project is a massive capital investment focused on a small well-defined area.1 The LRRT corridor is located in a region of general economic decline whose major characteristics are typical of many northeastern cities in the USA. Because of the regional out-migration of population and the intra-regional sustained trend of suburbanization, there has been a continuous decline in population in the central city from 1960 to the present.2 Another characteristic is the constant change in the composition of the regional labour force from traditional blue-collar industries toward an increase in the white-collar service employment. These trends are shown to have profound effects on shopping patterns, employment location and travel behaviour (see Chapter 4, section 4.2 and 4.3 for a general discussion of such trends). Several working assumptions were made in examining the effects of the project on the CBD area. These assumptions also served as a guideline for developing the methodology. First, it was assumed that the LRRT project will produce a large number of interrelated impacts which for analytical purposes may be categorized into four distinct impact groups: transportation, economic, shopping patterns and land use. Second, it has been assumed that no one model can simultaneously treat all of these impact types and therefore a set of models and techniques should be used. The third working assumption is that the potential effect of any of the above impact types will be enhanced or constrained by the regional trends mentioned above. Unlike growth areas, where a facility can be built in anticipation of generated demand for its use, investment in a declining area must consider the amount of activity that can be supported by the limited demographic and economic resources. The fourth assumption is that while the various effects of the LRRT project will be felt region wide, their main domain of influence will be in the immediate corridor and the CBD. Thus, the focus should be on impacts in these two areas. Last, it was assumed that the ability of the LRRT project to revitalize the CBD depends also upon other public and private sector policies. The importance of this assumption lies in the fact that development in other parts of the Buffalo region, which occur through private and public sector incentives may actually be conflicting with the LRRT objectives. The methodological framework of the study was designed on the basis of these working assumptions. It is presented in Figure 10.1. The figure shows how major trends in the Buffalo region are combined with a set of models to

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Figure 10.1 Methodological framework of LRRT analysis.

analyse the LRRT impacts. These impacts are categorized into distinct groups, which together influence the economic vitality of the CBD. Next, we elaborate on the principal components of the methodology, underlying trends and models. 10.2.2 Principal regional trends Above we asserted that in order to correctly examine the various impacts of the LRRT project on the CBD it is necessary first to analyse underlying major regional trends. These include demographic, employment, retail patterns and travel trends. Population and employment trends Table 10.1 shows the population and employment trends in Buffalo metropolitan area for the period 1950–80. From Table 10.1 it is seen that the city population decreased from a high of 580,000 in 1950 to a low of 357,000 in 1980, a reduction of over 38 per cent of the total number of city residents. While many of these people have

Notes: a MSA population in 1980 is approximately 300,000. b Estimates. P=population; TE=total employment; UE=unemployed; BCE=blue collar employment; WCE=white collar employment.

Sources: US Census, Population and Housing 1970; 1980; Erie and Niagara Counties, Regional Planning Board, 1975.

Table 10.1 Population and employment trends in Buffalo Metropolitan Statstical Area (MSA), 1950-80a

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relocated to suburban areas, even on the county level there was an overall population decline between 1970 and 1980 of about 100,000 residents. Population changes are of course related to concurrent employment changes. Between the years 1970 and 1980 Erie County employment declined from 422,000 to 403,000 employees, while in Buffalo blue-collar employment declined from 97,000 to 80,000 employees. The net employment figures have been affected by two phenomena: the outmigration of the labour force and the addition to the labour force of household members who previously did not work, mainly women. A further analysis of the employment trend shows that in this period two major shifts in the employment make-up have occurred. First, while blue-collar, city employment dropped 18 per cent between 1970 and 1980, service employment in the city has increased by about 47 per cent during this 10– year period. These figures point to a trend of structural change in employment make-up in which employment in manufacturing declines while that in the service sector rises. A second and related major shift is the significant increase in the rates of participation by women in the labour force. In 1980, 40 per cent of the total labour force in the metropolitan area were women compared with 35 per cent a decade ago. The importance of these trends lies in the fact that service-related jobs, in contrast with manufacturing, are the major industry category of the CBD. While in the last decade the region has lost employment, total employment in the CBD has remained stable. About 50 per cent of the total city’s white-collar employment was in 1980 concentrated in the CBD area (GBDF 1978). Retail patterns By its very nature the retail sector depends on its proximity to residential and employment areas. Historically the CBD enjoyed central roles with regard to retail and commercial activity, even though it has increasingly had to share its pre-eminence with the suburban areas over the decades. Over time, new construction of retail centres has taken place at an increasing rate at increasing distances from the inner city area. Retail malls have become progressively larger, both in terms of store space as well as the parking spaces provided. It is for these reasons that while in 1960 CBD sales were 27 per cent of city totals and 15.7 per cent of the SMSA’s total sales, in 1977 the figures were 13.1 per cent and 2.3 per cent respectively. A factor which has mitigated this trend is that patronage of some CBD retail outlets has been rising in the 1980s mainly because of the increase in the number of women who work at the CBD and also shop there. Nevertheless, the CBD area had over the years lost much of its retail dominance to the suburban areas, a fact which largely explains the decline in its economic viability (GBDF 1978).

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Table 10.2 Per cent transit use peak and offpeak, city, suburbs

Source: Paaswell and Berechman (1981). Notes: a City of Buffalo only (see Figure 10.2). b First ring (see Figure 10.2).

Transportation trends The Buffalo metropolitan area has the familiar twentieth-century pattern of circumferential and radial expressway systems providing a high level of accessibility throughout the region and especially within the suburban area. With an excellent highway network surrounding and bisecting the entire region, there are virtually no heavy congestion points in the area in peak periods. This benefit can ironically be partially attributed to the fact that the network was designed and constructed for anticipated regional population increases. The public transportation bus network also provides good service mainly within the city limits. Modal split figures, depicted in Table 10.2, are typical of most USA major cities. The overall transportation picture that emerges is that of a high level of accessibility throughout the region by private automobiles coupled with a good inner-city bus transit system. The fact that most households live within ten minutes of auto travel time from a major shopping centre indicates that transportation services in Buffalo were adequate at the time of the LRRT construction and provided high levels of accessibility to all key locations in the region. In summarizing the overall effect of these trends, three points should be recognized. First, the combination of population decline, an increasingly dispersed shopping pattern and high levels of accessibility throughout the region have focused economic development and activity location away from the downtown area. Second, current shifts of employment to whitecollar jobs in which women participate at high rates and the location of these jobs in the downtown indicate the potential of the CBD to capitalize on these conditions through future development. Third, given the current state of the transportation system, it is clear that the above nontransportation trends and impacts are the crucial factors for CBD revitalization.

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Figure 10.2 The Buffalo rail transit and development patterns in the metropolitan area.

10.2.3 Models used in the impact analysis As shown in Figure 10.1 the set of impacts of the LRRT project is divided into four categories for analysis. Therefore, the principal models formulated and used in the analysis are, in part, impact specific. The metropolitan region was subdivided into 34 zones for the purpose of the model analysis. Zonal division of the metropolitan area is depicted in Figure 10.2. (see Berechman and Paaswell 1983 for the basic data on land use, population and employment). Next we describe the four principal models which are used in the analysis: the economic base model, the access ability model, the shopping probability model and the urban land use model.

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Economic base model In addition to nearly $450mn of federal and local investment originally allotted for the LRRT (1978–83), another $300mn of public and private investment, which directly relates to the construction of the system, was planned. Much of the investment in offices, a convention centre, and a transit mall tie their origins to a 1972 master plan for the city of Buffalo that considered the transit system in the centre of Main Street to be the cornerstone of CBD revitalization. The impact of these investments on the Buffalo economy is likely to be realized through changes in the labour force directly related to the LRRT construction. These are of two types. The first is temporary and medium-term employment created by LRRT and related construction activities. Essentially this the multiplier effect as shown in Figure 7.2, Chapter 7. The second is long-term employment created by the LRRT system and LRRT-related new facilities like those mentioned above. Additional labour force impacts are likely to arise in the service sector as explained below. These labour changes constitute the economic development effect from this transport infrastructure investment. Of course they are time dependent, as certain time lags exist between the date of an investment and the time its economic growth impact becomes tangible. Other impacts from the investment relate to regional income, which is likely to rise and, in turn, affect local expenditures. An economic base model was used to evaluate the multiplier impacts.3 The model asserts that new non-services employment will generate demand for services of various types. These additional services in turn will create more jobs, which subsequently will generate further demand and jobs. These additional increments of employment, which represent the employment multiplier effect and were initiated by the LRRT total (direct and related) investment, are also used to compute the consequent increases in population and in regional income.4 The accessibility model (ACCESS) The ACCESS model was designed to measure changes in total accessibility within the Buffalo area on a zonal basis following the construction of the LRRT system. Accessibility is defined here as a combination of interzonal travel time and zonal activity level and is separately calculated for work and shopping trips. As these two trip types often occur at different times, peak and offpeak travel times by mode were used for computing changes in zonal total accessibility. The model is a derivative of Davidson’s (1977) model. Total accessibility of zone, i, Xi is assumed to be a function of three factors: intrazonal I T accessibility, X i , interzonal accessibility of zone i, by transit, X i and interzonal C 5 accessibility of zone i by car, X i Thus:

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(10.1)

The transit and car accessibility components are computed as gravity functions of the type: (10.2)

where aj is level of attractiveness of zone j, dij is travel time between zones i and j, and ß is an impedance parameter. Let denote accessibility before and after LRRT, respectively. Then change in total accessibility for service purpose,6 DSi, for zone i, is: (10.3)

where Oi is the number of households in zone i (denoted by Hi) times their (constant) trip rate (denoted by e), i.e. Oi=h·Hi, and g is impedance parameter for interzonal shopping trips. Change in total accessibility for work purposes, DWi, for zone i, is: (10.4)

where Di, is the number of employees in zone i, (denoted by Ei), times their (constant) trip rate (denoted by e), i.e. Di=e·Ei, and l is the impedance parameter for interzonal work trips. Total summation over all of (10.3) and (10.4) show the total region-wide changes in service and work accessibility caused by the LRRT project. The input data needed for the accessibility model 1 consist of base year data to measure 1980 pre LRRT accessibility, Xi , and 2 future year (1985) data, to measure post LRRT accessibility level, Xi . Shopping probability model A major indicator of the impact of the transit investment is the consequent change in level of retail activity in the Buffalo CBD area. As explained at the outset it was conjectured that the construction of the LRRT system connecting the city fringe with the downtown will enhance shopping in the CBD, which will be manifested in higher levels of retail trips and sales. To evaluate this hypothesized impact of the new transit system, it was necessary to develop and calibrate a model which simulates individuals’ propensity to shop at a given shopping facility, given their socio-economic characteristics, the set of all available regional retail facilities and accessibility variables. By changing the level of explanatory variables, mainly reduced

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travel times and increased retail floor space, attributed to the LRRT investment, it was possible to assess their impacts on shopping behaviour. A key assumption that underlies the use of this model is that trip frequency to a given shopping facility represents individuals’ choice probability of selecting that facility, given all other available regional retail outlets. Another major assumption is that all individuals sampled have the same choice set of shopping facilities. Three major determinants of retail trip frequency have been identified for the analysis. These are: individuals’ socio-economic attributes including income, car availability and household size; attractiveness of a retail centre including its size (i.e. its floor space), number of employees and volume of retail sales and accessibility to the shopping centre, mainly travel time.7 The estimated statistical model is: (10.5)

where r, s are indices of retail and residential zones, respectively. denotes the proportion or frequency of trips to a shopping centre located in zone r of total shopping trips taken by individual l. Hsk is a vector of k socio-economic attributes of individual l who locates in residential zone s. Art is the level of attractiveness of a shopping centre in zone r, as measured by variable t (e.g. retail floor space or number of retail employees). Lsr is a measure of accessibility between residential locations s, and shopping centres in r. C, a, b and g are parameters to be estimated. Notice that in equation (10.5) frequency of travel to a shopping centre in r is unaffected by the characteristics of other centres. Thus, competition between centres is not directly accounted for and the possibility that CBD shopping will gain because of some changes in other centres is not considered here. However, by requiring that in the estimation of equation (10.5), competition between centres is introduced indirectly. The results are indicative of the propensity to shop at a given centre, but are not intended to display the complete dynamics of shopping behaviour. We are primarily concerned with the pull that the delineated factors had on the relative magnitude of shopping attraction at a given retail location r. Urban land use model As mentioned above, the large investment in the LRRT was alleged to impact land use, in addition to its impact on travel and shopping patterns. Since changes in the land use system are a major determinant of urban revitalization there was a need to evaluate such changes relative to the entire study area, particularly the LRRT corridor. The specific model used for this purpose is a derivative of the well-known

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Garin-Lowry model with a considerable number of analytical and empirical changes made to meet the particular needs of this study. Since the analytical structure and operation of this model are rather well documented in the literature, here we will only specify the model’s allocation function used in this analysis (see Batty 1976; Foot 1978; Berechman and Small 1988 for a detailed description). For the purpose of allocating residential population to specific locations the following allocation function was used: (10.6)

and, (10.7)

where Tij is the number of employees travelling between work zone i and residential zone j, Ei is level of employment at zone i, Ai is a vector of gravity balancing factors, with Hj measuring residential attraction of zone j. Cij is the interzonal generalized travel cost matrix and d is an impedance parameter. The service location sub-model employs a similar allocation function to compute the number of service employees demanded by the population of zone j who work at zone i. The major inputs to this model included basic and non-basic employment, residential population, land availability, activity multipliers, employment and residential density constraints and employment and shopping travel times all disaggregated by zone (see Berechman and Paaswell 1983 for these data). The operationalization of this model requires several phases. First there is the calibration phase where the parameters of the allocation functions are estimated. Following calibration, the key variables within the model are altered in order to test the impact of major changes in the sub-region (LRRT corridor) and to simulate the outcomes of policy alternatives. This is the prediction stage. Since the model allows for the imposition of constraints on zonal activity levels to reflect physical limitations on land availability or density policies, there is a constraint procedure which can be introduced at calibration or the prediction phases. The models reviewed in this section were developed from a comprehensive package that provides a variety of tools for investigating the range of LRRT impacts (Paaswell and Berechman 1981). The following section presents the major tests performed and their results. 10.3 Tests and results The methodology and models outlined above were used for the evaluation of the LRRT four impact categories, namely, investment multiplier, accessibility, shopping pattern and land use. As explained in Chapter 7 (see also Figure

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7.2), the investment multiplier impact is not regarded as part of the potential economic growth effect of the project. In the present analysis, however, the increase in the labour force, which can be directly attributed to the LRRT investment, is computed and then introduced into the economic base model in order to project increments of population (P) and dependent service employment (S). 10.3.1 Investment multiplier results The magnitude of the LRRT investment was $450mn (in 1978 prices), and the LRRT-related investments, mainly in retail and commercial development, amounted to additional $300mn. Taking 65 per cent of these investments to generate short and medium term employment, a new labour force of 5,370 workers was computed. Any additional employment, if it occurred, would be due to the increase in population related to this new employment which, in turn, would generate demand for additional, population-related service employment.8 10.3.2 A ccessibility model results From equations (10.3) and (10.4) above, accessibility indices were computed for each zone for the current (pre LRRT) and projected year (post LRRT). The pre-LRRT interzonal travel time matrices were computed using actual network data and information on bus services including bus frequencies, location of stops and routes. The post-LRRT travel times were computed on the assumptions that the actual light rail travel times and bus operation parameters will be as those indicated by the system’s planners. The postLRRT information on and Oi and Dj, as well as on the parameters g and l (see equations 10.3 and 10.4) were obtained from the land-use model. The results of measuring travel time and total accessibility before and after LRRT, for work and service trip purposes, were used to rank the study zones from most accessible (rank 1) to least accessible (rank 34). The results are given in Table 10.3. These results elucidate a number of interesting points. First, there are major differences in the accessibility of a zone for work trip and service trip purposes. Not only are work trips carried out mainly at peak times while service (shopping mostly) trips are done mainly at offpeak times, but modal split also differs for these trips, so that more transit trips are done for work purposes at peak time. A second point is that for both trip types there are differences in the resultant rankings between time accessibility and total accessibility. However, within each trip type and accessibility measure there is almost no change in the ranking of the zones before and after the LRRT project. The principal implication of these results is that relative accessibility, however measured,

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Table 10.3 Comparison of zone rank by time and total accessibility for work and service trips, before and after LRRT

for a given trip type will not change significantly after the LRRT system begins operation. Another point worth observing is that the zones which are most accessible for work are suburban zones (e.g. zones 23, 24). The explanation is that major highways and roads service these zones quite adequately and that these zones already contain a relatively large number of trip-generating and attraction activities.

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The CBD on the other hand (zones 1, 2) is ranked medium for work trips, mainly because it is less accessible to car trips and is served by buses which provide high level service to this area, though not as good as the car does. For service trip purposes, the CBD zones would still have low to medium level of accessibility after the LRRT, mainly because most shopping trips are carried out using the car. Note, however, the impact of activity level on total accessibility rank of the CBD. Whereas zones 1 and 2 are ranked 34 and 32 respectively for service purposes on the time accessibility scale, they are ranked 23 and 17, respectively, on the total accessibility scale. The latter scale contains the impact of zonal activity level. Those zones in the study area with current high accessibility levels gained little with the introduction of the LRRT, while those zones adjacent to the LRRT corridor gained the most. However, these gains were not substantial enough to offset the high accessibility of suburban zones already well served by an extensive highway network. The results of the accessibility model runs lead to the following main conclusions. First, total accessibility and time accessibility in particular will change, but very moderately by the construction of the LRRT system. Second, given the planned route of the LRRT system, trip makers in most zones that are not immediately adjacent to it will still use the highway system or the current bus system in their daily trips. Hence it is very unlikely that current modal split ratios will change significantly once the system is in full operation. Third, given the results regarding the actual changes in the levels of accessibility in the zones adjacent to the LRRT route, it can be expected that for a special portion of the public residing in those zones, there will be a significant increase in actual and perceived accessibility. Those inner city residents who do not own an automobile or who are considered to be transportation disadvantaged (e.g. the elderly or the young) should get the most benefit of improved accessibility first to the CBD and eventually to the region. Fourth, since travel times in the post-LRRT period will not be greatly improved, the only way the CBD can increase its share of shopping and employment is if other factors which affect the level of CBD attractiveness are improved. 10.3.3 Shopping analysis results Recall that the shopping probability model (equation 10.5) contains variables of three types: households’ socio-economic characteristics, accessibility and attractiveness of a shopping facility. The socio-economic variables used were level of household’s income and car availability. Input information for these variables was obtained in the survey of shopping patterns in Buffalo (Paaswell et al. 1979), which included 246 observations with complete data. Location of households and their preferred shopping centres were also obtained from this survey. The accessibility measures used in the analysis were travel times by private car and by transit. In addition, it was necessary to determine which areas will be included as shopping

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destinations, as it was assumed that the CBD competes directly with major shopping centres and not with local or neighbourhood stores. Thus, for this analysis the entire Buffalo metropolitan area was divided into four ‘super’ retail areas namely CBD, Other City, Suburban Malls and all others. The attractiveness variables selected for the model were: number of retail establishments in each super retail area, total square feet of retail space, total number of retail employees and total retail sales. Seven principal tests were carried out.9 These were: • • • • • • •

Test 1: the entire 246 observations were used. Test 2: households with income categorized as low income. Test 3: households with income categorized as high income. Test 4: households which shop at the downtown and live inside the city. Test 5: households which shop at the downtown, but live outside the city. Test 6: households which shop at suburban malls and reside in suburban zones. Test 7: households which travel less than 20 minutes for shopping.

The results of these tests, in terms of the estimated parameters of model (10.5) are reported in Table 10.4. Several key points should be observed about the results from these tests. In all seven tests the value for the accessibility coefficient d is insignificant and small. It should be noted that 91 per cent of the sampled households live Table 10.4 Parameter estimates of the shopping probability model (10.5) seven tests

Note a Because of the importance of these coefficients for the analysis we report their estimates and standard errors in brackets. None is significant at 0.01 or 0.05 level.

a

= Household’s income coefficient = Car availability coefficient g = Coefficient of a shopping centre attractiveness variable d = Coefficient of an accessibility variable C = Regression’s constant F = F test for regression M = Number of observations for a given run NS = Not significant at 0.01 level b

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less than 20 minutes from their selected areas of shopping. Given the distribution of shopping centres in the Buffalo region, travel time variables were found not to affect households’ shopping choices. Improved travel times, therefore, would not alter prevailing shopping choices, holding constant all the other independent variables. The only variable whose coefficient is significant for all of these runs is level of attraction of the shopping area. This result is extremely important in the context of the present analysis. It implies that any increase in the share of the CBD in the regional retail activity through the LRRT is conditional upon the degree to which the system will increase the CBD attractiveness as a shopping area. It may do so by generating more and better retail outlets but not by affecting the CBD’s relative level of accessibility. Hence, the above results from the accessibility model, which indicate that the LRRT will have little if any impact on the relative accessibility of the CBD, may not diminish the importance of the LRRT system as a potential factor in improving the CBD attractiveness. We, next, elaborate on this result by presenting the results of the land use model. 10.3.4 Land use model results Following calibration, the model was used to predict land use impacts of potential changes generated by the LRRT system. The results of two runs, which directly relate to our objectives here, are shown below. The first is a test for the impact of the LRRT’s direct investment only. The second is a test for the overall impact of the project including changes in zonal attractiveness, in accessibility and taking into account the LRRT direct and related investments. 1

2

Prediction of LRRT’s investment effect: the direct effect of the investment is to increase basic employment. The additional basic employment as computed from the economic base model was introduced into the land use model and the resultant vectors of population and employment were observed. These results, in terms of per cent changes from original (base year) data, are reported in Table 10.5. Prediction of the LRRT’s overall impact: in this prediction run the effects of the total investment, of changes in the zonal level of attraction for activity location (mainly services) and of changes in accessibility were simultaneously introduced into the model. The predicted per cent change in the distribution of population and employment are reported in Table 10.5.

These results indicate that the impact of the investment factor alone is to increase service employment in the CBD (zones 1 and 2) while reducing residential population there. However, since total population in these zones is currently small, this latter effect can be ignored. On the other hand, base

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Table 10.5 Results of the two prediction runs of the land-use model

year level of service employment in the CBD is the highest in the region (45,687 employees). Thus, the predicted 34 per cent increase implies an addition of 15,330 employees which in itself is larger than any amount of service labour at any other zone, at the base year. The planned commercial and retail facilities in the CBD discussed above are the main sources for this new service employment.

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The predicted population distribution from the total impact of the LRRT suggests a similar decline of residents in the CBD. A more dramatic result is the large increase in population in major suburban zones (e.g. zones 21, 22, 23, 27). This additional population, which is generated by LRRT-related employment, is distributed by the model to these zones for reasons similar to those which underlie the long-run trends of suburbanization. These include greater availability of residential land, lower densities and high levels of accessibility. With regard to service employment, the results of the total impact test again show that the CBD is bound to benefit most from the LRRT system. This large gain (over 100 per cent) is mainly because of the increase in attractiveness of zones 1 and 2 for services, which corroborates the results obtained before from the other models. The new service employment is consistent with anticipated retail, commercial and cultural development there. In addition, the analysis shows a high per cent increase in service employment in zone 18, which is located at the northern end of the LRRT route (see Figure 10.2). This also indicates the positive effect of the system on zonal attractiveness, but because of the relatively small number of service employees in the base year in this zone the implications of this result are limited. The final set of results to be observed pertains to the impact of LRRT on zones immediately adjacent to its corridor. With regard to population, the current trend of population decline is not going to be reversed. Moreover, these adjacent zones are also not going to benefit much from the LRRT with regard to service employment. In contrast to the CBD zones, where the bulk of the physical development is planned, only very limited LRRT-related development will occur in those zones. When coupled with virtually no change in accessibility levels after the LRRT implementation, this lack of development explains the results for the adjacent zones. 10.3.5 Overall downtown results of the LRRT project From the results presented in this section the following major conclusions can be stated. 1

2

3

The downtown’s economy: the LRRT is expected to have a positive impact upon the economy of the downtown. This result will transpire by attracting additional service employment to the CBD area and is contingent on the implementation of additional private investments, some of which are related to LRRT investment. Accessibility of the downtown: the accessibility of the downtown area to all other zones in the region by mode and trip type will not change significantly after the LRRT construction. Shopping at downtown: if, as projected, the LRRT will have positive impact on downtown attractiveness, a larger share of the regional retail trade will be captured by the CBD, all other factors remaining the same.

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10.4 The Buf falo LRRT: postscript 10 In the early 1960s the Niagara Frontier Transportation Study, one of a number of seminal computer-assisted regional transportation studies, forecast the need for significant capacity improvements in a number of corridors serving Erie and Niagara Counties in New York State. Influenced by the strength of the auto, steel and chemical industries and grain mills that were the backbone of this manufacturing region, the planners believed all that was in the area’s future would be growth of employment, population and the services which support a healthy economy. The city of Buffalo had a population in 1960 of 530,000 and the two counties’ area of 1.2 million. A product of this planning process, although more than a decade and a half later, was the Buffalo Light Rail Rapid Transit system (LRRT). The LRRT, as opened finally in 1982, was a six-mile line serving the city of Buffalo along the ‘Main Street corridor’. It should be noted that by 1980 the city had begun a population decline, although population in the region was stable. The city was feeling the familiar effects of suburbanization— losing both people and jobs to the surrounding areas. Transit ridership on the bus only transit system was over 28,000,000 trips per year. The LRRT was expected to add 80,000 trips per day to this total. At that time, transit had captured fewer than 10 per cent of regional trips. The average travel time to work by all modes was about 20 minutes and by transit 30 minutes. During the next decade and a half Buffalo suffered a major loss in employment. All of the heavy manufacturing—steel, tyres, auto manufacturing—closed shop. Local government, the universities and other services picked up some of the employment slack but by 1992, the population had dwindled to 323,000. The thirty-eighth largest city in the country in 1980 had become the fiftieth largest by 1992. Its home county, Erie, had gone under one million for the first time in 40 years. Buffalo had lost a base of support for its transit—the basic population. The 180,000 jobs supported in the city in 1980 became 147,000 by 1992. In 1960, when planning begun on the transportation needs, more than one of every two jobs in the region were in the central city; by 1992 this had dwindled to less than one in three. In addition to losing population and having jobs become more suburban and dispersed, planners and decision-makers in Buffalo made a number of critical decisions that mitigated any potential success of the Light Rail. These included: 1

2

Keeping the CBD viable and attractive to cars. Parking policies were enacted to ensure easy access of cars to activities throughout the CBD and substantial new parking capacity was added along streets that paralleled the LRRT. No retail incentives were enacted, especially to attract high-end shoppers. In fact the city was inactive as developers put in place several million sq

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4 5

6

277

ft of new retail malls away from the CBD and away from the LRRT (or any transit). New highway capacity was added to bring people from the suburbs to the CBD. In fact, some of this capacity competed directly with the Light Rail. As a result of population declines and highway capacity increases, average travel time to work actually declined to less than 19 minutes. To make the LRRT ‘work’, bus routes were changed to feed the LRRT. Many of these one-seat rides from suburb or outer parts of the city now became two-seat rides, as passengers had to transfer. The NFTA and city did adopt a downtown free fare zone. However, this put significant pressure on the operating budget (the LRRT returned only 20 cents on the dollar of cost from the fare box). As a result there were many service cuts, making the system less attractive.

Today, NFTA has 28 million annual transit trips, and the LRRT was assumed to almost double this figure. While more than nationally promoted Portland, Oregon rail ridership, it is far from Calgary’s 140,000 daily riders. Buffalo has replaced some of its old industries with a baseball stadium and hockey arena, but little else draws people to the CBD. The system, designed for an active region and dynamic core, must now serve a car-oriented suburban region and hope for the day when the Buffalo CBD is rediscovered. 10.5 Key lessons from the Buffalo LRRT project The ex ante asserted significant impact of the LRRT on the attractiveness of the downtown as a centre of retail, commercial, cultural and entertainment activities is mainly due to a planned massive physical development in a welldefined small area. But attractiveness of an area is also determined by features like variety and quality of activities, personal safety and comfort, ease of access to retail outlets, upgrading of existing facilities and, most importantly, by competition from other accessible and attractive areas in the region. All these issues are affected by urban public policies and, as a result, it is apparent that proper private and public sector programmes are necessary to induce these features. The lack of regional or even citywide co-ordination of policies to ensure the attainment of the LRRT objectives is probably the most serious threat to the revitalization of the downtown. Conflicting highway, parking, transit and zoning policies are examples of this problem. Since the LRRT route does not extend to the suburbs, suburbanites who wish to use the system need to drive and park their cars at the nearest LRRT station. Given that very few parking facilities have been planned and that the highway system provides adequate access to the heart of the CBD, no significant changes in modal split can be expected. Moreover,

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express buses, which operate between suburban zones and the downtown, also reduce the effectiveness of the new system. Still another example of the consequences of lack of policy co-ordination is the concurrent development of retail and commercial facilities in suburban zones. Even though this development may not be as massive as the planned development at the downtown, the proximity of these facilities to residential population is likely to counterbalance the positive impacts of the LRRT on the attractiveness of the CBD. The Buffalo transit development was consistent with a 1972 downtown plan that linked CBD access via rail to downtown renewal. However, related investment projects like new hotels, new office buildings and a pedestrian mall, which integrate travel with adjacent activities, were not begun until transit construction funds were secure. In this sense, more than many other urban projects, the impact of transit investment and the stimulus that transit provides through its interaction with other activities are seen as the main determinants of CBD revitalization. Thus, the key general lesson from this study is that improved accessibility per se is neither a necessary nor sufficient condition for such a goal as CBD revitalization. A corollary to this conclusion is that capital transport investment must be targeted and the implications of the investment, land development, employment increase and location of service facilities must be clearly determined and enforced through complementary public policies. 10.6 Empirical evidence from other rail projects The study of the Buffalo LRRT project was an ex ante analysis. In this section we review empirical evidence from existing high-speed, inter-city rail projects. While the main effect of such projects is reduced travel time between cities, here we focus on their potential benefits from enlarged labour market areas and increased economic activity around the stations. We review the evidence from Japan and France, where high-speed rail links have been constructed. We also examine impacts from the California BART system. The focus here is on the local and regional impacts in terms of the development effects. 10.6.1 The Japanese Shinkansen high-speed rail system The Japanese Shinkansen have been in operation since 1964 when the Tokaido line was opened between Tokyo and Osaka. (See Figure 10.3.) Systematic analysis has been carried out on population and employment changes over time (1975–85), but with mixed results. Nakamura and Ueda (1989) found that higher than expected population growth took place in three of the six prefectures where the Shinkansen stations were located. More detailed analysis suggested that the greatest impact on population growth took place in locations where there was both a Shinkansen station and a high-speed road. However,

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Figure 10.3 The Japanese Shinkansen high-speed rail system.

the causality of the relationship was questioned as to whether the Shinkansen station was leading growth or growth would have taken place without the Shinkansen (Sands 1993). Multivariate analysis was used to unravel some of the relationships. The study concluded that ‘growth caused by the Shinkansen could be predicted for a region with 90 per cent accuracy’ (Sands 1993:260). It seemed that there were three principal conditions needed for growth. First, a high incidence of information exchange industries (business services, banking services, real estate) is required. This result seems to support the conclusion of Chapter 7 where agglomeration economies are seen as a prerequisite for development. Second, sufficient opportunities for higher education (universities) must be there. The third condition is good accessibility to a Shinkansen station. Growth rates in retail, industrial, construction and wholesale sectors were 16–34 per cent higher in cities with a Shinkansen

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station than in those cities without (Brotchie 1991). Yet, these high net figures may conceal a strong reallocation process of activity within a city from existing centres to new station-accessible centres. More localized studies have attempted to examine the effects of station location on development (Amano and Nakagawa 1990). The original Tokaido line had twelve stations, with three completely new stations located at peripheral sites near to the city. The other stations were located in city centres. The development impacts at the existing stations were minimal, but the new stations located at peripheral sites had substantial local impacts. At Shin Yokohama, some 15 miles south-west of Tokyo, a new station was opened with little initial impact. It was only when a new underground line was built to central Yokohama (4 miles away and cut the journey time from 30 minutes to 12 minutes with a high frequency service) that development took place and the station became an important stopping point on the Shinkansen. Perhaps this is a manifestation of the work of underlying network economies. Only limited development took place on the Yokohama side of the railway (the south side) because local residents have resisted development. On the non-Yokohama side of the railway (the north side) an edge city has developed with new offices in a highly accessible location (Hall 1995). The impact of the Shinkansen is clear at local level as population and employment growth rates are consistently higher in areas with a Shinkansen service than in those without, particularly in the information exchange sector and in the hotel and food service sectors (Sands 1993:268). Limited evidence has also been found on land value increases. Nakamura and Ueda (1989) found that land values in commercial areas rose by 67 per cent with a Shinkansen station. 10.6.2 The French TGV high-speed rail system The French Le Train à Grande Vitesse (TGV) system has been in operation since 1981 when the line between Paris and Lyon was opened. Connection to the TGV network has been seen as a ‘boost to the local economy’ (Bertolini 1998:166), but this impact may only be a redistribution as appropriate local economic conditions are also important (Troin 1995). On the TGV-PSE (ParisSud Est to Lyon), there were three new stations at Lyon Part-Dieu, Le Creusot and Maçon. It was only in Lyon that significant growth took place, as there was a heavy demand for office space around the new Part-Dieu station. By 1990, the TGV station had 40 per cent of the city’s total office space with a further 60 per cent of new projects. This amounted to a 43 per cent increase (1983–90) to some 250,000 sq m. The explanation was that the new station had good local access (as well as being two hours from Paris), with a high visibility and level of convenience for customers. In addition, there was little available space in central Lyon, so relocation was an attractive option for high tech service industries needing good access to Paris. Business trips by

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rail increased by over 50 per cent (Pieda 1991). These local advantages were not available in Le Creusot and Mâcon so little TGV-related activity took place. (See Figure 10.4.)

Figure 10.4 The French TGV high-speed rail system.

As the TGV network has been extended, the impacts elsewhere were also variable. Substantial growth has taken place at Le Mans, Nantes and Vendôme on the TGV Atlantique where local conditions were buoyant. A 20 per cent rent premium is typical in these new development locations. The TGV links

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have reduced travel time substantially to Paris (e.g. Vendome to Paris is now 42 minutes by TGV when previously it was 135 minutes). They have raised the image of the newly connected locations and opened up new locations for businesses to consider location (or relocation). However, as noted above, development has been inconsistent across station locations, as impacts have been variable and highly localized. The extent of development has depended on the overall economic strength of the local economy and the presence of service sector firms requiring access to Paris (Sands 1993). The clearest example of all these elements coming together is in the technopole concept at Euralille.11 A new high-speed railway station has been built as a hub for the TGV network in northern Europe. This, in turn, allows decentralization from Paris (60 minutes away), the provision of a service function to Brussels (30 minutes away) and a business bridgehead on the continent for London (at present 120 minutes away, but eventually 90 minutes away). In addition, there are plans to link the rail investments with international airports at Paris-Roissy and BrusselsZaventem and to expand the local Lille-Lesquin airport (Ampe 1995). At the new Lille Europe station there is a business/recreation complex, including a trade centre, congress, exhibition and concert facilities. New hotel accommodation is also being provided, with a large retailing centre, leisure facilities and public support services. The Lille Business School is being relocated here, together with new housing. The novel aspect of this combined transport and development package is that the investments have proceeded simultaneously. The management and finance have been carried out through the French system of Société d’Economie Mixte (SEM), which is based on partnerships between the public sector (mainly the infrastructure investment) and the private sector (mainly the associated business, retailing and housing investment). The main conclusion from this brief review of two high-speed rail investments is that the impacts are found both at the network and local levels. The network effects relate to the substantial increase in accessibility to the key national and international markets. The local impacts are more variable and relate to the presence of a buoyant local economy that can take advantage of the new opportunities offered by high-speed rail accessibility. Impacts are apparent mainly in the service and high-tech sectors, but also more generally across all business sectors. Image is important as is the availability of support services and facilities, such as those found in Euralille. Local road and rail connections also make a difference, particularly if the high-speed station is located at the periphery of existing urban areas (e.g. Shin Yokohama and Euralille). Finally, the need for supporting public policies at all levels cannot be over-emphasized.

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10.6.3 The San Francisco BART system A unique opportunity for examining the impact of an urban rail investment is provided by the Bay Area Rapid Transit (BART) system in San Francisco, which was opened in 1973 and now has over twenty years of data to assess its influence. (See Figure 10.5.) Early studies indicated that BART had a modest influence on land use and urban development, directly by improving accessibility and indirectly by inducing policies supportive of compact development (e.g. incentive zoning and redevelopment finance). It was also concluded that it was only where the necessary supporting conditions were present (e.g. buoyant local economy) that growth actually took place (Knight and Trygg 1977). The main conclusion reached with twenty years of evidence is that BART has had a very modest impact in a highly localized and rather uneven pattern. These are similar conclusions to the impacts of high-speed rail in France and Japan. The buoyant centre of San Francisco has been able to grow and maintain its dominant position in the urban hierarchy. The centre of Oakland has also benefited through its attractiveness for public and private investment, part of which is due to the high levels of public transport and road accessibility. Walnut Creek has attracted office development, Pleasant Hill has 1,800 apartments and condominium units within 400 m of the BART station and Fremont has a mix of transit-oriented developments (Cervero and Landis 1997). It should be noted that each of these last three stations is located towards the end of the BART network, where little existing development was in existence in 1973. Overall, the effects of BART seem to have been to maintain the dominance of the centre of San Francisco. Over 75 per cent of all office construction within 800 m of BART stations since 1973 took place here (40 million sq ft of office development in downtown San Francisco, compared with 12 million sq ft elsewhere in San Francisco). However, outside San Francisco, even greater office development took place near road-accessible suburbs like Pleasanton and San Ramon rather that in the rail accessible locations. Overall, 35 million sq ft of office space was built in areas unserved by BART (since 1973), compared to 9 million sq ft within 800 m of an East Bay BART station. In the housing sector provision has taken place in the BART accessible locations. But again this may have resulted from a policy decision to encourage transit-oriented housing developments (Cervero and Landis 1995b) and this emphasizes the pivotal role that the public sector has in supporting station area development (Cervero and Landis 1995b). So BART is clearly not a sufficient condition for station-related development to take place, but given the appropriate circumstances it can make an important contribution. The empirical evidence suggests that the non-BART served corridors had a 20 per cent greater increase in population than the BART served corridors.

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Figure 10.5 The San Francisco BART transit system.

The same picture is apparent for employment growth. BART’s locational advantage is clear for the FIRE (finance, insurance, real estate) and nonbusiness service sectors, particularly for the Richmond (the north) and Fremont

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(the south) lines. This advantage is reflected in higher office rents immediately around stations. The inevitable conclusion reached is that urban rail transport and land development have only a tenuous link. Where there are impacts, it seems that they are highly localized and in specific sectors. Even where development is specifically targeted at accessible rail locations, the impacts on patronage are limited. When these rather modest changes are placed within the city-wide context of change over a twenty-year period they become even less significant. The rather pessimistic view portrayed by Webber (1976) twenty years ago about whether BART would ever live up to expectations, either as a successful transport system or as a catalyst for urban development and economic growth, has been realized. 10.7 Conclusions Many rail investments are justified both as the basis of transport benefits (principally time savings) and economic development benefits (e.g. new jobs, higher rents, growth in property prices). Transport benefits alone, in many cases, are not sufficient to justify the enormous investment. Yet the analytical and empirical evidence suggests that the development impacts are not uniform and only occur where other economic conditions already favour development. These investments do not act as the catalyst for change, but they can act to reinforce a change that is already taking place (or is likely to take place). If the development impacts of rail investment are examined in the wider context of urban change and development related to urban road investment, then their impact is further reduced. The San Francisco evidence is particularly clear on this. Employment growth and residential growth was some 20 per cent higher in those corridors served only by freeways rather than those served by BART. One possible explanation for the small and variable impact of urban rail investment is the condition of ubiquitous accessibility found in many urban areas, often reinforced by cheap access to the private car. Any additional infrastructure, particularly where the network is already well developed, makes very little impact on the overall accessibility. As the road system is always going to be more available than the rail system, it is not surprising that road accessibility dictates where growth takes place. With high-speed rail the situation is somewhat different, particularly in Japan where population densities are quite high. Rail transport dictates development patterns in Tokyo and the other major Japanese cities as employment is highly centralized with limited parking, making land values a function of travel time to Tokyo station and other cities (Wegener 1995). In addition to the accessibility to central employment, high-speed rail stations also act as strong localized attractors for local employment centres. But here again the local conditions do not automatically lead to development and employment opportunities taking place. The necessary economic conditions

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have to be present and public sector involvement is necessary to attract interest from the private sector. Finally, the Buffalo LRRT project provides a generic lesson for all rail projects. That is, all of the potential positive impacts of the investment on the target area (Buffalo’s CBD) at best amount to a necessary but not a sufficient condition for economic revitalization. In this case the principal reason is that non-LRRT public policies may enhance or conflict with these benefits and thus reinforce or depress CBD revitalization by the LRRT. Providing a high level mass transit capacity such as the LRRT system, which is capable of bringing a large number of people to the CBD in short intervals, is one thing. What these people will do there is another issue. Notes 1 The city of Buffalo is within Erie County and Buffalo SMSA is made up of Erie and Niagara Counties. The study area is all SMSA, but major results of the analysis discussed below pertain mainly to the metropolitan area (Erie County) and the CBD (down town) area (Figure 10.2). 2 Between 1980 and 1990 Buffalo population has declined further to the present level of 330,000 inhabitants. 3 See Oppenheim (1980) for a review of this model. 4 An independent input-output model for Buffalo (Dickson 1978) gave values for the economic base model’s coefficients. 5 In the Berechman and Paaswell (1983) equation 1 was erroneously presented as a linear function of these components though it was not computed that way. 6 In this analysis retail activities were used as a surrogate measure for service activities. 7 The selection of these variables emanated from a separate study (see Paaswell and Berechman 1981). 8 These calculations are based on labour productivity rates of capital expenditures on labour and material (i.e. number of employees per $1mn investment) and the estimated yearly capital expenditures (see Dickson 1978; NFTC 1977 for data). This employment figure was then introduced into the economic base model using the 1980 county ratio of 2.5 residents to employee. Based on these figures it was expected that total net increase in population due to the LRRT would be in the range of 19,000 to 25,000 people, while the net increase in total employment would be 7,000 to 10,000 employees. In terms of income generated, the above capital expenditure was expected to generate $l,040mn in regional income over the LRRT investment’s period (1978–85). 9 For all tests retail floor space was used as a measure of attractiveness and the minimum of auto or transit travel time as the accessibility variable. 10 Prof. Robert Paaswell, Director, University Transportation Research Center, the City College, New York, has written this section. We thank him for his contribution. 11 The technopole concept relates to the concentration of transport modes and facilities with the development of technology parks and investment in high quality telecommunications infrastructure all in one location.

11

The economic impacts of airports

11.1 INTRODUCTION Since its inception in the first half of the twentieth century, commercial air travel has grown rapidly. In the 1970s and 1980s global air travel grew at around 6 per cent per annum, far outstripping growth rates in the global economy during this period. It is anticipated that this trend will continue for some time, before falling to a lower level of between 2 and 3 per cent. Recently, the growth rates have been further encouraged by growth in income levels, the globalization of economies and the demand for long-distance leisure travel. In addition there has been a fundamental restructuring of the airline industry. At a domestic and international level aviation markets have been or are being deregulated allowing airlines greater freedom to determine their own route network, while state-owned assets have been privatized, creating a more commercially driven environment. This has intensified the development of hub and spoke operations and in recent years the move towards the creation of global airlines, through code sharing, alliances, marketing partnerships, shared equity and acquisitions. Both growth and restructuring have had an impact upon the provision of infrastructure. Simultaneously, airports must handle an ever-growing number of passengers and freight, as well as respond to different demand patterns. In volume terms this pressure is particularly intense at existing hub airports, particularly those which perform an international role. In 1993 there were 29 airports in the world with more than 20 million passengers per annum. At present, airports plan to spend some £25bn (over US$ 40bn) worldwide on airport development to meet the expected increase in demand. In the transport context, air travel is the most dynamic growth sector. In this chapter we focus on the economic impacts of airports, principally concentrating on the employment effects and the impacts on the local economy. This involves the identification of the means by which these impacts can be isolated and measured and in greater detail analysed through two case studies. These case studies focus on London Heathrow, in particular on the current decision over the development of Terminal 5 (T5) and on Manchester where

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construction of a second runway is now under way. The material presented in this chapter is from secondary sources, with a clear focus on the economic development impacts of transport investments. Andrew and Bailey (1996) identify six different types of airport by passenger numbers and traffic mix and detail their economic impacts (see Table 11.1). The table shows that airports with similar volumes of traffic can generate quite different economic impacts because of fundamental differences in their traffic mix and because each segment of traffic sustains a different number of jobs. In turn an important determinant of the traffic mix will be the size and diversity of the local economy. Heathrow’s location in the South East of England, where approximately one-quarter of western Europe’s thousand largest companies have their headquarters (Pieda 1995), may be related to the high number of business passengers using scheduled flights from this airport (Andrew and Bailey 1994). We begin with a general discussion of the economic impacts of airports (Section 11.2). Subsequently, in Section 11.3, we analyse the case of Terminal 5 in London Heathrow airport, followed by analysis of Manchester’s second runway project, in Section 11.4. Major conclusions are presented in Section 11.5. 11.2 The economic impacts of airports Airports have a major impact on their locality (up to 15 miles) and more widely in the regional and international contexts. IATA (1991) suggests that the economic benefits of airports include wealth creation, employment generation, tax payments, travel and tourism and regional economic benefits. It should be noted that airports are also tourist attractions in themselves. For example, Gatwick is the fifth largest tourist attraction in the South East of England and has plans to develop a theme park and a visitors centre. The IATA (1991) report puts a global figure of US$710bn (1989) on the total economic impact of the air transport business, of which about 30 per cent is accounted for by direct expenditure and indirect expenditure, and the remainder is induced expenditure. The industry supports some 21.6 million jobs worldwide (1989) and this will increase to 30 million in 2010. Before we proceed with the appraisal of the employment impacts of airport investments, it is important to place this analysis within the context of the analytical framework proposed in Part III. In Chapter 7 we have distinguished between employment impacts from the investment multiplier and employment impacts from the primary (transportation) benefits of the project (see Figure 7.2). The former is mainly the result of financial leakage into the local economy and is a function of the magnitude of the investment rather than its type (e.g. a transportation versus a health project) and is a short-termphenomenon. The latter effect, sometimes referred to as the employment multiplier, is supposedly a ‘technical summary’ of the labour market equilibrium process

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(see Section 8.3), which is represented by a single number or a set of numbers, one for each labour category. In many cases employment multiplier numbers are obtained from input-output tables or from specific technical studies. Analytically, the use of employment multipliers assumes that a linear relationship exists between the volume of airport traffic and the consequent level of employment, and that this relationship holds for all airports irrespective of size or function (the mix of passenger and freight, national or international). This assumed relationship further presupposes the existence of two implicit but largely implausible assumptions. First, there are no economies or diseconomies of scale in airport operations so that labour increases in direct proportion to the increase in the volume of traffic. Second, labour productivity at airports remains constant, even though there is evidence showing that overtime labour productivity at airports, measured by passengers throughput per staff employed, has improved. An additional limitation on the use of employment multipliers is a lack of pertinent data to continuously update them. They are considered stable over time, irrespective of technological and temporal changes in labour markets. Given the above, it follows that employment multipliers are, at best, only crude measures of the employment impacts of airports’ development, which probably overestimate the true level of employment. Moreover, the application of a single multiplier number to all, or to very large categories of employment, adds to the inaccuracy of the employment results. Given these qualifications, the calculations of the expected increase in airport activity from future increase in the demand for air travel and from the construction of facilities like Terminal 5 to accommodate this demand are based on the use of such employment multipliers. A further step in the analysis of the labour impact of airports is to define and classify the types of jobs generated by airports. By disaggregating overall employment figures, attempts can then be made to understand how different types of job relate to each other, to effectuate the employment function of the airport and the wider economy. By quantifying these relationships, models can then be developed to predict the employment implications of future airport development. Many studies, both in the UK and USA, have undertaken this exercise as part of larger assessments of the economic importance of airports. There seems to be a broad consensus as to the types of jobs that airports support (CLES 1988; York Consulting 1991; Andrew and Bailey 1994). Table 11.1 presents this classification of airports by traffic mix and type of economic impacts. Given this characterization of airport types we can identify four classes of employment that they generate: •

Direct employment is created through the expenditure required to maintain the transport function (and ancillary services) of an airport. These jobs are entirely dependent upon the presence of the airport and

Source: Andrew and Bailey (1996).

Table 11.1 A classification of European airports by traffic mix, size and economic impacts

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•

•

•

291

are supported by revenues produced by the airport operator, airlines, passengers and freight. Direct employment is normally divided into those jobs which are on the airport site and those located outside the airport area. Indirect employment is generated by the subsequent airport-related purchases made by organizations in the above category. There may be further ‘rounds’ of expenditure from first level to second or third level suppliers. Induced employment is supported when further non-airport related purchases are made in the wider economy by those employed in the first two categories. Attracted employment is present when activities unrelated to the operation of the airport, nevertheless locate nearby to take advantage of increased accessibility and of agglomeration economies. This includes inward investment to the region around the airport and tourist developments.

The relationships between these categories can be summarized in an employment impact model, that links airport and airline revenues to the diffusion of expenditure through various types of interrelated organizations, which in turn creates direct, indirect and induced employment. Positioned alongside this expenditure-employment flow is the attracted employment category, where employment levels are not determined by input levels of expenditure (Figure 11.1).

Figure 11.1 Employment impact model. Source: Derived from IATA (1991) The Economic Benefits of Air Transport

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Indirect and induced employment are normally calculated with the use of multipliers. The attracted employment is analyzed more generally through qualitative interviews with firms in the main sectors involved. In calculating airport-induced employment, we should consider the ‘crowding out’ phenomenon. That is, employment can occur through the backward linkages (indirect and induced employment) where jobs generated by the airport investment lead to pressures on the labour and capital markets, resulting in adjustment and possible reductions in employment or investment in other sectors. Similarly, the forward effects (attracted employment) result in relocation of new firms near the airport and greater than expected growth rates in those firms, resulting in cost reductions and spatially differentiated growth rates. Again, crowding out may take place as existing economic activities are forced to locate away from the airport. The difficulty here is in identifying the dynamics of these various processes, which are all taking place simultaneously and over a period of time (Rietveld et al., 1998). Turning now to employment and non-employment related economic impacts, Andrew and Bailey (1996) suggest analysis at four levels to identify the precise contribution of airport development to a region: 1

2 3

4

Analysis of the structure of the region’s economy, together with the role that the airport plays within it. This links in with the classification and function of the airport (Table 11.1). Isolating and analysing the significance of each airport infrastructure variable that supports the regional economy. Assessing the economic impact of the airport business in terms of the different types of employment and purchasing effects identified above. Usually this entails application of employment and income multipliers and building in allowances for industry productivity. Adding the social dimension, given the airport’s potential effects on the wider labour market and the cultural activities within the region.

Based on this framework, we now examine the economic development impacts of a major form of airport infrastructure investment—a new terminal. 11.3 The economic impact of terminal 5 at Heathrow airport 11.3.1 Introduction The 1985 Airports White Paper (UK Department of Transport 1985) stated that aviation in the UK contributes 85,000 jobs and some £500mn (about US $900mn) to the exchequer (the treasury). Nearly two-thirds of foreign tourists come to the UK by air and this is a major growth sector. Expenditure by air travellers visiting the UK together with civil aviation earnings amounted to

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Table 11.2 Heathrow airport in the UK context, 1993

Table 11.3 Heathrow airport in the EU context, 1993

over £6bn in 1984. UK export efforts rely heavily on air transport with some £27bn of visible trade passing through UK airports (1984), three-quarters of it through Heathrow. Over the last ten years, air passenger traffic in the UK has grown by over 60 per cent from 70 million passengers (1984) to 112 million passengers (1993). Within this market, Heathrow is in a dominant position, accounting for some 42 per cent of all passengers and over 60 per cent of freight (Tables 11.2 and 11.3). Gatwick comes second on both counts, with Manchester in a distant third position. Heathrow is also in a leading position within Europe and is the world’s largest international airport (Table 11.3). The airport has two major impacts, one as an employer and the other as a facilitator of increased activity within the regional and national economies. In this section we concentrate on the impact of the proposed new terminal (T5) on employment, but we also cover some of the wider impacts (Section 11.3.4). 11.3.2 London Heathrow airpor t Heathrow airport is located 25 km west of London on the fringe of the builtup area. Originally conceived as a military airfield, it has been London’s preeminent civilian airport ever since opening as London airport on 25 February 1946, because of its close proximity to the city and relatively good road and later public transport links to surrounding areas. However, this location has

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Table 11.4 London area airports: traffic, employment, productivity and average passengers per aircraft

Source: From various published statistics. Table 11.5 Employment in the South East of the UK for standard industrial categories (SICs). in 1991

Source: NOMIS.

also made new development at Heathrow controversial over the years. It is built on prime agricultural land and lies within the protected strip of open space (the Green Belt that encircles London) where development has traditionally been restrained. Perhaps more critically, its main flight paths are directly above the urban mass of London to the east because of prevailing westerly winds. Thus, large numbers of people are exposed to the increased noise nuisance that may result from airport expansion. Aware of these problems from an early stage, consecutive governments have commissioned a plethora of reports over the years to investigate the issues surrounding airport development in London and South East England as a whole (Tables 11.4 and 11.5). In many cases, these have advocated the restraint of Heathrow, with growth directed elsewhere. Most notable of these has been the desire to distribute growth throughout the South East by implementing a structured airport expansion policy, underpinned by the creation of a third London airport (ultimately at Stansted), thereby limiting

pax/emp=Passengers per employee. ?P=Percentage change in productivity. Source: Andrew and Bailey (1996).

Table 11.6 Direct on-airport employment at Heathrow, Gatwick, Stansted and Luton airports between 1980–93, including labour productivity changes (1980-91)

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Table 11.7 Passengers and jobs at UK airports

Source: From various published statistics.

development at London airport (ultimately at Stansted), thereby limiting development at Heathrow and London’s second airport, Gatwick. However, development at Heathrow has endured the test of time. Despite this ongoing debate to limit its growth, the airport has expanded in its first half century to four terminals and its operations are entrenched in the physical and economic fabric of west London and beyond. It is the main domestic, European and intercontinental hub for British Airways, which alone has delivered much of the growth in passengers and freight during this time. Its attractiveness to other airlines operating in the South East is demonstrated by the decision of international operators such as New Zealand Air and Virgin Atlantic to transfer operations from Gatwick to Heathrow when the liberalization of airport policy allowed them to do so in the 1980s (Tables 11.6 and 11.7). Table 11.8 Total employment impact of the four London airports based on estimates of off-Airport and induced employment for 1993

Source: Based on Andrew and Bailey (1996).

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As shown by Table 11.6, there have been substantial increases in labour productivity in recent years, particularly at Heathrow and Gatwick, but most noticeably at Stansted. The decline in productivity at Luton is difficult to explain, but may relate to its higher on-airport employment to passenger ratio (Table 11.7). It is here that the difference between the three major UK airports (Heathrow, Gatwick and Manchester) is apparent on all criteriapassengers, employment and air transport movements. There seems to be a consistent ratio of about, 1000 jobs (direct) for every 1 million passengers for these three large airports. In the four London airports (Heathrow, Gatwick, Stansted, Luton), there are about 82,000 people employed on the airports and some 72.2 million passengers (880 jobs per 1 million passengers). The multipliers calculated by Andrew and Bailey (1996) are 1.186 for indirect employment and 1.24 for induced employment, giving an overall level of 1.47 for total employment (Table 11.8). These are considerably higher than those multipliers developed for Heathrow by Pieda (1995), where figures of 1.08 and 1.24 were used, giving an overall multiplier of 1.34 (Section 11.3.3).

11.3.3 London Heathrow terminal 5 The growth and restructuring of domestic and international aviation markets creates a potential opportunity for development at hub airports such as Heathrow. Within this context, Heathrow’s owners (BAA Plc) believe that it would be commercially advantageous to expand the capacity of Heathrow to around 80 million passengers in the early years of the next century, consolidating its position as an important international hub. In order to achieve this, the organization would like to construct a new terminal alongside the four existing terminals at the airport and have submitted a planning application to this effect. The scale of the Terminal 5 (T5) proposal has led to a planning inquiry into its merits, which is examining the wider social, environmental and economic impacts. Because of the issues at stake, this has already become the longest UK planning inquiry, running for over three years. To aid this decisionmaking process it is therefore important that the role of the airport as a generator of economic activity is understood.1 At Heathrow airport attempts have been made to quantify the number of jobs directly and indirectly related to the operation of the airport, and the number of induced jobs it supports in the wider economy (Pieda 1994). This has formed the basis of a model, which has then been used to predict the employment implications of future growth and development at the airport up to 2016, when there will be 80 million passengers if T5 is built (Pieda, 1995). The analysis has been undertaken for a twenty-five year period (1991–2016) with and without T5. It is clearly acknowledged that the

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accuracy of forecasts of this type are subject to internal assumptions and external changes. In 1991, Heathrow employed 52,300 people on site and a further 6,500 direct off site, giving a total workforce of 58,800 people. In addition to this direct employment, there was a 4,500 indirect employment and 15,200 induced employment (see Tables 11.8 and 11.9), giving an implied multiplier of 1.08 and 1.24 respectively for the additional employment from these two sources. The overall multiplier is 1.34. The employment impact has been evaluated within two spatial realms: the local labour market area and the UK as a whole. No attempt has been made to estimate the numbers of jobs created through attracted employment, although the importance of Heathrow to the national and international economy is reviewed shortly (Section 11.3.4). Direct impacts The direct impacts have been measured by identifying activities solely related to the operation of the airport. This has been supplemented by surveying the organizations providing these services in order to establish how many additional jobs are supported. These organizations included the airport operating company itself, 90 airlines, six in-flight catering companies, several hotels, numerous freight forwarding agents and a large number of retailers and other concessionaires. A key component of the Heathrow analysis has been the increase in productivity measured by number of passengers per employee or by air traffic movements per employee. Passenger throughput has increased at a much faster rate than air traffic movements per employee, partly because of the growth in aircraft size. Overall, productivity has increased by 4.7 per cent per annum (1971–91) as measured by passengers per employee. This level of increase is not expected to continue as it relates to an exceptional period Table 11.9 On-airport employment assumptions, 1991–2016

Source: BAA (1994). Note: Traffic support staff are 17 per cent of direct traffic related staff. Percentages give annual changes in productivity % p.a.=per cent labour productivity change per annum.

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when BAA was privatized. However, it is assumed that productivity will increase at a substantially higher rate with T5 than without (Table 11.9). The argument used here is that productivity grows faster with faster traffic growth. This has been true in the past at Heathrow. The net effect is that direct on-airport employment will decline by 7,500 (14 per cent) if T5 is not built, and increase by 2,100 (4 per cent) if T5 is built (Table 11.10). These changes must be placed against a background of growth in passengers to 50 million (without T5) or 80 million (with T5) in 2016. Over a twenty-five year period, direct on-airport employment will not change by a significant amount, despite a possible doubling in the numbers of passengers through the airport. This is in contrast with the high levels of employment generation created by the second runway at Manchester airport (see Section 11.4.3). Direct off-site employment will increase from the current level of 6,470 employed in hotels, air freight and in-flight catering, growing to 7,030 (without T5) and 9,230 (with T5). The figure for local indirect employment (within the study area) is 4,520. This is assumed to grow in proportion to traffic growth but is moderated by increases in productivity. Using forecasts from Cambridge Econometrics, Pieda (1994) extended the trends from 1991 to 2005 to 2016, giving substantial productivity gains. Indirect employment decreases in 2016 if T5 is not built (to 3,105) and increases only marginally if T5 is built (to 4,590). Finally, the induced employment is calculated through the use of multipliers (Table 11.10). A review of multipliers used elsewhere suggested a value of 1.3, but as 20 per cent of Heathrow jobs are outside the study area, the effective multiplier is scaled down by that amount to 1.24. The total effect of the new terminal on all employment is limited. Without the new terminal, employment will fall by 10,300 from the present (1991) 78,400 employees (a decline of 13.1 per cent), and even with the new terminal, the growth is limited to 6,200 (+7.9 per cent) (Table 11.10). The Pieda (1994) research acknowledges that forecasting over a twentyfive-year period, particularly in a rapid growth sector, is problematic. An extensive sensitivity analysis was carried out on the productivity Table 11.10 Heathrow employment impacts

Source: Based on Pieda (1994). Note: These figures relate to local employment impacts in the study area.

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assumptions and wider changes in the labour market in the South East. The overall conclusion reached is that the estimates are sound, but that assumptions made about changes in the regional economy, employment and the structure of the labour market all limit the robustness of the analysis. Previous attempts to predict change have all been unreliable, even over a much shorter period of time.

Indirect impacts Indirect impacts were evaluated using input-output analysis. Here expenditure linkages were explored between the organizations listed above and their suppliers. Because this required a detailed breakdown of purchases for a large number of goods and services, data were only collected from a sample of major employers such as British Airways, with results adjusted to estimate total impact. In 1991 it was estimated that 4,500 local people were indirectly dependent upon the operation of the airport.

Induced impacts Induced impacts were measured using multipliers. These tools help to quantify the relationship between direct and indirect employment levels and the number of jobs these support in the broader economy through spending and taxation, so that estimates can be made where no other data exist. In the case of Heathrow, multipliers have been used to estimate locally induced jobs. Locally, a multiplier of 1.24 was chosen, which means that it was assumed that for every 100 employees living in the local area, a further twenty-four jobs were supported, giving a total of 15,190 (see Tables 11.8 and 11.10). In many airport studies multipliers have been used to estimate direct, indirect and induced employment. Although this may be the best tool to use in the absence of suitable data, they should be treated with some caution. In the Heathrow study, for instance, the multipliers have been developed from secondary sources, namely general economic assessment tools and other airport studies. In the former, the UK multiplier has been based upon research examining the UK economic system in the 1970s. Since then the propensity to import goods and services from overseas has increased, which means that the multiplier used may overstate the benefits of airport expenditures upon induced employment in the UK. Yet the values of the multipliers are assumed to increase over time (see Table 11.10). The multipliers used in the other airport studies are themselves based on similar reviews, so the methodologies used to determine them can appear somewhat opaque and some circularity is implicit in the process (Twomey

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and Tomkins 1995). Airports are not generic organizations but complex points of interface between the air transport system and spatial economies. In this respect, the role that they perform varies significantly depending upon their position in the global aviation network and the economic context that they operate within. We return to this issue of multipliers in the conclusion (Section 11.5). The Pieda report estimated the construction employment effects of T5 at London Heathrow in terms of the direct jobs created over a five-year period (1997–2002) and the indirect and induced jobs created. Over the construction period (phase one of T5), it was estimated that 11,135 person years of work would be generated and which would in turn result in a further 3,787 jobs. The multiplier used (1.34) came from another review of studies carried out by Pieda (1994) and a decision was taken to use the mid-point of the range of multipliers from the most detailed study examined (McGuire 1983). These levels were much lower than those used in the Manchester study.

11.3.4 The national signif icance of Heathrow In addition to its role as a major employer in the South East, London Heathrow has an important national and international position as the leading UK and European hub.2 The employment impacts of Heathrow extend beyond the local effects (see Section 11.3.3) to its wider impacts in the national economy. Direct employment in 1993 was 58,700. In addition to this, some 44,100 people were employed nationally (indirect employment), resulting from an analysis of the amount and nature of non-wage and salary expenditures by firms that use the airport.3 This figure includes local employment (4,520) in the study area (the 18 local authorities) outlined in Section 11.3.3, and the wider indirect employment in the country as a whole (39,580). To calculate the induced employment, an empirically derived consumption multiplier was used to produce a figure for the actual numbers of people employed (88,730). The total national impact is 191,572 (1992–3), a figure which is over twice the employment supported in the Heathrow T5 study area (128,310 as compared with 63,220). Other evidence of Heathrow’s national and international role is more qualitative in nature. Accessibility to international markets means that Heathrow is important for multinational firms, particularly with respect to headquarters functions. The UK has the European headquarters of 37 per cent of the 1,000 largest industrial and financial companies. With the opening up of international markets, foreign direct investment has increased, with the UK being particularly attractive to inward investment from Japan and the USA. Heathrow is the main UK port in terms of value of trade for both

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imports and exports. In addition, the airport has a major role as a tourist destination for London and the UK. The majority of tourists (69 per cent) arrive in the UK by air, with Heathrow accounting for over 60 per cent of that figure (1990).4

11.4 The economic impact of the second runway at Manchester airport 11.4.1 Introduction There has been considerable debate in the UK over whether new airports should be built, whether new capacity should be concentrated in the South East, or whether the regional airports should be encouraged (see Table 11.7). Policy has urged the maximum use of regional airports to reduce timeconsuming journeys to central London along with the heavy road traffic congestion associated with the South East. It has also been argued that regional employment can be enhanced by the development of airports outside London. Over the last decade passenger traffic at Manchester airport has more than doubled to around 12.8 million passengers and freight has tripled to 89,000 tonnes (1993). It is the third UK airport after Heathrow and Gatwick. The airport has expanded with new terminal buildings in 1949 and 1962, further developments in 1973 and a dedicated domestic terminal in 1989. The second international terminal was operational in 1993, with a second phase being designed to cope with some 12 million international passengers. The most recent development has been the agreement to build a second runway so that international and new hub opportunities can be combined with a growing range of interlining activities. In 1990, over 80 airlines were operating services from Manchester to over 160 destinations, with scheduled international traffic accounting for 25 per cent of all passenger movements and a further 16 per cent related to domestic scheduled services, the remaining 59 per cent being charter services (Twomey and Tomkins 1995). Table 11.11 shows the estimated breakdown of passenger movements in Manchester. The charter market is expected to decline, but Manchester will expand as an international hub airport. Much of the discussion on the second runway revolved around the economic benefits of expanding air services at Manchester on employment in the region, particularly as it has suffered high unemployment, resulting from economic restructuring and the move towards post-industrial employment patterns (Chapter 4). The numbers of staff working at the airport will increase from 10,000 (1990) to 30,000 (2005) when the new runway is in operation (York Consulting 1994).

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Table 11.11 Forecasts of passengers through Manchester airport 1995–2005

mppa=million of passengers per annum. Source: Twomey and Tomkins (1995).

11.4.2 The second runway at Manchester airpor t As noted in Part I, it is often stated that the development of high quality transport infrastructure is an essential component for local and regional economies. The importance of a regional airport is that it supposedly performs a ‘double act’ in this process. It improves overall accessibility within the region and provides a major source of employment. Its indirect effects are also said to be significant in terms of indirect employment and the additional income (investment multiplier) that it generates within the regional economy. There may also be other less tangible effects as the airport makes a statement about the importance of the region and its image. However, not all the arguments for airport development are positive. The costs are largely environmental, with extensive land take requirements for the airport, plus additional support services and related industries. Airports may also act as an attractor, bringing new industry and services to the area, particularly from multinational businesses which need airport access. Airports are a major source of noise and airborne pollution (emissions) and generate and attract traffic, causing congestion on the surrounding road network. Much of the recent debate over the second runway proposal at Manchester airport revolved around the issues of land take, pollution, traffic and noise. In addition, the new runway will be built in ‘green belt’ land which has a high amenity and recreational value. Our main interest here is to establish the nature and scale of the employment arising from the airport. Research by the Ecole Nationale de l’Aviation Civile (quoted in Twomey and Tomkins 1995) demonstrates that about 1,000 new jobs are created on-site at an airport for every additional 1 million passengers, similar to figures for Heathrow, Gatwick and Manchester (see Table 11.7). Manchester airport conforms to this ‘rule of thumb’ with 10 million passengers and 10,000 workforce (1990). Forecasts to 2005 suggest about 30 million passengers and a workforce of 30,000. Manchester Airport Plc, which is responsible for running the airport operation, employs about 20 per cent of the 10,000, with the remaining 80 per cent accounted for by shops, hotels, air freight forwarders, passenger agencies, handling services, maintenance

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and support facilities. A study by the Centre for Local Economic Strategies (CLES 1988) showed that in addition to the 8,400 staff employed on site at the airport, a further 12,000 jobs were sustained in the North West region of England, mainly in the Manchester conurbation—a multiplier of 2.43.5 This study also commented that the airport was the single most important factor in the attraction of inward investment to the region, with over 13,000 new jobs being provided in the last five years (1983–8). More recent studies have been carried out by York Consulting on the economic impact of the airport (York Consulting 1991, 1994). The numbers given are different (Table 11.12), with a multiplier of 1.33 if direct and indirect employment are used, or 1.53 if induced employment is added to indirect employment. York Consulting (1991) also examined the broader impacts of the new investment (the transportation impact), using figures from the USA where these additional job estimates are calculated as two times of site employment or some 5 per cent of US GDP, though these figures seem high for the European situation.6 The analysis of Twomey and Tomkins (1995) of Manchester airport’s economic role within the North West region is based on establishing economic linkages through surveys covering the sourcing of supply requirements to the airport (Phelps 1993; Turok 1993).7 Their objective is ‘to establish an average reference pattern of linkage between industry sectors at a broad spatial level and then to examine the extent of divergence from that average at a smaller spatial scale, the region’ (Twaney and Tomkins 1991:201). From this linkage analysis it is possible to determine the number of jobs in any one sector supported by demand from another identified sector. The two sectors of interest are air transport (SIC 7500)8 and air transport support services (SIC 7640) in the North West region, and the linkages are with all other sectors in the local and regional economy. The data used come from the Census of Employment (1991) and Twomey and Tomkins (1995) acknowledge the limitations in terms of the classification used. For example, all miscellaneous transport services and storage jobs are grouped together (SIC 7700), so it is difficult to identify the airport-related jobs. Second, many airport jobs (e.g. airport catering is placed under catering not airports) are allocated by function rather than location and this may lead to underestimates. Table 11.12 Estimated regional employment impact of Manchester airport

Source: York Consulting Ltd (1991).

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Table 11.13 Direct employment linkages at Manchester airport, 1991

Source: Based onTwomey andTomkins (1995).

Third, the nature of the linkage data combines all support services for transport together. There is no difference made between other forms of transport (e.g. inland and sea transport), so apportionment has to be made which in turn can lead to inaccuracies. The category of miscellaneous transport services has been omitted, as it is not possible to identify the air-related activities. Given these data limitations, it is also important to be aware of key implicit assumptions made: ‘that there is no spatial variation in either the productivity with which air transport services are provided to industry or the unit intensity with which the latter purchases goods and services from industry’ (Twomey and Tomkins 1995:202). This analysis is intended to be restrictive as it only calculates the linkages between direct on-site employment, defined as employment of the air service sectors, and the local and regional economy. There is no attempt to measure the total employment attributable to the airport through direct off-site, indirect and induced jobs. The air transport industry employment (SIC 7500) in the North West Region9 (Table 11.13) covers those jobs which make purchases from the air transport sector. Business services account for the largest share at about 20 per cent (464 jobs). The air transport support services (Table 11.13) are more important than the air transport industry, with the service sector again dominating (75 per cent). Distribution, hotels and catering account for 35 per cent of air transport support employment, with major contributions from transport and communication (23 per cent) and business services (16 per cent). Looking at the employment linkages the other way round, namely the impact of Manchester airport in terms of North West Region industry jobs that are directly supported by the existence of the air infrastructure, a detailed sectoral profile can be built up. The 5,487 jobs (see Table 11.13) relate to fuel inputs, engineering and allied industries, and food and drink on the manufacturing side (about 30 per cent of the total). Again it is the service sector that dominates, including distribution companies, hotels and catering, transport and communications and business services (i.e. insurance and computing). The jobs sustained by air services are relatively small (6.2 per cent in Table 11.13). The total direct impact is about 12,000 jobs in the North West Region.

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Table 11.14 Regional economic impact of Manchester airport

Source: York Consulting Ltd (1991). Note: Data for this study is based on survey work, information from Mancester Airport Plc, York consultancy Ltd and other companies.

Other studies have tried to be more comprehensive in their estimates of the regional economic impact by extending the analysis to five categories (York Consulting 1994), to include indirect, induced and spin-off effects (Table 11.14). This analysis is based on estimating the total spending power in the North West Region (1993) resulting from the operation of Manchester airport. Through the use of multiplier analysis10, further rounds of expenditure were estimated so that a picture of the total economic impact of the airport could be established. The total effect can be defined as income or the corresponding number of jobs. Some discussion is presented about the assumptions made about leakage where expenditure takes place outside the economy (the North West region), and additionality which relates to new expenditurerather than expenditure that would have occurred anyway. With respect to the new runway at Manchester airport, York Consulting (1994) estimates that the net impact will be about 93 per cent of the gross impact. A spin-off effect is

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identified (Table 11.14) which relates to the inward investment in the region resulting from the transport infrastructure investment. The magic number relating to the injection of spending power is £620mn net of all taxes (1993) which was generated by the operation of Manchester airport (Table 11.15), with a further £257mn of direct income generated in the North West region. Manchester airport would then support about 12,200jobs within the airport and a further 6,900 jobs off—site (within a 20minute drive). This is a total of 19,100 jobs, which would translate to about 16,600 full time equivalent direct jobs. The figure of 12,200 is not dissimilar to that arrived at by Twomey and Tomkins (12,183 jobs, in Table 11.13). Most of these jobs were in the North West region (97 per cent), with 67 per cent coming from Greater Manchester and a further 26 per cent from Cheshire. The airport company employs about 13 per cent of the 12,200 people, with airlines (30 per cent), handling agents (15 per cent) and concessionaires (25 per cent) accounting for a further 70 per cent of the total. In addition to this direct employment, York Consulting (1994) estimates a further 3,800 net full-time equivalent indirect jobs and a further 6,200 net full-time equivalent induced jobs. The total employment created by Manchester airport would be 26,000 full-time equivalent jobs, or about 30,700 jobs in total. The net economic impact of the operation of the Airport was around £378mn of income (1993). Table 11.15 depicts the estimated impacts for future years. Table 11.15 Economic impact of Manchester airport

Source: Based on York Consulting (1994). Notes: The direct income (£mn) is made up of the following components: wages and salaries earned by residents in the North West region £150mn; Profits earned by companies based in the North West region £36mn; Expenditure on goods and services supplied by companies located within the NorthWest region £241mn; Leakages to residents and companies outside the North West Region and expenditure by companies located in other regions £193mn.

The York Consulting analysis (1994) then made two estimates for the year 2005, one without the new runway, the other with the second runway: •

•

Scenario R1: the level of operation assuming that only the current single runway is available—passenger traffic will increase to 22.8 million per annum by 2005. Scenario R2: the second runway will be constructed and ready for use in 1998. Traffic will increase to 29.3 million passengers per annum in 2005.

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Again, there has been a series of assumptions made about the relationship between passenger numbers, income from the airport and employment in the region. In addition, other factors are assumed to have an impact on airport operations. Productivity growth will result in a reduction in the number of on-site jobs per million passengers per annum. Customer care and regulation is likely to increase the number of staff per passenger, as there are increased requirements for high quality services and changes in other safety and security regulations. Economies of agglomeration may also exist as there is a concentration of services at the larger airports, which may result in more employment at the major hub locations. The Manchester airport study assumed productivity growth (including the impact of any changes in customer care and regulations) of 0.5 per cent per annum (1993–2005). This figure is much lower than that assumed at Heathrow for Terminal 5 and is contrary to the historic growth in productivity at Heathrow and Gatwick airports of about 3 per cent per annum (see Table 11.6). ‘Scale’ facilities (e.g. airline headquarters, maintenance facilities and conference centres) will support about 1,800 on-site jobs in scenario R2 in 2005, with a reduced figure of 1,000 on-site jobs in scenario Rl in 2005. With direct off-site employment, the same productivity figures have been used. For the indirect and induced employment, the productivity growth is higher at 2 per cent per annum (1993–2005) which is in line with changes expected in the regional economy. This difference is important as it suggests that the scope for productivity savings in the airport is much less than the economy as a whole. No explanation is given of this important assumption. Given these assumptions, Table 11.16 makes a summary of the expected impacts. Note that the net effect on the regional economy of the second runway is estimated to be £204mn and 12,200 full-time equivalent jobs in the North West region. Table 11.16 Summary of economic impact of Manchester airport: Operational effects

Source: Based on York Consulting (1994).

In addition to these permanent jobs, there will be short-term construction employment as the airport company (Manchester Airport Plc) and other airport-related firms invest in the new runway and terminal buildings. The

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estimates here are £518mn (1993 prices) for 1995–6 to 1999–2000 period with scenario R2 to include the new runway (£146mn) and other investment (£372mn) from Manchester Airport Plc and a further £192mn by other companies. The corresponding figures for scenario Rl (no runway) are £322mn (1993 prices) for the 1995–6 to 1999–2000 period from Manchester Airport Plc and a further £ 164mn from other companies. The arithmetic used here assumes a standard relationship between expenditure, income and employment. Each £1mn of expenditure would generate around £300,000 of regional income and around 22 person years of employment (no source given). As airport construction work is capital intensive, the actual figures used on the construction of the runway are significantly lower as £1mn of expenditure generates £210,000 of regional income and around 15 person years of employment. The multiplier effects must also be taken into account. For each £1mn of expenditure, there will be £100,000 of regional income and seven indirect jobs, with slightly higher effects for the induced regional income and jobs (calculated by multipliers; see notes to Table 11.17). Table 11.17 Regional economic impact of investment at Manchester airport, 1993–2005: construction effects

Source: York Consulting (1994). Notes: 1 Calculated by £486 million * £300,000 and £486 million * 22 person years per £1 expenditure-£486 million=£322 million from Manchester Airport Plc+£164 million from other companies. 2 Calculated by £564 million * £300,000+£146 million * £210,000 and £564 million * 22 person years+£146 million * 15 person years per £1 expenditure-£564 million=£372 million from Manchester Airport Plc+£192 million from other companies. 3 Calculated by £486 million * £100,000 and £486 million * 7 person years per £1 expenditure. 4 Calculated by £710 million * £ 100,000 and £710 million * 7 person years per £ 1 expenditure-£710 million=£564 million as above+£146 million as the investment costs of R2. 5 Calculated by multipliers of 1.36 and 1.40.

The above analysis covers the operational and construction effects of the proposed airport expansion. In addition to these two major effects, there are other spin-off effects, principally inward investment to the region and tourism.

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It is difficult to be precise on the employment generation here, as the method of analysis is heavily dependent on qualitative assessments of the competitive position of the region, inward investment and tourism in general in the UK, and a sensitive investigation of a few sample firms. York Consulting (1994) estimates that currently there are between 20,000 and 30,000 spin-off job opportunities which can be attributed to Manchester airport. Under scenario Rl this will increase to between 29,000 and 39,000 by 2005, with the corresponding range under scenario R2 being 36,000 to 46,000 jobs. These numbers are substantial and reflect the difficulty of unravelling the complex impacts of major investments, particularly types of inward investment (e.g. international companies) and particular sectors of the tourism market (e.g. high income air-based travellers). Although ranges are given to the figures, it may be equally important to test the sensitivity of the assumptions being made as the job opportunities created by these spinoff effects are nearly 50 per cent of the permanent operational jobs being generated. The multiplier is substantial and the numbers of jobs for Manchester airport are substantially higher than those for Heathrow Terminal 5 (see Section 11.3.3). The conclusion reached in this comprehensive study (York Consulting 1994) of the impact of Manchester airport and the second runway is that the airport will act as the dynamo for the regional economy of the North West over the next ten years. 11.5 Conclusion 11.5.1 Differences between the Heathrow and Manchester analyses The employment effects for Heathrow Terminal 5 and Manchester’s second runway were computed with different methodologies and results (Table 11.18). The T5 studies (Pieda 1994, 1995) examined the local study area impact and the national impact of Heathrow Terminal 5. The multipliers used (actual and implied) range between 1.24 and 3.26. The Manchester study used a standard relationship between expenditure, regional income and the amount of employment created. The implied multiplier for indirect to direct jobs is 1.33, but adding the induced figures raises the Manchester multiplier to 1.53, with the more recent studies having higher figures (York Consulting 1994). The crucial question seems to be the area over which the impact will be felt. The Heathrow study suggests that the national impacts are much greater than the local impacts, while the Manchester studies concentrate on regional impacts with only a limited recognition of the international attraction of the airport. The basic inconsistency in the studies is the claim that each seems to start by reviewing all the other studies. This process should lead to reduced

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variability, not enhanced variability in the multipliers as found here. Table 11.18 compares results from various studies reported in the literature, relative to the multipliers used and nature of measured effects. Table 11.18 Multipliers used to calculate employment at airports

Source: From various references. Note: Heathrow studies: Pieda (1994) quantifies local impacts as measured in the study area (18 local authority areas which contain at least 1 per cent of the airport’s workforce) and Pieda (1995) examines the national impact.

11.5.2 Major airpor t employment impacts Although this chapter has concentrated on two UK airports, it is also informative to place these detailed case studies into the international situation. Pieda (1995) undertook a review of all airports with more than 10 million passengers per annum to determine the employment effects (Table 11.19). Comparison is difficult as the study areas vary in size. The larger the study area, the greater the potential impact. Two measures have been developed to allow direct comparison: passengers per direct job and the combined multiplier (the relationship between direct employment and total employment). The productivity of the US airports is substantially higher than those elsewhere (e.g. Heathrow, Manchester, Schiphol). All the US airports have values in excess of 1,000 passengers per direct job, except Los Angeles (910) and John F Kennedy (650). The European figures are between 560 (Schiphol) and 700 (Heathrow). There is even greater variation in the multipliers, which range from 1.33 to 3.76. This again depends on the area of study, yet the national importance of Heathrow puts its figure in excess of 3.00 along with JFK and La Guardia. Most of the other airports are in the 2.00 to 3.00 range.

Notes: 1 Chicago O’Hare and Midway airports. 2 Dallas/Fort Worth airport. 3 John F.Kennedy airport. 4 Toronto LB Pearson airport. 5 Direct includes both on-airport and direct off-airport employment. 6 Combined indirect/induced multiplier. 7 Only direct and total employment estimates given. Indirect and induced employment has been inferred. 8 Derived by applying the inferred induced multiplier in the study to the direct and indirect employment. This was done to remove the induced employment associated with visitor employment. 9 Exact definition of total employment not given, but assumed to be consistent. 10 Only direct employment estimates given. 11 Total employment not available on a consistent basis. 12 Heathrow 1 is the study area (18 local authorities) and Heathrow 2 is the national impact.

Sources: Based on Pieda (1995) Tables 5. 1 and 5.2 and other sources, including Baltimore Washington International airport (1989), California Department of Transportation (1989), City and County of Denver (1987), Colorado National Banks (1989) and Port Authority of New York and New Jersey, (1990).

Table 11.19 International airports employment impacts

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11.5.3 Comments Two main conclusions arise from this analysis of a dynamic sector where demand is increasing at 6 per cent per annum and where substantial investments are taking place. First, there is a tremendous variability in the use of multipliers, the main instrument to assess the employment impacts of investments. Not only does the value vary, but so does the nature of the employment effect (direct, indirect and induced), as does the area over which the impact supposedly will be felt. There seems to be no consistency (see Table 11.19), even though most studies start by taking (weighted) averages of previous studies. As noted at the outset, the use of multipliers results in a crude analysis and this is particularly surprising for such a dynamic sector of the economy. More comprehensive analysis would be expected as the growth in air travel is likely to continue, given the substantial changes taking place in contemporary society (outlined in Chapters 3 to 5). Second, the assumptions used on productivity are crucial both to future employment within airports and to direct (and indirect) off-airport employment. Recent productivity gains (4–5 per cent per annum) are considered to be exceptional and have been attributed mainly to the increases in aircraft size—a one-off change. The forecasted gains in productivity are much lower (under 1 per cent per annum), and substantially below the expected changes for the regional economy as a whole. Small changes in these figures have substantial impacts on employment. The calculation of direct on-airport employment and the changes in levels influences all the subsequent related employment as the multipliers are applied to the on-airport employment figures. Any error in these is further compounded. It would seem that a more sophisticated approach is required that acknowledges economies of scale and of agglomeration in airport operations (see Tables 11.8 and 11.19), and that there are critical size thresholds which require more flexible assumptions on productivity. The nature of airport business (passenger or freight; business or charter; national or international; interlining or terminating) will also influence the ratios of passengers to employees. The composition of the airport workforce is also becoming more flexible (in line with other macroeconomic changes), as shift working and part-time employment replace traditional forms of employment. In addition to these two major limitations of current studies, there are broader questions raised about the real impacts of airports on local, regional and national economies. The development effects are taken for granted, even though they are difficult to quantify and require the existence of necessary conditions in germane labour markets. Although we can say what we mean by indirect and induced employment, it is extremely difficult to quantify them, particularly the latter. The question here is whether it is best not to quantify them at all until we can develop more sophisticated measures, or to accept that the crude measures we have should continue to be used

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with the appropriate caveats. One underlying issue is whether these indirect and induced jobs are wholly attributed to the existence or development of the airport or not. Are they new jobs or merely the redistribution of employment from one location to another (regionally, nationally or internationally)? Airport development may help to increase the competitiveness of one location over another, but is there a net benefit to the national economy as a whole? More importantly though, airport investments are of a substantial scale and one would expect identifiable employment impacts. Yet, even here the employment impacts of both Heathrow Terminal 5 and Manchester Airport’s Second Runway are small in relation to the scale of the labour market. Moreover, the consultants have argued that employment in airport related activities would be reduced (through productivity gains) if the new investment does not take place. Transport infrastructure investment is being justified in part on the basis of maintaining existing employment, not creating new employment. Acknowledgement Alan McLellan, a researcher at the Bartlett School of Planning in University College London, helped with the data collection and drafting of this chapter. We thank him for his contribution. Notes 1 The study area around Heathrow consisted of 18 local authority areas, which contain at least 1 per cent of the airport’s workforce. This accounted for about 80 per cent of the direct on-site employment (1992). 2 Nearly 30 per cent of the total passengers (47.6 million in 1993) are transferring through the airport. Given its primary position in the UK and Europe, there is a clear preference for airlines to use the airport, but the number of slots available is limited and valued at a premium, at least by the airlines that “own” them. Even though airport user charges are some 20 per cent higher at Heathrow than at Gatwick, Heathrow is still more congested, emphasizing the preference for it by international airlines. 3 Pieda (1995) used input-output analysis to calculate the purchases of the full range of goods and services required by companies operating at the airport. 4 This position is being eroded by the recent introduction of the Eurostar rail services between London and European cities, principally Paris and Brussels. 5 Note that the multiplier relates to all direct employment at the airport only disregarding all direct employment off the airport. So values are not directly comparable with those for Heathrow. Comparisons can be made with those figures in Table 11.12 (York Consulting Ltd 1991). 6 Twomey and Tomkins (1995) concluded that the size of this effect is 2.9 times onsite employment at Los Angeles international airport. 7 An alternative approach is to use area-specific input-output tables to indicate levels of economic connectedness (Szyrmer 1985, 1986). 8 SIC is the Standard Industrial Classification used in the UK to group jobs in the census of employment.

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9 The North West region includes the counties of Cheshire, Greater Manchester, Lancashire and Merseyside. 10 The ratio of total income (direct plus indirect plus induced) to the initial injection of spending power is termed the multiplier.

APPENDIX 11.1 METHODS USED IN ASSESSING THE ECONOMIC IMPACTS OF AIRPORT INVESTMENT There are three main groups of models that have been used to measure the economic development impacts of airports and these are summarized here together with illustrations of their use: • • •

Economic base models: relate changes in the goods and services sold within the region to goods and services sold outside the region. Econometric models: test relationships between the key economic variables through regression and other multivariate statistical methods. Input-output models: establish dependency relationships between economic sectors and make estimates of the induced impact sector by sector, and after aggregation for the region as a whole. Table A11.1 summarizes several airport studies.

Table A11.1 Summary of studies

12

Interpretation of impacts and policy conclusions

There is an unquestionable relationship between economic development and a liberal democracy…. The exact nature of that relationship is more complicated… And not adequately explained. (Fukuyama 1994:125) There is a high degree of empirical correlation between stable democracy and economic development. (Lipset 1959:72)

12.1 Introduction We started out in this book with the intention of answering a single question. Does transport infrastructure investment in well-developed economies promote economic growth primarily at the urban level? This book has taken us on a long journey and a considerable way towards finding the answer. But what have we learned? We have tried first to develop a conceptual framework to encompass the many issues involved. Second, we have examined how local and regional growth issues are affected by new trends in economics and lifestyle, as well as by changes in behaviour. At the heart of the book we review existing analytical methods, including the development of a microeconomic modelling approach, to examine the underlying forces that contribute to economic growth and the complex interrelationships at work. A series of empirical studies complement the analytical research where other policy impacts and location factors, which also have an important influence on the economic state of urban areas, are assessed. In this final chapter, we summarize our perspective on this important fundamental question and think through the implications for policymaking and for further research. Our intention is to be controversial and thought provoking, as this question is one of the key determinants of decisions on transport infrastructure investment and one of the principal unresolved challenges to transport researchers. Before we proceed we must re-emphasize a key point made throughout the book, namely that we do not question the potential ability of transportation

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infrastructure investments to produce transportation benefits such as travel time reductions. What we question, however, is whether there are additional benefits from these investments, generically referred to as economic development benefits, and how to measure them. Thus, it is the additionality and measurability of these assumed benefits that we are concerned about. Failure to properly identify and measure these alleged development benefits is bound to result in double counting of benefits, thereby running the risk of implementing the wrong projects. 12.2 Transport and economic development From the wealth of information, data and analysis assembled in this book, we come to the basic conclusion that in developed countries where there is already a well-connected transportation infrastructure network of a high quality, further investment in that infrastructure will not on its own result in economic growth. Transport infrastructure investment acts as a complement to other more important underlying conditions, which must also be met if further economic development is to take place. Additional transport investment is not a necessary condition, but acts in a supporting role when other factors are at work. There are three sets of necessary conditions: 1

2

3

The first, economic conditions, include the presence of underlying positive economic externalities, such as agglomeration and labour market economies, the availability of a good quality (well-trained and highly skilled) labour force and underlying dynamics in the local economy. This is a fundamental condition, as only when all these factors are positive and the local economy is buoyant will new transport investment, in conjunction with the other necessary conditions, have an economic development impact. Second, there are investment conditions that relate to the availability of funds for the investment, the scale of the investment and its location, the network effects (e.g. are there missing links in the network), and the actual timing of the investment. Transport infrastructure investment decisions are not made in isolation, so the nature of the investment, including its ‘place’ in the network, is also one of the necessary conditions that needs to be considered. These factors on their own are again not sufficient and this particular focus has been a limitation on much of the analysis found in the literature, which has tended to examine the spatial factors in isolation as the main focus of evaluation. The third set constitutes political and institutional conditions that are related to the broader policy environment within which transport decisions must be taken. To achieve economic development, complementary decisions and a facilitating environment must be in place; otherwise the

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Figure 12.1 Illustration of the necessary sets of conditions.

impacts may be counterproductive. Included in this group of factors are the sources and method of finance, the level of investment (local, regional or national), the supporting legal, organizational and institutional policies and processes, and any necessary complementary policy actions (e.g. grants, tax breaks and training programmes). Again, on its own, even a favourable political environment will not result in economic growth unless the other necessary conditions are also present. These three basic sets of necessary conditions are illustrated in Figure 12.1. As we have argued, individually, the necessary conditions will have little or no impact on development. Even if they are combined on a pair-wise basis, their effect will be limited. For example, in Figure 12.1, if only the investment and political conditions are present (box 2+3), we can expect accessibility changes, but since the necessary economic conditions are not present, economic growth impacts will not transpire. In that case, relative attractiveness of particular locations may change, but this is merely a redistribution of existing economic development rather than additional growth.

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Similarly, if only the investment and economic conditions are present (box 1+2), economic development effects from the investment may not follow for the lack of supportive policies, or because of the presence of conflicting transportation and landuse policies (see Chapter 10 on the Buffalo Light Rail investment). It is only when all three necessary sets of conditions are present and operating together that economic growth will ensue. Two important further conclusions follow on from this analysis. Transportation infrastructure investments are location related and have potential growth effects on local economies. Hence, actually to identify and measure the economic growth resulting from such investment, analysis must take place at the local level. It is at this scale that the impacts on local economic development, income levels, accessibility and employment should be assessed. On the practical level, as analysis becomes more aggregate, many of the impacts are ‘lost’ (Section 12.4). Second, if the economic growth effects are to be measured, then analysis has to move away from the primary concern with user benefits (conventionally travel time savings) to a much wider assessment of costs and benefits (Section 12.5). 12.3 Key questions and answers In the introduction (Chapter 1), we raised ten key questions which now also need to be answered. This is done in Table 12.1. As demonstrated in Table 12.1, we do not claim to have obtained definitive answers to all (or any) of them, but we hope that some progress has been made. Inevitably, as we make progress in one direction, new directions emerge so that we may be left with even more questions than answers. But that is the nature of research. In retrospect, the questions we raised in the introduction demonstrate the problems that we have faced, particularly in terms of the complexity of the processes involved and the new agenda. In the next sections, we further develop some of the more interesting debating points as a commentary on the key questions in Table 12.1 and we add some new dimensions to the discussion. 12.4 Dimensions of analysis In Chapter 2 (Section 2.4) we have discussed our methodological framework, within which we have structured the book. It consists of three dimensions: the scale of the analysis, the type of variables used to assess the investment and its impacts, and the time duration of the effects. The latter dimension is the most difficult to assess and in this book we examine it primarily through the case studies. Table 12.2 shows the relationships between the types of measured impacts and the level at which they are likely to be felt. Although we have presented extensive discussions about the national and regional levels of impacts (see Chapters 6 and 7), the main focus of the book is directed at the local impacts of transport infrastructure investments.

Table 12.1 Questions and answers

Table 12.1 Continued

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Table 12.2 Conceptual framework of analysis

We now illustrate the arguments regarding the presence of the necessary conditions for economic development to ensue and the ideas presented in this section regarding the scale of the analysis. Consider the following two examples of national and regional transportation investments, and implications for the local level (Section 12.4.3). 12.4.1 National level It is clear that all countries need a well-developed transport infrastructure to compete internationally in the new global markets. As trade barriers are reduced and new markets are opened up, it is essential to have high levels of accessibility. However, it is not only the quantity of the physical infrastructure that is important, but also the quality of the infrastructure in a much wider context (the political and institutional conditions in Figure 12.1). The physical infrastructure needs to be extended, not just to the links in the network, but also to the terminals and interchanges. These nodes are often the points of congestion which cause most delay. The control systems used on the network are crucial to ensure that maximum efficiency is gained from its use and decisions are based on high quality real time information. The management, information and control systems are also important, in many cases even more important than the physical infrastructure itself. In addition, the financial and organizational context within which the infrastructure is placed can again influence decisions on construction, maintenance, ownership, charging and responsibility. These elements are also the key to the effective use of any infrastructure expansion. Part of this responsibility relates to the externalities created by infrastructure use (e.g. noise, pollution, accidents and consumption of resources), and who is to pay for these (substantial) costs, whether it is society as a whole, the user or both.

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The most important ‘new’ dimension in this process is the use that is made of the network by the user. The choices available have increased substantially, particularly if the physical infrastructure is placed in the wider context of the changing technology, employment, substitution of time between work and leisure activities and firms’ production, and location decisions (the economic set of conditions in Figure 12.1). A high quality ubiquitous infrastructure allows an increased flexibility in the operations of firms and greater variability in individual travel patterns. Company structures have become much flatter with production being outsourced, efficient supply chains, lower stock levels and shorter production runs. Manufacturing processes are increasingly being customer driven, with the detailed specifications being provided by the customer. Efficient transport systems are required to facilitate these new production processes, but there also has to be the supporting managerial, organizational and technological infrastructures to allow this to work efficiently (the political and institutional set of conditions in Figure 12.1). 12.4.2 Regional level Here the concept of accessibility is central to the debate. It has been argued that changes in accessibility resulting from transport infrastructure investment cause a redistribution of employment between regions. It is unclear whether the changes in accessibility also create new activities, which we would call economic growth (see Figure 12.1, box 2+3). The conclusions reached here suggest that at the regional level redistribution will take place, often to the further advantage of the already accessible core parts of the country. But transport accessibility must be seen as part of a much wider concept of accessibility that includes availability of skilled labour, good quality locations, the necessary supporting infrastructure and local road and rail networks (Figure 12.1, economic conditions). It seems that the actors need to be able to manage the changing internal and external relationships to achieve the best economic output. This is what some (e.g. Storper 1993) have called the creative learning capacity. Changes in transport accessibility resulting from infrastructure investment form one element in that process. Figure 12.2 illustrates the regional arguments. In Figure 12.2 movement along the accessibility axis will not achieve economic growth on its own. Only where there are open dynamic systems (when the economic and political sets of conditions are present, see Figure 12.1) will it have a real impact, particularly where the existing infrastructure provides only poor levels of accessibility (quadrant 1). In dynamic systems with high levels of accessibility it will support growth (quadrant 2), but in the lower part of the figure (quadrants 3 and 4) improving transport accessibility is not a sufficient condition for economic growth (for the lack of the economic, investment and political sets of conditions). The capacity of the system has to be enhanced so that it can respond, i.e.

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Figure 12.2 Transport and economic development at the regional level.

move to the top part of the figure. This means that the skills and knowledge base have to be raised (i.e. introducing economic conditions). Strong links with local research and the informal contact networks have to be established and dynamic transactional relationships between manufacturers, local suppliers and customers are required. These are the key elements of the creative learning capacity that is a necessary prerequisite for economic growth at the regional level (see Figure 12.1). 12.4.3 Local level In addition, there is the need to examine the costs and benefits of new road investment in terms of the opportunities for economic growth and development. These effects should be examined at the strategic (county) and regional level so that the spatial consequences can be matched to the local traffic impacts. These important considerations are rarely included in the decision process. Even at planning inquiries where all material considerations should be raised, the local authority has to follow the strategy set out in the development plan. Privately, there may be great concern over the impact of a new road on the local economy and environment, but publicly the proposal is supported if it conforms to the general strategy set out in the development plan. If all local concerns were taken into consideration, the costs of the road would be increased and the benefits reduced, thus lowering its net value, possibly to a point where it ceased to be viable (Headicar 1996). The conclusion here is that decisions have to reflect the

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concerns at all levels and that they must also be placed within the political and institutional context. 12.5 A new proposal for project appraisal A major criticism we had of the national level analysis is that finding a rate of GDP growth from a certain increase of total capital accumulation actually tells us little about the contribution of the next infrastructure project (see Chapter 6, Section 6.2.2). That is, some projects have a high and positive impact on GDP growth whereas others may not. The national level analysis only tells us about the average contribution of capital accumulation. But since infrastructure investments are carried out project by project we need to analyse each separately where the general rate of return (or rate of GDP growth) from total public capital formation may at best serve as a kind of a guideline. Another issue to contemplate is that analysis of all planned infrastructure investment projects is done ex ante with little certain information about future trends and development. Hence the need to consider specific issues that may affect the potential impact of this investment with economic growth. In this book we have highlighted some of these factors. First is the need to consider each transport project within the framework of a local, regional or even national network. Secondly, for growth to occur it must be the result of improvements at the network level and not at the single project level. The effect of a given investment on growth can be dramatically different when it links, for example, two disjoint networks rather than being an additional link in an established network. Prioritizing objectives and criteria is a third factor. 1 2 3

Majority of benefits need to be transport related, since otherwise why invest in transportation facilities in the first place. Need to avoid double counting in measuring non-transport benefits. Need to show functional linkage between primary transportation benefits (e.g. accessibility improvements) and potential economic growth effects.

If transportation investment is to take place, we would suggest a twin approach where conventional cost benefit analysis is carried out on the project to determine the primary (transportation related) user benefits and costs of investment. To achieve a given rate of return, this analysis may account for some or all the necessary returns. If there is a shortfall, then a complementary analysis needs to take place that has a wider view of the investment proposal. As itemized in Table 12.3, this would include the contribution of the project to the transport network as a whole through network analysis. In addition the value added of the project would be assessed through its contribution to local employment, the potential for increases in productivity and the environmental impacts. Finally, it would

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Table 12.3 A suggested twin approach to project appraisal

also investigate the distributional impacts in terms of the spatial effects on the regional and local distribution of services and facilities, and the social impacts. This complementary analysis would use some of the methods and models proposed in this book. The contribution that the components outlined in Table 12.3 would make to the overall project assessment could be measured. If additional benefits can be shown and are sufficient to raise the cost-benefit analysis above the crucial level for the rates of return, then investment could take place. This more complex type of analysis seems to be increasingly important, as the conventional benefits from any proposed transport investment may be providing an ever-decreasing proportion of the total returns. 12.6 Decoupling transport from economic growth Historically, there has been a close relationship between the growth in demand for freight and passenger traffic and economic growth, as measured by GDP. The question raised in Chapters 1 and 2 related to the underlying rationale for this statistical relationship and whether it should (or would) continue into the future. Our conclusion is that there is no reason why we should have transport growth in line with economic growth. Indeed, there are strong efficiency and environmental arguments for breaking that link. We should be seeking to reduce the transport intensity of activities while at the same time maintaining economic growth: this is the decoupling argument. Travel can be broken down into three component parts—volume, distance and efficiency. The first two components are usually combined to give measures of performance (i.e. passenger km or ton km), but the third element is equally important as it relates to modes, travel time and price, the use of resources, technology and organizational factors. For example, in the freight sector

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efficiency can be increased through the use of logistics, flat organizational structures, new forms of handling, minimization of warehouse requirements and spatial organization to reduce distribution costs. All these measures can increase efficiency and reduce the volumes and distances travelled. However, they can also work to increase efficiency and the volumes/distances travelled; the arguments work in both directions. Dematerialization can make a more fundamental impact. As production becomes more service based, lighter products are moved about and miniaturization takes place with a much greater emphasis on quality and design. The net effect is that less volume is moved around so less travel is required. But as the material intensity of products decreases, material consumption may still increase as the economy is growing and demand is also expanding. It has been estimated that dematerialization could result in a 15 to 20 per cent reduction in freight volumes between 1995 and 2020 (Schleicher-Tappeser et al. 1998). This reduction must be placed against the expected increase in freight traffic of 80 per cent over this same period. Further reductions could be achieved through raising the durability of products so that they last longer, but here there is a trade-off between lasting quality and the need to take advantage of the latest technological innovations. The second major reorganization change could be the move from the global to the ‘glocal’1. Traditional arguments strongly favour concentration of production to take advantage of agglomeration economies. However, the development of flexible specialization (Piore and Sabel 1984, 1988; Chapter 4 ) has allowed a new complementarity between global networks and regional production for regional markets without the multinationals losing control. The multinationals still have the knowledge and control of information, but produce for local markets in a dispersed manner through outsourcing. Production units are downsized so that the new lean production methods can be introduced. For example, in the state of Baden-Wurttemberg in south Germany most suppliers for the car manufacturer Mercedes are based in and around Stuttgart. Certain components are ordered to the hour and must be available within a radius of 100 km (Schleicher-Tappeser et al. 1998). Local production networks provide the opportunity for short travel distances and a reduction in freight traffic. This same study estimated that the potential reduction in freight traffic through the use of regional markets, regional production and the reduction in international flow of goods (that have been produced for local markets) could amount to between 20–30 per cent (1995– 2020). There is a substantial potential for decoupling in the freight sector with the new forms of production outlined above. The scale economy arguments, together with economies related to specialization and the comparative cost advantages of producing large quantities for large markets, are now being questioned. Customer-driven requirements mean that products are now tailored to individual specifications so that smaller scale production units for

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regional markets become possible, provided that the knowledge and skills are available. However, there is still a long way to go as global cultures and internationalization have been instrumental in producing similar values, tastes and lifestyles, with the consequent loss of community and locality. Similarly, most of the changes in policy have tended to encourage greater internationalization through trade liberalization, market-based strategies, subsidies to farmers and other groups, deregulation in transport, privatization, and even markets for environmental and consumer protection. The most optimistic view is that in the freight sector transport demand could remain constant over the next twenty years (to 2020), with the strong implementation of decoupling strategies. In the passenger sector, the opportunities for stabilization in demand seem harder to envisage, particularly within the context of increased affluence and leisure time. Decoupling must again be seen as a combination of strategies to reduce the volume of traffic, the distance travelled and measures to increase efficiency, but at the same time maintaining economic growth. The decoupling arguments follow the same structure as in the freight sector with dematerialization of travel through less travel or travel by more efficient modes, and through establishing local travel patterns through the reorganization of the production and consumption patterns based on local and regional networks. In the context of this book, the important conclusion is that, even if there has in the past been a link between transport use and economic growth, there is no reason why this link should continue. The strong sustainability argument would suggest that if the link does exist it must be broken as it is unsustainable. We should be actively seeking to break the link (if it exists) between transport development and economic growth. 12.7 Complexity and causality There may be a Catch-22 situation in the analysis of the links between transport infrastructure investment and economic development. To make analysis tractable requires a series of simplifying assumptions to be made, but this in turn reduces the usefulness of the analysis. Even if more complex formulations are possible, then the limitations of the available data are quickly reached. This situation results in uncertainty and prevents clear conclusions. Even if it is possible to demonstrate that economic development is present in theory, it may be impossible to measure it in practice. This complexity is increasing, particularly when placed against new funding priorities and mechanisms, the changing industrial base, the balancing of economic priorities with social and environmental concerns, etc. One of the main themes that has been argued in this book is that even though simplification and strong assumptions may have been valid in the past (see,

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for example, Figure 7.1), that position has become less tenable. We must accommodate complexity in analysis and recognize the multi-dimensional nature of the links between transport, location, development and the many other new factors relevant to our understanding of these processes (see, for example, Figure 7.2). In our microeconomic analysis (Chapter 8), some of these factors have been included in the analysis with several different strategies available. But even here we have concentrated on work-related activities, single workers (not multiple workers), standard notions of values of time, and only two firms. Perhaps the complexity issue needs to be approached through a combination of methods that link together more formal modelling approaches with descriptive qualitative analysis that can make links between methods. Such an approach would begin to tackle the complexity in a robust and flexible manner so that a clear picture can be achieved. Complexity relates to one of the underlying problems investigated in this book, namely causality. One of the key elements in the basic economic argument is that there must be a set of causal relations between transport investment and economic development since otherwise there would no basis for the claims that transport infrastructure investments also promote economic growth. Public infrastructure investment is regarded as the trigger mechanism to the increasing of private capital rates of return through increases in private capital stock and labour productivity, which in turn result in higher total output and economic growth. We have argued that this causality argument is one of many that can be used and that the empirical evidence is weak. It seems doubtful that public infrastructure investment will lead to substantial increases in new employment, as most of the potential savings will be realized through increases in productivity with the existing labour force. Changes in accessibility may induce relocation, but only if those changes are above key thresholds and other factors are working strongly in the same direction, will these relocations further induce economic growth. In the microeconomic model developed in Chapter 8, we have explored the impacts of infrastructure investment on the choices that individuals make in how they choose to use their time. Whether additional time (resulting from reduced travel times following an infrastructure investment) is taken as leisure or additional work is a major question. The proliferation of leisure time activities in well-developed economies may mean that additional time is not used to increase labour supply. If that is the case, the causality linkage between accessibility improvements and economic growth may be rather weak and getting weaker over time. The conclusion reached is that the causality argument is weakening as there seem to be decreasing returns on public physical capital. Although we have not explored this issue in detail, the arguments for increasing returns on

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knowledge and technology may be seen as the new engines of economic growth rather than the transport infrastructure (Romer 1986). 12.8 Accessibility and proximity The evidence cited in this book is mixed on the role that accessibility changes have in generating economic development. It should be remembered that accessibility is a relative concept and infrastructure investment in one location may help that location, but at the expense of a competing location. The net effect may be marginal. More important though is the argument that in most advanced economies levels of accessibility by road (and rail) are already high. This means that most infrastructure investments will only affect the accessibility in the system as a whole in a marginal way. It is only where there is a major change in the system-wide accessibility (e.g. a new link joining two previously disjoint networks, or the opening up of a previously inaccessible location) that major relocation will take place. This suggests that in the future accessibility should not be investigated only as a relative concept, but that we should be searching for minimum thresholds above which change takes place. These thresholds are important if accessibility changes are seen as the means to improve the relative positions of regions, by increasing inward investment and employment. A second more subtle implication of changes in accessibility is that they enhance existing trends rather than creating new ones. If the conditions are advantageous for firms to relocate or set up a new business in some areas (for example, where the labour force has appropriate skills or where there are financial incentives), then improvements in the transport infrastructure may give one location preference over another. On its own, transport infrastructure is a second order location variable where there is a well-developed network, but in conjunction with other factors it may ‘tip the balance’ in favour of the (marginally) more accessible location. This debate relates to the complementarity found within networks. Accessibility has tended to be viewed as the impact of one new link on the network as a whole. But many investments are complementary, so competition is really taking place between systems, not individual links. Accessibility should not only be viewed as the changes taking place in one system (e.g. rail), but also the new competitive position of that system in relation to other systems (e.g. road). There is a strong optionality value in accessibility to a particular system, even if no use is actually made of it.2 New concepts of networks and accessibility are required to determine under which conditions the competitive position of one network will be changed as compared with another. This is a question of value added to influence expectations, to facilitate co-ordination and to ensure compatibility. Proximity is closely related to accessibility, but may also be important in the context of development. There are two conflicting processes at work, one of

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which leads towards proximity through agglomeration factors and the other which suggests that in a well-connected society non-proximity or diseconomies of agglomeration may operate. Many services can (and are) provided remotely. For example, in a telephone enquiry service, the person actually answering your question need not be in close proximity to you or to others. Similarly, many products can be outsourced to locations where labour and other production factors are cheaper. Provided that the charge to the user is low (i.e. a local rate telephone call or cheap products), proximity is not important. High quality communications and transport infrastructure facilities have allowed a greater flexibility in the location of many services and firms. Economies of scale play an important role in this process and locations that possess specialization tendencies may benefit more from a reduction in transport costs than other locations. Krugman (1991a) shows an interesting example of the impact of a reduction in transportation costs from infrastructure improvements on manufacturing location in the presence of scale economies in production. With high transport costs, production will take place in dispersed location (e.g. core and periphery). When transport costs fall there will be a shift towards production in one location (either the core or the periphery), which can switch if transport costs keep falling (see also Rietveld and Bruinsma 1998). All these factors may provide important insights into patterns of development at the national and international levels and may even help in our understanding of urban spatial structure (Anas et al. 1998). In the situation where transport costs are decreasing (or already low) and where there is a ubiquitous infrastructure, non-material flows may become more important (Burmeister and Colletis-Wahl 1997). The traditional framework, which treats transport as a cost, is becoming less relevant when trying to understand the spatial dynamics of industry. Recent research proposes a richer more complex set of relationships that are based on circulatory capacity. This concept includes the infrastructure and the organization and management of flows of goods, information and people (i.e. the services that can be produced). The links between infrastructure and the utilization of the production process are not deterministic. The role of transport infrastructure in a network economy is no more than a generic resource for circulation (Burmeister and Colletis-Wahl 1997:239). Particular forms of use and the consequent impacts depend on the strategies adopted by the actors in the production process. Proximity extends far beyond geographical accessibility to include the organization and degree of control of the flows of goods, information and people. 12.9 Transport investment and economic development: the role of policy design If there is one key lesson to be learnt from the analysis in this book, it is that of the crucial role that policy design can play in influencing and strengthening

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the potential impact of transportation infrastructure investment on local economic development. In Figure 12.1 we have highlighted the notion that for economic development to follow from transportation infrastructure investments, it is essential that three sets of necessary conditions be met. This representation, however, conveys the impression that all of these sets are equally important in affecting the economic development outcomes of infrastructure investments. Our aim here is to argue that this, in fact, is an incorrect portrayal of reality since political and policy decision making actually affects, directly or indirectly, the other two sets of necessary conditions. Diagrammatically we show this in Figure 12.3. In Figure 12.3, the circle labelled investment type refers to the particular nature of the investment: that is, the mode (e.g. in highway, rail, freight or airport); the investment’s scale, its location and whether it is a new link in an existing network, an expansion of existing links in an existing network or a new link that unites two disjoints networks. The circle labelled economic conditions refers to such conditions as agglomeration externalities in firms’ location, labour market externalities, the presence of network economies, and the presence of inefficiencies in spatial structures. Finally, the circle labelled policymaking, refers to key non-economic factors that influence economic growth. These include such elements as the organizational structure and range of responsibility of decision making and overseeing agencies, the nature of the legal system, to the government level at which decisions are made and, most importantly, to the political involvement of political organs. The shaded area in Figure 12.3 is where all three sets of

Figure 12.3 The role of policymaking in achieving economic growth.

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conditions are met and where economic development will emerge (compare it with the box 1+2+3, in Figure 12.1). What this diagram essentially shows is that the relative importance of each set of conditions is not equal. Policymaking, which affects both the economic conditions and, more importantly, the investment type, is the crucial factor in realizing economic growth benefits from a transportation infrastructure investment: hence, the special attention we have paid throughout this book to policymaking facets of transport infrastructure investment projects. In democracies, the ability of governments to co-ordinate their activities and design complementary policies to gain maximum economic development effects from their capital investments many times seems rather limited. Perhaps paradoxically, however, and to which the quotations from Fukuyama and Lipset at the beginning of this chapter attest, it is only in democratic societies that economic development reaches its maximum potential. Notes 1 2

Glocal refers to a combination of global and local, where production is still controlled by the large multinational companies, but produced locally for local markets under franchising and other arrangements. We should note here the concept of positive network externalities developed mainly for communication and computer networks. It implies that the value of membership to one user is positively affected when another user joins and enlarges the network (Katz and Schapiro 1994), hence the rationale for having more users joining and using the network. In transportation this argument may be valid in developing economies where new transport investment opens up undeveloped areas. In the context of this book, which focuses on transportation networks in highly developed economies, this argument may be invalid as there are strong congestion externalities present when another user joins the network.

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Index

Accessibility 11, 12, 23, 26, 36, 44, 45, 46, 47, 49, 52–53, 113, 124, 148, 168, 171, 174, 176, 191, 196, 208, 211, 217, 221, 227, 230–233, 239, 242, 247, 252, 258, 263, 266, 267, 271, 275, 278, 279, 282, 301, 302, 321, 324, 330, 331–332 benefits 197 effects 39 model 265, 269–271 Acid rain 119 Activity centres 213–214 relocation 163, 211 Additional growth 319 Additionality 77, 305 Agglomeration economies 46, 47, 92, 94–95, 114, 134, 168–169, 212–214, 216, 218, 219, 222, 228, 247, 249, 279, 328 Aggregate output 134 Air quality standards 123–124 Air transport 119, 287–316 Allocation function 268 Allocative externalities 167, 169–170, 172, 174, 187, 201 Amsterdam 251 orbital motorway 239, 251–253 Attracted employment 291 Backword linkages 212 BART system 257 see San Francisco Behaviour 45–47 see Travel, Transport Belgium 181–182, 191 Benefit-cost analysis 26, 157, 161, -197 see Cost benefit analysis Benefit cost comparisons 192 Benefit cost ratio 153

Blue Water Park development 248 British Columbia 182, 187 Buffalo 286 Buffalo Light Rail Rapid Transit 257, 258–277 Bus 276 network 263 Business confidence 253 Calgary 277 Canada 182 Canal 8 Capacity investment function 201 limitations 244 Capital accumulation 326 productivity 138 sunk costs 190 Car ownership 85–87, 110, 112, 119, Cars 244 Case studies 28 Causal linkage 37 Causality 36, 62, 115, 134, 135, 144, 146, 148, 149, –150, 156, 174, 253, 329–331 mechanism 33 paradigm 41 CBD 259, 262, 267, 271, 272, 273–274, 275–276, 276, 278 Central place theory 9 Channel Tunnel 72 CO2 emissions 115, 119 Combined multiplier 311 see Multiplier Commercial traffic 180–182 see Freight Communications 96 see Telecommunications networks 321 Compact city 121 see Sustainability development development 283

364

Index

Company structure 324 Comparative analysis 253 Competition effect 28 Competitiveness 17, 76, 119, 253 Complement 318 Complementarity 52, 331 analysis 326–327 factor 135 Complexity 329–331 Congestion 63, 78, 103, 104–105, 123, 251 see Toll roads cost 95, 97, 201–202 Connectivity 44 Construction activities 265 Construction employment 300, 308 Consumer surplus 51–52, 165–168 174, 175 Consumers’ behaviour 164 Consumption multiplier 301 Corridor 259 city 121 Cost benefit analysis 12, 81, 105, 326 see also Benefit-cost analysis Cost effectiveness analysis 193 Cost elasticity 142 see Elasticity Cost function 149, 154, 243 model 141–143, 151 Cost overrun 74 Cost savings approach 181 Cost of travel 46 Creative learning capacity 324–325 Crowding out 24, 106, 125, 31, 137 Decentralisation 101–102 Decision-makers 193 Decoupling 327–329 Delay option 191 Demand elasticities 188, 189, 203 see Elasticity Demand for labour 223, 245 Demand function 220 Dematerialization 328 Democracies 334 Demographic change 58, 108–112 Denmark 187 Density 120, 121 Developing countries 21–24 Development argument 7, 21 effects 13, 313 plans 242 pressures 239, 242 Differential employment shift 244

Direct employment 289, 291, 297–299, 307 see Employment Direct impacts 297–299 Discount rate 183–186 Discount shopping stores 243 see Retailing Discrete choice analysis 176, 179 Disutility of travelling 178 Double counting 166, 174, 193, 196 see Cost benefit analysis Durability 328 Dynamic model 136, 141 process 39 systems 324–325 Econometric analyses 11 model 48, 315 Economic base model 265, 269, 273, 315 base theory 25 conditions 315, 333 development 35 evaluation 127 growth 35, 47–50, 132 integration 75, 76–78 linkages 304–307 overhead capital 14, 60 performance 105 potential 245 rate of return 188 Economies of scale 46, 51, 76, 77, 114, 289, 307, 313, 332 Edge city 121, 280 Efficient management 136 urban form 172 Elasticity 180, 219 of costs 143 of output 138, 148, 153, 156 of substitution 203 Elderly 109, 111–112 Emissions 123 Employment 215, 230–233, 297, 303, 304, 310, 311 impact model 291 location 216 multiplier 288–289 taxes 71 Energy use 115, 121 Engineering needs assessment 157 England 288 Environmental 17, 116–117 costs 116, 119 effects 115–120, 170 impacts 58 regulation 118

Index

Equilibrium analysis 38–39 conditions 46 employment 228 Equity 58, 118, 158 effects 112–115 Euralille 282 Europe 84, 111, 124 European Commission 192 European Union 5, 25, 102–103, 108, 110, 194, 196 Evaluation see Cost-benefit analysis, Benefit-cost analysis Ex ante analysis 257, 258 Ex post analysis 237, 257 Expectations of investors 29, 322 Expenditure leakage 316 Externalities 43, 120, 133, 137, 188, 196, 212 Financial services 84, 100 Firm related growth measures 214 Firm’s location 16, 223 Flexibility 99 Flexible specialisation 92, 328 Floor space 247 Forward linkages 212 France 64 Free fare zone 277 Free rider 67, 75, 133 Freight distribution 14, 97, 329, Fremont 285 French A71 motorway 239, 249–251 French railways 71 see Railways, TGV Funding 137 Garin-Lowry model 268 Gatwick 293 GDP 4, 5, 18, 23, 35, 48, 50, 76, 80, 115, 326 General equilibrium model 136 General purpose technologies 158 Generalized Leontief models 143 see also Leonief cost function models Generalized travel cost 171, 175, see also travel, Transport costs Geographical scales 40 Germany 146, 184–185 Global cities 84, 100–102 Global production 58 Globalization 92, 101 effects 104 Glocal 328 Government failure 134

365

Government provision of infrastructure 133–134 Gravity functions 266 Great Britain 84–87, 110 Greece 185 Green belt 240–249, 242, 247, 303 Greenfield sites 239 Gross state product 148, 151 Headquarters functions 301 Heathrow 292–301, 310 see also London, Terminal 5 Heavy Goods Vehicles 244 see Freight distribution High tech service industries 280 High technology 242 Higher education 279 High-speed rail 61, 63, 80, 98, 101, 102, 103, 257, 278–282, 285 Highway expenditures 146 Household attributes 178 sector 215–217, 220–221, 223, 226 size 109 Hub 301, 302 airports 287, 297 See Heathrow, Manchester locations 307 Hypermarkets 243 see Retailing Hypothecation 71 Image 29, 53, 282, 322 Impact analysis 264–268 matrix 193 statement 192, 193 Increasing returns 113, 114 Incremental revenue 181 Indirect employment 291, 296, 297– 299, 302 see also Employment impacts 299 jobs 307 Induced demand 124 Induced employment 291, 296, 297–299 impacts 300 traffic 18, 49 Industrial reorganisation benefits 168 Inflexibility 186 Information exchange industries 279 Information technology 83 see also Telecommunication Infrastructure sector 223 Innovation 90, 119 Input prices 144 Input-output analysis 11, 48, 299, 315

366

Index

Interchanges 99, 323 Intermediate input 135 Intermodiality 45, 98 Internal rate of return 183 Internal trade theory 15 Investment 42–44 conditions 318 effect 273–275 financing 66–75 multiplier 164, 268–269, 302 see also Multiplier analysis type 333 Inward investment 301, 303, 308 Irreversibility costs 186 Israel 153 ISTEA 192 Japan 103, 109 Shinkansen see High-speed rail Job uncertainty and flexibility 90 Joint projects 70, 73–75 Journey length 112 Jubilee Line Extension 25, see also London Labour 216 force 259 force participation 230 market effects 24, 169, 196, 225, 228, 321 productivity 140, 145, 163, 211, 215 225, 229, 289, 294–296 see also Productivity Lagged relationships 145 Lakeside development 249 Land market 250 owners 251 rent 51–52 use model 273–275 values 9, 17, 46, 247, 251 Latest demand 124, 166, 254 Leakage 305 Leisure 84, 112 time 220, 221, 224, 229 Leontief cost function model 146 see also Generalized Leontief models Level of attraction 273 Life cycle 88, 112 Lifestyles 120 Lille 53 see Euralille Linkage analysis 304 Liverpool 6 Local economic conditions 27

economic growth 211, 212–214, 258 economy 128, 282 impacts 320 inquiry 247 labour market 297 level 320 Location decisions 37 theory 7–11, 25, 46, 51, 94 Logistics systems 6 Log-linear production function 151 London 63, 99–100, 240–249, 257, 292–301 Long-distance commuting 102 Long-term employment 265 Lumpy investment 177, 190 Luton 294 Lyon 280 M25 London orbital motorway 63, 239, 240–249 accessibility 243–246 Macroeconomic approaches 16–18, 25, 105 Maintenance 78 Manchester 310 airport 301–309 see also Hub Marginal social costs 170, 201–202 Market equilibrium 203 failure 133 potential measures 243–244 related growth measures 215 Marshallian districts 93 Measurement of benefits 165–168 Method of financing 38 Mode choice approach 181 Model see Accessibility model, Cost function model, Dynamic model, Econometric model, Economic base model, Garin-Lowry model, Generalized equilibrium model, Generalized Leontief model, Land use model, Multinomial Logit model, Production function based model, Shopping probability model, Time allocation model, Urban and regional model Monopoly 66 Motorization effect 110–112 Motorway 12, 63 see Amsterdam Orbital, French A71, M25 accessible locations 242 Multicriteria analysis 183, 192, 193

Index

Multinational businesses 303 corporations 92, 93 firms 301 Multinomial logit model 175–176 Multiple regression analysis 244 see also Regression analysis Multiplier analysis 296, 297, 299, 300, 305, 310, 311, 315 effect 10, 15, 39, 48, 105, 115, 173–174, 265, 308 Necessary condition 212, 318–320 Neo-Schumpeterarian theory 90 Net present value 163, 183 Netherlands 185, 191 Network 324 accessibility 174–177 capacity 159 development 214 economics 169, 177, 333 performance 40, 44–45, 102–107 see also Accessibility New economic geography 114 growth economics 113 settlements 121 New York 99, 212, 230, 258 New York State 276 Niagara Frontier Transportation Study 276 Noise nuisance 293 Non-transportation trends 263 Non-work travel 87 Normative economics 128 Oakland 283 Offices 243, 252 Opportunity costs 178, 180, 190, 207, 221 Optimal level of traffic 202 public capital 136 traffic volume 201 Optionality value 331 Osaka 278–280 Out-migration 262 Out elasticity 149, 150, 153, 155 see also Elasticity Outsourcing 328 Overall impact 273–275 Overspill principle 72 Pareto optimum solution 202 Paris 88, 280 Parking policies 276–277 Partial equilibrium 217 models 147

367

Participation 118 by women 262 Partnership 70, 75, 78–79, 83 funding 62 Pecuniary externalities 167 Perceived accessibility 271 see also Accessibility Planning agencies 251 applications 246, 247 inquiries 325 Pleasanton 283 Policy design 332–335 Policymaking 333, 334 Political and institutional conditions 318 Pollution 116–117, 303 Population and employment 260–262 Portland 277 Positive externalities 211 feedback 213 Post-industrial society 90 Price inelastic 204 Private capital investment 147 see also Investment capital productivity 20 goods 184 output 153 sector investments 25 Privatization 65, 68–70 Production 127, 218 costs 7 function 145, 218, 219, 226 function based model 20, 138–14, 149, 154, 156, 234 network 328 sector 215–216, 217–220, 223 Productivity 17, 18–20, 60, 68, 76, 77, 80, 106, 115, 132, 135, 147, 151– 152, 156, 298, 304, 307–309, 311, 316 change 142 gains 212 Profit function 234 model 143, 149 Profit maximization 218 Project appraisal 326–327 see Cost benefit analysis, Benefit cost analysis evaluation process 161 Property market analysis 246 rights 57, 68 Proximity 95, 245, 331–332

368

Index

Public acceptability 71 awareness 120 capital 132, 142, 144, 147, 148, 151, 154, 157 see also Investment capital stock 143–144 expenditure 4, 57, 63 goods 184 infrastructure investment 20, 135, 140 141–143, 156, 330 see also Investment inquiry 70 perceptions 60 sector borrowing requirement 68, 69 utilities 59 Railways 8 accessible 283 investment appraisal 194 projects 196 see also High speed rail Random utility theory 179 Rates of return 22 Real estate transactions 249 Redistributed traffic 254 Redistribution of employment 313 Regional airport 302 Regional development 5, 11, 14, 53, 61, 244 economic impact 305 income 310 shopping centres 247 strategy 242 trends 259, 260–264 Regression analysis 230, 253 see also Multiple regression analysis Relocation costs 171 decisions 171 Rent levels 252 see also Land values Residential location 88, 217, 222 Resources 116–117 Retail activity 266 centres 262 see also Regional shopping centres development 246–249 floor space 267 malls 277 patterns 262–263 warehouses 247 Returns to public investment 141 Richmond 284 Risk 66, 69, 71, 72, 73, 74, 186–191 aversion 189 free bonds 183

Road accessibility 283, 285 see also Accessibility pricing 67, 71, 203 traffic 3 transport informatics 97–97 Rural development agency 251 SAFER 251 San Francisco 257 BART 283–285 Saturation level 111 Scale economies 219 see also Economies of scale Service employment 262, 275, 305 Shadow price 147, 156, 188, 205, 207, 208 toll 65, 67, 73, 79 value parameters 141–143 Shamrock 96 organization 89 Shinkansen stations 278–280 see also Japan Shopping analysis 271–273 probability model 266 trips 265 see also Retail Sideways linkages 212 Simulation 225–230 Simultaneous equation model 153 Small time savings 180, 181 Social congruity 192 equilibrium 197, 202 groups 85 net value 188 overhead capital 14, 23, 60, 67–69 rate of return 48 welfare 133, 165, 234 Société d’ Economie Mixte 282 South Bronx 230–233 Spain 154 Spatial agglomeration 90 changes 171–172 efficiency 171 equilibrium 149, 172 organization 226 proximity 213 redistribution 172 relocation 196 Stansted 294 Stated preference data 179 preference techniques 177 Static equilibrium 216 Steady state 139

Index

Structure plans 242 Suboptimal 136 Suburban areas 262 malls 272 see Retail, Shopping zones 275 Suburbanization 259, 276 Sunk costs 189 Superstore 243 See Retail Sustainability 115–120 development 58, 117–118 growth 5 Sweden 154 Taste coefficient 221 Technological change 28, 57, 96–100, 188, 322 innovation 83, 116 related growth measured 214 revolution 5, 25, 100 solutions 120 substitution 122 Technopole development 93 see also Euralille Teleactivities 98, 122 Telecommunication 45 see also Communications networks 98, 100 systems 101 Telecommuting 89, 122, 135 Terminals 5 297–302 see London, Heathrow, Air transport Terminals 323 TGV 280 see also High speed rail Thresholds 331 Time accessibility 269–271 see also Accessibility allocation model 205 based analyses 321 preference 183 related changes 139 scale 40, 183–186 spent at work 205 spent in leisure 205 spent in travel 205 Tokyo 99, 278–280, 285 Toll roads 63, 65, 71, 97 Toronto 179 Total accessibility 269–271 see also Accessibility cost analysis 195 employment 307 production costs 27 Tourism 301, 308

369

Trade theory 51 Traffic calming 193 diversion 204 growth 104 Trans-European transport network 125 see High speed rail Transfer payments 48 Transit investment 146 oriented development 122 Translogarithmic cost function 143, 152 Transport behaviour 102–107 bonds 71 capacity 201 capital accumulation 132 costs 7, 49, 50, 61, 113, 229, 321 see also Generalized travel costs cost advantages 244 efficiency 79 infrastructure 35, 215–216 intensity 50, 80, 104, 327 sector 228 trends 263 Travel behaviour 37 congestion 216 costs 214 costs savings 162 demand 103 impacts 251 time 171, 176, 207, 201, 211, 224, 227, 243, 254, 263, 265, 267, 269, 273 see also Time accessibility time budgets 122 time savings 44, 163 Treatment of time 322 Trip chaining 178 Trip distribution models 175 Trip lengths 85–87, 103 Ubiquitous accessibility 285 see also Accessibility UK 104–105 Uncertainty 186–191 Urban and regional modelling 11–16 see also Models, Land use models congestion 52 form 120–123 hierarchy 9 land use model 267 regeneration 3 Urbanization economies 171 USA 84–87, 97, 103, 109, 110, 111, 124, 125, 145–146 rail 187

370

Index

Use 324 Utility function 205, 221, 226 maximization 178 Value added 163, 326 capture 29 of time 178–182, 205–208 see also Time, Travel time Volume capacity function 222, 227 Wage rate 220, 230 Walnut Creek 283

Warehousing 242 Weighting systems 193 Welfare 186 Willingness to pay 165 see also Tolls, Time costs, Congestion Women 110, 111 Work 265 journeys 87 patterns 84–89 time 216, 228 Yokohama 280