Risks of Private Investment in East Asian Toll Roads

Henley Management College

What are the key risks associated
with private investment in start-up
toll road projects in Developing East
Asian Economies?

Richard F. Di Bona
ID No.: 1005661

Dissertation submitted in partial fulfilment of the
requirements of Master of Business Administration
2006

Dissertation Richard F. DI BONA
Henley Management College (1005661)

ACKNOWLEDGEMENTS

I am indebted to many for assistance and advice given during the preparation of this

Dissertation. Firstly, to my supervisor, David Parker; also to all the staff of the Henley

Hong Kong office, and to Ken Bull in Henley.

Within transport planning and associated professions, there are simply too many people

to thank individually. I believe I have learnt something from almost everyone I have

worked with over the last 14 years, who afforded me the opportunity to work across a

fascinating mix of countries. Over the last couple of years I have picked the brains of

many colleagues and clients, past and present; and due to frequent commercial

sensitivity, many comments and discussions have been on an anonymous basis. Many

also acted as disseminators of my questionnaire and as “sounding boards” to discuss

ideas and informally corroborate “ball park” figures used in the Monte Carlo risk

simulations.

I should also like to thank Consolidated Consultants in Amman, for their assistance with

printing the Dissertation.

Finally and most importantly, I must thank my wife Mariles for her moral support

throughout the course of my MBA studies and our daughter Vanessa (for helping me

take my mind off of my studies for essential relaxation).

DissFinal i December 2006


DECLARATION

I confirm that this Dissertation is my own original work. It is submitted in partial

fulfilment of the requirements of Master of Business Administration in the Faculty of

Business Administration of Henley Management College. The work has not been

submitted before for any other degree or examination in any other university.

DissFinal ii December 2006


ABSTRACT

Since the 1980’s there has been a resurgence in private sector involvement in

infrastructure, especially in tolled highways, including in developing economies

(Malaysia, Mexico and Thailand were early adopters). Activity expanded during the

1990’s across much of Latin America and East Asia, the latter region being where the

author has worked extensively. Following a slowdown in the aftermath of the 1997

Asian Financial Crisis, activity has recently picked-up again.

The 1980’s and 1990’s were characterised by generally declining price inflation and

interest rates; whereas now there is evidence of them increasing. Based on the

Kondratieff Wave (long-term business cycle; a.k.a. “K-Wave”), price inflation and

interest rates could be expected to trend upwards significantly over the coming 10-15

years. This Dissertation seeks to determine whether this will significantly change the

nature of project risk. Thus the specific hypothesis is:

“There is a significant change in the nature and extent of project finance risks for

private stakeholders in East Asian toll roads during a period of increasing price

inflation and interest rates”

The focus is on inter-urban toll roads in Cambodia, Mainland China, Indonesia, Laos,

Malaysia, Myanmar, the Philippines, Thailand and Vietnam.

The Literature Review begins with basic taxonomy and a review of infrastructure

privatisation trends (globally and in East Asia), illustrating likely future demand.

Financial valuation methods are reviewed, suggesting that whilst FIRR and NPV can be

used, the upfront capital-intensity of toll roads makes annual ratios such as Return on

Capital Employed less relevant to ex ante project evaluation. Generic project risks are

DissFinal iii December 2006


then investigated, showing that most project-risks are “front-loaded” on toll roads. The

Kondratieff Wave is then introduced and its potential applicability discussed, followed

by Kuznets’ work on both infrastructure development cycles and development

economics. The implications of cycles on over-investment are then discussed, with

specific emphasis on the genesis and aftermath of the 1997 Asian Financial Crisis.

Transport modelling theory is presented, followed by discussion of traffic risks and

forecasting issues, resulting variously from uncertainty, institutional risks and

methodological weaknesses, but also demonstrating the primacy of economic growth on

outturn performance. Construction risks are also considered, followed by a brief

discussion of other issues (primarily related to governance and business norms).

Forecasts of toll road demand and construction cost have often been unreliable, with

serial underestimation of cost and overestimation of demand.

Environmental analyses of the East Asian countries studied are then presented, using

PESTLE and stakeholder analysis. Focussing on Thailand (for consistency with the

Literature Review’s analysis of the Asian Financial Crisis), recent economic

performance is assessed, suggesting that recovery is underway. Potential growth in

vehicle ownership and the demand for roadspace is then considered, benchmarking the

studied countries against more developed economies; this shows substantial up-side

potential. The performance of a number of Chinese expressways is then examined. The

opportunities and threats facing the studied countries are discussed, grouping the

countries into three categories corresponding to risk-versus-potential characteristics.

Finally, analysis of gold price and treasury bill rates are used to postulate the current

global economy’s position on the K-Wave, showing that it is likely in the early stages of

an upswing.

DissFinal iv December 2006


Next, practitioner perceptions, expectations and experience were tested using a

questionnaire survey (which generated over 160 responses; respondents having a mean

of 20.6 years’ working experience). These showed that legal and political factors were

deemed most significant; but once detailed evaluation (i.e. modelling) commences,

economic factors predominate. As expected, data were perceived as less available and

reliable in developing economies. However, no strong preferences regarding the choice

of modelling method were shown; rather that the approach should be tailored to each

project in turn. Under-forecasting demand seemed rare and over-forecasting it relatively

common, in line with Literature Review findings. There was evidence of transport

modellers being pressured by clients to adjust forecasts. There was also evidence that

many forecasters do not appreciate differences between equity- and debt-side evaluation

requirements. NPV and FIRR are both widely used in evaluation. Based on perceptions

of individual countries’ prospective toll road markets, the country categorisations

proposed in the environmental analysis were broadly supported (with the exception of

Indonesia being seen more bearishly by respondents). Interestingly, respondents seemed

to generally expect many symptoms of the K-Wave upswing, in terms of rising interest

rates and price inflation. However, they were not that convinced of the impacts of these

parameters on forecast performance.

Consequently, Monte Carlo risk simulation modelling was employed to quantitatively

test likely impacts of different risk elements. The model comprised traffic/ revenue

forecasts and financial analysis for a notional inter-urban start-up toll road facility.

10,000 model runs were undertaken, with each run tested over three economic

scenarios, namely:

DissFinal v December 2006


 “Conventional Case” based on recent previous forecast modelling assumptions (e.g.

interest rates, price inflation and economic growth at levels similar to recent years);

 “Respondents’ Case” based on expectations gauged from the questionnaire survey

(with slightly higher economic growth, interest rates and price inflation, but

markedly higher fuel cost inflation); and,

 “Kondratieff Case” based on K-Wave upswing conditions (higher economic growth,

interest rates and price inflation; though fuel price inflation at the same level as the

Respondents’ Case).

The Respondents’ Case tended to give the most optimistic results, but results were more

variable than in the Conventional Case. Meanwhile, results from the Kondratieff Case

appeared quite volatile, tending to support theory. Furthermore, interest rates were

shown to become substantially more important to overall risk as they rise; and price

inflation may also increase in importance. Under Kondratieff Case conditions, if

economic growth outstrips the impacts of rising price inflation and interest rates, then

projected returns can be quite significant.

What the above implies is that the nature and extent of project finance risks for private

stakeholders are indeed likely to change as price inflation and interest rates increase.

However, if investors can lock-in fixed-rate debt (e.g. issuing bonds) before interest

rates increase significantly, these risks can be mitigated. Price inflation subsequent to

the issuing of bonds would also serve to decrease the real value of debt outstanding. But

downstream refinancing is likely to prove increasingly costly (versus experience during

the 1980s and 1990s when cheaper refinancing was often available as a consequence of

DissFinal vi December 2006


declining interest rates). In summary, therefore the hypothesis is broadly supported by

evidence.

Approximate word count of main text is 16,900 words.

KEYWORDS

 Infrastructure project finance

 Demand forecasting

 Developing countries

 Risk analysis

 Long wave business cycle (Kondratieff wave)

 Economic growth

 Price inflation

 Interest rates

 Transport planning

 Start-up toll road facilities

DissFinal vii December 2006


TABLE OF CONTENTS
1. Introduction ............................................................................................................. 1
1.1 Terms of Reference/ Personal Development............................................................ 1
1.2 Applicability and Hypothesis ................................................................................... 2
1.3 Geographic Scope .................................................................................................... 3
1.4 Research Approach and Dissertation Structure ...................................................... 5
2. Literature Review.................................................................................................... 6
2.1 Historical Perspective and Basic Taxonomy ........................................................... 6
2.2 Economic Benefits of Transport Infrastructure Development ................................. 7
2.3 East Asian Transport Infrastructure Privatisation Trends ...................................... 8
2.4 Financial Valuation ................................................................................................. 9
2.5 Project Risk Analysis ............................................................................................. 14
2.6 The Kondratieff Wave ............................................................................................ 16
2.7 Kuznets Cycle, Kuznets Curve and S-Curves ........................................................ 18
2.8 Infrastructure Development, Cycles and Crises .................................................... 19
2.9 Transport Modelling .............................................................................................. 23
2.10 Traffic Risks and Forecasting Issues ..................................................................... 25
2.11 Construction, Operations and Maintenance.......................................................... 33
2.12 Other Considerations ............................................................................................ 35
2.13 Summary of Key Issues .......................................................................................... 37
3. Environmental Analysis ....................................................................................... 39
3.1 Introduction and PESTLE Analysis ....................................................................... 39
3.2 Political, Legal and Stakeholder Issues................................................................. 40
3.3 Economic Recovery ............................................................................................... 42
3.4 Vehicle Ownership ................................................................................................. 46
3.5 Traffic Performance of Existing Toll Roads .......................................................... 48
3.6 Opportunities and Threats ..................................................................................... 51
3.7 Postulated Position on K-Wave ............................................................................. 53
4. Questionnaire Survey ........................................................................................... 55
4.1 Purpose .................................................................................................................. 55
4.2 Design Concept and Sample Selection .................................................................. 56
4.3 Questionnaire Design and Survey Execution ........................................................ 57
4.4 The Survey Sample ................................................................................................. 58
4.5 Tollway Appraisal.................................................................................................. 62
4.6 Transport Modelling Issues ................................................................................... 65
4.7 Forecast Performance and Evaluation Criteria .................................................... 67
4.8 Countries’ Outlooks ............................................................................................... 70
4.9 Economic Outlook ................................................................................................. 73
4.10 Other Comments .................................................................................................... 75
4.11 Key Conclusions from the Questionnaire Survey .................................................. 75
5. Risk Simulation Modelling ................................................................................... 77
5.1 Introduction ........................................................................................................... 77
5.2 The Case Study and Its Parameterisation ............................................................. 78
5.3 Methodology .......................................................................................................... 82
5.4 Comparison of Cases under “Base Run” .............................................................. 84
5.5 Comparison of Simulation Results between Cases ................................................ 85
DissFinal viii December 2006


5.6 Analysis of Individual Risks ................................................................................... 88
5.7 Discussion of Results ............................................................................................. 91
6. Discussion and Conclusions.................................................................................. 92
6.1 Introduction ........................................................................................................... 92
6.2 Evaluation Criteria and Implications of the Time-Nature of Risk ........................ 93
6.3 Macro-Level Risks and Opportunities ................................................................... 94
6.4 Market Risks .......................................................................................................... 96
6.5 Forecasting Risks................................................................................................... 98
6.6 Is the Market Anticipating a Change in the Rules-of-the-Game? ....................... 100
6.7 What Lessons for Practitioners? ......................................................................... 101
6.8 Conclusions: Evaluation of Hypothesis ............................................................... 103
References: Literature ................................................................................................ 105
References: Internet Resources ................................................................................. 117
Appendices ................................................................................................................... 119

LIST OF TABLES
Table 2.1: Investment and Maintenance Needs in East Asia, 2006-2010 ......................... 8
Table 2.2: Bain and Polakovic Forecast Performance Statistics ..................................... 26
Table 2.3: Bain and Wilkins Ramp-Up Revenue-Adjustment Profiles .......................... 30
Table 2.4: Estimated Expressway Construction Costs .................................................... 34
Table 2.5: Operations and Maintenance Costs ................................................................ 34
Table 2.6: Summary of Key Risks and Issues ................................................................ 38
Table 3.1: Highlights of PESTLE Analysis .................................................................... 39
Table 3.2: Vehicle, Trip and Expressway Patronage Income Elasticities....................... 48
Table 4.1: Aggregated Respondent Experience Categories ............................................ 58
Table 4.2: Respondents’ Mean Years’ Experience in Various Fields ............................ 60
Table 4.3: Respondents with Experience in Study Area ................................................. 61
Table 4.4: Rankings of Macro-Level Risks by Respondent Category ............................ 63
Table 4.5: Rankings of Project-Level Risks by Respondent Category ........................... 64
Table 5.1: Basic Link Characteristics of Case Study Network ....................................... 79
Table 5.2: Assumed Trip Distribution (% by O-D Pair) ................................................. 79
Table 5.3: Comparison of “Base” Runs between Cases ................................................. 85
Table 5.4: Summary Results from Simulation Runs ....................................................... 86
Table 5.5: Rankings of Risk Categories’ Importance by Case ....................................... 89

LIST OF FIGURES
Figure 1.A: Map of East Asia ........................................................................................... 4
Figure 1.B: Research Approach ........................................................................................ 5
Figure 2.A: Standard & Poor’s Risk Pyramid ................................................................. 14
Figure 2.B: Transport Concession Risks ......................................................................... 15

DissFinal ix December 2006


Figure 2.C: Kuznets Curve and S-Curve......................................................................... 18
Figure 2.D: Indexed Thai Real GDP and M2, 1991-1999 .............................................. 19
Figure 2.E: Baht-US$ Exchange Rate 1994-2001 .......................................................... 20
Figure 2.F: Dollarised Thai GFCF 1994-2001 ................................................................ 21
Figure 2.G: Demand, Revenue and Price Elasticity of Demand ..................................... 27
Figure 3.A: Typical Concession Stakeholder Map ......................................................... 40
Figure 3.B: Thai GFCF 1993-2006 (Rolling Annual Average by Quarter) .................... 43
Figure 3.C: Thai GFCF 1993-2006 (Rolling Annual Average by Quarter) in US$ ....... 43
Figure 3.D: Thai GFCF, GDP and M2 in Baht, Indexed to 1995 ................................... 44
Figure 3.E: Thai GFCF, GDP and M2 in US$, Indexed to 1995 .................................... 44
Figure 3.F: Thai GFCF, GDP and M2 in US$, Indexed to 2000 .................................... 44
Figure 3.G: Currency Performance since 1994 ............................................................... 45
Figure 3.H: Currency Performance since 2001 ............................................................... 45
Figure 3.I: Relationship between Wealth and Roads Per Capita .................................... 47
Figure 3.J: Relationship between Wealth and Road Density .......................................... 47
Figure 3.K: Traffic Growth on Shanghai-Nanjing Expressway...................................... 50
Figure 3.L: Traffic Growth on Shanghai-Hangzhou-Ningbo Expressway ..................... 50
Figure 3.M: Interest Rates, Nominal Gold Price and Kondratieff Wave ........................ 54
Figure 4.A: Respondents by Experience Type ................................................................ 59
Figure 4.B: Respondents by Years of Experience .......................................................... 59
Figure 4.C: Respondents’ Global Experience ................................................................. 60
Figure 4.D: Respondents with Experience in East Asia ................................................. 61
Figure 4.E: Attitudes to Macro-Level Risks ................................................................... 63
Figure 4.F: Attitudes to Project-Level Risks .................................................................. 64
Figure 4.G: Data Availability and Reliability ................................................................. 65
Figure 4.H: Attitudes to Transport Model Types ............................................................ 66
Figure 4.I: Perceptions of Forecast Performance ............................................................ 68
Figure 4.J: Which Forecast Outputs are Considered? ..................................................... 69
Figure 4.K: How Often Are Which Criteria Considered?............................................... 69
Figure 4.L: Perceived Tollway Market Opportunities by Country ................................. 71
Figure 4.M: Impact of Experience on Country Perceptions ........................................... 71
Figure 4.N: Country Perceptions by Respondent Category ............................................ 72
Figure 4.O: Economic Expectations ............................................................................... 73
Figure 4.P: Economic Expectations by Respondent Group ............................................ 74
Figure 5.A: Case Study Notional Network ..................................................................... 79
Figure 5.B: Volume/Capacity-to-Speed Relationships ................................................... 83
Figure 5.C: Cumulative Probability Distribution of FIRR (excluding FIRR<0%) ......... 87
Figure 5.D: Cumulative Probability Distribution of Payback Period (years) ................. 87
Figure 5.E: Cumulative Probability Distribution of NPV at 16% ($m) .......................... 87

DissFinal x December 2006


GLOSSARY OF TERMS AND ABBREVIATIONS
ADB Asian Development Bank, Manila
ASEAN Association of South East Asian Nations
BOO Build-Own-Operate (concession form)
BOOT, BOT Build-Own &/or Operate-Transfer (concession form)
Billion One thousand million, being the international financial standard (as opposed
to the strict/ traditional British definition of a million million)
China For the purposes of this Dissertation, China is analogous to Mainland China,
being the People’s Republic of China, excluding the Special Administrative
Regions of Hong Kong and Macau and also excluding Taiwan.
CIA Central Intelligence Agency, United States of America
DBFO Design-Build-Finance-Operate (concession form)
EIRR Economic Internal Rate of Return comprising FIRR plus social impacts
Factory Gate Referring to prices of goods once manufactured but not transported, either to
port or end user.
FCO Foreign and Commonwealth Office, United Kingdom
FDI Foreign Direct Investment
FIRR Financial Internal Rate of Return
FOB Free On Board: being the price of cargo loaded onto a maritime vessel
GMS Greater Mekong Subregion, comprising Cambodia, Laos, Myanmar,
Thailand, Vietnam plus Guangxi and Yunnan Provinces of China
Guanxi meaning connections, a term covering business networks, political
connections and a broad sense of developing and maintaining goodwill; see
Appendix 6 for full definition
HHI Hopewell Highway Infrastructure Limited
IBRD International Bank for Reconstruction and Development, analogous with
WB
IPFA The International Project Finance Association
IRR Internal Rate of Return, taken to be analogous to FIRR
JBIC Japan Bank for International Cooperation and Development, Tokyo
JICA Japan International Cooperation Agency
K-Wave Kondratieff Wave or Cycle
KOICA Korea International Cooperation Agency
Kondratieff Spelling adopted for Kondratieff; alternative Latin spellings include
Kondratyev, Kondratiev (original Russian: Кондратьев)
NESDB National Economic and Social Development Board, Thailand
NPV Net Present Value
PBA Parsons Brinckerhoff (Asia) Ltd.
PPP Public Private Partnership (when discussing project financing models)
PPP Purchasing Power Parity (when discussing national income accounting
concepts, such as GDP and GDP per capita), this in contrast to figures
derived based on official exchange rates
ROT Rehabilitate-Own/Operate-Transfer (concession form)
SWHK Scott Wilson (Hong Kong) Ltd/ Scott Wilson Kirkpatrick (Hong Kong) Ltd
(including joint-consultant reports with Scott Wilson as one of the authors)
UNESCAP United Nations Economic and Social Commission for Asia and the Pacific,
Bangkok, Thailand
US$ United States Dollars
VOT Value of Time: equivalencing time and money in behavioural models.
WACC Weighted Average Cost of Capital
WB The World Bank, Washington, D.C.

DissFinal xi December 2006


1. Introduction

1.1 Terms of Reference/ Personal Development

For 14 years, I have worked in transport planning, economics and demand forecasting

across 20 countries/territories, mostly on transport infrastructure scheme appraisal, often

for privatisation, and usually in East Asia (covering rich, “tiger” and poor economies).

One reason for pursuing the MBA, the Business Finance Elective and this Dissertation

topic was to gain a more comprehensive understanding of projects’ financial risks.

Hopefully to make me a “better” demand forecaster and broader project appraiser.

During the course of my MBA I rekindled interest in aspects of economics, most

notably business cycles, leading me to the Kondratieff Wave. This postulates a cycle of

48-60 years duration; comprising inter alia phases of increasing interest rates and

commodity prices followed by decreases in same. Given recent increases in Federal

Reserve interest rates and commodity prices, Kondratieff theorists posit a

commencement of an “upswing” phase, qualitatively different from the “downswing” of

the 1980’s and 1990’s; potentially changing the relative importance of different aspects

of investment risk. Given most transport privatisation and associated literature and

experience are based on “downswing” conditions, reviewing these based on “upswing”

conditions could be timely.

Though focussed on profit maximisation (through risk management), better

understanding of changing risks should result in more efficient use of capital by private,

public and aid agency sectors alike.

DissFinal Page 1 December 2006


1.2 Applicability and Hypothesis

The Dissertation focuses on East Asia which is again emerging as a “powerhouse” of

economic growth, with commensurately strong demand for transport anticipated. The

World Bank (2003a) notes resurgent private sector involvement in infrastructure

provision since the 1980’s, with substantial tollway activity in East Asia (US$34 billion

during 1990-2001 into 149 projects). Although activity slowed following the Asian

Financial Crisis (AFC), by 2001 it returned to 1995 levels. Yepes (2004) expects

highways to be the second biggest infrastructure investment sector in East Asia during

2006-2010. In addition to providing profit opportunities, there is evidence that projects

could facilitate substantial economic growth in poorer economies, as well as “tiger”

economies (Corbett et al, 2006).

However, besides a potential legacy of over-investment prior to the AFC (Di Bona,

2002) suppressing the attractiveness of certain new projects, following 20 years of

declining interest rates and price inflation, it appears that they are now rising (Faber,

2002). Arguably this is connected with an upturn in the long-wave business cycle

(Kondratieff, 1926). Thus, the specific hypothesis is:

“There is a significant change in the nature and extent of project finance risks for

private stakeholders in East Asian toll roads during a period of increasing price

inflation and interest rates”



1.3 Geographic Scope

East Asia is a large, diverse region, including some of the World’s richest and poorest

societies, with differing political and legal systems and levels of economic openness.

This Dissertation is concerned with its developing economies, which are likely to

benefit as: manufacturing hubs for the world; markets in their own right; and/or, natural

resource providers. It is in such economies that transport infrastructure demand growth

may be most marked.

Whilst the literature review is deliberately broad, and the questionnaire survey relatively

so, the main focus is on inter-urban toll roads. Countries are included based on being:

 Sufficiently large (geographically) to accommodate inter-urban tolled highways;

 Developing economies; and,

 Countries where the author has at least some project experience.

The countries thus considered are: Cambodia, China1, Indonesia, Laos, Malaysia,

Myanmar, Philippines, Thailand and Vietnam; highlighted in Figure 1.A.

Appendix 1 gives key demographic and economic data on these countries and a few

others for benchmarking purposes. Appendix 2 gives headline transport statistics.

Whilst countries such as China are anticipated to continue requiring and attracting

investment in roads, increased scope for PPP is expected in other countries also.

1
Being Mainland China, i.e. excluding Hong Kong SAR, Macau SAR and Taiwan


Mongolia

N.Korea

S.Korea Japan
CHINA

Hong Kong
LAOS
MYANMAR PHILIPPINES

VIETNAM

THAILAND
Brunei
CAMBODIA
MALAYSIA

Singapore
INDONESIA
Timor-Leste

Source of base map: Google EarthTM 2
Figure 1.A: Map of East Asia

2
Study Area countries in red on yellow text. Other countries/ territories in black on grey text.


1.4 Research Approach and Dissertation Structure

The outline research approach is presented in Figure 1.B; also giving relevant Chapter

numbers.

1. Introduction and Hypothesis
Including definition of geographic scope

2. Literature Review 3. Environmental Analysis
Including a priori evaluation Including country economics and
and analysis thereof tollway market potential

4. Questionnaire Survey
Analysis of respondent
perceptions against findings of
Literature Review and
Environmental Analysis

5. Risk Simulation Modelling
Quantitative testing of impacts of
different economic assumptions and
evaluation of relative importance of
different risks, incorporating findings
of Chapters 2, 3 & 4

6. Discussion and Conclusions
Collating, comparing and
summarising findings from Chapters
2, 3, 4 & 5. Evaluation of initial
hypothesis and identifying areas for
possible future investigation.

Figure 1.B: Research Approach



2. Literature Review

2.1 Historical Perspective and Basic Taxonomy

Private transport infrastructure financing and operation dates back to at least the 19th

Century, including railways (e.g. UK and USA) and the Suez Canal. IPFA (2006) notes

following the First World War government resumed most infrastructure provision,

financing projects from public debt; subsequently developing countries followed this

practice, borrowing from development agencies (e.g. WB, ADB).

By the 1980’s, government debt constrained public financing of schemes, especially

given high interest rates; yet economic and demographic forces continued to demand

infrastructure. Thus was private involvement reborn.

There is much overlapping taxonomy regarding types of project privatisation. Guislain

and Kerf (1995) note a continuum of options for private sector involvement, from

supply and service contracts through leasing (wherein management of a built project is

let to the private sector in exchange for a revenue-share and/or up-front payment) to

Build-Own/Operate-Transfer (BOT, BOOT) and Build-Own-Operate (BOO); wherein,

the project is constructed then operated by the private concessionaire either in perpetuity

(BOO) or for a fixed period (BOT). Other forms include Design-Build-Finance-Operate

(DBFO) wherein the prospective concessionaire undertakes the design as well as build

of the project, often being wholly responsible for financing.



2.2 Economic Benefits of Transport Infrastructure Development

Whilst SACTRA (1994) questioned the benefits of additional trunk roads in developed

economies with built-out highway networks, in developing economies new highways

often facilitate economic development. Christensen and Mertner (2004) showed

Cambodia’s factory gate price advantage over China for garments negated by transport

costs: China FOB prices are lower than Cambodia’s. Di Bona (2005) noted

rehabilitation of Cambodia’s road networks transformed traffic levels and patterns;

subsequent quantification estimated nationwide road traffic levels increased 83.6%

above trend following the rehabilitation-to-date of roughly half of the trunk road

network3 (Corbett et al, 2006, p.A2-99). The benefits of transport infrastructure in

developing countries can be attested by increasing development aid for same (Luu,

2006).

In economic terms, rehabilitation greatly reduces generalised costs of travel (e.g. time,

fuel, vehicular wear-and-tear and hence fares/ tariffs). Buchanan (1999) recommends

governments only approve projects yielding a given socio-economic return, before

determining likely profitability.

Klein et al (1996) note privatisation appears to increase implementation costs, partially

due to private sector participation bringing true costs to light. It also increases funds

available for development.

3
83.6% estimated statistically, with traffic growth attributable directly to economic growth excluded.


2.3 East Asian Transport Infrastructure Privatisation Trends

Developing countries’ transport infrastructure privatisation began in earnest in the

1980’s, primarily with Malaysian, Mexican and Thai toll roads (WB, 2003a, p.126).

During 1990-2001, East Asia was the second largest market, attracting US$56 billion

private investment (41% of global total) into 229 projects (Ibid., p.135), particularly toll

roads: US$34 billion into 149 projects (Ibid., pp25-26 & p.143). By 2001, China had

attracted more private investment than any other country (US$23.6 billion), and

Malaysia the most per capita (US$582) (Ibid., p.136). Whilst activity slowed after the

1997 Asian Financial Crisis (AFC), by 2001 it returned to 1995 levels (Ibid., p.2). Table

2.1 illustrates substantial anticipated future expenditure (from Yepes, 2004); highways

are anticipated to require the second most investment of any infrastructure category.

Table 2.1: Investment and Maintenance Needs in East Asia, 2006-2010

(US$ million) (percent of GDP)
Investment Maintenance Total Investment Maintenance Total
Electricity 63,446 25,744 89,190 2.4 1.0 3.4
Telecoms 13,800 10,371 24,171 0.5 0.4 0.9
Highways 23,175 10,926 34,102 0.9 0.4 1.3
Railways 1,170 1,598 2,768 0.0 0.1 0.1
Water 2,571 5,228 7,799 0.1 0.2 0.3
Sanitation 2,887 4,131 7,017 0.1 0.2 0.3
Total 107,049 57,998 165,047 4.0 2.3 6.3

Buchanan (1999) notes the Malaysian boom in BOT highways followed the perceived

success of the North-South Highway (PLUS) concession in 1988, through which private

finance overcame public sector constraints and took-on risk, bringing private sector

skills and incentives to infrastructure operation. However, he believes PLUS appeared

profitable only because Government handed over 225km of existing expressway with

tolling rights.



In China several Provincial Governments established corporations for expressway

development. Soon after completing a flagship expressway, the company would be

listed with revenues raised used to acquire or develop additional highways4. This

relatively rapid listing contrasts with experience elsewhere (see Willumsen and Russell,

1998). Meanwhile, most foreign-invested BOT or leasing projects were Joint Ventures

(JV) with government retaining equity in the operating company.

Elsewhere in Asia, BOT concessions were the norm, though often undertaken by listed

firms. Operators occasionally issue bonds, although this practice is more widespread in

the Americas.

2.4 Financial Valuation

2.4.1 NPV and IRR

The decision to pursue a project and on what terms are primarily questions of project

valuation and risk. Higson (1995, pp.60-61) notes project value may be defined via Net

Present Value (NPV) or Internal Rate of Return (IRR). NPV values future cashflows as:

n
Ct
NPV  
1  r t
(1)
t 0

Where: Ct is net cashflow in period t

r is the discount rate (equivalent to opportunity cost of capital)

n is the number of periods covering the concession period

IRR expresses scheme value in terms of a percentage return on capital invested, being

the discount rate at which NPV is exactly nought:

4
See Appendix 3 for examples.


n
Ct
NPV   0
t 0 1  R t (2)

Ct can include social benefits of the scheme (see Section 2.2), as well as social costs

(e.g. displacement, environmental degradation etc; not covered in this Dissertation)

when used for social analysis.

The Fisher-Hirshleifer theorem (ibid, pp.66-67) states firms should undertake projects if

return is greater than investors’ required return. Highways require substantial up-front

investment and traffic flows often take a few years to build-up to “break even” levels;

attractiveness is greatly affected by timing of revenue receipts and the discount rate, as

well as by initial investment size.

Investors treat own target FIRR as strictly confidential; so no directly citeable values are

available. However, from the Author’s experience corroborated by off-the-record

conversations with fellow practitioners, a target FIRR of 16% p.a. is the usual threshold

required. This includes a modest risk premium (see 2.4.2); for particularly high risk

projects, or when capital is more expensive, FIRR would increase accordingly.

2.4.2 CAPM and WACC

The above assumes certainty regarding all project aspects, including: demand, price

inflation for inputs, selling price, construction cost and time, operating period, implicit

assumption of no sovereignty risks etc; yet uncertainty bedevils these parameters. The

Capital Asset Pricing Model (CAPM; ibid., p.123) suggests the return on a risky project

rj is:

rj  ri   j (rm  ri ) (3)

where: ri is the return on riskless borrowing/ lending



rm is the return on the money market as a whole

The risk premium for j is a proportion βj of overall market risk-premium, as follows:

 jm
j  (4)
m
2

Required return can also be calculated as Weighted Average Cost of Capital (WACC;

ibid, p.279):

E MV  K e   DMV  K d 
WACC 
E MV  DMV  (5)

Where: EMV is total market value of equity employed

DMV is total market value of debt employed

Ke is cost of equity, given by (6)

Kd is cost of debt, given by (7)

 Dividend 
Ke     ExpectedDi videndGrowth (6)
 Share Pr ice 

 
 Debenture Pr ice (%ofFaceValu e)   1  TaxRate 
InterestRa te
Kd    (7)
 

From (3) and (7) the Fisher-Hirshleifer theorem can be restated as pursue projects if:

ri   j (rm  ri ) 
EMV  K e   DMV  K d 
EMV  DMV  (8)

2.4.3 Treatment of Price Inflation

Often (especially in transport scheme appraisal) a constant inflation rate is assumed with

calculations based in real prices (akin to zero price inflation throughout). Such price

neutrality simplifies calculations; however, it does preclude analysis of price-risks

associated with individual project inputs and outputs.


2.4.4 Problems with CAPM and WACC

βj might theoretically be known for existing highways, but is unknown for new projects.

There may be insufficient local data to determine  m . β is intended for fully diversified
2

investors, rather than appraising a scheme in isolation. Higson (ibid., p.136) notes

CAPM assumes:

(i) perfect markets, without taxes and transaction costs, full, freely available

information and no-one with price-making power;

(ii) investors are rational, risk-averse, wealth-maximising, with homogenous

expectations of the future;

(iii) assets are marketable and infinitely divisible, with normally distributed

returns; and,

(iv) there is a risk-free asset for comparison.

Yet transaction costs can be substantial (professional fees, cross-border know-how, etc);

information is imperfect and expectations are heterogeneous. Given skill-sets required,

infrastructure investors are unlikely to be highly diversified. Highway projects’ size

makes them relatively illiquid. There may be no risk-free asset: money is only risk-free

if possible depreciation/ price inflation is ignored.

Lumby (1983) notes unless a project is financed with the same capital structure as the

firm itself (unlikely), WACC changes once the project is undertaken. Furthermore,

WACC assumes constant cashflows and that project systematic risk to equal that of the

company’s existing projects; both highly unlikely.



Ormerod (2005, p.173) notes whilst CAPM requires a normal probability distribution in

derivative markets, they exhibit power-law behaviour; this discrepancy caused the 1998

collapse of Long Term Capital Management. Whilst CAPM supports currency

diversification (e.g. in borrowing), Beaverstock and Doel (2001) note such borrowing

collapsed Steady Safe (an Indonesian taxi and bus firm) and in turn Peregrine

Investment Bank.

2.4.5 Financial Ratios

A number of financial ratios may be used to evaluate likely project performance and

risk. Given the capital-intensity of highway construction, coupled with typically long

lead-times for demand build-up (see 2.10.4), financial ratios may not always be as

relevant to ex ante project valuation.

Return on Capital Employed5 is likely to be poor for early years of a concession (unless

the project is highly geared). Likewise, Gross Profit Margin, Profit On Sales, Expenses

as Percent of Turnover, Sales to Capital Employed, Sales to Fixed Assets and Asset

Turnover all typically take many years to build-up to levels normally deemed acceptable

in many other businesses5.

Some of the above ratios might be improved by heavy borrowing, but such borrowing

and resultant debt-servicing increases the importance of Working Capital Requirements,

the Current Ratio and the Debt Service Coverage Ratio5. Standard & Poor’s relies on

Interest Cover (debt-service coverage) as the primary quantitative measure of a project’s

financial strength (Rigby and Penrose, 2001, p.28).

5
See Appendix 4 for definitions of these financial ratios.


2.5 Project Risk Analysis

Rigby and Penrose (2001) identify a pyramidal five-level framework for credit rating,

which can be taken as a proxy for overall project investor risk, shown in Figure 2.A.

Credit
Credit
Enhancement
Enhancement

Force Majeure Risk

Institutional Risk

Sovereign Risk

Project-Level Risks

Figure 2.A: Standard & Poor’s Risk Pyramid

Project-level risks comprise six broad elements, namely:

 Contractual foundations

 Technology, construction and operations: both pre-construction (e.g. construction

delay/ quality issues) and post-construction (e.g. Operations and Maintenance)

 Competitive position of project within its market: including industry fundamentals,

project’s competitive advantage/ likely market share, threats of new entrants, etc

 Legal structure, including choice of legal jurisdiction



 Counterparty risks: e.g. extent to which JV partners can contribute equity if/when

debt funding exhausted, reliability of suppliers, political risk guarantees, etc

 Cashflow and financial risks: in addition to expected cashflow, ability to cope with

interest rate, inflation, foreign exchange, liquidity and funding risks

George et al (2004) note the uncertainty inherent in start-up tollways requires flexible

financing approaches. Willumsen and Russell (1998) illustrate project-level risks as

shown in Figure 2.B. Predominating traffic/ revenue risks are discussed in Section 2.10.

Construction
Delay

Change Orders
Risk (nominal)

Construction
Costs

Ramp Up

Traffic &
Revenue

O&M
er
-2

-1

10
0

1

2

3

4

5

ov
d
an
H

Year

Figure 2.B: Transport Concession Risks

Sovereign and institutional risks are concerned primarily with the project’s country:

ratings usually constrained by government’s debt servicing/ foreign currency record,

reflecting risks of currency conversion and overseas transfer. Institutional factors



include business and legal institutions, which are often weak/ nascent in developing

countries, with concepts of property rights and commercial law not fully developed,

potentially leaving creditors/ investors exposed. La Porta et al (1997) found investor

rights in developing countries though limited, are generally better under common law

than civil law (especially French civil law, which often has weak enforcement).

Force Majeure risks includes “Acts of God” (floods, earthquakes, etc) as well as civil

disturbances, strikes, changes of law. Rigby and Penrose (2001) note toll roads are

typically less affected/ can return to normal service more quickly.

Credit Enhancement refers to insuring/ re-insuring specific risks. However, litigation

intrinsic in such claims can delay payment by years, so mitigation may be limited.

2.6 The Kondratieff Wave

Orthodox economics assumes given policies produce similar results at all times;

Ormerod (1999, pp.96-102) notes experience contradicts this, due to periodic exogenous

shocks. Others postulate cycles responding to exogenous shocks. But to some cycle

adherents, such “exogenous” shocks are mostly endogenous. Schumpeter (1939)

consolidated others’ preceding work, specifying three inter-related cycles:

 Kitchin (1923): based on fluctuations in business inventories (39+/– months)

 Juglar (1863): based on business investment in plant and equipment (7-11 years)

 Kondratieff (1926): based on development of new technologies/ sectors and impact

of their adoption on socio-economic conditions (48-60 years; a.k.a. “K-Wave”)

The K-Wave postulates periodic “Creative Destruction” (Schumpeter, 1950, Chap.VII)

intrinsic to industrial-capitalism. Not all cycle proponents accept the K-Wave:



Kindleberger (1996, p.13) calls it “possibly… dubious and elusive.” There is also

debate on periodicity. Whilst Schumpeter believed one K-Wave contained three Juglar

Cycles, each comprising in turn three Kitchin Cycles, Faber (2002, p.110) notes

Kondratieff never postulated precise periodicity.

Kondratieff’s empirical work identified a number of patterns within each cycle. Further

analysis by Schumpeter (1939), summarised by Faber (2002, pp.116-138) notes:

 Before and during the beginning of Upswings there are profound changes in

industrial techniques (based on new technologies) and/or involvement of new

countries in the global economy and/or development of new transport technologies.

 Social upheavals and international conflict are more likely during Upswings.

 Agricultural prices decrease during downswings; industrial prices hold steady or fall

slightly. During upswings, commodity price increases can create broader price

inflation. Interest rates also follow this cycle. As appears to have been the case in

recent years (see Section 3.7).

 Upswings are characterised by brevity of depressions and intensity of booms; the

opposite being true during downswings.

There are separate transitional phases at peaks and troughs, usually brief in relation to

Upswing and Downswing phases and largely ignored in the context of this Dissertation.

Appendix 5 presents K-Waves since 1787. Maddison (1995) estimated real global GDP

per capita rose 2.90% p.a. from the 1950s-1970s (K-Wave upswing); but declined to

1.11% p.a. until the 1990’s (K-Wave downswing).



2.7 Kuznets Cycle, Kuznets Curve and S-Curves

Kuznets (1930) identified a 15-25 year building construction cycle, concurring with

Schumpeter that innovation drives growth endogenously to the economic cycle. He also

postulated the Kuznets Curve (1955), plotting economic development against income

inequality: inequality increasing in the early stages of economic development,

plateauing then diminishing. Inequality can be measured using the Gini Coefficient

(Gini, 1912): 0 denoting perfect equality and 100 perfect inequality (one person has all

wealth).

This implies few might afford cars or tolls in the early phases of growth, but as

economies develop, tolls become substantially more affordable. Coupled with demand

saturation, this suggests an “S-Curve”, akin to the innovation/ adoption curve (Rogers,

1962). Figure 2.C shows this inter-relationship between a Kuznets Curve and S-Curve,

based on normal distribution.

Norm al Density/ Kuznets Curve
Cum ulative Norm al/ S-Curve

Figure 2.C: Kuznets Curve and S-Curve



2.8 Infrastructure Development, Cycles and Crises

Infrastructure may facilitate Upswings, but its short-term impact may trigger

Downswings, fostering “Creative Destruction” (Schumpeter, 1950): purging old

methods/ technologies for improved methods/ infrastructure to drive Upswings.

Lawrence (1999) argues major skyscraper completions are cyclical, preceding

recessions. But do build-out peaks precipitate recessions, or are they “peaks” due to

subsequent demand failure, uncorrelated with preceding build-out (as espoused by

Krugman, 2000)?

Di Bona (2002) analyses Thailand6, where the Baht’s flotation triggered the AFC.

Figure 2.D7 shows impressive real GDP growth until 1996, when close correlation with

M2 broke. Continued M2 growth refutes Krugman’s attribution of the AFC to demand

failure, which ignored structural causes.

200 300
Real GDP (1991=100)

180 260
M2 (1991=100)

160 220

140 180

120 140

100 100
1991 1992 1993 1994 1995 1996 1997 1998 1999

Real GDP M2

Figure 2.D: Indexed Thai Real GDP and M2, 1991-1999

6
Much of these Thai analyses originally presented in Di Bona, R.F. (2002) Surviving Bahtulism
7
Raw data from APEC (www.apec.org); analysis my own.


Before the AFC, Thailand enjoyed a virtuous economic development cycle: increased

wealth boosted investment returns, attracting further investment. Keynesian multiplier-

accelerator effects boosted growth, encouraging further development. Adaptive

expectations of investment returns fuelled excessive capital works and other

investments. Bangkok planned several new residential and business hubs, which could

not all be viable simultaneously: eventually supply outpaced demand.

The Baht’s July 1997 flotation coincided with doubts regarding the sustainability of

Thailand’s growth. Its depreciation (Figure 2.E8) ballooned offshore-financed corporate

debt. Ensuing capital flight intensified the crisis. Long infrastructure lead-times meant

there was still supply-in-waiting; many projects were stalled or abandoned. Figure 2.F9

shows GFCF collapsing with no noticeable rebound by 2001.

0.05
0.045
0.04
USD per THB

0.035
0.03
0.025
0.02
0.015
0.01
0.005
0
4 4 5 5 6 6 7 7 8 8 9 9 0 0 1 1
99 99 99 99 99 99 99 99 99 99 99 99 00 00 00 00
n -1 l-1 n-1 l-1 n-1 l-1 n-1 l-1 n-1 l-1 n-1 l-1 n-2 l-2 n-2 l-2
Ja Ju Ja J u Ja J u Ja J u Ja J u Ja Ju Ja Ju Ja J u

Figure 2.E: Baht-US$ Exchange Rate 1994-2001

8
Source data: www.fx.sauder.ubc.ca
9
Source data: www.nesdb.go.th and www.fx.sauder.ubc.ca


Hayek (1933) argues artificially low interest rates breed over-investment, precipitating

crises with debt- and investment-overhangs delaying recovery. Faber (2002, pp.192-

193) argues global liquidity injections following the 1995 Mexican crisis fuelled further

Asian speculative growth, delaying but ultimately amplifying and prolonging the AFC.

14,000

12,000

10,000
million USD (1988 prices)

8,000

6,000

4,000

2,000

0
1994 1995 1996 1997 1998 1999 2000 2001 2002

Gross Fixed Capital Formation Private Construction Government Construction
Land Development Construction And Land Development

Figure 2.F: Dollarised Thai GFCF 1994-2001

Faber (2002, p.69) notes cycles are “particularly violent in the case of emerging

economies, emerging industries and emerging companies, which grow and evolve

rapidly and are, therefore, capital-hungry.” Transport infrastructure construction is

especially capital-intensive.



Although infrastructure and utilities are often seen as defensive investments, Forsgren et

al (1999) argue toll road performance is cyclical, noting with reference to China (not

generally regarded as badly hit):

 Challenging business climate with (official) economic growth down to 7% p.a.

 Delayed construction of connector roads and reduced commerce reducing traffic

growth (and occasionally traffic declines)

 Debt service coverage (operating revenues) short of base projections

 Growing doubts as to willingness and ability of local partners to pay minimum

income guarantees to toll companies (note: these were abolished by decree in 2002)

 Increased refinancing and foreign exchange risks

 Periodic toll increases required to meet projections, yet approval process is opaque

 Problems with toll collection/ leakage

 Credit ratings deteriorating due to reduced credit quality of counterparties

In Indonesia, the rapid devaluation of the Rupiah in 1997, compounded by rapidly

increasing fuel prices, massive economic and political uncertainty and civil unrest,

substantially reduced Jakarta Intra Urban Tollroad traffic volumes (Ibid.).

Such patterns are not new. Despite railways driving America’s economic development

in the 19th Century, Faber (2002, pp.55-63) notes they exhibited cyclical booms and

crises. Moreover, historically overseas investors are often latecomers, repeatedly buying

peaks to sell-out in the immediate aftermath of crisis.



2.9 Transport Modelling

Corbett and Di Bona (2006) note transport models provide inter alia: assessment of

demand-side project risks; evaluation of alternative projects against one another; and,

forecasts of economic and financial returns, for use in project valuation. Traditional

“Four Stage” models (elucidated in Ortúzar and Willumsen, 1994) are outlined in

Appendix 7; but such models are data hungry so simplifications are common. Their

applicability to tollways has been questioned (Willumsen and Russell, 1998).

Usually the modelled area is divided into spatial zones. Traditionally, traffic to/ from

each zone is estimated based on land-use and corresponding trip generation rates.

However, given sparseness of robust land use data in developing countries, econometric

models of traffic levels are often used. Whilst Khan and Willumsen (1986) fitted S-

curve models to vehicle ownership and usage, often historical traffic counts are

regressed on corresponding income data to estimate income elasticities of traffic

demand, defined as:

  t1  t 0  
   t t   2  

T   0 1  
y 
T

Y   y1  y0  (9)
 

   y0  y1   2  
 

Where: to,t1 are traffic levels in periods 0 and 1

y1,y0 are income (GDP) levels in periods 0 and 1

As elasticities might not hold over time forecast values are adjusted, based either on S-

curves or a conservative assumption of gradually declining elasticities, taking implicit

account of longer-term demand saturation or improved logistic efficiency (decreased

lorry empty-running). Though these ignore vehicle ownership/ usage costs, Pindyck and

Rubinfeld (1981, pp.396-398) note Hymans’s (1970) model of USA vehicle ownership


shows such factors have short-term impacts, income-ownership relationships

predominating thereafter.

In developing countries tollway appraisals, driver interview surveys scaled using traffic

counts are often used to obtain trip patterns. Effects of other modes (e.g. rail) are

commonly omitted; impacts might be insignificant, or data unavailable.

In order to determine vehicle routeing, a variety of approaches are possible, including:

Network Assignment Modelling: Where the network is complex (roads parallel and

perpendicular to the toll-road significantly affecting patronage), network assignment

models should be used. In addition to interzonal trip matrices, the road network is coded

(e.g. length, capacity, tolls and relationships between speed and congestion). An

iterative assignment process is used, with link speeds recalculated to reflect congestion.

Typically forecasts are prepared for a base year, opening year and at 5 or 10-year

intervals thereafter, with intermediate years interpolated. Such models are calibrated by

adjusting network coding and often using maximum entropy matrix estimation (see Van

Zuylen and Willumsen, 1980) to better match traffic counts.

Logit-Based Corridor Modelling: A spreadsheet-based approach to model a corridor,

typically with one competing route (e.g. with no/ lower tolls and lower speeds). Traffic

is allocated between routes based on a logit function; (10) shows an absolute logit curve

for forecasting a new road’s traffic. For existing toll-roads incremental logit models

may be preferred, shown in (11). Commonly κ and λ would be estimated based on

previous studies (ideally existing toll-roads). Richardson (2004) notes a general bias

against using toll roads (κ<0). Forecasts may be prepared for selected years

(intermediate years interpolated) or for all years. Whilst congestion levels do not

feedback, increasing incomes make tolls more affordable.


1
PijXt 
, 
     GC ij ,t  GC ijX,t
L
 (10)
1 e
Where: PijXt is the share of trips i→j in period t using the expressway, PijXt  PijLt  1
, , ,

r
GCij ,t is the generalised cost for trip ij by route r (X=expressway, L=local

road), comprising equivalenced time and monetary elements in year t

κ, λ are calibrated parameters

  1  
   
 PijXt    GCij ,t GCijX,t   
L

IPij ,t  Obij ,t 0   X   Obij ,t 0  
X X , X 1  e  
 Pij ,t 0   
(11)
  
1

 
  1  e  GCij ,t 0 GCij ,t 0   
L X

 

Where: ObijX,t 0 is the base year observed expressway market share for trips ij

PijXt
, is forecast expressway share in year t (absolute logit); t=0 is base year

2.10 Traffic Risks and Forecasting Issues

Bain and Wilkins (2002) analyse toll-traffic uncertainty and traffic forecast error,

showing strong inter-correlation. Average initial year traffic was 70% of forecast

overall, 82% in lender-commissioned projections and 66% when commissioned by

others, suggesting commissioning party influence on forecasts: debt-financiers

relatively more concerned with down-side risk than equity-holders. Their Traffic Risk

Index (shown in Appendix 8) compares low and high risk factors for toll roads and

traffic forecasts in general.

Whilst initial year errors might be due to ramp-up (see 2.10.4), which Streeter and

McManus (1999) reckon can last 3-5 years, Bain and Polakovic (2005) note optimism

bias is “constant through Years 2 to 5” as shown in Table 2.2, signalling other errors



(discussed below). They also note drastic differences in forecasts by different parties for

the same projects, based in part on very different assumptions.

Table 2.2: Bain and Polakovic Forecast Performance Statistics

Operating Year Mean Actual/Forecast Traffic Standard Deviation
1 0.77 0.26
2 0.78 0.23
3 0.79 0.22
4 0.80 0.24
5 0.79 0.25

2.10.1 Toll Sensitivity and the Value of Time

Excepting “shadow tolling” (operator reimbursed based on patronage instead of user-

tolling), willingness-to-pay tolls is critical. Typically choice is between a slow, cheap

road and a fast toll-road; time and money equivalenced using the behavioural Value of

Time (VOT) to give “generalised cost.” Whilst higher tolls are usually preferred (see

2.10.4) sometimes they are too high (Wong and Moy, 2004). The price elasticity of

tollway demand is:

  q1  q0  
 
Q   q0 q1   2  
D 
p
  
P  p1  p0 
(14)
 

   p0  p1   2  
 

Where: ΔQ is change in traffic

ΔP is change in price (toll)

q1,q0 are traffic after and before toll change respectively

p1,p0 are new and old tolls respectively



Figure 2.G shows the relationship between demand, revenue and η. When tolls are

beneath the revenue maximising level (i.e. p<Prm) 0   D  1 , toll increases boost
p

revenue; when p>Prm  D  1 (toll increases decrease revenue).  D  1 when p=Prm.
p p

Willumsen and Russell (1998) note in developing countries Stated Preference surveys to

estimate  D and VOT are scarce and of uncertain quality. Reference is often made to
p

previous studies, factored for income levels. But the income elasticity of VOT, VOT is
y

complicated: as income increases, VOT rises (“income effect”), as does expenditure on

other products/ services (“substitution effect”) and possibly savings too (“savings

effect”), implying VOT  1 . In developed economies, Wardman (1998) suggests
y

VOT  0.49 ; Gunn and Sheldon (2001) advocate 0.35  VOT  0.7 . Cross-sectional
y y

analysis between developing countries suggests VOT  1 yet time-series analysis within
y

a country VOT  1 to growth VOT thereafter10.
y

Revenue
Maximisation -η
Demand

Total
Revenue

η= −1

Prm Price→

Figure 2.G: Demand, Revenue and Price Elasticity of Demand

10
Confidential source used in absence of public source.


Goods vehicles are of particular concern. Bain and Wilkins (2002) note in developing

countries long-distance tolls often exceed drivers’ wages, giving incentive to use

untolled routes (pocketing bosses’ toll money). Some studies (e.g. ADB, 2003) have

failed to establish any VOT for goods vehicles.

2.10.2 Competing Routes and Link Roads

Contractual guarantees theoretically limit competing routes’ development, presupposing

the contracting branch of government is willing and able to enforce such guarantees

across multiple government layers.

Jiangsu Expressway circumvented this risk by acquiring rights to highways parallel to

their flagship Shanghai-Nanjing Expressway and so manage (and toll) traffic on both

routes. However, when GZI Transport listed in 1997, it was assumed that the ferry

parallel to the (then) soon-to-open Humen Bridge would cease operation. But being

operated by a different local government, operation continued with fares undercutting

bridge tolls, attracting substantial goods vehicle volumes from the Humen Bridge.

Even when concessionaires gets first refusal at planned parallel routes, overinvestment

may result in excess infrastructure relative to traffic levels. Buchanan (1999) notes in

Malaysia those identifying schemes can often proceed (subject to financing) without

due diligence of impacts on existing BOT’s.

Though more important for urban projects, provision of adequate link roads is also

important. Congested approaches/ exits can result in “hurry up and wait” (Bain and

Wilkins, 2002), reducing tollways’ attractiveness.



2.10.3 Toll Increases and Revenue Guarantees

Contracts typically allow periodic price-indexed toll increases, or at a percentage of

price inflation. However, Forsgren et al (1999) note toll increase approval processes are

often opaque and beset with delay. Bain and Wilkins (2002) note tariff escalation is

often politicised, especially where there is little previous “tolling culture.” Sometimes

social unrest follows tolls’ imposition (Orosz, 1998) or toll increases, especially during

economic downturns (Dizon, 2002).

Some contracts give revenue guarantees to operators, underwritten by government.

However, China’s 2002 State Council directive scrapped such revenue guarantees

overriding contract provisions, leading to New World Development divesting from 13

toll roads and bridges (Chan, 2003).

Whilst non-toll revenues may be generated (e.g. service stations, advertising), Streeter

et al (2004) note their contribution is usually dwarfed by toll revenues.

2.10.4 Ramp-Up

Bain and Wilkins (2002) define ramp-up as information lag for users unfamiliar with a

new highway and general reluctance to pay tolls (see Richardson, 2004 for experimental

evidence). Streeter and McManus (1999) reckon on 3-5 years’ ramp-up and note this is

often underestimated in traffic forecasts.

Bain and Wilkins (2002) note ramp-up experience tends to cluster to extremes: either of

limited duration (even exceeding forecast traffic levels) or lagging for a long duration,

maybe never “catching up”, particularly for projects with a high Traffic Risk Index (see

Appendix 8). They derived revenue-adjustment factors as per Table 2.3 for use in

financial stress-tests.



Table 2.3: Bain and Wilkins Ramp-Up Revenue-Adjustment Profiles

Forecasts
Lenders Others
commissioned by
Traffic Risk Low Average High Low Average High
Year 1 revenue
-10% -20% -30% -20% -35% -55%
adjustment
Ramp-up duration
2 5 8 2 5 8
(years)
Eventual catch-up 100% 95% 90% 100% 90% 80%

2.10.5 Operating Costs

In addition to tolls, many models also apply distance-based monetary Vehicle Operating

Costs (VOC) reflecting fuel, maintenance, depreciation, etc. Whilst economic values for

these parameters are derivable, accurate behavioural values are often elusive. In practice

they may be used to reflect certain advantages of higher quality roads, whereon wear-

and-tear may be less and where smoother flow may yield fuel savings. However, these

are typically applied as fixed values with respect to distance and road-type, rather than

feeding-back modelled forecast speeds. Where there are larger VOC savings from an

expressway ceteris paribus there is more scope for higher tolls. However, there is an

issue as to who pays these costs (driver or employer).

2.10.6 Toll Leakage

Some vehicles use a facility without paying, either legitimately (e.g. certain government

or military vehicles) or illegitimately. There may be theft by toll-collectors and fraud by

administrators. Forsgren et al (1999) note toll leakage can be as high as 20% of

revenues. Sometimes computerised toll collection and auditing can restrain losses, but

on lower volume routes the cost of such measures might outweigh savings.



2.10.7 Induced Traffic

When a new highway significantly reduces transport costs or relieves congestion, it may

result in additional (induced) traffic. Corbett et al (2006, p.A2-99) report substantial,

rapid induction on Cambodia’s roads following rehabilitation. On green-field sites, it

may also over time enable expanded development, generating further traffic demand.

However, Willumsen and Russell (1998) note the difficulty of reliably forecasting such

effects; Bain and Polakovic (2005) report the prevalence of significant errors in induced

traffic forecasts.

2.10.8 Annualisation

Bain and Wilkins’ (2002) Traffic Risk Index shows projects with seasonal flow patterns

tend to be riskier. For inter-urban highways a “typical” day is usually modelled, with

results factored-up to annual forecasts. Thus seasonal changes might not be captured:

forecasts represent an expansion of one part of the annual pattern. Even when Annual

Average Daily Total (AADT) traffic is modelled, larger seasonal variations equate to

larger total variance between modelled day and actual day across the year.

For those projects where modelled hours are considered, mathematically the problem

increases, given further factoring from a “typical” hour (or perhaps AM peak and PM

peak) to a “typical” day. Conversely, when modelling a day, future congestion in peak

periods and its impact on effective daily capacities may be under-estimated.



2.10.9 Economic Effects

Economic risks feed through many elements of traffic forecasts:

 Overall travel demand (e.g. car ownership and usage, freight volumes, extent of

traffic induction)

 Willingness-to-pay tolls and try tollways (affordability; ramp-up extent and

duration)

 Toll leakage (incentive for malfeasance)

 Over-investment increasing likelihood of competing routes being built/ upgraded

Economic cycles affect most aspects of the economy and decision-making, including

evaluation assumptions adopted. Transport consultants define economic growth

scenarios either under guidance or instruction of commissioning parties. When

expectations are high more projects are evaluated, so proportionally more projects are

likely to founder on downturn (and be blamed on transport forecasts). This may create

cynicism regarding tollway investments extending into the early economic recovery,

resulting in under-investment in some areas, thence over-investment as returns on

operating (and newly opened) highways exceed expectations, thus creating a new

“error of optimism” (Pigou, 1920).

Luu (2006) and Gomez and Jomo (1999) cite governments in Vietnam and Malaysia

potentially over-expanding transport infrastructure development.



2.11 Construction, Operations and Maintenance

Construction cost overruns and delay (deferred/ lost revenue) may imperil initial debt

repayments. Rigby (1999) notes using engineering, procurement and construction (EPC)

contractors’ reputations to proxy technical risk is both commonplace and erroneous:

construction risks are often inadequately assessed. Based on UK experience, Flyvbjerg

and COWI (2004) recommend highway construction cost estimates be uplifted 15% if a

50% chance of overrun/ delay is acceptable, or by 32% if 20% chance acceptable.

Ruster (1996) notes construction cost overruns, delays and defects can be largely

mitigated by liquidated damages, performance bonds, warranties, contingency funds

and insurance. As revenue losses are rarely disputed during delay/ overrun arbitrations,

the focus of this Dissertation remains on demand-side risks. However, when the

contractor is the concessionaire, such risks should be analysed. Similarly, operations

and maintenance (O&M) risks should also be considered.

Table 2.4 shows estimated costs for new expressways in China and Vietnam. Whilst

costs are dependent on terrain, design standards and local labour and material costs,

there is significant difference between HHI costs and others (ADB potential projects),

unlikely wholly attributable to differences in local prices, or the difference between

Dual-2 and Dual-3 standard. A distance-weighted average of US$4.633m per km of

Dual-2 was derived, to be used in Chapter 5’s simulation model.

There is a trade-off between construction and subsequent operations and maintenance

costs. The latter also affected by periodic major maintenance (e.g. immediately before

concession handback). Literature review found little agreement as to how to gauge such

costs, and whether they should be related to construction or traffic flow/ revenue. Table

2.5 shows some public domain values; some confidential sources suggested using 6% of


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