Project Analysis in Developing Countries: Cost Benefit Analysis for Development 3031400135, 9783031400131

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Steve Curry John Weiss

Project Analysis in Developing Countries Cost Benefit Analysis for Development Third Edition

Project Analysis in Developing Countries

Stephen Richard Curry • John Weiss

Project Analysis in Developing Countries Cost Benefit Analysis for Development

3rd ed. 2023

Steve Curry International Development Economist and Consultant Chiang Mai, Thailand

John Weiss Emeritus Professor of Development Economics and International Consultant University of Bradford Bradford, UK

ISBN 978-3-031-40013-1    ISBN 978-3-031-40014-8 (eBook) https://doi.org/10.1007/978-3-031-40014-8 © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2000, 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

For Gisella (SC) For Francis (JW)

Preface to Third Edition

The first edition of this book, published in 1993, grew from material delivered on a Master’s level course at Bradford University, UK, where both authors then worked. Subsequently Steve Curry moved to the Asian Development Bank (ADB), where he was closely involved in preparing the ADB 1997 Guidelines on the Economic Analysis of Projects, whilst John Weiss worked as a consultant on training programmes to explain these Guidelines. Material reflecting this experience was incorporated in the substantial revision in the second edition published in 2000. Since then both authors have been involved in operational project work and professional training programmes on project analysis for developing country officials and staff of development agencies, as well as other related work on the evaluation of projects and programmes. Steve Curry retired from ADB, but has continued to consult on project and policy evaluations, as well as reviews of project analysis work. John Weiss retired from Bradford University and is now Emeritus Professor of Development Economics there. He was the lead consultant for the 2017 revision of the ADB Guidelines on the Economic Analysis of Projects. He continues to advise on project analysis methodology and applied project work and has conducted a number of reviews of past project analyses. The intention of the original book was to produce an accessible, but nonetheless academically sound, text that would be located somewhere between the technically demanding academic work on the subject and applied manuals from international agencies and governments. The target audience remains the same as for the earlier editions—a combination of postgraduate students, chiefly on International Development courses, plus vii

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professionals working on project planning and analysis for governments and international agencies. Any book written originally over 30 years ago will need revision to update references and explain trends in the literature, practice, and policy since then. In this case the 2000 version already pointed out the issues that have emerged as important since then. These are principally • The policy reforms which have removed the controls and interventions that affected key markets • The shift in the direction of policy to encourage a pattern of ‘inclusive and sustainable’ development leading a greater emphasis on distribution and environmental concerns • The change in public sector portfolios to physical and social infrastructure and away from internationally traded activities. Thus, currently in most developing countries project analysis is taking place in the context of open markets for commodities, services, and factors. Population growth and the spread of urbanisation have increased pressure on physical infrastructure and the pattern of economic growth has both raised inequality and had serious environmental consequences. The implications of these changes for project analysis were anticipated and discussed briefly in the second edition, but the new edition aims to take the analysis further. The central changes are the incorporation of new material on • refinement of the contingent valuation methodology for estimating willingness to pay for nontraded activities and where markets are missing, with the addition of a new chapter on estimating willingness to pay • the specification of the discount rate and its role in decision-taking, with the addition of a new chapter on the choice of discount rate • benefit quantification for different types of projects, including health impacts, with the addition of a new chapter • valuation of environmental effects relating to climate change and the use of eco-services, with the expansion of the original chapter on projects and the environment. These are in addition to editing revisions to all chapters to clarify the exposition and to update references, as appropriate.

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The authors thank Wyndham Hacket Pain of Palgrave and two reviewers for their support at the initial stage of the proposal. John Weiss wishes to thank Antonio Weiss and Rowena Cham for their help in preparing the manuscript. Chiang Mai, Thailand Leeds, UK 

Steve Curry John Weiss

Contents

1 Introduction  1 Basic Ideas   1 The Project Planning Process   4 Changing Context for Project Analysis   7 Other Approaches   8 Outline of the Book  11 2 Main  Features of Projects, Resource Statements and Financial Statements 15 Project Viewpoints  16 Prices  18 Types of Projects  19 Conventions Used in Drawing Up Project Resource Statements  22 Investment Costs  23 Operating Costs  27 Working Capital  27 Benefits  30 Some Project Resource Flows  31 Resource Statements and Financial Statements  36 Use of Constant or Current Prices  39 Discounting  40 Discount Rate for Financial Analysis  45 Discount Rate for Economic Analysis  47

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Contents

Conclusion  47 Further Reading  48 Bibliography  48 3 Project Criteria 51 Project Criteria for Single Projects When Benefits Are Valued  52 Benefit-Cost Ratio (BCR)  52 Net Present Value (NPV)  55 Internal Rate of Return (IRR)  56 Equivalence of Project Criteria  59 Different Points of View: Returns to Total Capital and to the Project Owner  59 Project Alternatives  61 Choosing Between Project Alternatives When Benefits Are Valued  62 A Further Approach to Choosing Between Project Alternatives  66 Decisions on Timing  69 Choosing Between Project Alternatives When Benefits Are Not Valued: Least Cost Analysis  71 Choosing Between Project Alternatives When Benefits Cannot Be Valued: Cost Effectiveness Analysis  74 Project Criteria and a Shortage of Investment Funds  76 Conclusion  79 Further Reading  80 Bibliography  81 4 Theory  Behind Economic Analysis of Projects 83 Background to Economic Pricing  88 Project Effects: Incremental and Non-incremental  91 Unit of Account and Numeraire 100 Application of Conversion Factors 103 Conflicts Between Economic and Financial Criteria 106 Conclusion 108 Further Reading 108 Bibliography 109

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5 The  Domestic Price System of Economic Analysis111 Traded and Non-traded Goods 111 Valuation of Traded Goods 113 Border Parity Pricing 115 Valuation of Non-traded Goods 119 Treatment of Labour Cost 125 Economic Value of Land 131 The Shadow Exchange Rate 134 Test for Trade Efficiency—Domestic Resource Cost Ratio 140 Conclusion 143 Further Reading 143 Appendix 1: Applications of Economic Analysis with Irrigation Project Illustration 143 The Project 143 Economic Analysis 148 Appendix 2: Irrigation Project and Domestic Cost Ratio 155 Bibliography 156 6 The  World Price System of Economic Analysis157 Rationale for World Price Numeraire 158 Equivalence of the Two Systems 160 Traded Goods in a World Price System 163 Non-traded Goods in a World Price System 164 Labour in a World Price System 167 Land in a World Price System 170 World Price Units and Willingness to Pay (WTP) 171 Irrigation Illustration Using World Price Numeraire 172 A More Detailed Analysis 177 Conclusion 180 Further Reading 181 Appendix 1: Semi-Input-Output (SIO) Analysis 181 Illustration of an SIO Application 185 Data Issues 191 Appendix 2: Semi-Input-Output System 192 Bibliography 194

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7 Estimating  Willingness to Pay195 Benefit Valuation in Non-traded Activities 195 Willingness to Pay (WTP) 199 Use of Surveys 204 Elicitation Approach 205 Benefit Transfer 213 Benefit Function Transfer 216 Conclusion 217 Further Reading 218 Appendix 1: Choice Experiment—Illustration from United Kingdom 218 Appendix 2: Benefit Function Transfer for Benefit Valuation for a Waste Water Treatment Project in China 221 Bibliography 225 8 Choice  of Discount Rate227 Specifications of the Discount Rate 228 Social Time Preference Rate 232 Shadow Price of Investment 235 Where Pinv Is Not Required 237 The Weighted SOC Discount Rate 238 Equivalence of the Two Approaches 240 Choice of Discount Rate 242 STP Rate and Declining Discount Rates 247 Is There a Case for Using Different Discount Rates for Different Sectors? 250 Fixed Sector Budgets 251 Hard to Value Sectors 251 Different Types of Benefit 252 Conclusion 252 Further Reading 254 Bibliography 254 9 Allowing for Uncertainty257 Sensitivity Analysis 258 Scenario Analysis 261 Risk Analysis and Use of Probabilities 262

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Reducing Risk 267 Further Reading 268 Appendix: Option Value and the Value of Waiting 268 Bibliography 271 10 Benefit  Valuation in Different Sectors273 Transport 273 Non-Incremental Benefits 275 Incremental Benefits 277 External Effects 281 Education Projects 283 Health Projects 292 Practical Applications of Cost Effectiveness 298 Benefit Valuation in Health 300 Conclusion 302 Further Reading 302 Appendix 1: Productivity Benefits and Transport Projects 303 Appendix 2: Urban Transport Project 306 Appendix 3: Education Project 310 Appendix 4: Health Project 314 Bibliography 317 11 Project  Analysis and Environmental Effects319 Introduction 319 Environmental Value 322 Methods of Estimating Environmental Values 325 Output- or Market-Based Approaches 327 Cost-Based Approaches 328 Revealed Preference 329 Stated Preference 335 Benefit Transfer 337 Meta-Analysis 340 Valuing Carbon and Other Emissions 342 Discounting and the Environment 347 Some Environmental Illustrations 352 Conclusion 356

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Further Reading 358 Appendix 1: Depletion Premium 358 Appendix 2: Meta-Analysis Using Ecosystem Service Database (ESVD) 362 Bibliography 364 12 Financial  Analysis of Projects367 Financial Sustainability 368 Financial Incentives 373 Charges and Cost Recovery 377 Price Changes 381 Conclusion 385 Further Reading 386 Bibliography 386 13 Income  Distribution Effects of Projects387 Projects with Important Distributional Effects 388 Estimating Income Flows from Financial Statements 389 Power Example: Cost and Benefit Flows at Financial Prices 391 Estimating Income Flows from Resource Statements 393 Economic Prices and Income Flows 394 Foreign Participation in Projects 402 Poverty Impact 406 Trade-Offs in Poverty Analysis 408 Conclusion 410 Further Reading 411 Appendix 13.1: Water Project Example: Distribution and Tariff Setting 411 Benefit Conversion Factors 417 Tariff Adjustment 417 Appendix 13.2: Weighting System for Project Distribution Analysis 425 Revaluing Savings 425 Revaluing Consumption 427 Bibliography 431

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14 Conclusions433 The Value System in Project Analysis 434 Empirical Estimation of Wider Effects 436 Future Application of Project Analysis 438 Conclusion 441 Bibliography445 Index453

List of Figures

Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 2.5 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 4.4 Fig. 4.5 Fig. 4.6 Fig. 5.1 Fig. 5.2 Fig. 6.1 Fig. 6.1 Fig. 7.1 Fig. 7.2 Fig. 7.3

Project benefits and costs: different types of projects Project life: technical (T), market (M) and economic (E) Life Economic life of a project Operating costs Project resource profiles BCR lines NPV curves NPV curves: project alternatives Incremental resource cost flow Output effects: competitive market Output effects: regulated market Input effects: competitive market Input effects: regulated market Traded project output Traded project input Transport and distribution element in border parity pricing Willingness to pay (WTP) Equivalence of world and domestic price systems SIO table direct coefficients Increase in supply causing price fall Original producers fully displaced by new project Identification of two price-consumption points without and with project Fig. 7.4 Two separate demand lines for different products Fig. 10.1 Demand for transport

21 24 26 28 33 54 56 63 66 92 94 95 96 99 100 117 120 163 182 196 197 200 203 278

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List of Tables

Table 1.1 Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 2.7 Table 2.8 Table 2.9 Table 2.10 Table 2.11 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 3.8 Table 3.9 Table 3.10 Table 3.11 Table 3.12 Table 3.13 Table 4.1

Scoring system: simple illustration Project perspectives and statements Project investment costs (000) Working capital calculation Carton project: resource flow statement ($000) Project financial statement Applying a discount factor as a weight Discounting a net benefit stream Discounted resource statement Discounting a constant annual amount Discounting and time profiles Financial cost of initial capital (FCIC) Resource statement: project criteria (Rs 000) Illustration of a resource flow with more than one IRR Resource statements: project alternatives (000) Comparison of NPV and BCR criteria Inconsistent results: NPV And IRR Incremental resource statement (Rs 000) Illustration of timing choice Least cost analysis: pipeline alternatives cost statements ($ million) Incremental resource cost statement (Rs 000) Cost effectiveness analysis: accounting skills Resource statements and investment constraint (Sh 000) Project criteria: investment fund constraint Project criteria: adjusted discount rate Basis for economic valuation

9 17 24 29 32 37 41 42 43 44 45 46 53 58 62 65 65 67 70 71 73 74 77 78 79 100 xxi

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List of Tables

Table 4.2 Table 4.3 Table 5.1 Table 5.2 Table 5.3 Table 5.4 Table 5.5 Table 5.6 Table 5.7 Table 5.8 Table 5.9 Table 5.10 Table 5.11 Table 5.12 Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5 Table 6.6 Table 6.7 Table 6.8 Table 6.9 Table 6.10 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5 Table 7.6 Table 8.1 Table 8.2 Table 9.1 Table 9.2 Table 9.3

Equivalence of the two approaches 102 Application of conversion factors 105 Illustration of conversion factor for non-traded activity: electricity122 Estimated returns to land at economic prices 132 Summary adjustments 133 Foreign exchange price comparison: illustration 136 Financial returns to Water Authority (Pesos million) 144 Financial analysis, costs, and benefits to farmers 145 Farmers’ cost (constant prices per hectare) 147 Price data (Pesos per kg) 147 Border parity prices (in Pesos/kg) 150 Economic analysis, costs, and domestic price system 151 Economic analysis, benefits, and domestic price system 154 DRC illustration 155 Numerical example in world and domestic price systems 161 Illustration of conversion factor for non-traded activity in world price system: electricity 165 Estimated returns to land at world prices 170 Economic analysis, costs, world price system 174 Border parity prices (BPP) (Pesos/kg) 176 World price conversion factors 177 Economic analysis, benefits, and world price system 178 SIO table—direct coefficients 186 Total primary factors per unit of output in productive sectors187 Full CF results 190 Willingness to pay from survey: weighted average 206 Illustration for willingness to pay 208 Illustration of benefit function transfer 217 Choice experiment attributes 220 Mean WTP calculation: solid waste management 223 Mean WTP calculation with transfer: solid waste management225 Consumption time preference rate using the Ramsey formula 234 Illustration of declining certainty equivalent discount rate 249 Sensitivity illustration with switching values 260 Scenario analysis illustration 262 Risk analysis: basic data 264

  List of Tables 

Table 9.4 Table 10.1 Table 10.2 Table 10.3 Table 10.4 Table 10.5 Table 10.6 Table 10.7 Table 10.8 Table 10.9 Table 10.10 Table 10.11 Table 10.12 Table 11.1 Table 11.2 Table 11.3 Table 11.4 Table 11.5 Table 11.6 Table 11.7 Table 11.8 Table 11.9 Table 12.1 Table 12.2 Table 12.3 Table 12.4 Table 12.5 Table 12.6 Table 13.1 Table 13.2 Table 13.3 Table 13.4 Table 13.5 Table 13.6

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Results of risk analysis 266 Data required by HDM4 model 276 Average cost savings without and with project (Pesos per vehicle km) 277 Summary of transport benefits and their valuation 282 Education project illustration: lifetime worker income with and without a project 286 Spreadsheet example: education economic analysis 288 Summary: education economic analysis 289 Illustration of cost per DALY ($) 297 Illustration of agglomeration benefits 305 Vehicle kilometres in city centre: difference with—without project308 Summary Results: Urban Transport 310 Education project: economic analysis 313 DALYs lost and averted by disease 315 Categories of ecosystem services 323 Sample of studies on valuation of eco-services 339 Use of meta-analysis to estimate value of forest cover 341 Sensitivity of social cost of carbon to discount rate ($/tonne CO2 in 2010 US dollars) 345 Project examples: Project A—mining project 353 Project examples: Project B—power rehabilitation (Shillings million) 354 Project examples: Project C—national park (Shillings million) 357 Economic value of gas 361 Illustration of meta-analysis 363 Financial internal rate of return: railway project 369 Comparators for financial indicators 370 Financial cost of initial capital (FCIC) 371 Financial internal rate of return: railway corporation 373 Return on equity 375 Average incremental cost illustration (at constant financial prices)380 Financial analysis: power project (constant financial prices) 392 Income changes from financial NPV 393 Decomposition of project costs and revenue 396 Economic analysis in domestic prices 399 Distribution analysis: divergence between economic and financial values 400 Summary distribution effect 401

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List of Tables

Table 13.7 Table 13.8 Table 13.9 Table 13.10 Table 13.11 Table 13.12 Table 13.13 Table 13.14 Table 13.15 Table 13.16 Table 13.17 Table 13.18 Table 13.19 Table 13.20 Table 13.21

Distributional analysis with foreign financing 405 Poverty impact: power example 407 Alternative measures of poverty impact: power example 407 Financial analysis (case 1: tariff = O&M costs) 412 Project cost breakdown and costs of water from alternative sources414 Economic analysis (case 1 tariff = O&M costs) 418 Summary results 420 Water case: distributional effects 421 Distributional effect: case 1 423 Water example: distributional effects with higher tariff (case 2) 423 Distributional effect: case 2 424 Savings-weighted NPV 426 Illustration of weights 428 Consumption weights 428 Weighted analysis 429

List of Boxes

Box 4.1 Box 7.1 Box 8.1 Box 8.2 Box 10.1 Box 11.1 Box 11.2

Differences: Financial and Economic Analysis  Willingness to Pay for Environmental Tourism: Kruger National Park South Africa Alternative Approaches to Discount Rate Discount Rate in United Kingdom, Green Book Data Requirements for DALYs Hedonic Price Model: Valuing Air Quality in Beijing Travel Cost Approach—Maasai Mara National Park, Kenya

87 211 231 243 296 331 334

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CHAPTER 1

Introduction

Project analysis involves estimating and comparing the beneficial effects of an investment with its costs. Such a comparison is done within a broader economic framework that provides the basis on which full costs and benefits are identified and valued. Project analysis originated more than 60 years ago, the main ideas developing simultaneously in different places. The last 40 years has witnessed an extensive application of project analysis methods, particularly in developing countries.1 This introductory chapter provides a brief outline of the basic ideas of project analysis and situates them in the broader process of project planning.

Basic Ideas The basic ideas of project analysis have a long history, but they became clear only with the first applications. These occurred simultaneously in different economic contexts. In the United States in the 1930s, a problem was formulated in relation to water resource investments. The costs of investing in and running such projects were relatively straightforward to estimate; what was not so straightforward was an estimate of the benefits 1  This term is used throughout the book. It corresponds with what the United Nations classifies as developing economies and economies in transition in their statistical databases; for example, UN World Economic Situation and Prospects 2022, UN, New York, 2022.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Curry, J. Weiss, Project Analysis in Developing Countries, https://doi.org/10.1007/978-3-031-40014-8_1

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water resource investments would generate. The beneficial effects distributed between many different types of users first had to be identified, and then valued. Moreover, there was no market in water provision which could yield an appropriate price to value water. At this time, guidelines were established for estimating the benefits and costs of water resource projects. In the context of a capitalist economy in recession, this use of project analysis can be described as extending quasi-market criteria to an area of infrastructure services where the full characteristics of a competitive market could not be established. This use of project analysis can also be viewed from a closely related but different perspective. It is no accident that this application was in the provision of services within the public sector. In a capitalist economy there is competition for resources between the public and private sectors; investments in the public sector often need to be resourced from incomes generated in the private sector. To justify public sector investments, it is useful to show that they also have productive effects. Project analysis can be used, therefore, not simply to analyse the effects of a project, but to justify it in relation to the alternatives available. In the 1930s also the first use of project analysis was made in the Soviet Union. The introduction of central planning at the end of the 1920s provided the investment framework; the overall rate of investment and its distribution between sectors was determined through the central planning mechanism. The emphasis was placed on expanding material production rather than social service sectors. However, within this context of planned investment several choices still had to be made at the sector and project level. For example, there were choices to be made about the best transport mode to meet a forecast demand; about the appropriate location for priority heavy industry investments; about the type of technology to be used in new investments; and about the scale of individual investments. Investment criteria were devised that allowed comparison not of the costs and benefits of specific investments but the alternative costs of generating given outputs. These forms of project analysis therefore differed. Whilst one form sought to analyse and justify the provision of infrastructure services the other sought the best alternative means of achieving productive effects. However, there was a similarity in the theoretical basis of project analysis developed in both contexts. The reference point was a competitive equilibrium that markets would produce either in a capitalist economy or under market socialism. The prices obtaining in such an equilibrium would

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indicate the marginal or additional value of resources and thus would be the appropriate ones to use in project analysis. Project analysis could therefore be rationalised as a means of formalising the best choice of investments by simulating a competitive market framework. The prices at which to value the benefits and costs of an investment have been a major concern in project analysis. An alternative basis for arriving at a set of prices for project analysis was proposed in the 1960s. Rather than relying on an abstract notion of competitive equilibrium, this alternative basis appeals to trade opportunities. For many countries, especially developing countries, trade opportunities are an important issue. A decision to develop domestic economic activities should take into account the possibility of relying on trade to obtain resources, possibly more cheaply. From this reasoning, international prices have come to be used in project analysis, not because they represent a competitive equilibrium but because they represent the terms upon which countries can participate in foreign trade or what can be termed trade opportunity costs. In the development context the commencement of significant concessionary aid flows to poor countries in the 1960s necessitated the development of a systematic methodology for assessing whether the projects financed from these funds provided an acceptable return to the recipient countries. It is no coincidence that some of the key ideas behind the methodology were developed virtually simultaneously within the Development Centre of the Organisation for Economic Cooperation and Development (OECD) and the United Nations Industrial Development Organisation (UNIDO). Subsequently work at the World Bank clarified and formalised the methodology. The main user of project analysis techniques in developing countries has been international agencies, since most have a formal requirement that a form of economic calculation be carried out for any project they finance. However, in principle the answers provided by project calculations are of direct relevance to national governments. The ideas behind project analysis in poor countries were also connected to critiques of development policy. For example, in debates over the choice of technology it was argued by some that developing countries needed to adopt recent technologies in some sectors, to raise productivity as fast as possible and to become internationally competitive. Others argued that developing countries should adapt the choice of technology to what was seen as an abundance of cheap labour and a shortage of domestic investment funds. Although this debate could not be resolved simply at the project level project analysis, by systematically outlining the costs and

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benefits of different alternatives, could indicate the technology choice for different sectors that would generate the greatest economic surplus for re-investment. Studies of the industrial import substitution process also concluded that protection of new investments had created profitable but high-­ resource-­cost industries that would never be internationally competitive. As an alternative to removing protection, it was recommended that new investments should be analysed as if there were no protection. Such an analysis conformed to using trade opportunity costs as the basis of economic valuation, and project analysis was a means of assessing the efficiency of this particular trade strategy. A similar approach was also derived from a consideration of domestic price policy. Where governments set many domestic prices especially for agriculture and food, production patterns did not necessarily conform to the international cost advantages of the economy. Again, use of an alternative set of prices based on trade possibilities was advocated as a means of improving resource allocation in agriculture. In the last 40 years or so other factors have influenced the form which project analysis has taken. There has been a considerable debate about the role of the state in development, and means of enhancing its effectiveness in what it does. There has been an increasing private provision of public goods through new forms of institutional arrangement and with an expected improvement in operational performance. The greater role of the private sector in managing public services means that the framework within which the analysis of particular investments takes place has changed. Institutional structures in part determine the point of view from which an investment is assessed. At the same time, there has been a reversion of public investment from the directly productive sectors to the initial focus on economic and social infrastructure, such as roads, health and education programmes. The valuation of infrastructure activities that do not enter directly into international trade must have a domestic not an external basis, and this has posed an additional challenge for project analysis.

The Project Planning Process Project analysis forms part of the broader process of project planning. Project planning focuses on discrete, new activities, involving a substantial commitment of investment resources. It consists of a set of procedures

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and techniques that can be applied, first, in the process leading up to a decision whether or not to invest and, secondly, in the implementing and organising of the new activity. Project planning is frequently conceived as a series of stages: the identification of investment possibilities; their preliminary investigation through a prefeasibility study; a more detailed investigation and implementation plan through a feasibility study; and a decision process accepting or rejecting the project. Once a project has been accepted further stages involve detailed design and finance negotiations; construction and commissioning; and full operation of the new activity. Project analysis plays a major role in those stages leading up to a decision whether to invest or not. Project analysis involves the comparison of those benefits and costs of an investment that can be quantified. Where benefits and costs affect only project owners and investors, this is a financial project analysis, and where they incorporate all effects on the wider economy, it is an economic analysis. At each stage of the project planning process, a decision is required whether to commit planning resources to the subsequent more detailed stage. The prospect of a project’s benefits exceeding its costs should be a major influence on such decisions.2 This can be briefly elaborated in relation to the first four stages outlined above. Project identification involves outlining the main characteristics of proposed new activities, including the scale of investment and the main economic and financial resources required to implement it. It also involves outlining the main costs of the project and the benefits that are likely to ensue. A preliminary analysis of the project’s costs and benefits at this stage would be based only on the simplest of forecasts and estimates. Projects that appear viable can be carried forward to the prefeasibility stage where a small team can investigate the technical basis of the proposed project and define the main alternatives—of scale, technology, and location—that are worth investigating. These alternatives need to be compared, and the overall project viability assessed. At this stage, project analysis should investigate the economic costs and benefits of all alternatives, 2  This book focusses primarily on the economic aspect of this decision, setting out a method for comparing economic costs and benefits, or project economic analysis. For ease of exposition, henceforth when the term ‘project analysis’ is used, it refers project economic analysis.

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without going into the details of financing and organisation; it is the overall viability of the project that matters here, regardless of how it is financed or organised. The feasibility-study stage involves a full assessment of viability, based on the chosen technical alternative and an implementation plan. An organisational and legal basis has to be defined for undertaking the investment and managing the operations. Sufficient financial resources have to be brought together on terms that can be met. However, the estimates of project benefits and costs, now defined in more detail, still provide a crucial element. The project analysis should now extend beyond the basic economic characteristics to include assessment of financial viability leading to a detailed financing plan and the distribution of benefits and costs between different participants. The comparison of costs and benefits will also have to be repeated from the viewpoint of different participants, for example, from the point of view of the project owners in particular, as well as the investment as a whole. This is because financial viability is a necessary condition for the continued operation of an economically justified project. Typically, investments require cooperation between a number of participants: owners, operators, lenders, workers or producers, government and even output users. An analysis, beginning from the same basic project description and statements, can be carried out from the point of view of each participant, recording their particular costs and benefits. A precondition for a successful and productive project is that all participants should share in the additional resources the project produces, at a sufficient level to justify their participation. Sometimes the relative shares will have to be resolved by negotiation; the government often plays a crucial role in resolving differences over relative shares, which can affect the surplus any project produces. A final decision on whether to proceed with a project will depend on a range of factors. An essential component of the decision is a comparison of a particular project with alternative investments. It is not sufficient that project benefits should exceed project costs: they must do so by more than in other feasible investments. Project analysis techniques must incorporate this comparative element so that the appropriate decision can be taken.

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Changing Context for Project Analysis In recent years, there have been significant changes in the context in which project analysis is applied. In the 1960s prevailing prices actually observed in domestic markets were often strongly influenced by government controls, such as import barriers, fixed exchange rates, minimum wage legislation and ceilings on domestic price. This meant that an analysis of a project based on these prices, would give a misleading picture of its economic impact. Much of the early methodology of project analysis focused on removing the effect of these controls by estimating economic prices (originally termed shadow prices), that would prevail in their absence. In the last 30 years or so the need for this type of adjustment has been reduced greatly. Policy reforms have removed many of the controls and interventions that affect key domestic markets. Analysis is often taking place in the context of open markets for commodities, services and factors. At the same time, funding modalities have multiplied to encompass resources applied under policy reform, or multi-investment funding programmes. Hence a preliminary assessment for investments must include whether a project investment is the appropriate modality to apply. The justification for extending analysis of projects beyond financial analysis has changed. Three cases can be identified whose relative importance has changed over time: (1) distorted markets subject to government controls, for example, foreign exchange, labour and utility prices; (2) incomplete markets where externalities are significant, for example, various physical and social infrastructure; and (3) absent markets where there is no price for project output, for example, a range of environmental goods and services. All economies have some of these features, but project analysis in developing countries initially focused predominantly on the first situation, emphasising how project analysis could get around the problems caused by what can be termed a ‘distorted economy’ policy. Whilst some aspect of this may still remain relevant in a limited number of situations, much of the challenge today in applying project analysis relates to incomplete markets, which mean that observed prices fail to reflect the benefit of producing or the full cost of using a good or service, or to the absence of a market entirely, so there is no reference point. These are not abstract theoretical problems as they are directly relevant in addressing some of the key contemporary development problems. The global population continues to grow, together with a substantial increase

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in urbanisation. There is increased pressure on physical infrastructure, and the pattern of economic growth has both raised inequality and had serious environmental effects relating to climate change, natural resources and bio-diversity. The challenge for project analysis is to incorporate these issues into the analysis of project investments, for example, through estimating the net effects of an investment on CO2 emissions. In addition, while the primary function of project analysis is to facilitate a decision on whether a proposed investment meets the decision-making criteria for investment or not, it can also be extended to provide additional information on the distribution effect of a project, including the effect on the poor. It can also be used to assess the incentives for all stakeholders, such as owners, workers, consumers and those financing the project. It is important that key stakeholders have a financial incentive to continue to participate in a project, as without their involvement the project may be unsustainable. This incentive can be tested in project analysis.

Other Approaches It is important to note that the type of methodology explained in this book is not the only way of viewing projects. Here we note two alternatives. The first is a form of impact assessment, sometimes termed multi-­ criteria analysis (MCA). In the simplest version this asks these questions: what are the main impacts created by a project, relative to what would happen without the project, and how can these be measured? The relevant impacts will usually be linked directly with the goals the project sponsors wish to aim for, which will vary between types of projects. For governments these are typically aspects like employment, balance between regions, effect on the environment and perhaps social goals like greater gender equality or improvement in the quality of life. Conceptually some of these will be easy to convert to comparable measurable units (like jobs created or CO2 emissions reduced), others like improvement in the quality of life are more difficult, but not impossible, to put in numerical terms. If common units can be agreed, project impact can be expressed in a set of numerical indicators, and alternative projects can be compared. If there is one overriding goal comparison between alternatives will be simple. For example, if two alternatives cost the same and the goal is job creation, one can simply select the alternative that has the largest impact measured by jobs created. However, if costs differ or if there are several project goals, the comparison is no longer so simple. A difference in cost

1 INTRODUCTION 

9

must be set against a difference in impact, and this involves a form of cost-­ effectiveness analysis, which is part of project analysis and is discussed here in Chaps. 3 and 8. Where there are multiple goals, impact scores measured in relation to different goals must be compared, and their relative importance must be allowed for. This is usually done by assessing a project’s contribution to a specific goal by a scoring system, giving a weight to the goal relative to other goals, and combining the weighted score in a measure of project impact. Weighted scores can then be compared for alternatives and if costs are the same, that with the highest score will be chosen. Table 1.1 gives a very simple example. Two options are compared. Option A has a stronger employment effect nationally, but a weaker effect in helping a poorer region. Option B has a regional focus, but contributes poorly to the environmental goal of emissions reduction. The weighting system places the greatest emphasis on employment creation, followed by emission reduction and then lastly regional income. With these figures the weight score for A is 6.7 and for B is 5.4, so if costs are the same, A will be chosen. Actual impact assessment can go to great lengths to develop suitable units and means of scoring, but ultimately requires a weighting system to compare project contributions in relation to the different goals. Such

Table 1.1  Scoring system: simple illustration Option

Goal

Units

A

Employment Environment

Jobs created CO2 emissions reduced Change in income

Income in target region Weighted score B

Weight on goal

8 5

0.5 0.3

6

0.2 6.7

Employment Environment Income in target region

Weighted score

Score 1 … 10

Jobs created CO2 emissions reduced Change in income

6 2

0.5 0.3

9

0.2 5.4

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S. CURRY AND J. WEISS

weights have no objective basis unless decision takers stipulate them for such a scoring exercise. Economic analysis of projects can also examine projects in relation to different objectives. It differs from MCA in two critical respects. First, as far as possible, it measures project effects in monetary terms; hence there is a common unit of either income or consumption.3 Hence for example employment or emissions impacts are given a monetary value, equivalent to income, and do not rely on judgement in relation to scores. Second, where goals or objectives may be in competition, it resolves this by using a weighting system, as far as possible derived from economic theory. The weights are the economic or shadow prices applied to value goods and factors of production. Critics point out that the underlying economic theory is open to different interpretations, and some of the values applied in project economic analysis are in turn based on subjective judgements (see, e.g. Chaps. 8 and 11). However, uncertain weights, with some theoretical basis, when applied consistently, offer reasonable grounds for comparison between projects. The second alternative approach applies a macroeconomic model to estimate the wider effects of a project on interrelated markets. The most common version of this approach is a form of computable general equilibrium (CGE) model. This is useful where an activity or policy change, such as a reform of foreign trade or a wide-ranging infrastructure reform, has major feedback effects on the rest of the economy. Such models are highly data-intensive and, even where they are available, are not suitable for the analysis of the majority of projects. The model is intended to reflect the equilibrium conditions in an economy at a point in time (the base case) where all markets are cleared on the basis of assumptions about key parameters and optimising behaviour by producers and consumers. A project or a policy change can be introduced as a ‘shock’ to the model and as all markets clear by assumption a new equilibrium will established. This allows the comparison between the new equilibrium and the base case to define the impact of the project or policy shift. Since CGE models can be built up from Social Accounting Matrices (SAMs) which allow for income and expenditure by different categories of household they can also be used to disaggregate the effects on the poor and other income categories. CGE models offer a means of capturing important macro or feedback effects from an activity. However, apart from their data requirements, they 3

 These may be treated differently under the circumstances explained in Chap. 8.

1 INTRODUCTION 

11

suffer from relatively high levels of aggregation in terms of sectoral groupings and, by comparing two equilibrium states, offer a snapshot ‘once for all’ impact effect rather than a gradual build-up of effects over time. In principle they can address some of the wider economic effects often linked with projects, such as induced additional investment stimulated by a project or important backward linkages to supplier activities or displaced production as competitors are forced out of a market. However, project analysis is based on the premise that in most cases projects are small relative to total activity in an economy and that where such additional effects are important, they can be estimated for an individual project by focusing on the most direct relationships, and ignoring any wider macroeconomic implications. Approaches for doing this as part of project analysis are discussed in Chap. 6, in the context of the methodology of semi-input-­output analysis and in Chap. 8 in the context of the appraisal of transport projects, which are often linked with wider effects.

Outline of the Book After this brief introduction to the basic ideas of project analysis, and the scope of its application, the content of this book can now be summarised. The results of project analysis are based on numerical calculations that summarise the benefits and costs over the life of an investment project. Chapter 2 introduces project statements—both resource and financial statements—which provide the means of organising this data. It also introduces the concept of discounting which allows benefits and costs arising at different point in time to be compared. Chapter 3 illustrates how a picture of project elements is drawn up using either financial data or modified for economic data. Decision-making requires clear criteria. The main measures of project worth are stated and applied in the case of a single project, or in the choice between project alternatives. These are explained for projects that generate a revenue or quantifiable benefits and those that do not. Chapter 4 elaborates on the competitive economic model underlying economic project analysis, and the circumstances in which a set of economic prices should be used to value project effects in the economy. It illustrates how this can be applied across all quantified benefits and costs, using a common numeraire or unit of account for aggregating project economic values.

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Chapter 5 gives more detail on how economic prices can be derived and the circumstances in which they should be used in an analysis where they replace financial prices. The basic data can be presented in alternative, but equivalent ways, depending on how world prices are converted into an economic value in domestic currency. Whenever there is a misalignment of the exchange rate for local currency the chapter explains how this can be allowed for by applying an alternative shadow exchange rate. This is termed a ‘domestic price analysis’. This adjustment to the exchange rate is important in applying a ‘trade efficiency criterion’, to assess whether particular activities should be produced nationally or imported. This was an important motivation for early work on project analysis in a development context. Because of the continued importance of international trade to most developing economies, the use of this criterion remains relevant in identifying trade opportunities. For small open economies where the majority of goods can be bought from or sold to the world market, there is a case for doing project economic calculations in units measured in world prices. Chapter 6 explains how this can be done and how the standard approach can be extended to capture a range of linkage effects through ‘semi-input-output’ analysis. This world price approach was seen initially as a means of conducting a very detailed application of trade efficiency criterion, although in practice the approach evolved into something directly equivalent to the domestic price calculation, only in different prices. For projects focused primarily on the domestic market, technically described as producing non-traded goods, benefit valuation must focus on the value these goods create for domestic consumers. This requires an estimate of consumer willingness to pay. This is now a major practical issue, because currently most public sector projects are of this type, focusing on physical and social infrastructure. Chapter 7 discusses empirical approaches to estimation of willingness to pay. A key parameter in coming to a decision for a proposed investment is the economic discount rate at which the value of benefits and costs are discounted, and as the standard for project decision-making. Approaches to the specification of the discount rate can vary with objectives. Chapter 8 surveys current thinking on this important parameter, focusing on the contrast between a social time preference and social opportunity cost rate. The justification for distinguishing between uses of income—either consumption or savings—is explained and its application is illustrated.

1 INTRODUCTION 

13

The application of project criteria involves forecast information of both quantities and prices. Alternative estimates of the data generate considerable uncertainty about the value of specific items and, therefore, of the overall estimate of economic returns. Chapter 9 illustrates common approaches to minimise the impact of uncertainty, focusing on sensitivity analysis and probability-based risk analysis with a brief discussion of the option of waiting. Although there is a consistent approach behind benefit valuation because of the differences between projects from different sectors, application of this approach can look superficially quite different. Chapter 10 gives examples of benefit valuation from three sectors—transport, education and health—explaining the different benefit valuation techniques used in each. Some projects are oriented directly to enhancing environmental conditions, and some to mitigation of environmental effects at the local and global level, and all projects have some environmental effect. This is now a critically important issue, and Chap. 11 provides an overview of approaches to valuation of environmental effects. A particular case now incorporated in many project analyses is the valuation of climate change effects, through estimation of changes in project effects on CO2 or other emissions. An economically acceptable project must also be financially sustainable. Chapter 12 illustrates how project analysis techniques can be adapted to test whether a project is financially sustainable. Chapter 13 extends the analysis by incorporating a project’s distributional effects. It explains the mechanics of income distribution analysis, and the derivation of poverty impact estimation using beneficiary weighting, where appropriate. The role of distribution analysis in decision-taking is considered. Chapter 14 draws some conclusions, pointing to some of the limitations of project economic analysis and highlighting what should be its key role as a check on bad project decisions. Some practical advice is offered in addressing some of the issues raised in earlier chapters.

CHAPTER 2

Main Features of Projects, Resource Statements and Financial Statements

A project involves the commitment of resources now to obtain extra resources in the future. Projects can be analysed from different points of view. This chapter begins by explaining some basic differences between project resource statements and project financial statements, and the prices used in drawing them up. A project statement includes all the inflows and outflows of a project according to the time period in which they occur. It involves the use of certain conventions about the valuation of resources and the time-frame into which they are put. These conventions can be applied to different types of projects. This chapter also includes a brief outline of different types of projects and the conventions used in drawing up the corresponding project statements. For decision-making purposes, the net effects of a project need to be added up. These net effects will occur in different years; adding them together involves defining the value of net effects in different years relative to a base year. This chapter also introduces the technique of discounting, which expresses all project effects in base-year values. Analysis of projects using financial prices can be used to assess the financial sustainability of a project that has been chosen for implementation. This chapter identifies some key indicators in the financial analysis of projects, including the assessment of the level of incentives for key project stakeholders.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Curry, J. Weiss, Project Analysis in Developing Countries, https://doi.org/10.1007/978-3-031-40014-8_2

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Project Viewpoints Any commitment of resources now to obtain extra resources in the future will involve different groups. A project can be analysed from different points of view. For example, a project to raise agricultural production in a particular area can be analysed from the point of view of the farmers who will produce the output and their households, the supplying and marketing agencies who will handle the inputs and outputs, the lending agencies involved in financing the project, and the government that stands to lose or gain revenue and commit expenditures through the project. For example, again, a factory project can be analysed from the point of view of the workers who will be employed there, the banks who will assist in funding, the government that will gain or lose from associated revenues and expenditures, and the owners of the project who will retain any net profits that are made. While each point of view can be made explicit, two project viewpoints carry particular significance. These are the viewpoints of capital as a whole or total capital, and the project owner or equity capital. Regardless of the range of participants in any project, it is important to look at a project as a whole, the viewpoint of total capital. An analysis from the viewpoint of total capital must include all the resource costs—investment, operating and working capital costs—as well as the resource benefits. These are combined in a project resource statement, showing the economic effects of the project. Such a statement allows assessment of the project from the point of view of its overall economic impact regardless of how it is financed and regardless of the project’s effect on the government budget. The underlying philosophy behind a project resource statement is that if projects can be demonstrated as beneficial from the viewpoint of total capital, then the economy as a whole will benefit from their implementation. How the beneficial effects are distributed between owners, producers, lenders and government will depend on financial arrangements, but a worthwhile project overall will be able to meet its financial commitments whilst generating additional resources for the economy. However, the financial arrangements cannot be ignored. Particularly, no project, however worthwhile, will be implemented unless there is a sufficient return to the owners—or, in the case of some projects where there are many who contribute their land assets as well as labour to production, the producers. From the owner’s point of view loan inflows and outflows affect their returns, as do tax or subsidy payments. These financial

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transactions need to be taken into account in a project financial statement so that the net effects of a project for the owner can be calculated. Although the resource costs and benefits form the basis of all project statements, they are supplemented by financial transactions to complete the financial statement. These financial transactions are open to negotiation, about loan terms and interest rates and sometimes about tax and subsidy rates; they can be varied to influence the return to equity capital in a way which the underlying resource costs and benefits cannot. In essence, the financial transactions are transfer payments which redistribute the benefits a project produces. This basic difference in perspectives can be stated more elaborately as in Table 2.1 where four points of view are compared. The viewpoint of total capital (2i) requires a resource statement drawn up at financial prices, which are the prices a project pays for its inputs and receives for its output. However, as discussed in Chap. 4, financial prices may not reflect the economic value of resources from the national point of view. Where this is the case, then an alternative set of prices known as economic prices will have to be applied in the resource statement to obtain a project statement from the point of view of the national economy (1). From the owner’s point of view, it is also useful to draw up a project statement independent of financing arrangements, which takes account of net taxes payable to the government (2ii). This will indicate the consequences of the project for private capital. It can form the basis of a judgement whether there are alternative projects which are better from the private viewpoint, independent of how any net benefits would be distributed between owners and lenders. The effect of specific financing arrangements for equity capital can be included in a separate statement from the owners’ point of view (3). Table 2.1  Project perspectives and statements Perspective

Project statement

1. National economy 2. Total capital  (i) Pre-tax  (ii) Post-tax 3. Owner: equity capital

Resource statement at economic prices Resource statement at financial prices Resource statement at financial prices adjusted for net taxes Financial statement = Resource statement at financial prices from owners’ viewpoint, adjusted for net taxes and financing

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The perspectives of total capital and the owner, or equity capital, are illustrated in this chapter. The perspective of the national economy forms the basis of Chap. 4 and most of the remaining chapters in the book.

Prices Both resource statements and financial statements can be drawn up using different prices. Both types of statement involve estimating the effects of a project in the future. There is a choice as to whether these estimated effects should be valued at a specified set of prices—at constant prices—or whether an attempt should be made to forecast prices in future years— current prices. Using constant prices to value the economic effects of a project is appropriate for decision-taking. The basic decision in project analysis is whether to invest in the project or not. The alternative is to assign the investment resources to other investments. Moreover, it is convenient for resources to be valued at present prices, which define their value in different uses when the investment decision is being made. If constant present prices are used throughout the project analysis—for future years as well as the initial year—then resources will be consistently valued at prices reflecting their value in alternative uses now. Future economic effects will be measured in the same units as present effects. This use of constant prices is in relation to both total capital and equity capital. From both points of view, the basic question to answer is this: is the project worthwhile? The answer depends upon a project statement with effects valued at constant prices, whether from the economy or the owner viewpoint. The use of constant price project statements requires two qualifications. The first relates to resource statements. These are usually drawn up first at constant financial prices. However, as indicated above, they can also be drawn up at constant economic prices, to better reflect project effects from the national point of view. A second qualification relates to changes in relative prices. Over the life of a project, it may be foreseen that some prices will rise or fall relative to others. For example, it may be foreseen that the price of energy inputs will rise relative to the present price for outputs and other inputs; or it may be foreseen that the price of an agricultural output such as rice may fall relative to the present prices of other resources, including labour. Where a particular price is expected to change in real terms, that is, relative to other items in the project statement, then the constant

2  MAIN FEATURES OF PROJECTS, RESOURCE STATEMENTS AND FINANCIAL… 

19

price analysis can be adjusted for this relative price change. This issue is discussed further later in the chapter.1 For projects to be implemented, a financial plan will need to be prepared. This financial plan must identify the total cash requirements of a project and the sources from which they will be met. For this purpose, a financial statement at current prices will be required, incorporating forecasts of general price increases and the specific rate at which prices will change for major inputs and outputs. It is easier to incorporate such price changes if the period for detailed financial planning is not too long. However, the use of current prices, although necessary to implementing a project, should make no difference to the decision whether to commit resources to a project or not. A project statement at current prices, if discounted by a discount rate at current prices, will give the same decision as a project statement at constant prices discounted by a discount rate adjusted to constant prices. The effect of discounting is discussed below. The point to be made here is that constant prices are sufficient for project decision-making. The construction of financial plans for implementing a project lies outside the scope of this book.

Types of Projects Basically, three types of projects can be identified, depending upon how new resources committed to them relate to existing economic activities. First, the largest type of project, around which project analysis grew up, involves new investment. New investments are designed to establish a new productive process independent of previous lines of production. They often include a new organisation, financially independent of existing organisations. Secondly, there are expansion projects which involve repeating or extending an existing economic activity with the same output, technology and organisation. Thirdly, there are updating projects which involve replacing or changing some elements in an existing activity without a major change of output. Updating projects involve some change in technology but within the context of an existing, though possibly re-­ formulated, organisation.2 With changing economic circumstances the balance between these types of projects may change. Whatever type of project is being analysed, the effect of using new resources has to be distinguished from the effect of resources that would be used without the project. The additional resource costs have to be identified; that is the resources that will be committed in a project over

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and above what would otherwise have been used. Similarly, the benefits over and above what would otherwise have occurred, have to be identified. Both additional costs and additional benefits have to be valued. For a new investment the whole of the output and the whole of the costs will be additional; for expansion and updating projects, the effects of the new resources have to be separated from the effects of resources without the project. The without-project scenario is as much a part of the forecast of project effects as the with-project scenario. Figure 2.1 illustrates the three types of projects. Figure  2.1A simply traces the total costs and total benefits of a new investment. Figure 2.1B traces the costs and benefits without and then with an expansion investment. It is the difference between these two streams of costs and benefits which describes the effect of the investment. Figure 2.1C does the same for an updating investment; the difference here is that the costs and benefits from existing operations will have a downward trend unless the new investment is undertaken. Project costs are generally easier to identify and estimate than project benefits. Project costs may be met directly by a particular institution; project benefits are frequently more diverse. A distinction can be drawn between directly productive and indirectly productive projects. Directly productive projects are those where the immediate costs and benefits accrue to a single organisation; a consequence is that this organisation is able to calculate and commit any resulting surplus to new activities. Indirectly productive projects, broadly speaking, are those where not all of the benefits derived from new resources accrue to the organisation responsible for carrying the costs. In these circumstances, any resulting surplus is not concentrated solely in the hands of a single organisation. Most public infrastructure projects, such as roads, are indirectly productive; the benefits accrue to users and producers whilst the costs are met by government. Of course, several projects, especially large ones, may be a mixture of directly productive and indirectly productive activities—for example, a rural development project involving both increases in agricultural output through farmer investment, as well as roads, schools and other infrastructure facilities. The importance of the distinction between directly and indirectly productive projects is that the benefits from new resources are more difficult to estimate in the case of indirectly productive projects. In a project analysis carried out from the national point of view, it is important to encompass as many project effects as possible in the project resource statement. This will include several effects that are external to the

2  MAIN FEATURES OF PROJECTS, RESOURCE STATEMENTS AND FINANCIAL… 

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A New investments

Benefits and costs

Benefits Costs

0 B Expansion investments

With benefits Without benefits With costs Without costs

0 C Up-dating investments

With benefits With costs Without benefits

Without costs 0

9

Fig. 2.1  Project benefits and costs: different types of projects

Years

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main organisation carrying out the project. For example, directly productive projects may have a detrimental impact on the environment, which will have no financial or economic impact on the project sponsor. All such effects, whether external costs or external benefits, should be identified and valued and included in the project resource statement as far as possible.

Conventions Used in Drawing Up Project Resource Statements The main conventions used to describe the scale and timing of costs and benefits in a project resource statement will be illustrated in relation to a new directly productive investment. There are four main elements of a project resource statement: investment costs, operating costs, working capital costs and benefits. These elements are frequently broken down into several different items. A value for each needs to be included in every time period in the resource statement, but to start with a convention needs to be established for separating out the different time periods. The usual convention is to draw up a resource flow using annual time periods; for discounting purposes (which will be explained later), all costs and benefits within a particular time period are treated as occurring at the end of the time period. The time-frame for a resource statement will then consist of a series of consecutive annual periods; the investment costs of a project will normally occur in the earlier periods. There is some choice about the nomination of the first period. If it is anticipated (generally for small investments) that the investment process could begin immediately a decision to accept a project is taken, then the first period can be nominated as period 0, and items falling within it can all be treated as though they occur immediately. If it is anticipated that even after a favourable decision it will take some time to organise the design and funding, and hence the investment expenditures themselves will occur sometime after the decision-making, then it is appropriate to nominate the first annual period as period 1, as if items falling within it, occur at least one year after the decision to invest.1 The significance of the distinction between the approaches is that if the first year is year zero, any cost and benefit item falling within this year is not

1  The examples in this book use both alternative assumptions, with the simple case cases assuming investment in year 0.

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discounted. Hence if similar projects are being compared, it is important to be consistent in the assumption used.

Investment Costs The items included under investment costs have to be broken down into three components—initial expenditures, replacement expenditures and residual values. Initial expenditures refer to the costs involved in establishing and commissioning a project. Replacement expenditures refer to the costs of equipment and other investment items in the operating phase of a project to maintain its productive capacity. Residual values refer to the value of all these investment items at the end of the project’s life, when production is expected to cease (or be substantially changed); in most projects it is unlikely that any residual value will be large enough to have a major impact on decision-taking. Initial investment expenditures will be one of the largest items in most project resource statements. For relatively small projects these expenditures may all occur in the first time period; for larger projects the expenditures will be spread over two or more years. Moreover, some projects may be explicitly designed in phases so that initial investment expenditures are spread over several periods. Where several periods are involved, land preparation works expenditure has to precede construction activities, which have to precede the purchase of machinery and commissioning activities. There will be an overlap between these different items within the time periods, but they will follow this general sequence. Investment items each have a different operating life. Land preparation works will be permanent and do not need to be repeated. However, other investment items such as buildings, machinery and vehicles have a limited life. Often buildings will last longer than the anticipated operating life of the project as a whole and will need maintaining but not replacing; however, machinery and vehicles will normally need replacing at fixed intervals. This is illustrated in Table 2.2, where machinery replacement costs are entered in the resource statement as an additional investment eight years after operations begin, and vehicle replacement costs are entered every three years. The replacement period will differ from item to item; the replacement cost is entered in the resource statement in the year before the replaced asset will be required in order to ensure continuous operation. Such replacements will be repeated as often as necessary within the overall length of time anticipated for project operations.

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Table 2.2  Project investment costs (000) Items

Replacement period (years) Years

Land preparation Building Machinery Vehicles

0 40 8 3

1 20 60

2 40 40 12

5

8

10

11

14

17

18a

12

(42) 0 (8)

40 12

12

12

12

Residual values; bracket indicate negative values, which are benefits

a

Fig. 2.2  Project life: technical (T), market (M) and economic (E) Life

T

M1

M2

E

Time

The project life will differ from case to case. It will affect the number of times replacement costs are incurred and the residual values of different assets. There are three bases upon which the project life can be determined (Fig. 2.2). The first is the technical life of the major replaceable assets, for example, machinery. It may be assumed that the project ends when the

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25

machinery is worn out. Within the life of the machinery, as in the example of Table 2.2, other items, like vehicles, will have to be replaced every three years and may not be worn out at exactly the same time as machinery, thus retaining a residual value. Alternatively, the project life may be determined by the market life of the output or, where benefits are not a marketed product, the period for which the benefits will satisfy a need. Usually this is the number of years over which there is a demand (or need) for additional output produced by a project. In the case where this exceeds the technical life of the major assets (M1, Fig. 2.2), these assets also will need replacing and will have a residual value at the end of the project life. Where the market life is shorter than the technical life of major assets (M2, Fig. 2.2), the latter will have a residual value without replacement. However, as a third possibility, the project life may be determined by the economic life of the major replaceable assets (E, Fig.  2.2) and this requires closer consideration of the replacement decision. Within the market life of the output, and with ageing machinery, it may be more economic to close a project down, or to replace the machinery before the technical life has been completed. Maintenance costs for an investment, for example, may evolve in two ways. Maintenance costs associated with one alternative may continue at a normal level which remains constant each year, but another alternative involves increased maintenance costs over time associated with longer down-time or increased operating costs per unit of output. At some time, maintenance and operating costs may increase to the point where it becomes economic to replace old machinery with new, not because it is fully worn out but because the maintenance and operating costs associated with a new machine are now sufficiently low to justify replacement. In this way, the economic life of major assets may differ from the technical or market life and involve an earlier replacement. This can be illustrated through Fig. 2.3 in which the line AA represents the operating costs per unit of output which increase over time with the age of the machinery. This increase is even more marked when maintenance costs per unit of output are added to operating costs as in line BB. The line BB has to be compared with line CC which, for the same level of output, represents the operating, maintenance and capital costs per unit of output of replacing the old machinery with new. The point D shows the point at which the operating, maintenance and capital costs of replacement are worth incurring by comparison with the increasing operating

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Costs C

B C

D

B A

A O

E

T

Time

Fig. 2.3  Economic life of a project

and maintenance costs of the old machinery. Hence, where T represents the technical life of the old machinery, E will represent its economic life. In many cases, the point D will lie within the technical life of the original investment. Thus, economic life of an investment depends on determining the best year in which to replace the main assets it has created.2 Only where the project life is determined exactly by the technical life of the major replaceable assets will these have a residual value of zero at the end of the project; other assets will still have a residual value even in these circumstances. Residual values for each investment item need to be entered as a resource benefit at the end of a project. These residual values can be given two interpretations. The first is that they represent the salvage value of remaining assets, the amount they would be worth if they were sold off. The process of selling assets may take some time, and so this salvage value can be recorded in the year after operations cease. Alternatively, residual values can be interpreted as the accumulated value of net benefits to be generated in the future from the remaining assets if they are adapted for other purposes rather than being sold off; these residual values can be

2  Any investment undertaken before the project should be treated as a sunk cost that is irrelevant for this decision

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written into the final year of project operations, as representing the value at that moment of a stream of returns in subsequent years. Whichever interpretation is given to residual values, a value has to be estimated. In practice, this is generally derived by allowing for the process of financial depreciation rather than by estimating salvage values or future net benefits directly. Hence, residual values are often estimated as the difference between the initial cost of an asset and the accumulated depreciation up to the end of the project. However, it should be noted that this widely used approach rather crudely uses an accounting concept of depreciation to derive an estimate of an economic concept, the value of assets at the termination of the project. Although conventionally entered under investment as negative costs, residual values represent the value of assets to the project at the end of its life and hence are benefits.

Operating Costs The level of operating costs will depend upon the level of output. Generally, operations will not start at the highest level the assets could sustain; capacity utilisation may build up over several years to the maximum sustainable level. The time profile of operating costs will generally follow the trend in output. However, the relationship between capacity utilisation and operating costs need not be simple. Some operating cost items may not vary with the level of output. Operating costs may be a combination of fixed and variable costs as illustrated in Fig. 2.4. Fixed operating costs will be incurred whatever the level of output; the variable costs will change as output increases becoming constant when maximum sustainable output is reached. Finding total operating costs involves adding these two elements together. Hence, total operating costs usually fall as a proportion of the value of output as higher capacity utilisation rates are reached.

Working Capital Working capital refers to the physical stocks needed to allow continuous production. These stocks have to be built up at the beginning of operations and may have a residual value at the end of the project life. There are three components of working capital—initial stocks of materials, final stocks of output and work-in-progress (Table 2.3). Initial stocks of materials are required at the beginning of production. The level of initial stocks will depend upon planned annual production levels. For example, if initial

28 

S. CURRY AND J. WEISS

Fixed and variable costs

Total

Variable

Fixed

Output

Fig. 2.4  Operating costs

stocks are to be maintained at one-twelfth of annual requirements, they will have to be increased each year as production rises to its highest sustainable level, and further incremental commitments will need to be recorded in the project resource statement (Table  2.3 rows 6 and 7). Initial stocks need to be purchased in advance of production increases, and so are recorded in the year before the output level to which they refer. Similarly, the production process will give rise to final outputs that will be stored for a period before distribution. Again, the level of final stocks will be estimated as a proportion of annual production and will involve annual increments until the highest sustainable output level is reached. Final stocks embody the initial materials and the value of resources such as labour or energy, committed through the production process. Final stocks should be valued not at the revenue that will be achieved when they are finally sold but at the value of total operating costs embodied in their production (Table 2.3, rows 8 and 9). Working capital also involves work-in-progress. At any point in time some materials will be passing through the production process. Such working capital is valued at the average of the initial stocks and the final

2  MAIN FEATURES OF PROJECTS, RESOURCE STATEMENTS AND FINANCIAL… 

29

Table 2.3  Working capital calculationa Capacity utilisation – (%) Years Operating costs   1. Fixed   2. Semi-variableb −Power   3. Variablec— Materials   4. Variablec— Labour   5. Total operating costs Working capital   6. Initial stocksd   7. Change over previous year   8. Final stockse   9. Change over previous year 10. Work in progressf 11. Change over previous year 12. Total working capital change

60

70

80

90

100

100

100

100

100

100

0

1

2

3

4

5

6

7

8

9

10

– –

100 100 100 100 100 100 100 100 100 100 60 65 70 75 80 80 80 80 80 80

–

120 140 160 180 200 200 200 200 200 200

–

60

–

340 375 410 445 480 480 480 480 480 480

70

80

90

100 100 100 100 100 100

10.0 11.7 13.3 15.0 16.7 16.7 16.7 16.7 16.7 16.7 – 10.0 1.7 1.8 1.7 1.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 –

28.3 31.3 34.2 37.1 40.0 40.0 40.0 40.0 40.0 40.0 28.3 2.9 2.9 2.9 2.9 0.0 0.0 0.0 0.0 0.0

0.0

0.4

0.4

0.4

0.4

0.5

0.5

0.5

0.5

0.5

0.5

–

0.4

0.0

0.0

0.0

0.1

0.0

0.0

0.0

0.0

0.0

10.0 30.4 4.7

4.6

4.6

3.0

0.0

0.0

0.0

0.0

57.3g

Some discrepancies owing to rounding Varies with half of the difference in capacity utilisation c Varies with the full difference in capacity utilisation d One-twelfth of annual materials for subsequent years e One-twelfth of output valued at production cost f In this case the production period is 10 days, and operations are for 250 days per year so that work in progress is [(row 8 − row 6 of previous year)/2] × 10/250 g Residual value shows all working capital recovered in last year, as production is closed down a

b

stocks for the same year. The quantity of working capital to be valued like this depends upon the production period, that is, the time it takes for initial materials to be transformed into a finished product, which can be quite short. For example, in Table  2.3, the production period is ten working days out of an annual level of 250 working days, so that the working capital tied up in work-in-progress will be small in relation to both total annual

30 

S. CURRY AND J. WEISS

output and initial and final stocks (Table 2.3, rows 10 and 11). The three components of working capital will generally have a residual value. This will be the total value of working capital at the highest sustainable level of output and will accrue in the final operating year as stocks are reduced with the ending of production.

Benefits There can be several benefits even of a directly productive project. Such a project may produce different types of output which are sold. These will be aggregated together to give the direct benefits of the project. Direct benefits are generally fully variable with respect to capacity utilisation; they are generally taken as equal to annual production less any increase in final stocks that is required as production builds up. The level of direct benefits will depend on factors outside the project, such as incomes and competing products, as well as factors determined within the project such as quality and in most cases, price. In addition, there can be indirect benefits, which accrue outside the project organisation. For example, an expanded supply from a project may create a new market and the opportunity to use surplus resources for the project’s suppliers. Such indirect benefits also ought to be included in a resource flow from the economic point of view although they will not appear in the financial statement from the owner’s point of view. These types of benefits are more difficult to estimate and will not necessarily be variable in relation to production. A key distinction in many projects is that between non-incremental outputs and incremental outputs. Non-incremental outputs are those produced by a project, which displace other supplies. For example, a road improvement project to facilitate the transport of goods may draw freight traffic away from rail transport, as well as lowering the cost for existing road users. The total freight being transported may be the same with the project as without the project, but with the project a higher proportion would go by road. Incremental outputs are those produced by a project in addition to outputs without the project. For example, the road improvement project may generate new traffic through reducing the costs of transport. The distinction between non-incremental and incremental outputs is important for the way in which project benefits are valued, as will be shown in following chapters.

2  MAIN FEATURES OF PROJECTS, RESOURCE STATEMENTS AND FINANCIAL… 

31

Central to any project are projections of future demand or need, as these will determine planned capacity and expected flows of benefits. Forecasts can be produced in a variety of ways with different degrees of sophistication. The simplest approach is to simply project the rate of past output growth into the future. This is clearly crude since theory points to the influence of income, product price and the price of substitutes. In addition, past projections can be undermined by shocks in a market, for example, due to a change in access to imports, and changes in taste and influences like advertising. A relatively simple projection links product demand with expected income growth, using an income elasticity.3 More sophisticated variants of this include the influence of price, at least of the good itself and possibly of substitutes. These effects on demand can be introduced into a regression framework or in more rigorous analysis in an economic model that captures demand and supply interactions.

Some Project Resource Flows The figures in a project statement can be represented as a set of annual cost and benefit flows. Different types of projects will have different resource flows, depending upon their technical and economic characteristics. An essential step in project analysis is to derive an accurate resource flow for the project being analysed, understanding not just the technical features which lie behind it but the assumptions on which the resource flow is based. The following illustrations show a series of resource flows for different types of projects. Table 2.4 is a resource flow for a light manufacturing plant. It involves a short construction period followed by a build­up of production to a stable level. Operating costs are largely variable, and so working capital is built up incrementally along with production. The net resource flow shows a pattern typical of manufacturing projects, with negative net resources in early years, including the first two years of operation, followed by a stream of positive net resources. Figure 2.5 illustrates in outline the cost and benefit time profiles of several other types of projects. Example A shows a continuous production chemical plant; operations begin at full capacity, there is very little replacement investment and the proportion of fixed operating costs is 3  An income elasticity gives the ratio of the percentage change in demand to a percentage change in income. A similar ratio of percentage changes applies to the price of a product and the price of one or more substitutes.

–

% capacity utilisation Capital costs: Land Buildingsa Plant and equipment Vehicles Working capital 1. Total capital costs Operating costs Material Labour Fuel and water Maintenance Overheadc 2. Total operating costs 3. Costs 1 and 2 4. Revenue 5. Project net resource flow (4–3) 8421 300 100 50 1000 9871

364 364

80

6

8421 300 100 50 1000 9871

– –

80

7

9971

8421 300 100 50 1000 9871

100 – 100

80

8

9871

8421 300 100 50 1000 9871

– –

80

9

9871

8421 300 100 50 1000 9871

– –

80

10

9871

8421 300 100 50 1000 9871

– –

80

11

9971

8421 300 100 50 1000 9871

100

100

80

12

9871

8421 300 100 50 1000 9871

– –

80

13

9871

8421 300 100 50 1000 9871

–

80

14

– (525) (1125)

16

7084

8421 300 100 5 1000 9871

1650

(1650)

–

– (2787)b – (2787) (1650)

80

15

9975 11,400 11,400 11,400 11,400 11,400 11,400 11,400 11,400 11,400 11,400 824 1165 1529 1429 1529 1529 1529 1429 1529 1529 4316

8788 10,235 9871

7368 280 95 45 1000 8788

363 363

70

5

Source: Adapted from COMSEC (1982, 18) Notes: All values at constant prices a Buildings have a 40-year life, plant and equipment have a 20-year life; vehicles are replaced every 4 years. b Working capital all recovered in last operating year c Overhead costs increased once only when 60% capacity is reached.

7125 8550 242 379

6100

6315 260 90 40 1000 7705

– 4275 5700 (6100) (1406) (100)

5681

–

5263 240 80 35 900 6518

100 365 466

60

4

6883 8171

4210 220 75 30 900 5435

3157 200 70 25 900 4352

365 365

50

3

5800

365 365

40

2

1329 1329

30

1

100 6100

100 1400 4500

0

Years

Table 2.4  Carton project: resource flow statement ($000)

2  MAIN FEATURES OF PROJECTS, RESOURCE STATEMENTS AND FINANCIAL… 

Benefits

A Chemical plant

Costs

B Holiday hotel

C Nuclear power plant

0

10

Fig. 2.5  Project resource profiles

20

30

40 Years

33

34 

S. CURRY AND J. WEISS

D Tree crop and processing plant

E Cattle project

F Cement plant expansion

G Up-dating plantation project

0

10

Fig. 2.5  (continued)

20

30

40 Years

2  MAIN FEATURES OF PROJECTS, RESOURCE STATEMENTS AND FINANCIAL… 

35

small relative to the flow of materials through the plant and the physical stocks of inputs and outputs the project requires. B is a typical holiday hotel project. There is large initial investment in landscaping and buildings and high fixed operating costs where staff are employed round-the-clock, full-time, in the face of an uncertain occupancy level. Variable operating costs, largely food and cleaning materials, are small, as is working capital. The net resource effects are very sensitive to the occupancy level. The investment requires regular replacement of operating assets and renewal of furnishings and equipment to sustain quality standards. C is of a nuclear power plant. The time profile includes a long construction period of 8 years with a lengthy operating period as well. The fuel-­ rods are purchased at the beginning of operations and are subsumed under investment. There is hardly any working capital as this is a non-storable product. Finally, the residual value of the nuclear power plant is negative since there are substantial costs of dismantling and disposal of materials at the end of the project’s life. D also shows a project with a long gestation period, a tree-crop project combined with a processing plant. However, the investment is not continuous. The first four years involve the phased planting of trees in a cleared area; because the trees do not start yielding fruit for six years after propagation, the processing plant does not need to be constructed until the sixth year. In addition, full bearing of the trees takes 10 years from propagation; the full benefits of planting will not accrue until 10 years after the completion of planting—that is, year 13. By that year all trees will be fully bearing for 30 years per tree. Without replacement of trees, benefits will start decreasing over the last four years of the project life. E involves an infinite project life. It relates to a cattle project on vacant land. An initial purchase of cattle stock is reinforced by extra purchases of stock over the years. The extent to which cattle are slaughtered and used builds up in a series of steps to an approximately stable level by the 15th year. The project operations are potentially infinite, since the investment— the cattle herd—reproduces itself. F relates to a cement expansion project based on an existing site and using existing distribution channels. The additional capacity investment utilises some of the same overhead facilities on site, so incremental costs are largely the variable costs in production. The joint use of existing facilities allows a low level of additional costs when production is building up to full utilisation.

36 

S. CURRY AND J. WEISS

Finally, G refers to an updating plantation project, where the additional investment is meant in part to bring back into use under-utilised existing assets. This generally involves a cost-saving in earlier years; in other words, the immediate benefits are lower operating costs after reorganisation and improvement of existing production, even before any expansion of production from the new investment takes place. These illustrations indicate the variety of cost and benefit relations over time in project statements for different projects. However, they all have a basic characteristic: the larger cost items precede the larger benefit items. When set out in a time profile, resources are committed now in the expectation of benefits in the future. This basic characteristic defines the investment problem: are the benefits in the future a sufficient return on the earlier costs? To answer this question requires a method for summarising the whole stream of benefits and costs of a project, and for applying decision-­making criteria.

Resource Statements and Financial Statements Resource statements incorporate the effects of a project from the point of view of total capital. The investment may be financed from different sources. Government may capture some of the net resources produced through taxation. Project statements incorporating these additional financial effects are termed financial statements. Only a brief outline of financial statements will be given here, based on two principal issues: first, how a resource statement should be adjusted for loans of all types, and for tax payments; secondly, how a resource statement should be adjusted when an investment is financed through foreign loans and equity, resulting in corresponding outflows from the economy. The financial statement from the point of view of the project owners may differ considerably from the resource statement from the point of view of total capital. This is illustrated in Table 2.5, where a project involving an investment cost of 5000 is financed in part through a loan of 3000. This loan inflow requires a series of loan repayment and interest outflows. The financial statement (before tax) differs from the resource statement; the net expenditure in the investment period by the project owners is smaller because of borrowing, whilst the net annual inflows after loan repayment and interest repayments are smaller also. An additional feature of any project is the tax that has to be paid on profits. The government allows profit to be calculated after interest

2  MAIN FEATURES OF PROJECTS, RESOURCE STATEMENTS AND FINANCIAL… 

37

Table 2.5  Project financial statement Year Outflows Investment cost Working capital Operating cost Loan repaymenta Interest Inflows Revenue Loan inflow I Resource flowb II Financial flow before taxc Calculation of tax  Profit after interestd  Depreciatione  Taxable profitf III Tax at 40% IV Financial flow to owners (II–III)

0

1

2

3

4

5

5000 50 1000 561 360

1000 524 288

1000 490 206

1000 458 144

– (50) 1000 428 72

2500

2500

2500

2500

2500

3000 (5050) (2050)

1500 579

1500 688

1500 804

1500 898

1550 1050

(2050)

1140 1000 140 56 523

1212 935 277 111 577

1284 873 411 164 640

1356 816 540 216 682

1428 763 665 266 784

Loan repayments over five years, deflated by an annual inflation rate of 7% Revenue minus investment cost minus working capital cost minus operating cost c Inflows minus outflows d Profit after interest is revenue minus operating cost minus interest e Depreciation over five years, deflated by an annual inflation rate of 7% f Profit after interest and depreciation a

b

payments and after a depreciation allowance related to the project’s stock of fixed assets.4 This taxable profit is the basis of an additional tax outflow, reducing the financial flow to the project owners. The financial statement is the basis for calculating a return to equity, that is the return to the project owners for the funds they have put in. The simple illustration of Table  2.5 has the same basic characteristic as the resource statements presented earlier; the large negative items appear at the beginning followed by the positive items. The investment problem for the owners is the same as from the point of view of total capital; are the benefits in the future a sufficient return on the earlier costs?

4  As explained in the notes to Table 2.5, items fixed in money terms—loan interest, loan repayments and the depreciation allowance are deflated by the expected inflation rate to give real values. This allows them to be included in a constant price analysis.

38 

S. CURRY AND J. WEISS

Project financial statements can be very varied. They will vary between different projects because of differences in financing arrangements and in the way that the tax and subsidy system affect each project. The return to owners can vary substantially for the same type of project because of such differences. Although it is essential that a project generates a sufficient return to owners for investment to take place, nevertheless the return to owners is not necessarily a good guide for investment decision-making when it comes to choosing between different types of projects. The financial and tax arrangements influence the distribution of benefits between lenders, government and project owners, but do not reflect the productiveness of the underlying investment from the point of view of total capital or the national economy. A particular problem in the use of financial and resource statements is the treatment of foreign loan and equity transactions. Foreign loan and equity inflows increase the command of a project over investment resources. Usually, they will be spent on investment items or working capital. However, the consequent interest and loan payments, and remitted profits, will reduce resources available to a project. Like other financial transactions, these should be included in the financial statement for the project. Whether such inflows and outflows should be included in a project resource statement depends on the specific circumstances. Foreign loan and equity inflows can be regarded as adding to total resources of the economy only if they were attracted by a specific project. Where such inflows would not take place without the specific project in question, they can be included from the point of view of total capital for that project, and the consequent interest and loan payments, and remitted profits, should be recorded as outflows. This may change the project’s effects on the national economy substantially. For example, where the interest rate on the foreign loan is higher than the return on the project the resources available to the national economy will be reduced. However, where the interest rate on the foreign loan is lower than the return on the project, the resources available to the national economy will be increased. It is not often the case that foreign flows should be included in a project resource statement. Where projects are dependent on multilateral or bilateral funds, the funding agency may have an investment budget from which it draws; allocation of funds to a specific project will not increase the total resources available to the economy. Similarly, directly productive projects using private capital inflows may not induce an additional inflow of

2  MAIN FEATURES OF PROJECTS, RESOURCE STATEMENTS AND FINANCIAL… 

39

resources to the economy, where there is a corporate investment budget that would be spent in the country anyway. Only where there is a clear case that foreign loan and equity inflows can be regarded as incremental resources available to the economy because of a particular project, should they be included in the resource statement.5

Use of Constant or Current Prices Most project analyses, both financial and economic, work in constant prices, which are usually the base (e.g. year 1) prices. This means that all costs and benefits are specified in prices of that reference year. The assumption is that general price inflation can be ignored for many purposes, if it affects all items equally. In practice this assumption is often used, although if accurate projections of future price rises are available for specific items (such as oil or important primary commodities), they can be incorporated in project analysis. This is done by allowing for relative price changes. Relative price changes refer to the rate of change of a future price, for a project input or output, relative to the rate of change of prices in general. If the general rate of change of future prices is expected to be r per cent per annum over the life of a project, and a specific output price is expected to increase at p per cent per annum, then the relative future price change for the output is given by:

1  p  1 1  r 



(2.1)

with all percentages expressed as a decimal. As a simple illustration, with general inflation projected to be 5% annually, and wages of workers employed on a project are governed by a negotiated wage settlement that allows only a maximum 3% increase annually, each year this scenario prevails the real wage per worker will be declining by 1.9%, as (1.03 / 1.05) −1) = 1.09% This relative price change can be positive or negative and where it is possible to incorporate such changes the important economic effects from a project in the aggregate should be captured.

5

 This issue is discussed further in Chap. 13 on Distribution

40 

S. CURRY AND J. WEISS

However, what is relevant for financial analysis and for distributional questions is that significant inflation can alter the distribution of project benefits and costs. This is because any project effect fixed in monetary (or nominal) terms has its real value reduced by inflation. This applies to allowances that reduce a project’s tax liability, such as the depreciation allowed on capital assets and interest repayments, and to any cash held as working capital. Also, any significant differential between national and foreign inflation over the life of a project can alter the real exchange rate for the economy and change the local currency cost of any foreign borrowing. Hence high inflation can have potentially contradictory effects. Price increases can shift the distribution of financial benefits in the direction of the government, through lowering the real value of depreciation allowances and thereby increasing tax liability on profits, but can also shift benefits towards investors, as the real costs of loan payments diminish over time. Also, a devaluing exchange rate will raise the cost of foreign borrowing. In principle a full financial and distribution analysis allows for this, although in practice getting accurate price projections is very difficult. In general, the assumption is that constant price analysis will yield an adequate result for assessing financial and economic viability, especially where price increases are expected to be fairly low and exchange rates fairly stable. Chapter 12 has a further discussion of this.

Discounting The total costs and benefits of a project can be added up over the full project life; however, this would assume that all resources used up or generated in different years are valued equally, so that the investment resources committed in the first year are of equal value to the benefits generated in the 20th year, for example. Conventionally, resources used up or generated in earlier years are valued more highly than resources in later years. The process of discounting applies a weight to the resources in different years to convert them to a common base year, which is usually year 1.6 The weight applied in different years is known as the discount factor, and it depends upon a chosen rate of discount, which measures the fall in value of net benefits over time. If the same level of costs will be incurred in two successive years, then the costs incurred in the second of the two years will be given a lower value relative to the same costs in the first year.  As discussed above, for simpler projects the base year could be year 0, in which case first-­ year costs or benefits are not discounted. 6

2  MAIN FEATURES OF PROJECTS, RESOURCE STATEMENTS AND FINANCIAL… 

41

This lower value can be specified by multiplying the second year’s costs by a factor of the following form: Discount factor 

1 1  r 

(2.2)

where r is the rate of discount expressed in decimals. For example, if it were appropriate to value the second year’s costs at a level 10% below that of the first year then r would be set at 0.10 and the discount factor would become 1 / 1.10 = 0.909 (Table 2.6). The comparison between 2 years can be extended to comparisons between many years. The costs and benefits of a project arising in different years can be weighted to give an equivalent value in the base year. Costs and benefits in subsequent years are weighted by the discount factor relevant to each year, where: General discount factor 

1

1  r 

(2.3)

t



and t is the year of the project life. The weight to be applied to the costs and benefits of different years then depends not just upon the rate of discount, r, but also on the number of years, t, over which the discounting is conducted. This is illustrated in Table 2.7 for two discount rates. The discounted net benefits are smaller in later years and for a higher rate of discount. This follows from the character of the general discount factor. The discount rate has only to be moderately high, say 12%, for the discount factor to become very small after a few years, and hence for discounted costs and benefits to be reduced to a negligible value. When discounting is applied so that all resources are Table 2.6  Applying a discount factor as a weight

Year First Second Costs Discounted costs (Discount rate 10%)

200 200

200 200 × 0.909 = 181.8

42 

S. CURRY AND J. WEISS

Table 2.7  Discounting a net benefit stream Years

0

1

2

3

4

5

Net benefit stream Discount factor at 10% Discounted net benefits Discount factor at 7% Discounted net benefits

0 1.000 0 1.000 0

100 0.909 90.9 0.935 93.5

100 0.826 82.6 0.816 87.3

100 0.751 75.1 0.763 81.6

100 0.683 68.3 0.763 76.3

100 0.621 62.1 0.713 71.3

revalued relative to the base year, year 0, the revalued resources are called present values. Discounting as applied in Table 2.7 involves two assumptions which it is necessary to make explicit. The first is that there is a constant rate of decline in the value of resources from year to year; thus, the discount rate is the same for all years of a project statement. The second is that the same discount rate is applied to all resources in a project statement, whether those resources are benefits or costs, and whether those costs are investment or operating costs. These assumptions can be varied, but the process of applying different discount rates in different years or to different resource effects needs careful justification.7 The effect of discounting can be illustrated by the following example involving a simple statement of project costs and benefits (Table 2.8). At a discount rate of 6%, the sum of the discounted benefits exceeds the sum of the discounted costs. The same result applies if the net benefits are discounted and summed directly. In addition, although the annual benefit and cost streams do not change between years, their discounted values do; discounted net benefits decline over time, owing to the lower value placed on resources in later years. It is sometimes useful to apply other factors derived from the general discount factor. In circumstances where there is an equal annual value or amount of resources to be discounted, use can be made of the annuity factor:

1  r   1 t r 1  r  t

Annuity factor 

(2.4)

7  Chapter 8 explains the theoretical case to justify a declining discount rate over time and discusses the case for using a different rate in different sectors.

2  MAIN FEATURES OF PROJECTS, RESOURCE STATEMENTS AND FINANCIAL… 

43

Table 2.8  Discounted resource statement Year

1

2

3

4

5

6

7

Costs Benefits Net benefits Discount factor (6%) Discounted costs Discounted benefits Discounted net benefits

100 0 (100) 0.943 94.3 0 (94.3)

20 50 30 0.889 17.8 44.5 26.73

15 50 35 0.839 12.6 42.0 29.4.

15 50 35 0.792 11.9 39.6 27.7.

15 50 35 0.747 11.2 37.4 26.1.

15 50 35 0.704 10.6 35.2 24.6.

15 50 35 0.665 10.0 33.3 23.3

Note: Figures in brackets are negative

For example, suppose that a small project will generate annual net benefits of 100 per year from years 1 to 10 inclusive, where the discount rate is specified as 0.06 or 6%. The general discount factor can be evaluated ten times for the ten different years and applied to the net benefits in the corresponding years, the results being added to give the present value of total net benefits (Table 2.9, columns 1–3). Alternatively, the annuity factor (AF) can be evaluated once, for r = 0.06 and t = 10, and applied to the annual amounts of net benefits. In this case the annuity factor is evaluated as 7.360. Applying this factor to the annual net benefits gives the total value of discounted net benefits as:

100  7.36  736.0

the same result as at the bottom of Table 2.9 [column (3)]. Evidently, the annuity factor is the sum of the discount factors for all ten years, as confirmed at the bottom of Table 2.9 [column (2)]. Of more use, especially in the construction of financial statements, is the capital recovery factor (CRF). The annuity factor (AF) converts an equal annual amount into an equivalent present value. The capital recovery factor (CRF) performs the reverse operation: it converts a present amount into an equivalent equal annual amount over a period of years, at a specified discount or interest rate. Suppose a loan of 736.0 were taken now, to be repaid at 6% per annum interest in equal annual instalments over ten years. Above we have 100 × 7.36 = 736.0

44 

S. CURRY AND J. WEISS

Table 2.9  Discounting a constant annual amount Year 1 2 3 4 5 6 7 8 9 10 Totals

Net benefits (1) 100 100 100 100 100 100 100 100 100 100

Discount factor (12%) (2)

Discounted net benefits (3)

0.943 0.889 0.839 0.792 0.747 0.704 0.665 0.627 0.591 0.558 736

94.3 88.9 83.9 79.2 74.7 70.4 66.5 62.7 59.1 55.8 736

Rearranging, this can be written as:

100  736.0  1 / AF

The equal annual amount 100 can be derived by multiplying the loan amount 736.0 by 1/AF. The expression 1/AF is the capital recovery factor, explicitly: Capital recovery factor 

r l  r 

l  r 

t

t

1

(2.5)

This use of the capital recovery factor can be illustrated by another example. Suppose a loan of 1000 taken now is to be repaid in equal annual amounts over eight years at an interest rate of 8%. The equal annual amount (x) to be paid can be calculated by:

x  1000  CRF

Here the capital recovery factor is evaluated as 0.174, and the equal annual amount at 174. This amount has to be paid each year for eight years to fully repay the loan and the associated interest. Discounting brings out the importance of timing in resource flows, which is emphasised by the two further illustrations below (Table 2.10).

2  MAIN FEATURES OF PROJECTS, RESOURCE STATEMENTS AND FINANCIAL… 

45

Table 2.10  Discounting and time profiles Equal undiscounted net benefits; different time profiles Discount rate 0.06 Present Value A

91.70

B

96.41

Years Net Benefits Net Benefits

1

2

3

4

5

6

-100

20

40

60

60

60

-100

48

48

48

48

48

2

3

4

5

6

-200

80

80

80

80

80

-100

-100

100

100

100

100

Equal undiscounted net benefits; different gestation periods Discount rate 0.06 Present Value A

129.24

B

125.05

Years Net Benefits Net Benefits

1

In the first, two alternative projects have the same undiscounted net benefits of 240, but the time profile is different. The sum of the discounted net benefits is higher in A, as the net benefits occur more quickly. In the second illustration, the principal difference between the alternatives is between a project with a short gestation period (one year) and one with a longer gestation period (two years). With discounting the net benefits of the latter (B) need to be considerably more than the former (A) to compensate for the delay in their arrival; otherwise, discounting will favour projects with shorter gestation periods.

Discount Rate for Financial Analysis Discounting must be applied to financial statements and for financial analysis actual market rates of interest and returns on alternative investment provide the basis for the discount rate. However, any discount rate will have to be expressed in real terms to be applied to resource or financial statements drawn up using constant prices. This means adjusting actual borrowing or lending rates, for example, for price increases using a recent

46 

S. CURRY AND J. WEISS

or forecast rate of increase of prices. The real rate of discount can be calculated as: Real rate of discount 

1  i   1, l  p

(2.6)

where i is the nominal rate and p is the annual average rate of increase of prices In applications to project financial statements, the point of view is that of the project owner, and so the project financial statement will include the costs of borrowed funds. A minimum condition for a project owner is that the project should generate resources in the future at a rate that will allow repayment of all borrowed funds. The cost of these borrowed funds needs to be expressed in real terms where the project financial statement is at constant prices; where there is more than one source of borrowed funds a weighted real cost of borrowing can be calculated. Table 2.11 gives an illustration of a financial discount rate for a project funded by a mix of equity, domestic loans and foreign loans. Allowing for different rates of inflation domestically and internationally, the weighted real financial cost of funds used by the project is just over 5%. This provides the basis for a financial discount rate.8 This issue is discussed further in Chap. 12. Table 2.11  Financial cost of initial capital (FCIC) Source Railway project Equity Domestic loan Foreign loan

Amount

Weight

Nominal interest (%)

Inflation rate (%)

Real interest (%)

2593 1460 1660

0.454 0.256 0.291

12.0 11.0 7.0

6.0 6.0 2.5

5.7 4.7 4.4

5713

Weighted average

5.1

 Application of such a discount rate to a financial statement assumes that the project owner will be satisfied if the return on the equity proportion of financing is the same as the real weighted cost of borrowing. However, the equity funds can usually be invested in alternative projects whose return to equity is independent of the rate at which funds are borrowed or lent, because capital markets do not necessarily reflect these opportunities. Hence it may be necessary to use additional data to estimate the returns available on equity investment elsewhere. 8

2  MAIN FEATURES OF PROJECTS, RESOURCE STATEMENTS AND FINANCIAL… 

47

Discount Rate for Economic Analysis For an analysis from the economy point of view, two basic rationales for the process of discounting can be given. The first refers to the opportunity cost of resources. The resources committed to the productive process in one project could alternatively be committed in another. For the first alternative to be preferable, the return on resources should be greater than the return on resources in the other project. More generally, for a project to be worthwhile it must generate a return on resources greater than in easily available alternative uses. The discount rate should be set equal to the return in alternative uses, to reflect the opportunity forgone when resources are committed to the project. This is an opportunity cost approach to the discount rate. The second rationale is based on subjective reasoning. It is presumed that resources will be committed to uses that generate additional consumption benefits in the future. However, most people prefer additional consumption sooner rather than later. They have a positive rate of time preference, implying that the same additional benefits in year 2 is worth more than in year 3, which is worth more than in year 4, and so on. This may be because the future is uncertain, or because people may expect their incomes and hence their consumption to be higher in the future and so consumption now is more valuable, or simply because most people have a very short time horizon. By this interpretation, the discount rate could be seen as representing the rate at which people’s valuation of a unit of additional consumption in successive years declines. This rate may differ for different people. For economic analysis of projects, a single rate of discount for project participants, often referred to as the social rate of time preference, can be set by the government for planning purposes, to represent some average for all groups or the planner’s view of what time preference should be.9 This is a time preference approach to the discount rate.

Conclusion A project involves the commitment of resources now to obtain extra resources in the future. The resources produced and used up by a project need to be recorded in a project statement. For decision-making purposes 9  Chapter 8 on the discount rate discusses these two perspectives on the discount rate in more detail.

48 

S. CURRY AND J. WEISS

these resources are valued at constant prices. A project financial statement will also be recorded at constant prices, but will indicate the effects of a project from the owners’ point of view, allowing for financial and tax transactions. There are several types of investment project. Comparison between different types of projects is made easier where clear assumptions and conventions are used in relation to the project life, replacement investments, operating costs, working capital, and other items included in project statements. Project resource statements, and the corresponding financial statements from the owners’ viewpoint, will have a common characteristic; initial net outflows during the investment period will be followed by net inflows in most operating periods. For decision-making purposes, it is necessary that the annual net flows be added together in some way. The technique of discounting provides a means of assigning relative weights to the net flows in different years before they are added together. The following chapter discusses how this information can be used for decision-making.

Further Reading The classic texts in the early literature on this topic are UNIDO (1972), Little and Mirrlees (1974) and Harberger (1972). The early chapters of these still provide the clearest rationale for the subject. It is also useful to review some of the guidance documents and manuals of international development assistance organisations to see how these concepts have been applied in practice, see, for example, EU (2014) and ADB (2017). In the UK context, the government has a helpful guide to the analysis of central government projects, HM Treasury (2022).

Bibliography ADB. (2017). Guidelines on Economic Analysis of Projects. Asian Development Bank Manila. COMSEC. (1982). A Manual on Project Planning for Small Economies. Commonwealth Secretariat. EU. (2014). Guide to Cost Benefit Analysis of Investment Projects. European Commission.

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49

Harberger, A. (1972). Project Evaluation—Collected Papers. Macmillan. HM Treasury, Government of UK. (2022). Green Book: Central Government Guidance on Appraisal and Evaluation, 2003, Updated 2022. Little, I., & Mirrlees, J. (1974). Project Appraisal and Planning for Developing Countries. Heinemann Educational. UNIDO. (1972). Guidelines for Project Evaluation. UN.

CHAPTER 3

Project Criteria

The construction of resource statements and the application of discounting are basic tools of project analysis. However, the purpose of project analysis is to facilitate decision-taking on proposed projects. This chapter outlines the criteria used for taking project decisions. It restricts itself to comparisons using discounted values of benefits and costs. Use of project criteria for decision-taking will be illustrated in two contexts. The first is when a decision is required about a single project proposal. A project resource statement is constructed to represent the effects of the project. Using an appropriate discount rate, a decision is required whether to proceed with the proposal or not. The second context involves choice between project alternatives. In this case, more than one project statement is constructed to represent a proposed investment under different conditions, for example, at different locations or using different technologies. Two decisions are required: which alternative to select, and whether to proceed with the project at all. For some projects, benefits can be quantified but not valued. In these cases, the best alternative, in terms of cost, can be identified, but a judgement may have to be exercised on whether or not to proceed with the project. Decision-taking can be from different points of view, and the project criteria discussed here can be applied to both financial and economic analyses. The primary focus of this chapter is on financial analysis, with the

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Curry, J. Weiss, Project Analysis in Developing Countries, https://doi.org/10.1007/978-3-031-40014-8_3

51

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economic discussion taken up in detail in subsequent chapters. The principal point of view adopted here is that of total capital, the whole of the investment in the project, regardless of how it is financed. This allows a comparison of the project, or the project alternatives, with other investments in the economy as a whole. However, reference must also be made to the point of view of the owner of the project.

Project Criteria for Single Projects When Benefits Are Valued To decide whether a single project proposal is acceptable or not, different project criteria can be used. The three criteria in frequent use for this purpose are the benefit-cost ratio (BCR), the net present value (NPV), and the internal rate of return (IRR). Each has its advantages and disadvantages and each needs to be used in a different way for decision-making. However, where resource statements are drawn up using the same information and assumptions these three criteria yield the same decision for single projects.

Benefit-Cost Ratio (BCR) For a project to be acceptable, the discounted value of its benefits should exceed the discounted value of its costs. Discounted benefits can be expressed in a ratio to discounted costs. Formally,

  B  t   / 1  d  BCR   C  t   / 1  d 

t

(3.1)

t



t

t



where, d is a rate of discount t is the number of years from the base year, for each year of the project period B(f) and C(t) are total benefits and total costs in year t The calculation of the BCR is illustrated in Table  3.1 for two different project resource statements using the same discount rate. In example A,

16.6

4607 5021 1.090 414

(1200)

1200

Note: Figures in brackets are negative

Materials Energy Labour Total costs Revenue BCR Net revenue (NPV) IRR (%)

200

Working capital

0

1000

Present values @ 8%

Investment Cost

Example A

Discount rate 0.08

230

400 80 90 570 800

1

300

500 100 100 700 1000

2–6

500

(200) 500 100 100 500 1000

7

Table 3.1  Resource statement: project criteria (Rs 000)

Fertiliser Seeds Labour Total costs Revenue BCR Net revenue (NPV) IRR

Investment cost Working capital

Example B

7.0

1968 1955 0.993 (13)

Present values @ 8%

(375)

30 20 200 690 315

40

400

0

65

30 20 200 250 315

1

65

30 20 200 250 315

2–6

105

30 20 200 210 315

(40)

7

3  PROJECT CRITERIA 

53

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S. CURRY AND J. WEISS

the BCR exceeds 1; in example B, the BCR is less than 1. Where d represents the opportunity cost of resources, then the BCR can be used for decision-making as follows: At the discount rate d: If BCR > 1.0, accept the project proposal If BCR  0, accept the project proposal If NPV  d, accept the project proposal If IRR  1.0, then NPV > 0, and IRR > d; accept the project If BCR  0, accept alternative A, as long as NPV(A) > 0 If Incr. NPV  0 If Incr. NPV = 0, either alternative can be accepted, as long as their NPV is positive However, at different rates of discount the decision might be different. At a discount rate higher than 10% the additional revenues may not prove adequate compensation. The IRR of the incremental resource statement, termed the incremental internal rate of return (Incr. IRR), can be compared with the discount rate. The incremental internal rate of return from Table 3.6 is 12%. At this rate of discount, the NPVs for both alternatives are the same. This incremental internal rate of return is greater than the discount rate of 10%, and confirms the adoption of alternative A. Formally the choice using the incremental internal rate of return method can be formulated as follows: If Incr. IRR > d, accept alternative A, as long as NPV(A) > 0 at d If Incr. IRR  0 at d If Incr. IRR = d, either alternative can be accepted as long as NPV > 0 at d The incremental internal rate of return can be looked at in a different way. Up to this rate of return, alternative A is preferable to alternative B, shown both by a direct comparison of their NPVs and by the NPV of the incremental resource statement; but beyond this rate of return, alternative B becomes preferable. The choice between alternative A and B changes at the rate of discount given by the incremental internal rate of return. For this reason, it is also called the cross-over discount rate, the rate at which the choice crosses over from one alternative to the other. For rates of discount above the incremental internal rate of return, the second alternative B should be preferred to the first A.

3  PROJECT CRITERIA 

69

As in the case of single project decisions, the use of the incremental internal rate of return to make choices between alternatives is subject to the limitations of the IRR measure; it may be negative, and it may not be unique. However, it is often calculated as a project indicator in order to test whether a project alternative can bear a certain rate of interest in its financing, or when there is uncertainty about what the exact discount rate should be. In the latter case, it is useful to have an idea of where the choice may change over from one alternative to the other.

Decisions on Timing In principle an important alternative for any project is the possibility of delaying the decision to invest and investing the funds in a relatively liquid asset that can be accessed when it appears more attractive to invest, for example because demand conditions have changed or because different technologies become available. Again, if alternatives, differing by start date are identified their net benefits can be compared using the NPV indicator and the one that creates the largest NPV should be selected. Application of this comparison requires that a single discount rate reflecting the use of the funds when they are not committed to a specific project be applied to each alternative, and that each alternative is discounted back to the same base year, not to the year in which the alternative is started. Algebraically this can be illustrated for a simple two-period project with investment (K) in one year and net benefits (B) in the second year. If there are three alternatives (A, B and C) with the same capital cost starting in years 1, 2 and 3, respectively, and a discount rate of r per cent then there will be three NPVs denoted by their start dates. Given the one-year lag B2 refers to benefits from investment in year 1, B3 refers to benefits from investment in year 2, and B4 refers to benefits from investment in year 3. Each alternative is discounted to the same base year (year 0). NPVA  B2 / 1  r   K / 1  r  2

NPVB  B3 / 1  r   K / 1  r  3

NPVC  B4 / 1  r   K / 1  r  4



2 3



70 

S. CURRY AND J. WEISS

Table 3.7 gives a numerical illustration for a slightly more complicated example. Again, there are three alternatives (A, B and C) starting in years 1, 2 or 3, but each alternative now has a 5-year life, so net benefits arrive between years 2 to 8 depending upon year in which the alternative starts. Net benefits are assumed to be determined by demand conditions and rise from year 2, peak in year 5 and then decline. A 6% discount rate is used for each, and each is discounted back to year 0. A comparison based on the respective NPVs shows alternative B, with a one-year delay for demand to build up between years 2 and 3, to have the highest NPV. It is therefore the preferred alternative. The difference in NPVs between investing now (NPVA) and delaying a year (NPVB) is 17.70 and is the value of waiting This discussion as in the rest of the chapter assumes that project data are known with certainty. It also assumes that the passing of time through delaying a project has no impact on the information available on future benefits, which would allow a later more informed investment decision. A key point about waiting to invest is the additional information it allows. Once uncertainty is introduced the timing decision becomes more complicated. In principle the fact that a project decision is irreversible and rules out future options, which may be preferable, imposes a cost that should be added to the project’s capital cost. Chapter 9 on uncertainty discusses the concept of an option value, which in theory becomes an additional cost, and implies a modification of standard project criteria.

Table 3.7  Illustration of timing choice Years

A B C A B C

1

2

3

4

5

6

7

8

−220 0 0 NPV 27.00 45.30 39.50

30 −220 0

50 50 −220

80 80 80

90 90 90

50 50 50

50 50

40

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Choosing Between Project Alternatives When Benefits Are Not Valued: Least Cost Analysis The previous example of technological choice involves the comparison of benefits and costs for different alternatives. Least cost analysis considers only the costs of two or more alternatives, treating the benefits as identical. Table 3.8 and Fig. 3.4 present an example of project alternatives relating to a water supply project. Here the same quantity and quality of water per annum can be delivered using pipes of different diameters. The smaller pipe will involve greater pumping costs and last for a shorter time. In order to make the alternatives equivalent, a salvage value of 15 million has been entered in alternative A since it has a working life beyond the 25 years of alternative B. The smaller pipeline alternative B costs less to install, but more to operate. The question in this case is whether the extra investment costs of the larger pipeline A are worthwhile given the associated savings in annual operating and maintenance costs. Figure 3.4 shows that for some rates of discount, the present value of costs for alternative B is higher, and for higher rates of discount, the present value of costs for alternative A is higher. Which alternative to choose again depends upon the discount rate. If the discount rate is set at 10%, alternative A will be chosen. Formally this would correspond to: At the discount rate d, minimise [PVC(i)], where PVC is present value of project costs, and i refers to a project alternative. For rates of discount below 10%, alternative A will still be chosen. Alternative B has the lower present value of costs only at discount rates above 12.3%. Table 3.8  Least cost analysis: pipeline alternatives cost statements ($ million) Discount rate 0.10

0

Installation costs Operating and maintenance costs Total costs PV total costs

90

90 270.2

Alternative 25 A 1–24 20

(15) 20

20

5

0

Alternative B 1–24

25

22.5

22.5

22.5

22.5

70

70 274.2

72 

S. CURRY AND J. WEISS

As the name implies, least cost analysis only addresses the question of how to minimise costs for undertaking a particular activity. It does not address the further question of whether the project is acceptable as a whole. The incremental internal rate of return criterion can be applied also in the context of least cost analysis, to determine whether an alternative with greater initial costs is worthwhile in terms of any savings in annual costs it may yield. In the example of Table 3.9 the alternatives not only involve a difference in investment and operating costs, but in the phasing of investment as well. The alternative involving the larger investment (method 2) would be completed more quickly. Figure 3.5 illustrates the incremental cost flow for these alternatives, obtained by deducting the costs of the alternative with larger, earlier investment from that of the alternative with smaller, later investment, at different rates of discount. The incremental cost flow involves negative values at the beginning, corresponding to the higher investment costs, followed by positive values, corresponding to the saving in operating costs. The question asked through the incremental cost flow is whether the larger investment costs of method 2 are adequately repaid by later saving in operational costs. Where the costs of method 2 have been subtracted from the costs of method 1: If Incr. IRR > d, accept alternative 2 If Incr. IRR  1.0. Second, movement to seek work on a project can entail additional costs, both for the migrant in terms of transport and possibly living cost and for society if additional infrastructure investment, for example in transport and housing, is needed. If these costs can be converted to a per worker cost, this would need to be added to the shadow wage, so SWR   i ai mi · CFi  

(5.5)

where β is the per worker additional cost in economic prices and other terms are as before. Finally, in some formulations the extra effort and its disutility associated with new project work is added as a subjective cost for workers; for example, because they have to leave home, or work for longer hours than in their previous activity. Including this the expression for the shadow wage becomes SWR   i ai mi · CFi    

(5.6)

where μ measures the subjective disutility. Whilst conceptually correct, the complications introduced in (5.4) to (5.6) are rarely addressed in practical estimates, which tend to focus on the direct opportunity cost based on foregone output elsewhere. This is expected to be the main element of the shadow wage. Another way of approaching these complications, which is said to offer a simpler and more practical alternative, is to use the supply price of workers to estimate a shadow wage.12 The supply price is the wage workers will need to accept a 11  This could be due, for example, to lack of realistic information on the prospects of employment in urban areas combined with lack of rural employment opportunities. 12  Harberger suggested this approach many years ago. For a more recent statement, see Jenkins et al. (2018) chapter 12.

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129

job. The advantage of this approach is that additional cost in terms of living expenses and compensation for extra effort or absence from home will be reflected implicitly in this supply price, although costs borne by society, not the individual worker, will not be covered. In the numerical example used above, if the wage workers are offered Rs 7000 per year and they pay deductions of 30% income tax and national insurance, their net income is Rs 4900. If this is the supply price (sometimes also termed the reservation wage), it implies that the additional cost that must be compensated is Rs 843 (the difference between earnings in the previous activities of export and domestic crop production, which total Rs 4057, and the net income workers accept to work for of Rs 4900). These figures do not allow for economic pricing, however, since the 38% premium on the value of export crops is not included and must also be allowed for.13 Hence, whilst providing a numerical value for subjective costs, this approach does not remove all of the complexity. Furthermore, it assumes that at the margin the market is sufficiently flexible to allow workers to make rational choices and seek employment up to the point that the wage offered exactly matches the minimum they will accept. Where there is a scarcity of labour, so workers are in excess demand, there is usually the assumption that the labour market for this type of labour will be sufficiently competitive for wages to reflect opportunity cost in terms of productivity elsewhere. Here valuation of labour should be from the demand side. There are two possibilities: • that additional demand from a new project attracts workers away from activities where they were previously employed full-time • that additional demand generates further supply through labour training or immigration. In the first case, the opportunity cost is the productivity of the workers in the full-time employment from which they are drawn, converted to economic prices. In practice, a common treatment for this type of worker 13  From this analysis the annual shadow wage cost will be the supply price (Rs 4900) plus 38% of the financial value of the export crops (Rs 3000 × 0.38), which gives Rs 6040. This is slightly higher (around 3%) than the result from the opportunity cost approach, when the uncompensated cost of Rs 843 is added to the original opportunity cost shadow wage estimate of Rs 5000.

130 

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is to assume that the market wage on a new project is a reasonable proxy for their productivity elsewhere, at economic prices. In terms of Eq. (5.2) for the SWR, this assumes that SWR  FWR  i ai mi

However, this is not necessarily the appropriate treatment for all skilled labour. In some countries trained or educated workers may be in surplus supply, if the skills they possess are not those required by new projects. Further, government wage controls or employer bargaining strength may prevent wages from rising in response to productivity changes. In both cases direct estimates of output forgone will be required, since the market wage will not be an adequate proxy. In addition, where opportunity cost is in terms of traded activity and where there is a significant premium on foreign exchange, this additional cost will not be accounted for in the project wage. In the second case, where labour supplies expand to meet project demand, opportunity cost is determined by the resources that go into generating this additional supply. For national workers, who are unskilled but receive training, the opportunity cost per worker will be output forgone, that they would have created without the training, plus the training cost per worker. Where foreign workers are required on a project the cost to the economy will be defined differently. If one assumes that the workers would not have immigrated in the absence of the project under analysis, they will have no direct output forgone for the national economy. Their economic cost will be the savings they send abroad as remittances, plus the economic cost of their consumption, when they spend locally what they do not remit. Algebraically the economic wage of foreign workers can be expressed as: SWRF  r.  FWRF  SER / OER   1  r  FWRF  CFc.  wage remitted   cost of loccal consumption  where SWRF is the economic wage of a foreign worker FWRF is the project wage r is the proportion of this wage remitted abroad

(5.7)

5  THE DOMESTIC PRICE SYSTEM OF ECONOMIC ANALYSIS 

131

(1 − r) is the proportion spent locally CFc is the conversion factor for the expenditure made by foreign labour SER is the shadow exchange rate OER is the project exchange rate If foreign workers earn Rs 1000 per month and remit Rs 500, the latter is a direct drain of foreign exchange at the project or actual exchange rate. Any premium on foreign exchange must be introduced by multiplication by the ratio of the shadow to the actual exchange rate. The remaining Rs 500 they spend domestically will generate indirect foreign exchange costs, leading to more imports or less exports, and use of non-traded resources. In theory several specific factors could be used. In practice, unless foreign labour costs are a major part of total project costs, it is often assumed that the expenditure they make at financial prices (1 − r) FWRF is an adequate measure of economic costs, so CFc = 1.0, In general, economic wage estimates can be made at different levels— nationally for broad categories of worker, such as skilled and unskilled; regionally for similar broad categories differentiated by region or economic zone; and finally at the project level for more specific types of workers. National and regional estimates are important for calculations of macro-level economic parameters and as initial approximate estimates for individual projects. However, within a country, labour markets can vary substantially and estimates of output forgone will require careful assessment of the type of workers required and where they are likely to be drawn from. For this reason, where labour is an important input into a project, detailed estimates of the economic wage should be made so that local employment conditions and migration trends can be allowed for. Where labour is only a minor input, national or regional estimates may be sufficient or the shadow wage can be assumed to be the project wage.

Economic Value of Land Land is not often a major item in industrial projects, but in agriculture and some infrastructure investments it can be far more significant. In a competitive land market, prices would equal the expected future gain from the purchase or rental of an additional unit of land. However, in many cases existing land markets are not sufficiently competitive for price to be an adequate guide to productivity. In urban areas land markets may be

132 

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dominated by speculative purchases by buyers whose intention is to hold the land to influence the price, and in rural areas land use may follow from custom rather than financial or economic calculation. The economic price of land is given by its opportunity cost—the net income at economic prices that could be obtained from the land in its most likely alternative non-speculative use. Where land for a factory site was previously unused, its direct opportunity cost will be zero, since no output would be lost by building the factory on the land, and only landclearing costs can be properly attributed to the project. If land is a major project cost, it is best treated as a project-specific item whose value is estimated by taking account of the situation in the project area and the most likely uses of the land. A numerical example can illustrate the procedure. A sugar mill is to be established that requires the expansion of sugarcane farming. The aim is that small farmers previously growing cotton will shift to cane, a higher value crop. The cost of land that is considered here is not that for the factory site, but the much larger area that was previously used for cotton cultivation. This land cost must be included as part of the cost of sugarcane supplied to the mill. Here the opportunity cost of the land is the return per acre at economic prices if farmers continue to grow cotton. This return is the difference between the value of cotton output per acre minus all purchased inputs, such as fertiliser and pesticide, minus also an allowance for the costs of family labour, with in principle all items converted from financial to economic prices. This is shown in Table 5.2. Table 5.2  Estimated returns to land at economic prices

Output  Cotton Inputs  Family labour  Fertiliser  Pesticide  Bullocks  Water Net return

Financial prices (Rs per acre)

CF

Economic prices (Rs per acre)

56.00

1.25

70.00

20.0 5.5 5.5 11.0 5.5 8.5

1.00 0.95 0.95 1.00 1.00

20.0 5.2 5.0 11.0 5.5 23.3

5  THE DOMESTIC PRICE SYSTEM OF ECONOMIC ANALYSIS 

133

In this example cotton and the alternative export crops that farmers might grow have border parity prices 25% above prices paid to farmers, once a premium of foreign exchange is introduced (CF = 1.25).9 Family labour is valued at the relevant market wage in the area of the project, which is used to cost their time. Fertiliser and pesticides are imported at low import duties and even after adjustment for foreign exchange their conversion factors are 0.95. Bullocks and water are non-traded items, and for lack of data their financial prices are taken as their economic prices. Using the CFs to value the data at economic prices, the opportunity cost to the economy per acre of land used for cotton cultivation is Rs 23.3 per acre. This must be included in the cost of cane cultivation, since only if the benefits from the sugar mill are sufficient to cover all costs to the economy, including those generated by the diversion of the land to cane cultivation, will the project be justified. The overall adjustments for arriving at economic prices for project outputs and inputs are summarised in Table 5.3. Table 5.3  Summary adjustments Project item

Treatment

Traded goods

Valued at border parity prices—border price plus or minus adjustment for costs of transport and distribution. Where appropriate, shadow exchange rate used for foreign exchange effects Where these can be increased to meet the needs of new projects, valued at unit economic costs of additional production with all cost items Where these are in fixed supply, priced at their value to users Where these lead to additional consumption, valued at willingness to pay. Where they replace other supplies, valued at economic costs saved Where workers in excess supply, valued at output forgone elsewhere as a result of their new employment on a project Where workers in excess demand, valued at their market wage Where foreign workers involved, remittances are a direct foreign exchange cost valued at shadow exchange rate, and local expenditure converted to economic costs where necessary Opportunity cost defined as net returns from land in alternative use, with returns measured at economic prices

Non-traded inputs

Non-traded outputs

Labour

Land

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The Shadow Exchange Rate Up to this point we have not explained the derivation of an economic value for foreign exchange, what we have termed the shadow exchange rate (SER). The comparison of this value with the official or project exchange rate (OER) gives the premium on foreign exchange.14 The shadow exchange is what is sometimes termed a national parameter, as it applies to all projects in the same economy that generate, save, or use foreign exchange. We first give a theoretical explanation of how the valuation should be carried out before turning to the simplifications that are typically used in practice. In theory, the shadow exchange rate should reflect the welfare change from the additional demand for or supply of a unit of foreign exchange created by a project. Foreign exchange is valuable because it allows domestic residents to access goods that they would otherwise not have access to and thus welfare change can be measured in units of consumption. In principle, a project can lead to a range of possible effects—for example more imports or domestic use of potential exports by a project itself, or expansion of exports or lower imports by others. In addition to these direct effects a large project’s impact on supply and demand in the foreign exchange market may be sufficient to alter the exchange rate, causing price effects that in turn change both imports and exports. In practice tracing all of these repercussions will be highly complex.15 If the prices of traded goods in local currency change because of a small adjustment to the exchange rate created by a project, trade elasticities for individual items can be used to estimate their share in marginal trade. Once marginal trade shares are known they can be used as weights in a comparison between the value to consumers of the traded goods made available by a project (where it adds to or saves foreign exchange) or used by a project (where it is a net importer) and the world price value of these goods. If value to consumers is in national currency and world price value is in foreign currency, this weighted comparison gives an exchange rate, which we term the shadow exchange rate. 14  In principle, as discussed below it is possible that over the life of a project the premium is negative, so the real value of foreign exchange is overstated. This will arise if the national currency of the project country appreciates over time. 15  The treatment of foreign exchange discussed here can be seen as partial in that it ignores how funding for the foreign exchange expenditure impacts on non-traded goods through substitution effects; see Jenkins et al. (2018) for a discussion of impact on non-traded activity.

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Where it can be assumed that the prices at which traded goods are sold in the domestic market (their domestic financial prices) reflect consumer willingness to pay, this procedure can be illustrated algebraically as DPj DP SER   ai . i   a j . OER i WPi j WPj

(5.8)

where SER is the shadow exchange rate and OER is the actual rate facing a project, i and j are import and export goods, respectively ai is the share of import i and aj is the share of export j in additional expenditure of foreign exchange DPi and DPj are the financial prices of i and j, and WPi and WPj are the border parity prices of i and j with foreign exchange values converted into local currency at the OER. As indicated above, the import price elasticity of demand and the export price elasticity of supply can be used to derive the relative shares in marginal trade.16 The procedure can be illustrated with a simple numerical example. Trade for an economy is composed of only two goods, machines which are imported and rice which is exported. For simplicity trade is in balance, so exports equal imports. The share of these goods in additional foreign trade is determined by their trade elasticities. The import elasticity of demand for machines is −1.2, whilst the export elasticity of supply for rice is 0.5. As exports equal imports, the weights are simply determined by the

16  The import price elasticity of demand is the percentage change in import quantity demanded divided by the percentage change in import price and the export price elasticity of supply is the percentage change in export quantity supplied divided by the percentage change in export price. Assuming the country concerned is a price taker on the world market the weights ai and aj can be derived from the trade elasticities, so that for import i, ai = −Mi.fi/ (∑Xj.fj − ∑Mi.fi) and for export j, aj = Xj.fj /(∑Xj.fj − ∑Mi.fi), where fi and fj are the trade elasticities for i and j, and Mi and Xj are the initial values of i and j, without the project valued at the OER. The negative sign on M.fi is because price elasticity of demand fi is negative, so to get the trade share from the summation of total trade a negative sign is need.

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Table 5.4  Foreign exchange price comparison: illustration Commodities Rice Machines Weighted average SER/OER

Weights

Financial price (Rs)

World price (Rs)

Price ratio

0.29 0.71

800 2000

900 1400

0.89 1.43 1.27 1.27

relative elasticities.17 A price survey has been carried out to identify domestic prices for these goods. The weighted price comparison is in Table 5.4 and the weighted average price ratio is 1.27. The interpretation of this example is that where a project’s additional demand for foreign exchange causes a rise in the Rupee/US dollar exchange rate to Rs 10/US$ (a devaluation of the Rupee), this will lead to less imports and more exports. Domestic users and consumers will forgo imports of machines because they are now more expensive, and also forgo consumption of rice as now more is shifted to the export market, where sales are more profitable with the exchange rate change. If the price in the domestic market, termed here as the financial price, is used for willingness to pay, which is taken as the measure of the worth of these goods, this implies that on average foreign exchange is 27% more valuable than indicated by the actual exchange rate. In this case, with the OER Rs 10 per US dollar, the SER is Rs 12.7. The type of detailed analysis required for a comprehensive price comparison is rarely available. In practice a short-cut approximation based on several key simplifying assumptions is usually applied. These are 1. that trade elasticities for different exports and imports are equal, so that current shares in trade can be projected into the future 2. that domestic prices facing consumers of traded goods reflect their willingness to pay, so no consumer surplus is involved 3. that domestic prices facing producers reflect marginal economic cost 4. that the difference between the domestic prices of traded goods and their world prices (cif and fob) is determined solely by taxes and

17  If Mi = Xi, then ai = −Mi.fi/(∑Xj.fj − ∑Mi.fi) reduces to −fi/(fj-fi) and aj = Xj.fj /(∑Xj. fj  −  ∑Mi.fi), reduces to fj/(fj-fi). Hence the weight on imports of machines is 0.5/1.2+0.5 = 0.29 and on exports of rice it is 1.2/(1.2 + 0.5) = 0.71.

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subsidies on foreign trade18 or alternatively that the effect of any non-tariff restrictions on trade can be converted into a tariff equivalent percentage 5. that the effect of any non-tariff restrictions on trade can be converted into a tariff equivalent percentage 6. that the exchange rate used in the comparison reflects the long-run real value of foreign currency expected over the life of a project. Critical to the approach is the assumption that taxes on trade, or their equivalent, can be used to approximate the difference between the value of traded goods to consumers in terms of their willingness to pay and their world price. This implies that for an import good subject to an import tariff t, its value to consumers DP equals its world price WP plus t (DP = WP + t). Any additional indirect tax, if applicable, should also be added and any subsidy (s) should be deducted, as the subsidy lowers the price domestically; hence for a subsidised import DP  =  WP  −  s. For an export good diverted to the export market by demand from a project, any export tax (x) reduces the price received by producers, whilst any export subsidy (s) raises it. Hence for an export DP = WP − t and for an import, DP = WP + s. With these assumptions the valuation of foreign exchange is simply based on the rate of trade taxes and subsidies in the economy and the formula for the ratio of the shadow exchange rate to project exchange rate is SER  M  Tm  Ti  Sm    X  S x  Tx   OER M  X 

(5.9)

where M and X are the total value of imports and exports in a given year, converted into national currency at the official exchange rate Tm and Tx are total trade taxes on imports and exports, respectively Ti is total indirect taxes on imports Sm and Sx are total trade subsidies on imports and exports, respectively. 18  This is equivalent to valuing goods after they are released from customs with the relevant trade taxes and subsidies paid, the so-called customs-gate prices. This means local transport, distribution, and port costs can be ignored.

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All values in (5.9) should either refer to the same year or be averages over the same period. The logic of the formula is that the annual value of imports at domestic prices will be their total annual cif value (M) plus total annual import taxes (Tm) plus any annual indirect taxes Ti minus annual import subsidies (Sm).19 Similarly the total annual value of exports at domestic prices will be the annual fob value (X) minus annual export taxes (Tx) plus annual export subsidies (Sx). Despite its very approximate nature, this expression for the SER/OER has the advantage of using data that is generally readily available, apart from the difficulty in sometimes identifying values for trade subsidies. When countries pursued relatively protectionist trade strategies use of Eq. (5.9) often resulted in relatively high premia of foreign exchange, particularly where the effect on non-tariff barriers, like import quotas and licensing, could be captured in price terms. Today, taxes on and barriers to foreign trade are now much lower. The effect has been that use of Eq. (5.9) typically results in low SER/OER ratios (e.g. from 1.0 to 1.10) and consequently the economic valuation of foreign exchange may have relatively little impact on project results.20 Hence this raises the question of whether the link between domestic and world prices can simply be based on the exchange rate in the market, provided the value of the currency is free to vary in response to demand and supply trends. The answer to this lies in what is perceived about the long-run (that is over the life of a project) real stability of the exchange rate. A flexible exchange rate can be misaligned with the value implied by the underlying fundamentals of an economy and its trading prospects. In some countries this may provide a much stronger reason for focusing on the economic value of foreign exchange than the minor effect of trade taxes and controls. Where it is judged that the real exchange rate over the life of a project will change, a real adjustment should be incorporated in (5.9). Theoretically a real exchange rate change is a change in the price of traded goods relative to non-traded goods. The numerator of (5.9) is the economic value of the traded goods accessed by foreign exchange, and 19  Indirect taxes, other than specific export taxes, are not normally applicable to exports and hence indirect taxes on exports are not included in the formula. 20  No systematic database exists but a review over 20 years ago found an average ratio of around 1.10 and the policy change in most countries since then will have almost certainly lowered this; see Lagman-Martin (2004).

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with a real adjustment this value will change relative to non-traded goods (with both in a project’s base year prices). If EER is the long-run sustainable real exchange rate, consistent with current economic policies over the life of a project, measured in national currency per unit of foreign currency, it must be compared with the actual exchange OER in the same base year prices. If so, a real devaluation of the local currency implies EER/OER  >  1 and a real appreciation implies EER/OER  w, while w > d. Expression (8.1) will therefore give a lower NPV than (8.2) and (8.2) will give a higher value than (8.3) (as w > d). However, under a number of scenarios, (8.2) and (8.4) give the same result, although it is usually argued that in principle for projects with long lives the time preference-­shadow price approach is preferable, because it explicitly allows for society’s view of the value of future project impacts.

Social Time Preference Rate More recent developments in the theoretical literature have focussed on the discount rate based on a social time preference (STP) rate, which can, where necessary, be combined with a shadow price for investment. This discount rate gives the rate of decline in the value of consumption over time and corresponds to what is labelled as d in Box 8.1. Once willingness to pay is accepted as the basis for project benefits and opportunity costs, as discussed in Chap. 4, the natural unit in which to express all project effects will be units of consumption. The STP discount rate reflects their fall in value over time, principally because with a growing economy, on average in the medium term, future recipients of consumption will be richer than current recipients. As noted above, in a perfect capital market d can be derived from the interest payments to savers, as a measure of the compensation they require to postpone consumption. In real-world conditions there are a range of interest rates available for different financial products with varying degrees of risk and taxes and banks’ profit margins create a wedge between lending and borrowing rates. For this reason, the STP is usually estimated empirically rather than being based on an observed market interest rate. This version of the discount rate is usually estimated using a formula derived theoretically from the conditions required for long-run optimality in savings and economic growth and often described as the Ramsey formula.5 Although in principle rates can vary between individuals, the time preference estimated in this

5

 The original work was Ramsey (1928).

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way is usually seen as that of policy-makers acting as representatives of society, in what is termed a normative version of the discount rate. Theoretically, policy-makers will be maximising social welfare on an optimal growth path, where investment returns equal society’s valuation of the cost of waiting for consumption. In the formula, the discount rate is determined by a wealth effect, which allows for the fact that recipients of future consumption will be richer than present-day consumers, plus a time preference effect, which is the cost of waiting, including the risk of catastrophic change that undermines the ability to enjoy future consumption.6 The equation is d  gc.n  p

(8.5)

where d is the consumption time preference rate, gc is the projected growth of per capita consumption, n is the elasticity parameter reflecting the declining marginal utility of consumption, and p is a measure of time preference.7 A policy maker’s concern for inequality can be reflected in a higher value of n, which is usually set at between 1.0 and 2.0.8 The parameter p can reflect different things including pure impatience (sometimes described as a ‘selfish’ motive), fear of death, and risk of catastrophic events. In empirical work it is typically set at no more than 1% or 1.5%, so the value of d tends to be strongly influenced by what is assumed about future consumption growth. Empirical estimates for the STP rate tend to produce relatively low figures in the range of 2% to 5%. The analysis must be country-specific, as n and p represent the normative views of decision makers and gc is a country forecast. Of these parameters, as noted, n reflects an aversion to inequality 6  Catastrophic change can include natural disasters, like earthquakes and floods, as well man-made political crises. 7  It should be stressed that the assumption behind (8.5) is that any project analysed using this formula is marginal, in the sense that it is not large enough to have an impact on the growth of consumption (gc). For non-marginal projects, d become endogenous to the analysis and the standard approach to project analysis does not apply; see Dietz and Hepburn (2013) for a theoretical discussion. 8  The rate of decline of marginal utility of 1.0 implies, for example, that if consumption rises by 10% between two years, the marginal value to society of an additional dollar of consumption also declines by 10%. Similarly, where n is 2.0, the marginal utility of an additional dollar of consumption declines by twice the rise in consumption, that is, 20%.

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Table 8.1  Consumption time preference rate using the Ramsey formula Discount rate Growth gc Elasticity n Time Preference p Discount Rate d

3.0 1.5 1.5 6.0

3.0 1.0 1.5 4.5

2.5 1.5 1.0 4.8

2.5 1.0 1.0 3.5

2.0 1.5 1.5 4.5

2.0 1.0 1.0 3.0

1.5 1.0 1.0 2.5

both intra and intertemporal. There is a large technical literature attempting to derive usable empirical values applying a range of estimation procedures. If n is treated as a normative value based on policy makers’ preferences arguably the most appropriate procedure is a revealed preference approach looking at the value of inequality aversion implied by tax policies. A common approach is to derive n from the difference between marginal and average tax rates for different categories of tax payer, on the grounds that the greater the difference the stronger the weight placed by governments on reducing inequality.9 A review of the technical literature conducted a few years ago suggested consensus estimates of n = 1.5 and p  =  1.5%.10 For illustrative purposes these values can be combined with different forecasts for consumption growth to show the sensitivity of estimates for d to growth, allowing as well for the alternative values of n = 1.0 and p = 1.0%. Table 8.1 summarises different outcomes for the STP rate for different combinations of parameter values. The range is wide from over 6% with the consensus values and a relatively high growth forecast, to 2.5% with a more modest growth forecast and a lower assumed elasticity value. Normally country estimates using the Ramsey formula derive relatively low discount rates compared with estimates that include an opportunity cost element, with higher income countries having the lowest STP estimates. For example, there is a 3.5% estimate for the discount rate for the United Kingdom (HMT, 2020), an estimate of 4.5% for France (Quinet, 2013), 3.5% for the richer members of the 9  From this approach n = ln(1 − t1)/ln(1 − t), where t1 is the marginal tax rate and t is the average, and ln refers to natural logarithms. Moore et al. (2013) calculate an average value of n for the United States of 1.35 based on this approach. 10  See Freeman (2018). This work reviewed practice in the UK public sector and compared assumptions used there with the wider literature.

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EU, 5.5% for the poorer members (Florio, 2014; EU, 2014), and 3.5% for the US (Moore et al., 2013). The differing results between countries with higher and lower income levels are driven by the assumed greater potential for growth in consumption per capita in the latter, due to an expected process of ‘catch-up’ growth. As is shown in Table 8.1, what is assumed about growth, combined with the elasticity parameter, drives the results. Given that growth forecasts can be uncertain and n is a subjective parameter, about which there can be disagreement, setting a precise value for d can be controversial.11 Equation (8.5) is based on an underlying theoretical framework and requires estimates for only a small number of parameters, so it is clear why it is attractive as a means of identifying a value for the discount rate. If governments and private investors can borrow as much as they need for investment from either the domestic or international markets, there will be no opportunity cost concerns (as additional investment will not displace other investment) and the STP can be used as the discount rate. However, the complication arises in the common situation in lower and middle-income economies, where there is a scarcity of savings because this ready access to borrowing is not available. Technically, this means that the marginal returns on investment exceed the time preference discount rate (so from Box 8.1, q > d). This defines a savings scarcity, which must be addressed in some way, as additional investment will be more valuable than consumption foregone. One way to address the scarcity of funds for investment is to apply an opportunity cost discount, however, this has the disadvantage theoretically of not addressing the impact of time and waiting on the value of benefits. Hence, the approach of giving a premium value to funds for investment.

Shadow Price of Investment Apart from some exceptions noted below, where savings are scarce and access to borrowing is limited, theoretically, use of a STP requires that it be combined with a shadow price of investment (Pinv). This should be 11  An extension of the Ramsey model adds the need to allow for a precautionary effect where there is uncertainty over the growth of future consumption. In the face of uncertainty there will be a demand to invest for the future and Gollier (2011) shows how the conventional estimate for d is reduced for an economy subject to uncertainty, as measured by high volatility in growth. For an economy with low volatility the introduction of a precautionary effect in the calculation of d has little effect.

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used to increase the proportion of project investment cost that is financed by scarce savings and increase the value of project benefits that are saved or reinvested.12 The value of a unit of income saved and therefore available for investment must be compared with a unit of income that is consumed. This requires estimating the flow of consumption over the life of a representative unit of income invested and discounting it to the present at the STP rate. There are different formulae for this depending on what is assumed about the life of the unit of investment and how much of the annual benefits are reinvested to create additional consumption. In the simplest case, there is no reinvestment of benefits and all are consumed in the year they arise. Here Pinv = q / d

(8.6)

where q is the annual marginal return on investment and d is the STP rate. Hence, for example, where q is 10% and d is 4%, Pinv is 2.5 and one marginal unit of income saved and invested is worth 2.5 units of consumption. Once reinvestment out of benefits is allowed for, the value of Pinv is increased substantially. In the extreme case of an investment with an infinite life, the formula for a return in perpetuity, but including a reinvestment rate (s), is Pinv  1  s  q / d  sq



(8.7)

For example, for values of q = 0.10 and d = 0.04, plus a reinvestment rate of 15%, (s = 0.15), Pinv is 3.4.

 Early work following Little and Mirrlees (1974) used saved income, rather than income consumed as its numeraire. This meant that the consumption displaced or generated by a project needed to be converted into units of savings equivalent. The formulae for the savings premium (Eqs. (8.6) to (8.8)) could be applied, however now income consumed was divided by the value of Pinv rather than income saved multiplied by Pinv. With savings, not consumption, used as the numeraire an opportunity cost not a time preference discount rate has to be used. 12

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If the investment depreciates, so it has a limited life, the formula changes. Now Pinv  1 – s   q    / d  sq  1  s  



(8.8)

where all items are as before and δ is the depreciation rate. If the average life of an investment is 10 years, so δ is 0.10, and q = 0.10 and d = 0.04, plus a reinvestment rate of 15%, (8.8) gives a much lower and arguably more realistic value for Pinv of 1.5.13

Where Pinv Is Not Required If the shadow price of investment parameter is unnecessary the analysis and data requirements for using a STP rate are simpler. Hence, it is worth reviewing where Pinv may not be required, when using the time preference discount rate. As noted earlier, what is not contentious is that where governments can borrow as much as they need to invest at the going interest rate, there will be no scarcity of savings and q = d. Hence, d will be the appropriate discount rate as a new project will not displace any investment. In practice at least in a low-income context, this situation is likely to be relatively rare. Second, where all projects’ costs and benefits are distributed in the same way between savings (which are all invested) and consumption, using a premium for investment through Pinv affects the absolute difference between benefits and costs (that is the project NPV), but will not affect rankings of different projects based on their NPVs. This follows since both benefits and costs will be increased by the same proportion and the adjustment by Pinv will be redundant. Third, where a project’s funding has no impact on investment elsewhere, Pinv has no relevance. This again requires strong assumptions, for

 A short-cut approach that has been suggested is to rank recently approved projects discounted at the time preference rate by their benefit/cost ratios. As costs can be treated as a proxy for discounted investment and benefits for discounted consumption, the benefit-cost ratio for the marginal or least attractive project can give an estimate for Pinv (Spackman, 2017). 13

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example that funds for public sector projects are raised by taxation or user charges, not borrowing from the capital market, and that none of the benefits of the project are saved or re-invested. In principle, where taxes are raised to fund a project the distortionary cost associated with these, the so-called marginal cost of taxation, should be added to project costs. This scenario where project funding comes from taxation underlies much of theoretical case for the time preference rate as opposed to a version of the weighted SOC discount rate. Thus, there are situations where in principle a STP discount rate can be used on its own, without an allowance for opportunity cost. However, they may not be relevant in a development context, where there may be a lack of funding for high return activities due variously to a poorly functioning system of financial intermediation, a weak capacity for tax implementation, or inaccessibility to foreign borrowing. The difficulty of producing an accurate estimate of Pinv is one of the reasons the suggestion was made to incorporate both time preference and opportunity cost in a single weighted discount rate.

The Weighted SOC Discount Rate The SOC rate, or Harberger rate, addresses the problem of a shortage of savings by including the opportunity cost of capital q in its discount rate. The SOC rate (w) is defined as w  f1.q  f 2.d  f 3.b

(8.9)

where q is the marginal return on investment foregone, d is the compensation savers require to give up current consumption, and b is the cost of foreign borrowing. The weights (f1, f2, and f3) are the respective shares of displaced investment (usually assumed to be from the private sector), additional domestic savings, and additional foreign borrowing. As funding for new investment is taken to come through the capital market and will necessitate a small adjustment in this market, the negative interest elasticity of investment demand (εd), and the positive elasticities of domestic savings (εs)  and

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foreign savings (εf ), in response to a change in interest rates, are used to determine these weights.14 If there is no scarcity of savings, so governments can access all funds they wish from financial sources, the STP and SOC two approaches agree that the discount rate should be the consumption time preference rate (d). This follows since none of the funds for the project will displace other investment and if foreign borrowing occurs, in theory, it will only take place up to point at which its marginal borrowing cost b equals d. Hence, in the weighted discount rate f1 = 0 and f2 = 1.0 or if foreign borrowing occurs its cost equals d. Hence, the choice between the alternative approaches the SOC and the STP is relevant only where there is shortage of funds for investment. There are different ways of estimating the parameters in Eq. (8.9) for the SOC, but in practice there is only one major difference between the data requirements for (8.9) and those for the STP and Pinv.15 The SOC rate requires values for the opportunity cost of investment (q), the compensation required by domestic savers, (d), the savings rate (s), the foreign borrowing rate (b), and the relevant elasticities (which will determine the savings and borrowing rates s and b). The STP plus Pinv approach requires similar values for the opportunity cost of investment (q), the compensation required by domestic savers, (d), and the savings rate (s) and if Eq. (8.8) is used for Pinv, the depreciation rate. It does not include the cost of foreign borrowing. 14  Hence, where D is the pre-project investment demand, Sd is the pre-project domestic supply of savings, and Sf is the pre-project foreign supply of savings







f1 

 d. D  s.Sd   f .Sf   d. D

f2 

 s.Sd  s.Sd   f .Sf   d. D

f3 

 f .Sf  s.Sd   f .Sf   d. D







15  Harberger and Jenkins (2015) summarise the approach to estimation favoured by Harberger himself.

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The key conceptual difference between the original statements of the alternative approaches is that in the SOC, d is based on observed net of tax real savings rates not derived from Eq. (8.5), which is heavily dependent on the subjective parameter n, reflecting aversion to inequality.16 In terms of the weights in Eq. (8.9), the expectation from the empirical literature is that the responsiveness of investment to interest rate changes is likely to be significantly higher than for either domestic or foreign savings. Empirical work on the responsiveness of savings to programmes of interest rate liberalisation implies a very low responsiveness of total savings (although the form of savings may change), which means a low elasticity εs. Hence, the return on investment (q) will have the highest weight in Eq. (8.9), bringing the result closer to an opportunity cost discount rate.17

Equivalence of the Two Approaches It can be shown that if the same parameter values are used (so foreign borrowing is ignored, the same time preference figure is used, and the same savings rate applies both to the project under analysis and the national discount rate given by Eq. (8.9), then use of a weighted SOC rate is equivalent to use of a STP rate plus Pinv. This can be illustrated with a very simple example, although the result can be generalised to more complex cases.

16  Harberger and Jenkins (2015) recommend starting from national accounts data to identify the net income paid to owners of capital. This is in contrast with the normal procedure initially suggested by Harberger, and used previously in the SOC calculation of taking real interest rates net of tax on government bonds or savings deposits, as a measure of time preference. The recommended change is on the grounds that actual tax rates collected can vary significantly from official rates with lack of transparency in the application of tax allowances and concessions. 17  In their estimate of the discount rate for South Africa Jenkins et al. (2011) use values for the investment elasticity (εd) of −1.5, domestic savings elasticity (εs) of 0.5, and foreign savings elasticity (εf) of 1.5. For a typical middle income country Harberger and Jenkins (2015) suggests that weights of f1 = 0.6, f2 = 0.1, and f3 = 0.3 are not implausible, but this is no more than illustrative. The largest variation between countries might be expected in the weight on foreign borrowing, since how far such borrowing will respond to a demand for new funds will be determined largely by country conditions relating to the openness to foreign capital flows and the organisation of the financial sector.

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An investment (K) of $1 mill in year zero, is financed 75% by displacing other investment (f1  =  0.75) and 25% by reducing consumption (f2 = 0.25). It has a perpetual yield of consumption benefits of $100,000 annually. It is assumed that there is no foreign borrowing, the marginal return on investment is 12% (q = 0.12) and that the consumption time preference rate is 4% (d = 0.04). This gives a weighted discount rate of 10% (0.75 × 0.12 + 0.25 × 0.04). The present value of a perpetuity is the annual sum divided by the discount rate, so using the weighted discount rate the present value of benefits is $1 million ($100,000/0.10) and the NPV of the project is zero.18 Showing equivalence requires a value for Pinv and that the same proportions between foregone investment elsewhere (75%) and reduced consumption (25%) apply for both the project’s costs and benefits and for the SOC rate. The simplest estimate for Pinv is to use Eq. (8.6) and take the ratio of the return on investment (q) to the STP rate (d). As q is 0.12 and d is 0.04, this gives a value of 3.0. From the perpetuity formula, the present value of $100,000 of benefits in perpetuity discounted at the STP rate is $100,000/0.04 = $2.5 million. Capital cost is composed of $750,000 displaced investment and $250,000 foregone consumption. Using the shadow price of investment of 3.0 gives a total cost of $2.5  million (0.75 million × 3 + 0.25 million = 2.5 million). Hence, in this case, also the present value of costs equals that of benefits, so the NPV is zero. Critically this equivalence between the SOC and STP approaches does not hold if the proportions of displaced investment and consumption for the project in each year of its life differ from those used in the weighted discount rate. Hence, in terms of data requirements, there is little to choose between the two approaches apart from a different specification of time preference (d). In one it is estimated from a formula in line with theory, but arguably subjective and difficult to estimate, and in the other it is estimated from market data, which is itself subject to error, particularly in relation to the transparency of tax charges. In relation to the critical assumptions needed for equivalence, the key point is that in principle there is no reason why the split between consumption and investment affecting an individual project should match the national split used in the weighted discount rate. Since

18  This result holds for an investment with a perpetual yield of q which is all consumed, discounted at d; for derivation see UNIDO (1972, pp. 175–177).

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the savings effect of a project will be determined in large part by its distributional implications, in principle, the flexibility to allow for varying savings effects between projects, is a key advantage of the STP/Pinv approach.19 The point that Pinv is difficult to estimate because of uncertainty over the key parameters, q, d, and s, is true, but equally each of these are applied in the SOC approach, so the uncertainty is in fact shared by both approaches. In higher-income economies, no doubt in part because the savings scarcity is less of an issue due to more effective financial intermediation, governments have adopted a time preference approach (normally without a value for Pinv). Box 8.2 summarises the approach applied in the United Kingdom to estimate a rate of 3.5% and clearly documented in various official publications.

Choice of Discount Rate The review above of the principles on which a discount rate value for decision-making can be derived indicates that there is no simple answer to the question of which of the STP or SOC approaches a country should adopt in setting the discount rate. Both have points in their favour and under certain circumstances will be exactly equivalent. In practice, neither can be applied with the rigour required for full accuracy, so the choice in large part depends on how the rate is to be used and on what is a plausible scenario in relation to government sources of funding for new projects. In relation to calculation both approaches are difficult to implement accurately and both require assumptions and shortcuts. If one follows the recommendation for the best-practice approach to calculation of the SOC rate (as in Harberger & Jenkins, 2015), it requires a detailed breakdown of national accounts data to derive estimates for both the opportunity cost of capital and the rate of return to savers. At various stages assumptions need to be made on which data to include and which to exclude. In relation to the opportunity cost of capital, in all countries measurement of the capital stock is particularly difficult, with the need to allow for economic, not financial, depreciation. Also, in many countries, observed real market rates of interest are too affected by taxes and bank margins to indicate real

19  However, if the SOC rate varies between projects and the same saving-consumption split is used for the project and the discount rate, the equivalence is re-established.

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Box 8.2 Discount Rate in United Kingdom, Green Book

In recent years a number of international agencies have used the STP approach to estimate the discount rate for their member countries, for example, EU (2014, Annex II), and ADB (2017, Appendix 18). Perhaps the clearest illustration of the approach in practice by a national government can be seen in the United Kingdom. The UK government (HMT, 2022) in the ‘Green Book’ has provided a clear and in some ways novel approach to the STP discount rate using the Ramsey equation. The first use of this approach in the United Kingdom public sector came in 2003, when a rate of 3.5% was introduced as an alternative to the previous opportunity cost rate based on the marginal return to investment in the private sector. This time preference estimate has been refined slightly and checked in subsequent years (HMT, 2022). The approach is novel because 1. it reviews the empirical values for key parameters based on the current academic literature20 2. it reduces the discount rate over time to allow for uncertainty 3. it introduces a separate lower discount rate for non-monetary health effects The Green Book rate uses Eq. (8.5) above in the main text with the following values; future growth of average consumption per capita in the United Kingdom, gc = 2%; the elasticity of marginal utility of consumption, n  =  1.0, and the time preference adjustment p = 1.5%. So, 1 × 2% + 1.5 gives 3.5% for the STP discount rate. Of these parameters, as noted, n reflects an aversion to inequality both intra and intertemporal. There is a large technical literature attempting to derive usable empirical values for n applying a range of estimation procedures. The review of Freeman et al. (2018) finds the average of revealed preference approaches is 1.5 and suggests that plausible values for n lie between 1.0 and 2.0, so the Green Book estimate is at the bottom of this range. (continued )

 See the review of Freeman et al. (2018).

20

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Box 8.2  (continued)

Time preference in the Green Book discount rate has two elements—a pure impatience effect and an allowance for catastrophic and systemic risk that affects all projects. Catastrophic risk covers natural disasters and pandemics but also man-­made problems and technological advancements, and a small component for systematic risk affecting all projects. The sum of these is given as 1.5% composed of a pure impatience figure of 0.5% and an allowance for catastrophic events of 1.0%. Project-specific risks are to be allowed for in the benefit-cost analysis of individual projects. These figures are checked by Freeman et al. (2018) and found to be broadly in line with empirical estimates in the academic literature. Their overall conclusion is that the original value for the discount rate of 3.5% ‘appears to lie within the middle ground of the current literature’ and therefore does not need revision. The Green Book introduced the concept of declining discount rates to address uncertainty over time. Academic work in this area by Weitzman (2001) and Gollier (2002) is cited in justification. As explained in this chapter, this was in response to arguments in the theoretical literature that once uncertainty is allowed for, the discount rate should be calculated on a probability-weighted basis. The result will be a certainty equivalent discount rate that must decline over time. The Green Book uses a gradual declining discount rate schedule, so that the standard rate of 3.5% applies up to year 30 of a project’s life. Beyond this the rate declines by 0.5% over different time periods; hence, the discount rate is 3% for years 31–75, 2.5% for years 76–125, 2.0% years for 126–200, 1.5% for years 200–300 and 1% beyond year 300. This is a gradual decline from a relatively low starting point, and although in line with a widely accepted theory, it is unclear if in practice the magnitude of the reduction will make a major change to many project decisions. There is also the suggestion in HMT (2022) that for long-lived projects that affect future generations, the discount rate could be reduced further by removing the ‘selfish’ element of 0.5% referring to pure time preference or impatience. (continued)

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Box 8.2  (continued)

Hence, for such projects the rate up to year 30 will be 3.0%, with additional reductions for the subsequent years. Again, how far this will impact practically is unclear. The innovation introduced in the most recent version of the Green Book (HMT, 2022) relates to the treatment of benefits of health projects. The argument is that in appraisals in the United Kingdom these are normally in units relating to health impact (in the United Kingdom in QALYs or in the development context in DALYs) not monetary units and for the health impact on utility, the wealth effect due to rising income and consumption can be ignored giving a discount rate of 1.5%. HMT (2022) also makes clear what was not explicit in the first version of the Green Book, that its specification of the discount rate is based on a couple of key assumptions 1. that overall government expenditure is not affected by any individual project but is determined by macro-economic policy and hence 2. that the level of taxation and private sector investment or borrowing is unaffected by any individual project. This means that issues like the cost of raising additional taxation or of investment foregone elsewhere can be ignored, thus allowing the discount rate to focus only on the valuation of consumption benefits from projects at different points of time. returns. Hence, the relatively straightforward data of observed real interest rates has to be replaced by estimates of returns to savers calculated indirectly from the national accounts. Finally, the results for returns to capital and savers plus the cost of foreign borrowing must be weighted. Again, there is uncertainty over the true elasticities required to derive the weights. Superficially, the time preference approach looks much easier to apply, since most versions base the estimate on the Ramsey formula (8.5), which contains only three parameters. However, there are major difficulties in application. Two of the three parameters in the formula—the inequality

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aversion elasticity and the time preference rate—are subjective parameters and although the possible range of plausible values has been narrowed in the academic literature there remains uncertainty over what value to use. The result for the time preference discount rate is particularly sensitive to what is projected for the growth of consumption, combined with the value used for the aversion to inequality parameter. In addition, in the presence of a shortage of savings, the theoretically rigorous approach using a time preference discount rate, requires that the opportunity cost of the investment displaced by a project is captured by applying a shadow price of investment to all the savings effects a project creates. These can be both negative, when a project diverts savings from other investment, and positive, when project income is reinvested. To estimate this parameter for the shadow price of investment requires similar data on the marginal return on investment to that used in the SOC approach. Hence, the same points on the accuracy of this data apply in the STP approach. For a given timepreference discount rate, the alternative formulae for the shadow price of investment are highly sensitive to the estimate used for the marginal return on investment and the rate of reinvestment out of this return. As noted above, theory tends to favour the time preference approach combined with a shadow price for investment, because of the flexibility it offers in addressing time preference through the discount rate and opportunity cost through the shadow price of investment. Separating these two aspects of a project means consumption at different points in time can be treated consistently allowing for the factors that make consumption now of higher value than consumption in the future. For short-term projects (e.g. with a life of no more than 30 years), this advantage may not matter greatly. For longer-term projects, use of an opportunity cost or weighted discount rate, where it exceeds significantly the time preference rate, will arguably give an excessively low value for distant benefits and costs. At a 10% discount rate, for example, a project cost of $ 1 mill 50 years in the future will be valued in the present at only $8500 ($1 million × (1/ (1.1)50 = $8519). The reason for this very low valuation is that it is assumed that an investment of $8500 today will be put to various uses that will grow in value at a compound rate of 10% for 50  years. However, this assumption of constant reinvestment at a 10% return over a long period of time is implausible and not a good way of assessing longer-term project effects. The time preference rate provides the way of comparing benefits or costs at different points in the future, for example, in years 2, 50, and

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100, using consistent criteria based on diminishing marginal utility and judgements about time preference for consumption. In addition, as noted above, the STP approach allows for differential treatment between projects. In principle, there is no reason why the impact in terms of displaced investment as a proportion of costs or of induced investment as a proportion of benefits should be the same between projects. This is the implicit assumption in the SOC approach, where the elasticities determine the respective shares regardless of the distribution of the benefits and costs of a project. In principle, STP combined with a shadow price for investment allows for flexibility, although it must be acknowledged that in practice in appraisals it may be difficult to operationalise this flexibility by applying different displacement and reinvestment rates between projects.

STP Rate and Declining Discount Rates The STP approach has also been associated in the literature with the use of declining discount rates over time. As discussed above time preference rates are typically below 5% and are usually well below estimates of the opportunity cost of capital, as defined as the marginal return on investment. This has the effect of a higher weight to long-run effects and as discussed in Chap. 9 is in line with increasing concern over how environmental effects can be address in project economic analysis. In addition, there are now well-established theoretical reasons, why discount rates might not just be low, but also show a declining trend over time.21 This is based on the argument that once uncertainty is allowed for, the discount rate should be calculated on a probability-weighted basis. Introducing uncertainty requires a distinction between a discount factor and the discount rate and between unweighted and probability weighted discount factor and discount rate. The discount rate is the percentage change in a variable between periods, such as a 3% decline between two years. The discount factor (DF) is the adjustment to a variable to convert it to a value in the present. Its size is determined by the discount rate (DR) and the year involved (t) relative to the present. The general expression is DF  1 / 1  DR 

t



 See some of the key contributions, Weitzman (2001) and Gollier (2002).

21

(8.10)

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Hence, for a 3% rate and a year 2 years in the future, the DF is given by the expression DF  1 / 1  0.03 

2



which gives a DF of 0.9426. Uncertainty means there cannot be a 100% probability that any individual DF will be the true one. In principle, there will be a range of possible DFs, with different probabilities. The weighted average of these, with weights determined by their probabilities, gives a certainty equivalent discount factor (CEDF). A discount rate is implicit in any DF and is given by rearranging (8.10) so

1  DR 

t

 1 / DF, and



DR  1 / DF1/ t  1 The same procedure can be followed with the certainty equivalent values, so any certainty equivalent discount factor (CEDF) will be based on a certainty equivalent discount rate (CEDR). Hence, for a CEDF the implicit CEDR is CEDR  1 / CEDF1/ t  1

It can be shown that once probabilities are introduced, even where these do not change over time, there will be a gradual decline in the discount rate, which will ultimately end at the lowest of the possible rates. This can be illustrated in a simple numerical example. There are three possible discount rates, 5%, 3%, and 1%, with an assumed equal (33%) probability of being the true rate. Hence, for year 1, the certainty equivalent discount factor will be 0.333  1 / 1  0.05    0.333  1 / 1  0.03    0.333  1 / 1  0.01   0.9711





The weighted certainty equivalent discount rate is derived from the expression CEDR = 1/CEDF1/t – 1.

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Table 8.2  Illustration of declining certainty equivalent discount rate Discount factors DR 0.05 0.03 0.01

Probability 0.333333 0.333333 0.333333 CEDF 1/CEDF CEDR

Year 1

Year 30

Year 50

Year 70

Year 100

0.952381 0.970874 0.990099 0.971118 1.029741 0.029741

0.231377 0.411987 0.741923 0.461762 1.026091 0.026091

0.087204 0.228107 0.608039 0.307783 1.023847 0.023847

0.032866 0.126297 0.498315 0.219159 1.021922 0.021922

0.007604 0.052033 0.369711 0.143116 1.019631 0.019631

DR is discount rate DF is discount factor CEDF is certainty equivalent discount factor CEDR is certainty equivalent discount rate

Hence for year 1, where t = 1, CEDR = 1/0.9711 − 1, which gives 0.0297 or 2.97%. Table 8.2 repeats the calculation for the years 30, 50, 70, and 100. It shows a gradual decline in the certainty equivalent discount rate from just below 3% in year 1, to 2.6% in year 30, reaching just below 2% in year 100. This is despite the fact that neither the possible discount rates nor the probability weighting placed on them changes over the period. These declining rates allow a higher weight to be based on long-run costs or benefits, such as environmental impacts.22 The key difficulty in applying the approach is in determining the trend rate of decline. Empirical estimates tend to draw on observed data on market rates, but these offer only approximations to future developments. Nonetheless the theory behind the case for declining rates summarised above, coupled with concerns over equity between generations and some evidence on individual preferences for consumption over time, has meant that the academic case for declining rates is now widely accepted. Several governments, such as those in the United Kingdom, France, and Norway have applied declining discount rates to public sector 22  The academic literature on has expanded the case for declining rates in recent years and has incorporated broader issues of macro-economic risk into the discount rate discussion. Freeman et al. (2018) review these arguments.

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projects, whilst others such as the United States and Germany address the issue by applying a lower rate to projects with longer-term effects.23 In the development context the use of declining rates has not yet become common, but the example of practice in developed economies may have an influence. A practical complication is that with a declining discount rate project accept or reject decisions must be taken based on the NPV (or benefit cost) indicators, as there will be no unique discount rate with which the IRR can be compared. Where governments are used to applying the IRR indicator in decision-making, this will require an adjustment in procedure. Adjustment in this way for uncertainty is normally associated with an STP approach to discounting, but in principle as the adjustment is based on probabilities of true values it can apply to alternative specifications of the discount rate, such as the weighted SOC rate. For most discount rates a noticeable decline will only start after about 30 years, so will only have a practical impact for longer-­lived projects, usually with an environmental impact. However, these are the type of projects where high constant rates can undervalue long-term effects, either positive or negative. This can be seen in Chap. 10 where the social cost of carbon estimates for the damage cost of climate change can be highly sensitive to the rate of discount used for these long-run effects. A declining rate, with say a modest decline of drop of 1 percentage point every 30 years, could increase damage costs significantly.

Is There a Case for Using Different Discount Rates for Different Sectors? The standard argument based on efficiency in the use of government resources is that there should be a single test discount rate applied across the public sector. Exceptions to this rule have been discussed, and in some cases applied, but only in the case of environmental and health effects is the case strong.

23  Groom and Hepburn (2017) discuss the influence of theory on government practice in setting discount rates.

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Fixed Sector Budgets Where the budgeting process is rigid, in the sense of providing fixed budgets for each sector, then, for example, the opportunity cost of a health project will be determined by the return on the marginal health project, not by the return on the marginal agricultural (or other) project, and vice versa. This means that what has been termed here q, reflecting the marginal return on investment, in theory will be specific to different sectors. In this scenario of fixed sector budgets, a new project will not raise additional funds on the capital market or to borrow abroad. Therefore, in the SOC formula for w (8.9) q will differ by sector, and f2 and f3 are zero, so w = q. This argument is not relevant for the specification of the discount rate in the STP approach as opportunity cost does not figure directly in the calculation. However, in practice, the assumption of total rigidity in public sector budgeting is extreme and differential discount rates by sector are rarely estimated based on this approach. Hard to Value Sectors More common is the argument that in some sectors it is more difficult to quantify benefits than in others. Hence, these sectors will be discriminated against if they are compared with the same test or hurdle discount rate as the more ‘directly productive’ sectors (i.e. those where benefits are easier to value). Examples of sectors where it may be difficult to put a monetary value on all of the possible benefits typically include the environment and health, and sometimes education. Whilst this approach is an understandable and pragmatic approach to the problem of benefit valuation in some sectors, it is open to the criticism of being arbitrary, since there is no numerical basis for the differential between sectors. Good practice suggests that the correct approach is to use the normal discount rate, but to try to put monetary values on the additional, as yet unquantified, benefits. If this proves impossible, and without including these benefits the project appears unacceptable, the next step is to determine the value these unquantifiable benefits have to take to reach the target rate of return (or a positive NPV if that is the decision criteria used). The realism of this switching value then needs to be assessed on the basis of the evidence available. If it proves implausibly high, then there is no case for accepting the project. Hence, theory does not support use of different discount rates between sectors based on hard to value benefits.

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Different Types of Benefit When the STP rate is used there is a theoretical case for distinguishing between sectors depending upon the nature of the benefits they create, although the argument here has no relevance for SOC rates, based on the Harberger method. The key driver of the STP rate is the impact of rising income on the utility or satisfaction derived from future consumption, sometimes termed the wealth effect. It has been argued that there will be certain types of benefits for which the wealth effect (gc.n in Eq. (8.5)) will not apply. This is because rising income in the future should not reduce the satisfaction derived from such benefits. This argument has been raised in relation to two sectors—health and the environment. For health, for example, the UK Green Book suggests a different lower discount rate for health projects of 1.5%, as opposed to the general rate of 3.5%. This is derived by removing the wealth effect (gc.n) from (8.5) and basing the discount rate on pure time preference and catastrophic risk (p) alone. This is based on the argument that there is no reason why the value placed by consumers on good health declines as they get richer. A similar argument has been used in relation to environmental effects where it is argued that the wealth term should not apply as the wellknown depletion of the environment means that consumer valuation of environmental assets should be rising, not falling, over time due to their increased scarcity. This approach has led to the recommendation of ‘dual discounting’, with a separate rate for environmental benefits and costs within the same project, derived by omitting the wealth effect and basing the STP rate on pure time preference and catastrophic risk.24 The issue is discussed further in Chap. 10 in the section on health benefits and Chap. 11 on the environment. In practice, dual discounting within the same project remains rare and, as discussed in Chap. 11, the concern behind it can be addressed other than by using a separate discount rate.

Conclusion For any approach to estimating the discount rate, there are simplifications. The SOC rate arguably does not address fully time preference for consumption at different points in time, since it includes a large opportunity

 See, for example, Kula (2012) and other references cited there.

24

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cost element in the weighting of the rate and may understate long-run effects, such as environmental damage. The time preference alternative addresses the value of time, but without a shadow price of investment will omit an allowance for the opportunity cost of investment. The shadow price of investment is subject to considerable uncertainty. Omission of this shadow price can be justified under the assumptions set out above, but they are fairly restrictive and may not apply in many middle and lower middle-income economies. Given the theoretical arguments in favour of an STP rate and the difficulty in estimating Pinv accurately, a way around this is to use the STP as discount rate, without distinguishing between savings and consumption effects, but to rank projects by the NPV to capital cost ratio. At a minimum, projects should have a positive NPV at the time preference rate and therefore the ­NPV/capital cost ratio is positive. Projects are accepted until the budget is exhausted. The issue of opportunity cost is addressed by varying the cut-off point for the ratio, so the final acceptable ratio will depend on the availability of funds. Here the discount rate can be set at the STP rate as opportunity cost is covered by the ranking process, and the discount rate can focus only on time preference. This may provide a practical solution to the need to address both time preference and opportunity cost, without resorting to an uncertain estimate of Pinv. This is provided there is enough information on a portfolio of possible projects, so that they can be assessed simultaneously. In the past in a development context, it was common for economic project analysis to use relatively high discount rates (e.g. in the range of 10% to 12%). They were opportunity cost rates and were to reflect the scarcity of funds and the assumed high marginal returns on investment. Such high rates now look somewhat dated, both because of the low real returns on financial assets found in most countries in recent years, which have been well below these figures, and because of the growing acceptance of the academic arguments on the need to value time accurately to address issues relating to long-run environmental costs and to introduce uncertainty. The key conclusion from the more recent academic literature on discount rates is that simple (that is unweighted) opportunity cost rates will distort decisions and that if a savings constraint is an issue, there are ways in which the saving scarcity can be taken into account in combination with a time preference rate. However, the result will be a discount rate below the type of opportunity cost rates applied earlier.

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Further Reading Text books like Boardman et  al. (2017) provide an introduction to the theory behind the discount rate. In the project context the original work on the time preference rate is in Feldstein (1964 and Marglin, 1963). For more recent assessments and references, see Moore et al. (2013) and the very useful review by Freeman et al. (2018). The social opportunity cost approach is explained in Jenkins et al. (2018) chapter 8 and Harberger and Jenkins (2015).

Bibliography ADB. (2017). Guidelines on Economic Analysis of Projects. Asian Development Bank Manila. Boardman, A., Greenberg, D., Vining, A., & Weimer, D. (2017). Cost Benefit Analysis Concepts and Practice (4th ed.). Cambridge University Press. Dietz, S., & Hepburn, C. (2013). Benefit-cost Analysis of Non-marginal Climate and Energy Projects. Energy Economics, 40, 61–71. EU. (2014). Guide to Cost Benefit Analysis of Investment Projects. European Commission, Brussels. Feldstein, M. (1964). The Social Time Preference Discount Rate in Cost Benefit Analysis. Economic Journal, 74, 360–379. Florio, M. (2014). Applied Welfare Economics: Cost Benefit Analysis of Projects and Policies. Routledge, London. Freeman, M., B. Groom, & M. Spackman. (2018). Social Discount Rate for Cost Benefit Analysis, A Report of Her Majesty’s Treasury. . Gollier, C. (2002). Time Horizon and the Discount Rate. Journal of Economic Theory, 107(2), 463–473. Gollier, C. (2011, July). On the Underestimation of the Precautionary Effect in Discounting. Toulouse School of Economics. Groom, B., & Hepburn, C. (2017). Reflections: Looking Back at Discounting Policy. Review of Environmental Economics and Policy, 11(2), 336–356. Harberger, A. (1972). Project Evaluation—Collected Papers. Macmillan. Harberger, A., & Jenkins, G. (2015). Musings on the Social Discount Rate. Journal of Benefit Cost Analysis, 6(1), 6–32. HM Treasury, Government of UK. (2020). Green Book Review: Findings and Response. HM Treasury, Government of UK. (2022). Green Book: Central Government Guidance on Appraisal and Evaluation, 2003, Updated 2022.

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Jenkins, G., Kuo, C-Y., & Harberger, A. (2011). The Economic Opportunity Cost of Capital: Cost Benefit Analysis for Investment Decisions. Development Discussion Paper, 2011–8, Queens University, Canada. Jenkins, G., Kuo, C.-Y., & Harberger, A. (2018). Cost Benefit Analysis for Investment Decisions. Cambridge Resources International. Kula, E. (2012). Discounting: How Do We Ensure Intergenerational Equity? In J. Weiss & D. Potts (Eds.), Current Issues in Project Analysis for Development. Edward Elgar. Little, I., & Mirrlees, J. (1974). Project Appraisal and Planning for Developing Countries. Heinemann Educational. Marglin, S. (1963). The Opportunity Cost of Public Investment. Quarterly Journal of Economics, 77, 274–289. Moore, M., Boardman, A., & Vining, A. (2013). More Appropriate Discounting: The Rate of Social Time Preference and the Value of the Social Discount Rate. Journal of Benefit Cost Analysis, 4(1), 1–16. Quinet, E. (2013). The Socio-economic Evaluation of Public Investment. Mimeo Commisariat General a la Strategie et al. Prospective, Paris. Ramsey, F. (1928). A Mathematical Theory of Saving. Economic Journal, 38(152), 543–559. Spackman, M. (2017). Social Discounting: The SOC/STP Divide. Working Paper 207. Centre for Climate Change Economics and Policy. UNIDO. (1972). Guidelines for Project Evaluation. UN. Weitzman, M. (2001). Gamma Discounting. American Economic Review, 91(1), 260–271. Zerbe, R., & Bellas, A. (2006). A Primer for Cost Benefit Analysis. Edward Elgar.

CHAPTER 9

Allowing for Uncertainty

The techniques of project analysis have been considered so far as if the basic data, which they use, is known with certainty. However, both technical and economic information is used in the form of forecasts, and is subject to considerable uncertainty.1 It is possible to conceive of different values, based on experience, for the fundamental technical relations in any productive process, and for the project costs and benefits at either financial or economic prices. For most project data, a range of values can be found or predicted, yielding the possibility of different conclusions in the application of project worth measures. Variations in some technical or economic values will be more significant than others. Moreover, some will be subject to greater uncertainty, or a greater range of likely values. An apparently acceptable project may appear risky, if small variations in the predicted values cause the project decision to change; other projects yielding a lower rate of return may appear more robust in the sense that the project decision is unaffected by changes in key variables. An initial attempt to identify those variables that are likely to affect most the project outcome is provided by sensitivity analysis. By varying values one at a time, as explained in the first section below, the effect 1  A common technical distinction is between uncertainty, which is lack of knowledge on the probability of outcomes and risk, which refers to a probability distribution of possible outcomes.

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on the project worth measures can be calculated, and for each variable, the range of variation within which the project decision remains the same can be identified. In addition, estimation of the switching value for a key parameter, which is the value at which a decision on a project changes, will be a critical piece of information, as it allows a focus on a critical area of risk. Where a range of estimates are available for key variables, sensitivity analysis can be complemented by scenario analysis. This involves analysing a project for different scenarios, such as the base case, treated as the most likely, the worst case, and the best case. This gives a range of possible outcomes. The second section illustrates this with a simple example. Neither sensitivity nor scenario analysis considers the likelihood of changes in key variables occurring. However, if a probability distribution of possible values for key variables can be estimated, their variation can be allowed for and the indicators of project worth can be recalculated. The technique of risk analysis, illustrated in the third section, allows a number of variables to vary simultaneously, in either complementary or contradictory fashion and with different probabilities. This technique can be applied particularly to the most significant variables identified through sensitivity analysis. Sensitivity, scenario and risk analysis each provide additional information for decision-taking. Waiting to see how events change, and investing the funds in short-term assets, is also an option and the appendix to this chapter explains the logic involved. However, these forms of analysis only identify the likely origin and extent of uncertainty. In practice, it is important to try and reduce or control uncertainty. Brief mention is made in the final section below of how this might be done at the analysis as well as implementation stages of projects.

Sensitivity Analysis Measures of project worth are first calculated using the best estimate of inputs and outputs and the discount rate. The project decision will be based on these best estimates. However, what would be the effect on the project worth measure of variation in the estimates? By how much would a project item have to vary before the project decision is changed? These are the questions that sensitivity analysis is designed to address, in order to identify the variables to which the project is most sensitive. For any project statement, some items will have a significant effect on the project outcome. Where discounting is applied, variation in items

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occurring in earlier years will have a greater effect than variation in items in later years. It is common, for example, to investigate the effects of investment cost-overruns or delays in construction that lengthen the investment period. Other items liable to have a major impact include the economic values of the main inputs and outputs. The application of sensitivity analysis involves varying one project item at a time and measuring the effect on project worth. Because this is easier to interpret in absolute terms, the measure of project worth generally employed in sensitivity analysis is the net present value (NPV). This is illustrated in Table  9.1 which contains the resource flow for an agro-­ processing project. This is shown at economic prices.2 At a discount rate of 10%, the best estimate of the economic NPV is 163.7. The items in each row of the resource flow have been varied by 10%; cost items have been increased (and the terminal values where appropriate), whilst the economic value of revenue has been decreased. The first column in the bottom part of the table shows the percentage change in the NPV as a result of the 10% variation. The variation in many project items could come about through variations in quantities or prices, or a combination of both. At this stage of the analysis, the source of variation is not as important as its likely effect. Table 9.1 shows that some items have a very substantial effect on the NPV, particularly the revenue, and the cost of materials. On the basis of the best estimate and a 10% discount rate, this is close to a marginal project since the NPV, although positive, is not very high at the given discount rate. Sensitivity analysis can be conducted for a range of plausible variations in key variables. Unrealistically high variations should be avoided, and in practice, a range of 10%–20% is the most common range used. An alternative way to present the results of sensitivity analysis is to ask by how much a project item needs to change before the project decision is affected and the project switches from being acceptable to unacceptable or from unacceptable to acceptable. The so-called switching values for each project item are presented in the second column in the bottom part of Table  9.1. Items with a relatively low switching value indicate that the project decision is very sensitive to changes in the item. In this example, the project decision is very sensitive to variation in benefits and material costs; very small percentage changes in these items result in a negative 2  This can be interpreted as the flows at financial prices adjusted by conversion factors derived from the procedures discussed in Chaps. 5 and 6.

1000 1000 200 0 0 0 0 0 2200 0 −2200 0.1 £163.66

Construction Equipment Working Capital Materials Power Transport Skilled Labour Unskilled Labour Total Cost Benefits Net Discount rate NPV

Construction Equipment Working Capital Materials Power Transport Skilled Labour Unskilled Labour Benefits

Increase for cost, decline for benefits

a

450 50 25 150 100 775 900 125

1

Effect of 10% change in each variable on NPVa

0

Cost

Year

675 75 38 150 150 1088 1350 262

2

49 62 12 301 34 17 56 67 603

%

900 100 50 150 200 1400 1800 400

3

Table 9.1  Sensitivity illustration with switching values

900 100 50 150 200 1400 1800 400

4

900 100 50 150 200 1400 1800 400

5

900 100 50 150 200 1400 1800 400

7

900 100 50 150 200 1400 1800 400

8

16.4 16.4 83.3 3.3 29.4 58.8 1.9 14.9 −1.7

Switching value % change

900 100 50 150 200 1400 1800 400

6

900 100 50 150 200 1400 1800 400

9

−200 900 100 50 150 200 700 1800 1100

−500

10

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NPV rather than a positive one. In practice, projects are typically sensitive to capital costs, so delays that cause cost-overruns in real terms can be serious for the viability of a project, and the valuation of the main project benefits and key resource inputs. Use of switching values is a more effective means of applying sensitivity analysis than simply testing for given percentage changes in all project variables. It allows a focus on individual parameters and the posing of the key question: what is the likelihood of the switching value (e.g. in the fuel price for a transport project or the main crop price for an agricultural project) being reached? In addressing this, experience and market knowledge will be needed to give an implicit judgement on probabilities.

Scenario Analysis Whilst sensitivity analysis can be helpful in estimating the impact of individual variables on a project, uncertainty relates to more than one variable. A relatively simple way of allowing for variations in more than one parameter is to construct alternative scenarios for a project, such as most likely, worst case, and best case. Most likely will involve the base case values, whilst worst case covers the most pessimistic values for key values and best case covers the most optimistic. This approach allows an estimate of the range of possible outcomes depending on how accurate the projections of key variables are. Scenario analysis is helpful where a relatively large range of estimates are available for key parameters. For example, there will be some projects which are highly sensitive to the impact of climate change, and as is well known, there are a wide range of climate projections in the technical literature. To take a very simple example, benefits from an agricultural project will be dependent on an increase in crop yields from the project. However, these yields will in turn be influenced by climatic conditions relating to temperature change and rainfall. Rising temperatures and lower precipitation will reduce yields, whilst higher precipitation will raise yields. These are external factors that the project itself cannot control. The base case analysis uses a single projection for these two climatic variables from a model that the project team have confidence in. In the long-run, this model predicts a temperature rise of 1.5 degrees centigrade and a rise in precipitation of 12.5% relative to the historical mean. These assumptions are used to derive a most likely estimate for the project NPV and IRR.

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Table 9.2  Scenario analysis illustration

Precipitation rise Temperature rise Project NPV at 5% Project IRR

Best case

Base case

Worst case

13.5% 1 degree $50.3 million 9%

12.5% 1.5% $10.1 million 6%

10% 2.6 degrees −$ 30.0 million 3%

However, in this case, there are over 20 alternative models with differing projections. A review of the results of these narrows down the range. Using 10% as a cut-off for plausible values, it finds that in over 90% of cases, the projections are in the range 10% to 13.5% for precipitation and 1 degree to 2.6 degrees for temperature change. The worst-case scenario is thus a high temperature rise of 2.6 degrees and low precipitation rise of 10%. The best-case scenario is the low temperature rise of 1 degree and the high precipitation rise of 13.5%. NPV and IRR values can be calculated for the worst and best-case scenarios and compared with the results from the base case values. This gives a range for possible outcomes for a project and allows decision makers to judge, for example, if the risk of a worst-case outcome is too much to incur. Table 9.2 illustrates this. Here the project is acceptable if the base case scenario occurs, with a 6% IRR above the discount rate of 5% and hence a positive NPV. However, there is also the risk of a loss of $30 million in the worst case. If the project is approved, implicitly it implies that decision takers are judging that the probability of a $30 million loss is not so great as to offset the benefits that could rise from the other two scenarios. This type of information is clearly useful, but without formal probabilities being attached to the alternative scenarios, it cannot be incorporated quantitatively in decision-taking criteria. This is addressed by the procedure of risk analysis.

Risk Analysis and Use of Probabilities Base case values used in project calculations are the most likely values from the assessment of the project team concerned. Implicitly these are expected values, which are possible future values weighted by a rough judgement on the probability of different values occurring. For example, an output

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value for an export crop of Pesos 100/kg might be based on an approximate judgement that there was a 20% chance of the true price being 80/ kg, a 60% chance of it being 100 and 20% chance of it being 120. Occasionally for projects where considerable data is available, project calculations could be done more formally with expected values for all key output and input variables, so that for item j, the base case value (Bj) is a probability-weighted value with a range of different possible values weighted by the probability of their occurrence. If this is done systematically, the NPV and IRR results will also be expected values based on the probabilities. The true probability distribution behind project calculations is itself not known and by definition is therefore uncertain. Sensitivity analysis as discussed above provides a relatively simple way of changing assumptions about probabilities and it is very widely used as a standard procedure in project calculations. In addition, it is possible to formalise these implicit judgements in a risk analysis, which allows for both the possibility of simultaneous variation in all key parameters of a project and the probability that these will vary around their mean expected value. This allows a judgement on how likely it is that the decision to accept or reject the project would be wrong due to the risk involved. The following section introduces risk analysis, which allows for the simultaneous random variation of probabilities of all key project variables, within a defined functional form. This also introduces amendments to the standard criteria on project acceptability. Risk analysis involves a fuller assessment of possible variation. Its purpose is to provide a probability estimate of how likely a project decision is to be wrong. Risk analysis begins from the best estimates contained in the initial resource flow and from the effect of variation given by sensitivity analysis; but now different variables are considered simultaneously. The actual outcome for a project may vary from the original best estimates. A range of values above and below these best estimates can be defined: for example, in the illustration in Table 9.3, values 10% below and 20% above the best estimates are used. This is a relatively conservative range for variation. Some project items can be estimated with greater certainty than others. Although it is convenient to use the same range of variation for each variable considered in risk analysis, the probability of the different values in

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the range occurring will differ.3 For example, given the optimism with which projects are often prepared, some items like investment costs are more likely to vary upwards rather than downwards from the best estimate, whilst others like revenue are more likely to be below rather than above the best estimate. A probability should be attached to each variation relative to the base case, to reflect the likelihood with which the different values in the range will occur. The sum of these probabilities must total 1.0 for each variable. The effect of varying values within a range can be calculated through sensitivity analysis. It is the additional probability estimates associated with each variation that represent the essential feature of risk analysis. Where do these probability estimates come from? For some variables, they may come from past evidence, for example, of fluctuations in prices, outputs, or of material ratios in different production processes. For other variables, intuitive guesses may have to be made on the basis of experience. For this example, Table 9.3 summarises the effect on the NPV of variation in each variable, presents the probabilities associated with each Table 9.3  Risk analysis: basic data Variation Investment cost

Materials cost

Materials ratio

Unskilled labour

Benefits

Probability Change in NPV Range Probability Change in NPV Range Probability Change in NPV Range Probability Change in NPV Range Probability Change in NPV Range

−20%

−10%

0

10%

20%

0.1 +200 00–09 0.1 +988 00–09 0.05 +988 00–04 0.1 +200 00–09 0.1 +1974 00–09

0.1 +100 10–19 0.1 +494 10–19 0.05 +494 05–09 0.1 +110 10–19 0.2 +987 10–29

0.4 0 20–58 0.6 0 20–79 0.8 0 10–89 0.8 0 20–79 0.5 0 30–79

0.2 −100 60–79 0.1 −494 80–89 0.05 −494 90–94 0.1 −110 80–89 0.1 +987 80–89

0.2 −200 80–99 0.1 −988 90–99 0.05 −988 95–99 0.1 −220 90–99 0.1 +1974 90–99

 In risk analysis software for different variables for the same project, it is possible to specify different distributions, with a normal distribution the most common, and different means and standard deviations around the mean. This is an alternative to the simpler approach illustrated here. 3

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variation, and also converts these to a set of two-digit random numbers. The random numbers are a means of making random choices within the range for each variable. The probabilities for a particular variable, which sum to 1.0, are associated with numbers between 00 and 99. For investment cost, for example, the probability of 0.1 for investment cost being 20% less than the best estimate is associated with the 10 values 00–09; the probability of 0.1 for investment cost being 10% less than the best estimate is associated with the 10 values 10–19; and so on. A randomly selected two-digit number then identifies a particular value for investment cost. This information is presented for five variables. Risk analysis now proceeds by the selection of sets of 5 two-digit random numbers, identifying the variation in each variable to which they relate, and adding up the total effect on the NPV. This has been done 100 times to yield 100 estimates of the NPV. These 100 estimates in each case are distributed around the best estimate of the NPV (and around their own mean value). The results of the risk analysis are presented in Table 9.4. The expected NPV by this process is larger than the best estimate and positive and confirms the earlier decision that the project is acceptable. However, there is a very large range of values around the best estimate and the expected NPV, and there are many positive and negative NPV values, implying considerable risk. The proportion of negative and positive values can be calculated. Here 39% of the NPV values are negative. If the project is accepted because of its positive NPV at best estimates, there is a 39% chance that the decision will turn out wrong.4 These are the essential results of the risk analysis. They contain more information than simply the best estimate, but also provide a dilemma for those who have to decide. For this project, there is a substantial chance of being wrong. It may be thought unacceptable to risk the 39% chance of a negative NPV. It is for this reason decision takers may opt for a simple rule of thumb, such as not accepting projects with risk to failure above a 4  Strictly these percentages are sample estimates (from a sample of 100 observations) of true values. For simplicity these proportions can be taken to represent the probability that a decision will be wrong. With random selection of values for each variable, the different values for the NPV are distributed around their expected value and most samples of more than 20 observations will approximate to a normal distribution, where the proportion of results lying within a specified range can be calculated. It is this, which allows the calculation of the probability of a project decision being wrong.

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Table 9.4  Results of risk analysis NPVs Sample range: Expected value Distribution of NPVs above 3000 2500 2000 1500 1000 500 0 −499 −999 −1499 −1999 −2499 −2999 below −3000 Probability:

Economic prices (NPV = 164) Highest Lowest

to to to to to to to to to to to to Positive NPV Negative NPV

2852 −3786 369

2999 2499 1999 1499 999 499 −1 −500 −1000 −1500 −2000 −2500

0 2 10 5 11 8 25 12 14 8 3 1 0 1 61 39

threshold (such as 25%). The counter-argument is that where a public agency or private firm accepts large numbers of projects risks can be pooled across the portfolio, so that risk of failure can be offset by chance of success. This argument does not apply where a particular project is large relative to the portfolio of a firm or the budget of a government. Here the potential cost of failure may have major consequences for the financial stability of a firm or the macro-stability of a country, and potential gains from smaller projects may not be large enough to ignore any risk involved with the large project. Risk analysis is most important for large and marginal projects, with a rate of return just above the discount rate. For projects with a much larger rate of return, the probability of a negative NPV with variation in the major variables is likely to be small. Aside from the case of very large projects for which the consequences of project failure are particularly severe, the main effect of risk analysis is likely to be on decisions among

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alternative marginal projects. If all project analyses include a risk analysis conducted on similar lines to that in the illustration above, then the less risky among the marginal projects can be chosen.

Reducing Risk Identifying the effects of variation in major variables, and investigating the likelihood of their combined variation, provides considerable information on the risks associated with a project. It suggests where it may help to try and reduce the risk. Where project risk is deemed to be important, it can be reduced at both the analysis and implementation stages of a project. An analysis carried out using the most pessimistic estimates for each variable shows the amount that has to be available as a contingency reserve in the worst case. Reducing risk should aim at improving the project results at least to this extent so the reserve is no longer necessary. In many cases project results can be improved by a re-design of the project. Alternative technologies, locations, output mixes, and scales should be investigated. Re-design can also include the phasing of investment so that the production lessons learnt in early phases can be applied in later phases, improving the overall performance. Each course of action will have its own risks; lower expected NPV results will probably have to be traded off against lower risks. Risk reduction can also be achieved by choosing projects for which there are well-known precedents. Replication of small-scale projects rather than commitment to a few large-scale projects can reduce risk. Investment decisions can be seen in terms of programmes of investments rather than one-off projects. Copying and adapting from imported technologies under supply and management contracts can also reduce production and marketing risks. These forms of dealing with risk, however, cannot easily be incorporated into project analysis calculations or decisions on individual projects. Moreover, such approaches lose the potential benefits of developing new technologies or learning-by-doing. For projects where there are several sources of risk, a different approach may have to be adopted. The illustration above revealed a project highly sensitive both to changes in revenue and in the materials ratio. Where considerable uncertainty attaches both to output sales and technology, then the best approach may be to initiate pilot production on a small scale. Pilot production will allow the technical ratios and the output to be tested and improved. It will allow a re-estimation of the main project variables,

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and a reduction in risk for a full investment. However, this does not mean that the full investment will necessarily become acceptable; a reduction in risk may be associated with a smaller estimate of the project’s net benefits. A better design for the project may result from these actions; but in practice, there will still be considerable uncertainty about project effects. A major tool to use in project implementation to reduce risk is contracting. Generally, the longer for which a contract runs, the more certain the elements contained in the contract. Long-term contracting can be applied both to the purchase of inputs and to the distribution of outputs. It can be applied in management and technical and marketing agreements, to encompass pricing, profit sharing and exchange rate calculations. Again, however, achieving greater stability, or less uncertainty, in project effects may be purchased at a lower level for the net benefits. A simple means of reducing risk is to delay a decision on a project until more information is available. Delay can reduce risk, but also entails a cost as project benefits will be received later. There is therefore a trade-off involved. Appendix summarises a means of formalising this trade-off.

Further Reading Pouliquen (1970) was one of the seminal statements on risk in project analysis. Textbook discussions are in chapter 6 of Jenkins et  al. (2018), chapter 8 of Florio (2014). Weiss and Ward (2012) have a short survey. Belli et al. (1999) give an example of the application of risk analysis to an education project.

Appendix: Option Value and the Value of Waiting The additional information to be obtained by waiting for committing to a final project decision offers another way of addressing uncertainty. The discussion of an option value, based on the value of waiting, occurs in both the financial and environmental economics literature and is based on the premise that 1. an investment decision on a project is generally irreversible, at least in the short-term, and rules out other uses of the resources involved 2. when the future is uncertain, delaying a project will provide more information on benefits and costs in later years.

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The option value is the difference between the net benefits from a project (usually expressed as an NPV) after a delay and the net benefits if the project is undertaken immediately. Where this is positive, it gives the benefit of waiting. By definition, a positive option value implies an immediate decision to invest is not correct and will not maximise net benefits. Ignoring a positive option value imposes a cost as it means future benefits are being lost. Hence the argument that if a project goes ahead immediately, the positive option value should be added to project investment costs. A negative option value arises where the costs created by delaying benefits exceed any additional information obtained by waiting. In this case the decision to invest immediately will maximise net benefits. A helpful explanation of the logic involved is given in the context of a project involving development of a forest area. The choice will be between ‘Conservation’ to preserve the ecological services offered by the forest or ‘Development’ to create marketable products, such as crops or timber. For simplicity, the discussion is for two periods. In period 0 (i.e. the present), the choice is Development, which rules out future conservation, and Conservation, which allows the option in period 1 of either developing or conserving. Development benefits are D0 and D1, for the two time periods are assumed to be certain. The conversion benefits in the present (V0) are known with certainty, but in period 1, may be either high (Vhigh) with a probability of p or low (Vlow), with a probability of p − 1.5 A conventional cost-benefit comparison, which ignores the option of waiting, compares Conservation and Development. Hence Development is justified if its expected net benefits (ED) exceed those of Conservation (EP). Hence to justify Development requires D0  D1  V0  p.Vhigh  1  p.Vlow



Waiting one period entails allowing the forest to function normally with a known benefit of V0 and investing the funds that would have been spent on the project elsewhere for one period at the discount rate, so their present value remains constant. On the assumption that more information will be known on the uncertain Conservation benefits in year 1, these may be either pVhigh or 1 − pVlow. If they are low, the Development option can 5

 D and V can be thought of as present values.

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be taken, but if they are high, the Conservation option may be justified. Hence, with waiting, there are three possible outcomes (a) waiting and then developing, so total benefits over the two periods (ED) are V0 + D1 (b) waiting and then conserving with high conservation benefits, so benefits (EP) are Vo + pVhigh (c) waiting and then conserving with low conservation benefits, so benefits (EP) are Vo + 1 − pVlow The optimal decision will depend on the size of ED relative to EP, which in turn depends on the uncertain benefits from conservation. The probability p will not be known in the period 0 when the immediate decision on whether to invest has to be taken. By assumption in this example, it is a random value in period 0, that will be known in period 1, because waiting has provided more information. If Vlow occurs, the decision should be to wait and develop, but if Vhigh occurs, the decision should be to wait and conserve. The value of waiting (EW) in period 0 and then with more information choosing the best option in period 1, is given by the expression EW  V0  pVhigh  1  p  D1



The logic is that waiting and not developing gives a known conservation value in year 0 of V0. In year 1, if Vhigh occurs, conservation will be chosen, and if Vlow occurs, the choice will be development with a benefit of D1. Hence in year 1, benefits are a probability-weighted sum of Vhigh and D1. The economic value of waiting EW will always be higher than the immediate choice of conservation (EP) provided D1 is greater than Vlow and p is less than 1.0 The second comparison must be with the choice of immediate development ED. Immediate development is assumed to give certain benefits so ED  D0  D1

Hence ignoring the option of waiting requires that immediate Development creates benefits greater than waiting, so ED > EW. Alternatively, where waiting has more benefits, so EW > ED,

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option value is the loss EW − ED created by taking an immediate decision to develop.6 The argument is that the decision rule to justify Development should be the requirement ED > EW, not the weaker one that benefits of immediate development exceed those of continuing with conservation (EP), so ED > EP. Where the value of waiting (EW) exceeds benefits from both immediate development (ED) and continued conservation (EP), the difference between EW and the larger of ED and EP gives the negative option value created by taking an immediate decision. In theory, it can be seen as a cost imposed by foregoing the option of waiting and should added to the cost of a project, where waiting is ignored.7 In practice, it is rare to see detailed option value calculations included as part of project costing; however, the general principle that decisions can be delayed to allow more information to be collected is an important one. This is particularly the case where projects involve relatively large irreversible investments. Where waiting allows more information over the future (e.g. in the growth of demand) it provides a means of addressing uncertainty over the probability distribution of future outcomes. As discussed in Chap. 3, conventional analyses of project timing, to compare alternatives with different start dates by the NPV (discounted to the same base year), may forgo this opportunity, as it makes no allowance for the availability of additional information.

Bibliography Arrow, K., & Fisher, A. (1994). Environmental Preservation, Uncertainty and Irreversibility. Quarterly Journal of Economics, 88, 312–319. Belli, P., Khan, Q., & Psacharopolous, G. (1999). Assessing a Higher Education Project: a Mauritius Feasibility Study. Applied Economics, 31, 27–35. Dixit, A., & Pindyck, R. (1994). Investment Under Uncertainty. Princeton University Press. Florio, M. (2014). Applied Welfare Economics: Cost Benefit Analysis of Projects and Policies. Routledge.

6  This discussion is based on Pearce et al. (2006), which in turn draws on the original work of Arrow and Fisher (1994). Pearce et al. (2006) show that to justify immediate development requires that ED  >  EP  +  E.max(D1  −  V1), where EP is the benefit of conservation, and E.max(D1  −  V1) is the expected value of the maximum difference between development benefits in period 1 and conservation benefits period 1. 7  This is the argument made by Dixit and Pindyck (1994).

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Jenkins, G., Kuo, C.-Y., & Harberger, A. (2018). Cost Benefit Analysis for Investment Decisions. Cambridge Resources International. Pearce, D., Atkinson, G., & Mourata, S. (2006). Cost Benefit Analysis and the Environment. OECD, 128–130. Pouliquen, L. (1970). Risk Analysis in Project Appraisal. World Bank Staff Working Paper 11, World Bank, Washington DC. Weiss, J., & Ward, K. (2012). Projects and Risk. In J. Weiss & D. Potts (Eds.), Current Issues in Project Analysis for Development. Edward Elgar.

CHAPTER 10

Benefit Valuation in Different Sectors

The earlier chapters have discussed the principles behind economic analysis of projects, and Chap. 7 has given a detailed discussion of estimating willingness to pay. Chapter 11 looks at valuation approaches for environmental effects, which are now highly relevant elements that need to be incorporated in a full analysis. This chapter looks at projects from sectors where the public sector is active in most economies. Benefits need to be estimated differently in each to allow for the varying types of project effects. The principle of valuation in terms of the incremental-non-­ incremental distinction remains relevant, although the application of these principles differs between sectors. Simplified examples are discussed from three sectors—transport, education, and health. Each sector is predominantly non-traded and for projects in these sectors, how benefits are treated is by far the most important issue in their economic analysis. The Appendices give three examples of practical applications of economic analysis in these sectors; at the same time, they illustrate practical shortcuts that are used frequently.

Transport Transport covers a range of activities including roads, rail, air, ferries, and ports. The common denominator is that projects of this type are all activities which facilitate travel of persons and goods, either by lowering cost, increasing speed or improving quality of travel experience, or a © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Curry, J. Weiss, Project Analysis in Developing Countries, https://doi.org/10.1007/978-3-031-40014-8_10

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combination of these. Demand for travel arises because these services allow individuals, households and firms to access leisure or productive activities and to purchase and produce goods and services and employ workers. To apply the valuation principles discussed in previous chapters requires the price-quantity and incremental and non-incremental framework discussed in Chaps. 4 and 7. The starting point for analysis is the traffic flow projections with and without a project. These could be based on a full transport planning model that takes account of different modes of transport in an area and allows for diversion caused by a project. There will be decomposition by type of vehicle and user, by purpose of trip and by time of travel. Cost estimates will be derived from within the model. For large urban planning transport schemes, such as a metro expansion, construction of a new ring road around an urban conurbation or a new airport or port, it is probably essential that a version of such a model be used. For less large-scale transport projects, a partial approach is often applied. For example, a simple, approach to forecasting traffic flows for road projects can be derived from combining macro or regional forecasts for income growth with transport income and price elasticities. For a road project this can allow for differences between vehicle types (such as lorries, passenger cars and scooters) and trip purpose (such as leisure and business) by applying different elasticities. To illustrate a simple version of this approach makes future traffic between points i and j (Tfij) a function of income growth from the base year (g), base year without-project traffic (Tij1), the travel cost between i and j with a project (Gij) faced by users, and an elasticity parameter (n) reflecting the proportionate change in traffic flow for a proportionate change in travel cost, which is negative and varies by vehicle type. Where G1ij is base year travel cost and an income elasticity of demand of 1.0 is assumed



Tfij  g T1ij G ij / G1ij



n



(10.1)

Hence, assuming an income growth of 4%, a base year traffic of 100 in year 1, a forecast reduction of generalised travel cost (GTC) of 8% between years 2 and 1, and an elasticity parameter of 0.5, projected traffic in year 2 will be 109.6.1 1  This is termed an ‘own-cost elasticity’ model and this and more complex versions are discussed in detail in Department of Transport, Transport Analysis Guidance (TAG), Variable Demand Modelling—Key Processes; www.webtag.org.uk.

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Non-Incremental Benefits For most transport projects, non-incremental benefits can be estimated through cost savings, where costs are generalised travel costs that cover the full cost to the economy of the transport service provided. These savings cover the effect of the project on existing and expected traffic without a project, often termed normal traffic, and on traffic diverted from other routes or modes as a result of a project. These savings will be in operating costs of the relevant form of transport (cars, buses, trucks, planes, ferries, and so forth) and in driver and passenger time. In addition, there may be benefits (or costs) external to the project in lower (or higher) emissions or accidents, which need to be included as either lower cost or additional benefits (or higher costs). Cost savings should follow the principles discussed in Chaps. 4, 5 and 6, so for example, taxes saved are not an economic cost and labour time saved should be valued at a shadow wage where otherwise surplus workers are involved. Where a shadow exchange rate is used in the analysis, any traded costs saved, for example for fuel, should be adjusted by the shadow exchange rate factor. Taking road projects as an example, once future traffic flows are projected, preferably differentiated by vehicle type, a key question is how travel cost is affected. Typically for roads the main savings in costs will be in terms of lower vehicle maintenance and fuel costs, and savings in travelling time. In this analysis, care should be taken in ensuring that the correct comparison of vehicle operating costs without and with the new project is made. This is important if the existing road network is becoming over-­ crowded or is poorly maintained. Failure to improve the road network may lead to sharply rising costs per vehicle in the future, and the gains from a new project must be based on a comparison between costs with the project and future costs on the unimproved road, not with current costs on the road. For time savings, where the purpose of travel is known, and the savings cover travel for business reasons, the gain will be in terms of the opportunity costs of labour, for example for drivers, which can be valued at either the wage paid or a shadow wage, where underemployment is an issue. Where leisure travel is involved, there is a difficulty in valuing travel time for leisure as opposed to productive purposes. Normally some proportion of the average daily wage, such as 0.33, is used, as a crude approximation for the benefits involved.

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For most road projects in developing countries, for many years it has been standard practice to calculate travel cost savings from a software model developed by the World Bank, the Highway Development and Management model version 4 (HDM-4).2 This software models the impact of road surface improvement on travel cost, including speed. Travel cost will depend on vehicle type, distance, terrain and road surface, both with and without a project. Basic data on road and vehicle characteristics, plus prices for key cost items must be entered into the model. In addition, origin-destination survey data are required for vehicle usage with and without a project. The impact of the project is calculated for two alternative measures of road surface quality—one with and the other without the project—with surface quality usually measured by the International Road Roughness Index (IRI). Table 10.1 summarises the type of data required by the model. This model will allow estimates of travel costs for road users under the specified conditions, relating to terrain, speed, vehicle characteristics and so forth. Table 10.2 gives a simplified example of the type of cost decomposition that might be used. The comparison is between average costs per vehicle km for trucks, buses and cars for an older unimproved without-­ project road and a new improved version after project implementation.

Table 10.1  Data required by HDM4 model Road characteristics

Vehicle characteristics

Cost

Rise (m/km) Fall (m/km) Curvature (degrees/km)

Average speed (km/hour) Vehicle weight (tonnes) Power to weight (bhp/ tonne) Vehicle usage and age (km, years)

Vehicle price, new Tyre price, new Price of fuel and lubricants

Roughness (m/km) Width (metres) Surface moisture content (%) Rainfall (mm/year)

Maintenance labour ($/ hour) Vehicle crew cost ($/hour) Overhead

Source: ADB (2013) Box 7.2

2  Version 5 is now used. For the analysis of smaller scale project catering for lower traffic volumes in rural areas, an alternative software the Roads Decision Model (RED) is available.

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Table 10.2  Average cost savings without and with project (Pesos per vehicle km) Trucks and buses

Fuel and oil Tyre wear Vehicle maintenance Wagesa Total Savings

Cars

Old road

New road

Old road

New road

50 40 80

50 34 64

22 10 32

20 6 20

25 195

21 169 26

10 74

6 52 22

Includes cost of unpaid car drivers’ time

a

In practice, costs would need to be separated further by vehicle type. This data will be at constant financial prices, and where there are important distortions of prices, an adjustment would need to be made to convert figures to economic prices. What could be significant is to ensure that where the fuel used is internationally traded, its border parity value is used. Where fuel is either subsidised or taxed, this would require removing any subsidy, import duty, or indirect tax. As a first approximation, road user benefits for normal traffic per vehicle km are given by average travel cost savings (such as Pesos 26 and 22 per vehicle km in Table 10.2).

Incremental Benefits Incremental benefits arise from the traffic stimulated directly as a result of a project and are additional to any normal (without-project) growth that is covered by the non-incremental effect. Incremental effects are referred to as generated traffic and their value is often approximated by half the unit generalised travel cost savings. This is on the grounds that the analysis in Chap. 7, Fig. 7.3 can be applied to the demand for travel to estimate the area under the demand line, with travel cost equivalent to price and the horizontal axis showing demand for travel in physical units in a measure of distance, such as vehicle kilometres or passenger kilometres. This approach to approximating willingness to pay requires that travel cost is defined appropriately for the users involved. The relevant costs will be those faced by the traveller, so operating costs of a bus are not relevant for passengers,

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Vehicle cost per mile

D

P C E

P1

C1

D1

O

Q

Q1

Vehicle miles

Fig. 10.1  Demand for transport

but fares are, while operating costs of a lorry including toll charges are relevant for a haulage company. Externalities, like effects on emissions or accidents from generated traffic, must be included separately as project costs or benefits, but will not be used for estimating willingness to pay, since they are not part of a price or cost facing users. Figure 10.1 illustrates this for a road example, but the principle applies to other modes of transport also. The demand for transport is a function of the cost of using the service. Costs on alternative modes of transport or other roads determine the maximum that users are willing to pay to use a project road. With a demand curve of DD1 in Fig.  10.1 a new project improves an existing road and lowers costs per vehicle from OC to OC1. There are gains to normal without-project traffic Q and new generated traffic (Q1−Q). The gain to the former is the unit cost saving for normal traffic and is shown by the area CPP1C1. Generated traffic (Q1 − Q) gains the difference between what users are willing to pay for the services of the road PEQ1Q, and what they actually pay P1EQ1Q. The net gain to new users is the consumer surplus triangle PEP1. This is equivalent to the generated vehicle miles (Q1 − Q) multiplied by half the average saving per mile [(OC  −  OC1)/2]. Total benefits B from normal and generated traffic are thus

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B   Q   OC – OC1  Q1 – Q  0.5 OC – OC1



279

(10.2)

By rearranging this reduces to



 

B  0.5 OC  OC1  Q  Q1



(10.3)

So, benefits equal the sum of with and without-project traffic multiplied by half of generalised cost saving, often termed the rule of half. The rule of half as discussed here is still used frequently in practical work, although it is recognised that it breaks down under several conditions, for example where the price-quantity relation is non-linear and where the cost change is large.3 Particularly relevant for transport projects are where the generalised cost changes created by a project are large and the with-project outcome is totally different from the without-project case (e.g. where a transport project transforms the economic base of an area due to major induced investment). Here, as discussed below, wider effects have to be considered. Willingness to pay will give users’ perception of the value of the journey whether for productive or leisure purposes. This means if it is work-related, willingness to pay reflects the economic value of the journey to the user. However, for this to equate to the value of the additional economic activity previously constrained by the lack of transport services requires the very strong assumption of a perfect market, with, amongst other things, no price distortions in goods and factor markets and perfect information on the part of users. Where relatively small changes in travel cost are created by a project, using willingness to pay estimates derived from the rule of half, for example, may give an adequate approximation for the economic benefit from generated traffic. However, where large changes are involved direct estimates of the value of additional activity (net of the additional costs involved) are required to replace the willingness to pay estimate.

3  An approximate approach was suggested for some transport projects as a way of overcoming the linearity assumption. This is termed ‘numerical integration’ and involves identifying a small number of price-quantity points on a non-linear demand line and using the rule of half to estimate the area under each. ADB (2013) explains the procedure and illustrates. It shows that the approach only makes a difference for a price elasticity more negative than −1.0.

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This output value must be attributable to a project, so that it would not have been forthcoming in its absence, and it should be net of the resource costs involved in producing the output. For example, if a new road links farmers with an urban market, the gain in terms of generated traffic will be the value of their additional output—that is, the addition to what they would have produced without the road, minus the value of the inputs used to produce this additional output; such inputs will include seed, fertiliser, pesticides, the farmer’s own time and the cost of hired labour, the cost of purchase or hire of oxen for ploughing and any capital cost of land clearing or other infrastructure facilities. Collecting such data at the project level can be difficult in the absence of detailed surveys of economic activities in the area of location of a project. The treatment of generated traffic stimulated by a new project is both potentially very important, particularly where the project provides access to a market that was previously inaccessible or changes the land use pattern in an area, but is also often very difficult to quantify. The theoretical discussion of these output effects focusses on the mechanisms involved.4 Of these the key will be any investment induced by the transport project, that would not otherwise have taken place, and any additional productivity growth in the area linked to the project, again due solely to the project. Estimation of induced investment could be addressed as part of a spatial planning exercise through a computable general equilibrium model. Whilst relevant for large projects designed to transform the land use pattern and productive base in an area, such models are complex and data-­ intensive and are not designed for individual projects. Where there is a strong case that a transport project will create a benefit through induced additional investment (e.g. investment in a hotel in response to a road which allows tourist access to the area), the appropriate procedure is to include the induced investment and the associated benefits and operating costs in the appraisal of the original transport project. Thus, in the hotel illustration, the investment and operating costs of the hotel must be added to the costs of the road, and the benefits from the hotel will be added to the other benefits arising from the road. However, large induced investments will be relatively rare and should only be treated in this way where it is clear that they would not have taken place without the initiating project. To address this accurately, the transport project needs to be planned in conjunction with the anticipated 4

 See Laird and Venables (2017).

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investment, so that the costs and benefits of the latter can be included in an integrated appraisal covering both the transport project and the induced activity. Any potential benefits from induced investment need to be compared with the costs involved before they can be associated with transport projects. In practice, planning projects in this integrated way requires data on the induced activity, which is rarely available, and hence this type of joint appraisal is rarely carried out. An approach to estimating productivity gains arising from agglomeration benefits in areas linked by a project has been applied in more detailed analyses for larger projects. In terms of productivity gains in an area owing to transport projects, there is now a body of evidence that has been drawn on to give an operational way of incorporating possible benefits. This is based on the theory of agglomeration benefits from externalities and the benefits of specialisation as producers and consumers are concentrated together. Appendix 1 illustrates this approach.

External Effects In addition, as noted above, where external effects are involved, the willingness of travellers to pay and cost savings must be supplemented by additional estimates of benefits. For transport projects, the key external effects are on accidents and on emissions. The net effects in relation to these must be given an economic value and either added to costs or benefits. Accident costs can include infrastructure damage, cost of medical treatment, loss of earnings, the human cost of suffering, and loss of life. Generally, these are specific to particular project locations, and precise estimates are difficult to make. Some international estimates are available for higher-income countries, and these are sometimes scaled for differences in income per capita between countries. Estimating emissions from transport projects is now an important step in appraisal. Emissions will include not just CO2 effects from energy use, but also potentially particulate matter and nitrogen oxide and sulphur dioxide, as well as noise pollution effects. Some of these other emissions (like particulate matter) may have individual economic prices available in the literature, but others may be valued as CO2 equivalents and valued at the economic value of CO2 (see Chap. 11). Some emissions such as particulate matter will vary with location and type of transport activity making estimates of their level created by a project more difficult. Forecasts for emissions with and without a project can be done with varying degrees of

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detail from the use of simple emission coefficients per vehicle/km to sophisticated planning models, which allow for various growth and substitution effects between transport modes. For larger urban transport schemes, use of modelling of this type has become relatively common. Wider environmental effects from transport projects are possible, but as far as possible, efforts should be made to minimise these as part of project design. For example, any negative impacts on the local eco-system from road, rail, or airport construction should be identified in a preliminary environmental impact assessment and mitigatory measures should be built into project design and the cost of this mitigation included in the project cost. Similarly, any resettlement of communities affected by the construction should be included in costs. Hence, it is only damage that is not mitigated or compensated that needs to be included as an extra cost created by a project. How important this will be will vary with the nature of projects and the effectiveness of regulations aiming to enforce this type of mitigation. In summary, transport projects can in principle create a range of benefits, user benefits in lower cost and times savings, accident reduction benefits (although some projects may increase accidents with higher travel numbers), lower emissions (although again some projects may raise them) and induced economic activity. Appendix 2 gives a case study illustrating some of these. The treatment of the main benefits from transport projects is summarised in Table 10.3. Table 10.3  Summary of transport benefits and their valuation Category

Valuation

Examples

User benefits normal traffic

Generalised travel cost savings

User benefits generated traffic

Half-generalised travel cost savings

Wider economic benefits

Estimates of net economic effects Accident reduction or increase Change in emissions

Vehicle operating and replacement cost, value of time, user charges Willingness to pay for additional journeys approximated by half value of savings Land use change, induced investment, productivity growth Impact on number of crashes valued at economic cost Valued at CO2 and CO2 equivalent

Accident benefits Emissions benefits

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Education Projects Benefit valuation in social sectors like health and education is far more controversial than in relation to physical infrastructure sectors, like water or transport. This is partly because there may be substantial external benefits, so that the welfare gains from such projects affect more than those who may purchase or use project output. For example, a better-educated population is both more innovative and skilled, as well as more health-­ conscious and socially concerned. Whilst individuals attending a new school may benefit in the long run from their greater productivity, the health and social factors will create wider welfare gains, and the ability to innovate can raise incomes for many not directly affected by the school. Under these circumstances, individual preferences for the output of such social sector projects will understate the full value of the services to the economy. In some cases, it may be considered too complicated and inaccurate to value the benefits of a project in monetary terms. Where this is the case, or it is judged to be too controversial, cost-effectiveness analysis, as discussed in Chap. 3, can be applied. However, whilst noting these concerns over the external benefits from education, there is a long-standing tradition in project analysis of valuing benefits at the estimated net incremental income education provides.5 This is on the grounds that education raises productivity, which is a direct economic benefit, and under the assumption of a competitive labour market, where earnings reflect productivity, additional income will give the monetary value of this productivity. As gains to individuals provide the basis for willingness to pay, if access to education services raises future income, rational individuals should be willing to pay for education up to the point that its additional benefit equals its additional cost. Hence, even if there is no direct charge for education (only indirect charges through taxation), additional earnings can be used to reflect individuals’ willingness to pay for their education and give a value for projects’ incremental effect. Furthermore, this was rationalised on the grounds that as educational externalities are usually deemed strongly positive, benefits based on incremental earnings should give minimum estimate of true benefits. In terms of non-incremental effects, there are three main possibilities. In the first there is a pure cost-saving effect, where a project saves costs per pupil, for example through school reorganisation, whilst maintaining 5

 This was often termed a ‘human capital’ approach to education planning.

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education standards. This gives non-incremental benefits as the economic value of the costs saved. The second possibility is that the project has a non-incremental effect in that it educates the same number of pupils as without the project, but creates a ‘quality’ effect, by improving teaching methods and standards. In this case the earnings differential due to this quality effect provides the value of non-incremental benefit. Finally, it is also possible that cost savings are at the expense of quality and will be wholly or partially offset by a decline in the differential per student, so net non-incremental benefits will be the difference between the cost saving and impact on earnings. The assumption of perfectly competitive labour markets underlying this approach is a strong one; the approach is approximate and rests on several critical assumptions. As noted, any wider external benefits for the rest of society are not included. Second, any projected increase in earnings cannot be assumed to be caused solely by education; factors like social status or restrictive employment practices may also be at work. Third, where labour markets are distorted—for example, owing to employer collusion or lack of information—earnings will not reflect productivity and thus, even if all earnings differentials are caused by education, true benefits will not be estimated. In establishing annual benefits from an education project, there are also complications relating to potential unemployment, opportunity costs, and the investment of households in education. These can be seen in a general statement of annual net benefits from an education project. Here, for year t, net benefits NBt are NBt  Yw. 1  p t   Ywo t  A t  Ct



(10.4)

Where Yw is projected earnings with an education project, Ywo is projected earnings without the project, p is the probability of graduate unemployment, so 1-p is the probability of obtaining a job with an earnings premium due to the project. A is the private cost of education borne by a student’s family (such as lost earnings whilst studying and cost of education material or tuition fees), and C is the cost of education borne by the project concerned, including capital cost (e.g. for school building or maintenance). In an economic analysis C and A should be valued at economic prices.

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285

What is assumed about the wage premium post-graduation on completion of education or training is critical in most education projects. An earnings-age profile for different education categories is needed, so the average earnings for different levels of education attainment can then be compared, and the earnings differential per pupil due to the project can also be calculated over a working life. For example, the earnings of someone who has completed secondary education can be compared with those of someone who has completed only primary education. The time profile of earnings differentials over the working lives of the individuals in different cohorts can be discounted to the year of completion of education/ training to give annual benefits from education/training for that year. The simplest approach is to take the difference in average earnings by education category in the project base year and to project this difference over the life of a project. Average earnings data by education category can come from a range of sources, such as a recent Labour Force Survey, from the judgement of those planning a project or from prospective employers and in the case of universities and technical and vocational institutes, from studies of the subsequent employment of their graduates. The procedure for estimating benefits from differential earnings can be illustrated for a very simple example of a school project. As a result of the project, children entering the school receive six-year of primary and five-­ year of secondary education, while without the project they would receive only six-year of primary education, with no secondary education. The output of the project is therefore additional secondary graduates. The project has a construction period of 1 year so that by year 2, children are attending school. Each year a cohort of 100 children enters the school. It is assumed that with the project, all children join the school in the first year of primary and remain until the end of their secondary education, with no drop-outs and all children progressing between years. By year 7, the first cohort has finished primary, and by year 12, they enter the labour market. When they leave school, all children earn Pesos 10,000 per year, assumed to remain constant in real terms over a 40-year working life. Without the project, all children would leave school after only primary education, and then work at an income of Pesos 6000 per year for 46 years. Hence the education premium is Pesos 4000 per year. An additional minor benefit is the saving in primary school cost in the first six years of education, since without the project, children would have gone to another school that can now either save costs or take other pupils. This is ignored in the example.

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Table 10.4  Education project illustration: lifetime worker income with and without a project Cohort

(100 * Pesos 10 000)

(100 * Pesos 6000)

1 2 3 ⋮ 10

With project years 12–51 With project years 13–52 With project years 14–53

Without project years 7–51 Without project years 8–52 Without project years 9–53

With project years 21–60

Without project years 16–60

The working life of the school is sufficient for 10 cohorts of 100 children to go through the school. The first cohort of 100 children enters the labour market in year 12 and leaves in year 51; the second cohort enters in year 13 and leaves in year 52; the third enters in year 14 and leaves in year 53; and so forth until the final cohort enters in year 21 and leaves in year 60. Children in the first cohort would have commenced work in year 7 without the project; those in the second cohort in year 8 and so forth. Table 10.4 illustrates the procedure, with Pesos 10,000 and 6000 average annual lifetime earnings of students, with and without the project, respectively. Once annual figures for earnings with and without a project are available for each year of its working life, they must be discounted to a value in the present (i.e. the project’s base year). Whilst this is not a major issue in terms of complexity, it can be cumbersome in spreadsheet calculations. For example, as in the simple numerical example given above, the first set of graduates enter the labour market in year 11 of the project and work until year 50, the second set enter in year 12 and work until year 51 and so forth. It is possible to discount all these future income flows directly back to year 1, so where r is the discount rate for year 11 the discount factor is 1/(1 + r)11 and for year 12 it is 1/(1 + r)12, and so forth. The alternative way of discounting, which is arguably intuitively clearer, is to use what is termed double discounting. This treats each year of project operations separately and discounts future lifetime earnings back to the year the graduate potentially enters the labour market (in this case year 7). The present value of total future earnings in that year is then entered as the annual benefit in the overall NPV and IRR calculation. Hence this procedure involves discounting twice, once to bring future income to a value in the year the graduate potentially enters the labour market and then

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secondly to discount this present value to the project base year.6 In this example, all incremental earnings will first be discounted back to the year the graduate could have taken up work (with negative earnings in the first six years whilst in secondary education). For cohort 1, this is year 7 of the project, for cohort 2 it is year 8 and so forth. In the final project calculation, the present value of income in year 7 becomes the benefit for that year and is then discounted by the discount factor for year 7, that is (1/1 + r)7, and similarly for subsequent years. In this example, earnings foregone from not working earlier in without-­ project employment must be deducted from the with-project earnings. This means that for each cohort there will be a total cost of 0.6 million in the first six years of secondary school (6000 times 100 pupils), whilst attending secondary education. Once graduates enter the labour market, the total net earnings are 0.4  million (100 times 10,000 minus 6000), although in the first years of secondary education there is a cost of 0.6 million in earnings foregone. To keep the presentation simple, future earnings are calculated for a working life of 20 rather than 40 years and a terminal value of 8.0  million is added to the year after the end of this 20-year period to capture the additional earnings beyond year 20. Using a 6% discount rate for each year of project operations, the present value, when discounting to the potential year of entry to the labour market, is 2.66 million. This is the benefit for each year of the project’s working life to be compared with project costs. Table 10.5 has the spreadsheet calculation where these figures are derived. With some assumptions about the cost of the project, an economic NPV and IRR can be given. Investment costs of the school, incurred in year 1, are Pesos 5  million and annual wages and running costs are Pesos 1.2 million. It is assumed that earnings reflect productivity, so gains to students, before income tax, are treated as economic benefits. Some building equipment used in the construction of the school pays an import duty of 10%, and this is deducted from the capital cost to give a net of tax figure of Pesos 4.8 million. The project’s working life is assumed to be the time needed to allow all ten cohorts to pass through the education system 6  The calculations could also be done by taking the year of actual entry to the labour market as the base for discounting future income, in this case year 12. When this base is used the opportunity cost of income foregone by staying in education would need to be discounted separately. Here to keep the presentation simple, the incremental income figures are net of foregone income, so this additional step is not required.

Table 10.5  Spreadsheet example: education economic analysis Pesos (millions) Cohorts year

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 Present Value

0 0 0 0 0 0 −0.6 −0.6 −0.6 −0.6 −0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0 0 0 0 0 0 0 0 0 2.66

0 0 0 0 0 0 0 −0.6 −0.6 −0.6 −0.6 −0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0 0 0 0 0 0 0 0 2.66

0 0 0 0 0 0 0 0 −0.6 −0.6 −0.6 −0.6 −0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0 0 0 0 0 0 0 2.66

0 0 0 0 0 0 0 0 0 −0.6 −0.6 −0.6 −0.6 −0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0 0 0 0 0 0 2.66

0 0 0 0 0 0 0 0 0 0 −0.6 −0.6 −0.6 −0.6 −0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0 0 0 0 0 2.66

0 0 0 0 0 0 0 0 0 0 0 −0.6 −0.6 −0.6 −0.6 −0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0 0 0 0 2.66

0 0 0 0 0 0 0 0 0 0 0 0 −0.6 −0.6 −0.6 −0.6 −0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0 0 0 2.66

0 0 0 0 0 0 0 0 0 0 0 0 0 −0.6 −0.6 −0.6 −0.6 −0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0 0 2.66

0 0 0 0 0 0 0 0 0 0 0 0 0 0 −0.6 −0.6 −0.6 −0.6 −0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 0 2.66

0 0 0 0 0 0 0 0 0 0 0 0 0 0 −0.6 −0.6 −0.6 −0.6 −0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 8 2.66

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Table 10.6  Summary: education economic analysis Pesos (millions) Costs Years

Investment

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 NPV IRR

4.8

Benefits Operating 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

Earnings

2.66 2.66 2.66 2.66 2.66 2.66 2.66 2.66 2.66 2.66

Net −4.8 −1.2 −1.2 −1.2 1.46 1.46 1.46 1.46 1.46 1.46 1.46 1.46 1.46 1.46 1.46 1.46 2.14 9%

(which is year 16 of the project life), but benefits continue until year 41, when all ten cohorts have worked for 20 years. These are reflected with the addition of a terminal value for future earnings. The result is an economic NPV of 2.14 million and an economic IRR of 9%. At a 6% discount rate the project is acceptable (Table 10.6).7 Even this relatively simple approach poses issues for empirical estimation. First, there is the question of the education premium, that is, the differential earning that can be attributed to the project. The approach of comparing average incomes in the base year by education category is highly simplified for two main reasons; first it assumes the percentage premium is constant over time when the expectation is that earnings are likely to peak at different ages for workers with different levels of education. 7  The example is simplified but broadly corresponds to procedures on actual projects. For a detailed discussion of the economic appraisal of a higher education project funded by the World Bank, see Belli et al. (1999).

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Workers without formal technical qualifications, for example, might learn on the job and catch-up in skills over time, so the premium of earnings of those with technical qualifications, whilst initially high, might decline over time. In addition, technical change may make some skills redundant and erode the differential in later life. In the higher education sector, however, the gap between university graduates and secondary school completers may widen over time, as the effect of seniority and cumulative effect of education on productivity may operate more strongly in graduate occupations with a strong technical content. Second, and critically, the approach assumes that all of the observed difference in earnings can be attributed entirely to the improved education offered by a project. This implies that other factors likely to affect average earnings—such as innate ability, location, and gender—are similar on average for the education groups that are being compared, so the only difference is in access to education.8 More sophisticated econometric approaches to the estimation of the education premium are available, which can partly address these issues. Where detailed household survey data is available, a cross-sectional regression analysis can be applied to link earnings to education level, controlling as far as possible for age and other relevant factors. A widely used approach explains earnings by schooling and experience (proxied by age minus years in school). The coefficient on the years of schooling variable gives the impact of an additional year of schooling on wages.9 A simple general version of the model applies an analysis across households ln  E     p.Dp  s.Ds  t.Dt  1 .X  2 .X 2  .



(10.5)

Here, E is annual earnings and ln is natural logarithms, and Dp, Ds, and Dt are dummy variables for primary, secondary, or tertiary level completion (dummy is 1.0 for completion and zero otherwise), X is experience in years, α is a constant and μ is the error term. The expression (βs/βp)−1 as a percentage gives an approximate measure of the wage premium for

8  This is clearly an extreme assumption and early analyses of education projects usually applied an ‘alpha coefficient’ typically around 0.7, implying that of an observed wage differential 30% at least was due to factors other than education; see Woodhall (2004) and Psacharopoulos (1995). 9  This is often termed applying a Mincer function after Mincer (1974), who used US Census data to relate earnings to years of schooling and work experience.

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secondary graduates over primary graduates, and (βt/βs)−1 gives the premium for university over secondary graduates. Different specifications of this model are possible depending on the level of detail applied in controlling for other factors that might influence earnings, such as gender, location and sector of activity. In addition to the complication in estimating the education premium, estimates of lifetime earnings, with and without a project, require assumptions on the drop-out or non-progression, rate for students entering education or training and on the employment rate once they have entered the work force.10 Only by allowing for these effects will an accurate estimate of incremental earnings be possible. Historic data on drop-outs, with a downward adjustment for an expected positive impact of a project, would normally be used. For unemployment rates for graduates the two main options are Labour Force Survey data, preferably differentiated by sector or by region or alternatively surveys of graduate employment conducted by education institutions. Such surveys can offer more accurate insights, but are more frequently available for technical and vocational training than for schools or universities. Whatever the source, there is always a danger in relying on past figures to predict the future, and given the importance of the assumption on the unemployment rate, its switching value could be given in the sensitivity analysis for the project (see Chap. 9). Thus far no reference has been made to economic pricing for education projects. As in the transport case, in highly distorted economies, it is necessary to adjust for factors like a misaligned exchange rate or high taxation or subsidisation of input costs. For example, an overvalued exchange rate for national currency will understate the cost of imported teaching equipment like computers, and subsidised building materials will misleadingly lower the cost of constructing school or university buildings. Where such effects are important financial prices faced by a project should be replaced by economic prices. In most economic analyses based on the assumption that benefits are captured by incremental earnings, there is no need to use a shadow wage rate to revalue these benefits. This is because possible unemployment over a graduate’s working life, which is the key part of the shadow wage estimate, should have already been built into the calculation. In principle, where future earnings have a high traded good component  For consistency, an assumption is required on the employment rate without access to the services of the project, since it could be misleading to assume without-project earnings are certain. 10

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and the exchange rate is misaligned, the traded element in incremental earnings should be adjusted by the difference between the shadow and actual exchange rate (the SERF). In practice, as decomposing future earnings into traded and non-traded elements is very difficult, this adjustment is rarely made. This is equivalent to assuming all incremental productivity gains are non-traded activities.11 As noted initially, education is a sector where analysts may be reluctant to express education benefits in monetary terms, given the difficulty of capturing wider effects that go beyond gains to individuals themselves. The response to this is to use cost-effectiveness analysis for assessing the overall viability of a proposed education project, or to choose between alternative versions of a project. As discussed in Chap. 3 this requires a comparison of a measure of educational impact in non-monetary terms with project costs, with both impact and costs discounted to the present over the life of the project. This approach is only meaningful if a suitable impact measure is available, such as numbers of pupils or test scores. It should not be too difficult to compare different schemes for school reconstruction in the aftermath of a flood or earthquake, for example, on the basis of cost per student. Similarly, there may be alternative ways of raising learning standards to a broadly comparable level, for example computer or book-based, whose costs can be compared. However, large expansions in teaching or training provision, with expected important quality effects, will not lend themselves readily to a cost comparison. Appendix 3 has a simplified case study of the economic analysis of an education project.

Health Projects For benefit valuation of health projects, there can be even greater complications. Health benefits can manifest themselves in a variety of ways—for example in fewer days of illness for the sick and fewer days spent caring for them, reduced medical expenditures on drugs and treatment facilities, longer life expectancy and an improved quality of life. Where benefits are in terms of savings in cost, conceptually they pose no special problem, and normal cost valuation principles can be applied. For example, time freed for productive work can be valued at the economic value of labour, given by a shadow wage. In relation to public sector planning many health  The equivalent adjustment for a foreign exchange effect in a world price system is to apply the SCF to revalue incremental earnings. 11

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projects have a cost-saving component, particularly in the context of restrictions on health budgets. Various reorganisations, for example, often aim to shift health delivery from hospitals to local clinics or community health workers, with a view to saving cost. As such, their benefits in cost savings will be a project’s non-incremental effect. However, in practice, such reorganisation rarely has zero health consequences, so in principle, it can have incremental effects, which must also be valued. Difficulty in valuation is raised by the categories of reductions in mortality and morbidity improvements in terms of less days of ill health. Furthermore, healthier individuals are both happier and benefit the rest of society, for example through reduction in infectious disease, higher productivity, and innovation. Although attempts have been made to quantify all these aspects, the notion of life valuation, in particular, remains highly controversial. For this reason, much economic planning in the health sector focusses on a cost-effectiveness approach. This section focusses initially on the application of cost effectiveness, before turning to attempts to place a monetary value on benefits from changing health conditions due to a project. As with any cost-effectiveness analysis, a measure of impact must be compared with the costs of achieving it. A cost-effectiveness indicator (CEI) for a health project can be summarised as CEI  PV  Cw  Cwo  / PV  HIw  HIwo 



(10.6)

Where C is cost, HI is a measure of health impact and w and wo refer to with and without a project. PV refers to discounted figures for C and HI over the life of the project. In health analysis, considerable effort has gone into identifying a suitable impact measure. HI can be related to the specific goal of a project, such as children immunised or pregnant mothers seen, or to inputs provided to the health sector, such as doctors trained or bed nights provided. This is a process approach to cost effectiveness and allows a relatively straightforward comparison between the costs of alternative ways of achieving a narrow goal. However, such approaches do not allow for the health outcomes from different health interventions. To address this requires that different health conditions be converted into a common unit, so the impact of a project can be measured by the changes in the number of these units it creates.

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In the development context, the most widely used unit for measuring health impact is the Disability Adjusted Life Year (DALY).12 DALYs represent levels of loss caused by ill health and death, and the sum of DALYs avoided is a project’s health impact. In the DALY weighting system, the reference point is years of death averted (YDA), which have a weight of 1.0. All other health conditions averted by a project have a weight of less than one, which varies with the severity of ill health with the condition. These disability weights range upwards from just below 0.1 for a mild condition, described as ‘limited ability to perform at least one activity’, to 0.92, where the patient is very severely disabled and ‘needs assistance with activities of daily living’.13 The weighting system used is complex, in that it adjusts not only for the severity of the health condition averted, but also by the age of those whose years of life are saved or whose years of illness are averted. If society’s view is that the benefit from saving an extra year of life is influenced by the future productivity of those affected (which is a controversial view), then saving the lives of those of working age will create a higher social gain than saving the lives of the elderly and the very young. Age weights are calculated so that the total burden of disease is the same with and without age weights, which requires that some weights are above 1.0 and some below. The original DALY age weighting gave a weight of more than 1.0 to those aged between 9 and 54 and a weight of below 1.0 for those below 9 and above 54 years of age. Hence DALY calculations involve the product of two sets of weights, one for the severity of the condition averted and the other for the age of the patient involved. For example, as noted a year of the most serious non-­ fatal condition (where the patient needs assistance for the most basic daily activities) has a disability weight of 0.92. For a young patient of 10 years of age, the age weight is 1.086 and the product of the two gives an overall weight of 1.0 (0.92*1.086 = 1.0). However, for someone aged 60 the age weight is 0.874 and the overall weight is 0.80. This implies that averting a

12  This was developed originally by the WHO and World Bank for the Global Burden of Disease Study; see Murray and Lopez (1994, 1996). Another widely used measure of health impact is the Quality Adjusted Life Year (QALY), which is similar but uses a different weighting system. For a comparison see Sassi (2006). 13  See Murray (1994) in Murray and Lopez (1994).

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year of this serious condition at age 10 is 25% more socially valuable than averting it at age 60, which is a strong and controversial result.14 Estimating DALYs requires epidemiological data on the incidence of a disease or health condition in the population and the different levels of severity for those affected, as well as average age at the onset of the condition, and average age at premature death. In addition, to establish impact caused by the disease or health condition, national life expectancy data will be needed to identify expected age of death without the condition. The total burden of ill health from a condition is measured by total DALYs composed of the sum of years of life lost from a condition (YDA), years affected by disability before premature death (YD), years of chronic disability (YC) and years of temporary disability (YT). Although the DALY indicator was developed originally to assess the burden of disease in an economy, and thus as a macro-indicator, it can be adapted to project analysis. Comparisons between health projects require assumptions about how effective they will be in reducing key parameters, like disease incidence and age of premature death, and thus in changing DALYs, relative to what they would be without a project. Hence total health impact from a project will be the change in DALYs it creates, chDALY  chYDA  chYD  chYC  chYT

(10.7)

where ch refers to change in. In summary, Box 10.1 indicates the basic data required for calculating the change in DALYs overall, or for a project application. Use of cost effectiveness in economic analysis for health projects requires that project costs in economic prices be compared with its impact, in this case in terms of DALYs averted.15 Both project impact and costs should be linked with particular years of a project’s life, so that impacts and costs can be discounted to the present. Hence CEI  PV  Cw  Cwo  / PV  DALYw  DALYwo 

 See Anand and Hanson (1998).  Some illustrative examples are given in ADB (2000).

14 15



(10.8)

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Box 10.1  Data Requirements for DALYs DALY Data requirements Incidence of cases (per 1000 population at risk) Fatality rate per case (%) Chronic permanent disability rate per case (%) Temporary disability rate per case (%) Proportion of year to which disability applies (%) Average age at onset of disease/illness Average age of premature death with disease/illness Life expectance at different ages Disability weights on weights on chronic and temporary disability Age weights

where Cw and Cwo are costs with and without a project and DALYw and DALYwo are total DALYs, with and without a project. PV refers to present values for both costs and DALYs. The procedure can be illustrated using a simple example of a vaccination project covering a fatal condition, and so impact is only in years of death averted (YDA). The population at risk is children below one year of which there are 1 million, and the project has enough vaccine and staff for 100,000 children annually for three years. The incidence of cases is 40% of the population at risk. The fatality rate with the disease is 100%, and the probability of survival without the disease is 95%. All of the illness occurs before the age of 1, and without the disease, life average expectancy is 65 years. For simplicity, male and female illness data are treated as the same. Years of death averted can be calculated from the expression





YDA  IN  FR  SR  PV  ao..ad   w 

(10.9)

Where IN is incidence of the condition (in cases per 1000 of population); FR is the case fatality rate; SR is the survival rate without the condition between ao (the average age of onset) and ar (the average age of death with the condition); ad is the average age of death without the condition;

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PV(ao … ad) is the present value of years of death averted; w is the weight attached to each year and * refers to multiplication. In this example, annually 100,000 children will be vaccinated, and 40% of these would have caught the illness (IN = 0.4) and of these all would have died (FR = 1). Without the illness, 95% of these would have survived to the age of one. Hence of the 100,000 vaccinated children, 95% of 40,000 would have died without the project, whilst 5% will die anyway from another condition (SR = 0.95). If 38,000 deaths (40,000 times SR) are averted of children up to the age of one, total years of death averted will be determined by their life expectancy. On average, this is taken to be 65 years per child. Total years of death averted is the present value of 1 to 65 discounted initially to the year the vaccine is administered. These are taken to be years 1 to 3 of the project, as its working life is three years. The procedure of double discounting discussed in the previous section is then used to discount the annual present values with base years of 1, 2 and 3 to the project base year. As the effect is to avert a year of death this has a disability weight of 1.0 and for simplicity it is assumed the age weight for children is also 1.0.16 Table 10.7 gives the calculations. Project cost is $0.9 million annually to cover vaccines and staff cost and is spread evenly over three years. The deaths averted are for 65 years. The present value of 1 year over 65 years Table 10.7  Illustration of cost per DALY ($) Total

100,000

IN FR SR w

0.4 1 0.95 1

Annuity factor r AF=41.33779

0.015

Children at risk Life expectancy

38,000 65

years 1 2 3

Million $ Project costs 0.9 0.9 0.9

Million DALY 1.570836 1.570836 1.570836

Present Value Cost per DALY

2.62 3.4654

0.76

16  Strictly, as noted in the text, in the DALY system, the age weights differ with age and for children are above 1.0 when they reach working age.

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is calculated using a discount rate of 1.5% and the corresponding annuity factor of 41.3.17 The present value of project cost is $2.62 million, and the present value of its health impact is 0.76 million DALYs, giving a cost per DALY of just under $3.5. Discounting is used to reflect the cost of waiting, and most health cost-­ effectiveness analyses (including the original DALY analysis) use a time preference not an opportunity cost discount rate, typically in the range of 1.5% to 3%.18 As discussed in Chap. 8, a theoretical reason to justify a lower-than-normal social time preference (STP) rate is that not all of the terms in the formula for this rate (see Eq. 8.5) apply to health benefits. There the discount rate is determined by a wealth effect, which allows for the fact that recipients of future consumption will be richer than present-­ day consumers, plus a time preference effect, which is the cost of waiting. The argument is that ethically the wealth effect should not be included, as it implies that society places a declining weight on good health as people become richer. As estimates of pure time preference are typically in the range of 0.5% to 1.5%, excluding the wealth effect results in a health discount rate of no more than 1.5%.19 Whilst there is no uniformity in practice in choice of discount rate for health projects, there is broad agreement that it should be based on time preference and, under some specifications, should be lower than the standard STP rate. Practical Applications of Cost Effectiveness Despite the considerable data requirements, cost-effectiveness analyses using DALYs are now common. Particularly in poorer countries, the results indicate a very wide range of costs per DALY averted between conditions. Costs are lowest for early childhood interventions such as basic vaccines and immunisation, and are also low for treatment of conditions like malaria and tuberculosis, whilst they are very high for more complex 17  An annuity factor gives the value of 1 unit discounted over a period of time (n years) at a specific discount rate (r). The formula for an annuity factor (AF) is AF = 1 − (1 + r)−n / r. In this case it is 41.3, meaning discounting 1 year over 65 years at 1.5% gives a present value of 41.3 years. 18  This is in part due to the view that opportunity cost should not be considered as health projects provide a basic need and should not compete for funds with other projects. 19  As discussed in Chap. 8, this is the suggestion in the most recent version of the UK Green Book (HMT, 2022), which argues for a health discount rate of 1.5%, rather than the standard STP rate of 3.5%.

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conditions like cardiovascular disease and schizophrenia.20 A key question is how the results of these studies should be used. The most efficient approach in terms of maximising health impact from a given health budget is to set a minimum target for cost per DALY and to only fund activities that are cost effective by this criterion. If the health budget is fixed and funds are not used fully by projects that pass this test, the target cost threshold can be raised. Alternatively, if too many projects pass the test and additional funds cannot be found, the threshold will need to be lowered as a means of rationing. In practice, most low and middle-income countries do not operate their health system in this way, in part because of lack of systematic information on costs per DALY or any alternative measure of impact from different interventions. Often, where cost per DALY is calculated, a rough rule of thumb is used. This draws on a suggestion from the WHO that cost per DALY can be compared with a country’s GDP per capita and will be acceptable provided cost is below three times GDP per capita, with a cost per DALY equal to GDP per capita classed as highly cost effective. Those health interventions whose cost per DALY exceeds three times GDP per capita should not normally be undertaken.21 Although highly approximate and lacking any clear theoretical basis, this approach has been interpreted as creating an implicit value for a year of life averted. For a country with a GDP per capita of $3000, for example, it implies a year of life averted is worth between $3000 and $9000. Recent research has questioned the credibility of this rule of thumb, suggesting that in low and lower middle-income economies, the cost of achieving a DALY averted is less than GDP per capita. Such studies draw on statistical relationships from international cross-country data linking numbers of DALYs with total health expenditure per capita, rather than on case-studies of specific interventions. For example, with data for 2016 one study finds that for countries with a low score by the UN welfare measure, the Human Development Index (HDI), average cost per DALY averted is 34% of GDP per capita, and for countries with a medium score 20  For example, Jamison et al. (2006) report costs per DALY for a coronary bypass graft 360 times the cost for tuberculosis treatment. Very low costs per DALY for preventative treatment of infants for malaria are reported by Hutton et al. (2009) and for neonatal immunization against tetanus in Griffiths et al. (2004). 21  This draws on WHO (2001), which estimated damage costs from the loss of each DALY at approximately one to three times GDP per capita.

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by the HDI, the average cost is 67% of GDP per capita. For countries with higher scores by the HDI, average cost is above GDP per capita, but still well below three times.22 There are econometric complications in allowing for factors other than health expenditure that can impact on DALYs. If these are not allowed for this will bias the results, so that cross-country results such as these can understate the costs involved. However, this is not the only study to question the simple rule of thumb, and where it is still used, it may be giving misleading guidance.23 Benefit Valuation in Health In addition to uncertainty over the appropriate cost threshold for interventions to avert DALYs, strictly a cost-based approach can inform priorities, but not set them, and cannot reveal whether an intervention should be funded. There will be trade-offs between treating complex health conditions and using funds on more basic ones, where health impact will be greatest. In practice, health allocations are a balance between different goals. If costs alone were used to determine health funding allocations, more medically complex conditions, like heart surgery, would not be undertaken. Despite the controversy over whether human life can be given a monetary value, some economic analyses give monetary values to health benefits and subject health projects to conventional project calculations. In principle, this could aid in setting trade-offs, but the implication of this approach is that health projects are seen as competing with projects from other sectors and should be subject to the same time preference discount rate as other projects. For example, if health benefits are given a monetary value, they are being treated as equivalent to any other form of private consumption and hence the argument on the omission of the wealth effect from the discount rate does not apply. There are basically two approaches to the valuation of life. One is to attempt to assess individuals’ willingness to pay to reduce their chances of death, and the other is to use a measure of monetary income to value life. In terms of willingness to pay, as discussed in Chap. 7, this can be either 22  See Daroudi et  al. (2021). Grouping countries by HDI score is intended as a partial control for other factors that can influence numbers of DALYs. The authors acknowledge that adding other explanatory variables to the model will lower the coefficient on health expenditure and thus raise the cost per DALY averted. 23  Ochalak et al. (2015) discuss the econometric complications, whilst also questioning the rule of thumb (see also Woods et al., 2016).

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through an assessment of past behaviour in a revealed preference approach or through a survey attempting to elicit views in a stated preference format. Revealed preference is most directly observed through the wage premium for taking a riskier job; for example, working in a mine with a history of accidents as opposed to open cast mining. Where a quantifiable risk is lowered by taking a less well-paid but less risky job, the income reduction is implicitly a valuation decision. Contingent valuation studies can also be used to establish willingness to pay for lower risk of death, although interpretation of the results is complicated by the influence of the age and wealth of respondents, the time profile of risk reduction and the quality of life after survival. If an estimate of willingness to pay to reduce risk of death can be obtained, it can be combined with data on the reduction in risk involved to derive a value of statistical life (VSL). For example, as an illustration, from a survey it is found that average willingness to pay to reduce risk of death from 5 in 10,000 to 3 in 10,000 is $100. This means that over a population of 10,000, if the survey results are representative, total willingness to pay would be $1 million. This sum corresponds to 2 lives saved (the reduction from 5 to 3 deaths per 10,000). As total willingness to pay is $1  million to save two lives, this implies that if respondents are well informed the VSL equals $500,000. Most studies on VSL have been for developed higher-income economies, and when applied in a development context, the normal procedure is to scale for the difference in income per capita between the reference country and the country of location for the project involved. This procedure has been recognised as a highly unsatisfactory form of benefit transfer. Even accepting the accuracy of the VSL for the country for which the calculation was carried out, the scaling down of this to the level of a poorer country implies a strong and controversial ethical judgement. For example, if on average, based on their own behaviour or stated views, the VSL for an EU citizen is Euro 2 million, the justification for taking a value of Euro 0.5 million for citizens of a country whose GDP per capita happens to be 25% of the EU country level, is very weak. The relevant preferences will be of those exposed to the risk, not those of a richer group in another country. The alternative is to value life based on lifetime productivity, usually approximated by valuing DALYs at GDP per capita or some multiple of GDP per capita. Whilst easier to apply than an approach based on willingness to pay, this version lacks any theoretical basis and is open to

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manipulation to derive a desired result on a project. It is unclear whether a life should be valued at GDP per capita or a multiple of this. As noted above, the WHO rule of thumb for cost effectiveness implies a wide range of one to three times GDP per capita. Although somewhat unsatisfactory, this approach has been used quite frequently in practice, where it is judged that health projects should be subject to NPV and IRR calculations. In summary, benefit valuation in health remains controversial, and for health projects, there is a strong case for focusing as far as possible on cost-­ effectiveness analysis. A cost-effectiveness example for a health project is summarised in Appendix 4.

Conclusion This chapter has discussed economic analysis and particularly benefit valuation in three sectors—transport, education and health. Whilst the general principles set out in earlier chapters are applied, the way in which benefits are approximated varies in each sector. Establishing benefits accurately involves the distinction between non-incremental output (which replaces other supplies) and incremental output (which adds to use by consumers). The former will be valued at its supply price, based on costs saved elsewhere, and the latter at the demand price based on consumer willingness to pay. In transport, the main benefits are savings in travel costs including time plus an allowance for the generated traffic, and externalities such as emissions, and may include induced economic activities. In education, they are incremental earnings plus any savings in cost. In health, the approach is either cost effectiveness, where benefit valuation is judged too problematic, or benefit valuation based on a statistical value of life estimate often derived from a benefit transfer or a valuation of DALYs averted, using GDP or a multiple to approximate a year of life. The treatment of benefits in different sectors is a critical element that economic analysis must focus on, and this chapter has illustrated the variety of methods that have to be used.

Further Reading For transport, ADB (2013) has a discussion and detailed illustration for a transport project. The website of the Department of Transport, UK Department of Transport, has a very useful section on practical guidance for transport project analysis; Transport Analysis Guidance (TAG),

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www.webtag.org.uk. For education, Psacharopoulos (1995) and Woodhall (2004) are clear introductions. Jimenez and Patrinos (2008) discuss the approach at the World Bank. Potts (2012b) has a helpful overview. For health, the key concepts are illustrated in ADB (2000) and Weiss (2012).

Appendix 1: Productivity Benefits and Transport Projects A fairly close association has been found in the research literature between economic density and productivity, so where concentration of economic activity is higher, so is productivity. A potentially important role of any transport project (whether road, rail or air) is to reduce economic distance by lowering cost and time of travel. The benefits in terms of productivity can be derived by measuring the impact of a project on economic density [often termed economic mass) in a location and using evidence from the research literature to derive an estimate of how this might raise productivity. For any one location linked with other locations as a result of a transport project, economic density or mass is measured by the sum of the economic activity in each linked location weighted by economic distance from the original location. In the most common application of this approach, the economic activity in a location is measured by employment (N) and economic distance by generalised travel cost (GTC). Hence for original location, i linked with a series of locations j by a project, Economic Mass (EM) for location i is measured as

EMi  dij. Nj

(10.10)



Where dij is the economic distance between i and location j, Nj is employment in j, and summation is for all trips i to j, and summation for all j. The weight on location j is given by the inverse of the generalised transport cost between i and j, allowing for a decreasing impact of agglomeration on productivity as distance increases. Hence, dij  1 /  GTCij





(10.11)

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Where GTCij is generalised travel cost between i and j and α is a distance decay parameter, reflecting the declining impact of agglomeration as distance from i rises. A transport project, whether a new project or improvement of an existing facility, will change economic mass by reducing GTCij (and depending on the model possibly also Nj). Therefore, EMi can be calculated with and without a project and the greater the difference between the two situations the greater the potential impact on productivity through agglomeration effects. In principle, EMi can be calculated for different transport modes and different sectors, although in practice aggregates of different modes may be used. Once the change in EM due to a project is estimated, a relationship between EM and productivity growth taken from the research literature is applied to derive an estimate of the impact of the project connectivity on productivity in the locations linked by the project. If the change in EM occurs only once, the growth in productivity will occur only once, but can be assumed to stabilise at the higher level over the life of the project, so it remains a constant annual benefit. If agglomeration benefits are present and the scale of agglomeration effects is approximated adequately by EM, there should be a relationship between a change in EM and a change in productivity, and this in turn implies a relationship between a change in GTC and productivity. Estimates of the elasticity of productivity with respect to economic mass are available from the research literature.24 Once these have been used to derive an estimate of the change in productivity due to a project’s agglomeration effect, benefits can be approximated by the estimated productivity growth multiplied by the product of the numbers employed in i with the project and the average wage in i. This annual benefit figure for an individual year can be increased in real terms over time in line with whatever is assumed about the value of working time in the overall transport analysis. Annual agglomeration benefits can then be added to other annual benefits. To illustrate with a simple numerical example, a road improvement project reduces travel cost from town a, to three other towns b, c and d. Information on employment in these towns and travel costs between town a and the three towns with and without the project is given in Table 10.8. 24  Estimates for the UK suggest productivity elasticity parameters between 0.02 and 0.08, varying between sectors. 1.0 is broadly a midpoint value for α; see UK, Department for Transport, Wider Economic Impacts—Transport Analysis Guidance (TAG), 2020 for further information.

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Table 10.8  Illustration of agglomeration benefits Distance GTC/ from a km without project $

GTC/ km with project $

Total journey cost without project $

Total journey cost with project $

Weight 1/GTC without project

Weight N 1/GTC with project

EM EM without with project project

b 50 km 1.04 c 20 km 1.04 d 80 km 1.10 Total

0.95 0.95 0.98

52.0 20.8 88.0

47.5 19.0 78.4

0.0192 0.0210 50,000 961.5 0.0481 0.0526 80,000 3846.2 0.0114 0.0127 60,000 681.8 5489.5

1052.6 4210.5 765.3 6028.5

A distance decay factor of 1.0 and an elasticity of productivity with respect to economic mass of 0.05 are used to estimate the impact on productivity. For town b total journey cost without the project is 50 km times $1.04 without and 50 kms times $0.95 with the project, giving total journey costs of $52 and $47. 5, respectively. The weight on town b is given by the expression 1/GTCα. As α is taken as 1.0, this reduces to 1/GTC, or 1/52  =  0.0192 and 1/47.5  =  0.0210, without and with the project, respectively. When these weights are applied to the employment in b of 50,000, they give the economic mass (EM) for town b. With the project, EM for b rises from 961.5 (0.0192 times 50,000) to 1052.6 (0.021 times 50,000). The calculation is repeated for towns c and d, and the result for each town is summed to give a total for EM. Of the three towns, c is both nearest to a and has the largest economic activity, as measured by employment. Therefore, it has the highest weight in the calculation of economic mass. However, as costs to each town fall, the weight on each town increases. Without the project, total economic mass (EM) is 5489.5, whilst with the project due to the fall in GTC, it rises to 6028.5, even assuming no impact of the project on employment in the other towns. The proportionate rise in EM is 9.8%. With an elasticity for productivity with respect to economic mass of 0.05, this implies a rise in productivity sustained in each town over the life of the road project of approximately 0.55%. When this percentage is applied to the total wage bill in each town, this gives an estimate of the value of the productivity benefit. Even assuming no further change in travel cost, this benefit will be constant in each year of the project life. If cost falls annually, productivity benefits will increase further.

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There is considerable uncertainty attached to calculations such as these given the fact that unique elasticities for a specific location are highly unlikely to be available. Nonetheless they provide a means of deriving order of magnitude estimates for a possible effect from transport projects, which are intended to create economic corridors to link production clusters in different locations. However, it will only be under clearly defined circumstances, that is where a direct link between production clusters is established by a project, that agglomeration effects will be created. Such estimates have been made for projects in the UK, where the practical version of the methodology was developed, and for other higher-income economies, although they often tend to be low relative to overall benefits.

Appendix 2: Urban Transport Project This case illustration is based on an actual project, and although the explanation and numbers have been simplified, the analysis summarised is that followed in practice. This transport project aims to strengthen the public road transport network and shift traffic away from private cars to electric buses and non-motorised options. Project demand is based on existing traffic flows and expected traffic growth, whilst altering behaviour patterns away from conventional fossil-fuel-based travel. The project finances the introduction of electric buses and international best practice technologies in relation to urban transport planning. These allow the computerised management of the bus fleet, and the introduction of electric charging points, pedestrianised zones and a street layout that encourages cycling and greater walking. Elements include bus lanes and bus stops, bicycle and pedestrian lanes, improved traffic lights and a computer-based bus management and information system. Benefits relate to normal and diverted traffic and include lower emissions due to the switch from internal combustion engine (ICE) vehicles, and reduced congestion; time savings; and reduced vehicle operating and maintenance costs for the vehicles that continue to use the city centre after the introduction of the project. The example follows the procedures discussed in Chap. 5 using the domestic price system at 2020 constant prices for calculation purposes. 1. Taxes and Subsidies are removed from all financial prices. 2. Price contingency and interest during construction are removed from capital cost at financial prices.

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3. In principle all benefit and cost flows should be disaggregated into the resource categories traded, non-traded, skilled labour and unskilled labour. However, the taxes on foreign trade are very low, and macro-­analyses treat the existing real exchange rate as stable and in line with macro-economic fundamentals, so no shadow exchange rate adjustment is made (SERF = 1.0). Hence, there is no need to distinguish between traded and non-traded goods. 4. Labour costs are a relatively small part of total costs and unskilled labour is likely to be involved only in project construction. The labour component of construction cost is adjusted by a shadow wage rate factor (SWRF = 0.75), on the grounds that there is underemployment in the project location. 5. A 6% time preference discount rate is applied. The timing assumptions used in the analysis are that the project starts in year 1, and becomes fully operational in year 6, continuing until year 23, with a terminal value of 10% of the original capital cost. Investment costs are phased to match the phasing assumed in the financial analysis. Operating and maintenance costs are set at 5% of capital costs and replacement expenditure on additional buses and electric charging points is required periodically. Capacity utilisation is assumed to build up in stages rising from 60% in year 3 and reaching full capacity in year 6. Calculations are in constant US dollars. For the analysis, a transport planning model is used to create a detailed set of transport projections for the city centre area. This compares the business as usual (BAU) case based on existing transport sources and recorded commuter preferences with the intervention scenario, where the project alters transport options and preferences. In both cases, the same rate of population growth is applied in the projections. The model projects a rise in the number of electric and hybrid buses and a substantial decline in the number of ICE cars. This major set of changes in the transport profile of the city centre will create a wide series of effects not all of which could be quantified accurately. Table 10.9 from the transport model gives the difference between projected vehicle kilometres with the project and those in the BAU case over the life of the project. Kilometres by bus are projected to rise substantially and those by private ICE cars to fall substantially, with a more modest rise in electric-powered vehicles.

– – – – – – – 3,757,820 7,059,859 9,990,582 12,615,361 14,985,116 17,139,810 19,111,069 20,924,158 22,599,466 24,153,628 25,600,367 26,951,141 28,215,625 29,402,083 30,517,647 31,568,538 32,560,229 33,497,579 34,384,929 35,226,183

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045

– – – – – – – (84,174,343) (195,648,180) (309,292,152) (420,896,201) (527,329,498) (626,322,534) (716,333,952) (796,523,193) (866,843,777) (928,181,014) (982,296,303) (1,031,343,595) (1,077,120,704) (1,120,597,744) (1,162,003,225) (1,201,208,501) (1,238,049,361) (1,272,464,922) (1,304,509,852) (1,334,318,781)

Auto—Gas – – – – – – – (2,849,803) (11,989,126) (24,215,938) (38,601,229) (54,581,884) (71,897,123) (90,498,617) (110,388,426) (131,356,719) (152,691,295) (173,098,099) (191,070,275) (205,545,465) (216,309,330) (223,862,842) (229,009,254) (232,517,884) (234,980,527) (236,801,063) (238,236,662)

Auto—Diesel – – – – – – – 76,136,438 131,096,905 170,354,467 197,775,893 216,351,768 228,406,871 235,750,703 239,785,986 241,589,034 241,971,981 241,533,423 240,701,209 239,769,184 238,928,572 238,294,285 237,926,358 237,846,901 238,053,054 238,526,584 239,240,739

Auto—EV – – – – – – – 53,993,287 94,326,349 124,372,523 146,662,161 163,094,104 175,094,056 183,733,188 189,816,960 193,951,651 196,594,177 198,089,422 198,698,185 198,618,138 197,999,508 196,956,849 195,577,854 193,929,971 192,065,358 190,024,606 187,839,539

MC

– – – – – – – (181,159,416) (318,341,390) (422,543,795) (502,004,584) (562,895,007) (609,838,676) (646,300,243) (674,876,431) (697,513,958) (715,672,712) (730,447,931) (742,661,699) (752,931,447) (761,721,262) (769,380,296) (776,171,541) (782,293,373) (787,895,690) (793,092,004) (797,968,495)

eBike

Note: Brackets indicate negative figure Note: Bus is public bus; Auto-gas is petrol-fuelled car; Auto-diesel is diesel car; Auto-EV is electric car; MC is motor cycle; eBike is electric bicycle

Bus

Year

Table 10.9  Vehicle kilometres in city centre: difference with—without project

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The measurable benefits calculated are • Reduction in CO2 and PM2.5 emissions • Saving in VOCs and travel time for the cars that continue to enter the city centre with the project due to lower congestion and shorter travel times • A reduction in car-related accidents due to the lower car traffic Data available to value the effects of the project were limited, and several important effects had to be estimated based on figures used on similar projects elsewhere in the same country. Estimates for additional buses come from the transport model. The cost of $100,000 per bus is used in the project costing, and 200 buses are included in project capital cost with additions and replacements, periodically. Data on average cost per vehicle km for ICE cars were taken from a recent similar project at $0.228/vehicle km, and 5% of this figure is assumed to be saved to cover both vehicle operating cost and time savings. The coefficients for CO2 and PM2.5 emissions per vehicle km also come from the same project. On the basis of international estimates, CO2 is valued at $40/tonne escalated at 2% each year in real terms to reflect growing environmental damage, and PM2.5 is valued at $124,000/metric tonne. In principle, this price, as a measure of damage cost, should pick up the effect of health damage per tonne of pollutant, so no additional estimate of health effect is needed. Accident data come from the city records, and fatalities are valued at $295,000, which is an estimate from the literature for European countries scaled for the difference in income per capita between the project country and the countries to which the estimate applies. To be conservative in the calculations, it is assumed all non-fatal injuries are non-serious (such as minor fractures) and are valued at $10,000. A 50% reduction figure for accidents as a result of the project was used in the transport model and was applied in the analysis. The approach is conservative since there are other effects that are not captured, but which would raise the net benefits of the project. For example, there are likely to be additional health benefits to pedestrians, not reflected in the valuation of reduction in air pollution. Table 10.10 summarises the results giving the present value of costs and the different categories of benefit, as well as the economic NPV (ENPV) at 6% and the economic internal rate of return (EIRR). The ENPV is positive at 6%, and at approximately 13%, the EIRR is above the test rate of

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Table 10.10  Summary Results: Urban Transport Present values at 6%

$ million

Costs Capital O&M Benefits CO2 emissions reduction PM2.5 emissions reduction VOC savings Accident reduction ENPV EIRR

95.49 111.67 132.55 2.05 99.22 54.89 84.16 12.7%

6%. Allowing for the unquantifiable effects that are not included suggests the project is clearly acceptable. As expected, the main benefits are in terms of emissions reduction and savings in time and operating costs for vehicles that continue to use the city centre. Accident reductions also appear relatively important. It should be noted that for this type of project, where emissions reduction is the key objective, a major assumption is involved. CO2 emissions reduction is a global benefit; however, as CO2 emissions are driven primarily by the use of fossil fuel-based sources of energy, which for most countries are internationally traded goods, inclusion of CO2 reduction as a global benefit implies the assumption that due to the project global use of these energy sources will be lower. This means that where the fossil fuels are imported, when demand falls due to the project, they will not be exported elsewhere from their country of origin. Similarly, if the resources are available nationally it implies that demand fall due to the project is not replaced by exports or use elsewhere in the economy. Under some circumstances, these may be strong assumptions and are beyond the control of an individual project or its sponsors.

Appendix 3: Education Project The following is a simplified version of the economic analysis of an actual education project to improve teaching standards and quality. The project is wide-ranging and is designed to

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• Provide support for the Ministry of Education to improve the management and information base of the education system with a view to reducing administrative costs. • Provide in-service training to teachers to raise teaching quality and education standards. • Provide investment for improvements in school infrastructure and teaching support material, such as computers, books and learning centres. The project has a two-year period before any benefits arrive with a ten-year operating period. Costs are both initial start-up costs and annual operating costs. The economic analysis uses a domestic price numeraire, but shadow pricing is minor. Taxes on trade are low, and the real exchange rate is assumed be at a long-run level, so the shadow exchange rate seems close to the project rate, and no exchange rate adjustment is made. Any taxes on project costs are removed. Labour cost is primarily for professional work, and teachers and other staff are assumed to be paid a salary that reflects productivity. Two main forms of benefit are identified, with constant financial price values assumed to reflect economic values in domestic prices. First, there is a non-incremental benefit given by the estimated saving in cost due to improvements in the efficiency of the Ministry of Education arising from the technical support provided by the project. Based on expert judgement on what is feasible, the original analysis assumes this to be 0.5% of total annual recurrent expenditure in each year of operations. As this is not subject to any tax, it is not adjusted. Second, whilst the project has no effect on the numbers of pupils coming through the system, which is driven by population growth, it is intended to improve progression and retention rates at the secondary level. For example, an improvement from 80% to 90% between grades 10 and 12 is forecast. This means that relative to without the project, there will be more school graduates with a secondary school certificate. For every cohort of 100 pupils entering the secondary level, there will be an extra ten pupils (or 10%) as completers rise from 80% to 90%. The benefits of this per additional graduate are assumed to be reflected in the higher earnings associated with secondary-level graduation. These higher earnings are estimated by applying a version of the Mincer function to Socio-Economic Survey data on earnings. The model applied makes wages a function of age, education level, gender, location in terms

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of urban or rural, and sector of employment. Education is captured by a set of dummy variables which take a value of unity where a particular education level is attained (e.g. incomplete secondary, completed secondary, technical/vocational, college/university). The reference point for the analysis is earnings of those without any education qualification and the coefficient on the different variables shows the impact of these variables on wages relative to wages of those with no education. The coefficient on the dummy for incomplete secondary is 0.15 and on completed secondary 0.44. This implies that controlling for other factors, like age, gender, and location, secondary graduates have wages 44% above wages of those without education, whilst those who have an incomplete secondary education have 15% higher. Hence, by these figures, there is approximately a 25% wage premium associated with secondary completion relative to non-­ completion.25 Those without education are assumed to earn the minimum wage, and this is forecast to remain constant in real terms. The econometric results used to estimate future wages for the two education categories (secondary completers and non-completers), and the difference between these is the wage differential due to the project. When the numbers of additional pupils remaining to complete the secondary level because of the project are multiplied by the lifetime earnings differential, this gives the second category of benefit. Employment surveys have revealed an unemployment rate of 10%, largely determined by macro-­ economic conditions, which the project cannot affect. Hence the final adjustment is to allow for 10% unemployment amongst the additional completers resulting from the project. Earnings are calculated on a lifetime basis, assuming a 40-year working life, and discounted back to the year of secondary graduation at a 3% discount rate. The original analysis used a 12% opportunity cost rate, but this illustration has chosen to use a lower time preference rate as more appropriate for social sector projects that are assumed not to compete for funds with productive sector projects. These calculations for benefits (B) can be summarised as follows B   MC  0.05 t  Cohort t   0.9  0.8    PVE  0.25   0.9



(10.12)

25  For example, if those without education earn 100, those with incomplete secondary will earn 115 and those with completed secondary will earn 144 and 144/114 = 1.25.

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Where MC is the annual cost of the Ministry of Education and (MC *0.05)t is the non-incremental benefit in year t; Cohort is the number of pupils entering grade 10  in year t and the additional number completing is Cohortt * (0.9 − 0.8); PVE is the present value of forecast lifetime earnings without the project discounted at 3%, so PVE * 0.25 is the differential due to the project. The forecast average unemployment rate of 10% must be applied, so incremental earnings from those in work are (PVE * 0.25) * 0.9. Costs are the capital cost of the project relating to buildings, equipment and teaching materials, whilst operating costs are for consultancy services, training and additional salaries. Financial prices net of any taxes are used. In addition, for those who now remain in secondary school because of the project an estimate of earnings foregone in the years between grades 10 and 12, when in the absence of the project, they would have entered the labour market, is added to project operating cost. This is valued at the minimum wage. Table 10.11 gives the results in constant prices. All coefficients are assumed constant, and the flow of students is assumed to stabilise after year 5 of the project, so the value of the stream of differential earnings

Table 10.11  Education project: economic analysis Costs

Benefits

Naira million years

Capital

1 2 3 4 5 6 7 8 9 10 11 12 NPV IRR

7.7 7.1

Operating

2 2.2 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5

Efficiency

3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2

Productivity

1.8 2.1 3.6 3.6 3.6 3.6 3.6 3.6 3.6 3.6

Net −7.7 −7.1 3 3.1 4.3 4.3 4.3 4.3 4.3 4.3 4.3 4.3 18.15 20%

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reflecting productivity benefits, is constant after that. Efficiency benefits are the assumed saving in Ministry of Education costs. These are relatively large because of the size of the Ministry budget. The result is an NPV of Naira 18 mill at 3% and an IRR of 20%. Although the numbers have been changed, this is a similar result to that obtained in the original appraisal. It reflects the view that investment in education can have high returns in most economies.

Appendix 4: Health Project This Appendix summarises the approach to the project analysis of a health project in a relatively low-income country where contagious and preventable diseases are common causes of premature death. The aim is to illustrate the approach adopted in an actual appraisal, rather than on the precise calculations. The project is to improve community-based delivery of preventive, primary, and curative care. The focus is on reducing premature deaths and illness from a range of conditions affecting maternal and neonatal disorders, nutritional deficiencies and sexual health. A particular aim is to improve management of childhood illness by altering household behaviour and improving diagnosis and treatment of children. The analysis first requires an estimate of current disease burden in the country in terms of both premature mortality and morbidity measured by units of DALYs lost as a proportion of the population. This is obtained for the most recent year (2019) from the Global Burden of Disease database produced by the Institute of Health Metrics and Evaluation, University of Washington (www.healthdata.org/gbd). This source gives estimated DALYs lost per 100,000 of the population for the country concerned due to different diseases. The project is located in a specific region, and it is assumed that the area served by the project has the same health characteristics as the country as a whole. Hence the ratio of the population of the project area to the population in the country is multiplied by the total national burden of disease data (32%) to give the numbers of DALYs lost in the project area due to the different conditions. Based on academic research for the country concerned as well as the wider literature, the health conditions in the area are divided into preventable conditions that can be addressed by the effective low-cost interventions that the project will introduce and those that cannot be prevented in this way. This division is based on judgement and inevitably will be

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Table 10.12  DALYs lost and averted by disease

All causes Preventable diseases Maternal/ neonatal Respiratory infections Other infectious diseases HIV/AIDs Malaria Nutritional deficiencies Cervical cancer Sexual violence Total

DALYs lost per 100,000 population

DALYs lost in project area

% of DALYs avertable by project interventions

Potential DALYs averted in project area

32,344

266,195

2783

22,907

63

14,386

4433

36,483

52

19,007

531

4367

47

2061

475 28 655

3907 232 5391

75 63 52

2923 145 2820

143 18

1176 152 74,615

31 23

365 36 41,743

approximate. Once the list of preventable conditions is identified the number of DALYs lost to each condition in the project area gives the target number of DALYs that the project is to address. Table 10.12 taken from the project report gives the list of preventable diseases, the DALYs lost from each per 100,000 of the population, the DALYs lost in the project area from each condition at the outset of the project, and the percentage of these judged to be avoidable through the interventions to be introduced by the project. Applying this percentage to the number of lost DALYs by condition gives a total for potential reduction in DALYs. The estimation procedure can be summarised as follows DALY averted   Total DALY    POPa / POPtotal    x    y 



(10.13)

Where Total DALY is the burden of disease estimate for the DALYs lost in the country calculated by multiplying DALYs per 100,000 by total population (in units of 100,000); POPa is the population of the project area

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and POPtotal is the national population; x  is the proportion of DALYs that relate to preventable conditions that can be addressed by the project, and y is the assumed effectiveness of the project. Critical are the judgements on x, the classification of conditions as preventable through what are termed proven interventions, and y, the effectiveness of the project. Implicit in the estimation is the assumption that y = 1, as conditions that are preventable are assumed to be prevented. This may be over-optimistic and bias the results. Table 10.11 shows that, by far, the most important conditions that the project will address relate to maternal and infant health and respiratory infections, including tuberculosis. Interventions that the project can introduce at the community level to address these include safe delivery and postnatal care for new-born babies, full vaccination coverage, and early antibiotic treatment for respiratory infections. The figures on DALYs in Table 10.11 are projected over a 30-year life of the project by expected population growth in the project area to give annual DALYs averted as the project output. The economic analysis is based on cost effectiveness and the annual stream of DALYs is discounted at a 3% discount rate to give a present value of 0.95 million DALYs. Project costs cover staff wages, buildings, equipment, training and pharmaceuticals. To allow for continued operations over a 30-year period, periodic replacement costs were included, as well as regular maintenance costs. For the cost-effectiveness analysis, data in financial prices were adjusted by removing taxes and the price contingency as well as financial charges of interest and loan repayment. It appears that no additional shadow pricing, for example of foreign exchange or labour, was used. As discussed in this chapter cost-effectiveness analysis in health requires the ratio of the present value of project cost to the present value of a measure of health impact, with both discounted at the same rate. In this case this gives a cost per DALY averted. Decision-taking requires a judgement on whether the resulting cost per DALY is acceptable. The project used the criteria of comparing cost per DALY with either GDP per capita or three times GDP per capita. By both criteria, the project is acceptable and meets the WHO criteria of very cost effective, as the cost per DALY is well below GDP per capita (less than 10% of GDP per capita). However, in the particular analysis for this project, the calculations are undertaken incorrectly; the project cost stream was discounted at 8%, while the DALY stream is discounted at 3%. The reason for departure from normal practice is not given, but this may have been based on the idea that the

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opportunity cost of the funds that go to the project needs to be incorporated in some way. Where a limited health budget is involved, the opportunity cost issue is best dealt with by ranking interventions by their cost per DALY, at a time preference discount rate, and selecting those with the greatest health impact, until the budget is fully allocated. Trying to use an additional discount rate to do this confuses two different rationales for discounting in the same calculation. Discounting health cost at a higher rate artificially reduces the cost per DALY. In this case, even using 3% to discount project costs still gives a cost per DALY of well below GDP per capita (around 15% of GDP per capita). This result supports the argument, reviewed in this chapter, that the criterion of comparing cost per DALY with GDP per capita may no longer be appropriate for decision-taking in poor countries with weak health systems, as there is a wide range of early interventions that reduce DALYs at a relatively low cost.

Bibliography ADB. (2000). Handbook for the Economic Analysis of Health Sector Projects. Asian Development Bank. ADB. (2013). Cost Benefit Analysis for Development: A Practical Guide. Asian Development Bank. Anand, S., & Hanson, K. (1998). DALYs: Efficiency Versus Equity. World Development, 26(2), 307–310. Belli, P., Khan, Q., & Psacharopolous, G. (1999). Assessing a Higher Education Project: a Mauritius Feasibility Study. Applied Economics, 31, 27–35. Daroudi, R., Sari, A., Nahvijou, A., & Faramarzi, A. (2021). Cost Per DALY Averted in Low and Middle Income Countries. Cost Effectiveness and Resource Allocation, 19, 7 (online publication). Griffiths, U., Wolfson, L., Quddus, A., Younus, M., & Hafiz, R. (2004). Incremental Cost Effectiveness of Supplementary Immunization Activities to Prevent Neonatal Tetanus in Pakistan. Bulletin of the World Health Organisation, 82(9), 643–651. HMT (2022). HM Treasury, Government of UK. Green Book: Central Government Guidance on Appraisal and Evaluation, 2003, Updated 2022. Hutton, G., Schellenberg, D., Tediosi, T., Macele, E., Kahiqwa, E., Mas, X., Trapero, M., Tanner, M., Trilla, A., Alonso, P., & Menendez, C. (2009). Cost Effectiveness of Malaria Intermittent Preventive Treatment in Infants in Mozambique and Republic of Tanzania. Bulletin of the World Health Organisation, 87(2), 132–139.

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Jamison, D., Breman, J., Measham, A., Alleyne, G., Claeson, M., Evans, D., Jha, P., Mills, A., & Musgrove, P. (Eds.). (2006). Disease Control Priorities in Developing Countries. Oxford University Press. Jimenez, E., & Patrinos, H. (2008). Can Cost Benefit Analysis Guide Education Policy in Developing Countries? World Bank Policy Research Working Paper, 4568. Laird, J., & Venables, A. (2017). Transport Investment and Economic Performance: A Framework for Economic Analysis. Transport Policy, 56, 1–11. Mincer, J. (1974). Schooling, Experience and Earnings. Columbia University Press. Murray, C. (1994). Quantifying the Global Burden of Disease: The Technical Basis for Disability Adjusted Life Years. In C. Murray & A. Lopez (Eds.), Global Comparative Assessments in the Health Sector. WHO. Murray, C., & Lopez, A. (Eds.). (1994). Global Comparative Assessments in the Health Sector. WHO. Murray, C., & Lopez, A. (Eds.). (1996). The Global Burden of Disease. Harvard School of Public Health. Ochalak, J., Lomas, J., & Claxton, K. (2015). Cost per DALY Averted Threshold for Low and Middle-Income Countries; Evidence from Cross-Country Data. CHE Research Paper 122. University of York. Potts. (2012b). Measuring Benefits from Education. In J. Weiss & D. Potts (Eds.), Current Issues in Project Analysis for Development. Edward Elgar. Psacharopoulos, G. (1995). The Profitability of Investment in Education. HCO Working Paper, World Bank. Sassi, F. (2006). Calculating QALYs, comparing QALY and DALY calculations. Health Policy and Planning, 21(5), 402–408. Weiss, J. (2012). Project Appraisal in Health: Cost Effectiveness Approaches. In J. Weiss & D. Potts (Eds.), Current Issues in Project Analysis for Development. Edward Elgar. WHO. (2001). Macro-Economics and Health: Investing in Health for Economic Development. Woodhall, M. (2004). Cost Benefit Analysis in Educational Planning. UNESCO, IIEP. Woods, B., Revill, P., Schulper, M., & Claxton, K. (2016). Country Level Cost Effectiveness Thresholds: Initial Estimates and Need for Further Research. Value in Health, 19(8), 929–935.

CHAPTER 11

Project Analysis and Environmental Effects

Introduction This chapter addresses how the environmental effects of projects can be incorporated in project analysis. It begins by briefly discussing the basis for environmental values used in project economics before explaining the main approaches in the literature for putting numerical values on these effects. These values need to be incorporated in a project resource statement and like other effects in the future need to be given a value in the present. How to discount environmental effects has been a topic of considerable debate and the chapter reviews the main alternatives for doing so. All projects have some environmental effects, either positive or negative, since either directly or indirectly all projects will create some demand on natural resources, and will create some waste products to be assimilated by the environment. Since these effects are frequently not the subject of market transactions, prices are not available to reflect them, and they remain one of the most obvious examples of external effects. The heightened awareness of the impact of human activity on the climate and the far-reaching consequences of climate change means that policy discussions in all countries now stress the importance of environmental sustainability to ensure that projects do not make demands on the environment that are excessive relative to the current stock of natural capital. How far environmental considerations are critical for the analysis of an individual project

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Curry, J. Weiss, Project Analysis in Developing Countries, https://doi.org/10.1007/978-3-031-40014-8_11

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will vary with its characteristics; a simple fourfold classification of projects may serve to illustrate the point. 1. Environmental projects where the main objective is to produce an environmental benefit, either in terms of an improvement to the environment or the avoidance of damage that would otherwise occur. Examples are a project to preserve wetlands as a natural park and thus to avoid costs created by the loss of important species or habitat; rehabilitation of a power plant to remove gas emissions into the atmosphere; and an irrigation improvement scheme to correct waterlogging and associated soil salinisation problems that reduce crop yields. 2. Projects with non-environmental objectives, but with significant environmental by-products. Examples include a timber project that creates deforestation and a loss of benefits associated with natural forests; a factory where industrial waste is not disposed of adequately leading to contamination of a nearby river and an effect on fish stocks and human health; a dam and its associated infrastructure that changes traditional patterns of human and natural life; a low-­ income housing scheme that worsens air pollution owing to the heating system used by poor households. All of these examples relate to negative by-products; however in some cases, there can also be effects, which whilst not central to the original objectives of a project, nonetheless are beneficial to the environment; for example, a tree replanting project, where the main aim may be to produce timber but there are major additional benefits in terms of reduced soil erosion as trees provide shelter from water run-off and winds. 3. Projects with non-environmental objectives and relatively minor environmental by-product costs. Examples include a mining project where waste tailings are produced that may be a health hazard if they are untreated; a road where improper digging of embankments could result in gradual soil erosion; a dam where heavy river flows could result in downstream siltation or flooding. In cases such as these, whether problems emerge will largely depend on whether the original project makes provision for the expenditure necessary to remove or mitigate the possible damage in the future. 4. The vast majority of projects create a by-product effect on the environment through the emissions, in CO2 and other gases, associated

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with the energy used, various agricultural practices directly and ­indirectly in the construction and operation of the project, as well as various agricultural practices, such as deforestation and animal husbandry. It is now widely accepted that additional emissions of gases that contribute to climate change should be included as a project economic cost, where a project adds to emissions, or a benefit where it reduces them. In some cases, the emissions effect may be minor whilst in others valuation of emissions can have a major impact on the project decision and lead to a redesign or rejection of the project. For the first category of project under (1) an assessment of environmental benefits will be central to any appraisal. If a full project analysis is to be carried out, the environmental consequences of these environmental projects must be both quantified and valued. In terms of our examples, this means estimating the full range of benefits from preserving wetlands (whether species diversity, plant-based medicines or eco-tourism), the quantities of gas emissions reduced by rehabilitation of a power plant, and the higher crop yields after the removal of waterlogging. The physical quantities identified must be valued at a price that reflects their economic value and is consistent with the prices used for non-environmental project costs and benefits. This full environmental analysis is demanding in terms of time and can be conceptually complex. Where the difficulties to valuing environmental benefits are judged too severe, an alternative is to stop at the identification stage and simply list the range of effects; then a qualitative judgement can be made on whether these effects appear substantial enough to justify the project. Where a physical environmental target is set (e.g. reducing gas emissions or waste water disposal to a particular standard) cost effectiveness analysis can be used to select the cheapest alternative. For projects in category (2), by definition, environmental effects matter. The key policy objective should be to ensure as far as possible these environmental effects are internalised in project costs, so that the project’s owners invest to either remove or mitigate the negative environmental effects. These costs must then be included in the analysis. However, where some external environmental effects remain these must be identified and valued. Project acceptance will require that benefits exceed the value placed on costs, including all environmental effects.

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For projects in category (3), by definition, environmental effects, whilst tangible, will be relatively modest at least at the level of an individual project. Here the most important issue for analysis will be to ensure that practical technical standards are adhered to, so that projects themselves invest to avoid any serious negative environmental consequences from their actions. Where there is a well-designed environmental policy that enforces appropriate standards, probably the majority of new projects can be put under this heading, and detailed environmental assessment of the direct effect of projects can be left to the subset of projects under headings (1) and (2). The weaker the overall policy framework, the more necessary it will be in a project analysis to raise issues relating specifically to environmental costs and benefits. In addition, as reflected under category (4), for all projects, there is the possibility of some global climatic effect since any economic activity that uses energy will create CO2 and related gas emissions, and there are a range of other economic activities which can impact on the climate. Projects with little direct impact on the environment can have a major indirect effect through the emissions they create and its consequences for temperature increase. The global environmental impact of projects has become a major feature of project analysis in recent years. In the case of emissions this is usually addressed by estimating the net emissions of CO2 (or its equivalent in other emissions) created or reduced by a project and giving these emissions an economic price. Hence, a project that adds to emission has its costs increased, and a project that reduces them has its benefits increased.

Environmental Value A full economic analysis incorporating environmental effects requires some basis for establishing environmental values, so that these can be added to values arising from other project activity. Environmental values can be applied not only in the context of individual projects with environmental effects, but also in an assessment of the merits of changes in policy that impact on the environment. In discussions of environmental value, it is conventional to distinguish between use and non-use value, since unlike other activity environmental effects can have a value that is in addition to any benefit or cost arising from the direct use of the environment. Considering use values first, the environment is valuable to users because it provides a range of services, so-called ‘eco-system services’; for example,

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Table 11.1  Categories of ecosystem services Category

Examples

1. Provisioning goods and services 2. Regulating and maintenance services

The supply of food, energy and materials; for example, food crops, water, fish, fuels and timber The control of waste, toxics, the flow of liquids, solids and gases; for example, flood protection, purification of air and water, waste absorption, disease control and climate regulation Access to beauty spots and wildlife

3. Cultural services

Source: Adapted from the UN System of Environmental and Economic Accounting (www.seea.un.org)

flood protection, air purification, assimilation of waste and the supply of aesthetic benefits, such as scenic views or rare species. Table 11.1 gives a categorisation of these services.1 This range of services has been categorised in the technical literature in relation to different biomes, where a biome is an ecosystem covering different habitats. Current estimates show a wide range of average values per biome, with not only great variation between types of service, but also major differences between studies of the same type of service. For example, with values in 2020 constant dollars per hectare, the range is from $159,000 per hectare for coral reefs and $119,000 for tropical forests to $1600 for grassland and $5300 for temperate forests. In each case, the maximum value found is well above the sample mean, indicating a wide variation between studies.2 A further distinction is sometimes drawn between direct uses of the environment (e.g. when tourists visit beauty spots) and indirect uses (e.g. various forms of farming). In addition, because of uncertainty regarding the future supplies of non-renewable or non-reproducible environmental resources, users may be prepared to pay a premium simply to guarantee that supplies will be available in the future. This is termed option value and is a form of insurance premium. Since it is related to future use of the environment, option value is a form of use value. 1  A definition of an ecosystem is provided by the UN Convention on Biological Diversity: ‘a dynamic complex of plant, animal and micro-organism communities and their non-living environment interacting as a functional unit’. Ecosystem services are the benefits from an ecosystem. 2  These figures come the studies collected in the Ecosystem Services Database reported in de Groot et al. (2020). The authors treat these averages as no more than illustrative of the wide range found and caution against their use for benefit transfer.

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Non-use value, on the other hand, arises because the environment and its resources may be deemed valuable in their own right independently of any use. Thus, individuals may value environmental resources or natural species (e.g. tropical forests or a rare butterfly) not because they will ever use or even see them, but because they are judged to be of intrinsic value in their own terms. The motives here could be a mixture of genuine altruism and a sense of responsibility to future generations, including individuals’ own families. The spread of contributions to wildlife charities is explicable in these terms. This form of value is termed existence value. Use value is common to all commodities and underlies the concept of willingness to pay (WTP). However, what distinguishes environmental goods and services is option values and intrinsic or existence values, which are not normally attached to other commodities. Total environmental value is thus composed of the sum of use and non-use values. However not all potential sources of value can simply be summed to give total value, since some sources of value may be incompatible; for example, some direct uses of tropical forests may preclude the protection of rare wildlife and thus reduce existence value. Hence total environmental value must be based on a compatible set of environmental uses. In addition, different approaches to valuation of the same effect are normally alternatives, so that values derived from one approach cannot be added to values derived from another. For example, one might attempt to value the impact of a factory on a rural area and derive three alternative estimates of environmental cost: one by estimating lost agricultural productivity; the other by estimating changes in agricultural land prices; and the third by asking the farmers what compensation they would need to make them as well off as before the factory was built. Adding together values from more than one of these approaches would usually be double-­ counting. Similarly, for a dam project that resettles a local community, it would be double-counting to involve a compensating resettlement project (provided this meets the needs of those affected) and the income loss they would be expected to sustain from not being able to engage in their traditional fishing or farming activity (provided they could do this in their new location).

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Methods of Estimating Environmental Values Many ecosystem services are non-marketed, as they are not sold directly on a market. Hence there is no financial price that can be applied or adjusted for an economic analysis. Even where there is a market transaction, such as a fee for entry to a nature reserve or beauty spot, this will often greatly understate the value placed by users on the services of the location. A large literature has grown up on means of quantifying environmental values in the absence of markets, although for reasons of practicality, the techniques that are often discussed intensively in the theoretical literature (e.g. the hedonic pricing and the travel cost methods discussed below) are often difficult to apply in operational project work due to lack of time and data.3 In considering means of estimating economic values, it is conventional to distinguish between several broad approaches, with the literature sometimes using different terminology for the same approach. Here five separate approaches are suggested, with sub-categories within each. • Market or Output-based approaches: included here are attempts to link the net value of output changes with environmental services, using prices in those markets as a measure of value. The approach captures the opportunity cost of a development option, in terms of the net output value lost in environmental benefits, when the services of the ecosystem are reduced by a project. The most sophisticated version of this approach involves an environmental production function model, with a measure of environmental services as an input. Market or Output-based approaches as sometimes referred to as a ‘change in productivity’ approach. • Cost-based approaches: included here are the costs of providing the services of the environment through man-made means in a replacement cost approach, and the cost of preventing a damaging environmental impact, in a ‘preventive expenditure’ approach. Alternatively,

3  A review of different approaches is given in UN (2021) and USAID (2018). Often estimates of environmental value make little or no reference to the issue of the numeraire and the procedures discussed in Chaps. 5 and 6. Where this is the case, implicitly a domestic price numeraire is used. As discussed in Chap. 5 this requires that, where necessary, a shadow exchange rate is applied to the value of traded items, including any environmental effect that involves traded goods.

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costs may be measured as damage averted. In an optimal scenario, each cost concept will be equal, but this is not a real-world case, so a choice needs to be made on the most likely scenario. • Revealed preference approaches: here observable financial data are used to infer a value for a non-marketed ecosystem service. The best-­ known examples are the travel cost approach, which uses data on travel costs to infer a demand curve for visits to recreation sites and the hedonic price model, which uses data on market transactions, principally in property markets, to infer a value for particular aspects of the environment, such as air quality or noise pollution.4 • Stated preference approaches: here surveys are conducted to ask respondents on their valuation of the environment, with the question usually framed in terms of willingness to pay a certain level of fee or tax to preserve specific features of the environment. Alternatively in choice experiments, respondents are asked to rank alternatives with different environmental attributes to infer a monetary value for specific attributes, such as air or water quality. • Benefit transfer: this approach involves transferring a value estimated in one context, by any of the four approaches outlined above, to a new context. It is recognised as the least accurate of the possible approaches, but also the simplest to implement. The different approaches to environmental valuation have been well known for many years. There is no simple solution to the question of which should be used to value specific ecosystem services, since the answer will vary with the context involved. Complexity and significance of the service, data availability, technical expertise of analysts and time and resource available must all be borne in mind. Generally, output-based and cost-based approaches are easier to apply as they focus on direct potentially measurable effects, but may not capture all the value created by a service. Revealed preference approaches are now widely applied, but typically in research studies or in analyses of very large and significant projects, rather than in standard project analyses. Stated preference studies are accepted as the only way to capture some non-use values, but can be

4  Some classifications, such as USAID (2018), define revealed preference most widely and for example, include cost-based approaches under revealed preference, on the grounds that expenditures reveal a minimum valuation of a service.

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complex to do properly, and there is always a risk of bias in the results. Generally, in terms of environmental services, reviews of research studies suggest that market- or output-based approaches have been most commonly applied for the valuation of provisioning services, while cost-based approaches have been used for regulating services. Revealed preference and stated preference approaches have been used primarily for cultural and recreational services.5

Output- or Market-Based Approaches Operational project analysis often focusses on the use value component of environmental value, and estimates it by the value of the tangible goods and services whose availability is affected. The approach requires the establishment of a relationship between a project and environmental parameters (such as soil or air quality) and then a second relationship between these parameters and production.6 The most obvious examples of this approach relate to projects which alter the pattern of land use and thus change output; where output change results from the environmental effect of a project, the economic value of this output gives the basis for use value. A timber project, for example, will reduce the products that can be obtained from the forest in its natural state. Their value may not always be easy to estimate because some of the products may not be sold in a market, so that proxy values based on marketed alternatives must be used. The loss of natural forest products will be a cost to be offset against the benefits of timber sales. From a positive perspective, planting of trees as shelter beds will be expected to raise crop yields and the value of these crops will provide an estimate of environmental benefit from tree planting. Similarly, natural habitats, if preserved, provide a range of services for agriculture; for example, natural pollinators can support agricultural productivity, agricultural pests can be controlled naturally by wild predators like birds and bats, and coastal mangroves can support the reproduction of fish species. In all cases of valuation based on an impact on output, it will be important to specify accurately the link between a project, the environmental  See Markandya (2016).  A focus on tangible output changes created by a project’s environmental effects involves an environmental production function, where the environmental service is an input into production [see Barbier et al., 2023]. 5 6

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change it causes, and the resulting change in output. In principle, as noted above, what is required is an environmental production function that can be used to isolate the impact of the environmental change from all other factors. Once the corresponding output change is estimated, it should be converted into an economic value following the principles discussed in earlier chapters; in brief, if the output is internationally tradable, it should be valued at its world price net of whatever local costs are involved; if it is non-traded and incremental, it should be valued at willingness to pay, and if it is non-incremental at costs saved.

Cost-Based Approaches An alternative way of valuing environmental benefits can be used where eco-services can replace an equivalent man-made provision. Here the avoided costs that result provide the definition of benefit. Costs represent a use of resources, so a change in cost affects the availability of output somewhere in the economy. Hence this is another version of valuing an environmental effect through an output change. An example in relation to ecosystem services from a project for protection of natural habitat would be preservation of wild pollination, saving farmers the cost of alternative measures like use of bee hives. Alternatively, where a project is designed to remove environmental damage, the question posed is what costs will be saved if a project removes the need for another solution to be found. An eco-service example is benefits from protection of coastal mangroves, valued at the avoided cost of coastal walls or other forms of man-made protection against flooding. This cost-based approach assumes that in the absence of the project the alternatives that define benefits would actually be pursued, so in the examples, farmers would need to invest in bee hives or coastal protection would need to be constructed. Hence, using costs saved to value benefits is equivalent to assuming that environmental benefits are wholly non-­ incremental. Where they are not, other approaches are needed. On the cost side of a project, ideally adequate mitigation measures to avoid environmental damage should be built into a project’s design and included as part of costs. Where it is expected that there are additional costs not internalised in this way, this will necessitate expenditure to remove the environmental damage the project creates. This is a negative external effect and estimates of the cost of damage removal should be included in the project analysis. Where the environmental costs involved

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refer to the cost of mitigating environmental damage after it has occurred, the approach is often referred to as one of replacement cost; this is in contrast with a focus on the cost of removing or abating the damage at source, so that no harmful effects actually occur, referred to as preventive expenditure. In principle, there is no reason why these two approaches should give the same estimates of environmental cost, since depending on circumstances, it may or may not be cheaper to allow damage to happen and then compensate for it, than to prevent it from occurring in the first place. What is important for an individual analysis is to assess both alternative costs before the project is implemented and incorporate whichever is more likely to occur. In terms of the impact of a project on costs, a special category of effect relates to the treatment on non-renewable resources. Appendix 1 discusses the additional cost arising from the fact that current use by a project of a non-renewable asset reduces future availability for others users. This creates a cost additional to the cost of extracting the resource, termed a depletion premium (DP). Strictly a cost-based approach to damage will be the sole basis for the value of an environmental effect only where the expenditure involved, either preventive or replacement, is successful in removing all of the relevant negative environmental effects. Where it is only partially successful, there will still be some environmental damage to be valued as forgone output and included alongside the environmental expenditure that has been or will be undertaken. For example, if a project causes soil erosion this may be compensated by greater use of chemical fertiliser, so the cost of fertiliser becomes the relevant replacement expenditure. However, if the compensation is only partially effective there will still be some loss of productivity to be included in the analysis as an environmental cost, in addition to the cost of fertiliser. As discussed below a major example of a cost-based or damage approach relates to the valuation of the CO2 emissions created or saved by a project. Economic value is often determined by modelling estimates of the global damage created by the effect on higher CO2 emissions on climate change.

Revealed Preference Revealed Preference describes approaches, which assume that the price of an environmental good or service not sold directly in a market can be revealed by observing its impact on other markets that are affected

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indirectly by the good or service. This requires analysing data from the behaviour of consumers in existing markets, as a guide to their willingness to pay for the environmental output of a project. The key requirement is that the market used must be influenced by environmental effects in a quantifiable way. The best-known example of this approach to valuation is a hedonic price model, which uses property prices, usually for residential property, to value environmental attributes like air quality or access to green spaces. Here, a statistical relation is established to explain variation in house prices; independent variables used will include size, age, location and other property characteristics. In addition, a variable must be included that reflects an environmental dimension of the property; for example, noise exposure in the form of noise from either road, rail or air traffic, or air quality resulting from traffic or factory pollution, or more aesthetic features such as access to natural scenery. This inferred impact on property prices can be used to value the effect of a project on these aspects of the environment. The theory behind this is that the price consumers are willing to pay for an item will be determined by its mix of attributes or characteristics. Hence property prices reflect the utility their owners receive from residing in the property. For example, if prices are lower where pollution is higher, then if the statistical analysis can fully control for the effect of all others factor, this suggests that willingness to pay for the property is lower in the presence of pollution and the contribution to lower prices caused by higher pollution provides a measure of willingness to pay to reduce pollution. Hence, isolating the effect of the relevant environmental variable on the property price, whilst controlling for the effect of all other influences, and if done properly and if property owners are fully informed, can in principle provide an estimate of the marginal value of improvements in characteristics, like noise levels, or air quality. Values derived in this way can be applied, for example, to value the benefits of projects to reduce pollution or reduce noise, or can be applied to value costs from projects which raise noise levels or add to pollution. The approach need not be restricted to valuation of noise or pollution and can be applied to any feature which impacts property prices, such as access to parkland or scenic views. The hedonic price approach requires detailed data on property markets. It is necessary that prices there are not driven by speculative pressure leading to strong cyclical swings, as this will undermine their accuracy as a guide to consumer preferences. Further there is the more fundamental restriction that it is difficult to argue that environmental effects in relation

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to biological resources, such as genetic, species and ecosystem diversity, impact residential property prices. Hence the type of impact that can be valued in this way is limited. Nonetheless hedonic pricing models have been applied in a development context. Box 11.1 summarises the results of a study of the property market in Beijing, which has used property prices to value improvements in urban air quality.

Box 11.1  Hedonic Price Model: Valuing Air Quality in Beijing

This research study utilises data on a random sample of transactions across all residential areas of Beijing for the period 2013–2016. This data reports average unit sales prices, as the mean prices for transactions of apartments across all blocks of flats in a community within a year. A limitation of the data is that no measure of property size is included as the prices are averages across apartment blocks, with the implicit assumption that these are apartments of a standard size. The average unit prices across 6740 communities are the dependent variable for the analysis. Data on air quality is available for six major pollutants starting from 2013. The pollutants sulphur dioxide (SO2), ground-level ozone (O3), carbon monoxide (CO), nitrogen oxide (NOx) and particulate matters PM10 and PM2.5 are included in the analysis, but the main policy focus is on particulate matter PM2.5. Data was collected on the community-level characteristics for each property. These cover age of property, a measure of green vegetation in the community, a ranking of the educational quality of primary schools in the area, and distance of each community from the nearest subway station and the second ring road as a measure of access to the city centre. Higher levels of pollution are expected to reduce property prices, while the different location factors associated with the different communities in the city will also have an influence. Higher age of property, and further distance to a subway station and the ring road, will each be expected to lower prices, whilst higher ranking of primary schools and extent of green vegetation cover will be expected to raise them. In addition, to address the concern that there may be unobservable location factors influencing prices, fixed effect dummies (continued)

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Box 11.1 (continued) are introduced for the 24 geographical divisions in which the communities are located. To address the effect on property prices of local regulations, separate dummies are used for each of the 16 administrative divisions of the city. The full model to explain property prices is expressed as Pijt   ijt  1.E ijt  2.H ijt  1.L ijt   ijt



(11.1)

Where P is the average property price, E represents the level of pollutants covered, H is a vector of community characteristics (including age of property, vegetation, quality of schools, and distance from subway and ring road), L is the set of location dummies, α is a constant, i stands for community i, j is for district j, and t is year t and ε is the error term. The beta coefficients give the impact on property prices of a 1% change in each of the explanatory variables. Different versions of the model are run but the preferred version uses a cross-sectional panel model pooling all the annual observations and a semi-log functional form, so price P is in natural logarithms. The results are broadly stable across years and largely conform to expectations, with the exception that the index of green vegetation area coverage in each district is not significant, implying that it is either poorly estimated or the greenness of an area does not influence demand for property and therefore prices. The measures of pollution level are nearly always strongly significant and negative. In terms of willingness to reduce particulate matter, the results indicate the average home buyers would be willing to pay between $48 per ug/m3 and $63 per ug/m3 to avoid 1 ug per m3.7 This is derived by applying the estimated β1 coefficient for PM2.5 to the average house price per m2 across the sample with the results varying between the different versions of the model. This willingness to pay range can be used in analysis of projects to improve urban air quality or in an assessment of the benefits of an urban planning policy aimed at limiting emissions. Source: Yingdan et al. (2020)

7

 Ug is the metric measure of milligram.

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Another variant of the revealed preference approach relies on data on travel costs, including the cost of time, as well as the direct cost of a journey, to derive measures of value for a site to be visited. From an environmental perspective, it is most relevant for the valuation of transport projects that allow increased visits to national parks or natural beauty spots. In such cases, the value of services provided by the parks or sites is highly unlikely to be fully reflected in admission charges and the decisions of visitors, often to incur high costs to travel to the parks or sites, can be interpreted as a recognition of this value. The general approach is to use survey data collected from visitors to estimate a quantity-price relationship and thus to infer a demand curve. In this analysis, the price paid by visitors is the full cost of accessing the location which is travel cost (including where relevant the depreciation of any traveller vehicles) plus the cost of time spent travelling and the admission fee, if any. The quantity effect will be a measure of time spent at the site (such as hours or person nights). If a demand curve can be identified, it will be possible to derive an estimate of willingness to pay for individual travellers, which can be aggregated to give a total benefit. The approach is data-intensive, as it requires a survey of users of the facility involved and relatively sophisticated econometrics to derive consumer surplus estimates. Further, there are practical problems where travellers visit more than one site or combine a recreational visit with a work visit. In such cases a way must be found to allocate the estimated benefits between sites or between activities. Usually this involves framing a question to respondents to the survey that will reveal the proportion of the enjoyment they receive that they would attribute to the site that is being valued. There are also important conceptual issues regarding the valuation of time spent travelling. The money wage for responding is usually the reference point, and time spent travelling is typically valued at a proportion of this, such as 30%–50%, on the grounds that this is leisure not working time. However, if travelling is seen as particularly arduous there may be a case for using a cost of time above the wage rate. For reasons of data-intensity and estimation complexity, whilst there have been many research studies that have used this approach, in a development context its use for practical assessments has been limited. Nonetheless, Box 11.2 illustrates the approach used in a research study for a game park in Kenya. The data derived there are relevant for addressing the policy question of how far park charges can be raised to reduce the park’s dependence on donor and central government funding.

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Box 11.2  Travel Cost Approach—Maasai Mara National Park, Kenya



The logic of the travel cost approach is that the demand for the recreational services of a park can be explained in a demand function where travel cost replaces the normal price for a service and willingness to pay for the services can be derived from this curve. This study estimates willingness to pay for the services of the Maasai Mara National Park, Kenya. To estimate willingness to pay, the study used an onsite survey administered to 400 visitors, which had questions on the total round-trip costs to Kenya from country of origin, total cost of the trip including not just travel, but also lodging, meals and entrance fees, time spent travelling, and days spent in the park, and elsewhere in Kenya. To address the complication raised by multi-purpose trips, a subjective question was asked about the enjoyment visitors obtained from their stay in Maasai Mara, their stay elsewhere in Kenya, and their stay in other countries during the trip, with enjoyment proportions having to sum to 100. The cost of time is another key issue in this type of study and here was almost 50% of total cost. It was derived from responses from a question on annual income. Annual reported income was converted to an hourly rate assuming a 40-hour week and 260 working days per year. The opportunity cost of time for both time spent travelling to the park and time whilst onsite in the park was taken as 33% of this hourly rate. A demand function for the services offered by the park was constructed taking the dependent variable to be explained as days spent there and explaining this demand by round-trip travel cost to the park, onsite costs whilst there, and a series of characteristics of the respondents. Simplifying a little, the demand function estimated can be written as D i    1 TCi  2 OC i  3 X ij   i

(11.2)

Where i refers to each individual respondent, D is the number of days of stay in the park per group, TC is round-trip travel cost to the (continued)

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Box 11.2 (continued) park, OC is onsite daily costs whilst there including the entrance fee, εi is the error term and X is a vector measuring j different characteristics of respondents. These are age, gender, income, number of trips to Sub-Saharan Africa, awareness of other similar parks, proportion of total trip enjoyment derived from Maasai Mara, and whether the trip was part of a tour package. The results are largely as expected. Round-trip and onsite costs are strongly significant and negatively related to days of stay, whilst income is also significant, but with a positive effect. Gender and age appear to have no significant effect, whilst awareness of other parks, trips to Africa and share of enjoyment all appear to have a positive effect on days of stay. Being part of a tour package has a negative effect, as it is likely to build inflexibility into travel plans and will not allow extension beyond the booked days. Various specifications are reported, and in double-log form, the coefficient on round-trip cost β1, as the proxy for price, can be interpreted as a price elasticity of demand. This means total willingness to pay can be found by integrating the area under the demand curve relating trips to round-trip cost over the relevant price range. This allows the difference between total willingness to pay and actual round-trip expenditure, that is consumer surplus (CS), to be estimated from the expression CS = (−1/β1)*100.8 In the preferred version of the model, this gives a consumer surplus of $328 per person per visit or over $115 per day. This is large relative to the entrance fee actually charged at the time of $80 per day and indicates considerable scope to raise charges in the park to increase revenue. Source: Mulwa et al. (2018)

Stated Preference The valuation approaches discussed so far have the significant limitation that they address only use value of the environment. If the other components of environmental value—that is, option and existence value—are to be quantified this can be done only by direct survey techniques that elicit

8

 Multiplication by 100 is required due to the logarithmic form used.

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a subjective response on the value placed on the environment. In effect to capture total economic value what is sought is either individuals’ willingness to pay to conserve environmental benefits or willingness to accept compensation for environmental costs. The best known of the direct survey approaches to derive an average willingness to pay, the contingent valuation methodology is discussed in Chap. 7, and Box 7.1 illustrates its application to value the services of a game park in Africa. Chapter 7 Appendix 1 has also illustrated the use of a related survey technique, choice experiments, to value specific attributes of environmental services, in this case water. In principle, provided respondents understand clearly the full environmental effects about which they are being questioned and answer in a truthful and unbiased fashion, this approach offers an important means of establishing environmental values, based on individual preferences. It has been used extensively in policy contexts and has been a major growth area in environmental economics. However, since environmental effects are not marketed commodities, it may be difficult for respondents to think of them in monetary terms; it is no coincidence that the survey approach has been used more frequently in developing countries in the water supply sector, since in many countries poor people have to buy water from vendors and are used to thinking of water as a marketed commodity, like any other. This is different from environmental effects, like recreational parks, scenic views, or species conservation. Nonetheless, environmental valuation provides the best-known and highly influential example of the application of the contingent valuation survey technique. This is its use to assess the non-use value damages created by the 1989 Exxon Valdez oil spill in Alaska. A widely debated study used the technique to estimate how much households located far away from the accident (and thus not directly involved as users) would be willing to pay in tax to prevent similar accidents that create environmental damage and in particular serious loss of wildlife; in effect the existence and option values of the environmental and wildlife assets damaged by the spill. The debate around the study led to the appointment of a special panel of distinguished academics to review the approach and which produced a series of recommendations that still provide guidance on best-­ practice.9 Much attention is focused on getting the procedures for applying the methodology correct. 9  Carson et al. (2003) explain the original study. The review of the methodology and recommendations is Arrow et al. (1993).

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Specifically, the panel recommended • Careful sampling of respondents • In-person interviews • An elicitation format based on a single yes/no choice to accept a particular payment level • An accurate description of the scenario and the benefits from accepting payment • Conservative design features to create a downward bias in the estimates • Checks on respondents’ understanding • De-briefing questions • Careful pre-testing of the questionnaire As discussed in Chap. 7, these recommendations still form the basis of good practice estimation for this type of study. In operational project work, it will only be large and high-profile projects with significant environmental effects that will have the resources to mount surveys that follow best-practice procedures to estimate environmental valuation. However, there are many research studies that show what can be done.

Benefit Transfer Given the technical complexity of some of the environmental valuation techniques discussed here and the difficulties in carrying out primary research studies for individual projects, in practice more approximate and simpler approaches are often used. Empirical estimates of the monetary value of various types of environmental effects, particularly ecosystem services, are now available for developing countries. Many of these are the result of detailed research exercises rather than work carried out as part of the analysis of particular projects. The methodologies involved are becoming increasingly well known and, in some instances, it may be possible to transfer environmental values—that is, to use estimates derived for other purposes or other places, perhaps from a general study, in the analysis of a particular project. For example, estimates of the per-hectare value of nontimber forest products from a study on a particular forest region in Brazil could be used to value benefits of a forest conservation project in a neighbouring country with similar forest cover. This is an example of the unit transfer approach discussed in Chap. 7.

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Databases of environmental valuation estimates are now available.10 To illustrate, Table 11.2 summarises the results of a sample of studies covering a range of ecosystem effects, which have been converted into monetary values. These refer to the estimated economic benefits from natural forests, wetlands and mangroves. For example, natural forests provide a source of pollination that can protect crops like coffee and increase crop production, while natural vegetation can prevent soil erosion and help maintain crop yields. Natural forests can also provide benefits like foodstuffs, construction materials and natural medicine with a market value. Areas like wetlands and mangrove forests or swamps offer protection against flooding or storm damage, which affect both physical assets and activities, like fishing.11 The implication is that for projects that will change and develop these natural habitats, these ecosystem benefits will be lost. Thus, they are opportunity costs that must be included in an appraisal of the proposed development to ensure that the benefits from development are enough to cover these losses. On the other hand, for projects to preserve and protect natural habitats, these functions create benefits that can be compared with the costs of protection and preservation. For example, environmental valuation of natural forests is an area where considerable work has been done. Estimates of the non-timber benefits of forests give the opportunity cost of developing forests for timber. The development alternative can be justified only if it creates economic gains in excess of these opportunity costs; if it does not, the conservation alternative is preferable. In each case in Table 11.2 the results are derived from detailed research studies, typically applying an ecological production function approach, whereby an environmental input is combined with labour, capital and man-made inputs to derive an estimate of its contribution to output, which is then given an economic value. Benefits are expressed in annual monetary values per hectare, although individual values are not directly comparable as they are in dollars in prices of different years and refer to widely different situations.

10  USAID (2018) summarises these. The best documented of the databases is the Ecosystem Services Value Database (ESVD) [see De Groot et al., 2020]. 11  Barbier et al. (2023, Table 1) give another summary of case studies estimating ecosystem benefits.

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Table 11.2  Sample of studies on valuation of eco-services Eco-service

Description and Estimated Value

Source

Natural pollination Natural pest control Coastal mangroves Coastal mangroves Wetlands

Wild pollinators from natural forest, protective ffect on coffee output, Costa Rica: $130/ha/pa Pest control by birds, protective effects on coffee output, Costa Rica: $75–$310/ha/pa Protective effect of coastal mangroves on fisheries, Thailand: $33–$110/ha/pa Coastal protection from storm damage from mangroves, Mozambique: $26/ha/pa Coastal protection offered by wetland from hurricane damage, USA: $250–$51,000/ha Wetland flood protection, USA: $30–$70/ha/pa

Ricketts et al. (2004) Karp et al. (2013) Sathirathai and Barbier (2001) Narayan et al. (2017) Costanza et al. (2008) Watson et al. (2016) Vogl et al. (2017)

Wetlands Soil moisture retention Forest products

Natural vegetation increasing soil water retention as protection against soil erosion, Kenya: $68–$479/ ha/pa Non-timber forest products, including food, Bolivia, Honduras: $18–$47/ha/pa

Godoy et al. (2002)

Source: Adapted from USAID (2018)

The difficulty in applying a simple unit value benefit transfer approach with this type of data is that estimates such as these are often highly context-­specific and very uncertain. Most of the results in Table 11.2 are given as a relatively wide range reflecting this uncertainty and the fact that a number of different crops or activities can be involved. For example, the wide range of estimates of the benefits of protection from storm damage created by coastal mangroves reflects the different types of physical assets and livelihoods that are protected in different locations. Similarly, the range of benefits from soil moisture arising from allowing natural vegetation to grow alongside farm land arises from the different possible crops affected. In addition, strictly all economic values for environmental effects should be in the same price units as other project effects. However, many studies do not make clear whether they are using economic or financial prices and, if the former, whether they are estimating environmental economic effects in world or domestic price units. Hence the values indicated cannot always be applied directly in a project analysis and require adjusting at least for choice of the base-year price and exchange rate.

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Meta-Analysis Clearly, there will always be a need to check the realism of a unit transfer approach, and there will be some environmental values which are too specific to a particular area or type of effect to be used in this way. This is one of the reasons why a meta-analysis approach has been applied to ecosystem values. Meta-analysis involves using a large sample of studies on environmental valuation. These are studies, often from a range of countries, which explain an environmental value (such as willingness to pay for aspects of the environment) by a similar set of variables. These studies have to use a comparable methodology, with equivalent explanatory variables, and should meet minimum standards of statistical rigour. The meta-analysis combines the data from the sample of studies to estimate an average relationship between an environmental value and a set of variables reflecting a series of key characteristics. Provided this relationship holds for the area of a new project, by entering the values of the explanatory variables that apply to the project location, this relationship can be used to predict value in the project area. The approach can be illustrated using a meta-analysis of studies of the willingness to pay (WTP) for the recreation use value of forests.12 A meta-­ analysis explains the value of forest cover (US$ per hectare) across a sample of studies by the variable total population density (persons/residents per sq km), GDP per capita (US$), mean annual temperature (C) and species richness (number of species per hectare). The relationship found across the sample in a double-log regression is Y  8.375  0.562.POP  0.566.GDP  0.0178.TEMP  1.113.SPEC

Where Y is estimated value of forest cover, POP is population density, GDP is GDP per capita, TEMP is temperature and SPEC is species richness. All variables are in natural logarithms. Use of this result of the meta-analysis can be illustrated for a forest conservation project in a location where the following data apply; Population density: 500 residents per sq km; National income: GDP per capita $4600; Temperature: 15C; Species richness: Numbers of tree species 200.  The example is adapted from USAID (2018).

12

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Table 11.3  Use of meta-analysis to estimate value of forest cover Variables

Data

Ln

Pop Density (persons/sq km) 500 6.214608 GDP/capita (US$) 4600 8.433812 Temperature (C) 15 2.70805 Species (numbers/hectare) 200 5.298317 Constant

Coefficient LnxCoefficient 0.562 0.566 0.0178 1.113

Exponential

3.492609751 4.773537356 0.048203294 5.897027229 −8.375 5.83637763 342.5363

Where ln is natural log and exponential gives the anti-log

If these location-specific values are entered in the equation from the meta-analysis, provided the relationship found there applies to the project location, this will give a first approximation for the recreational value of forests for use in the analysis of new project that would develop all or part of the forest area. Table 11.3 explains the calculation. The result is a WTP of close to US$ 350 per hectare. It provides a first estimate of the opportunity cost per hectare of a project to develop a forest area and thus remove forest cover and forego the recreational use of the forest. As the equation is in double-log form, the data on the explanatory variables must be converted to natural logs and then multiplied by the coefficients from the equation and the constant term added. The result of 5.8364 is a value in natural logs, and this must be converted to a whole number taking the exponent. The final result is a WTP of US$ 343 per hectare. Appendix 2 gives a more detailed example of how data from the Ecosystem Service Value Database can be used in a meta-analysis to derive a preliminary value. Benefit transfers, either of the simple unit value type or those based on meta-analyses, will provide only first approximations, which always need to be checked for their relevance to any individual project. However, they can be used in a preliminary assessment of a project (or policy change) to establish how the results look, allowing approximately for valuation of the expected environmental effect. Where sensitivity analysis has already been applied, the benefit transfer value can be compared with the switching value for the environment effect concerned to judge the risk of an environmental value changing the accept/reject decision on a project (see Chap. 9).

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Valuing Carbon and Other Emissions The most significant environmental value, as in principle it can affect all projects, is the economic value placed on greenhouse gas emissions, created by a project or saved by a project. The application of a value for the net CO2 emissions or their equivalent in other gases created by a project is a potentially important way of incorporating the objective of climate protection in public sector investment planning. It requires an accurate estimate of the volume of emissions, created or reduced, in each year of a project’s operating life and application of a price (often termed the social cost of carbon) to value these. Greenhouse gases create costs for the global economy as they lead to long-run increases in temperature, which can have major economic consequences such as sea level rise, flooding, reduction in agricultural production, and increased risk of catastrophic events. These are costs imposed globally, although their incidence will vary between countries, depending on geography and resource endowments and the ability of countries to respond with measures of mitigation or adaptation. It is now standard practice for international agencies funding development projects to put a value on the economic damage from emissions, since given their mandate to encourage international development, they have an obligation to assess the full effect of any project they support. This means that in project analysis calculations, where a project creates emissions, its costs are raised by adding an estimate of damage cost and where it lowers emissions, by adopting a less polluting alternative, its benefits are increased. National governments may take a different view in their appraisal of projects in their own country, since they may not wish to consider effects that arise outside their borders. However, there is an increasing awareness that all countries lose from climate change and it is now becoming more common for national appraisals also to include a value for greenhouse gas effects.13 If a global perspective is taken, it is important to note that where an individual project in country A reduces emissions, for example replacing coal-based electricity generation by wind-based technology, if this is treated as a global benefit the implication is that the coal

13  Emissions can also have local effects, for example, where the health of the population in the vicinity of a project is affected. In principle, these health damage costs should also be estimated.

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that would have been used in A is not extracted and used in the power sector in country B, with the same coal-based technology. This can be a strong assumption to make unless the regime of global agreement on national targets for emission reduction is strictly enforced. Whilst the principle of valuing climate change effects, either positive or negative, and including these as either additional costs or benefits, is widely accepted, operationalising this in terms of specific prices is not straightforward. This is often done using an estimate of the social cost of carbon, defined as the future economic damage caused by emitting an additional tonne of carbon dioxide (CO2) into the atmosphere. CO2 is not the only damaging gas, and where economic values for other emissions are available from the research literature, these can be applied; where they are not available, this is usually dealt with by converting other gases, such as nitrous oxide (NO2), into equivalent units of CO2 so that the CO2 price can be used to value them. The CO2 price per tonne used to value a projects’ effect in terms of emissions must be in prices of the same base year as all other project components and is conventionally given in US dollars and treated as a traded cost or benefit, on the grounds that climate change creates a global cost that all countries will suffer from. Values for the cost of carbon to use in economic analysis can be based on different approaches. Some countries or groupings of countries have established a ‘cap and trade system’ whereby a national ceiling is placed on emissions and individual producers are given the right to emit a fixed amount. Low-emitting firms who do not use their full quota, can trade the unused allocation in a market for carbon permits, which high emitters can purchase. The per tonne price in the market gives a carbon price, which reflects the willingness to pay of producers. Whilst directly relevant for the financial analysis of a project, such a price is likely to seriously understate the social cost of carbon defined as a measure of economic damage from emissions. The price in a carbon market will be closely related to the overall emissions ceiling used. A market based on a high overall ceiling is likely to result in a much lower price than a more restrictive system that sets a low overall emissions target. Hence there is no objective market price. In addition, producers’ willingness to pay to emit will reflect their assessment of the private gains from doing so, not the full economic damage caused by emissions. Hence where such a market exists, it is best treated as a minimum or floor estimate of the value of carbon.

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A rigorous economic valuation uses a model—an Integrated Assessment Model—with both economic and climatic components—to estimate economic damage, usually at a global level. This requires that the model specifies the links between economic activity (e.g. GDP and sectoral growth), change in CO2 and other emissions due to increased economic activity, the concentration of emissions in the atmosphere and its resulting impact on temperature, the impact of temperature change on aspects of the natural environment (e.g. on sea and river levels, wind speed and soil quality), and the economic value of the damage caused by these changes (e.g. damage from flooding, and agricultural losses due to lower crop yields). These are all complicated relationships with considerable uncertainty, not just in the climatic relationships, but in the damage effects and the way in which they should be valued. This uncertainty is heightened by the fact that the effects are projected into the very long run and thus need to be discounted to the present to obtain a base-year price.14 The uncertainty can be dealt with by running a model for different scenarios with different levels of effects; for example, for high, medium or low increases in temperature. Alternatively, a technically more rigorous approach uses probability-weighting for these scenarios, for example using the approach to risk analysis discussed in Chap. 9 in the context of an individual project. These models have been criticised for their inability to deal with the type of catastrophic risks and associated uncertainty created by tipping points in climate change; this omission may create a serious downward bias in the results for the social cost of carbon.15 Differences in key assumptions and model parameters mean that there is no consensus in the modelling literature on a unique value for the price of CO2 for use in either project or policy analysis. In addition, the choice of discount rate to use in such models to estimate future damage cost can create a wide range of possible values for the social cost of carbon. For example, Table  11.4 shows the range of estimates of the carbon price from one well-known model for different discount rates between 2.5% and 5%. A higher discount rate has a very significant impact in lowering the estimated future damages caused by emissions. Even an increase in the discount rate of only 0.5 percentage

 Pindyck (2017) has a critical assessment of these models.  See Stern et al. (2021).

14 15

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Table 11.4  Sensitivity of social cost of carbon to discount rate ($/tonne CO2 in 2010 US dollars) Discount rate

2020

2025

2030

2050

2.5% 3% 4% 5%

148.0 87.3 40.9 22.6

152.0 95.9 45.8 25.7

164.6 104.9 51.1 29.1

235.7 156.6 81.7 49.2

Source: Nordhaus (2017, Table 1)

points (from 2.5% to 3%) lowers the cost of CO2 per tonne by 40% in 2020 (from $148 to $87.3).16 As an alternative approach, many countries have now set national targets for emission reduction as part of their contribution to the international agreement on climate change. Analysis can work from the nationally agreed target (e.g. achieving carbon neutrality by a specific year in the future) and estimate the cost-minimising path of abatement cost to achieve the target. This in effect ranks known technologies by their cost per tonne of CO2 removed, with the cost of the marginal technology setting the CO2 value for analysis. When used as a carbon price, this implies a project that increases emissions will cost the economy the marginal per tonne cost of abatement, as this cost must be incurred by another party in the economy if the national emission target is not to be missed. Similarly, this cost is saved, since where a project reduces emissions, this will save the marginal abatement cost expenditure by someone else. Estimates of abatement costs are also based on complex models that require critical assumptions. Nonetheless, they have the advantage of being country-specific, so that differences in production structure, natural resource endowment, and nationally agreed emissions targets are allowed for. The expectation is that for a low-income country, with a relatively low emissions per head of population and therefore a low national emissions

16  As discussed further below, there is considerable disagreement on how best to discount future environmental effects. The study cited here used a baseline discount rate of 4.25% (calculated as an average of the opportunity cost of capital in the US and other economies), which gave a most likely value for CO2 of $37 in 2020 (in 2010 dollars). This is a relatively low figure for CO2 and much less than that obtained with discount rates that are defined in terms of social time preference.

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reduction target, the CO2 price derived from this approach will be significantly lower than that for a high-income country with a much higher level of emissions per capita. The Report of the High-Level Commission on Carbon Prices which recommended this approach suggested that for most countries meeting their targets required a carbon price of between $40– $80/tonne in 2020 and $50–$100 in 2030 for use in project and policy analysis (World Bank, 2017). This is to be increased annually by 2.25% to allow for increasing pressure on environmental resources creating a real price increase. In principle the emissions content of a project should be derived from both its direct and indirect effects, particularly the indirect effects created by the energy inputs required by the project. In practice there are limits as to how far back in the chain of production it will be possible to trace these indirect effects. However, at least the emissions content in the electricity supplied to a project should be estimated.17 Given the uncertainty over the precise CO2 price there is no single correct solution for incorporating the CO2 effect in project analysis. If a national perspective is taken, one can use an estimate of marginal abatement cost for the country in which the project is located and apply this consistently across projects. The carbon price can be adjusted over time if more information on abatement cost becomes available. Alternatively, from an international perspective, for example in analyses of international agencies, one can take a price range for a global damage estimate per tonne of CO2 (at a consistent discount rate) where the range is sufficient to cover all plausible and comparable estimates. The top of the range would be a price that no plausible study exceeds, and the bottom of the range would be a price that no plausible study is less than. To illustrate, using the price range from the High-Level Commission on Carbon Prices quoted above, implies a price range of $40–$80 in 2020 rising to $50–$100 in 2030 and growing in real terms beyond that. For 2030, for example, the bottom of the range, $50, could set a low carbon price for an analysis, and the top, $100, could set a high price. If a project that has positive net emissions will have its cost increased by the use of a carbon price. If it fails the test of acceptability in terms of the NPV/EIRR 17  If the electricity supplied to a project is valued at economic prices, with a cost of carbon included in the cost, it will be unnecessary to add the indirect emissions arising from the use of electricity. Provided the appropriate carbon price is used, inclusion of this indirect effect will be double-counting.

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criteria at the low carbon price, it should be rejected. If it passes the test of acceptability at the high carbon price it should be accepted. If it’s switching value for the carbon price is within the price range (e.g. $50–$100 in 2030), the outcome is ambiguous and the project should be examined again to see if it can be redesigned in a way that either lowers cost, including emissions, or raises benefits. For a project that saves emissions, and therefore benefits from the carbon price, the conclusions are reversed. If it passes the test at the low carbon price, it should be accepted, and if it fails the test at the high carbon price, it should be rejected. Again, with a switching value within the chosen price range the project needs to be reconsidered. There are more rigorous ways of dealing with uncertainty in the carbon price, such as using probabilities for the carbon price and other key parameters, but this procedure is a relatively simple way of clarifying the effect of incorporating a value for emissions in project analysis.

Discounting and the Environment If environmental effects can be quantified and valued, they must be incorporated in a project analysis in the same way as other benefits and costs. This requires that they be subject to discounting to derive their present value. However, the use of common discount rates to adjust environmental effects has provoked considerable debate and remains controversial. It will be recalled from Chap. 8 that the conventional basis for the discount rate is either one of social time preference or opportunity cost. Most discussions of environmental economics regard use of an opportunity cost discount rate as inappropriate, since it assumes that resources created by a project can be reinvested to produce future income. If this logic is applied to environmental effects it implies, for example, that a project with long-­ run environmental cost of US$ 50 million in year 100 of its life will result in only a very small cost in the present. For example, with a discount rate of 6%, reflecting returns available elsewhere in the economy, the present value of this cost is only US$ 0.15 million.18 The implication is that if this very small sum is set aside today and invested at an annual rate of 6%, with all returns reinvested again at 6%, in 100 years’ time it will have grown to

18  The discount factor for year 100 at a 6% discount rate is (1/1+0.06)50, which is 0.002947 and 50 times 0.002947 equals 0.15.

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50 million and thus potentially at least will provide a fund to either mitigate the environmental damage or compensate those affected by it. Hence, given its very low present value equivalent, at relatively high discount rates, long-run costs or benefits have little effect on present value results and thus on project decisions. However, even accepting this scenario, there is a further problem that is basic to the critique of environmental valuation. This is that certain aspects of the environment may not be substitutable by material goods. If certain environmental resources are irreplaceable, in order to protect the interests of future generations, the only solution is to require that in the aggregate, new projects do not reduce the natural stock of this resource. This is often termed the use of sustainability criteria. The original suggestion to operationalise this was that whenever the natural capital stock is damaged by a project or group of projects, a compensating scheme to reinstate the original level of natural capital must be set up.19 The cost of this compensating project must be included in the analysis of the initial project or projects that cause the damage. In practice there is generally a recognition that full compensation may not be possible and that it may be difficult to organise and finance a single compensating project for a group or programme of environmentally damaging projects. Hence there will be uncompensated environmental costs, as well as expected environmental benefits, from some types of projects that must be valued in project economic analysis. As costs and benefits occur over a project life, this requires that they be converted to a value in the present and a discount rate will be required for this purpose. The transfer of levels of the discount rate based on opportunity cost and used typically for relatively short-term productive sector projects with tangible marketed outputs, to the appraisal of long-term and normally highly uncertain environmental effects has caused considerable debate and much unease. For example, there is the argument that the relatively high discount rates derived from estimates of opportunity cost, for example in the 6% to 12% range, are not only unrealistic in terms of the assumption that returns can always be reinvested at this rate, but create a bias against projects with longer-term beneficial effects and a bias in favour of those with long-term costs, as referred to above.

19  This suggestion, for example in Markandya and Pearce (1994), was made in part to allow a conventional discount rate to be applied to projects with major environmental effects.

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If the opportunity cost approach to discounting is doubted for projects with long-term environmental effects, use of a social time preference approach to the discount rate, with some adaptation, may address this concern. As discussed in Chap. 8, theoretically the time preference rate is meant to reflect society’s weighting of units of consumption at different points in time. Here the lower weight placed on future as opposed to present consumption is determined by income growth and concerns over future uncertainty, not by opportunity cost. The role of the discount rate is not to ration scarce resources, but to assess accurately the relative values of goods or income arising at different points in time. Time preference discount rates are inherently subjective and can be viewed from different perspectives (e.g. from that of different individuals, of society as an aggregation of individuals, or of the government as the representative of society). Attempts at empirical estimation normally attempt to establish a general rate for society or the government. This approach usually leads to estimates of a time preference rate of discount that is equal to or only slightly above the average growth of per capita consumption. This has the advantage for environmental analysis that it will normally produce discount rates that are well below those derived from an opportunity cost approach that is based on the returns available on other projects. The main debate on the original analysis of the economic cost of climate change focused on the choice of a low-time preference discount rate, as opposed to one based on market rates reflecting opportunity cost.20 When the discount rate is specified as a time preference rate theoretically it is necessary to express all project benefits and costs in terms of units of consumption, which can then be discounted at a rate which reflects preferences for consumption now rather than later. This requires that all environment effects be converted into units of consumption to be discounted in the same way as all material (i.e. non-environmental) goods and services. This chapter has discussed ways of estimating values for environmental effects to allow this. Some critics question how far monetary values can adequately reflect environmental benefits and costs, and given the nature of these calculations, many will be no more than approximations. However, even if exactly accurate values could be obtained, there is

20  The contrast was a rate of close to 4% or one between 1% and 0 [see Nordhaus, 2007; Stern, 2007].

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still a more profound limitation of the standard approach to time preference. As discussed in Chap. 8, the basic formula for the time preference discount rate makes it determined by a wealth effect, which allows for the fact that recipients of future consumption will be richer than present-day consumers plus a time preference effect, which is the cost of waiting, including the risk of catastrophic change that undermines the ability to enjoy future consumption. As normally applied, this formula implies that as a society gets richer its valuation of future consumption (the utility derived from an additional unit) declines. The key point is that this need not apply to environmental outputs, which are becoming scarcer and which cannot be substituted by material goods; or alternatively, if it does apply, any decline in valuation need not be at the same rate as for other consumer items. This argument leads to the suggestion of a separate environmental discount rate, that is, dual discounting within one project with a separation between environmental and non-environmental benefits and costs and the environmental ones discounted at a lower rate. If the problem is a rising scarcity of environmental assets, there are two separate ways of addressing it, which involve approaches that can be equivalent. The first is to apply a different discount rate for all environmental effects, whilst keeping the normal discount rate for all others. This dual discounting raises practical complications for project analysis. For example, it requires a clear distinction between environmental and non-­ environmental aspects of a project or between environmental and non-environmental projects, and it assumes all environmental effects should be treated as becoming scarcer at the same rate. Also, the IRR becomes an ambiguous criterion, as there is no single discount rate for it to be compared with. To avoid such confusion, the second approach is to keep the normal discount rate, but to increase the real value of all environmental effects to address the scarcity issue, if necessary, using a different price increase depending on the assets involved.21 This requires that the environmental estimates reviewed in this chapter, including that for CO2, be increased in real terms by an estimated or

21  Weikard and Zhu (2005) show theoretically that the difference between the discount rate for consumption in general and for the environment is given by the price change for environmental assets relative to consumption goods.

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assumed inflation factor for the environmental effect concerned. For example, for a constant price environmental damage cost of C growing in real terms by x per cent annually, after 50 years this cost will be C*(1 + x).50 This real adjustment can be combined with discounting at the normal time preference discount rate of i to give the present value of this cost in year 50 of C*(1 + x)50/(1 + i).50 If x equals i, this is equivalent to not discounting, and if x exceeds i, despite projecting 50 years ahead the present value of environmental damage will have grown in real terms. A similar argument applies for environmental benefits. In terms of valuation, this real adjustment is equivalent to discounting at a lower rate of i − x. Hence using a relative price change allows a relative adjustment for environmental value, whilst applying a single discount rate for all project items. The additional information required is a value for the real price increase for environmental assets created or used by a project. This is illustrated in Table 11.6 with a simple example, where emissions valuation discounting at 3% has the same present value as discounting at 6%, whilst including a 3% rise in the price at which emissions are valued. This approach removes the need for a separate discount rate, but poses the practical challenge of forecasting the relative price rise involved for each environmental good or service. As discussed in this chapter, it can be very difficult to derive unique base-year economic prices for a range of possible environmental effects, and adding price forecasts creates a further level of uncertainty. A crude expedient used in some of the models that estimate the social cost of carbon is to assume an annual relative price increase, such as 2% or 3%, for all environmental effects to address this relative price shift.22 Both alternatives for addressing the growing environmental scarcity pose challenges for practical work. Probably more common now than use of a separate environmental discount rate is the relative price adjustment approach, based on relatively simple assumptions about relative price increases for environmental assets. In principle this type of adjustment should apply to all projects, but often it will be only for relatively large projects and relatively important environmental effects that this type of adjustment is made.

 Nordhaus (2017), for example, uses a 3% relative price increase.

22

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Some Environmental Illustrations To illustrate some of these concepts, we turn to three simple illustrations of the use of environmental calculations. We use three examples to reflect projects that correspond to the first three environmental categories identified at the outset of the chapter. It will be recalled that these correspond to: • environmental projects whose main purpose is to improve the environment • projects for whom an environmental effect is significant and generally negative, but a by-product of other non-environmental activity • projects for whom an environmental effect is both a by-product and relatively minor. These categories are illustrated in reverse order. The numbers used are simplified for exposition. Values are in constant economic prices, with some relative price changes to allow for rising environmental scarcity.23 Project A is a mining project to produce iron ore. Its main environmental effect is its impact on water pollution levels in a nearby river. National environmental regulations require the project owners to prevent pollution by including in project investment the construction of a sedimentation lake to store polluted water to prevent the entry of solids into the river and a new waste water treatment plant for the town nearest to the mine. Here we have an illustration of a compensating project (the sedimentation lake and water treatment plant) being planned at the same time as and as part of the original project. Since in this case the project owners are required to meet the cost of the compensating project the environmental impact of the project is internalised as part of project costs. Table 11.5 gives benefits and costs for project A over a 30-year life of the project. Benefits are in terms of revenue from sales of iron ore and costs are investment costs, both direct costs and those from the compensating project, and operating costs (which cover both extraction cost and an allowance for depleting a non-renewable resource). In addition, a cost is added each year for the net emissions created by the project. These are valued from a national perspective as the cost of carbon abatement necessary to meet the country’s climate commitments.24 This cost is taken as  As these are simple projects, investment is in year zero.  The implication is that by increasing emissions this abatement expenditure per tonne of CO2 will have to be incurred to meet the national carbon target. 23 24

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Table 11.5  Project examples: Project A—mining project $ million Years Benefits Costs Mine Investment Compensating Project Mine Operating cost Net emissionsa Net Benefits NPV at 6% IRR %

0 0

1 0

2……………………………….31 45……………………………….45

150 0 0 5 −150 48.9 7

200 40 0 6 −240

0…………………………………0 0…………………………………0 5…………………………………5 2…………………………………4.7 38………………………………35.3

Emissions rise in value by 3% annually

a

$30/tonne rising at 3% annually. The example uses a 6% discount rate reflecting the opportunity cost of resources committed to the project. The analysis shows a positive NPV at a 6% discount rate, when the costs of the compensating project are included as part of the costs of project A. The implication is that project A can generate a sufficient surplus for the economy from its mining activity, whilst allowing its harmful environmental consequences to be fully mitigated. Project B is a power plant rehabilitation. The benefits from rehabilitation are two-fold: there is a saving in fuel since the plant will run more cost-effectively, whilst producing the same output of power. In addition, rehabilitation will reduce the volume of CO2 gas emissions into the atmosphere per kwh. The environmental effect, in terms of reduced CO2 emissions, is not the primary objective of the rehabilitation, but it is nonetheless an important form of benefit. As it is partly funded with financial assistance, a global perspective is taken. The CO2 savings are valued on the basis of the estimated social cost of carbon from the modelling literature. A value of US$ 50 per tonne, rising annually in real terms by 3%, is taken as a best estimate. Hence the value of this environmental benefit is given by the volume of emissions reduction per MWh of electricity production multiplied by this value for CO2. Costs for project B are the investment cost of rehabilitation; the rehabilitation will only allow further production for another ten years at the same output level, so that beyond year 10 benefits, both fuel saving and emissions reduction, will cease. Calculations are in Table 11.6 in constant

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Table 11.6  Project examples: Project B—power rehabilitation (Shillings million) Year

0 1 2 3 4 5 6 7 8 9 With emissions benefit Present value of emissions NPV at 6% IRR % Without emissions benefit NPV at 6% IRR %

Benefits

Costs

Fuel savings

Emissions reductiona

Emissions reductionb

Rehabilitation Net Benefits

8 8 8 8 8 8 8 8 8 8

5 5.15 5.30 5.46 5.63 5.79 5.97 6.15 6.33 6.52

5 5 5 5 5 5 5 5 5 5

70 0 0 0 0 0 0 0 0 0

44.0

44.0

−57 13.15 13.30 13.46 13.62 13.79 13.97 14.15 14.33 14.52

36.5 19 −7.6 3

Emissions value rises in real terms by 3% annually and is discounted at 6% Emissions value constant but discounted at 3%

a

b

Shillings. In the case of project B, the treatment of the environmental effect is critical to the analysis. With the cost saving for emissions included, the project has a positive NPV and an IRR of 19%, which is well above the discount rate of 6%. However, should the emissions effect be omitted, the project becomes unacceptable with a negative NPV and an IRR of 3%. The implication is that it is both important to include the environmental effect and to value it as accurately as possible. In addition, Table 11.6 shows that the use of a single discount rate of 6%, combined with a relative price increase in the value placed on emissions of 3% annually, is equivalent to discounting emissions at 3% (i.e. the difference between the standard rate of 6% and the relative price rise), whilst keeping the value placed on emissions constant. Both calculations give a present value for emissions of Shillings 44 million.

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Project C illustrates an environmental project. It is a project to develop and protect a national park area for both the preservation of wildlife and to attract high-income eco-tourism. Here investment costs of the project are the initial costs of constructing buildings for the park administration and associated cost in setting up the park. There are also low operating costs in terms of the employment of wardens and gardeners. The emissions effect of the project is low, and an estimate of the cost of emissions is included as part of operating costs, rather than shown separately. The main form of cost, however, is the opportunity cost arising from loss of income suffered by the native inhabitants of the area, who would have used the parkland for cultivation of rice and the collection of forest products, such as fuelwood in the without-project case. The net value of these forgone products, that is the value of the goods concerned minus the cost in terms of time and resources that would have been expended to obtain them, defines an annual opportunity cost per acre of land set aside for the project. As a traded good rice can be valued at its world price, with appropriate deductions to obtain the border parity value and an adjustment for the shadow exchange rate. Traditional forest products are more difficult to value. Here it is assumed that a value for fuelwood and other important forest products can be taken from research studies from other locations. Estimated opportunity cost per acre multiplied by the total area covered by project C gives the income loss to the economy from the new land use. Benefits of project C are the consumer satisfaction derived from the preservation of natural areas and the species that breed there. For the purpose of valuation, a survey approach is employed, which asks tourists who currently visit the area close to the site of the project how much more they would be willing to pay for their trips if they were able to see the wildlife and habitat that will be available in the park. This is an attempt to capture the total environmental value of the park facilities. However, as only tourists likely to visit the park are questioned the existence component of total value covering the intrinsic worth of environmental assets, rather than their use value, will not be identified. For the national analysis, benefits are taken as domestic tourists’ willingness to pay plus the expected revenue collected in entrance fees from foreign tourists.25 For simplicity numbers of domestic and foreign tourists visiting the park each year are held

25  As non-residents it is conventional to assume that any consumer surplus of foreigners is not part of national benefits. Hence only funds collected from them in charges are relevant.

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constant, but with increasing scarcity of natural assets willingness to pay is projected to grow at 2% per year in real terms. For foreign tourists it is assumed that half of this growth is captured in higher charges, so revenue collected grows at 1%. Annual benefits are defined as the projected number of domestic tourists in each year multiplied by their estimated average willingness to pay plus the projected number of foreign tourists in that year multiplied by revenue collected from them. For the analysis, the project life is taken as 20 years. In recognition that the national park covers self-regenerating resources that can last beyond this date, a terminal value is included to reflect benefits after year 20. Here this is taken to be 20 times the year 20 annual figure and entered in year 20. All values are in constant Shillings. The results for this example are in Table 11.7. Project C appears to be acceptable with a positive NPV at 6% and an IRR of 8%, when an allowance is made for increasing environmental scarcity. While the projected willingness to pay of tourists is high enough to cover project costs and thus to justify the project, the beneficiaries will be better-off tourists, whilst the losers from the project will be the much poorer inhabitants of the area of the park. The latter lose from the lack of access to the park area for rice cultivation and the gathering of forest products. The present value of the income equivalent of the gain to domestic tourists (109 million) is high enough to compensate the losers (78 million), but the project itself has no built-in compensation mechanism. Payment of compensation would require government action, funded from entrance fees paid by visitors.

Conclusion This chapter has considered ways in which environmental effects can be quantified and incorporated in conventional project calculations. This is an area in which considerable research is taking place and the chapter has tried to give a sense of the richness of the literature and the developments in empirical estimation. However, it is necessary to distinguish between detailed research studies on environmental valuation, of which there are now many, and actual applied project work. In the latter many approximations, such as the frequent use of benefit transfers, have to be made. Also, despite the major strides that have occurred in environmental valuation, very considerable uncertainty remains in relation to both the identification and the valuation of environmental effects. This uncertainty is illustrated

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Table 11.7  Project examples: Project C—national park (Shillings million) Year

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 NPV at 6% IRR % Present Value of domestic willingness to pay at 6% Present Value of farmers opportunity cost at 6%

Benefits

Costs

Net

Domestica

Foreignb Investment Operating Opportunity cost of land

Benefits— Costs

0 5 5.100 5.202 5.306 5.412 5.520 5.631 5.743 5.858 5.975 6.094 6.217 6.341 6.468 6.597 6.729 8.864 7.000 7.141 142.825 9.1 8 109.4

0 1 1.01 1.020 1.030 1.041 1.051 1.061 1.072 1.083 1.094 1.104 1.116 1.127 1.138 1.149 1.161 1.173 1.184 1.196 23.922

−30 0.5 0.61 0.722 0.836 0.953 1.071 1.192 1.315 1.441 1.569 1.700 1.832 1.968 2.106 2.247 2.390 2.536 2.686 2.837 75.747

30

0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

0 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 90

78.3

Measured by willingness to pay Measured by revenue collected

a

b

by both the range of values that have been suggested for particular impacts on the environment and by the methodological debates, relating particularly to the role of discounting and the most appropriate means of quantifying total environmental value. There is still a long way to go before environmental appraisal, in the sense of the incorporation of

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environmental benefits and costs into an economic project analysis, becomes as relatively routine as the other areas of analysis covered in this book. Nonetheless a strong sensitivity to environmental concerns is now widespread and substantial progress has been made in addressing these concerns at the project level and also for policy purposes.

Further Reading Pearce et al. (2006), updated in OECD (2018), is the best overview of environmental economics as applied projects. Anand (2012) has an introductory discussion. USAID (2018) is a good simple introduction to methods of eco-system valuation, while Pearce et  al. (2002) has many detailed case studies. Markandya (2016) and UN (2021) give details on environmental valuation of the ecosystem.

Appendix 1: Depletion Premium Discussions of environmental valuation have refocused attention on the fact that development projects may use resources whether natural habitats and ozone layers or minerals or fossil fuels, which cannot be renewed or replaced. The point of principle is that the cost of using such resources is not just the cost of their extraction, but also the foregone opportunity of using them in the future arising from the fact that because of their finite supply current use means that future income (or future benefits that are equivalent to a value in income) from these resources are lost. The cost of this income forgone is what constitutes the depletion premium. The full economic cost of using a non-renewable resource is its cost of extraction plus the depletion premium, which captures the use-value that others forgo as a result of current use. Hence, an important part of environmental valuation relates to its estimation. There are two issues here, one relating to ex ante appraisal and the other to pricing policy. First, the full economic cost of non-renewable natural resources must be included in the appraisal of any project using these resources. Unless the benefits from a resource extraction project are high enough to cover costs, including the depletion premium, by definition the project cannot be making the best use of the limited resources involved. Second, unless the depletion premium is included in the price charged for the resource, current users will not be paying a price that

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reflects the negative impact of their use of the resource on society’s future income. Estimation of the depletion premium is based on the principle that for a particular rate of resource extraction to be justified the net unit value of the resource—that is, the price per unit minus the cost of extraction— must rise at the same rate as the discount rate. If the discount rate is an opportunity cost rate, the logic is that if the net unit value rises by less than the rate of discount it will pay to increase the extraction rate to obtain more resources and invest the proceeds from their sale at the discount rate. If the reverse holds and the price rise is greater than the rate of discount it will pay to extract less, keep the resource in its natural state and let it appreciate in value. Similar arguments apply if the discount rate is one of time preference. The interpretation is that if the appreciation in price is less than the cost of waiting it makes sense to extract the resource, and if the appreciation is greater than the cost of waiting it makes sense to keep it in its natural state. The application of this principle allows the formulation of the depletion premium (DP) in any year t as: DPt   Pt  n  Et  n  / 1  r 

n



(11.3)

where Pt + n is the value of the resource at the time it is exhausted Et + n is the extraction cost in the year of exhaustion r is the discount rate n is the number of years between year t and the point of resource exhaustion.5 The depletion premium is the per unit opportunity cost of the depletable resource. Equation (11.3) implies that in any year it is given by the future value of the resource at the point that it is fully depleted (P) minus the extraction cost saved (E), since the resource is no longer depleted, discounted back to the year in question. Hence there are four influences on the DP: the value placed on the resource at the time of its exhaustion, the extraction cost, the discount rate, and the length of life of the depletable resource.

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Of these, in Eq. (11.3) the discount rate is that rate used for the project concerned, while extraction costs can be estimated from technical data. Of greater uncertainty are likely to be the length of life of the resource involved and the economic value of the latter at the point of depletion. In principle these latter parameters are interdependent and will also vary with the discount rate and extraction costs. Formally their correct valuation requires a programming model that solves for the different parameters simultaneously. In practice empirical estimation may be much more approximate. A further complication is that the final value of the resource P can be defined in two alternative ways. Where the resource has a substitute, its unit value at the point of full depletion will be determined by the equivalent per unit cost of the substitute. The charge for the resource cannot go beyond this since by definition there will be a cheaper substitute available. For example, fuels have substitutes and the value of a natural gas deposit per million Btu at the point of depletion could be specified as the cost of the quantity of fuel oil required to give the same energy as a million Btu of gas. As a traded good, fuel oil should be valued at its world price. Hence, if the gas field is estimated to have roughly 15 years of working life, the forecast price of oil 15 years in the future gives the basis for the estimation of the value of gas at depletion. However, there will be some non-renewable environmental resources with no substitutes. If environmental assets like wetlands and endangered species of animals or plants are treated as completely non-substitutable resources, so the value becomes infinite, and Eq. (11.3) cannot be applied. Hence where environmental assets have no replacement value, the concept of a depletion premium is meaningless. For non-renewable minerals and fossil fuels, where the terms in Eq. (11.3) are quantifiable, the policy concern is that users should pay the full cost of using the resource, including the depletion premium in the price they are charged. Moreover, there is a case for taxing the depletion premium component of the charge rather than letting it remain as profit in the hands of the resource owners. This is on the grounds that as tax revenue the income from the depletion premium can be set aside in a fund to finance the development of substitutes for the non-renewable resources that are currently being depleted.

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These principles can be illustrated with a simple numerical example using the case of a natural gas field. Extraction cost of gas is $0.5 per mill Btu, while the field is estimated to have an approximate working life of 15 years on the basis of current projections of demand (although strictly these will vary with the price charged for the gas). Fuel oil is taken as the substitute fuel and after 15 years, the equivalent economic cost of fuel oil is estimated at $4.0 per mill Btu. The discount rate is 10%. It is assumed that both fuel oil and extraction costs are already at economic prices (including emissions effect) and thus reflect economic values. With this information Eq. (11.3) can be used to derive both the depletion premium and the full cost of using natural gas on new projects for each year from 1 to 15 (Table 11.8). In Table  11.8 the cost of using natural gas (and its economic value) rises gradually to the replacement cost of the gas in year 15 of $4.0 per million Btu. The increase in the cost of gas usage is due to the rise in the depletion premium at the rate of discount of 10% annually. The figures in the final column give the full economic value of gas. This is the figure for the cost of gas to be used in each year of the project calculations and also to be charged to gas users. Table 11.8  Economic value of gas Year

Depletion premium

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

(4.0 − 0.5)/(1.1)14 = 0.92 (4.0 − 0.5)/(1.1)13 = 1.01 (4.0 − 0.5)/(1.1)12 = 1.12 (4.0 − 0.5)/(l.1)11 = 1.23 (4.0 − 0.5)/(1.1)10 = 1.35 (4.0 − 0.5)/(1.1)9 = 1.48 (4.0 − 0.5)/(1.1)8 = 1.63 (4.0 − 0.5)/(1.1)7 = 1.80 (4.0 − 0.5)/(1.1)6 = 1.98 (4.0 − 0.5)/(1.1)5 = 2.17 (4.0 − 0.5)/(1.1)4 = 2.39 (4.0 − 0.5)/(1.1)3 = 2.63 (4.0 − 0.5)/(1.1)2 = 2.89 (4.0 − 0.5)/(1.1) = 3.18 0

Extraction cost

Economic value

0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0

1.42 1.51 1.62 1.73 1.85 1.98 2.13 2.30 2.48 2.67 2.89 3.13 3.39 3.68 4.00

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Appendix 2: Meta-Analysis Using Ecosystem Service Database (ESVD) A meta-analysis is a more sophisticated version of a benefit function transfer, where the coefficients to be applied to project-specific data, come from a statistical analysis of data from a large number of studies in the literature, with control variables included for the methodology used in each study and checks on data consistency and quality. The existence of large databases on environmental values, such as the ESVD, make this approach possible. However, it will only be applicable for certain types of service where there is a large enough and sufficiently consistent literature and where benefits from the service can be defined clearly enough. Furthermore, there is the important question of whether a project study conducts an original meta-analysis or uses one from the literature. It is clearly more complicated to do the former. De Groot et al. (2012) has a useful illustration of how the ESVD data can be used in meta-analysis to provide guidance values. They use the illustration of valuation of wetland services based on 244 studies from the database. The results from these studies are used in a cross-sectional regression of the form

lny i  a  bw.Xw i  bc.Xc i  bs.Xsi  u i

(11.4)

where i is 1 … 244 and refers to individual studies from the ESVD, yi is a vector of wetland service values from the ESVD, a is a constant, b is a vector of coefficients on the explanatory variables and u is a vector of residuals. Three types of explanatory variables are included. Xw cover characteristics of the wetland involved, such as geographical area in hectares, and wetland types, such as freshwater, wooded or salt brackish marshes. Xc refer to socio-economic and geographic characteristics of the study site, such as GDP per capita, population within 50 kms radius of study site, and measures in hectares of wetland and rivers/lake abundance within 50 kms radius. Xs covers the estimation approach used in the different studies whose results are used in the analysis, with a dummy of 1.0 to reflect use of a particular method, such as hedonic pricing or production function. Hence to derive a site-specific value, the b coefficients from Eq. (11.4) must be combined with X values for the site concerned. Table 11.9 reports the variables used and the results. Statistically significant results are found for the wetland area size (with a negative sign

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Table 11.9  Illustration of meta-analysis Variable

Definition

Coefficient

Standard Error

Dependent constant Study site area Fresh water marsh Wooded marsh Salt brackish marsh GDP/capita Population within 50 km Wetland abundance within 50 km Lake and river abundance within 50 km Hedonic pricing Travel cost Replacement cost Net factor income Production function Market price Opportunity cost Choice experiment N = 244 Adjusted R2 = 0.443

ln of $/hectare/pa ln of hectares Dummy Yes = 1, No = 0 Dummy Yes = 1, No = 0 Dummy Yes = 1, No = 0 ln GDP/capita $ ln Population ln of hectares ln of hectares

1.386 −0.321 0.576 0.681** 1.489*** 0.339*** 0.203*** −0.203*** 0.092

1.890 0.055 0.443 0.303 0.480 0.303 0.480 0.110 0.093

Dummy Yes = 1, No = 0 Dummy Yes = 1, No = 0 Dummy Yes = 1, No = 0 Dummy Yes = 1, No = 0 Dummy Yes = 1, No = 0 Dummy Yes = 1, No = 0 Dummy Yes = 1, No = 0 Dummy Yes = 1, No = 0

−1.219 −1.658* −0.567 1.355*** 1.298** −1.391*** −0.726 −0.573

0.047 0.077 1.112 0.426 0.495 0.635 0.392 0.804

Source: de Groot (2012, Table 4) ** indicates statistical significance at 5% level; *** indicates statistical significance at 1% level

indicating diminishing returns per hectare as scale is increased); income per capita and population within the 50 km radius (with a positive sign indicating the value of services rise with income and population); dummies for certain types of wetland (with freshwater marshes appearing to generate a lower value of services); wetland abundance within 50 kms (with a negative sign indicating a substitution effect with some service benefits captured by adjacent wetlands); finally the negative signs on some of the dummies indicate that studies using these methods generate a lower value of services than that in the omitted method in the regression, which is contingent valuation). In a meta-analysis the data on an individual site can be combined with the coefficients from the cross-sectional study to give a first approximation estimate for a new location. However, the authors enter a series of caveats on the need to check the accuracy of this result. They stress values for services will be site and time-specific and that the type of averages reported

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in large databases will be illustrative not definitive. They caution that if environmental service valuation is critical to a project, it can be ‘duly informed’ by a transfer value, but this should not be a substitute for a detailed original calculation.

Bibliography Anand, P. (2012). Environmental Valuation. In J.  Weiss & D.  Potts (Eds.), Current Issues in Project Analysis for Development. Edward Elgar. Arrow, K., Solow, R., Portney, P., Leamer, E., Radner, R., & Schuman, H. (1993). Report of the NOAA Panel on Contingent Valuation. Federal Register, 58(10), 4601–4614. Barbier, E., Mensah, A., & Wilson, M. (2023). Valuing the Environment as an Input, Eco-system Services and Developing Countries. Environmental and Resource Economics, 84, 677–694. Carson, R., Mitchell, M., Hanemann, M., Kopp, R., & Presser, S. (2003). Contingent Valuation and Lost Passive Use: Damages from the Exxon Valdez Oil Spill. Environmental and Resource Economics, 25(3), 257–686. Costanza, R., Perez-Maqueo, O., Martinez, M. L., Sutton, P., Anderson, S. J., & Mulder, K. (2008). The Value of Coastal Wetlands for Hurricane Protection. Ambio, 37, 241–248. De Groot, R. Brainder, L., vsn der Ploeg, S., Costanza, R., Bernard, F., Braat, L., Christie, M., Crossman, N., Ghermandi, A., Hein, L., Hussain, S., Kumar, P., McVittie, A., Portela, R., Rodriguez, L., ten Brink, P., van Beukering, P. (2012). Global Estimates of the Value of Ecosystems and Their Services in Monetary Units. Ecosystem Services, 1, 50–61. De Groot, R., Brander, L., & Solomonides, S. (2020). Update of Global Eco-System Service Valuation Database. Foundation for Sustainable Development, Report 2020-06, Wageningen, Netherlands. Godoy, R., Overman, H., Demmer, J., Apaza, L., Byron, E., Huanca, T., Leonard, W., Pérez, E., Reyes-Garcia, V., Vadez, V., Wilkie, D., Cubas, A., Mcsweeney, K., & Brokaw, N. (2002). Local Financial Benefits of Rain Forests: Comparative Evidence from Amerindian Societies in Bolivia and Honduras. Ecological Economics, 40(3), 397–409. Karp, D. S., Mendenhall, C. D., Sandí, R. F., Chaumont, N., Ehrlich, P. R., Hadly, E. A., & Daily, G. C. (2013). Forest Bolsters Bird Abundance, Pest Control and Coffee Yield. Ecology Letters, 16(11), 1339–1347. Markandya, A. (2016). Cost Benefit Analysis and the Environment: How to Best Cover Impacts on Biodiversity and Eco-system Services. OECD Environment Working Paper 101, OECD, .

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Markandya, A., & Pearce, D. (1994). Natural Environments and the Social Rate of Discount. In J.  Weiss (Ed.), The Economics of Project Appraisal and the Environment. Edward Elgar. Mulwa, R., Kabubo-Mariara, J., & Nyangena, W. (2018). Recreational Value and Optimal Pricing of National Parks: Lessons from Maasai Mara in Kenya. Journal of Environmental Economics and Policy, 7(2), 204–222. Narayan, T., Foley, L., Haskell, J., Cooley, D., & Hyman, E. (2017). Cost-Benefit Analysis of Mangrove Restoration for Coastal Protection and an Earthen Dike Alternative in Mozambique. United States Agency for International Development. Climate Economic Analysis Development, Investment and Resilience (CEADIR). Nordhaus, W. (2007). A Review of the Stern Review on the Economics of Climate Change. Journal of Economic Literature., 45(3), 686–702. Nordhaus, W. (2017). Revisiting the Social Cost of Carbon. Downloaded from www.pnas.org OECD. (2018). Cost Benefit Analysis and the Environment: Further Developments and Policy Use, Organisation for Economic Co-operation and Development, Paris. Pearce, D., Atkinson, G., & Mourata, S. (2006). Cost Benefit Analysis and the Environment. OECD, 128–130. Pearce, D., Pearce, C., & Palmer, C. (Eds.). (2002). Valuing the Environment in Developing Countries. Edward Elgar. Pindyck, R. (2017). The Use and Misuse of Models for Climate Policy. Review of Environmental Economics and Policy, 11, 100–114. Ricketts, T., Daily, G., Ehrlich, P., & Michener, C. (2004). Economic Value of Tropical Forest to Coffee Production. Proceedings of the National Academy of Sciences, 101(34), 12579–12582. Sathirathai, S., & Barbier, E. (2001). Valuing Mangrove Conservation in Southern Thailand. Contemporary Economic Policy, 19(2), 109–122. Stern, N. (2007). The Economics of Climate Change: Stern Review. Cambridge University Press. Stern, N., J. Stiglitz, and C. Taylor. (2021). Economics of Immense Risk, Urgent Action and Radical Change. NBER Working Paper 28472. National Bureau of Economic Research. UN. (2021). Monetary Valuation of Ecosystem Services and Assets for Ecosystem Accounting, NCAVE and MAIA. UN. USAID. (2018). Integrating Ecosystem Values in Cost Benefit Analysis. USAID. Vogl, A., Bryan, B., Hunink, J., Wolny, S., Apse, C., & Droogers, P. (2017). Valuing Investments in Sustainable Land Management in the Upper Tana River Basin, Kenya. Journal of Environmental Management, 198, 78–91.

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Watson, K., Ricketts, T., Galford, G., Polasky, S., & Oniel-Dunne, J. (2016). Quantifying Flood Mitigation Services: The Economic Value of Otter Creek Wetlands and Floodplains to Middlebury, VT. Ecological Economics, 130, 16–24. Weikard, H., & Zhu, X. (2005). Discounting and the Environment: When Should Dual Rates By Used? Economic Modelling, 22, 868–878. World Bank. (2017). Report of the High-Level Commission on Carbon Prices. World Bank. Yingdan, M., Gao, L., Zhang, J., & Wang, J. (2020). Valuing Urban Air Quality: A Hedonic Price Analysis in Beijing, China. Environmental Science and Pollution Research, 27(2), 1373–1385.

CHAPTER 12

Financial Analysis of Projects

The techniques discussed in the earlier chapters apply to analyses in both economic and financial terms and both types of analysis are required. While this book focuses principally on economic analysis, the economic viability of projects depends on the financial sustainability of the management unit implementing and operating the project. The economic benefits will occur only if the financial resources are available to undertake the investment and maintain project operations. Project analysis at economic prices to judge whether an investment is worthwhile must be accompanied by an analysis at financial prices. For revenue generating projects, the financial return of the project must be compared with its financial obligations in order to assess its financial sustainability. Financial sustainability is taken up in the first section of this chapter. The second section relates to financial incentives. Economically worthwhile investments rely on different project participants for their implementation. Project participants will be taking a risk that their effort or financial resources will yield a return. A financial analysis should also be undertaken from the point of view of the major participants on whom the project relies, to assess the return on the commitments they make. For major investors this means analysing the return to their equity investment. For farmers or small business persons this means assessing the increase in their annual incomes generated by the project. In each case a judgement is necessary as to whether the financial incentive to invest funds and time will be worthwhile. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Curry, J. Weiss, Project Analysis in Developing Countries, https://doi.org/10.1007/978-3-031-40014-8_12

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Many publicly funded projects set charges for their outputs below full cost recovery levels. Some publicly funded goods have no charges at all. There are circumstances where economic efficiency is promoted through financial subsidies to specific activities that have substantial economic externalities. However, reliance on financial subsidies may undermine financial sustainability, especially where governments have difficulty in raising additional revenues. There should be an analysis of subsidy levels where there are no charges or where charges do not fully meet financial obligations, and of the financial and economic effects of changing the level of charges. Such an analysis can be combined with an analysis of the fiscal impact of a project. The third section of this chapter deals with the issue of cost recovery and subsidies. Project analysis for economic viability and financial sustainability is undertaken at constant economic and financial prices respectively. However, both domestic and international inflation, and exchange rate changes, can affect the financial returns to investors and the financial requirements of governments. Moreover, inflation can bring about a liquidity crisis in early project stages where revenues have not yet built up sufficiently to cover costs. The fourth section illustrates some effects of inflation on financial sustainability and incentives.

Financial Sustainability Financial sustainability will be ensured if a revenue generating project will be able to meet all its financial obligations. A means of assessing financial sustainability is to compute the return to the project at constant financial prices and compare the result with a standard thought to ensure financial stability. At the same time, computation of a financial rate of return for a project is the first step in understanding the distribution of financial benefits the project will generate. Table 12.1 shows a financial internal rate of return (FIRR) calculation for a railway project. The project involves construction of a new railway. It also involves additional investment expenditures for expanding the capacity of the existing railway lines with which it will connect. Taking all the investment and operating expenditures together at financial prices, the FIRR, based on the additional railway revenues the project will generate, is 9.3%. Is this financial rate of return sufficient to guarantee the financial sustainability of the project?

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Table 12.1  Financial internal rate of return: railway project Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Capital cost

O&M cost

570 1143 1600 1257 1143 0 480 480 480 480 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 20 32 230 303 374 429 495 539 582 626 645 667 686 706 727 739 749 762 774 786 798 810

Revenue

Net revenue

0 0 0 13 94 454 686 908 1066 1229 1394 1565 1737 1810 1882 1955 2026 2099 2128 2156 2185 2212 2241 2269 2298 FIRR

−570 −1143 −1600 −1264 −1081 224 −97 54 157 254 855 983 1111 1165 1215 1269 1320 1372 1389 1407 1423 1438 1455 1471 1488 9.3%

One basis for answering this question is to compare the FIRR for the project with the financial opportunity cost of capital (FOCC). This refers to the opportunity for returns given up when funds are committed to the railway project. It measures not the cost to the national economy, which is given by the discount rate, but the return to total capital resources invested in an alternative project at financial prices. It can be argued that unless at least the FOCC is generated by the project, the financial stability of the project may be threatened by two factors: first, the likelihood of owners and operators withdrawing their earnings for investment elsewhere so that replacement expenditures cannot be made during the project life; second, the need to rely on favourable lending terms that may not be renewed later in the project life. In our example, it is estimated that the financial

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Table 12.2  Comparators for financial indicators Indicator of financial worth

Comparator

Public funds Financial internal rate of return (FIRR) FIRR Net of tax FIRR Private funds Return on equity (ROE) Average incremental cost (AIC) Current price financial return

Financial opportunity cost of capital (FOCC) Financial cost of initial capital (FCIC) Net of tax FCIC Country/sector required return Financial charge or tariff Inflation adjusted FOCC/FCIC

opportunity cost of capital for the country concerned is 12%—that is, investors can generate a financial return in real terms of 12% through other investment opportunities. The FIRR is lower than the FOCC, meaning that the project may suffer from the subsequent withdrawal of financial support. Comparators for different financial return indicators are given in Table 12.2. There is another basis for comparison. The FIRR for the project can be compared with the actual financial cost of initial capital (FCIC) for the project itself. This value is a weighted average of the real costs of the equity and loans that will finance the project, and is illustrated in Table 12.3. The railway project will be undertaken with a mixture of government equity, domestic loans, and international loans. The government equity is associated with a notional financial return, while the domestic and international loans are at fixed interest rates, converted to real terms in domestic currency.1 The weighted average financial cost of initial capital is 5.1%, lower than the FIRR. This shows that the project will be able to meet the costs of the funds required initially to finance its investment. However, there still remains the risk that profits will subsequently be taken out of the project to generate a greater financial return elsewhere, rather than reinvested in replacement and expansion. In addition, this second comparison presumes that the funding arrangements for the project are already defined and agreed. If the funding arrangements change, the FCIC will also change. 1  There may be a required financial return for government corporate investment that can approximate this. In theory it will depend on how the equity is financed and the cost of raising additional funding.

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Table 12.3  Financial cost of initial capital (FCIC) Railway project Source

Amount

Weight

Nominal interest (%)

Inflation rate (%)

Real interest (%)

Equity Domestic loan

2593 1460

0.454 0.256

12.0 11.0

6.0 6.0

5.7 4.7

Foreign loan

1660

0.291

7.0

2.5

4.4

5713

Weighted average

5.1

Railway corporation Source Equity Domestic loan Foreign loan

Amount

Weight

Nominal interest (%)

Inflation rate (%)

Real interest (%)

2350 1460 1660

0.430 0.267 0.303

12.0 11.0 7.0

6.0 6.0 2.5

5.7 4.7 4.4

5470

Weighted average

5.0

Railway corporation (after tax) Source Equity Domestic loan Foreign loan

Amount

Weight

Nominal interest (%)

Inflation rate (%)

Real interest (%)

After income tax (%)

2350 1460

0.430 0.267

12.0 11.0

6.0 6.0

5.7 4.7

4.0 3.3

1660

0.303

7.0

2.5

4.4

3.1

5470

Weighted average

3.5

The two comparators for the FIRR, the FOCC and the FCIC, respond to different questions. The first answers the question: are investors in the railway likely to reinvest their funds in its operations to maintain its operations in the long run? The second answers a different question: are the financial returns sufficient to meet financial obligations to the equity owners and lenders? A positive answer to the first question generates greater security; if the FIRR exceeds the FOCC then in most cases involving

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public funds it will exceed the FCIC also, and the long-term financial sustainability of the project will have greater assurance.2 Two variations on the use of the FIRR for assessing financial sustainability of revenue generating projects can now be mentioned. First (as for the railway project), the overall project FIRR is not associated with a particular organisation or administrative unit. It combines the financial returns to the corporation that will operate the new railway as well as the organisation responsible for the rest of the railway system. These may not be the same entity. If the new railway line is to be operated independently, the financial sustainability of the new line itself requires a similar comparison, but excluding the associated expenditures on the linking lines for which it is not responsible. This comparison is presented in Table 12.4. Without the additional expenditures on the linking lines, the FIRR for the corporation that will run the new railway is higher than for the project as a whole; some of the project expenditures will be met by the railway ministry, not the new railway corporation. The FIRR for the railway enterprise is 9.6%. Adjusting the FCIC also to refer only to the new railway, the FIRR of 9.6% is now significantly higher than the FCIC for the new railway alone, which is only 5.0%. A second variation on the use of the FIRR is to adjust the calculations for taxation. In the case of the new railway, there is a 5% sales tax on all revenues. This will accrue to the government, not the railway corporation. Also, the net profits of the corporation will be taxed at a rate of 30% after allowing for initial losses. When these two taxes are subtracted from the revenues and added to the enterprise costs respectively, the net of tax FIRR for the railway corporation falls from 9.6% to 4.3%. At the same time, the FCIC should be adjusted. The interest payments on borrowing will be tax allowable. This will reduce the effective cost of borrowing, resulting in a FCIC after tax of only 3.5% (Table 12.3). The adjustment for taxes in both the calculation of the FIRR and the cost of initial capital focuses attention on the net returns to the railway corporation. However, it assumes the financial structure of the project is already known including the marginal rate of tax on net profits. Again, the first comparator for the FIRR, the FOCC, provides a surer assessment of financial sustainability than the project-specific FCIC comparator. 2  However, if investment funding for infrastructure comes from the private sector the required return of the investor may be very high because of the perceived risk. In such cases the FCIC will also be high and various guarantees may be needed from government on repayments to access funding.

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Table 12.4  Financial internal rate of return: railway corporation Year Net revenue 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

−570 −1143 −1600 −1264 −1081 224 −97 54 157 254 855 983 1111 1165 1215 1269 1320 1372 1389 1407 1423 1438 1455 1471 1488

Less links

Adjusted net revenue

0 0 0 140 103 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 FIRR

−570 −1143 −1600 −1124 −978 224 −97 54 157 254 855 983 1111 1165 1215 1269 1320 1372 1389 1407 1423 1438 1455 1471 1488 9.6%

Less business Less income tax tax 0 0 0 1 5 23 34 45 53 61 70 78 87 91 94 98 101 105 106 108 109 111 112 113 115

0 0 0 0 0 0 9 113 177 220 262 315 373 433 459 485 510 535 560 571 581 591 600 610 619 FIRR

Adjusted net revenue −570 −1143 −1600 −1125 −983 201 −140 −104 −73 −27 523 590 651 642 662 686 709 732 723 728 733 736 743 748 754 4.3%

Financial Incentives The FIRR is compared to the FOCC to assess whether a project will be financially sustainable. However, the cost of equity funds that is included in the FOCC depends upon the source of those funds. In most cases, the equity funds for public sector projects will be government funds in one way or another. These funds are more or less guaranteed once the government decides to go ahead with a project, and the government has the power to secure extra funds through changes in taxation and charges. However, governments often try to attract private sector funds into public

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provision of goods and services. This may require a reassessment of the level of financial returns that are necessary for a project to go ahead. The private sector will consider two key factors in deciding whether or not to invest in a public sector project. The first is the level of demand for the good to be produced and the financial return that will be generated for the investors. The second is the risk associated with private investment, not just the market risk but the risk of changes in government policy affecting ownership and use of profits. The latter reason in particular means that the cost of private equity funds in public sector projects is often considerably greater than the government’s FOCC. Whereas a government may have an FOCC of around 12%, a private investor in the same project may require a return of around 20% in real terms. This can raise the cost of initial capital significantly. The same applies for public sector corporations that have been instructed to operate on commercial terms, and which are seeking to expand their sources of investment funding through bond issues and other financing mechanisms. To assess the incentive for private sector involvement in a public sector project the rate of return on equity (ROE) should be calculated. The FIRR from the point of view of the implementing agency needs to be adjusted for loan financing and taxation. Loan inflows and loan payments at real interest rates should be incorporated into the project statement at constant financial prices. Tax payments should be calculated on the basis of the relevant tax regime, on a post-interest and depreciation basis, with both expressed in real terms. Table 12.5 provides an illustration for the railway project. The equity investment inflows are compared with the profit net of interest, tax, and loan payments. The IRR on this financial flow provides the real ROE, here calculated as 5.8% after tax. The real return on equity can be higher or lower than the constant price FIRR. For many public sector projects, where loans may be given at less than full market rates, the return on equity is likely to be above the FIRR. In the common case, as in the railway illustration, the FIRR is greater than the real financial cost of initial capital. The FIRR can be envisioned as a weighted average of the real return to lenders and to owners. If the FIRR is greater than the financial cost of initial capital, then the return on equity is likely to be greater than the overall FIRR. The only adjustment that may intervene in this situation is deduction of taxation to the government. This may reduce the return on equity also below the FIRR. If the FIRR is already below the financial cost of initial capital then the return on equity will be even lower, both before and after tax, and the

Loan inflow

Loan payments

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

570 1143 1600 1257 1143 0 480 480 480 480

0 0 0 20 32 230 303 374 429 495 539 582 626 645 667 686 706 727 739 749 762 774 786 798 810

0 0 0 140 103

570 1063 0

0 50 1252

0 0 120 117 115 112 109 106 104 101 99 96 94 92 90 87 85 83 81 79 77 75

0 0 0 116 109 103 97 92 87 82 77 73 69 65 61 58 54 51 0 0 0 0 0 0 0

0 0 0 1 5 23 34 45 53 61 70 78 87 91 94 98 101 105 106 108 109 111 112 113 115

0 0 0 0 0 0 9 113 177 220 262 315 373 433 459 485 510 535 560 571 581 591 600 610 619

0 0 0 13 94 454 686 908 1066 1229 1394 1565 1737 1810 1882 1955 2026 2099 2128 2156 2185 2212 2241 2269 2298 ROE

0 −30 −468 −1358 −1207 −14 −347 −303 −264 −210 437 421 489 485 511 541 569 598 641 649 656 661 743 748 754 5.8%

Year Capital cost O&M cost Less links Foreign Domestic Foreign Domestic Business tax Income tax Revenue Return on equity

Table 12.5  Return on equity

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project will not be attractive to the private investor in any case. In this example, the post-tax FIRR (4.3%) is greater than the real cost of loans (a little over 3%), while the return on equity after tax and loan payment is higher than the post-tax FIRR (at 5.8%). There is no unique rate with which to compare the ROE even for a particular country. The basic question is whether the return on equity is sufficient to attract private investment funds into a particular sector. Each sector will have a different level of risk associated with its operations, and therefore the required return on equity may vary from case to case. In general, the ROE required to attract private investment funds is higher than the FIRR necessary to cover the financial cost of initial capital of publicly funded projects and projects may require some form of guarantee of charges or quantities. This raises questions about tariff levels and structure and the estimated demand for goods and services at different tariff levels. The ROE can be calculated for different assumptions about the level of tariffs and the level of demand. Adjustment of the financial returns calculations for taxation allows different tax regimes to be compared. There are different combinations of indirect and direct taxes that can be imposed on a project, and different methods for imposing the taxes. Each will have an effect on the government budget, revenues on the one hand, and the investor’s after-tax equity return on the other. The possibility exists for negotiation that will arrive at a tax regime that is favourable to both parties. In any project, the government’s financial requirements may not be the same as the investors. For example, the government may be willing to accept a flow of tax revenues equivalent to a government sector financial return of 12%, while private investors want a return of 20% because of risk factors. With its higher discount rate, tax flows in later years will be worth less in present value to the investor than to the government. Hence different tax regimes can be compared on the basis of the worth in present value of the tax payments to the enterprise and the worth of the same tax receipts to the government, using different financial discount rates. Not all participants in a project will think in terms of formal rates of return calculations, so this type of analysis is only relevant for the institutions and formal sector investors involved. However, for example, farmers whose additional output in response to the availability of irrigation water, or small-scale suppliers of made-up cloth to a textile factory, need to respond positively to the incentives created by a project, if the target rates of return are to be achieved. They will need to make extra effort and invest

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some of their own funds and this requires a judgement on whether the activity is worthwhile. The annual income they will expect to receive needs to be high enough to justify the effort and cost involved. They may not do this formal calculation themselves, but where changes in behaviour by large numbers of dispersed small producers are required, the project sponsors need to be confident that the expected future income to participants is high enough for them to perceive it as worthwhile. The viability of a project depends upon an adequate level of financial incentive for each project participant. A financial analysis of the project from the point of view of different project participants is an integral part of project analysis, and must accompany the calculation of expected economic returns.

Charges and Cost Recovery The level at which prices or tariffs are set can have major implications for the financial analysis of public sector projects; there are many services publicly funded that make direct charges on their users. While privately funded services and goods also inevitably involve charges for their outputs, the economic principle behind any charging is that the charges set should be equal to the marginal costs of providing the good or service. In some circumstances the marginal cost will include only a very low value of additional operating and maintenance (O&M) costs. However, where new investment is required, the marginal cost also includes an annualised value of capital costs to expand the supply. Often, this principle is not observed in practice. For public goods, where it is not possible to exclude any who wants to use a facility and where one user does not preclude use by others, the marginal costs for each user may be very small. For example, the marginal costs of using a public road are the extra maintenance required as a result of one extra vehicle moving along it. However, there are transactions costs to suppliers and users of implementing a charge. The transactions costs of charging may be much greater than the marginal cost for each user and therefore it is not worth installing a charge system on such public goods. The same may be true in the areas of basic health and basic education. Hence the benefits from expanding the provision of such goods will occur as external benefits to the users. Such external benefits, where transactions costs exceed the marginal costs of supply, provide a justification for subsidised provision of public goods.

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Another circumstance in which the principle of setting charges equal to marginal costs of supply is ignored is when goods and services are set aside for provision by the private sector at a minimum expected financial rate of return. This rate of return will have to include the recovery of operating and maintenance costs and a return on the investor’s capital costs. For a privately funded toll road, for example, the toll levels may be set well above the marginal costs of supply because of these financing requirements. The marginal costs of supply for a good or service are not fixed for ever. They can change as technologies change. For example, the marginal costs of power generation may change because of a shift from coal to gas, or other energy sources, or the construction of a new generating plant. The marginal costs of health provision may change because of a reorganisation and decentralisation of the health service. The marginal costs of water supply may change through the introduction of private sector management techniques and incentives. In the setting of charges, due recognition should be given to ways in which institutional structures and performance can be improved to reduce the future costs of supply. The marginal costs of supply are also determined by the structure of the market. In a competitive market it can be expected over time that the marginal costs of different suppliers would converge, and would also reduce as new technical and managerial technologies are introduced. On the other hand, where supply is largely monopolised in public or private hands, as in the provision of many public goods, financial charges may be fixed in the medium term through the supply contracts entered into between government and suppliers and the regulatory regime that has been imposed, and may not reflect marginal costs. Where supply is partly in private hands, such as for toll roads, and partly in public hands, such as for public roads, it is not only that the marginal costs of different roads are not accurately reflected in charges, but also the relative costs of using one road rather than another, and hence the distribution of traffic between them, will not be adjusted to the relative marginal costs of the roads. In comparing charges with costs, the relevant costs are the costs of the least cost option for expanding the supply of a good or service. In project analysis, the long-run marginal least cost of supply can be approximated by the annualised supply costs of an investment project. For example, if an urban water supply project is being analysed to increase the treated water supply, the annualised cost of supply can be calculated by comparing the costs of the project with the extra water it will supply. This is illustrated in Table 12.6. The capital and operating and maintenance (O&M) costs of a

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water supply project are discounted to provide a total discounted project cost. The water to be provided by the scheme is also discounted. The discounted project cost can be compared with the discounted value of the water and the cost per m3 of water can be calculated, generally referred to as the average incremental cost of supply (AIC).3 Hence



AIC 

PV  Ct 

PV  Qt 

where PV is discounted present value over t years, Ct is project cost in year t and Qt is quantity of output in year t. If AIC is at financial prices a financial discount rate (FCIC as defined above) should be used. A similar calculation can be carried out for an economic AIC using economic prices and the economic discount rate for the project. The example of Table 12.6 shows the calculation of average incremental cost at financial prices. The tariff which will cover cost at a 6% discount rate is 33 cents (300.19/913.38) and one which will cover only O&M costs is 13 cents (117.64/913.38). Where charges for a project output using the least cost technologies do not ensure the financial sustainability of the project from direct revenues, there are two options. The first is to provide financial subsidy to the project. This may involve an additional economic cost. This economic cost is related to the effects of the financing of the subsidy. How would the government finance an additional subsidy payment? Either it will increase charges to another category of users, ensuring cross-subsidies at charge levels higher than the marginal costs of supply for those other users; or it will increase tax revenues; or it will finance the additional expenditures through deficit financing. Each of these options has its own effects. Some argue that imposing extra taxes to finance the subsidy payments in particular will increase the economic costs associated with government intervention in the economy. However, such arguments can be applied to all forms 3  The fact that quantity of water is also discounted often causes confusion. The AIC should be interpreted as the charge which if imposed on output in each year of the project life will allow the project to cover all its costs, both capital and O & M. To derive this the AIC must be the ratio of discounted costs to discounted physical output.

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Table 12.6  Average incremental cost illustration (at constant financial prices)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 PV at 6% AIC at 6%

Cost

$ mill

Capital

O&M

39 90 73 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 3 6 6 7 7 8 8 8 9 9 9 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 117.64 0.13

Total

Quantity m3 millions

39 90 73 23 3 6 6 7 7 8 8 8 9 9 9 10 10 10 10 44 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 300.19 0.33

0 0 0 0 50 60 62 64 66 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 913.38

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381

of government expenditure, and do not need to be associated with a specific project investment. The second option in a situation of financial loss is to change the charges, which will be the response for a private sector project. In some circumstances, an increase in charges may resolve the question of financial sustainability. However, the increase in charges may also reduce the economic benefits of the project. Where demand is sensitive to charge levels, an increase in financial charges may involve a significant reduction in demand and, where the value of output is greater than its cost, a reduction in net benefits. Partly, this option may be undertaken deliberately, under programmes of demand management. If an increase in water charges, for example, reduces the demand for water, it may be possible to delay expansion investments, at an economic saving greater than the loss of economic benefits through increasing charges. The option of demand management should always be considered as an alternative to, and in conjunction with, the timing of investments. Financial charges could also be reduced. In competitive circumstances, for example, in competition between rail and road services for passenger traffic, a reduction of rail passenger charges may increase the number of passengers and the revenues from passenger services, especially long-­ distance services that compete with growing road services. If this option is taken to try and ensure the financial sustainability of a particular service, consideration must also be given at the same time to the quality of the service, in terms of reliability, speed, and safety, against which the changes in charge levels may be set. Whatever option is taken, as noted at the outset of this chapter, the financial sustainability of a project is critical for success. Where the AIC is below the tariff either a long-term project subsidy must be guaranteed or ways found of removing the divergence between tariff revenue and costs.

Price Changes Calculation of a financial and an economic rate of return for a project is generally done using constant prices—that is, ignoring the effects of future price changes. However, prices do change. In particular a financial analysis of a project may look significantly different if expected price changes are built into the analysis than if they are not. The focus finally is still on the real rate of return, economic and financial, that a project will generate and whether this is sufficient to justify the investment. However, the inclusion

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of price changes in the analysis may even affect these basic project decisions in some cases. The main reasons for undertaking a project analysis in current prices rather than constant prices are to establish the financial sustainability of a project through identifying the full amount of project financing that will be required, and to identify the full distribution of project effects accepting that price changes can affect different project participants differently. There are three components of price changes that need to be considered. First is the question of relative price changes. As noted in Chap. 2, relative price changes can and should be included in constant price project analyses anyway and correspondingly in a current price analysis. Relative price changes refer to the rate of change of a future price, for a project input or output, relative to the rate of change of prices in general. If the general rate of change of future prices is expected to be R% per annum over the life of a project, and a specific output price is expected to increase at P% per annum, then the relative future price change for the output is given by:



 1  P   1 Rate of relative price change    1  R     with all in percentages expressed as a decimal. This relative price change can be positive or negative. The financial sustainability of a project can be affected especially by relative price changes between output prices and input prices. Where relative price changes have already been incorporated into the constant price project statements, there is no need for further adjustments for this effect when constructing the project statements in current prices. The second issue relating to price changes is that of the expected increase in prices in general, that is overall inflation. This rate of increase, R% per annum, needs to be applied to all project outputs and inputs to construct a current price project statement. Thirdly, the exchange rate between domestic and foreign currency may change. This will also affect the project financial flows in the future, both the future financial flows for project outputs and inputs and the future flows for foreign loan inflows and payments. The future exchange rate is more difficult to predict than the rate of general price changes. It may be affected by several factors: such as the relative price changes between

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international and domestic goods, changes in productivity, and the extent of foreign exchange inflows to a country such as remittances and capital flows. A common assumption is that the real exchange rate remains constant in the future, meaning that the relative purchasing power between domestic and international goods remains the same.4 A simple interpretation of this is that the project or official exchange rate (OER), will adjust over time to the difference between the rate of general price increases in the international economy, S% per annum, and the domestic economy, R% per annum. The future exchange rate is then given by:  1  R t Future exchange rate, year t  OER    I  S  t 

   

with all percentages expressed in decimals. This particular assumption and calculation is consistent with economic analyses that implicitly assume a constant real exchange rate, that is, a constant ratio between the value of traded and non-traded goods over the life of a project. The rate of general price increase and future exchange rate changes can be included in project statements drawn up at current prices, and a comparison made with those drawn up at constant prices. To do this requires the following procedures. First, the constant price resource flows at financial prices, which already incorporates relative price changes, must be adjusted for the rate of general price increases. This means that all project outputs and inputs must be adjusted upwards over time at the general rate of inflation per annum. Second, depreciation allowances that were previously used after inflation adjustment, so they decline in real terms each year, must be entered at their full nominal value. Third, and similarly, interest payments that previously used an inflation adjusted interest rate must now be now entered at the full nominal interest rate. Finally, all foreign flows, whether resource flows at financial prices or foreign loan inflows and outflows, need to adjusted by an adjustment factor representing the difference between future domestic and international price increases, in short, they require adjustment for a constant real exchange rate scenario.

4

 See the discussion on the shadow exchange rate in Chap. 5.

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Incorporation of these price changes will have several effects. First, the financing requirements for the project are increased. This relates to the increase in domestic prices and also to changes in the nominal exchange rate. Second, the liquidity of the project can be affected, especially in the transition between construction and operation, where the financing requirements for initial inventories and working capital add to the investment financing requirements. Third, the value of foreign loan payments can be affected by a change in the exchange rate. If the exchange rate depreciates loan inflows in earlier years will yield lower domestic currency amounts per unit of foreign currency than the loan payments to be made in future years. Hence if there is a real currency depreciation, project financial sustainability will be undermined for those projects using foreign loans to finance sales in domestic currency, and will be enhanced for those projects generating foreign currency revenues. Where the domestic currency appreciates the reverse will be the case. Fourth, the extent to which the project will pay income taxes will depend on the effects of price and exchange rate changes on depreciation allowances, especially where these are based on historic costs, and on interest and loan payments. All these effects combine together to influence the project financial performance at current prices and will determine whether in any year of operations the project’s funding requirements exceed its income from sales, equity, or loans. With a negative net cash flow in any year, additional funds must be sought. The inflation adjustments described above, also influence the financial rate of return on a project from the point of view of different participants. The key participants here are investors, lenders, and the government. Price increases can shift the distribution of financial benefits in the direction of the government, through the effects of tax regulations including depreciation allowances. Price increases can also shift the distribution of financial benefits towards investors, as the real costs of loan payments diminish over time. In addition, the overall financial return on a project as well as its distribution between these different parties, can be affected positively or negatively by significant differences in inflation rates between countries and hence exchange rate changes. Project statements incorporating price increases yield measures of financial worth that need to be compared with discount rates that are also adjusted for inflation. For example, a real discount rate of 12% adjusts to a nominal discount rate of 21% if there is an expected increase in general prices of 8%, calculated through:

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Nominal discount rate   1  0.12   1  0.08    1

385



Hence whereas a constant price project statement may be discounted at 12%, or the financial return compared with 12%, the current price project statement should be discounted at a rate of 21%, or the nominal financial return compared with a discount rate of 21%. Having noted all this, in general, a constant price analysis will yield an adequate result for assessing financial sustainability and incentives, especially where price increases are expected to be fairly low and exchange rates fairly stable. It is only in a high inflation environment, where significant price increases and corresponding changes in exchange rates are expected, will it be necessary conduct a current price analysis. However, given uncertainty in predicting inflation, the most important aspect of this analysis will be a focus on the short-term cash flow position of a project rather than a longer-term analysis over a project’s working life. Hence calculation of current price NPV and IRR indicators will be both difficult and highly uncertain and should not be the central focus of a current price analysis. Whilst strictly a full analysis of the distribution of financial benefits requires inflation adjustments, doing this accurately over the full life of a project requires very strong assumptions about the accuracy of inflation projections and will be very uncertain. In practice it is rarely carried out.

Conclusion The economic benefits of a project will occur only if the implementing agencies have sufficient financial resources to sustain their operations. For revenue earning projects the financial return at constant prices can be calculated and compared with the cost of capital. This comparison can be affected substantially by whether the implementing agency is subject to income tax or not. Where private sector funds are used in public sector projects, the required financial return to provide sufficient incentive to invest will be higher. Many goods and services are provided by the public sector with a charge on the user. The user charge may differ from the marginal costs of supply in financial or economic prices, for many reasons; where transaction costs are too high, where the market is monopolised, or where a specific financial return is required as a financial incentive. A comparison between the average incremental cost of supply and the proposed charges will indicate

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any subsidy that will have to be met from general government reserves. The raising of charges to try and reduce a financial subsidy will be at the cost of some decline in demand and therefore economic benefits. Price changes, and especially exchange rate changes, may undermine the financial sustainability of a project. To complement the calculations of financial returns and subsidy levels at constant prices, a similar calculation at forecast current prices and exchange rates will show whether a project will have sufficient funds to meet all its obligations. It also provides a basis for fully estimating the distribution of financial returns between different project participants.

Further Reading Jenkins et al. (2018). Chapter 3 discusses financial analysis as prelude to a full economic analysis. Potts (2002) discusses financial analysis in chapter 5, while chapter 12 is a useful discussion of the impact of inflation and exchange rate changes. For an example of an operational manual for the financial analysis of projects, see ADB (2019).

Bibliography ADB. (2019, October). Financial Analysis and Evaluation: Technical Guidance Note. Asian Development Bank. Jenkins, G., Kuo, C.-Y., & Harberger, A. (2018). Cost Benefit Analysis for Investment Decisions. Cambridge Resources International. Potts. (2002). Project Planning and Analysis for Development. Lynne Rienner.

CHAPTER 13

Income Distribution Effects of Projects

Projects can be analysed from different perspectives. So far, the discussion has focused primarily on their effects in terms of resource use. Projects are assessed on the efficiency with which they use existing resources—an acceptable project being one which generates more total national income than could be obtained by committing the same resources elsewhere. This chapter focuses on a project’s effect on the distribution of income. It is possible to trace the distributional effects of projects and to identify gaining and losing groups or stakeholders. This should be an important piece of information for decision-takers, since most projects will have negative or positive income effects for different groups. Estimates of income changes created by a project can be given as additional information alongside the standard indicators of project worth from economic and financial calculations. Distribution analysis can inform both project design and the choice between competing projects. If target groups, such as the poor, are found initially to gain only a modest proportion of project benefits this may lead to a redesign of the project. Similarly, where different projects offer alternatives for reaching a target group, distribution analysis can be used to assess their relative effectiveness and cost. In addition, it can be used to explore the consequences of alternative choices in a project, for example, different levels of tariffs for water or power charges. Finally, it is possible to explore the trade-off between growth and distribution

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Curry, J. Weiss, Project Analysis in Developing Countries, https://doi.org/10.1007/978-3-031-40014-8_13

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objectives formally through a weighting system; the methodology for doing this is explained in the second Appendix to the chapter. The chapter begins by discussing the type of project likely to create significant changes in the distribution of income. It then discusses the net financial flows created by the financing arrangements of a project, before considering how information from an economic analysis, on divergences between financial and economic prices, can be used to identify further changes in project economic flows. These can be combined to identify the income changes resulting from a project. These are discussed at four levels: between classes and groups, between the poor and the non-poor, between savings and consumption, and between nationals of an economy and foreigners. The methodology involved is illustrated in a case-study.

Projects with Important Distributional Effects The procedures outlined below can be applied to any type of project. Projects which have a positive impact on the distribution of income in a specific country are those where net benefits are distributed in a more egalitarian manner than is average income. Such projects will be those for which a significant part of benefits go to lower income and under-­privileged groups, such as unskilled workers, small farmers, artisans, small traders, and low-income consumers. The type of project likely to have this impact will possess several characteristics: • employment of relatively large numbers of unskilled workers, in either construction or operations • production of consumption goods for low-income groups, which were either previously unavailable, or only available at a higher price • production of intermediate inputs used by low-income producers that were either previously unavailable or available only at a higher price • generation of backward linkages so that additional jobs can be created for unskilled workers in domestic activities that supply inputs to the new project.1 1  It is linkages between a project and suppliers of non-traded goods that are relevant. Where traded inputs are involved, it is normally assumed that additional demand from a project will affect the balance of payments (with more imports or less exports) rather than domestic supply.

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389

From this list of characteristics, it should be clear that what is important from a distributional point of view is not the sector of investment, but the type and design of the project involved. For example, although it is sometimes implied that investment in agriculture has more beneficial distributional effects than investment in industry, this is by no means inevitable. Investment in a mechanised or estate farming system may be less progressive in distributional terms than investment in industrial projects for relatively simple low-income goods, utilising labour-intensive techniques of production. In addition, as discussed below, the financing arrangements involved whether with foreign or local sources of finance, can also affect a project’s distributional outcome.

Estimating Income Flows from Financial Statements The starting point for an analysis of the income effects of projects must be the results of an analysis at constant financial prices, since financial prices determine the money revenues and costs of projects. The largest income flows from financial statements are likely to be the profits received by project owners, taxes paid by a project to the government or subsidies received by it, and any income transfers arising from the financing arrangements of a project. In terms of the mechanics of tracing distributional effects, it is easier to work with discounted present values for revenue and cost flows of the project, and then to allocate both the financial and economic price NPV of a project into net flows to different groups. This means that instead of calculating the income effects for an individual year, all annual project costs and benefits are converted to a present value by discounting. This greatly simplifies the treatment of distributional issues, although it will not be strictly accurate where the relationship between financial and economic prices for individual items changes over time, in which case conversion factors (CFs) involved are not constant. However, provided one can assume constant CFs the calculation of all cost and benefit streams in present values will be the appropriate starting point for distributional analysis. The exact income groups used for the identification of income flows can vary between projects but it is desirable to have some consistency, so that the comparative position of different projects can be assessed. Normally it would be desirable to include the following groups:

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• The government, as the origin of taxes and subsidies and the owner of public sector projects. There is an ambiguity regarding the treatment of government income, since in principle if additional income changes for the government are large enough, they could affect tax payments, with higher government income used to reduce taxes, and lower income causing a rise in tax rates. Where tax changes follow a change in government income the ultimate gainers and losers will be tax payers. However, if one assumes that no individual project is large enough to affect tax policy this complication can be avoided, and all changes in government income can be treated as income effects for the government. • Equity owners of projects, who in some cases will be the government. • The rest of the private sector—that is, owners of enterprises other than project equity owners, who may supply a project with inputs or use its output. • Various households who will be affected directly or indirectly as suppliers of labour to a project or as purchasers of output; these may be distinguished by income level, whether they are above or below a poverty line, or perhaps by region of location; • Lenders of financial capital, who could be either the banking sector or private depositors. Which group is relevant depends on who bears the cost or receives the benefit of the income transfers that arise from the financing of a project. For example, where the real interest rate of a loan to a project is below the financial opportunity cost discount rate, that is, a rate fully reflecting the financial cost of funds (so the project is being subsidised), and where the banks pass on this low interest charge to savers as a low deposit rate, this will be a cost to depositors. Alternatively, if in these circumstances savers receive a real deposit rate equal to the financial discount rate the banks themselves will be meeting the cost of supplying cheap credit to a project; • Foreign income recipients, such as foreign equity holders or foreign workers. The allocation of the income changes that can be identified in an analysis at financial prices is illustrated here using a simplified example of a power project.

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

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Power Example: Cost and Benefit Flows at Financial Prices The project introduces electricity to households in previously unconnected areas. Approximately 10% of project output is non-incremental in that an equivalent power usage would have been provided from non-grid sources (such as kerosene or fuel wood). On a kwh basis the cost from these sources is 20% above the national power tariff. Willingness to pay for new users is estimated to be 10% above the tariff they would have occurred without the project. Investment takes place in year 1 at a cost in constant prices of Pesos 40 million. Operating costs are constant at Pesos 3.35 million per year. Operating life is 10 years from year 2 to 11. There is no terminal value for project assets. The project is run by a private company and the capital cost is financed 50% by equity and 50% by a loan. The loan is at a 5% interest rate from a Development Bank and is repayable over five years. Profits tax is at a rate of 20% and a depreciation allowance, calculated on a straight-­ line basis, is allowed as a deduction against tax liability. The project’s financial NPV represents the surplus of income over cost with both measured at constant financial prices—that is, the prices actually received or paid by the project. It thus represents an income gain that will go to different groups depending on the financing and taxation arrangements of the project. Using a 10% discount rate to reflect the opportunity cost of funds, in this example, the financial NPV is Pesos 0.78 million. The financial IRR is 10.5%. The project owners do not receive all of the net returns from the project since they must repay the loan of Pesos 20 million and pay Profits tax to the government. However equally they do not finance all of the investment cost of the project since their equity is only 50% of project cost. Table 13.1 gives the relevant project financial flows. Project owners gain the discounted value of the annual returns to equity. The present value of this income stream is an income in excess of what would be generated by investing Pesos 20 million elsewhere at 10%. The government gains the discounted value of annual profits tax payments. However, as the ultimate owner of the Development Bank, which is offering low-cost credit, it loses from the interest rate subsidy. At this stage we do not identify other income effects for the government, which will include indirect tax payments on inputs used by the project. These are captured in the economic calculations and are discussed below. Finally, there is an interest rate subsidy for this project, since the loan carries a real

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Table 13.1  Financial analysis: power project (constant financial prices) (Pesos million) Investment costs Traded Non-traded Unskilled labour Total investment costs Operating costs Traded Non-traded Unskilled labour Taxes Total operating costs Loan Loan repayments Interest Principal Depreciation Revenue, of which Incremental sales Non-incremental sales Taxable profit Profits tax at 20% Net return to equity NPV to equity Net return to project NPV to project

1

2

3

4

5

6

7

8

9

10

11

1.68 1.34 0.17 0.17 3.35

1.68 1.34 0.17 0.17 3.35

1.68 1.34 0.17 0.17 3.35

1.68 1.34 0.17 0.17 3.35

1.68 1.34 0.17 0.17 3.35

1.68 1.34 0.17 0.17 3.35

1.68 1.34 0.17 0.17 3.35

1.68 1.34 0.17 0.17 3.35

1.68 1.34 0.17 0.17 3.35

1.68 1.34 0.17 0.17 3.35

0

1 4.00 4 10

0.8 4.00 4 10

0.6 4.00 4 10

0.4 4.00 4 10

0.2 4.00 4 10

0 0.00 4 10

0 0.00 4 10

0 0.00 4 10

0 0.00 4 10

0 0.00 4 10

0 0

9 1

9 1

9 1

9 1

9 1

9 1

9 1

9 1

9 1

9 1

0 0 −20

1.65 1.85 2.05 2.25 2.45 2.65 2.65 2.65 2.65 2.65 0.33 0.37 0.41 0.45 0.49 0.53 0.53 0.53 0.53 0.53 1.32 1.48 1.64 1.80 1.96 6.12 6.12 6.12 6.12 6.12

0.46 −40

6.65 6.65 6.65 6.65 6.65 6.65 6.65 6.65 6.65 6.65

30.00 6.00 4.00 40.00

20.00

0.78 (Pesos million) Financial NPV of which Project owners (equity) Project lenders Government

0.78 0.46 −2.20 2.52

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

Table 13.2 Income changes from financial NPV

393

(Pesos million) Financial NPV at 10% Of which Project equity owners Project lenders Government

0.78 0.46 −2.20 2.52

interest charge of 5%, whilst the opportunity cost of funds is 10%. This situation could arise for example due to controls on interest rates or to the impact of inflation, here assumed to be zero for simplicity.2 In this case there will be a loss to lenders given by the difference between the discounted value of the loan and the discounted value of the stream of annual principal and interest repayments. This loss is a transfer between lenders and the project owners and hence is not part of the net income flow given by the financial NPV.  The present value at 10% of this set of income changes is shown in Table 13.2:

Estimating Income Flows from Resource Statements In addition to the flows created at financial prices, it is also necessary to identify the transfers arising as a result of divergences between financial prices and economic prices. This is because economic prices reflect the opportunity cost of utilising resources on a project, and the benefits of producing output. In other words, as has been discussed in earlier chapters, economic not financial prices cover the real income effect of a project. If an input costs more at financial than at economic prices—for example, because of a tax—the real income loss to the economy owing to the use of the input is less than the value placed on it in financial analysis. A project which pays the financial price for the input loses this value, so that someone else must gain the difference between the full opportunity cost and that met by the project. Where a tax is involved, this will be a gain to the government. Similarly, if the economic price of an output is above its financial price, there will be an income gain to the economy not captured in the financial calculations. Again, some group must gain this additional value. 2  Inflation reduces the real value of items fixed in money terms, like loan repayments and depreciation allowances.

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These points can be summarised by stating that divergences between financial and economic prices are due either to distortions in the functioning of markets or to the existence of externalities. Someone must either gain or lose from distortions or externalities and their income change will not be picked up when, as above, income flows are identified from the financial analysis. Hence it is necessary to focus on the divergence between financial and economic values and the income changes these create. A more formal way of making this point is to use the identity. NPVecon  NPVfinan   NPVecon  NPVfinan  ,



(13.1)

where, NPV econ is the economic NPV that gives a project’s addition to national income and NPV finan is the financial NPV. To trace the full distributional effect of a project requires first establishing the income flows from NPV finan and then those that arise from the term in brackets in (13.1) (NPV econ − NPV finan).

Economic Prices and Income Flows Identifying the difference between economic and financial prices for traded goods requires a comparison between border parity prices, as defined in Chap. 5, and prices received or paid by a project. The main source of divergence will be trade taxes and controls, which create gains to the government and traders who have access to scarce imports. In addition, in any economy with a significant degree of protection there will be a significant economy-wide divergence between financial (that is, domestic) and economic (that is, largely world) prices for traded goods.3 In such a situation there will be an income effect not reflected in the financial price calculations. What is involved is a premium value on traded relative to non-traded goods. The conventional procedure is to treat it as a form of tax on the suppliers of traded goods, such as exporters and foreign suppliers of capital, and correspondingly a form of subsidy for the users of traded goods, like importers. The tax and subsidy will be administered by the central bank through the exchange rate it offers for foreign currency and 3  This overall divergence lies behind the concept of a shadow exchange rate factor (SERF) and its inverse the standard conversion factor (SCF).

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395

hence the net income change created will fall on the government.4 Where the exchange rate is not misaligned with the real value of foreign currency, no income effect will be created. For non-traded goods, different cases can be identified. For incremental non-traded inputs in variable supply, their cost must be decomposed, and inputs valued at economic prices. Divergences between economic and financial values will arise through the effect of taxes and controls on trade, through the existence of surplus profits, and through the payment of market wages above the productivity of workers in alternative occupations. These divergences will create income changes for the government, traders, private capitalists, and labour. Where non-traded non-incremental outputs and inputs in fixed supply are involved, in theory valuation should be based on consumer willingness to pay. Where the latter exceeds the financial price there is an extra gain (for an output) or cost (for an input) not reflected in the financial calculations. A detailed distributional analysis would try to identify the pattern of use of the non-traded goods concerned, and attribute these additional gains or losses to different groups. Finally, a direct income effect created by the divergence between financial and economic prices is through the employment effect of projects. Where there is significant under-employment of labour, opportunity cost measured by output forgone in a worker’s alternative activity generally will be below the financial wage on new projects. This implies that workers will experience an income gain as a result of moving to a new project, given by the difference between their new wage and that in their alternative employment.5 In addition government income can also be affected by additional employment. Where workers previously produced traded goods, so that their output forgone is in terms of traded commodities, it is often assumed 4  This assumption is a simplification. For example, where controls in the form of quotas are used to restrict imports the foreign exchange premium will go in part at least as surplus profits to quota licence holders. In addition, as discussed briefly in Chap. 5, part of the foreign exchange premium may be caused by a fundamental misalignment of the real exchange rate. This may be dealt with by increasing trade taxes, in which case the conventional assumption regarding government income may be valid. On the other hand, the policy response may be a deflationary policy to reduce domestic income in general to support the misaligned official exchange rate. In this latter case the additional income loss will be spread widely between residents of the country. Hence the view that it is government income alone that is affected by the aggregate divergence between domestic and world prices will be true only under certain restrictive assumptions. 5  The cost of the additional consumption this will allow is discussed in detail in the Little and Mirrlees (1974) approach to the shadow wage and is summarised briefly in Appendix 13.1.

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Table 13.3  Decomposition of project costs and revenue

Traded Non-traded Labour Taxes

Investment (%)

Operating cost (%)

Revenue (%)

75 15 10 0

50 40 5 5

0 100 0 0

that the drop in domestic output that occurs when workers move to employment on a new project is made up by expenditure on additional imports. Thus, the government will gain from the tariffs collected on the imports. The logic of these arguments can be illustrated by extending the previous power project example. Now we decompose the investment and operating costs and project revenue into different resource categories to allow for their economic valuation. The breakdown is given in Table 13.3. The traded element of costs refers to imported equipment required for the investment and spare parts for operations. Non-traded inputs cover locally supplied materials. Labour required for both the investment and operations phase is all unskilled. Project benefits are 90% incremental, valued through consumer willingness to pay, and 10% non-incremental valued through cost savings. Both are treated as wholly non-traded, since for simplicity it is assumed that either there is no foreign exchange element in costs. The economic analysis is conducted using the domestic price numeraire, with a shadow exchange rate (SER).6 For a distributional analysis, use of domestic prices as the numeraire has the important advantage that the financial and economic analyses will be at the same price level, since financial values, reflecting prices actually charged, will be in domestic units. Hence, when the domestic numeraire is applied, the economic calculation of income changes arising from the financial flows can be added directly to

6  It is simpler to use distribution analysis in conjunction with this unit. If world prices are used there is an extra step which is often misunderstood. This involves converting financial price data to the world price level by multiplication by the standard conversion factor. Only then will the income changes from the financial analysis be comparable with the economic price values. Hence to apply Eq. 13.1 requires that NPV finan and NPV econ both be at world prices.

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

397

those arising from the distortions and externalities that are captured in the economic analysis. In the economic analysis of the power example, the following procedures are adopted. First, for the traded inputs to the project, for simplicity all transport and distribution costs from the port to the project are ignored, with traded items treated as wholly foreign exchange costs. A more detailed analysis could disaggregate between the foreign and non-traded components of traded items valuing the latter separately. Secondly, it has been calculated that the overall average divergence between domestic and world prices for traded goods in the economy is 15%. This implies a ratio of the shadow to the official exchange rate of 1.15, and a foreign exchange premium value, of 15%. The implication is that when the central bank supplies foreign currency to importers, the importers are receiving a 15% subsidy; conversely exporters are in effect being taxed since their foreign currency earnings are being converted at a rate that is less favourable than the real value of foreign exchange to the economy. Traded items, which in the financial analysis are expressed as costs in local currency at the official exchange rate, therefore have a conversion factor of 1.15 to capture the real impact of the use of foreign exchange on these items. Thirdly, it is assumed that non-traded items that are inputs to the project are supplied under non-distorted conditions, so that in domestic prices economic and financial values are equal. Thus, these items have a conversion factor of 1.0. Fourthly, the unskilled project labour has an opportunity cost to the economy, measured by output forgone in domestic prices in the workers’ most likely alternative activity. It is estimated at 80% of the project wage and the conversion factor is 0.8. Without the project it is assumed that the workers’ wage is equal to this output forgone and that they pay no income tax. Fifthly, approximately 10% of project output is non-incremental in that an equivalent power usage would have provided from non-grid sources (such as kerosene or fuel wood). On a kwh basis the economic cost from these sources is 20% above the national power tariff, so the conversion factor for non-incremental power supplied by the project is 1.2. Willingness to pay for new users is estimated to be 10% above the tariff, so the conversion factor for incremental output is 1.1. Sixthly, all indirect taxes on project inputs are omitted as transfers, and hence have a conversion factor of 0.

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Finally, for simplicity, the economic discount rate is taken to be 10%; this is the same as the discount rate used in the financial calculations to reflect the opportunity cost of capital to the enterprise.7 Table 13.4 gives the economic calculations for the project. The NPV is now Pesos 3.28 million which gives the full contribution of the project to national income. The financial NPV of the project, it will be recalled, is Pesos 0.78  million. Hence there is a difference of Pesos 2.50  million between the financial and economic calculations that must be allocated between particular groups to add to the distributional information derived from the financial analysis. Whenever an item has a conversion factor which differs from unity there will be income changes created by a project that are not picked up in the financial analysis. Table 11.5 shows how the divergence between financial and economic values creates a set of income changes for the participants in the project. Power users value their incremental or additional power at 10% above the tariff. Their gain is the difference between what they are willing to pay and what they actually pay, and is given in the last column of the table. It includes 10% of the financial revenue of the project from incremental power use. It also includes the non-incremental power usage that replaces other energy sources at a cost 20% above the tariff. For this proportion of sales users gain the difference between what it would have cost them without the project and what they actually pay with the project.8 Project workers gain the difference between their wage with the project and what they would have earned without the project.9 This net gain is given in the penultimate column of the table. Without the project they are assumed to earn their economic wage, which is 80% of the actual project wage. Hence 20% of the project wages bill will be a gain to workers. Finally, the government is affected in two ways. The net gain is given in the related column in the table. It gains the indirect tax payments made on project inputs required during operations. However, it loses the foreign exchange premium. This loss arises because the project has traded—that is, foreign exchange costs—in both its investment and operating phases, 7  Using a lower economic discount rate will affect absolute gains to different groups, but not the proportion of benefits going to different groups. 8  For simplicity this assumes that users pay the full economic cost from non-grid sources of supply, so that savings are fully a gain to them. 9  The shadow wage is defined in terms of the opportunity cost of labour and for simplicity the additional aspects discussed in Chap. 5 are assumed to be unimportant.

Investment costs Traded Non-traded Unskilled labour Total investment costs Operating costs Traded Non-traded Unskilled labour Taxes Total operating costs Total costs Benefits Incremental power Non-­incremental power Benefits–costs Economic NPV Discount rate Economic IRR

(Pesos million)

CF

0.00 0.00 0.00 0.00 0.00 43.70 0.00 0.00 −43.70

1.15 1.00 0.80 0.00

1.10 1.20

3.28 10% 12%

34.50 6.00 3.20 43.70

1.15 1.00 0.80

1

9.90 1.20 7.70

1.93 1.34 0.13 0.00 3.40 3.40

0.00 0.00 0.00 0.00

2

Table 13.4  Economic analysis in domestic prices

9.90 1.20 7.70

1.93 1.34 0.13 0.00 3.40 3.40

0.00 0.00 0.00 0.00

3

9.90 1.20 7.70

1.93 1.34 0.13 0.00 3.40 3.40

0.00 0.00 0.00 0.00

4

9.90 1.20 7.70

1.93 1.34 0.13 0.00 3.40 3.40

0.00 0.00 0.00 0.00

5

9.90 1.20 7.70

1.93 1.34 0.13 0.00 3.40 3.40

0.00 0.00 0.00 0.00

6

9.90 1.20 7.70

1.93 1.34 0.13 0.00 3.40 3.40

0.00 0.00 0.00 0.00

7

9.90 1.20 7.70

1.93 1.34 0.13 0.00 3.40 3.40

0.00 0.00 0.00 0.00

8

9.90 1.20 7.70

1.93 1.34 0.13 0.00 3.40 3.40

0.00 0.00 0.00 0.00

9

9.90 1.20 7.70

1.93 1.34 0.13 0.00 3.40 3.40

0.00 0.00 0.00 0.00

10

9.90 1.20 7.70

1.93 1.34 0.13 0.00 3.40 3.40

0.00 0.00 0.00 0.00

11

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

399

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S. CURRY AND J. WEISS

Table 13.5  Distribution analysis: divergence between economic and financial values (Pesos million) Investment costs Traded Non-traded Unskilled Total investment costs Operating costs Traded Non-traded Unskilled labour Taxes Total operating costs Total costs Benefits incremental Benefits non-­ incremental Benefits–costs

Financial

CF

Economic difference

−27.27 −5.45 −3.64 −36.36

1.15 1.00 0.80

−31.36 −5.45 −2.91 −39.73

−4.09 0.00 0.73 −3.36

−9.36 −7.49 −0.94

1.15 1.00 0.80

−10.76 −7.49 −0.75

−1.40 0.00 0.19

−1.40

−0.94 −18.71

0.00

0.00 −18.99

0.94 −0.28

0.94 −0.47

0.19

−4.56

0.91

−55.08 50.27

1.1

−58.72 55.30

−3.64 5.03

5.59

1.2

6.71

1.12

3.28

2.50

0.78

Central government

Project workers

Power users

−4.09 −4.09

0.73 0.73

0.19

5.03 1.12

−4.56

0.91

6.15

but generates no income in foreign exchange. The government must supply foreign exchange to the project through the central bank and does so at the official exchange rate that does not reflect the full value of foreign exchange. Hence the government is implicitly subsidising the project through the exchange rate. The discounted value of these income changes is identified in the last three columns of Table 13.5. They can now be added to those from the financial calculations to give the net income changes created by the project. The relevant income flows are set out in Table 13.6. The overall picture is that this project creates significant shifts in income, with some groups gaining and others losing relative to the without project situation. The main beneficiaries are project users who receive a benefit in the form

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

401

Table 13.6  Summary distribution effect Income from financial analysis Present value (pesos million) Project owners Development Bank Government Project workers Power users Total

0.46 −2.20 2.52

0.78

Income from divergence economic and financial

Net income

−4.56 0.91 6.15 2.50

0.46 −2.20 −2.04 0.91 6.15 3.28

of a consumer surplus. However, this surplus arises in part because they and the project owners receive two important subsidies. Credit to the project is provided at a subsidised interest rate and its foreign exchange requirements are provided at an under-valued exchange rate for foreign currency. The government and the lending institution the Development Bank are the losers from the project. The government’s losses from its foreign exchange dealings are only partly offset by its additional income from the project in indirect and profits taxation (the latter is reported in Table  13.6 under the income flows from the financial analysis). As the government is also responsible for the Development Bank its total loss is 4.24 million. Workers employed on the project also gain, as a result of the beneficial employment effect of the project. Whether or not the distributional outcome shown in Table  13.6 is judged socially acceptable will depend largely on how deserving the main beneficiary group, the power users, are relative to others who could benefit from government funding and subsidised loans from the Development Bank. Distributional analysis of this type does not itself answer this question, but it sets out the information that is required if the issue is to be addressed. In most projects, changes to key prices can be used to alter the project’s distributional outcome. For example, here if power users were not considered a target group for income redistribution, the distributional outcome could be altered by removing the interest rate subsidy. This would result in higher charges for users, who are the main beneficiaries in the base case.10 10  Changes to the tariff or the exchange rate are not project-specific issues. Decisions to alter these would need to be taken on wider grounds than simply the impact on an individual project.

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Foreign Participation in Projects Up to this point the discussion of income distribution effects has not distinguished between nationals and foreigners. However, foreigners—for example foreign banks, transnational firms, governments, or foreign workers—can all be involved with projects. The difference of principle in the treatment of income flows to foreign as opposed to national groups created by a project is that conventionally project analysis has a national perspective. Hence net income received by foreigners from a project will be a cost to the economy, since it is an income outflow, whilst income losses to foreigners will be a gain, or form of income inflow, to the national economy. Apart from the trading links in supplying imports or buying exports, which are handled through the valuation of traded goods, there are several ways in which foreign groups will be involved with projects. These include • supplying loan or external finance • direct foreign investment in some or all of the equity of a project • as foreign labour. The distributional aspects of these links will be discussed in turn in relation to the power project example. A key issue in relation to foreign financing is whether the inflow of funds is linked specifically with the project under consideration. If it is not—for example, if there is a general assistance budget or loan programme available for any project not just one in particular—then all the financial transactions involved, both inflows and outflows, will occur regardless of which project is selected. This means that any gain or loss to the economy resulting from foreign financing arrangements cannot be treated as a benefit or cost linked with an individual project. In our example of the power project, one can change the original assumption and now assume that the loan of Pesos 20 million is from a foreign source. The project gains as a result of the loan finance since the discounted flow of repayments is less than the present value of the original loan. Where domestic lenders provide the loan, which is the assumption used up to now, the gain of Pesos 2.20 million is a domestic transfer, since it is a gain to the project owners, but a loss to another domestic group, project Lenders. As a transfer it is not included as a benefit in the economic NPV calculations. Similarly, if the loan comes from a

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

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foreign source, but is not tied to a specific project, the net gain of Pesos 2.20 million will again not be included as a benefit. However, in this example, if the foreign loan is tied specifically to the project, so that it would not be available for other projects, the financing arrangements with foreign creditors cannot be ignored. The net gain arising from the difference between the discounted value of the loan and the outflows associated with it becomes a net gain to the economy and a loss to foreigners. Losses to foreigners have no social value in an analysis from the national perspective, so that the net value of the project in economic terms is now increased. The logic of this is that the economy has gained by receiving a loan greater than the value of repayments associated with it; or in other words it has gained from a loan whose real interest rate is below the economic discount rate reflecting returns available on other investments. Similar procedures apply to assistance flows. Given their concessional element one would expect the loan repayments to be below the value of the loan inflow by a higher proportion than in the case of commercial credit. The ratio of discounted repayments to the present value of the aid inflow is what is termed the grant element of aid. The principle involved for foreign equity participation in projects is similar to those for loan finance, and it is likely that more foreign investments than foreign loans will be linked specifically to particular projects. Where investment is not linked, so that the same investment inflows of equity and outflows of repatriated profits would arise regardless of what project is selected, the foreign investment arrangements of a project can again be ignored.11 Foreign workers employed on a project will experience an income gain if their earnings in their new employment exceed what they could have earned in their alternative occupation in their country of origin. However, following the procedure that only income changes for nationals are relevant in a national project calculation, income gains to foreign workers are not incorporated as a benefit of a project. The cost to the economy of employing foreign labour is the proportion of their income sent home as remittances, a direct foreign exchange cost, plus the cost of their local consumption expenditure, either diverting non-traded goods from 11  This treatment assumes equal rates of profit and profit repatriation of these profits in whichever project the foreign investments are committed to. This is obviously a simplification, but the theoretically correct procedure of comparing outflows with those from the marginal alternative foreign investment project is not practical.

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domestic consumers or, where traded goods are involved, creating foreign exchange costs. To illustrate the treatment of foreign financial involvement in the power project it is assumed that the equity is foreign investment that would not otherwise have come to the economy and that the loan is also from a concessional foreign source and is tied specifically to the project. Foreign funding creates a benefit in the year in which it is received and an annual cost when it leads to outflows in the form of repatriated dividends and loan repayments. Here we assume that 60% of annual net of tax profits which accrue to equity are repatriated in the year in which they are made. Table 13.7 summarises this situation. Two new rows are added to cover foreign financing. Net foreign equity covers discounted equity flows with the initial investment of Pesos 12 million in year 1 having a positive sign and the subsequent repatriated dividends a negative sign. The net position is negative, for the national economy, at Pesos −0.28 million at financial prices. This is because outflows exceed the initial investment, since the return on equity is higher than the 10% discount rate. In the case of the loan its receipt in year 1 (as an inflow) has a positive sign, whilst subsequent repayments (as outflows) have a negative sign. The net position of Pesos 2.20 million is positive since the interest rate subsidy means that the original inflow is greater than subsequent repayments. As the subsidy is now assumed to come from foreigners, it is a national benefit. These flows at financial prices are all in foreign currency, and must be adjusted by the 15% premium. This creates an additional cost to the government when foreign exchange is supplied for the net equity outflows and an additional benefit for the government from the loan, since net inflows are positive. Comparing the result with that shown originally in Tables 13.2 and 13.4, the project economic NPV under these arrangements has increased from Pesos 3.28 million to 5.49 million. This is for two reasons: first the country now benefits from an interest rate subsidy worth Pesos 2.2 million at financial prices and 2.53 million at economic prices (adjusted by the 15% foreign exchange premium); second the country loses from the net foreign equity outflow of Peso 0.28 million at financial prices and 0.32  million at economic prices. The net change is thus Pesos 2.21, which when added to the original 3.28 gives the result of Pesos 5.49. In other words, the financing arrangements for this project involving foreign equity and loan create an increase in the economic return of the project.

Investment costs Traded Non-traded Unskilled labour Total investment costs Operating costs Traded Non-traded Unskilled labour Taxes Total operating costs Total costs Benefits incremental Benefits non-­incremental Benefits–costs Foreign financing Net foreign equity Net foreign loan National benefits–costs

(Pesos million)

−0.28 2.20 2.70

−9.36 −7.49 −0.94 −0.94 −18.71 −55.08 50.27 5.59 0.78

−27.27 −5.45 −3.64 −36.36

Financial

1.15 1.15

1.10 1.20

1.15 1.00 0.80 0.00

1.15 1.00 0.80

CF

−0.32 2.53 5.49

−10.76 −7.49 −0.75 0.00 −18.99 −58.72 55.30 6.71 3.28

31.36 −5.45 −2.91 −39.73

Economic

Table 13.7  Distributional analysis with foreign financing

−0.04 0.33

−1.40 0.00 0.19 0.94 −0.28 −3.64 5.03 1.12 2.50

−4.09 0.00 0.73 −3.36

Difference

−0.04 0.33 −4.27

−4.56

0.94 −0.47 −4.56

−1.40

−4.09

−4.09

Central government

0.91

0.19 0.91

0.19

0.73 0.73

Project workers

5.03 1.12 6.15 0.28 −2.20 −1.92

Power users Foreigners

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

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Poverty Impact The analysis discussed above does not distinguish among particular target groups that public policy wishes to reach. A frequently stated objective for projects especially funded by concessional assistance is that they should provide benefits for those in poverty. The target group ‘the poor’ can be defined as those on an income level below a nationally defined poverty line. From this perspective distributional analysis can identify the different groups affected by a project and then estimate the proportion of their benefits going to the households or individuals in each group who fall below the poverty line. For users and workers, where poverty is likely to be concentrated, accurate estimates will require additional survey data. A particular practical problem relates to how government income is treated, since in principle government income that funds a project could have reduced poverty if used elsewhere. Estimating this opportunity cost in terms of benefits to the poor foregone is one of the key practical difficulties in applying this approach. Empirical estimates usually rely on estimates of the share of welfare spending in government budgets and assumptions about the poverty impact of this expenditure.12 The power example, but excluding the assumption of foreign financing, is again used to illustrate the data required and the procedure involved in estimating the poverty impact of a project. It is assumed that households below the poverty line are 50% of power users and that they receive 50% of user benefits. None of the project employees is assumed to be part of the poor. It is assumed that of additional government expenditure 0.10 would go as benefits to the poor. The same proportion is used for the Development Bank expenditure. Table 13.8 summarises the project’s poverty impact. Once the income change for those in poverty has been estimated the information can be presented in different ways. For example, • the absolute value of the income changes for the poor • the proportion of net benefits from a project that accrue to the poor (sometimes termed the poverty impact ratio) 12  For example, if welfare-focussed programmes, like consumer subsidies or rural health clinics, are 10% of total budgetary expenditure and if it is assumed that these are well-­targeted at the poor, so 90% of beneficiaries are below the poverty line, then the benefits foregone in terms of poverty reduction will be 9% of government income. This example assumes taxation levels are unaffected by any individual project, as an option to the government is to lower taxes if its income increases due to a project or raise them if its income falls.

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Table 13.8  Poverty impact: power example Group

Income change (Pesos million)

Share of poor

Poverty impact (Pesos million)

0.46 −2.20 −2.04 0.91 6.15 3.28

0 0.1 0.1 0 0.5

0 −0.22 −0.20 0 3.07 2.65

Project owners Development Bank Government Workers Power users Net

Table 13.9 Alternative measures of poverty impact: power example

Absolute gain to the poor Share of net benefits going to the poora Share of total user benefits going to the poorb Gain to poor relative to change in government incomec

Pesos 2.65 million 81% 43% 63%

2.65/3.28 2.65/6.15 c 2.65/4.24 a

b

• the proportion of user benefits from a project that accrue to the poor • the ratio of the present value of income changes for the poor to the present value of the change in government income resulting from the project. Table 13.9 gives the four poverty indicators discussed above for the power example. For the numbers presented the project generates 2.65 million of net income for the poor. It appears a reasonable (but not highly targeted) form of poverty targeting with 81% of net benefits going to those below the poverty line and 43% of total user benefits going to the poor. Including the operation of the Development Bank the change in government income is a net expenditure of 4.24 million, and the gain to the poor of 63% of this figure. These indicators are most directly relevant for poverty-focused projects where the primary objective is to improve the position of the poor, but in

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principle can be used for any project. Each indicator provides slightly different information and for decision-taking needs to be compared with an acceptable benchmark, probably based on past experience with targeting projects. The first shows how effective the project is in creating benefits and welfare improvement for the poor, as welfare improvement is linked with an absolute change in income. The second and third indicates whether the project is an effective form of poverty targeting. The fourth indicates the project’s cost-effectiveness in the use of government income to address poverty. However, the effects of a project on non-poor beneficiaries and the private sector in general are not addressed by any indicator. None of the approaches for project comparisons is without problems. Comparison by the absolute gain to the poor neglects a consideration of effects on non-poor groups and could lead to the acceptance of loss-­ making projects that reduce rather than increase total national income. Comparing projects by the proportion of net or user benefits going to the poor avoids the problem of acceptance of projects with negative net benefits, but there is still a trade-off to consider since projects with a low NPV but a high proportion going to the poor may need to be compared with those with a higher NPV, a lower absolute gain to the poor and a lower share going to the poor. Use of the cost-effectiveness indicator also ignores effects on groups other than those below the poverty line. One aspect of distribution analysis is that it can inform project pricing since higher charges will raise revenue, but restrict demand and alter the distribution of benefits. Appendix 13.1 has a more detailed project example, of a water project, illustrating the effect of pricing and the implicit trade-offs involved in decision-taking, once poverty objectives are introduced.

Trade-Offs in Poverty Analysis If poverty impact is introduced into project decision-making, important trade-offs will be involved between efficiency, growth, and distributional objectives. As noted above, comparison by the absolute gain to the poor neglects a consideration of effects on non-poor groups and could lead to the acceptance of economically inefficient projects that reduce rather than increase total national income. The early literature of project economic analysis considered trade-offs in relation to efficiency (measured by a project’s economic NPV), growth (as measured by its impact on savings and hence future investment), and

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distribution (measured by the consumption changes a project creates). In the analysis of a project the trade-offs were to be addressed by putting numerical weights on the savings and consumption effects created by a project, with higher consumption weights for those below a benchmark level (such as average or poverty line consumption). These revalued effects were to be combined into the measure of project worth, a weighted economic NPV and or weighted economic IRR.13 In principle, for public sector projects if the weights accurately reflected the view of decision-­ takers on the trade-offs involved, decisions on project acceptance should be based on these weighted indicators. This should be regardless of the original unweighted economic NPV or economic IRR.14 The details of the procedures for weighting are discussed in Appendix 13.2, but the logic is explained briefly here. Using savings and consumption weights requires that all project effects be converted to a common unit or numeraire. This was usually consumption going to the average consumer.15 Hence the net benefits of a project should be separated into those creating additional consumption and additional savings, which will depend on the proportion of income saved by the different groups. Where a group loses as a result of a project this will result in lower consumption and savings. Consumption weighting gives a weight of more than 1.0 to consumers with below average consumption per capita and a weight of less than 1.0 to those with above average consumption per capita. Savings weighting requires a number for the future units of consumption going to average consumers created by one unit of investment. As the additional consumption created by the saving and subsequent investment arises in the future, the stream of consumption must be given as a present value by discounting at the time preference rate. The valuation of a unit of savings requires the calculation of the parameter Pinv, discussed in Chap. 8 in relation to the discount rate. Whilst the approach provides a technical solution to the problem of trade-offs and although the methodology of weighting is well known, it is now applied only rarely in practice. This is in part owing to complications regarding its additional data requirements. If a weighting methodology is  See UNIDO (1972), Little and Mirrlees (1974) and Squire and van der Tak (1975).  This proposal was controversial, as it could create loss-making projects and potentially be a high-cost way of transferring resources to the poor; see for example Harberger (1978). 15  In principle the average consumption numeraire could be in either domestic or world prices, although it is simpler and easier to explain when domestic prices are used. 13 14

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applied for comparisons between projects, it is essential that the resulting set of weights be applied consistently. Otherwise, it would be easy to manipulate results to achieve desired outcomes in terms of favouring particular projects. Importantly, as discussed in Appendix 13.2, the use of a particular set of consumption weights is essentially subjective, since their value will vary with an assumed elasticity parameter n, reflecting a social or government preference for greater equality. In the absence of any agreement on this parameter there is the possibility of inconsistency in decision-­ taking on comparisons between projects, where different weighting schemes are applied. Practice now favours estimating the unweighted distributional effects of projects and using this income change data, perhaps combined with some simple ratio calculations in relation to poverty impact, as discussed above, to aid decision-taking on project design or acceptance. There is a recognition that distributional effects can be important for some projects, but that the poor can be reached by a variety of measures and that biasing the selection of projects in favour of those with a particular distributional outcome is not always the most effective means of alleviating poverty.

Conclusion In principle, it is possible to go into considerable detail in tracing through the income effects of projects, particularly those arising from divergences between financial and economic prices. However, in practice it will be difficult to go much beyond the stage of identifying in broad terms the main groups affected. A typical breakdown could be between government, private producers, skilled labour, and unskilled labour. The estimates are no more detailed than the economic analysis on which they are based. Typically project analysis focuses on direct or first-round effects from an investment. Where indirect effects are also captured in estimates of economic NPV, for example through induced investment and linkages utilising the approach of semi-input-output analysis (see Chap. 6) the income changes they create can also be subject to the procedures of distribution analysis outlined here. It is also possible to estimate the income inflows and outflows arising from foreign involvement in projects. Failure to go beyond aggregate groupings is largely due to the crude assumptions required to allocate income changes between different groups. Distributional analysis of the type discussed here will be useful in addressing some current concerns relating to projects. Stakeholder analysis stresses that different groups will be affected by any project and if the

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

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project is to be implemented successfully there must be some supporting groups who have a stake in its success. Distributional analysis provides important information in this context since it shows quantitatively gainers and losers and thus allows an assessment of the likely supporting and opposing groups. While limited, this information may nonetheless be an important input into project decision-taking, particularly if it is possible to estimate how the poorest groups are affected by utilising survey evidence on the characteristics of consumers, workers, and other producers affected by a project. In addition, given the weak revenue base of governments in many lower-income countries, the impact of a project on government income and potentially savings is of considerable concern. Expenditure not matched by additional income can lead to macro-economic instability as government deficits are increased. Hence it can be important to assess how a project is likely to affect government income and perhaps to redesign the project where it is judged to create major problems for government finances. These are each important pieces of information.

Further Reading The procedure for conducting distribution analysis is explained in UNIDO (1972) and is applied in project cases in UNIDO (1980). For other examples, see Fujimura (2012). The methodology of distribution weighting is set out in Little and Mirrlees (1974) and Squire and van der Tak (1975). A more recent discussion with more up-to-date references is in Florio (2014) Chapter 7; see also Brent (2006). For an alternative approach to weighting see Harberger (1984).

Appendix 13.1: Water Project Example: Distribution and Tariff Setting The project is an addition to the water supply system and will connect 20,000 households who previously had no access to mains supplies. Before the project these households had been obtaining water from water vendors, at very high prices, from private wells, or from standpipes. Basic data on the project are given in Table 13.10. Costs and benefits are in million constant Pesos. The project has a 3-year construction period and a 20-year operating life. Total investment costs are Pesos 160 million, with expenditure of Pesos 40 million in the first two years and 80 million in the third. The project is run by a Water Board but its investment cost is financed 100% by an equity contribution from the central government.

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Table 13.10  Financial analysis (case 1: tariff = O&M costs) Year

1

Population (000) 20.00 Consumption With project (m3 0.00 million) Without project 0.00 (m3 million) Incremental 0.00 consumption Revenue (Pesos million) Non-­incremental 0.00 consumption Incremental 0.00 consumption Total revenue 0.00 Costs (Pesos million)  Investment Traded 24.00 Non-­traded 6.00 Unskilled labour 10.00 Operating and maintenance costs Traded 0.00 Non-­traded 0.00 Unskilled labour 0.00 Total costs 40.00 Net revenue −40.00 NPV (O&M costs) Discount rate NPV (quantity sold) Incremental O&M cost Tariff

NPV = −129.53

117.77 10% 39.26 3.00 3.00

2

3

4

5

6

7

8

9

10

20.00

20.00

20.00

20.60

21.20

21.90

22.50 23.20

23.90

0.00

0.00

5.00

5.15

5.30

5.46

5.63

5.80

5.97

0.00

0.00

3.00

3.00

3.00

3.00

3.00

3.00

3.00

0.00

0.00

2.00

2.15

2.30

2.46

2.63

2.80

2.97

0.00

0.00

9.00

9.00

9.00

9.00

9.00

9.00

9.00

0.00

0.00

6.00

6.45

6.91

7.39

7.88

8.39

8.91

0.00

0.00

15.00

15.45

15.91

16.39

16.88 17.39

17.91

24.00 6.00 10.00

48.00 12.00 20.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00 40.00 −40.00

0.00 0.00 0.00 80.00 −80.00

10.50 3.00 1.50 15.00 0.00

10.82 3.09 1.55 15.45 0.00

11.14 3.18 1.59 15.91 0.00

11.47 3.28 1.64 16.39 0.00

11.82 3.38 1.69 16.88 0.00

12.17 3.48 1.74 17.39 0.00

12.54 3.58 1.79 17.91 0.00

413

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

11

12

24.60

13

14

15

16

17

18

19

20

21

22

23

25.30 26.10

26.90

27.70

28.50

29.40

30.30

31.20

32.10

33.10

34.00

35.10

6.15

6.33

6.52

6.72

6.92

7.13

7.34

7.56

7.79

8.02

8.26

8.51

8.77

3.00

3.00

3.00

3.00

3.00

3.00

3.00

3.00

3.00

3.00

3.00

3.00

3.00

3.15

3.33

3.52

3.72

3.92

4.13

4.34

4.56

4.79

5.02

5.26

5.51

5.77

9.00

9.00

9.00

9.00

9.00

9.00

9.00

9.00

9.00

9.00

9.00

9.00

9.00

9.45

10.00 10.57

11.16

11.76

12.39

13.03

13.69

14.37

15.07

15.79

16.54

17.30

18.45

19.00 19.57

20.16

20.76

21.39

22.03

22.69

23.37

24.07

24.79

25.54

26.30

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0 00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

12.91 3.69 1.84 18.45 0.00

13.30 3.80 1.90 19.00 0.00

13.70 3.91 1.96 19.57 0.00

14.11 4.03 2.02 20.16 0.00

14.53 4.15 2.08 20.76 0.00

14.97 4.28 2.14 21.39 0.00

15.42 4.41 2.20 22. 03 0.00

15.88 4.54 2.27 22.69 0.00

16.36 4.67 2.34 23. 37 0.00

16.85 4.81 2.41 24.07 0.00

17.35 4.96 2.48 24.79 0.00

17.88 5.11 2.55 25.54 0.00

18.41 5.26 2.63 26.30 0.00

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The Water Board is instructed to set a water tariff that at constant prices is sufficient to cover operating and maintenance (O&M) cost over the life of the project. This can be interpreted as an average incremental operating cost tariff and is equal to the discounted value of operating cost divided by the discounted quantity of water output. This gives a tariff of Pesos 3 per m3 of water. Part of the water supplied by the project is non-incremental, since it replaces supplies from non-mains sources (vendors, wells, standpipes). It therefore needs to be valued at its supply price. The other part of project output is incremental or additional since it adds to households’ use of water. This must be valued at the demand price. Without the project, water consumption is 150 m3 per household. A Consultancy study estimates that of this consumption on average roughly 65 m3 per household would have come from water vendors; 55 m3 would have come from private wells, and 30 m3 would have come from standpipes. With the availability of mains water, it is estimated that per household use will rise immediately to 250 m3 owing to the convenience and greater hygiene of mains supplies. The annual growth of water consumption after the project goes into operation is estimated at 3%, in line with population growth. It is assumed that without the project there would be no growth in water use. With these assumptions projected water use with and without the project is given in Table 13.10, which also shows the overall financial analysis results for Case 1, where the tariff is set at Pesos 3/m3. A set of information has been compiled for the economic analysis of the project (Table 13.11). All costs for alternative sources of water supply are also assumed to be non-traded. The charge from water vendors is estimated to have a monopolistic margin of Pesos 8 per m3, so that the economic cost of supply from Table 13.11 Project cost breakdown and costs of water from alternative sources

Traded Non-traded Unskilled labour

Investment cost (%)

Operating cost (%)

60 15 25

70 20 10

Cost from without-project sources Water vendors Wells Standpipes

Pesos/m3 19 9 2

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

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vendors is Pesos 11 per m3. This is based on the cost of supply from wells plus a margin of Pesos 2 per m3 to cover transport and distribution. The shadow exchange rate (in Pesos per US$) is estimated to be 15% greater than the official rate at which the project foreign exchange costs have been converted into local currency. There is a labour surplus in the economy and when measured at domestic prices the ratio of the economic wage to the prevailing project wage for unskilled labour is estimated at 0.80. Based on studies elsewhere the price elasticity of demand for mains supplied water is taken to be −0.50, and this is used to assess the impact of a rise in the water tariff. The national economic discount rate is 10%. The following section gives the economic analysis of the project using a domestic price numeraire. Since the project as it is planned initially will require a subsidy from the central government, two separate cases are considered. Case 1 uses the original average incremental cost tariff and case 2 uses a tariff 40% above the initial tariff. With a foreign exchange premium of 15% and no distortions in the supply of non-traded inputs to the project the relevant CFs for outputs and inputs in a domestic price system are as follows: Domestic price level CFs SER/OER = 1.15/1.0 Traded Non-traded Unskilled labour

1.15 1.0 0.80

In considering the value of output it is necessary to distinguish between the supply price, for non-incremental output, and the demand price, for incremental output. Benefits from non-incremental water are valued as a weighted average of the cost of the alternative supply sources, using the economic cost of supply from vendors rather than the monopoly price. Hence: Pesos/m3

Weight

Financial price

Economic price

Vendors Wells Standpipes Weighted average

0.43 0.37 0.20

19 9 2 11.93

11 9 2 8.47

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As non-traded items, under the assumption used, no further adjustment is needed. The economic value of the saving in cost of the alternative supply sources is thus Pesos 8.47/m3 in domestic price units. For benefits from incremental water an assumption is made for the value of willingness to pay. Using the assumption of a linear demand curve this can be valued approximately as the average of price without the project (Pwo) and price with the project (Pw). Here Pwo is the weighted average financial cost from the alternative supply sources of Pesos 11.93/m3. Pw is the average incremental operating cost tariff, where:

Pw 

PV  O 

PV  Q 

and PV(O) is the discounted value of operating cost in financial prices. PV(Q) is the discounted quantity of output. Here: = Pw

117.77 = 3.0. 39.26

Therefore, for case 1 the value of incremental output is:

 Pw  Pwo  2

.

or

11.93  3.00  2

 7.47.

For case 2 a higher tariff of Pw plus 40%—that is, Pesos 4.2/m3—is charged. Now the value of incremental output is:



11.93  4.20  2

 8.07.

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Benefit Conversion Factors In this case all benefits, both cost savings and willingness to pay, are non-­ traded since all the supply price value refers to a saving in non-traded costs. There will be different CFs for benefits depending upon whether the water use is incremental or is replacing without project consumption, and also upon the financial tariff charged. The set of CFs for water benefits in a domestic price system are given below: Water benefit CFs

Non-incremental consumption Incremental consumption

Case 1

Case 2

(8.47/3.0) = 2.82 (7.47/3.0) = 2.49

(8.47/4.20) = 2.02 (8.07/4.20) = 1.92

The benefit CFs must be applied to the financial revenue collected by the project to convert benefits to economic terms. For example, from Table 13.10 in year 4, revenue collected is 9.0 million for non-incremental consumption and 6.0 million for incremental consumption. Hence for the economic analysis the economic value of these two components of output is 9.0 * 2.82 = 25.4 and 6.0 * 2.49 = 14.9, respectively. Tariff Adjustment A tariff increase of 40% creates a new tariff of Pesos 4.2/m3. The higher tariff will reduce demand for water and thus reduce both output and operating cost. The new level of use can be found approximately by using a simple demand elasticity estimate of −0.50. Since the rise in price is 40% the fall in demand will be 20%. A 20% reduction in demand means a new water use of 200 m3 per household instead of 250 m3 and therefore a total consumption in the first year of operations of 4  million m3 instead of 5 million. The price elasticity used here refers to piped water and therefore does not correspond to the initial demand response when charges were lowered and mains replaced non-mains supplies. After the new water use in the first year of operations the same 3% annual growth due to population can be assumed. The economic resource table for the project under this first tariff case is given in Table 13.12. The economic NPV for the project is 49.82 mill.

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S. CURRY AND J. WEISS

Table 13.12  Economic analysis (case 1 tariff = O&M costs) (Pesos million)

Benefits Non-­incremental consumptiona Incremental consumptionb Total benefits Costs

Year CF

1

2

3

4

5

6

7

8

9

10

2.82

0

0

0

25.4

25.4

25.4

25.4

25.4

25.4

25.4

2.49

0

0

0

14.9

16.1

17.2

18.4

19.6

20.9

22.2

0

0

0

40.3

41.5

42.6

43.8

45

46.3

47.6

 (i) Investment Traded Non-traded Unskilled labour

1.15 1.0 0.8

27.6 6.0 8 41.6

27.6 6.0 8 41.6

55.2 12.0 16 83.2

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

 (ii) Operating costs Traded

1.15

0

0

0

12.1

12.4

12.8

13.2

13.6

14.0

14.4

Non-traded Unskilled labour

1 0.8

0 0 0 41 6 −41.6

0 0 0 41.6 −41.6

0 0 0 83.2 −83.2

3.0 1.2 16.3 16.3 24.1

3.1 1.2 16.8 16.8 24.7

3.2 1.3 17.3 17.3 25.3

3.3 1.3 17.8 17.8 26.0

3.4 1.4 18.3 18.3 26.7

3.5 1.4 18.9 18.9 27.4

36 1.4 19.4 19.4 28.1

Total costs Net costs/benefits

NPV: 49.82; Discount rate: 10%; EIRR: 14% a Financial revenue multiplied by CF for non-incremental consumption b Financial revenue multiplied by CF for incremental consumption

419

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

11

12

13

14

15

16

17

18

19

20

21

22

23

25.4

25.4

25.4

25.4

25.4

25.4

25.4

25.4

25.4

25.4

25.4

25.4

25.4

23.5

24.9

26.3

27.8

29.3

30.8

32.4

34.1

35.8

37.5

39.3

41.2

43.1

48.9

50.3

51.7

53.2

54.7

56.2

57.8

59.5

61.2

62.9

64.7

66.6

68.5

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

14.9

15.3

15.8

16.2

16.7

17.2

17.7

18.3

18.8

19.4

20.0

20.6

21.2

3.7 1.5 20.0 20.0 28.9

3.8 1.5 20.6 20.6 29.7

3.9 1.6 21.2 21.2 30.5

4.0 1.6 21.9 21.9 31.3

4.2 1.7 22.5 22.5 32.1

4.3 1.7 23.2 23.2 33

4.4 1.8 23.9 23.9 33.9

4.5 1.8 24.6 24.6 34.9

47 1.9 25.4 25.4 35.8

4.8 1.9 26.1 26.1 36.8

5.0 2.0 26.9 26.9 37.8

5.1 2.0 27.7 27.7 38.9

5.3 2.1 28.5 28.5 39.9

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Table 13.13  Summary results

Case 1 (tariff = O&M costs) domestic price level Case 2 (tariff = O&M costs + 40%) domestic price level

Economic NPV at 10% (Pesos million)

Economic IRR (%)

49.8

14

24.1

12

The same table and calculations have been undertaken including the second, higher tariff case. The effect of the higher tariff is to reduce the financial deficit of the Water Board, but to lower the economic benefits of the project. This follows since a higher tariff reduces use of water and the economic value of water to consumers exceeds its cost of production. Thus, lower water use reduces the benefit of the project to the economy. As is indicated in Table 13.13 below, the economic NPV becomes 24.1mill. The project is acceptable at a 10% test discount rate in either tariff case. However, its economic return is significantly lower in case 2, since the higher tariff reduces water use. The groups associated with the project are the Water Board (funded ultimately by the Government), the Government, Water Consumers, Water Vendors, and Labour. The distribution analysis follows the same approach as discussed above, first allocating the financial price information and then the income effects created by the divergence between economic and financial prices. As in the power example, since the conversion factors applied to correct for distortions are all constant over time, it is possible to work with income changes in present values rather than with annual figures. For tariff case 1, Table 13.14 gives the financial NPV data and the difference between economic and financial values. The Water Board bears the cost of the financial operations of the project, at the first tariff (case 1); this is a negative financial NPV of −129.53. Since the government is assumed to cover the losses of the Board, this in effect is a cost to the government. The economic NPV for case 1 is 49.82 and the following explains how it is allocated between the different groups. For water benefits, there is a distinction between incremental and non-­ incremental consumption. For the additional or incremental water, water consumers gain the difference between the economic value of the water, which reflects their willingness to pay, and its financial value, which is what

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

421

Table 13.14  Water case: distributional effects Case 1 (tariff = O&M costs) Benefits Previous water Incremental water Costs investment of which Foreign exchange Local Labour Operating of which Foreign exchange Local Labour Net benefits

Financial

CF

Economic

Difference

57.57 60.21

2.82 2.49

162.47 149.85

77.72 19.43 32.38

1.15 1.0 0.80

89.37 19.43 25.91

11.66 0 −6.48

82.44 23.55 11.78 −129.53

1.15 1.0 0.80

94.81 23.55 9.42 49.82

12.37 0 −2.36 179.35

104.90 89.64

they actually pay.16 The conversion factor for incremental consumption is 2.49, indicating that willingness to pay is approximately 150% greater than the tariff payments. For the non-incremental consumption, the economic value is determined by the supply cost; the complication here is that 43% of this consumption came from supplies of water vendors, who charged a monopoly margin. This monopoly margin is not part of the economic benefit of the water, since this is determined by the economic cost, at a normal return on capital. In this case, therefore, water consumers gain more than the difference between the financial and economic value of the water, since their gain is determined by the difference between what they were previously paying (which includes the monopoly margin) and what they pay with the project. In per unit terms the economic value of this water is Pesos 8.47 per m3, the tariff with the project is Pesos 3 per m3, but consumers previously paid 11.93 per m3. Hence the gain to consumers is Pesos 8.93 per m3; there will be a loss to water vendors of Pesos 3.46. Hence the net income changes from this form of water sale per m3 are as follows: Financial 3.0

Economic 8.47

Difference 5.47 of which Gain to consumers Gain to vendors

(11.93–3.0) = 8.93 (8.47–11.93) = −3.46

 Using willingness to pay as the benefit measure assumes that consumers are sufficiently well informed for the valuation of any health benefits to be incorporated in this measure. 16

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S. CURRY AND J. WEISS

Foreign exchange effects of the project all arise on the cost side, since it is assumed that there are no traded costs in the cost of water vendors. Such foreign exchange costs also create an income change which is not picked up in the financial analysis. This is because the official exchange rate does not reflect the real value of foreign exchange. A shadow exchange rate (SER) of 15% above the official, in terms of local currency per unit of foreign exchange, means that in the economic analysis foreign exchange costs are 15% greater than those in the financial analysis. As discussed, in this case since the project causes extra imports the foreign exchange premium is lost and there is a cost to the government; thus, for example, if foreign exchange is diverted from other imports as result of the project and if expenditure on these imports would have generated tax revenue for the government of 15% of their cif value, 15% of the financial cost of these imports can be treated as a loss of income to the government. Thus, the foreign exchange transactions of the project create a cost to the government in addition to the financial loss sustained by the Water Board. Finally, the last income change to be identified relates to Labour. The conversion factor for Labour of 0.80 implies that the supply price of Labour at domestic prices is 80 per cent of the wage paid by the project; hence 20 per cent of wage income can be treated as a payment to Labour in excess of the income that could have been earned elsewhere and any costs associated with project employment. For simplicity it is assumed that there are no tax payments on workers’ income. The overall distributional effects for this first tariff case are summarised in Table 13.15. It can be noted that water vendors lose when the mains supply is expanded, while at this tariff the water consumers for the project gain very substantially. The picture changes somewhat in case 2 when a higher level of tariff, 40% above the initial rate, is assumed. The new results are set out in Tables 13.16 and 13.17. Now gains to consumers are lower since financial charges for water are higher. Consequently, there is a smaller financial loss to the Water Board and hence to the government. The loss to water vendors is unchanged. There is some change in operating costs due to the lower level of output; hence there is a smaller gain to Labour and a slightly smaller loss to the government from the foreign exchange element in operating cost. In distributional terms the key trade-off between the two cases is that a smaller gain to consumers is balanced against a smaller loss to the government. One can pose the question whether a saving of Pesos 40.17 million to the government justifies reducing consumer benefits by Pesos 65.43 million. Theoretically the answer to this will depend on what one

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

Table 13.15 Distributional effect: case 1

423

(Pesos million) Present value at 10% −129.53 261.06 −66.52 8.64 −24.03 49.82

Water board/governmenta Water consumersb Water vendorsc Labourd Governmente Economic NPV

The Board loses the financial NPV Water consumers gain the difference between the economic value of water and its financial value at the tariff, plus the loss to vendors c Water vendors lose Pesos 3.46 per m3 of previous water consumption. The total loss can be calculated by multiplying in the present value of previous consumption at financial prices (when water is valued at the tariff of 3.0) by the conversion factor 3.46/3.0 d Labour gains the difference between its financial cost and its economic wage e Government bears the loss of the premium on foreign exchange a

b

Table 13.16  Water example: distributional effects with higher tariff (case 2) (Pesos million) Present values at 10%

Benefits Previous water Incremental water Costs investment of which Foreign exchange Local Labour Operating of which Foreign exchange Local Labour Net benefits

Case 2 (tariff = O&M costs + 40%)

Economic

Difference

Financial

CF

80.59 51.31

2.02 1.92

162.47 98.55

81.87 47.24

77.72 19.43 32.38

1.15 1.0 0.80

89.37 19.43 25.91

11.66 0 −6.48

62.95 18.84 9.42 −91.84

1.15 1.0 0.80

75.85 18.84 7.54 24.08

9.89 0 −1.88 115.92

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S. CURRY AND J. WEISS

Table 13.17 Distributional effect: case 2

(Pesos million) Present value at 10% Water board/governmenta Water consumersb Water vendorsc Labourd Governmente Economic NPV

−91.84 195.63 −66.52 8.36 −21.55 24.08

The Board loses the financial NPV Water consumers gain the difference between the economic value of water and its financial value at the new tariff plus the loss to vendors c Water vendors’ losses are the same as in case 1, since the level and value of previous consumption is not affected by the new tariff d Labour gains the difference between its financial cost and its economic wage e Government bears the loss of the premium on foreign exchange a

b

assumes about the cost of raising government income. If it is difficult and therefore costly to generate additional income for the government (e.g. due to high distortionary costs imposed by additional taxation) there might be a relatively high premium placed on government income. In this case it would have to be as high as 63% to justify the higher water tariff. The link between the poverty impact of a project and this form of analysis can be made by identifying how income changes for the different groups affect the poor. In this instance it will require identifying what proportion of the gains to consumers and workers (and also of the losses to vendors) will accrue to those in poverty and similarly what proportion of government income would otherwise have been spent on poverty reduction programmes. One could assume, for example, that none of the project workers is in poverty, because their supply price for labour is relatively close to the project wage. However, all water consumers might be below the poverty line. In this case a lower tariff increases income to the poor by 63% more than the loss of income to the government. Whether this is justified will depend on the implicit valuation of these two types of income.

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425

Appendix 13.2: Weighting System for Project Distribution Analysis In principle, distributional objectives can be incorporated in project selection by assigning weights to savings or consumption changes to different groups. These weights can then be either applied to the most efficient version of a project, in terms of the highest NPV at economic prices, or alternatively used to compare different versions of the same project that have different distributional outcomes. Weights can also be applied in the assessment of projects from a regional perspective, separating revaluing benefits going to residents of target regions. The weighting of flows to different groups allows revised NPV and IRR measures to be calculated and hence allows a distributional concern to be built into conventional decision-taking criteria. Revaluing Savings As discussed in Chap. 8, if there is a scarcity of savings in the economy a premium value must be attached to the savings effect of a project. There may be problems in a government’s taxation capacity, a weak system of financial intermediation to channel funds from domestic savers to borrowers or the inability of a government to borrow internationally, creating a scarcity of savings. This may mean that economically viable projects are not being implemented for a shortage of funds. Chapter 8 has explained the parameter for a shadow or economic price for investment (labelled there Pinv), which can be used to estimate the number of units of income consumed by an average consumer, which equal a unit of income saved. The parameter Pinv compares a unit of income saved and invested with the future stream of consumption this investment will create. Estimation of Pinv requires data on the marginal return on investment, the proportion of this saved and reinvested, and the social time preference discount rate required to convert the stream of future consumption to a value in the present. With the assumption that the future consumption accrues to consumers with the average level of consumption, if the savings created by a project are multiplied by Pinv this will convert a project’s savings effect into the equivalent in average units of consumption. Hence, for example, if the value of Pinv equals 1.5, this means that a unit of income saved (regardless of who makes the saving) is equivalent to 1.5 units of income consumed by the average consumer.

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S. CURRY AND J. WEISS

Table 13.18  Savings-weighted NPV Pinv

1.50

Change in Group

MPS

income

Saving

Consumption

Weighted savings

Weighted income

Project owners Development Bank Government Workers Users: poor Users: non-poor Total Weighted NPV

0.30 1.00 1.00 0.10 0.00 0.10

0.46 −2.20 −2.04 0.91 3.08 3.08 3.28 1.43

0.14 −2.20 −2.04 0.09 0.00 0.31 −3.70

0.32 0.00 0.00 0.82 3.08 2.77 6.98

0.21 −3.30 −3.06 0.14 0.00 0.46 −5.56

0.53 −3.30 −3.06 0.96 3.08 3.23 1.43

Note: MPS is marginal propensity to save

Introducing a savings effect into economic analysis requires that the income changes identified for different groups in the distributional analysis be disaggregated into changes in consumption and changes in savings. This requires estimates of the savings propensity of these groups. The approach can be illustrated in Table 13.18 using the figures for distributional effects for the power example in Table 13.8 of the main text and estimates of the proportion of income saved. As the poorest group, poor power users do not save, while non-poor users and workers save 10% of additional income, and owners as the richest save 30%. For the Development Bank and the Government funds could have been lent for another project so the savings rate is 100%. Using a value of Pinv of 1.5 means that the savings effect of the project must be increased in value by 50% to express it in consumption units. When the revalued savings are added to the unweighted consumption change the result is a NPV of 1.43  million, which is well below the unweighted value of 3.28. The reason why the results are different is that the distributional effect of the project has been relatively egalitarian with most of the benefits going to project users, who have a low or zero savings rate. The groups losing are the Development Bank, and the Government, who are assumed to reduce their lending to another project as a result. This type of weighting system is highly sensitive to values for Pinv and assumptions about savings propensity.

13  INCOME DISTRIBUTION EFFECTS OF PROJECTS 

427

Revaluing Consumption Unlike Pinv, which does not vary with who makes the savings, the value of consumption weights varies depending on the beneficiary. The best-­ known form of consumption weighting involves a simple formula that assumes that due to social or government preferences for equality the social value placed on a unit of consumption declines at a constant rate for all consumption levels. Application of this approach requires two parameters. The first is a reference level of consumption that will have a weight of unity. The main candidate for this reference level is average per capita consumption in the economy, but alternatives include a poverty line estimate or the income at which individuals become eligible for government subsidies or welfare payments. The second parameter is technically the elasticity of the social utility function for consumption and is the same parameter n in the discount rate formula (expression (8.5) from Chap. 8). It reflects the rate at which the weight for the consumption of an individual or group declines as per capita consumption rises and in principle captures the strength of society’s preference for equality. In Chap. 8 it was used in comparisons of the value of consumption between different time periods. Here it is used in comparison between consumers at the same point in time. The consumption weight formula is thus: di   Ca / Ci 

n



(13.2)

where di is the weight for group or individual i. Ci is per capita consumption income for i. Ca is the consumption or reference income, which we assume is the national average per capita income. n is the elasticity parameter. With (13.2) weights will decline the higher is Ci relative to Ca, that is, the better-off is i relative to the national average, and the higher is n, the stronger is society’s commitment to equality. Use of this formula can be illustrated using values of n of 1.5 and unity (Table 13.19). If there is no scarcity of savings, consumption and savings are equally valuable and the weights from (13.2) can be applied to all income, with figures for income replacing those for consumption in the weighting formula.

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Table 13.19 Illustration of weights

Consumption (Ca = 100) Ci = 50 Ci = 80 Ci = 200 Ci = 300

Table 13.20 Consumption weights

Average consumption National average Project owners Development Bank Government Workers Users: poor Users: non-poor

n = 1.5 Weights

n = 1.0

2.83 1.40 0.35 0.19

2.00 1.25 0.50 0.33

1 100 300 100 100 80 60 80

1 0.33 1 1 1.25 1.67 1.25

Application of consumption weighting to the power project requires data on national average consumption and the average consumption of the different groups involved with the project. Table 13.20 gives this data for each group and the resulting consumption weights, taking a value of n = 1.0. The national average consumption is 100 and poor users have an average consumption of 60% of the national average and non-poor users (that is those above the poverty line) have an average consumption 80% of the average. Project owners have an average consumption three times the national average and project workers one of 80%. It is assumed that the Government and Development Bank investment foregone would have created consumption benefits for average consumers.17 Hence it is revalued by Pinv. With these assumptions the revalued savings and consumption effects of the project are combined in Table 13.21.

17  Note that this assumption is for simplicity since to be consistent with the assumption on poverty impact used in Table 13.8, 10% of consumption benefits from this investment will go to the poor (with a weight of 1.67) leaving 90% for average consumers with a weight of 1.0. Hence the consumption weight for beneficiaries of government investment should be 1.07 (1.67 * 0.1 + 1.0 * 0.9) and a weighted Pinv for this investment would be 1.5 * 1.07 = 1.6.

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429

Table 13.21  Weighted analysis Change in Average consumption National average Project owners Development Bank Government Workers Users: poor Users: non-poor Total

1

Weights

100 300 100 100 80 60 80

1 0.33 1 1 1.25 1.67 1.25

Combined weighted NPV

Weighted

consumption

Saving

consumption

Saving

0.32 0 0 0.82 3.08 2.77 6.98

0.14 −2.2 −2.04 0.09 0 0.31 −3.70

0.11 0 0 1.02 5.12 3.45 9.71

0.21 −3.3 −3.06 0.14 0 0.46 −5.55

4.16

The result is a weighted NPV of 4.16 million, now above the original unweighted result of 3.28 million. The reason is the egalitarian distribution effect which provides benefits to poor power users, who have a high consumption weight of 1.67. Projects with an inegalitarian distribution effect, where benefits go to better-off groups, will have a weighted NPV that is lower than the unweighted result. When detailed data are available and weights are applied consistently across different projects, they can be ranked by their weighted NPVs. In this example, the project is likely to look attractive relative to alternatives because of its egalitarian distributional effect. It should be noted that this discussion is based on the assumption that the numeraire of the analysis is units of consumption. The same calculation can be re-done where savings are the unit. Where this is the case the two weights can be combined with changes in consumption first multiplied by the distribution weight di and then this weighted consumption figure divided by Pinv, to convert to an equivalent in savings. Hence the combined weight for consumption is di/Pinv, whilst change in savings is unweighted as savings are the unit in which effects are measured. This is the approach in Little and Mirrlees (1974) and is seen most clearly in their treatment of the shadow wage. Rather than adopting output foregone in alternative employment as the only economic cost of

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labour their specification of the shadow wage (SWR) adds the additional consumption effect created by employing a worker. Here using their notations SWR 

c1  1 s c  m

(13.3)

where m is opportunity cost in output foregone, assumed to be all consumed without the project, c is consumption level with a project and, c1 is the extra resources associated with employing a worker (such as extra housing) and s is the weight placed on additional consumption by a worker. In the terminology used in this Appendix, for worker j, s is equivalent to dj/Pinv. Here the cost of employing a worker is any extra expenditure associated with their consumption (c1) minus the social value of the additional consumption created by the project (c − m)/s.18 Although the methodology of weighting is well known, it is now applied in practice only rarely. Apart from the data requirements and the subjective basis for parameter n that determines weights, weighting schemes can create what appear to be implausible outcomes, since they can imply the justification of a high level of economic inefficiency in pursuit of distributional goals.19 For example, with n  =  1.0 a project with benefits of 25 and costs of 100 (that is, with a net loss of 75 before any weighting) would be justified if the benefits went solely to those with an income of half the national average (and thus a weight of 2.0), whilst its costs were borne solely by those with an income twice the national average (and thus a weight of 0.5). The point here is that whilst raising the income of the project beneficiaries by 25 may be justified in terms of social priorities, doing it at a net cost of 75 is likely to be a very inefficient means of reaching this target group. The expectation would be that there would be less costly means of affecting the transfer (e.g. through subsidy schemes or targeted work programmes) than implementing a loss-making project.20  Squire and van der Tak (1975) have a clearer explanation of this than the original analysis in Little and Mirrlees (1969, 1974). 19  See for example, the discussion in Harberger (1978). 20  In principle this issue could be addressed by estimating the cost in loss of economic efficiency of raising government revenue to fund subsidies or welfare programmes. This cost is normally defined in terms of losses of producer or consumer surplus, per dollar of tax revenue. This cost plus the administrative cost of the programme per dollar transferred to the target group could then be compared with the equivalent cost, in terms of benefits foregone, of a project supported on distributional grounds. However, information for such a detailed comparison is rarely available. 18

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Bibliography Brent, R. (2006). Applied Cost Benefit Analysis (2nd ed.). Edward Elgar. Florio, M. (2014). Applied Welfare Economics: Cost Benefit Analysis of Projects and Policies. Routledge. Fujimura, M. (2012). Projects and the MDGs: Estimating Poverty Impact. In J. Weiss & D. Potts (Eds.), Current Issues in Project Analysis for Development. Edward Elgar. Harberger, A. (1978). On the Use of Distributional Weights in Social Cost-Benefit Analysis. Journal of Political Economy, 86(2), 87–120. Harberger, A. (1984). Basic Needs Versus Distributional Weights in Social Cost-­ Benefit Analysis. Economic Development and Cultural Change, 32(3), 455–474. Little and Mirrlees. (1969). Manual of Industrial Project Analysis in Developing Countries (Vol. 2). OECD. Little, I., & Mirrlees, J. (1974). Project Appraisal and Planning for Developing Countries. Heinemann Educational. Squire, L., & van der Tak, H. (1975). Economic Analysis of Projects. John Hopkins University Press. UNIDO. (1972). Guidelines for Project Evaluation. UN. UNIDO. (1980). Practical Appraisal of Industrial Projects. UN.

CHAPTER 14

Conclusions

The previous chapters have outlined and discussed the main features of project analysis, particularly relating to the economic analysis of projects. This has included a full discussion of the ways in which economic prices can be applied in project analysis using different numeraires, as well as broader issues relating to the environmental and distributional effects of projects. The focus has been to combine theoretical discussions with practical illustrations. In many instances there remains a major gap between research studies which develop best-practice techniques drawn from the academic literature and what is feasible in practical project applications. It is important to stress that project economic analysis was developed principally to avoid accepting projects that were not economically justified. This included cases where financial analysis would approve the wrong projects, for example because they were financially attractive simply because of the protection from imports offered to domestic producers, or financially unattractive because the environmental benefits they provided did not generate revenue for the project. In all cases, the purpose is to arrive at better project decisions and to improve the effectiveness of investments, rather than offering rigorous technical solutions to the problem of valuation of project effects. In short, project economic analysis is intended to offer a pragmatic solution to the question of whether the benefits of a project are an adequate return on its costs. Project analysis has continued to develop in the last decades but at the same time there remain limitations © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Curry, J. Weiss, Project Analysis in Developing Countries, https://doi.org/10.1007/978-3-031-40014-8_14

433

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also. This concluding chapter outlines some of the remaining limitations whilst referring to the more recent developments covered in the preceding chapters. In terms of limitations, a major problem, although generally not made explicit, is the value system underlying project analysis: questions arise as to whether forms of project analysis are appropriate in all types of economy. A further problem is the issue of how far the partial and static nature of most applications may fail to pick up all relevant project effects. An additional issue is how far the adjustments implied by a detailed application of the original methods, designed on the assumption that developing countries were ‘distorted economies’, remain relevant in an era when most countries have removed the most serious controls on the functioning of markets. Related to this is the question of how far project analysis can be applied to policy lending which is designed to fund policy reform and not necessarily project investments. Finally, there is the question of how far project economic analysis can produce realistic and operational estimates of economic value for activities not transacted through a market, and for which proxy values must be estimated if they are to be included in project calculations. These issues will be discussed in turn, before the authors present their own conclusions on the continued role of project analysis in investment decision-taking.

The Value System in Project Analysis There remains a weakness in the value system which provides the basis for the economic analysis of projects in the development context. The value system for traded goods is discussed here before that for non-traded goods. World prices represent the terms on which an economy can participate in foreign trade, and are therefore relevant when planning investments that either use or produce traded goods. Use of world prices to value traded goods is sometimes confused with advocacy of an external policy of free trade. However, a number of situations can justify interventions in trade to promote or encourage new projects. The most significant of these are the use of surplus resources, such as underemployed labour, and cost reductions and quality improvements over time through learning and technical change. In principle, each of these possibilities can be incorporated in project analysis. A trade efficiency test in relation to the production of traded goods is an important step in assessing economic efficiency and is built into project analysis. It remains relevant today.

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However, border parity pricing means that even for traded goods there is a domestic component of value arising from the transport and distribution costs of moving goods to and from the border. For geographically large economies these non-traded costs can be a significant part of the border parity price, so that for such economies there is a greater divergence between economic value and cif or fob prices for traded goods than in smaller economies. This stems from the natural protection that inland projects in large countries enjoy in relation to import competition. The main difficulty in the value system employed in project analysis lies in the treatment of non-traded goods whose use or production affects only their availability to domestic users. Where non-traded outputs substitute for other goods—that is, they are non-incremental—their value can be estimated through the supply price of the goods they displace. However, where non-traded goods are incremental, in the sense that a project’s output adds to use in an economy, willingness to pay by the domestic user is the basis for value. Apart from the practical difficulty of measurement, willingness to pay indicators are derived from the individualistic theory of welfare economics. They reflect individual not social preferences and are based on the existing distribution of income. If the fairness of the distribution of income is challenged, it will undermine the acceptability of this approach. The ways around this objection each have difficulties. One way is to use consumption weights to adjust the willingness to pay of different groups by the weight relevant for that group. In this way, the fact that higher incomes allow the rich to express a greater willingness to pay than the poor can be counterbalanced by a higher consumption weight given to the latter. This approach is limited by the difficulty in obtaining acceptability for a set of consumption weights and the fact that in practice this form of project analysis has not been applied often. The second approach is to recognise willingness to pay as an unsatisfactory basis for value for some non-traded outputs, particularly those that are either major elements of consumption of the poor (such as low-income food) or are part of social consumption (like education and health), and to treat these as merit wants with a premium value. Although this solution was suggested many years ago it has rarely been used. Such valuation will be arbitrary and it faces the same problem of obtaining an agreed weight. For projects producing such goods, valuation of benefits may not be possible, in which case project analysis will have to concentrate on meeting physical targets with the minimum cost in economic terms.

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The same valuation problem relates also to the valuation of nontraded goods and services that are in fixed supply and are direct and indirect inputs into other sectors. The more such goods there are, the more the valuation system will depart from the direct application of world price equivalents of project inputs and outputs. As indicated already, this may be the case for locally oriented projects in large countries where a range of products, because of cost and quality, may be effectively non-traded. It may also be the case in small economies pursuing a relatively autonomous development path, or in countries faced by trade embargoes. Although trade liberalisation increased markedly from the 1990s onwards, nonetheless valuation by willingness to pay has taken a much larger role in the application of project economic analysis to public sector projects than when the original texts on project analysis were written in the 1970s. Public investments are now typically concentrated in the non-­ traded branches of infrastructure and social sectors. New investments in traded good sectors, like manufacturing and agriculture, now tends to be private investment. In principle economic calculations continue to be relevant for privately owned projects, however, without some form of public financing, for example from a Development Bank, they are very unlikely to be applied. Hence inconveniently for analysts, new public investment and international development assistance, for which project analysis is most widely used, is predominantly in the sectors where benefit valuation is most complex. One of the significant developments in this context has been the theoretical and practical estimation of the discount rate, as outlined in Chap. 8. Two main alternatives are the time preference rate and the weighted social cost of capital. The former can address the cost of waiting for benefits, but will require other means of addressing a scarcity of funds. The latter combines the two aspects in a single rate, but in doing so in practice is likely to be weighted towards its opportunity cost element and therefore may not allow adequately for the cost of time. For this reason, theoretical developments have favoured a time preference approach with the rate declining over time.

Empirical Estimation of Wider Effects Project analysis has seen a diversification of types of projects and the types of project effects that have been included in the analysis. Some of these have been outlined in preceding chapters. Particularly important are the

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greater emphasis on the incorporation of environmental effects in a project analysis. This includes putting economic values on the local environmental effects of projects, as well as allowing for their global impact through the effect of emissions on climate change. Given the policy challenge posed by climate change and the associated temperature rise, the need to address this effect in project decision-making is now very clear. Once environmental effects are incorporated in an analysis the issue arises of how they should be discounted. Chapter 11 discusses ways of doing this. The distributional dimension of projects has always been a relevant consideration and remains so. Most governments and all donors talk of need for ‘inclusive growth’. Project analysis provides a means of assessing the expected distributional impact of a project. Project estimates cannot be precise but offer an initial view of who are the main beneficiaries. Despite this broadening of the scope of project analysis, it remains a legitimate criticism of project economic analysis that the partial equilibrium approach it generally uses may not pick up wider effects of a project. In one version of the methodology the impact of a project on secondary markets is omitted on the assumption that these markets are competitive, so market or financial prices reflect economic benefits and costs. Hence there is no need to incorporate the wider effects of a project in these markets. This means an input to a project purchased from a competitive domestic market will be priced at its marginal cost and an output sold to a secondary market will be sold at a price reflecting consumers’ willingness to pay. In both instances, there will be no surplus incomes in secondary markets to be added as a project net benefit. In a development context this approach to secondary markets was not used and assumptions of surplus resources, such as labour, and underutilised capacity in supplier sectors were introduced. In practice, whilst economic pricing of labour was common, tracing surplus incomes in input suppliers was limited. Where this type of analysis was used it tended to focus on first-round effects, rather than going further back in the supply chain. The methodology of semi-input-output analysis, which involves building an input-output table around an individual project, offers a solution, but is demanding and has very rarely been applied in practice, due to data complexity. Similarly, computable generable equilibrium models can integrate a project into a model of the economy including the responses of investors and producers to the incentives a project creates. However, this type of complex equilibrium model is an alternative to the common simpler partial equilibrium approach of project analysis and should be seen as

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separate technique. It has a relevance for the analysis of large projects, with a big impact, particularly in a local area. However, given the data and modelling expertise required, it is not a substitute for project analysis. The second area where project analysis has difficulty in terms of data complexity relates to dynamic effects, for example, from learning and technical change, which can lead to reductions in unit costs or improvements in product quality over time. This is important for the application of the trade efficiency criterion. If dynamic effects are not allowed for, this procedure will allocate investment only to activities that are currently competitive internationally, and will ignore the long-run dimension of changing efficiency over time. The existence of such dynamic effects is the basis for the infant industry case for protection and there is evidence that they have been important in the industrial success of a number of economies. These effects, whether internal to a project or spread to others as externalities, are additional benefits that should be included in a project analysis. Omission of dynamic effects will thus be equivalent to judging a project on the basis of an economy’s existing and not potential comparative advantage. Where projects are designed to be genuinely innovative, in that they offer totally new products it will be very difficult to draw accurate market forecasts. Hence it will be particularly difficult to assess risky new projects, for which public funding is requested. This limits the usefulness of project analysis in assessing the portfolio of Development Banks, designed to support innovation, for example. The current thinking behind the modern version of industrial policy is that Development Bank lending can be spread across a range of high-risk innovative proposals, many of which may fail, but whose losses will be more than offset by a few high return successes. Identification and estimation of dynamic effects at the project level remains a major problem, particularly in sectors where technical change is rapid.

Future Application of Project Analysis As discussed in earlier chapters, much of the details of project analysis methodology were developed during the 1960s, when many developing countries were characterised by heavy government involvement in terms of price, trade and capital controls, as well as significant state investment and planning restriction over private sector activity. This government involvement was seen as a major factor in creating divergences between

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financial prices and the opportunity costs to the economy of resources. A primary purpose of the initial applications of economic analysis of projects was to identify high-cost import substitute activities that might be financially profitable due to protection, but which were unjustified in economic terms. During the 1980s and 1990s many developing countries introduced economic adjustment programmes involving reduced public sector activity. These adjustment programmes, involving a package of economic policy changes, were oriented to shifting resources into the export sectors and away from protected import substitute activities. They also involved removing many of the restrictions on the operation of markets for goods, services, and other productive inputs. This drive towards greater market liberalisation, whilst still not universal, has continued so that the policy context for project analysis is now very different for the majority of countries. An important question is whether such liberalisation measures that aim to bring prices more in line with opportunity costs remove the need for the economic analysis of projects. The answer given here is that this is not the case, for several reasons. First, market liberalisation can certainly reduce the gap between financial and economic values, but this gap will never be removed entirely. For example, taxes and subsidies will continue to be used by governments, although these create divergences between domestic and world prices for traded goods, and between domestic prices and the opportunity costs of production for non-traded goods. In any case, different countries have different economic and political systems and some will be closer to market liberalisation than others. In all economies there will be some monopoly or surplus profits, and under-capacity working, both of which will also contribute to a divergence between financial prices and opportunity costs in nontraded sectors. In addition, the nature and quality of regulation can strongly influence whether decisions are taken on the basis of a correct analysis of risks, or whether the risks of borrowing are under-estimated through, for example, selective allocation of credit. Reform in markets for foreign exchange, labour, and capital can bring the official closer to the shadow exchange rate, financial wages closer to economic wages, and interest rates closer to the economic discount rate. However, equality between market-determined prices for these factors and their opportunity costs to the economy will obtain only where an economy has no distortionary taxes and subsidies, and is perfectly competitive in the sense of having complete mobility and full employment of

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resources, full information on opportunity costs, and no external effects. This is not a realistic real-world scenario. Market liberalisation programmes in a number of developing countries have reduced the scale of divergence between financial prices and economic values, and trade liberalisation has been particularly important in narrowing the gap between domestic and world prices. However, divergences will persist. None the less the implication of this more liberalised policy environment is that the primary focus of attention must shift away from the estimation of parameters that reflect economy-wide distortions towards project-specific problems of benefit valuation in non-traded sectors. The second reason why economic analysis remains relevant is that increasingly investments, both public and private, are affecting aspects of life where goods and services are not transacted through a market. Environmental impacts, both local and global, are the obvious examples. A great deal of effort in recent years has been devoted to ways in which value can be estimated for activities where there is a ‘missing market’, and hence no financial price. This is an area where further research is needed, both to refine empirical estimation procedures and to test for the plausibility of values derived from short-cut approaches like benefit transfers and meta -analyses. In areas, like social sector delivery through education and health projects, where market transactions do occur, for many years the market solutions to valuation have been recognised as inadequate. Again, this is an area where current approaches could be improved. Finally, as part of the economic reform agenda in developing countries, governments frequently finance broad policy reforms, often using a combination of international assistance and their own budgets. Such reforms can involve the restructuring of individual sectors, like education or health where a range of providers are reformed, or reforms to a range of institutions like restructuring of banks, public enterprises or government departments. There have been attempts to use the techniques of project analysis to identify and value the economic benefits and costs of these initiatives. This is generally easier on the benefit, rather than the cost, side. Critical here is to identify the transmission mechanism through which a policy change is judged to create economic benefits. For example, reorganisation of schools or hospitals typically aim to raise productivity and thus reduce costs. Banking sector reform may aim at lowering the spread between lending and deposit interest rates, with a view to encouraging borrowing for investment, with resulting benefits from the higher investment. Analysis can review the plausibility of the assumptions used and the

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techniques of project economic analysis can be used to put numerical values on these effects. However, estimating the full cost of such reforms is very difficult. The expected administrative costs will be known from the agreed budget, but the indirect costs in terms of the disruption caused by reform will be difficult to assess and value. For example, in the short-term, with health sector reform, less patients may be treated or less funds may be lent as part of financial sector restructuring. Experience from other countries or macro-­ economic modelling can provide evidence of what the economic effects will be, but this will be limited. In practice, even with a discussion of the net benefits of policy reform, it is rare to see this converted into the standard project indicators of NPV and IRR.

Conclusion It is important to recognise the limitations of project analysis, particularly given the time and effort that is required to undertake an economic analysis of a project. Further, ex post evaluations by international agencies of the projects they have financed frequently reveal that all does not go as in the original project plan and that revised project indicators can be below expectations. Reasons for poor project performance are varied, and can be the result of an unstable operating environment, for example caused by macroeconomic or political instability, as well as by internal failings in the project itself, such as unrealistic ex ante planning or poor management. Sound ex ante analysis of projects will not guarantee ex post success, but it should help create the conditions for it. The central purpose of project analysis, as it has been discussed here—the analysis of investments from the point of view of national economic objectives—seems to be as important as it was 50 years ago Given the continued relevance of project analysis, how does the original methodology stand the test of time and how has its emphasis changed? The central principles on which project economic analysis is based—the idea of resources having opportunity costs, and that there is a cost of waiting, so effects in the future are less valuable than in the present—remain valid. Project economic analysis defines opportunity cost in terms of the economy, not individual investors, and uses a discount rate defined in economic terms. As indicated above also, changes in many countries strengthened the case for time preference rather than opportunity cost discount rates.

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With the focus of the majority of public sector projects on non-traded sectors for physical and social infrastructure, recent empirical developments in methodology have concentrated on ways of estimating benefits from such projects. In particular there has been progress in efforts to quantify the theoretical concepts of willingness to pay and consumer surplus, particularly through survey-based techniques. Refinements to the contingent valuation methodology in particular have seen its application become increasingly common. Similarly, there have been extensions to environmental valuation involving a range of techniques including contingent valuation, benefit transfers, meta-analysis, and global models of climate change. However there remains a gap between what has been done in the research literature and what is feasible in practice. While project analysis whereby financial prices for factors, like foreign exchange and unskilled labour, are replaced by economic prices, retains a relevance in certain types of economy, in a majority of cases, even in a development context, it is no longer the major focus of attention. Given the continued importance of foreign trade and the potential impact of new projects on the trade balance, foreign currency misalignment and how far foreign trade is taxed, still need to be assessed. However, practical approaches to estimating the shadow exchange rate have not been extended from the original literature and remain relatively simple. Their application may sometimes affect a project decision, but ways of estimating project benefits in specific sectors will need to be the primary focus. In labour surplus economies, or where there are large regional disparities in economic activity, adjustments to wage costs for otherwise underemployed or unemployed workers may continue to be necessary. This offers a way of encouraging employment creation through project decision-­making. However, there are many projects where direct labour costs are only a minor part of total project costs and where shadow pricing labour will make relatively little difference to decision-making. Indirect labour costs estimated, for example, through input-output procedures allow for linkages with supplying sectors. However, this type of wider effect is not captured unless the semi-output or a modelling approach is applied to a project. For labour’s economic valuation, relatively complex formulae were developed in the early literature to revalue wage costs, for example allowing for not just direct opportunity costs, but also the costs of extra consumption, resettlement, and extra effort. These have not been refined in recent years and their full application is now rare, apart from research studies, and unlikely to add much to project selection.

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Project economic analysis cannot provide precise numerical answers to all questions about a project. However, it can help to clarify key aspects of a project such as—is the project the best use of resources available; how does it benefit consumers; who gains and who loses from the project; how do its costs compare with foreign competitors; how is government revenue affected; how does the project impact on the environment and global climatic conditions; what does the implementing agency need to do to ensure project success? This book has set out the techniques used to address these questions and hence lead to more informed decisions. Finally, project analysis as a planning technique is capable of providing valuable information on project sustainability in the widest sense. As part of an economic project calculation more than just the narrow economic desirability of a project will be determined. The financial calculations on a project form the starting point for analyses of projects with marketed output and they will determine whether the project is financially sustainable— in other words, whether financial returns are high enough to attract private investors or to allow public operation without a subsidy. Where an adequate environmental calculation can be incorporated this adds a new dimension of environmental sustainability. In addition, the distributional analysis of a project identifies the gaining and losing groups affected by a project and hence its sustainability in a social sense. Hence a planning technique developed many decades ago is sufficiently flexible to address contemporary policy concerns.

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Index1

A Adjustment programmes, 439 Agriculture, 4, 91, 112, 126, 131, 185, 187, 189, 191, 217, 327, 389, 436 A matrix (in SIO table), 181–185, 192 Annuity factor (AF), 42, 43, 298, 298n17 Asian Development Bank (ADB), vii, 48, 218, 243, 279n3, 295n15, 302, 303 Assets (terminal value), 16, 23–27, 35–37, 40, 69, 86, 86n4, 88n6, 123, 192, 215, 252, 253, 258, 329, 336, 338, 339, 350, 350n21, 351, 355, 356, 360, 391 Average incremental cost (AIC) at economic prices (AIEC), 379 at financial prices (AIFC), 379, 380

B Barbier, E., 327n6, 338n11 Benefit-cost ratio (BCR), 52–55, 243n13 Boardman, A., 80, 108, 218, 229n1 Border parity prices (BPP), 115, 116, 121, 121n7, 123, 123n10, 127, 133, 135, 160, 165, 166, 170, 171n6, 176, 394, 435 By-product effect, 320 C Capital costs, 16, 22, 25, 37, 69, 70, 122–124, 140, 166, 183n12, 231, 241, 253, 261, 280, 284, 287, 306, 307, 309, 313, 377, 378, 391 Capital recovery factor (CRF), 43, 44, 123, 142n24, 192

 Note: Page numbers followed by ‘n’ refer to notes.

1

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. Curry, J. Weiss, Project Analysis in Developing Countries, https://doi.org/10.1007/978-3-031-40014-8

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454 

INDEX

Compensating project, 348, 352, 353 Competitive market input effects in, 95 output effects in, 92, 95 Constant prices, 18, 19, 37n4, 39, 40, 45, 46, 48, 83, 147, 306, 313, 351, 374, 381–383, 385, 386, 391, 414 Consumer surplus (CS), 97, 98, 119, 120, 136, 172, 199, 199n2, 202, 203, 278, 333, 335, 355n25, 401, 430n20, 442 Consumption conversion factor (CCF), 168, 171, 172, 172n8, 180, 183 incremental, 417, 418, 421 non-incremental, 417, 418, 420, 421 time preference, 230, 231, 233, 234, 239, 241, 247, 252 weights, 409, 410, 427–429, 428n17, 435 Contingent valuation (CV), viii, 204, 210, 211, 217–219, 221, 222, 301, 336, 363, 442 Conversion factor (CFs), 102n11, 103–105, 108, 118, 118n4, 122, 123, 126, 127, 131, 133, 140, 148–150, 152–154, 159, 159n4, 160, 162–171, 173, 176, 177, 179, 182–191, 193, 194, 259n2, 389, 397, 398, 415, 417, 420–423 Cost-effectiveness analysis, 9, 61, 74–76, 80, 86, 283, 292, 293, 302, 316, 321 Cost effectiveness ratio (CER), 75, 76 Cost recovery, 368, 377–381 Credit, 107, 390, 391, 401, 403, 439 Cross-over discount rate, 68 Current prices, 18, 19, 39–45, 115, 382–386

D Data requirements, 10, 180, 237, 239, 241, 296, 298, 409, 430 Depletion premium (DP), 86n4, 137, 329, 358–361 Depreciation, 27, 37, 37n4, 40, 87, 185, 237, 239, 242, 333, 374, 383, 384, 391, 393n2 Disability adjusted life year (DALY), 245, 294–302, 297n16, 299n20, 299n21, 300n22, 314–317 Discount factor (DF), 40–43, 247, 248, 286, 287, 347n18 Discounting, 11, 15, 19, 22, 40–45, 47, 48, 51, 55, 57, 72, 75, 80, 227, 229, 231, 236, 250, 252, 258, 286, 287, 287n6, 297, 298, 298n17, 317, 347–351, 354, 357, 389, 409 Discount rate, viii, 12, 19, 41–43, 42n7, 45–47, 47n9, 51, 52, 54–59, 58n1, 63–65, 64n3, 68–72, 76, 76n5, 78–80, 83, 104, 106, 108, 123, 140–142, 142n24, 144, 147, 149, 153, 162, 177, 192, 214n15, 227–254, 258, 259, 262, 266, 269, 286, 287, 289, 298, 298n17, 298n19, 300, 307, 312, 316, 317, 344–351, 345n16, 347n18, 348n19, 350n21, 353, 354, 359–361, 369, 376, 379, 384, 385, 390, 391, 398, 398n7, 403, 404, 409, 415, 420, 425, 427, 436, 439, 441 Distribution analysis, 13, 40, 387, 396n6, 400, 408, 410, 411, 420, 425–426 Domestic price numeraire, 101, 106, 111, 143, 157, 157n1, 160, 311, 325n3, 396, 415

 INDEX 

Domestic price system conversion factors in, 106, 123, 148, 159, 160 equivalence with world price system, 159–167, 169, 177 land in, 87, 132, 170 non-traded goods in, 12, 90, 90n7, 91, 96, 98, 101, 103, 111, 138, 139, 142, 148, 157, 159–161, 163, 403, 434, 439 skilled labour in, 148, 166, 307, 410 traded goods in, 90, 91, 98, 101, 102, 106, 112–115, 135, 136, 138, 160, 161, 163–164, 172, 195, 291, 325n3, 355, 397, 402, 404, 434–436, 439 unskilled labour in, 123, 125, 148, 155, 166, 307, 410, 415, 442 Domestic resource cost ratio (DRC), 140–143, 142n24, 155, 156 Dynamic effects, 158, 438 E Economic efficiency, 153, 368, 430n20, 434 Economic prices, 7, 11, 12, 17, 60, 84, 85, 87, 98, 99, 104, 107, 108, 113, 115, 118, 121–124, 126–130, 132, 133, 140, 141, 148, 157, 160, 161, 163–166, 169–171, 176, 179, 191, 193, 257, 259, 277, 281, 284, 291, 295, 322, 346n17, 351, 352, 361, 367, 385, 388, 389, 393–401, 396n6, 404, 410, 425, 433, 442 Economic wage, 126, 127, 130, 131, 167, 169, 398, 415, 423, 424, 439 Education benefits, 86, 292

455

Education projects, 268, 283–292, 289n7, 290n8, 310–314 Electricity, 122–124, 161, 165, 166, 200–202, 342, 346, 346n17, 353, 391 Electricity conversion factor, 122, 165 Environmental benefits, 252, 320, 321, 325, 327, 328, 336, 348–351, 353, 358, 433 costs, 253, 322, 324, 328, 329, 336, 347, 348 discount rate, 350 effects, viii, 8, 13, 195, 247, 252, 273, 282, 319–364, 437 Equity capital, 16–18 return on (ROE), 374–376, 404 Exchange rate official (OER), 103, 118, 118n5, 121, 122, 134–140, 135n16, 150, 153, 155, 160–164, 166, 169, 171n6, 173, 180, 383, 395n4, 397, 400, 422 shadow (SER), 12, 101, 103, 106, 117, 118, 118n4, 118n5, 121, 122, 134–143, 142n22, 150, 153, 155, 156, 159–163, 160n5, 165, 166, 169, 172, 173, 179, 180, 275, 307, 311, 325n3, 355, 383n4, 394n3, 396, 415, 422, 439, 442 Exports, 89, 90, 98, 99, 106, 112–116, 112n1, 115n3, 127, 129, 129n13, 131, 133–138, 135n16, 136n17, 138n19, 155, 156, 158, 170, 176, 183, 185, 186, 186n13, 189, 263, 310, 388n1, 402, 439 External benefits, 22, 199, 218, 283, 284, 377 External costs, 22

456 

INDEX

Externalities, 7, 60, 85, 86n3, 278, 281, 283, 302, 367, 394, 397, 438 F Feldstein, M., 254 Financial analysis, 7, 15, 40, 45–46, 51, 60, 83–88, 104, 145–146, 307, 343, 367–386, 392–394, 396n6, 397, 398, 401, 412, 414, 422, 433 Financial discount rate, 46, 376, 379, 390 Financial internal rate of return (FIRR), 80, 83, 368–374, 376 Financial opportunity cost of capital (FOCC), 369–374 Financial prices average incremental cost at, 379, 380 income flows at, 393 Financial statements, 11, 15–48, 115, 389–390 Financial sustainability, 15, 60, 153, 367–372, 379, 381, 382, 384–386 Florio, M., 108, 181, 235, 411 F matrix (in SIO table), 184 Foreign exchange, 7, 101, 102, 104, 106, 107, 111, 112, 114–116, 119, 122–124, 123n9, 123n10, 126, 130, 131, 133–143, 134n14, 134n15, 142n22, 148, 149, 153, 156, 158–160, 162, 165–167, 169, 170, 179, 180, 181n11, 182, 183, 183n12, 185–188, 188n14, 292n11, 316, 383, 395n4, 396–398, 400, 401, 403, 404, 415, 422, 439, 442 Foreign income recipients, 390 Foreign investment, 402–404, 403n11 Foreign loans, 36, 38, 39, 46, 382–384, 403

Foreign participation in projects, 402–404 Foreign savings, 239, 240, 240n17 Free trade, 434 G Gittinger, J., 81 Gollier, C., 235n11, 244 Government equity, 144, 370 income flows, 389, 401, 402 investment, 6, 88, 228, 235, 370n1, 381, 428n17, 438 policy, 112, 374, 440 Green Book, 243–245, 252, 298n19 H Harberger, A., 48, 128n12, 230n4, 238, 239n15, 240n16, 240n17, 242, 252, 254, 411 Health benefit valuation of, 13, 283, 292, 300–302 projects, 75, 245, 251, 252, 292–298, 298n18, 300, 302, 314–317, 440 Hedonic price, 326, 330–332 Household (income flows), 390 House prices, 330, 332 Human life, 300 I Imports price of, 7, 89, 90, 103 quotas, 89, 112, 113, 138 Import substitution policy, 89 Incentives, 8, 15, 60, 83, 107, 112n1, 158, 206, 367, 368, 373–378, 385, 437 Income distribution effects, 387–430

 INDEX 

Income flows, 286, 389–390, 393–402 Income weighting, 427 Incremental consumption, 417, 418, 420, 421 cost, 35, 72, 147, 150, 155, 415, 416 incremental net present value, 68 inputs, 95–97, 121, 395 internal rate of return, 68, 69, 72, 80 output, 30, 93, 94, 96–98, 120, 150, 152, 154, 171, 176, 195, 197, 198, 201n3, 202, 217, 302, 397, 415, 416 Infant industry, 438 Infant industry protection, 438 Inflation, 37, 37n4, 39, 40, 46, 139n21, 213–215, 215n16, 351, 368, 382–386, 393, 393n2 Infrastructure projects, 20 Inputs incremental, 95 non-incremental, 121, 171, 395 produced, 121, 181, 184, 186, 187, 191, 192 Institutions, 20, 291, 376, 401, 440 Integration technique, 120n6, 202n4 Interest rates, 17, 38, 43, 44, 227–229, 232, 237, 239, 240, 240n16, 245, 370, 374, 383, 390, 391, 393, 401, 403, 404, 439, 440 Internal rate of return (IRR), 52, 56–60, 58n1, 65, 66, 68, 69, 72, 78–80, 102, 104, 144, 153, 156, 158, 162, 177, 227, 228, 250, 261–263, 286, 287, 302, 314, 350, 354, 356, 374, 385, 425, 441 Inventories, 384 Investment incentives, 60, 367, 385, 437 Irrigation projects, 142–155, 176, 177

457

J Jenkins, G., 80, 108, 143, 239n15, 240n16, 240n17, 242, 254, 268, 386 K Kuyvenhoven, A., 177n9 L Labour conversion factor, 104, 123, 126, 131, 166, 167, 189 cost, 106, 123, 125–131, 142, 149, 155, 156, 307, 311, 442 in excess demand, 129 in excess supply, 167 opportunity cost of, 89, 106, 123, 124, 129, 130, 142, 148, 159, 166, 179, 189, 275, 395, 397, 398n9 skilled, 123, 125, 130, 148, 166, 183, 307, 410 surplus, 88, 123, 125, 185, 186, 275, 415, 434, 437, 442 under-employed, 179, 434, 442 in world price system, 163, 167–170 Land in domestic price system, 131–133 opportunity cost of, 87, 104, 132, 133, 142, 170, 171, 355 in world price system, 163, 170–171 Learning, 209n12, 292, 311, 434, 438 Least cost analysis, 61, 71–72, 74, 80 Life valuation, 293 Linkage effects (SIO approach), 179 Little, I., 48, 88n6, 158n2, 181, 236n12, 395n5, 409n13, 411, 429, 430n18

458 

INDEX

Loans, 16, 17, 36–40, 37n4, 43, 44, 46, 316, 370, 374, 376, 382–384, 390, 391, 393, 393n2, 401–404 Londero, E., 143 M Maintenance cost, 25, 26, 316 Marginal cost, 121, 122, 164, 191, 231, 377–379, 385, 437 Marginal productivity, 125 Marginal revenue, 114 Marginal utility, 214, 215n16, 233, 233n8, 243, 247 Marglin, S., 230n3, 254 Markandya, A., 348n19, 358 Market life of a project, 25 Market prices, 83, 84, 88, 89, 107, 114, 115, 117, 120, 131, 140, 141, 222, 343 Material cost, 155, 259 Materials ratio, 264, 267 Mining project, 320, 352, 353 Mirrlees, J., 48, 158n2, 181, 236n12, 395n5, 409n13, 411, 429, 430n18 Mitigating environmental damage, 329 N National park project, 334–335 Natural resources (depletion), 358–361 Net benefits, 17, 26, 27, 40–43, 45, 58–60, 69, 70, 77–79, 87, 162, 231, 268, 269, 284, 309, 381, 388, 406–409, 437, 441 Non-incremental benefits, 121, 275–277, 284, 311, 313, 328 consumption, 417, 418, 420, 421

inputs, 94, 95, 121, 171, 395 outputs, 30, 92, 93, 96–98, 121, 195, 197, 198, 217, 302, 391, 395, 397, 415 Non-renewable resources (depletion premium), 358, 360 Non-traded activities, viii, 91, 104, 115, 122, 123, 134n15, 161, 164, 165, 179, 180, 191, 195–199, 292 Non-traded goods in domestic price system, 111, 157, 160 valuation of, 12, 91, 96, 98, 119–124, 199, 436 in world price system, 160, 163–167 Non-traded inputs in fixed supply, 121, 395, 436 in variable supply, 164, 198, 395 Non-use value (environment), 322, 324, 326 Numeraire distribution analysis and, 396 economic pricing and, 100–103 willingness to pay and, 100 O Official exchange rate (OER), 103, 118, 118n5, 121, 122, 134–140, 135n16, 150, 153, 155, 160–166, 169, 170, 171n6, 173, 180, 383, 395n4, 397, 400, 422 Operating and maintenance cost, 71, 306, 307, 378 Operating cost, 22, 25, 27, 28, 31, 35–37, 42, 48, 66, 72, 77, 78, 106, 122, 144, 150, 153, 155, 156, 177, 275, 277, 278, 280, 309–311, 313, 352, 355, 391, 396, 414, 416, 417, 422 Operating surplus, 185, 186, 189

 INDEX 

Opportunity cost discount rate, 47, 64, 78–79, 104, 236n12, 238, 240, 246, 253, 298, 347, 348, 353, 359, 390, 391, 441 of capital, 76n5, 229, 238, 242, 247, 345n16, 398 of labour, 123, 179, 398n9 of land, 87 of non-traded goods, 195–199 of production, 148, 162 of resources, 47, 54, 353 Option value of environment, 323, 336 of waiting, 268–271 Organisation for Economic Cooperation and Development (OECD), 3 Output effects in competitive market, 92, 95 in regulated market, 94 Owner, project, 5, 6, 16, 36–38, 46, 46n8, 59–60, 80, 108, 352, 389, 391, 393, 401, 402, 428 P Pearce, D., 218, 271n6, 348n19, 358 Physical stocks, 27, 35, 87 Potts, D., 303, 386 Pouliquen, L., 268 Poverty impact, 13, 406–408, 410, 424, 428n17 Poverty-oriented projects, 406–408 Power project, 390, 392, 396, 402, 404, 428 Prefeasibility study, 5 Present values, 42, 43, 58, 71, 77, 79, 87, 141, 155, 221, 227, 231, 241, 269, 269n5, 286, 287, 296–298, 298n17, 309, 313, 316, 347, 348, 351, 354, 356,

459

376, 379, 389, 391, 393, 402, 403, 407, 409, 420, 423 Preventive expenditure, 325, 329 Price-consumption points, 200 Price elasticity of demand, 93, 95, 115n3, 135, 135n16, 197n1, 201, 335, 415 of supply, 93, 95, 135, 135n16 Price system domestic, 111–155, 157, 159–164, 159n3, 166, 169, 171, 171n6, 172, 177, 181n11, 188n14, 306, 415, 417 equivalence of domestic/world, 163, 165, 172 world, 157–194, 292n11 Primary factors, 159, 159n4, 177, 179, 181, 182, 184–190, 192–194 Private sector charges and cost recovery, 381 incentives, 374, 385 income flows, 389–391 Probability (risk analysis), 13, 263 Producers’ prices, 390–391 Productivity, 3, 88, 89, 104, 125, 127, 129–131, 148, 158, 169, 176, 280, 281, 283, 284, 287, 290, 292–294, 301, 303–306, 304n24, 311, 314, 324, 325, 327, 329, 383, 395, 440 Profits, 16, 36–38, 40, 107, 123–125, 148, 183, 185, 189, 192, 232, 268, 360, 370, 372, 374, 389, 391, 395, 395n4, 401, 403, 403n11, 404, 439 Project analysis basic ideas, 1, 11 economic adjustment programmes and, 439 planning process and, 4 value system in, 434–436

460 

INDEX

Project criteria equivalence of, 59 for project alternatives, 51, 61–69, 71–72, 74–76 for single projects, 51, 52 with shortage of investment funds, 76–79 Project financial statements, 15, 17, 37, 38, 46, 48, 115 Project identification, 5 Project life, 24–27, 35, 40, 41, 48, 140, 231, 289, 305, 348, 356, 369, 379n3 Project resource flows, 31–36, 103, 108 Project resource statements conventions used in, 15, 22–23 main features of, 15–48 project alternatives, 62 Project viewpoints, 16–18 Property prices, 330–332 Protection of environment, 319–322 Public goods, 4, 377, 378 Purchasers’ prices, 183, 185, 191 R Railway project, 368–370, 372, 374 Ray, A., 158n2, 172n7 Regulated market, 94, 96 Rehabilitation projects, 354 Relative costs, 378 Relative prices, 18, 19, 39, 144, 147, 150, 158, 176, 351, 351n22, 352, 354, 382, 383 Repatriated profits, 403 Replacement cost, 23, 24, 142n24, 316, 325, 329, 361 Resettlement projects, 324 Residual value, 23–27, 30, 35 Resource allocation, 4, 113, 228 Resource depletion, 358–359 Resource extraction cost, 359

Resource flows, 22, 30–36, 44, 58, 68, 103, 104, 108, 259, 263, 383 Resource statements conventions used in, 22–23 main features of, 15–48 project alternatives, 62 Return on equity (ROE), 374–376, 404 Risk analysis, 13, 258, 262–268, 344 Risk reduction, 267, 301 Road project, 274–276, 305 Rule of half, 97n9, 200, 204, 279, 279n3 S Salvage value, 26, 27, 71 Savings cost of, 36, 72, 98, 116, 121, 198, 275–279, 281, 282, 284, 293, 302, 311, 396 foreign, 89, 140, 239, 240, 240n17 premium, 236n12 Scott, M., 143, 177n9 Semi-input output (SIO) advantage of, 179–180 approach, 142n23, 180, 184 data requirements, 180 Sensitivity analysis, 13, 257–261, 263, 264, 291, 341 Shadow exchange rate (SER), 12, 101, 103, 106, 117, 118, 118n4, 118n5, 122, 134–143, 142n22, 150, 153, 155, 156, 159–163, 160n5, 165, 166, 169, 172, 173, 179, 275, 307, 311, 325n3, 355, 396, 415, 422, 439, 442 Shadow prices, 7, 10, 84, 140, 143, 230–232, 235–237, 241, 246, 247, 253 Shadow wage, 104, 123, 126, 128, 129n13, 131, 166, 275, 291, 292, 395n5, 398n9, 429, 430

 INDEX 

Skilled labour in domestic price system, 307 in world price system, 166 Social sector projects, 283, 312 Social time preference (STP), 12, 229–243, 246–253, 298, 298n19, 345n16, 347, 349, 425 Squire, L., 158n2, 181, 411, 430n18 Standard conversion factor (SCF), 101, 103, 161, 164–166, 169–173, 176, 177, 179, 180, 183, 183n12, 185, 186, 189–191, 292n11, 394n3, 396n6 Statistical life, 301 Stern, N., 344n15, 349n20 Stiglitz, J., 344n15 Subsidies, 16, 17, 38, 60, 85, 89, 93, 97, 106, 107, 137, 137n18, 138, 144, 148, 153, 160, 182, 185, 186, 218, 277, 368, 379, 381, 386, 389–391, 394, 397, 401, 404, 406n12, 415, 427, 430, 430n20, 439, 443 Sunk costs, 26n2 Supply average incremental cost of, 379, 385 marginal cost of, 121, 377–379, 385 price elasticity of, 93, 95, 135, 135n16, 197n1 Survey approach, 195, 199, 204, 336, 355 Sustainability environmental, 319, 443 financial, 15, 60, 153, 367–372, 379, 381, 382, 384–386 Switching value, 148n25, 251, 258–261, 291, 341, 347 T Tariffs, 89, 95, 96, 103, 104, 106, 107, 113, 124, 137, 144, 150,

461

153, 165, 185, 191, 200–202, 204, 219, 376, 377, 379, 381, 387, 391, 396–398, 401n10, 411–424 Taxation, 36, 229, 230, 238, 245, 283, 291, 372–374, 376, 391, 401, 406n12, 424, 425 Technical life (of project), 26 Technology, 2–5, 19, 51, 61, 62, 69, 89, 197, 224, 267, 306, 342, 343, 345, 378, 379 Terminal value, 259, 287, 289, 307, 356, 391 Total capital, 16–18, 36–38, 52, 59–60, 80, 369 Tradable goods, 112, 113, 158 Trade effects of projects, 89–90 efficiency, 12, 111, 140–143, 159, 434, 438 opportunities, 3, 12 opportunity cost, 3, 4, 90, 98 Traded goods in domestic price system, 111–113 valuation of, 91, 113–115, 402 in world price system, 163–164 Transaction costs, 385 Transport costs, 106, 113, 183, 186, 186n13, 191, 303 Transport project, 11, 97n9, 261, 274, 275, 279–282, 279n3, 302–310, 333 Travel cost, 274–277, 279, 302, 304, 305, 325, 326, 333–335 U Uncertainty, 13, 69, 70, 179, 180, 192, 210, 214, 215, 227, 235n11, 242–248, 250, 253, 257–271, 300, 306, 323, 339, 344, 346, 347, 349, 351, 356, 360, 385

462 

INDEX

Under employment, 125–127, 275, 307, 395 Unemployment, 123, 125, 139n21, 284, 291, 312, 313 United Nations Industrial Development Organisation (UNIDO), 3, 48, 108, 143, 218, 230n3, 411 United States Agency for International Development (USAID), 325n3, 326n4, 358 Unskilled labour in domestic price system, 307 in world price system, 166 Updating investments, 20 Use value (environment), 335 V Valuation of benefit, 86, 435 of environment, 326, 356 van der Tak, H., 158n2, 181, 411, 430n18 W Wage economic, 127, 130, 131, 169, 398, 415, 439 financial, 188, 189, 395, 439 shadow, 104, 123, 126, 128, 129n13, 131, 166, 275, 291, 292, 395n5, 398n9, 429, 430 Water supply project benefit valuation of, 71 distribution analysis of, 411–424 financial analysis of, 411–414

Weiss, J., vii, ix, 268, 303 Weitzman, M., 244 Welfare gains, 283 Willingness to pay (WTP) approximate measures of, 421 contingent valuation approach to, 211 for environment, 211–213 for non-traded inputs, 171 for non-traded outputs, 171 and numeraire, 172 With and without project comparison, 83 Workers in excess demand, 125, 169 in excess supply, 125, 167 Working capital, 16, 22, 27–31, 35, 38, 40, 48, 76, 384 Work-in-progress, 27–29 World Bank, 3, 181, 276, 289n7, 303, 346 World prices, 12, 90, 91, 98, 101–103, 106, 108, 112–115, 124, 127, 134, 136–138, 140, 153n27, 156–177, 157n1, 179, 180, 181n11, 183, 186, 188, 188n14, 189, 328, 355, 360, 395n4, 396n6, 397, 409n15, 434, 436, 439, 440 World price system conversion factors in, 165, 168, 177 equivalence with domestic price system, 163, 167 land in, 170–171 non-traded goods in, 164–167 skilled labour in, 166 traded goods in, 163–164 unskilled labour in, 167–169