Commodity Prices and Development
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Commodity Prices and Development
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Commodity Prices and Development Edited by Roman Grynberg and Samantha Newton
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Great Clarendon Street, Oxford ox2 6dp Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide in Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries Published in the United States by Oxford University Press Inc., New York ß Commonwealth Secretariat 2007 The moral rights of the author have been asserted Database right Oxford University Press (maker) First published 2007 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate this book in any other binding or cover and you must impose the same condition on any acquirer British Library Cataloguing in Publication Data Data available Library of Congress Cataloging in Publication Data Data available Typeset by SPI Publisher Services, Pondicherry, India Printed in Great Britain on acid-free paper by Biddles Ltd., King’s Lynn, Norfolk ISBN 978–0–19–923470–7 1 3 5 7 9 10 8 6 4 2
Contents
List of Figures List of Tables Notes on Contributors Introduction Roman Grynberg and Samantha Newton
vii xi xiii 1
PART I The Issue of Declining Commodity Prices 1. The Problems of Commodity Dependence Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
7
2. Secular Decline in Relative Commodity Prices: A Brief Review of the Literature Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
17
3. Long-Run Trend in the Relative Price: Empirical Estimation for Individual Commodities Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
35
4. Analysis of Movements in the Productivity and Prices of Selected Tropical Commodities in Developing Countries, 1970 to 2002 Euan Fleming, Prasada Rao, and Pauline Fleming 5. Commodity Value Chains Compression—Coffee, Cocoa, and Sugar Jaya Choraria
PART II
68
136
The Implications of Declining Commodity Prices
6. Estimating Foreign Exchange Loss due to Declining Commodity Prices Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
163
v
Contents 7. Marginalization of LDCs and Small Vulnerable States in World Trade Bijit Bora, Roman Grynberg, and Mohammad A. Razzaque
PART III
175
Mitigating the Impacts for Commodity Dependent Countries
8. Instruments for Addressing Commodity Price Behaviour Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
269
9. Commodity Prices and the Debt Relief Initiative Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
278
10. Aid Flows and Commodity Prices Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
301
References Index
329 345
vi
List of Figures
1.1 Share of Agricultural Products in Global Merchandise Exports
10
1.2 Per Capita Exports and Primary Exports as Percentage of Merchandise Exports in 144 Developing Countries
10
1.3 Share of LDCs, SVs, and HIPCs in World Merchandise Exports: 1950–2001
12
1.4 Relationship between Real GDP Growth Rate and Share of Primary Exports in Total Merchandise Export Volume in Developing Countries
12
2.1 Grilli–Yang Relative Price of Primary Commodities and its Changes Over Time
24
3.1 Relative Prices of 13 Commodities: 1900–2001
41
3.2 Real Prices of Broad Commodity Groups
46
3.3 Estimated Growth in Relative Prices for Broad Commodity Groups
46
3.4 Trend (1960–2002) Growth Rates for Individual Commodities with UNCTAD Data
49
3.5 Trend Growth Rates since the 1960s: Grilli–Yang versus UNCTAD Data
50
4.1 Export Quantity Index for Selected Commodities in All Sampled Countries, 1970–2002
75
4.2 Export Quantity Index for Selected Commodities in Sampled Commonwealth Countries, 1970–2002
76
4.3 Export Quantity Index for Selected Commodities in Sampled African Countries, 1970–2002
76
4.4 Export Quantity Index for Selected Commodities in Sampled African Commonwealth Countries, 1970–2002
77
4.5 Production Functions, Technological Change, and Technical Efficiency Change
81
4.6 Export Price Index for Selected Commodities in All Sampled Countries, 1970–2002
96
4.7 Export Price Index for Selected Commodities in Sampled Commonwealth Countries, 1970–2002
97
4.8 Export Price Index for Selected Commodities in Sampled African Countries, 1970–2002
97
vii
List of Figures 4.9 Export Price Index for Selected Commodities in Sampled African Commonwealth Countries, 1970–2002
98
4.10 Export Price and Import Price Indices for All Commodities in All Sampled Countries, 1970–2002
99
4.11 Export Price and Import Price Indices for Tree Crops in All Sampled Countries, 1970–2002
99
4.12 Export Price and Import Price Indices for Field Crops in All Sampled Countries, 1970–2002
100
4.13 Annual Rates of Change in Labour Productivity in All Sampled Countries, 1970–2002
102
4.14 Annual Rates of Change in TFP in All Sampled Countries, 1970–2002
103
4.15 Annual Rates of Change in TFP in Commonwealth Countries, 1970–2002
106
4.16 Trends in Export Unit Values and TFP in Jamaica, 1970 to 2002
116
4.17 Trends in Export Unit Values and TFP in Fiji, 1970 to 2002
117
4.18 Trends in Export Unit Values and TFP in Solomon Islands, 1970 to 2002
117
4.19 Trends in Export Unit Values and TFP in Ghana, 1970 to 2002
118
4.20 Selected Countries with Lower Rate of TFP Growth to Rate of Decline in Export Unit Value, 1970 to 2002
120
4.21 Trends in Export Unit Values and TFP in Malaysia, 1970 to 2002
121
4.22 Selected Countries Experiencing Rates of Decline in Export Unit Values and TFP, 1970 to 2002
121
4.23 Trends in the Single Factoral Terms of Trade in Nigeria, 1970 to 2002
124
4.24 Trends in the Single Factoral Terms of Trade in the Central African Republic, 1970 to 1998
125
4.25 Trends in the Single Factoral Terms of Trade in Papua New Guinea, 1970 to 1998
126
4.26 Trends in the Single Factoral Terms of Trade in Costa Rica, 1970 to 2002
127
4.27 Trends in the Single Factoral Terms of Trade in Mauritius, 1970 to 1998
127
4.28 Trends in the Single Factoral Terms of Trade in Solomon Islands, 1970 to 1998
128
4.29 Trends in the Single Factoral Terms of Trade in Sri Lanka, 1970 to 2001
128
4.30 Trends in the Single Factoral Terms of Trade in Kenya, 1970 to 1998
129
4.31 Trends in the Single Factoral Terms of Trade in Sierra Leone, 1970 to 1998
130
4.32 Trends in the Single Factoral Terms of Trade in Trinidad and Tobago, 1970 to 1998
130
5.1 Coffee: Cameroon-UK
151
5.2 Coffee: Ethiopia-UK
151
viii
List of Figures 5.3 Coffee: Kenya-UK
152
5.4 Coffee: PNG-UK
152
5.5 Coffee: Tanzania-UK
153
5.6 Coffee: Ghana-UK
153
5.7 Sugar: farm gate-to-retail price spreads
154
5.8 Sugar: farm gate-to-retail price spreads
154
5.9 Sugar: Mauritius-US
155
5.10 Sugar: Mauritius-EU
155
5.11 Sugar: Fiji-US
156
5.12 Sugar: Fiji-EU
156
5.13 Sugar: Brazil-US
157
5.14 Sugar: Thailand-US
157
5.15 Sugar: Australia-US
158
6.1 Composite Relative Commodity Price Index and its Changes
166
6.2 Volume and Purchasing Power of Exports
168
6.3 Foreign Exchange Loss as a Percentage of Primary and Merchandise Exports
171
7.1 Share of LDC Exports in Global Merchandise Exports, 1950–2000
182
7.2 Share of Small States in Global Merchandise Exports, 1950–2000
182
7.3 Share of Small States and LDCs in Commercial Services Exports
186
7.4 Declining Importance of Small States and LDCs in World Export (Merchandise Plus Services) Trade
187
7.5 Share of Small States and LDCs in World Trade Transactions
188
7.6 Aggregate Exports (Merchandise Plus Services) of Individual LDCs ($million)
190
7.7 Aggregate Exports (Merchandise Plus Commercial Services) of Individual Small States
191
7.8 Share of Individual LDCs in World Aggregate Exports, 1980–2000
192
7.9 Share of Individual Small States in Aggregate Global Exports, 1980–2000
193
7.10 Marginalization of Individual LDCs in Aggregate Exports (Merchandise plus Commercial Services), 1980–2000
197
7.11 Marginalization of Individual Small States in Total Exports (Merchandise plus commercial services), 1980–2000
197
7.12 Net Shifts in 1995–2000 as Percentage of 1990–94 Average Exports (Merchandise Plus Services) for LDCs
206
7.13 Net Shifts in 1995–2000 as Percentage of 1990–94 Average Exports (Merchandise Plus Services) for Small States
206
7.14 Trends in Marginalization and Growth of Real GDP in LDCs
207
ix
List of Figures 7.15 Trends in Marginalization and Growth of Real GDP in Small States
207
7.16 Composition of Exports in LDCs: Primary vs. Manufacturing
209
7.17 Share of Primary and Manufacturing Exports in Small States
209
7.18 Share of Agriculture in World Exports and World Exports-GDP Ratio
212
7.19 Scatter Plot of lnMAR and lnAGX
213
7.20 Scatter Plot of lnMAR and lnGLO
214
7.21 Plot of Variables and their Correlograms
217
7.22 Scatter Plot of lnMARSS and lnAGX for Small States
223
7.23 Scatter Plot of lnMARSS and lnGLO for Small States
223
7.24 Share of LDCs and Small States in Global Inflow of FDI
228
9.1 Real Commodity Price Index and Real Outstanding Debt in HIPCs
281
9.2 Projected Export Growth and NPV Debt-to-Export Ratio for Countries that have Reached Completion Point
289
9.3 Average 1990–99 Actual Export Growth Rate vis-a`-vis Projected Growth Rate for HIPCs
291
10.1 Composite Relative Commodity Prices and Aid Flows to LDCs, 1980–2000
303
10.2 Composite Relative Commodity Prices and Aid Flows to HIPCs, 1980–2000
303
10.3 Composite Relative Commodity Prices and Aid Flows to Small States, 1980–2000
304
10.4 Aid Flows to Mali and Cotton Prices
304
10.5 Aid Flows to Papua New Guinea and the Real Cocoa Price
305
10.6 Aid Flows to Togo and the Real Phosphate Price
305
x
List of Tables
1.1 Commodity Export Dependence in LDCs, SVs, and HIPCs
8
1.2 Large Share of Export Earnings from a Single Commodity in LDCs, SVs, and HIPCs
9
2.1 Summary of Findings on Secular Decline in Commodity Prices
31
3.1 Regression Results for 13 Commodities (with Updated Grilli-Yang Series: 1900–2001)
44
3.2 Regression Results for Broad Commodity Groups as in UNCTAD Commodity Price Bulletin: Annual Data (1960–2002)
45
4.1 Contributions by Selected Commodities to Export Earnings and Agricultural Output
73
4.2 Estimates of Trends in Export Quantities of Selected Commodities, 1970 to 2002
75
4.3 Major Exports of Selected Commodities
90
4.4 Proportion of the Total Value of Crop Output Contributed by the Selected Commodities in 1990
92
4.5 Estimates of Trends in Export Unit Values of Selected Commodities, 1970 to 2002
95
4.6 Estimated TFP Model
110
4.7 Estimated Labour Productivity Model
112
4.8 Aggregate Rates of Change in TFP and Export Prices
114
4.9 Comparison of Rates of Change in TFP and Selected Commodity Prices
116
4.10 Trends in the Single Factoral Terms of Trade
123
5.1 Breakdown of 2003 Raw Sugar Sales
148
6.1 Estimated Foreign Exchange Loss by Individual LDCs, SVs, and HIPCs (US$ million in 1984–86 prices)
169
6.2 Cumulative Foreign Exchange Loss from some Selected Commodities, 1985–2000
172
7.1 Absolute Volume of Exports
177
7.2 Absolute Volume of Merchandise Imports
179
7.3 Absolute Growth of Merchandise Exports ($billion)
180
7.4 Trend Growth Rates of Exports (per cent)
181
xi
List of Tables 7.5 Exports of Commercial Services ($billion)
183
7.6 Imports of Commercial Services ($billion)
184
7.7 Absolute Growth of Commercial Services Exports ($billion)
185
7.8 Growth Rates of Exports of Commercial Services
185
7.9 Volume of Export Trade (Merchandise Plus Commercial Services) ($ billion)
186
7.10 Total Trade Transactions of Different Country Groups ($billion)
188
7.11 Growth Rates of Merchandise and Services Exports from Individual LDCs
194
7.12 Growth Rates of Merchandise and Services Exports From Individual Small States
195
7.13 Average Change in Exports of LDCs in the 1990s
199
7.14 Average Change in Exports of Small States in the 1990s
200
7.15 A Summary of Trends in Marginalization of LDCs in the 1990s
201
7.16 A Summary of Trends in Marginalization of Small States in the 1990s
203
7.17 Fall in Commodity Prices in Real Terms
210
7.18 Computed F Test Statistics and Critical Values
215
7.19 DF and ADF Tests for Unit Roots
216
7.20 PHFMOLS Estimates of the Model
220
7.21 Short-Run Error Correction Model
222
7.22 Unit Root Test for lnMARSS
224
7.23 Short-Run Error Correction Model
225
7.24 Official Financial Flows ($ million)
227
8.1 Salient Features of Five Important International Commodity Agreements
271
9.1 Foreign Exchange Losses from Commodities and Outstanding Debt
281
9.2 Debt Relief Initiatives
283
9.3 Debt Relief for HIPC Countries
284
9.4 Debt Indicators in Developing Countries and HIPCs, 1999 (%)
286
9.5 Actual and Projected Debt Service Indicators for HIPCs that have Reached Decision Point
294
9.6 Hypothetical Cost of Compensation for HIPCs
297
10.1 Cost Estimates for a Joint Diversification Fund for LDCs, HIPCs and Small States (US$ million in 1984–86 prices)
312
10.2 Hypothetical Burden Sharing among Donors
314
10.3 ODA/GNI Positions of Donors after the Hypothetical Contributions to the Joint Diversification Fund
315
xii
Notes on Contributors
Bijit Bora
Counsellor, Economic Research and Statistics Division, World Trade Organization, Geneva, Switzerland.
Jaya Choraria
Research Fellow, Economic Affairs Division, Commonwealth Secretariat, London, United Kingdom.
Euan Fleming
Senior Lecturer, Department of Agricultural and Resources Economics, The University of New England, Armidale, Australia.
Pauline Fleming
Lecturer, School of Economics, The University of New England, Armidale, Australia.
Roman Grynberg
Deputy Director, Economic Affairs Division, Commonwealth Secretariat, London, United Kingdom.
Philip Osafa-Kwaako
Research Fellow, Economic Affairs Division, Commonwealth Secretariat, London, United Kingdom.
Prasada Rao
Professor, School of Economics, University of Queensland, Australia.
Mohammad A. Razzaque
Lecturer, Department of Economics, University of Dhaka, Dhaka, Bangladesh.
xiii
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Introduction Roman Grynberg and Samantha Newton
More than fifty developing countries depend on three or fewer commodities for more than half of their exports. Thirty-four of the Less Developed Countries (LDCs) rely on primary commodities to contribute at least half of their export earnings; for seventeen of them, primary commodities contribute more than 75 per cent. Twenty-two Small Vulnerable States (SVs) rely on commodities for more than 50 per cent of exports. Similarly, 32 of the 42 HIPCs are predominantly exporters of primary commodities. In fact, reliance on only a single commodity for a large share of export earnings is quite common in these countries, exposing them to the risk of export earnings instability as a result of price shocks and, perhaps even more significantly, falling purchasing power of exports over the long run in the face of the declining real price of the commodity in question. Over the past two decades the prices of nearly all the major agricultural commodities declined in real terms. Whether the terms of trade have moved unfavourably against primary commodities has been a subject of great controversy in the development economics literature since 1950 when Prebisch and Singer first hypothesized the problem. Despite contrasting evidence, in recent times there is a broad consensus for the long-run deterioration in relative commodity prices. The research carried out by such influential multilateral organizations as the World Bank and IMF has contributed to the formation of this consensus position. The studies documented in this book, the outcome of five years of research at the Commonwealth Secretariat, add further weight to Prebisch and Singer’s hypothesis. The empirical investigation presented in Chapter 3 of this text provides evidence of the presence of a statistically significant declining trend in the relative price of most individual commodities, much higher rates of decline being observed for the more recent period. It was found that over the past century, the estimated trend growth rates for most commodities fell between 0.79 and 1.43 per cent per annum. Much higher rates of decline are observed
1
Introduction over the relatively more recent period. Between 1960 and 2002 the aggregate relative price of commodity has fallen at an annual rate of 1.82 per cent with the corresponding figures for individual commodities ranging from 0.9 to 3.50 per cent. UNCTAD (2004a, p. 22) observed that the net effect of the secular decline in prices depends on two things—the extent to which world market prices are transmitted to producers and whether higher export volumes (eg through productivity and yield improvements) make up for falling prices. Chapter 4 addresses UNCTAD’s later point. The study, focusing on tropical commodity (coffee, cocoa, copra, palm kernel oil, coconut oil, palm oil, rice, cotton and sugar) dependent developing countries, investigates whether producers of commodities in developing countries have compensated for falling producer prices by increasing total factor productivity and whether falling export prices have been compensated for by rising total factor productivity of commodities at the national level in developing countries. It was found that very few of the countries studied had achieved rates of productivity growth that even matched, let alone counteracted, the rate of change in real prices. In determining the extent to which world market prices are transmitted to producers, the issue of a long run secular decline in the relative price of primary commodities must be considered in the context of the issue of a decrease in the producer’s share of retail value over time. Despite numerous quantitative studies providing evidence to illustrate the extent of the problems of commodity prices, historically there has been a lack of quantitative analysis of the evolution of the producer’s share of total retail value. However, Chapter 5 details a study of commodity value chain compression for coffee, cocoa, and sugar. The study uses time series data of prices along entire commodity chains from raw material, in a commodity exporting developing country, to final retail product, in a developed consuming country, in order to provide descriptive analysis of the evolution of farm gate-to-retail price spreads. Comparisons are made across the commodities studied and across countries in order to provide insight into the causes of changes in the farm gate-to-retail price spread over time. The evidence gathered on widening farm gate-to-retail price spreads (equivalent to a decrease in the farmer’s share of retail value over time) illustrates the plight of farmers in commodity exporting developing countries. Interestingly, evidence suggests that the compression suffered by sugar farmers in Fiji and Mauritius, countries enjoying preferential trading agreements with the EU, was less severe than for farmers in countries which did not benefit from the Sugar Protocol. The persistent weakness of real commodity prices presents serious challenges for export earnings and domestic incomes in commodity dependent countries. Secular decreases in real prices of commodities have caused lower purchasing power of primary exports, on which most of these countries rely predominantly for financing their imports. The resultant foreign exchange
2
Introduction losses relative to the total primary and merchandise exports of many of these countries are quite substantial. It is estimated that during the period 1995–2000, the countries comprising LDCs, HIPCs and SVs, suffered a cumulative foreign exchange loss of US$37 billion due to weakness in commodity prices (i.e. about US$6 billion per annum). For LDCs, the average annual loss is estimated to be about US$2.3 billion while the corresponding figures for HIPCs and SVs are US$5.5 and US$0.6 billion respectively. For many countries cumulative losses from a single commodity were found to be very large. For commodity dependent poor countries, persistent downward trends in real commodity prices, unallayed by higher export volumes, have resulted not only in significant foreign exchange losses but also in a failure to derive much benefit from the ongoing process of trade liberalization and globalization. We have attempted (in Chapter 7) to explain marginalization of LDCs and SVs in merchandise exports in terms of falling share of agricultural products in total global exports and in terms of world export-GDP ratio. The study establishes a valid long-run statistical relationship, indicating that these factors explain about 91 and 85 per cent variation in the declining share of world trade of LDCs and SVS respectively. A review of existing and recent instruments in international commodity policy finds that these instruments did not address the issue of long-run weakness in primary commodity prices. While price stabilization was the principal motive of the international commodity agreements, nevertheless they attempted, through market intervention, to raise the depressed price levels for a number of commodities. However, since the collapse of commodity agreements, there has not been any significant initiative to revive the prices of commodities. IMF external compensatory financing and EU-STABEX schemes focused only on the shortfalls in absolute export earnings and export earnings from commodities and commodity prices were not specifically targeted. On the other hand, various commodity protocols under EU-ACP trade arrangements guaranteed preferential prices for specific commodities exported by some selected suppliers. The scope of such preferences was very limited; however evidence suggests that farmers of the specific commodities covered in countries that benefited from the arrangements may have suffered less severe compression of commodity prices than those that did not benefit; e.g. sugar farmers in Fiji and Mauritius. Most commodity dependent low-income countries have also become heavily indebted and are included in the World Bank-IMF sponsored HIPC initiative. While the HICP debt relief initiative is commendable, the failure to address the problems of weakness in commodity prices adversely affecting export earnings prospects of the beneficiary countries threatens the credibility of the scheme. It is argued that a permanent solution to the problem of debt crisis lies in the structural shift in composition of the export basket of these countries. We propose an expansion of the HIPC initiative to include all LDCs
3
Introduction and SVCs and a supplementary debt-relief support, which would provide additional debt relief to the HIPCs in the event of adverse trends in commodity prices leading to unsustainable debt burden. Aid flows to LDCs, HIPCs, and SVs have not attempted to compensate for the losses incurred by the recipient countries as a result of the secular decline in commodity prices. Sustained weakness in commodity prices requires export diversification and structural changes in the economy. The international community can support the attempts toward diversification made by poor, commodity dependent countries. The study thus proposes the establishment of a multilateral fund that would provide resources needed to support diversification projects in commodity dependent developing countries. Illustration of hypothetical schemes shows that contribution by the donors to a multilateral diversification fund on the basis of some proportion of terms of trade loss suffered by LDCs, HIPCs, and SVs would increase the donors’ current ODA/ GNI ratio only marginally. The problems faced by commodity dependent LDCs, HIPCs, and SVs are formidable. Although diversification is the most appropriate response to the problem of the secular decline in commodity prices, long-term transformation in the economy can be a slow process and in the long-run the success will depend on a host of such factors as the development of human resources, institutional capacity building, poverty alleviation, and appropriate domestic policy and environment. By granting increased aid flows and debt relief, and providing assistance to encourage production of non-traditional export items, the international community can play a proactive role in the development of the commodity dependent poor countries. Only concerted efforts both at the domestic fronts of these countries and co-operation extended by the international community can help mitigate the problem of the world’s most vulnerable economies.
4
Part I The Issue of Declining Commodity Prices
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1 The Problems of Commodity Dependence Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
For a long time, commodity prices have been a source of considerable interest among academic researchers, and have been a major cause of concern for policy-makers and a harsh reality in the lives of poor people in countries that rely predominantly on primary production and exports. Primary commodity prices are not only associated with violent fluctuations, but have also exhibited a long-run declining trend relative to manufactured goods. When the declining terms of trade for primary commodities was first catapulted into prominence (Prebisch, 1950 and Singer, 1950), concerns were expressed that it would lead to unequal distribution of gains from trade between the primary producing developing countries and the developed economy suppliers of manufactured goods. In today’s world, however, the exports of developing countries as a group are dominated by manufactured items and consequently commodity production does not act as a divide between the North and the South.1 Nevertheless, for the overwhelming majority of the economies classified, not mutually exclusively, as least developed countries (LDCs), small vulnerable states (SVs) and heavily indebted poor countries (HIPCs), dependence on the production of primary commodities remains a major challenge for development.2 In 34 LDCs (about 70 per cent), primary commodities contribute at 1 Industrialized countries are still the dominant suppliers of primary commodities in the world market. According to UNCTAD data, in 2000 developed market economies accounted for 64 per cent of world’s primary exports (excluding fuels). Primary exports (without fuels) constituted about 11 per cent of total merchandise exports of both the developed and developing countries. 2 LDCs are considered to be the poorest countries of the world and are frequently deemed to be structurally handicapped in their development. This study uses the December 2001 list of 49 LDCs as defined by the UN Economic and Social Commission. On the other hand, because of the small size of their domestic economies, their remoteness and isolation and their economic vulnerability and susceptibility to natural disaster, SVS are also confronted with
7
The Issue of Declining Commodity Prices Table 1.1. Commodity Export Dependence in LDCs, SVs, and HIPCs Dependence greater than Dependence between 75 % 50–75 %
Dependence between 25–49 %
Dependence less than 25 %
Solomon Islands (96%)*§ Burundi (92%)*y Suriname (92%)§ Uganda (92%)*y Samoa (91%)*§ Ethiopia (90%)*y Niger (90%)*y St Vincent and Grenadines (90%)§ Rwanda (89%)*y Zambia (89%)*y Malawi (88%)*y Tonga (84%)§ Belize (82%)§ Kiribati (82%)*§ Madagascar (81%)*y Nicaragua (81%)y Vanuatu (81%)*§ Congo, D. R. (80%)*y Guyana (80%)§y Jamaica (80%)§ Gambia (79%)*§y Guinea-Bissau (79%)*y Sao Tome and Principe (79%)*§y Guinea (76%)*y
Benin (48%)*y Seychelles (48%)§ Afghanistan (45%)* Cyprus (42%)§ Djibouti (40%)*§ Lao PDR (37%)*y Eritrea (36%)* Haiti (36%)* Mauritius (34%)§ Tuvalu (34%)*§ Barbados (25%)§
Bhutan (19%)* Nepal (19%)* Gabon (18%)§z Bangladesh (16%)* Cambodia (16%)* Botswana (15%)§ Equatorial Guinea (13%)*§ Lesotho (12%)*§ Republic of Congo (8%)y Antigua and Barbuda (7%)§ Trinidad and Tobago (5%)§z Yemen (5%)*zy Malta (4%)§ Angola (2%)*zy
Mauritania (73%)*y Somalia (73%)*y ˆ te D’Ivoire (71%)y Co Ghana (71%)y Tanzania (71%)*y Chad (70%)*y Honduras (69%)y Mozambique (68%)*y Myanmar (68%)*y Papua New Guinea (67%)§ Grenada (66%)§ Maldives (66%)*§ Burkina Faso (65%)*y Kenya (65%)y Togo (65%)*y Bolivia (64%)y Comoros (64%)*§y Mali (64%)*y Central Af. Rep. (63%)*y St Lucia (61%)§ Liberia (60%)*y Sudan (60%)*zy Cameroon (59%)y Swaziland (57%)§ Vietnam (54%)y Dominica (55%)§ Senegal (55%)*y St Kitts and Nevis (53%)§ Fiji (52%)§ Cape Verde (51%)*§ Sierra Leone (50%)*y
Note : *indicates that the country is a least developed country, § a small vulnerable state, z an oil producing country and y a highly indebted poor country. The figures within the parentheses are average commodity dependence for periods 1980, 1985, 1990, 1995, and 2000. The dependence on primary commodity is estimated excluding the contribution of fuels in total merchandise exports. Source : Authors’ estimates based on data from UNCTAD.
least half of export earnings; for 17 of them, primary commodities contribute more than 75 per cent (Table 1.1). In the case of SVs, there are 22 countries (about 63 per cent of all SVS) where commodities account for more than 50 per cent of exports. Similarly, 32 of the 42 HIPCs (88 per cent) are predominantly overriding problems constraining their economic development. The definition of a small state covers all countries with a population of less than 1.5 million, and also includes Botswana, Jamaica, Mauritius and Papua New Guinea, even though they have populations above the threshold (Grynberg and Razzaque, 2003). Finally, the group of HIPCs comprises 42 poor countries that have accumulated unsustainable external debt. The definitions of LDCs, SVs and HIPCs are not mutually exclusive: 13 small states are LDCs of which two are also HIPCs, and 32 LDCs are HIPCs. Only eight HIPCs are neither LDCs nor SVs. Altogether, 81 countries can be considered as either LDCs, small states or HIPCs. Appendix 1.1 gives a list of these countries.
8
Problems of Commodity Dependence Table 1.2. Large Share of Export Earnings from a Single Commodity in LDCs, SVs, and HIPCs Commodities
50 per cent or more
20–49 per cent
10–19 per cent
Crude Petroleum
Angola, Gabon, Republic of Congo, Yemen
Cameroon, Equatorial Guinea, Trinidad and Tobago, Papua New Guinea St Vincent, Honduras Jamaica, Suriname
Vietnam
Bananas Bauxite Cashew Nuts Cocoa Coffee (Arabica) Coffee (Robusta) Copper Copra and coconut oil Cotton Diamond Fish Gold Jute Livestock Iron Ore Rice Sugar
Guinea Guinea Bissau Sao Tome and Principe, ˆ te d’Ivoire, Ghana Co Burundi, Ethiopia
Cameroon Rwanda
Honduras, Nicaragua
Uganda
Cameroon
Zambia
D. R. Congo, Papua New Guinea Solomon Islands
Kiribati
Mauritania
Benin, Chad, Mali, Sudan Central Af. Republic Mozambique Ghana Mali Mauritania Mauritius, Swaziland, Guyana, St Kitts and Nevis
Tea Timber
Tobacco Uranium Vanilla
St Lucia
Equatorial Guinea, Lao PDR, Solomon Islands
Burkina Faso D.R. Congo Senegal, Maldives Mali, Guyana Bangladesh Niger, Sudan, Nicaragua Guyana Belize
Kenya, Rwanda Cambodia, Central Af. Republic, Gabon, Ghana Myanmar, Papua New Guinea, Swaziland
Malawi Niger Comoros
Source : Cashin et al. (1999).
exporters of primary commodities. Not only do these three groups of countries rely heavily on commodities, but their exports are also concentrated either on a single commodity or on a limited range of exports. In 40 countries (out of a total of 81 LDCs, SVs and HIPCs), three leading commodities account for more than 50 per cent of export earnings (Appendix 1.2). Reliance on a single commodity for a large share of export earnings is quite common in these countries (Table 1.2), exposing them to the risk of export earnings instability as a result of price shocks and falling purchasing power of exports over the long run in the face of the declining real price of the commodity in question.
9
The Issue of Declining Commodity Prices 0.2 0.18 0.16
ratio
0.14 0.12 0.1 0.08
1997
1994
1991
1988
1985
1982
1979
1976
1973
1970
0.06
Figure 1.1. Share of Agricultural Products in Global Merchandise Exports Note and source: Agricultural exports data are from FAO Commodity Yearbook (various issues), while the data on merchandise have been taken from UNCTAD (2002).
ln (per capita exports in US$)
12.0
y = −0.021x + 6.4612 R2 = 0.1159
10.0 8.0 6.0 4.0 2.0 0.0 0.0
20.0
40.0
60.0
80.0
100.0
Primary exports as % of merchandise exports Figure 1.2. Per Capita Exports and Primary Exports as Percentage of Merchandise Exports in 144 Developing Countries Note: Oil-rich developing countries have been excluded. Data on per capita exports are for 1998–2000 average. The vertical axis shows the natural logarithm of per capita exports.
10
Problems of Commodity Dependence There are serious problems associated with excessive dependence on commodity production and exports. On the demand side, low-income elasticity of demand for primary commodities, together with technological advances resulting in declining intensity in the use of raw materials, has exerted a downward pressure on the expansion of overall consumption. Indeed, during the past three decades the share of agricultural products in global merchandise exports has more than halved—falling from about 18 per cent in 1970 to less than 8 per cent in 2000 (Figure 1.1). On the supply side, the improvement of technology, the emergence of new suppliers and the agricultural policy of developed countries have contributed to a rapid expansion in world commodity supplies (Reinhart and Wickham, 1994). The resultant imbalance, stemming from the surge in supply vis-a`-vis depressed demand, has caused a secular decline in relative commodity prices. The declining terms of trade would suggest reduced purchasing power of exports of countries predominantly dependent on primary commodities. This problem is further exacerbated by the interaction between price-inelastic and low-income elasticity of demand for commodities. That is, when the demand is not increasing, the revenue from a commodity with price-inelastic demand will fall if supply is increased. The consequence, known as the ‘adding-up’ problem, is that all commoditydependent countries cannot achieve high export growth. The cross-country experience suggests an inverse relationship between the degree of dependence on primary commodities and per capita exports among the set of developing countries (see Figure 1.2). The low price and income elasticity of demand, falling share of agricultural products in global merchandise exports, and ‘adding-up’ problem imply that if a group of countries continues to specialize in primary products, it will be marginalized in world trade. Between 1950 and 2000 LDCs’ share in world merchandise exports thus fell from more than 2.5 per cent to about 0.44 per cent (Figure 1.3). As most HIPC members are also LDCs, the former closely resembles the marginalization trend of the latter. Finally, the share of SVs in world export trade dropped from 0.5 to 0.2 per cent.3 Apart from the declining terms of trade, commodities have experienced widespread shocks in their prices. Typical large negative shocks have been found, with a year-on-year price fall of 44 per cent and a direct loss of income for given export quantities estimated to be 7 per cent of GDP (Collier, 2002).4 On the other hand, positive price shocks are known to have generated 3 It needs to be mentioned here that falling shares in world export volume may not be a problem as long as a country’s exports grow at some fair rate. However, many commoditydependent countries have been subject to frequent falls in absolute export revenues. With already low export volumes, if these countries cannot increase their share in world trade, globalization will only contribute to more skewed distribution of gains from trade. 4 Collier (2002) estimates that each dollar of direct loss from large terms of trade shock costs the economy US$3.
11
The Issue of Declining Commodity Prices 4 3.5 LDCs
per cent
3
SVs
HIPCs
2.5 2 1.5 1
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
1970
1968
1966
1964
1962
1960
1958
1956
1954
1952
0
1950
0.5
Figure 1.3. Share of LDCs, SVs, and HIPCs in World Merchandise Exports: 1950–2001 Note: Oil-rich countries are excluded. Source: Authors’ estimates.
‘Dutch disease’ effects for non-commodity export and import-competing sectors (Yabuki and Akiyama, 1996). On the whole, the commodity-dependent countries have grown more slowly than others (see Figure 1.4) and the overwhelming majority of them saw declines in purchasing power parity
Real GDP Growth Rate (1980 –2000)
12
y = −0.0275x + 4.6955 R2 = 0.1181
10 8 6 4 2 0 −2 −4 0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
Primary exports as percentage of Merchandise Exports Figure 1.4. Relationship between Real GDP Growth Rate and Share of Primary Exports in Total Merchandise Export Volume in Developing Countries Note: Based on 116 countries for which data are available.
12
Problems of Commodity Dependence (PPP) adjusted per capita incomes (Birdsall and Hamoudi, 2002).5 In 1999, the average real GDP per capita (adjusted for purchasing power) was lower in nonoil commodity-exporting LDCs than it had been in 1970 (UNCTAD, 2002b). There has also been a clear link between dependence on exports of primary commodities and the incidence of extreme poverty. It has been found that the type of export in which poor countries specialize makes a big difference in their degree of economic success and pattern of poverty. In particular, about Appendix 1.1. List of LDCs, Small States and HIPC Countries Countries
LDCs
Afghanistan Antigua and Barbuda Angola Bahrain Bangladesh Barbados Belize Benin Bhutan Bolivia Botswana Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad Comoros Congo ˆ te d’Ivoire Co Cyprus DR Congo Djibouti Dominica Equatorial Guinea Eritrea Ethiopia Fiji Gabon Gambia Ghana Grenada Guinea Guinea-Bissau Guyana Haiti
Yes
Small States
HIPCs
Yes Yes
Yes Yes
Yes Yes Yes Yes Yes
Yes Yes Yes
Yes Yes Yes
Yes Yes Yes
Yes Yes Yes Yes
Yes
Yes
Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes
Yes
Yes Yes Yes Yes Yes Yes Yes Yes
Yes Yes
Yes Yes Yes Yes
Yes Yes Yes
Yes (Continued)
5 The lower growth prospect of commodity-dependent economies is often referred to as ‘resource curse’ in development economics literature, where a rich endowment of natural resource is considered to be detrimental to industrialization or even development of institutions. See Bonaglia and Fukasaku (2003) for a review.
13
The Issue of Declining Commodity Prices Appendix 1.1. (Continued ) Countries Honduras Jamaica Kenya Kiribati Lao PDR Lesotho Liberia Madagascar Malawi Maldives Mali Malta Mauritania Mauritius Mozambique Myanmar Nepal Nicaragua Niger Papua New Guinea Rwanda Samoa Sao Tome and Principe Senegal Seychelles Sierra Leone Solomon Islands Somalia St Kitts and Nevis St Lucia St Vincent and the Grenadines Sudan Suriname Swaziland Togo Tonga Trinidad and Tobago Tuvalu Uganda United Republic of Tanzania Vanuatu Vietnam Yemen Zambia
LDCs
Small States
HIPCs Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes
Yes
Yes Yes
Yes Yes Yes
Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes
Yes Yes Yes
Yes Yes
Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes
Yes
Yes Yes Yes
Yes
Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Source: The lists of LDCs and HIPCs are from UNCTAD (2002b). Small states include those countries as listed in Grynberg and Razzaque (2003). There are thirteen countries that are both small states and LDCs, viz. Cape Verde, Comoros, Djibouti, Equatorial Guinea, Gambia, Kiribati, Lesotho, Maldives, Samoa, Sao Tome and Principe, Solomon Islands, Tuvalu and Vanuatu. * indicates that the oil-exporting small-LDC, Equatorial Guinea, is not included. Among the 49 LDCs 32, i.e. 65 per cent, are highly indebted poor countries. Four small states viz. Comoros, Gambia, Guyana, and Sao Tome and Principe are also classified as HIPC countries. Two countries, Gambia and Sao Tome and Principe, have all three characteristics, being LDCs, small states and HIPCs. Bolivia, Cameroon, Congo, Ghana, Honduras, Kenya, Nicaragua, and Vietnam are the only eight countries that do not fall into either LDCs or small states but are HIPCs.
14
Problems of Commodity Dependence Appendix 1.2. LDCs, HIPCs, and Small States and Their Leading Exports Countries
Three Leading Commodities (1997–99)
Afghanistan
Grapes and Raisins, Hides and Skins, Crude Materials (incl. Flowers) Fish, Beverages Dist Alcoholic, Wood Fuels, Diamonds, Coffee Fuels, Iron, Oil Palm Fish, Jute and Bust Fibres, Tea Sugar, Beverages Dist Alcoholic, Fuels Sugar, Bananas, Fish Cotton, Cottonseed, Oil Palm OrangesþTangþClem, Wheat/Flour, Fruit Freshens Oilseed, Fuel, Soybean Oil Diamonds, Bovine Meat, Hides and Skins Cotton, Sesame Seed, Hides and Skins Coffee, Tea, Sugar Wood, Natural Rubber, Fish Fuels, Wood, Cocoa Fish, Apples, Wood Diamonds, Wood, Cotton Cotton, Live Animals, Crude Materials (incl. Flowers) Vanilla, Essential Oils, Cloves (whole þ stems) Fuels, Wood, Sugar Cocoa, Fuels, Coffee Tobacco, Roots and Tubers, Dairy Products Diamonds, Coffee, Wood Sugar, Crude Materials (incl. Flowers), Fish Sugar, Tobacco, Cocoa Fuels, Wood, Cocoa Sesame Seed, Hides and Skins, Fish Coffee, Hides and Skins, Sesame Seed Sugar, Gold, Fish Fuels, Wood, Manganese ore Groundnuts, Fish, Groundnut Oil Cocoa, Diamonds sorted, Gold Nutmeg, Mace, Cardamom, Fish, Wheatþ Flour Bauxite, Alumina (Al Oxide, Hydroxide), Fish Nuts, Fish, Cotton Gold, Sugar, Bauxite Coffee, Fish, Mangoes Coffee, Bananas, Fish Alumina (Al Oxide, Hydroxide), Sugar, Bauxite Tea, Coffee, Fuels Fish, Copra, Crude Materials (incl. Flowers) Wood, Coffee, Tin Ore Wool, Greasy, Food Wastes, Vegetables Prepared Natural Rubber, Wood, Fuels Fish, Coffee, Cloves (wholeþstems) Tobacco, Tea, Sugar Fish, Wood, Copra Cotton, Live Animals, Groundnut Oil
Antigua and Barbuda Angola Bahrain Bangladesh Barbados Belize Benin Bhutan Bolivia Botswana Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Rep. Chad Comoros Congo ˆ te d’Ivoire Co Cyprus DR Congo Djibouti Dominica Equatorial Guinea Eritrea Ethiopia Fiji Gabon Gambia Ghana Grenada Guinea Guinea-Bissau Guyana Haiti Honduras Jamaica Kenya Kiribati Lao PDR Lesotho Liberia Madagascar Malawi Maldives Mali
Average Share (per cent) (1997–99) 48.58 2.57 71.00 66.36 8.65 19.44 52.48 37.86 7.42 23.31 73.20 41.45 88.91 40.45 44.10 21.54 73.15 52.44 65.48 85.83 60.00 43.14 86.25 7.22 34.11 89.06 6.11 79.42 33.85 93.22 19.03 61.88 23.17 59.92 75.42 90.98 18.68 34.89 61.15 46.07 80.38 10.32 3.02 14.56 54.19 70.96 71.70 45.13 (Continued)
15
The Issue of Declining Commodity Prices Appendix 1.2. (Continued ) Average Share (per cent) (1997–99)
Countries
Three Leading Commodities (1997–99)
Malta Mauritania Mauritius Mozambique Myanmar Nepal
Tobacco, Fish, Beverages Dist Alcoholic Iron ore and concentrates, Fish, Fuels Sugar, Fish, Crude Materials (incl. Flowers) Fish, Nuts, Wood Wood, Fish, Pulses Roots and Tubers, Pulses, Nutmeg, Mace, Cardamom Coffee, Fish, Bovine Meat Uranium, Live Animals, Tobacco Gold, Copper ore, Wood Coffee, Tea, Hides and Skins Fish, Copra, Fruit Prepared Cocoa, Fish, Coffee Fish, Fuels, Groundnut Oil Fish, Fuels, Cinnamon (Canella) Fish, Coffee, Cocoa Wood, Fish, Oil of Palm Live Animals, Bananas, Fish Sugar, Beverages Non-Alcoholic, Beverages Dist Alcoholic Bananas, Fruit Fresh, Pepper (White/Long/ Black) Bananas, WheatþFlour, Rice
1.14 72.43 23.28 42.92 45.08 8.97
Sesame Seed, Crude Materials (incl. Flowers), Coarse Grains Alumina (Al Oxide, Hydroxide), Rice, Fuels Sugar, Fruit Prepared, Other Citrus Fruits Nat. Ca Phosphate, Cotton, Coffee Pumpkins, Squash, Gourds, Fish, Crude Materials (incl. Flowers) Fuels, Beverages Non-Alcoholic, Sugar, Copra Coffee, Fish, Crude Materials (incl. Flowers) Nuts, Coffee, Fish
28.69
Copra, Roots and Tubers, Wood Fuels, Rice, Fishery Commodities Fuels, Fish, Coffee Refined Copper, Sugar, Cotton
67.02 32.76 89.30 49.61
Nicaragua Niger Papua New Guinea Rwanda Samoa Sao Tome and Principe Senegal Seychelles Sierra Leone Solomon Islands Somalia St Kitts and Nevis St Lucia St Vincent and the Grenadines Sudan Suriname Swaziland Togo Tonga Trinidad and Tobago Tuvalu Uganda United Republic of Tanzania Vanuatu Vietnam Yemen Zambia
36.67 93.70 57.92 69.62 76.43 92.11 41.72 35.00 26.61 95.13 41.20 36.61 55.68 68.48
84.50 23.24 61.75 82.02 51.77 16.30 65.94 42.17
Source : UNCTAD database.
four-fifths of extremely poor people live in those least developed countries that are mainly primary producers (UNCTAD, 2002a). The probability of becoming heavily indebted is also higher for commodity-producing poor countries. About 50 developing countries depend on three or fewer commodities for more than half their exports; 37 of these have been categorized as HIPCs.
16
2 Secular Decline in Relative Commodity Prices: A Brief Review of the Literature Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
Whether the terms of trade have moved unfavourably against primary commodities and the developing countries dependent on them has been the subject of intense interest and debate in the trade and development literature since the publication of articles by Prebisch (1950) and Singer (1950) some 53 years ago. The issue of movement of the terms of trade is essentially an empirical question and the hypothesis of a long-term trend decline in relative commodity prices has been the subject of one of the liveliest debates in the empirical economics literature. Statistical and econometric tests have been applied to produce evidence and counter-evidence. As Sapsford and Balasubramanyam (1994) appositely observe: ‘ . . . declining long-run trend hypothesis has in recent years established itself as an important test bed, upon which time series statisticians nowadays routinely evaluate their latest trend estimation procedure’.1 The basic objective of this chapter is to provide a brief review of the literature concerning the secular decline in commodity prices in an attempt to recapitulate the main issues in the controversial empirical research works and to identify any broad consensus that may have appeared in the recent past.2 A summary of selected studies highlighting the methodologies and data used, as well as key findings, is presented in Table 2.1.
1
Sapsford and Balasubramanyam (1994), p. 1737. Important previous reviews include Greenaway and Morgan (1999), Sapsford and Balasubramanyam (1994), Sapsford and Morgan (1994) and Sapsford and Singer (1998). Note that we restrict ourselves to the literature on secular decline in relative prices. Other issues, such as volatility and co-movement of prices, are not addressed. 2
17
The Issue of Declining Commodity Prices
2.1. The Genesis of the Debate Classical economists predicted a long-run improving trend in the prices of primary commodities relative to those of manufactures.3 According to the classical view, primary commodity production tends to be subject to diminishing returns and technological progress is likely to be more rapid in manufacturing than in agriculture. If prices are related to costs, the interaction of these two forces will lead the ratio of prices of primary products to those of industrial goods to rise (Thirlwall, 1989). In contrast, Prebisch (1950) and Singer (1950) identified a number of factors that were considered actually to have contributed to the deterioration of the net barter terms of trade (NBTT) for agricultural products.4 As summarized in Athukorala (2000), these are: (a) lower price and income elasticity of demand for primary products than manufactured goods; (b) technical progress that economizes on the use of primary raw materials in the manufacturing process; (c) technological superiority of developed countries and the control exercised by multinational enterprises based in these countries of the use of sophisticated manufacturing technology; and (d) monopolistic market structure in developed countries, combined with competitive conditions in both commodity and labour markets in developing countries. Compared to today’s ‘high-tech’ time series econometrics, the methodology employed by both Prebisch (1950) and Singer (1950) was very simple in providing the evidence on the declining relative price of primary products to that of manufactured goods for the time period covering the latter half of the nineteenth century to about the first half of the twentieth century (Sapsford and Balasubramanyam, 1994).5 By pulling together two sets of overlapping series, Prebisch observed that the NBTT of the United Kingdom for the whole of its merchandise trade had registered a secular improvement during the period from 1876–80 to 1946–47. Since for most of the period under consideration the UK was the world’s most important exporter of manufactures and importer of primary products, Prebisch interpreted this evidence to imply a secular deterioration of the NBTT of primary products traded worldwide. On the other hand, in his descriptive analysis of the problems of specializing in primary production, Singer referred to some statistics reported
3 For the nineteenth century there is some evidence in favour of the classical economists (see Sarkar, 1986). However, since the beginning of the twentieth century, things have changed markedly; it will be shown below that the dominant strand of the recent literature takes the view that there is a fall in the terms of trade for primary commodities. 4 The net barter terms of trade for primary products is defined as the ratio of an index of export prices of primary products to an index of import prices of manufactures. 5 The term ‘high-tech’ time series econometrics comes from Sarkar (1986) and refers to the relatively recent development of unit roots and cointegration techniques and their application to macroeconomic data.
18
Secular Decline in Relative Commodity Prices by the UN to make the point that ‘ . . . the trend of prices has been heavily against sellers of food and raw materials and in favour of sellers of manufactured articles’ (Singer, 1950, p. 477), together with some underlying potential reasons for such a tendency. This is how the Prebisch-Singer (PS) hypothesis came into existence, to be debated for the rest of the twentieth century and beyond. The objections raised against the PS hypothesis, together with the issues explored in the subsequent empirical literature, can be summarized as: (i) the misleading evidence emanating from the inappropriateness of UK terms of trade; (ii) the arbitrariness of the time span; (iii) the use of inadequate data; (iv) the statistical procedure; (v) the omission of other important variables in the analysis; (vi) the failure to take into account improvements in the quality of products; and (vii) the fact that developing countries are not the only exporters of primary commodities (Diakosavvas and Scandizzo, 1991; Sarkar, 1986; and Spraos, 1980). The use of UK terms of trade to draw conclusions about the overall relative price of primary commodities has been discussed at length by Spraos (1980) and Sarkar (1986), who find that the choice of the indicator was not unjustified.6 In the later period, much better data have been used, so this issue is not a major concern. The argument that developing countries are not the only producers and exporters of primary commodities also has no effect on the positive component of the hypothesis, but its implications might be different from those deduced by Prebisch and Singer, i.e. the relative distribution of gains from trade between the ‘centre’ and the ‘periphery’. As regards the criticism of the PS hypothesis that manufactured goods are more subject to quality improvements, potentially making the NBTT of primary goods appear worse, it should be mentioned that there is no measurement of differential qualitative change in the two types of products. Improvements in quality have also taken place in primary commodities and to what extent the prices of manufactured goods reflect more of the upward drift on account of quality improvement is not known (Grilli and Yang, 1988; Sarkar, 1986; Spraos, 1980).7 In the following discussion, therefore, this review will attempt to cover the other four issues as they have been explored in a number of important and influential empirical works. 6 Spraos (1980, p. 113) concludes, ‘ . . . the evidence of Britain’s NBTT to an inference about the relative price of primary products vis-a`-vis manufactures in world-wide trade was not misleading as to direction though it gave an exaggerated impression of the magnitude of deterioration’. On the other hand, Sarkar (1986, p. 361) observes: ‘ . . . Prebisch was to a large extent justified in choosing the NBTT of Britain . . . as ‘proxy’ for the terms of trade of the industrial region vis-a`-vis the agrarian region of the world’. 7 Grilli and Yang (1988) and Bleaney and Greenaway (1993) cite a few studies that have attempted to measure the effects of changes in the quality of manufactured goods on their prices. However, these studies are unlikely to be representative of the manufacturing goods sector as a whole. On the other hand, there is no study of the impact of quality improvement on primary product prices.
19
The Issue of Declining Commodity Prices
2.2. Empirical Findings to the mid-1980s Between 1950 and 1980 few studies were undertaken to verify the PS hypothesis empirically, although discussions relating to the causes of the change in terms of trade attracted considerable interest.8 In an early attempt Wilson et al. (1969), as reported in Diakosavvas and Scandizzo (1991), considered the NBTT and income terms of trade of developed and least developed countries for the period 1950–65. Taking 1950–53 as the base years, the study found that between 1954–57 and 1962–65 LDCs’ NBTT fell from 98.3 to 90.7. However, it was Spraos (1980) who introduced solid statistical tools into the analysis by empirically estimating the linear trend equations using Singer’s data and its extended version compiled by the author himself.9 He found that the relative price series for the 70-year period up to the outbreak of World War II provided support for the PS hypothesis, although the statistical series used by Prebisch exaggerated the rate of deterioration. However, Spraos observed that the declining rate was not stable and became very weak (‘open to doubt’) if the trend equation was estimated using the dataset extended to 1970. In other words, the finding was to be seen as the PS hypothesis being subject to the chosen time span. In a subsequent paper, Sapsford (1985) pointed out the problem of structural break in the trend equation estimated by Spraos. The method of trend estimation (or any model) is usually based on the assumption of parameter constancy and the potential problem associated with the stability of the parameters can be tested statistically. There could be several problems with stability of the regression coefficients, viz. only the intercept might change from one sub-sample to another, only the slope parameters might change, or both could change.10 Extending the data series considered by Spraos to 1982, Sapsford’s Chow (1960) test for structural stability supported a once-for-all upward shift in relative price in 1950 without any significant change in the declining trend between the pre- and post-war sub-periods. Sarkar (1986) and Thirlwall and Bergevin (1985) have also investigated the differential rates of declining commodity prices for different periods. Earlier in this paper Singer was criticized over his choice of time span, 1876–1938, as the terminal date was marked by the depression of the 1930s which was argued to have been responsible for exaggerating the negative trend. Fitting the trend 8
Diakosavvas and Scandizzo (1991) provide a list of all such studies. The linear trend equation for estimating the growth rate for any variable Y takes the form of lnYt ¼ a þ bT þ et , where ln stands for natural logarithm, a and b are respectively intercept and slope parameters and T is the time trend with, say, 1 for the beginning year of the sample to n, where n is the number of periods under consideration. 10 Consider the equation lnY ¼ a þ bT þ cD þ d(b D)T, where D is a dummy variable indicating a break point in the data and all other variables are defined as above. In estimation if c is significant but not d, this will result in intercept shift only. The significance of d only will result in shift in slope parameter while the significance of b and d will result in changes in both the intercept and slope coefficients. 9
20
Secular Decline in Relative Commodity Prices equation to League and UN series on the NBTT of primary products for the two periods 1876–1929 and 1876–1938, Sarkar observed that both series exhibited a statistically significant declining trend and the inclusion of the data for the 1930s only accentuated the existing declining trend.11 Considering the postwar period, Sarkar’s results show that the exclusion of petroleum from the group of primary commodities results in a trend decline rate of 0.89 per cent per annum.12 On the other hand, Thirlwall and Bergevin were interested in the differential rates of decline between the two sub-periods 1954–72 and 1973–82. The trend deterioration in the case of the first sub-period was estimated at 1.2 per cent per annum, while the corresponding rate for the latter period was found to be as high as 2.5 per cent per annum.13
2.3. The Grilli-Yang Study and Subsequent Empirical Investigations The empirical work that gave new impetus to the investigation of trends in commodity prices and provided the strongest evidence since the launch of the PS hypothesis is that by Grilli and Yang (1988). The most important contribution of Grilli and Yang was to prepare a consistent dataset. The authors first gathered US dollar price indices of 24 internationally traded non-fuel commodities for 1900–86, and then used them to construct an aggregate price index with 1977–79 values of world exports of each commodity used as weights. To obtain the relative price of primary commodities, Grilli and Yang used the UN index of unit values of exports of manufactured goods from industrial countries (MUV) as the deflator.14 The original MUV series had two breaks for the years 1915–20 and 1939–47, which the authors filled in by interpolation. Having constructed the new series of the relative price of primary commodities, estimation of the linear trend equation by OLS resulted in a statistically significant trend growth rate of about –0.6 per cent per annum.15 11 The use of data as provided by Lewis (1952) for 1870–1929 yields the lowest declining rate of 0.29 per cent per annum, while the dataset of Schlote (UN, 1949) for 1938–1976 provides the highest rate of 0.84 per cent. 12 This is because of the oil price shock of the 1970s that produced a sharp increase in fuel prices. However, it is now standard practice to consider the NBTT of non-fuel commodities while examining the issue of secular decline. 13 Thirlwall and Bergevin (1985) did not undertake the stability test as suggested by Sapsford (1985). 14 As reported in Grilli and Yang (1988), the estimate of the trend growth rate is not sensitive to the choice of deflator. The use of the US manufacturing price index instead of MUV would have produced similar results. 15 Grilli and Yang’s work is not the only attempt in compiling a very long-run series in commodity prices. Earlier, based on data reported in Schlote (1938), W. A. Lewis constructed a long data series for 69 years starting from 1870. The Economist’s ‘index of industrial commodity prices’ uses data since 1862 and is updated regularly. Apart from these, Diakosavvas and Scandizzo (1991) have also attempted to construct a data series, which remains unpublished, for as many as 14 commodities. However, because of the revision of commodity composition
21
The Issue of Declining Commodity Prices Partly because of the availability of consistent and long-time series data as provided by Grilli-Yang, and partly because of the advent of modern time series econometrics of unit roots and cointegration, the period since the late1980s has witnessed a renewed interest in applied works on commodity prices. Studies using the newly developed applied econometric techniques to test the conclusion reached by Prebisch and Singer about the secular decline in commodity prices have become a regular phenomenon. The first such notable study was by Cuddington and Urzua (1989); following the development in time series econometrics, they argued that the traditional trend equation estimation for obtaining the long-run growth rate was only valid if the underlying series had been a stationary one.16 If, on the other hand, the variable under consideration has a unit root (i.e. the series is non-stationary), the traditional trend growth equation will have to be modified.17 This modification requires the transformation of a non-stationary series into a stationary one and running a difference stationary (DS) model of the following type as originally proposed by Nelson and Plosser (1982).18 in The Economist’s index and the discontinuation of Lewis’ data series, there are major problems in using them in empirical application. On the other hand, a wider commodity coverage than Diakosavvas and Scandizzo and the systematic use of them in constructing a weighted aggregate series have made the Grilli-Yang dataset the most acceptable. 16 A time series is stationary if its mean, variance and auto-covariance are independent of time. By now there is compelling evidence that many macroeconomic time series are indeed non-stationary, which has some significant implications for regression analyses employing OLS. It has been shown that OLS regressions involving non-stationary data might produce not only inconsistent and inefficient estimates but also ‘spurious’ or nonsense relationships. In other words, one could obtain highly significant correlation between variables although in reality there might not exist any such relationship. One interesting example of spurious regression is illustrated by Hendry (1980) to show that there has been a strong positive relationship between the inflation rate and the accumulated annual rainfall in the United Kingdom! 17 Whether a variable is non-stationary can be determined by testing for the existence of a unit root in its data generation process. The two most popular tests for unit roots are the Dickey Fuller (DF) and Augmented Dickey Fuller (ADF) tests. The DF test is based on the equation: DYt ¼ t þ (c 1)Yt1 þ xT þ et where Y is the variable under consideration, D is the first difference operator, subscript t denotes time period, T is the time trend and e is the error term. The null hypothesis for this test is that (c 1) ¼ 0 (i.e. Yt is non-stationary) against the alternative of (c 1) < 0 (i.e. Yt is stationary). The t-test on the estimated coefficient of Yt1 provides the DF test for the presence of a unit root. In the presence of non-stationary variables the distribution of t-test is non-standard and the special critical values for the distribution of the non-standard t-test in the above model have been tabulated by Dickey and Fuller. The ADF test, on the other hand, is a modification of the DF, which involves augmenting the DF equation by lagged values of the dependent variables to ensure that the error process in the estimating equation is residually uncorrelated. The null and alternative hypotheses in the ADF equation are the same as the DF regression and so are the critical values. Note that a series without a unit root is also known as a trend stationary process (TSP) while the one with a unit root is a difference stationary process (DS). 18 This is represented by: DlnYt ¼ a þ et , where all variables are defined above and D denotes transformation of the variable into a stationary series. Since the left-hand side in the equation is the proportional growth rate in Y, an estimate of the trend growth rate according to this method is obtained by regressing the growth rate of the relative commodity price against a
22
Secular Decline in Relative Commodity Prices Therefore, for Cuddington and Urzua (1989) the type of equation to be used for the estimation of trend growth rates critically depended on the test for unit root in the relative commodity price series. Visual inspection of the data on relative price of commodity as constructed by Grilli and Yang showed a big spike in 1921, prompting Cuddington and Urzua to consider a once-for-all drop in relative prices for that year in light of which they employed the Perron (1989) test for unit to determine the time series property of the underlying variable.19 The results allowed them to conclude that the relative commodity price series was non-stationary and accordingly they opted for a DS model which yielded a trend rate not significantly different from zero. Cuddington and Urzua also employed the Beveridge-Nelson (1981) technique to decompose commodity price movements into permanent and cyclical components and found that roughly 39 per cent of average shock to NBTT was to be viewed as permanent, while the rest was cyclical. Similarly, in another study Cuddington (1992) applied the unit root test to determine the time series property of each of 24 commodity price indices of Grilli and Yang (1988), together with the comparable data for oil and coal. Thirteen commodity price indices appear to be difference stationary process (DSP) while the remainder can be modelled as trend stationary process (TSP). Of the 26 commodities, only five are found to have a negative trend, while in all other cases the hypothesis of a secular decline in prices is rejected. Like Cuddington and Urzua (1989) and Cuddington (1992), Newbold and Vougas (1996) have applied various univariate time series techniques to determine whether the aggregate relative primary commodity price index is trend or difference stationary. From these tests no conclusive inference could be made about the unit root property of the variable. The authors found evidence for the PS hypothesis when the series was considered to be TSP, but in the case of DSP there was no overwhelming evidence. Despite the ambiguous results derived from the unit root tests, the authors preferred the difference stationary constant, with an error term. This transformation ensures that the residual term is white noise, which otherwise turns out to be non-stationary in the case of the dependent variable possessing a unit root. The transformation from non-stationary to stationary usually requires differencing of the variable. Following Engle and Granger (1987), a variable having unit root on its level but not on its first difference is called integrated of order one and is often denoted as e I(1). A second or higher order of differencing might also be required to eliminate the unit root from the data-generating process, although most non-stationary series appear to be e I(1). Non-stationarity of the residuals in the time series regression is considered to be an important problem leading to the potential problem of spurious relationship. On the other hand, even if a regression comprising non-stationary variables yields stationary residuals, the estimated equation may still show a valid long-run relationship. Engle and Granger (1987) show that if two variables, Yt and Xt , are both e I(1), they will have a valid long-run relationship (usually said to be ‘cointegrated’) if residuals from the OLS regression of Xt on Yt are e I(0). 19 It has been shown that investigation of whether a series is TSP or DSP using standard DF and ADF tests can lead to wrong inferences if structural breaks are ignored (Perron, 1989; Zivott and Andrews, 1992).
23
The Issue of Declining Commodity Prices 2.00
Relative Price of Primary Commodities
1.75 1.50 1.25 1.00 0.75 1900
1910
1920
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
20
0 −20 Changes in the Relative Price of Primary Commodities 1930
1940
1950
1960
1970
1980
1990
2000
Figure 2.1. Grilli-Yang Relative Price of Primary Commodities and its Changes Over Time
model over the trend stationary alternative and concluded the PS hypothesis to be ‘non-proven’. The econometric evidence to nullify the PS hypothesis has been subjected to detailed scrutiny by Sapsford et al. (1992) and Leon and Soto (1997). According to Sapsford et al. (1992), the unit root testing procedure of Cuddington and Uruza was inappropriate, as the equations estimated to ascertain the order of integration of variables contained many insignificant lagged periods of the dependent variable, exclusion of which would have rejected the null hypothesis of unit root in the data, thereby establishing the superiority of the TS model as against the DS one employed by the authors. Another major problem of Cuddington and Urzua’s findings is related to the plausibility of the 1920–21 decline in relative commodity price as reflected in the Grilli and Yang (1988) dataset. Sapsford et al. argue that the 50.3 per cent fall in the relative price for that particular year may be called into question as the commodity price series constructed by Schlote (1938) reports this fall at 13.5 per cent only. If the decline for 1921 in the Grilli-Yang dataset is replaced by the extent of fall in Schlote (1938), a replication of the Cuddington and Urzua exercise re-establishes a significant downward trend in relative price over 1900–83 (Sapsford et al., 1992).
24
Secular Decline in Relative Commodity Prices On the other hand, Leon and Soto (1997) challenge the findings of Cuddington’s (1992) analysis of a declining rate for 24 individual commodities in the Grilli-Yang dataset. They followed the same approach as Cuddington, but instead of using Perron’s (1989) test, considered the unit root testing procedure of Zivott and Andrews (1992). In Perron’s methodology the test for structural break at a particular time is selected on the basis of data inspection, while Zivott and Andrews’ technique allows for determination of the break point endogenously and statistically. Application of this endogenous break point methodology resulted in 20 commodities (of a total of 24) becoming TSP and to defy the conclusion reached in Cuddington (1992) significant and negative trends were observed for as many as 17 commodities. The evidence from decomposition of time series into permanent and cyclical components by Cuddington and Urzua (1989) has also been disputed in subsequent studies. In Ardeni and Wright (1992) a structural time series approach, following Harvey (1989), is undertaken to decompose the aggregate composite commodity price index of Grilli and Yang (1988) into permanent, cyclical and residual components. The authors’ results demonstrate a permanent trend decline in the relative price of commodities at a rate of 0.6 per cent per annum. The experiments in this paper also do not provide support for the 1921 structural break affecting the trend declining rate to any significant extent. These findings are also corroborated by another important study by Reinhart and Wickham (1994). Using the IMF quarterly data on an all non-fuel real commodity price index from the first quarter of 1957 to the second quarter of 1993, Reinhart and Wickham first tested for unit roots and failed to reject the null hypothesis of non-stationarity, which led them to implement Beveridge and Nelson’s (1981) ARIMA and Harvey’s (1989) structural time series approach to decompose the series into permanent and temporary (or cyclical) components. From the results of both experiments it became clear that the weakness in commodity prices has been permanent in nature. Another study that seems to contradict the PS hypothesis of stable declining terms of trade of primary products is that by Powell (1991). Powell considered cointegration analysis to test for a long-run relationship between a commodity prices index and an index of unit values of manufactures (MUV in the GrilliYang dataset). Both were in nominal dollars and both were found to be nonstationary. Cointegration between the variables, together with the value of the cointegrating parameter being equal to one, would be interpreted as evidence against secular declining terms of trade. The Johansen test for cointegration results showed that the variables were cointegrated with the long-run parameter not statistically significantly different from one only when three outliers of 1921, 1938, and 1975 are controlled with a ‘jump term’. From this, Powell concludes that commodity terms of trade are stationary but with three sharp breaks. However, the major problem is that the same results can also be interpreted as a stepwise version of the PS hypothesis with permanent drops
25
The Issue of Declining Commodity Prices in those three years. Besides, although outliers are controlled with a jump dummy, no attempt is made to consider the changes in the cointegration parameter between the outliers. Cashin and McDermott (2002) and Hadass and Williamson (2002) have published two recent studies using different data from those used by Grilli and Yang (1988). Cashin and McDermott (2002) employed The Economist’s index of industrial commodity prices over the period 1862–1999.20 They estimated the trend decline rate in the series to be 1.3 per cent per annum— more than double the estimate made by Grilli and Yang (1988). The local trends (i.e. the trend over a decade) are found to vary remarkably from 2.7 per cent in the 1910s to as high as 6.9 per cent in the 1990s. The authors, however, could not find any evidence for a break in the long-run (1862–1999) trend, although the highest possibility of the appearance of such a break occurred in 1917. Setting the sample to 1917–1999 yields a declining rate of 2.3 per cent—much larger than estimated for the full length of the series. On the other hand, Hadass and Williamson (2002) employ a completely different methodology. Unlike the international prices of primary commodities relative to manufactured foods, they gathered the terms of trade data for 1870–1940 in the home markets of 19 countries, which they then divided into the ‘centre’ and the ‘periphery’ using the average unskilled wage or GDP per capita criteria.21 They found that the terms of trade defined as the price of agricultural products relative to that of manufactures improved in every region, which was consistent with their hypothesis of ‘transport revolution’. The main problem with the Hadass and Williamson study is that their sample is limited to only nineteen countries and none of the developing countries in the sample truly reflects the typical poor commodity-dependent nation.22 What becomes obvious from the above discussion is that most post-Grilli-Yang studies are plagued with the unit root testing procedure with inconclusive evidence about the exact time series properties of the variable. This problem is essentially inherent in the weak and low power of the unit root testing procedure and as Harris (1995) points out, the most important problem faced when applying the unit root test is their probable poor size and power properties.23 20 The real annual data of The Economist’s index of industrial commodities consist of the nominal industrial commodity price index (dollar-based with base 1845–50 ¼ 100, weighted by the value of developed country imports), deflated by the GDP deflator of the United States. 21 The authors observed that the share of primary exports in total exports could not be used to define the centre and periphery as during the sample period primary goods dominated world trade and countries both in the North (Europe and America) and in the South (mainly Asia), as included in the sample, had the same degree of dependence on primary commodities. 22 Developing countries included in the sample are Argentina, Burma, Egypt, India, Korea, Thailand, and Taiwan. 23 The problems of unit root testing procedure have been known for a long time; Engle and Granger (1987) also highlighted the low power of the DF and ADF tests. Considering the strengths and weaknesses of the testing procedures, Gujarati (2003, p. 820) concluded that ‘as yet there is no uniformly powerful test of the unit root hypothesis’.
26
Secular Decline in Relative Commodity Prices This is often reflected in the tendency to over-reject the null hypothesis when it is true and under-reject it when it is false. Even studies applying the modern time series techniques to the PS hypothesis are aware of this problem. For example, Newbold and Vougas (1996), having applied all the rigorous techniques in the arsenal of unit roots econometrics, realize that the econometric tests are relatively uninformative on the question of whether the relative price of primary commodities is trend stationary or integrated of order one. It is also clear from the above that whether or not any of the violent fluctuations in the time series of commodity prices has led to a structural break has been the subject of significant statistical controversy.24 While the regression methodology is capable of testing for structural breaks, how they affect the unit root property of a variable has not yet been settled in the applied econometrics literature. While the weakness of traditional unit root testing procedures in the presence of structural breaks is supposedly overcome by Perron (1989) or Zivott and Andrews (1992) type tests, none of the procedures can consider more than one structural break in the data. In a recent attempt Kellard and Wohar (2002), employing the Lumsdaine and Papell (1997) methodology for searching two endogenously determined break dates, confirm the trend stationarity of 15 individual commodity prices (out of the 24 in the Grilli-Yang dataset), 12 of which appear to have a declining trend, as opposed to only five found by Cuddington (1992). Even before wondering at the contrasting evidence, one might ask: why test for only one or two structural breaks in the data and why not more? Therefore, it would not be inappropriate to conclude that, despite the problem of violent fluctuations in the time series of commodity prices, existing econometric procedures are still uninformative in terms of determining how these affect the underlying time series properties. How the variables need to be modelled even when they are integrated has been a matter of careful investigation in econometric theory and applied econometric techniques (e.g. Banerjee et al., 1993; Charemza and Deadman, 1992; Engle and Granger, 1987; Harris, 1995; Hendry, 1995 and 1999). The most important and uncontroversial lesson of this literature is that mere differencing of the variables to transform them into stationary series and using them in OLS regression is tantamount to wiping out long-run information and should be avoided. The suggested procedure is to use some kind of cointegration technique, which makes it possible to obtain both the longrun and short-run estimates of the model. Therefore, even if the time series of relative prices of primary commodities is considered to be non-stationary,
24 For example, Sapsford (1985) considered 1950 to be the year that led to a shift in the intercept of the trend equation, while Cuddington and Urzua (1989) favoured 1921. Powell (1991), on the other hand, introduced a jump term in his regression equation to capture outliers corresponding to 1921, 1938, and 1975.
27
The Issue of Declining Commodity Prices a simple estimate of equation (1) by OLS as implemented in Cuddington and Urzua (1989) and in other studies should be problematic. Only one study (Bleaney and Greenaway, 1993) avoids the problem of unit root testing procedure, yet formulates a more general specification of the trend equation that encompasses both trend and difference stationary models.25 The specification used by the authors follows an error-correction modelling approach and is thus consistent with a cointegration technique. Updating the Grilli and Yang (1988) aggregate relative prices for primaries, Bleaney and Greenaway’s model provides a trend decline of about 0.84 per cent for the period 1902–91. Since the relative commodity price is found to be unusually high in the earlier part of the twentieth century, to avoid the exaggeration of the downward trend particular emphasis is given to the sample covering 1925–91. This yields a trend growth rate of 0.7 per cent per annum. The results also support a ‘oncefor-all’ drop in the relative prices of primary commodities after 1980.
2.4. Structural Models Low income elasticity of demand, declining intensity of primary resource use in the industrial countries and supply surge in agricultural production are thought to be the most important reasons for the long-run downward trend in real commodity prices.26 However, only a few empirical studies have been undertaken to explain the trend in commodity prices with other economic time series through structural models. As noted in Sapsford and Singer (1998), Borensztein and Reinhart (1994) attempted to explain recent depressed commodity prices by extending the traditional approach to the demand side to include the political and economic transition in Eastern Europe and the former Soviet Union. On the supply side, they emphasized the pressures brought about by the debt crises of the 1980s. Others, however, focused on an explanation of long-term decline in relative prices. Bloch and Sapsford in a number of papers (1992, 1997, 2000) explicitly referred to the explanation advanced by Prebisch (1950) and Singer (1950) with regard to the differences in competitive environment between primary and manufacturing production. In Bloch and Sapford’s models, therefore, wages and prices in primary production are competitively determined, while in the manufacturing sector they are 25
The approach taken by Bleaney and Greenaway (1993) is elaborated in the next section. The increase in supply of agricultural commodities is the result of the entry of new exporters into international markets (e.g. during the 1980s Malaysia and Indonesia became major suppliers of cocoa) as well as of technological progress (e.g. the development and diffusion of fertilizer-pesticides-irrigation mechanisms in crop production). Land under cultivation has also increased in many parts of the developing world along with a sustained increase in yields. For some commodities, the agricultural policies of the industrial countries have also contributed to the rapid expansion in world commodity supplies (Reinhart and Wickham, 1994). 26
28
Secular Decline in Relative Commodity Prices determined by mark-up pricing and union-employer bargaining. Both the level of the mark-up in the manufacturing sector and the wages in either sector may be affected by output levels or by the prices of both types of goods. Estimating the model for the world economy (i.e. using the data on aggregate commodity prices, industrial production, overall manufacturing wages, etc.), the authors find some support for the difference in market structure as contributing to the downward trend in the terms of trade.27 However, the main problem of the analysis is that some of the key variables in the model are not statistically significant.28 On the other hand, the analytical framework of Deaton and Laroque (2003) makes use of Lewis’s (1954) argument that as long as there is an infinitely elastic supply of labour at the subsistence wage, commodity prices cannot rise and may even decline with local technical progress.29 In this model. commodity supply is assumed to be infinitely price elastic in the long run, and the rate of growth of supply responds to the excess of current price over the long-run supply price. On the other side, demand is related to the level of world income and to the price of the commodity. Deaton and Laroque fitted the model for six commodities over the years 1900–1987.30 The results of the empirical investigation appear to be mixed, with variables of interest in a number of equations failing to become statistically significant.
2.5. Concluding Observations From the above review of the literature it may be reasonable to conclude that there is now a broad consensus on the long-term trend deterioration in relative commodity prices. Whilst the trend rate of decline may differ between individual commodities, on the basis of the very long-run data the magnitude of the estimates ranges from 0.6 to 2.30 per cent per annum. There is also some evidence that weakness in prices in the most recent past has been much steeper than the long-run average rate. 27
Their results show that for the period 1948–93 the adverse impacts on the terms of trade of primary products due to a trend difference in wage growth and the trend increase in markups in manufacturing are almost exactly offset by the impact of strong growth of manufacturing production. 28 The model used by Bloch and Sapford is highly aggregative in nature. The authors admit the problem of data, especially with respect to capital stock. The data on wages in the primary sector are proxied by a weighted average of agricultural wages in Mexico, Sri Lanka, India, Chile and Turkey. 29 In his original article, Lewis (1954) considered the price of sugar and real wages of workers in the West Indies. He argued that wages cannot grow because of unlimited supplies of labour at the subsistence wage. Therefore, the benefits of technical progress in sugar production accrued not to workers but to consumers in industrial countries (Deaton, 1999). 30 The implementation of the model requires information on commodity prices, total production of the commodities and world GDP.
29
The Issue of Declining Commodity Prices Nonetheless there are studies where the authors are still sceptical regarding a long-run trend decline. However, the weight of the evidence has certainly led to changes in the position of the World Bank and the IMF with regard to relative commodity prices (Sapsford and Singer, 1998). Until the 1980s, the World Bank and the IMF preferred to take the view that there was price volatility (without a downward trend), despite the existence of statistical evidence on the secular declining trend. However, since the late 1980s, work undertaken by both Bank and Fund economists has confirmed a long-run secular decline in the net barter terms of trade of primary commodities. However, relatively little has been done to explain long-run commodity price behaviour in terms of other factors. Several reasons have been given for the weakness in commodity prices, but robust statistical evidence supporting any of the alternative hypotheses is still unavailable.
30
Table 2.1. Summary of Findings on Secular Decline in Commodity Prices Study
Methodology and Data
Main Finding
Prebisch (1950)
A simple examination of the data by splicing the two partially overlapping series of Schlote (1938; 1952) and the United Nations (1949). The data corresponded to the net barter terms of trade of the UK for the whole of its merchandise trade, the inverse of which could be considered as the terms of trade of primary commodities.
Between the 1870s and 1930s the ratio of prices of primary to manufactured goods fell by 38 percentage points (as shown by Prebisch, Table 1 (p. 9).
Singer (1950)
Descriptive analysis of the problems of specialisation in the primary sector.
No data or statistics have been used for illustration but the author refers to a UN publication to make the point that ‘ . . . the trend of prices has been heavily against sellers of food and raw materials and in favour of the sellers of manufactured articles’ (p. 477 and footnote 4). Also provides reasons for the declining trend in relative prices.
Spraos (1980)
Linear trend equation fitted by the ordinary least squares regression was used to estimate growth rates. Used the dataset as considered by Prebisch (1950) and also compiled a new series to take into account the post-World War II period.
The author found that the balance of evidence from the range of relative price series for the 70-year period up to the outbreak of World War II provided support for the Prebisch-Singer hypothesis, although the statistical series used by Prebisch exaggerated the rate of deterioration ‘at worst by a factor of more than three’ (p. 126). However, if the sample was extended to 1970, the empirical evidence became ‘open to doubt’.
Sapsford (1985)
Linear trend equation corrected for autocorrelation by the CochraneOrcutt iterative method is used to estimate the growth rate. Chow test is carried out to examine the possibility of a structural break between the pre- and post-World War II period as implicit in the findings of Spraos (1980). The dataset used by Spraos is extended to the early 1980s.
An upward intercept shift in the post-war period is observed, but the shift occurs without any significant alteration in the downward trend as between the pre- and post-war sub-periods. The estimated long-run trend growth rate for the period 1900–82 is 1.29 per cent per annum.
Thirlwall and Bergevin (1985)
Trend growth rate estimation for two different sub-periods of 1954–72 and 1973–82. The United Nations quarterly data are used in the analysis.
The trend deterioration for real commodity prices turns out to be 1.2 per cent per annum between 1954 and 1972 while the estimated rate of decline for 1973–82 appears to be more than double at 2.5 per cent per annum.
Sarkar (1986)
Trend growth rate estimation for different periods, pre- and post-World War II, to examine whether Prebish-Singer results are subject to the time span chosen. Data used are taken from League (1945), Lewis (1952), Prebisch (1950), Schlote (1952) and various UN sources and correspond to aggregate price index.
The trend growth rates for 1876 and 1938 range between 0.29 to 0.84 per cent per annum. For the period 1953–80 the trend rate is affected by the inclusion of petroleum in the group of primary commodities because of the oil shock of 1970s. Exclusion of petroleum, however, results in a trend decline rate of 0.89 per cent per annum. (Continued)
Table 2.1. (Continued ) Study
Methodology and Data
Main Finding
Grilli and Yang (1988)
The authors compile US dollar price indices of 24 internationally traded non-fuel commodities for 1900–86. Then an aggregate price index is constructed with 1977–79 values of world exports of each commodity used as weights. The UN index of the unit value of exports of manufactured goods from industrial countries is considered as the deflator. The linear trend equation is used to estimate the growth/ decline rate in the aggregate relative price index.
The relative price of non-fuel primary commodities is estimated to have fallen by 0.6 per cent per annum. Significant negative trends emerge for most principal commodity sub-groups such as food, non-food agricultural and cereals.
Cuddington and Urzua (1989)
Time series models and the Perron (1988) unit root test are employed to make the distinction between trend stationary and difference stationary processes. In addition, the Beveridge-Nelson (1981) technique is used to decompose price movements into permanent and cyclical components. The Grilli-Yang aggregate index of non-fuel commodity prices (deflated by the unit value of exports of manufactured goods from industrial countries) is used in the empirical investigation.
There was a permanent drop in the level of relative primary commodity prices in 1921 but apart from that there is no evidence of secular deterioration. Roughly 39 percent of the average shockto the NBTTcomes out as permanent while the remaining 61 per cent is cyclical and dies out within three years. The permanent component has a one-time drop in 1921 but since then grows at a rate of 0.3 per cent (positive) per year.
Diakosavvas and Scandizzo (1991)
Simple linear and quadratic trend equations are estimated employing the generalised least squares (GLS) procedure. Data on prices of 19 commodities for 1900–82 have been gathered from different sources. The UN index of unit value of exports of manufactured goods from industrial countries has been used as the deflator.
For eight commodities, a declining and significant trend is discernible, but for six others there is counter evidence.
Powell (1991)
Cointegration analysis undertaken to test for a long-run equilibrium relationship between commodity prices and manufactured goods’ unit values. Used the Grilli-Yang aggregate commodity price index and the index of unit value of manufactured goods from industrial countries.
Controlling for three outliers in 1921, 1938 and 1975, cointegration between commodity and manufactured goods prices is found, with the cointegration parameter being unity. This is then interpreted as the evidence against a ‘stable declining commodity terms of trade’.
Cuddington (1992)
Time series techniques are used to determine whether each of the 24 commodity price indices, as prepared by Grilli and Yang (1988), plus two others on oil and coal contain unit roots or can be modelled as TSP.
Thirteen commodity price indices appear to be DSP while the rest are TSP. Of the 24 individual commodities, only five are found to have a negative trend as predicted by Prebisch and Singer, while the others have either zero or positive trends leading to the rejection of the secular decline in relative prices of commodities hypothesis.
Sapsford et al. (1992)
The Perron unit root test as in Cuddington and Urzua (1989) is applied, but only with lags that are statistically significant to test for the existence of TSP versus DSP data generating process in the relative commodity price index of Grilli and Yang. The data compiled by
The Perron test with low order significant lags leads to the rejection of the unit root in Grilli-Yang series as found by Cuddington and Urzua (1989). The secular decline is found to be sensitive to the amount of relative price fall in 1921. If the relative price fall in the Grilli-Yang series is replaced by
Schlote (1952) are used to express scepticism about the massive fall in commodity prices relative to those of industrial goods as reflected in aggregate relative price series constructed by Grilli and Yang.
an equal amount of Schlote (1952) dataset, a significant downward trend in commodity prices is established for 1900–86.
Ardeni and Wright (1992)
The structural time series approach of Harvey (1989), where the components of the time series are decomposed into the trend, cycle and residuals, and the Grilli-Yang aggregate real commodity price index (updated to 1988) are used.
The estimated trend growth rate is found to be negative and the rate is 0.6 per cent per annum.
Bleaney and Greenaway (1993)
Considered a general error-correction specification of the trend equation that encompasses both trend stationary and difference stationary models. For empirical exercise the Grilli-Yang (1988) relative commodity price index is used, updated to 1991.
Since the relative commodity price is unusually high in the earlier part of the twentieth century, to avoid the exaggeration of the downward trend particular emphasis is given to the sample covering 1925–91, in which case the trend rate is estimated to be 0.7 per cent per annum. The evidence of a ‘once-for-all’ drop in the relative prices of primary commodities after 1980 is found.
Reinhart and Wickham (1994)
The ADF, Phillips-Perron and Perron tests are used to check for unit roots and structural breaks in the data. ARIMA and structural approaches are used to decompose the time series into permanent and cyclical components. IMF quarterly data on all non-fuel real (aggregate) commodity price index deflated by the IMF index of manufacturing export unit values of industrial countries for 1957:I—1993:II.
Both the ARIMA and structural decomposition techniques present a similar result: the bulk of the price weakness is associated with the secular component and there is no evidence of an abnormally large cycle. Irrespective of the technique used, the downward trend is found to have steepened towards the end of the sample.
Newbold and Vougas (1996)
Univariate time series techniques are used to determine whether the aggregate series of relative prices of primary commodities can be modelled as TSP or DSP. The series of the relative prices of (aggregate) primary commodities under investigation is the one prepared by Grilli and Yang for 1900–87 and subsequently extended by Bleaney and Greenaway (1993).
The evidence of secular decline depends ‘to a substantial degree’ on whether the time series of relative prices is assumed to be trend stationary or integrated of order one for which the authors’ conclusion is that the usual econometric tests are relatively uninformative. In the case of TSP, the best estimate of downward drift is in the neighbourhood of 0.8–0.9 per cent per year, unless the experience of 1921 when there occurred a big fall, is discounted, in which case the figure falls to about 0.64 per cent. However, if the relative price series is considered to be difference stationary, there is no overwhelming evidence of any downward drift. The authors find that the case for trend stationary is not strongly established and therefore their conclusion is that the Prebisch-Singer hypothesis is ‘non-proven’.
Leon and Soto (1997)
Considered a test for finding structural breaks in the data endogenously as developed by Zivott and Andrews (1992). Used the same dataset of 24 commodity price indices as Grilli and Yang (1988).
In the case of 20 (out of 24) commodities, relative price indices turned out to be TSP. Negative and significant trends to support the PS hypothesis were found for 17 commodities.
Kellard and Wohar (2002)
A unit root testing technique developed by Lumsdaine and Papell (1997) that allows for two endogenously determined break dates (unlike the Zivott and Andrews (1992) test that searches for just one) is used to determine the data-generating process. The long-run trend is estimated by adopting ARIMA specification. The dataset comprises the same 24 commodities as in Grilli and Yang, but the figures are updated to 1998.
The tests lead 15 commodity prices to be classified as trend stationary. In various specifications with different dummies as required by the unit root test results, only 12 commodities were found to have negative time trend for 50 per cent or more of the time, providing ‘modest’ support for the PS hypothesis. The authors note, ‘ . . . [H]owever this result is sensitive to the decision criterion adopted and one should caution against any quick judgements as to the robustness of the PS hypothesis’ (p. 14). (Continued)
Table 2.1. (Continued ) Study
Methodology and Data
Main Finding
Cashin and McDermott (2002)
The Economist’s index of industrial commodity prices covering the period 1862–1999 is used. The trend growth rate is estimated for three sub-periods to examine whether there has been any change in the trend rate.
There has been a downward trend in real commodity prices of about 1.3 per cent per year over the past 140 years. Although not statistically significant, the highest possibility of structural break is detected in 1917. The average annual rate of decline between 1971 and 1999 is estimated to be 2.3 per cent. No support for a break in the long-run trend decline in commodity prices.
Hadass and Williamson (2002)
A completely different methodology is used. Data on the terms of trade in home markets for a number of 19 sample countries between 1870 and 1940 are gathered. The sample countries are then divided into ‘centre’ and ‘periphery’ using such indicators as the unskilled real wage and GDP per capita criteria.
The terms of trade are found to have improved in every region during the sample period, which is explained by the ‘revolution’ in the transport sector. In fact, for the period 1870–1940, the terms of trade are found to have improved more in the periphery than in the centre. ‘However, consistent with Singer’s prediction, these positive relative price shocks had an asymmetric impact in centre and periphery, boosting growth in the centre and suppressing it in the periphery’ (p. 22).
3 Long-Run Trend in the Relative Price: Empirical Estimation for Individual Commodities Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
In this chapter we estimate the trend growth rate in relative prices for individual commodities. Most studies consider the aggregate or composite relative price index in order to examine the validity of the PS hypothesis. However, individual commodity prices rather than the composite price index are more important for countries in ascertaining their problems or prospects related to export earnings and balance of payments emanating from trends in commodity prices. Some commodities might be subject to much steeper declining rates than the overall relative price index, in which case the movement in the aggregate price index would hardly reveal the practical consequences for countries specializing in them. In fact, the Prebisch–Singer thesis can also be considered for each of the major commodity groups (such as food, agricultural raw materials, minerals, etc.) and for the individual products comprising the broad classifications. It might also be of interest to see whether the hypothesis holds for all commodities, and if not, whether some characteristic features can be identified for the commodities that do not experience deteriorating net barter terms of trade. Most importantly, any general policy conclusion can only be deduced if a similar trend is revealed for most individual commodities. The empirical results, as provided in Bleaney and Greenaway (1993), show that different broad categories of primary commodities appear to exhibit price behaviour which is different from the aggregate relative price index. If this is so, then the examination of price behaviour at the individual level should be the most appropriate way of evaluating trends in commodity prices.
35
The Issue of Declining Commodity Prices
3.1. Methodology The literature review in the previous section included studies investigating individual commodities, usually with data taken from the work of Grilli and Yang (1988) or its updated version. However, these studies (e.g. Cuddington, 1992; Kellard and Wohar, 2002; and Leon and Soto, 1997) place too much emphasis on testing unit root properties in order to determine the appropriateness of trend stationary vis-a`-vis difference stationary models for estimating the trend equation. The results of these studies are highly influenced by whether the relative price data (for any individual commodity) are to be considered as TSP or DSP (Cuddington, 1992) and how many break points are being explicitly tested for in the process of determining the time series property of the variable under consideration (Leon and Soto, 1997; Kellard and Wohar, 2002). The underlying econometric tests have low power, as well as methodological issues that are as yet unsettled.1 Therefore, using these estimation techniques is unlikely to be informative. One alternative to avoiding the unit root testing procedure and the ensuing pitfalls is to follow the methodology used by Bleaney and Greenaway (1993) by constructing a general error correction model that encompasses both the trend and difference stationary models. Instead of prior testing of time series properties of the data, this methodology aims at minimizing the possibility of uncovering a spurious trend by appropriately allowing for possible dynamics involved in the determination of the trend rate. Despite the standard practice in modern applied time series econometrics of testing for integrating orders of variables before running a regression, the use of such a framework that does not require prior testing for unit roots may be appropriate, given very recent developments in the field. In fact, Pesaran et al. (2001) have devised a new approach to testing for the existence of a valid long-run relationship between variables which is applicable irrespective of whether the underlying variables are stationary, integrated of order 1 or mutually cointegrated. It has been argued that using this procedure it is unnecessary to establish the order of integration of the variables prior to estimation of the long-run relationship and that therefore, unlike typical applications of cointegration analysis, this method is not subject to the well-known shortcomings associated with the pre-testing techniques. The recent development thus supports the methodology employed by Bleaney and Greenaway (1993), especially when it has been demonstrated that the determination of unit root properties for commodity prices series with violent fluctuations is anything but straightforward. Further, the Bleaney-Greenaway approach happens to be a special case in the Pesaran et al. framework. In the following we outline the methodology 1
For example, how to choose a break point in unit root testing procedure or how many breaks are to be considered.
36
Long-Run Trend in the Relative Price adopted by Bleaney and Greenaway (1993) and relate this to the framework of Pesaran et al. (2001). Consider the standard trend equation: lnRP ¼ a þ bt þ u
(1)
where RP is the relative price and all other variables are as defined in the previous section. According to Cuddington and Urzua (and all others follow them), equation (1) can only be employed if lnRP is trend stationary. If lnRP is non-stationary and is ~I(1), the relevant model to be estimated is: D lnRP ¼ b þ u
(2)
Instead of using (1) or (2), Bleaney and Greenaway started with an autoregressive model with a time trend included: lnRP ¼ a þ bt þ clnRPt1 þ u
(3)
The main difference between (1) and (3) is the inclusion of a lagged dependent variable as a regressor. Equation (3) can be rearranged to obtain: D lnRP ¼ a þ bt þ c lnRPt1 þ u
(4)
where, c ¼ c 1. Equation (4) becomes an ideal error-correction model if c is negative, statistically significant and greater than 1, (i.e. 1 < c < 0). In that case, the change in lnRP is negatively related to its current level and this will pull back the short-run deviations to the steady state long-run trend path. By contrast, if c ¼ 0, lnRP may be considered as a random walk with increasing variance over time. In essence, an error-correction representation in (2) is only possible if the prices of primary products and manufactured goods are cointegrated.2 In the estimation of (4), if b 6¼ 0, and c < 0, lnRP has a non-zero deterministic trend, i.e. it has a long-run tendency to revert to a non-zero trend following any short-term disturbances. The combination of b ¼ 0 and c ¼ 0 will imply no long-term trend of lnRP but the series tends to be pulled back towards its historical mean. Thus both ‘b < 0 and c ¼ 0’ and ‘b FU , the null is to be rejected and a valid long-run relationship among the variables may be ascertained. If F < FL , then no long-run relationship exists; finally, if FL < F < FU , the test is inconclusive. Pesaran et al. (p. 290) clearly point out that ‘[I]f the computed Wald or F-statistic falls outside the critical value bounds, a conclusive inference can be drawn without needing to know the integration/cointegration status of the underlying regressors.’5 From (5) it is observed that if there is no other explanatory variable (apart from the trend term), the Pesaran et al. specification becomes the standard Dickey-Fuller unit root testing equation—just as the one used by Bleaney and Greenay (1993). Under such a circumstance, the statistical significance of the lagged level dependent variable will be regarded as a proof of the long-run relationship. However, if the dependent is non-stationary on its level, the
with drift, if the estimated b is positive, it is more probable that it will be greater than its current value in the future and the opposite is true if b turns out to be negative. 4 Pesaran et al. give both the critical values for Wald and F-statistics. In this paper we will only consider the F-statistics. 5 In equation (5) pi and di give the short-run estimates of the parameters. The long-run parameter values can be obtained by noting that there is no change in hthe isteady state such that: DXt ¼ DZt ¼ 0. This would imply the long-run coefficient on Z as: gj .
38
Long-Run Trend in the Relative Price distribution of T-statistics is non-standard and Pesaran et al. suggest that the critical value for testing the statistical significance of the lagged level dependent variable in the absence of any other explanatory variable will correspond to Dickey and Fuller’s (1979) unit root T-statistics.6 Therefore, an error-correction type trend equation model that encompasses both trend and difference stationary models such as the one in (4) not only avoids the problems of unit root testing procedures but is also justified. In Dickey-Fuller type equations, such as the one in (4), special importance is given to the problem of serial correlation. The concern over the presence of serial correlation is usually addressed by the inclusion of one or more lags of the dependent variable as regressor.7 Thus a more general form of equation (4) can be written as:
DlnRP ¼ a þ bT þ
m X
h DlnRPt1 þ FlnRPtm þ ut
(6)
i¼1
X where, F ¼ I hi And the long-run trend rate is given by: b ¼ F1 :
3.2. Estimation Results We now turn to the results. Except for one instance, the data used here are for prices of individual commodities. Two different datasets have been used to obtain the information on prices. First, an attempt was made to gather the data on individual commodities in Grilli and Yang (1988). Of the 24 commodities, data was obtained on 13 covering the period 1900–87.8 These are cocoa, coffee, tea, bananas, sugar, rice, wheat, maize, cotton, jute, palm oil, copper and tin.9
6 This, in effect, implies that in the absence of any other explanatory variables (apart from the constant and trend term) the statistical significance of the lagged level dependent variable is to be considered as evidence for a valid long-run relationship irrespective of the unit root property of the data. 7 A general practice in the case of annual data is to include at least one lag of the dependent variable and then to check for the residual autocorrelation problem. For quarterly data at least four lags are used. 8 We thank Angus Deaton for providing us with the Grilli-Yang data on commodity prices for these 13 commodities. From an e-mail communication, it was learnt that the World Bank no longer has access to the information on the individual commodities price series used in the Grilli-Yang study. 9 The commodities for which information could not be obtained were aluminium, beef, hides, lamb, lead, rubber, silver, timber, tobacco, wool and zinc.
39
The Issue of Declining Commodity Prices The series was then updated to 2001 using comparable information.10 All data were gathered in nominal US dollars and then the unit value index of the manufactured goods exports of the industrial countries was used as the deflator to compute commodity-specific net barter terms of trade.11 Apart from the Grilli-Yang dataset, the UNCTAD database on commodity prices was used to estimate the trend growth rate for as many as 60 individual commodities.12 The longest span of the data available from UNCTAD is 1960–2002. In most cases these data were available in US dollars and the unit value index of manufactured goods exports of developed market economy countries was used as the deflator.
3.2.1. Trend growth rates of relative prices for commodities in the Grilli-Yang dataset Figure 3.1 plots the updated 13 commodity-specific relative prices in the GrilliYang dataset. All relative prices exhibit wide fluctuations with spectacular peaks and troughs. Nevertheless, even a casual look at the graph clearly reveals a declining trend in the net barter terms of trade of rice, wheat, maize, cotton and palm oil. For bananas a strong declining trend is discernible from around 1930 and for tea and jute from the mid-1950s. Apart from two skyscrapers, a deteriorating trend in the real price of sugar is also clear. Tin is the only commodity that witnessed a strong positive trend until the early 1970s, largely because of the success of the International Tin Agreement (ITA). Since then, the real tin price began falling before the major crash of the mid-1980s, which coincided with the collapse of the ITA. The most striking feature of Figure 3.1 is that since the 1970s a strong downward trend in the real prices of all commodities is apparent. Table 3.1 provides the regression results for the commodities in Figure 3.1. It needs to be mentioned here that except lnRPt1 , for all variables the standard t-ratios are valid, which implies that as a rule of thumb if the t-ratio is greater than 2 the respective coefficient is statistically significantly
10 Apart from jute, price series for commodities were updated using the information in various issues of Global Economic Prospects, published by the World Bank. Price data in the International Financial Statistics Yearbook of the IMF were used to build the series on jute for 1987–2001. 11 Note that Grilli and Yang (1988) used MUV as the deflator. For later periods we use what UNCTAD now publishes as the unit value index of manufactured goods exports from the developed market economy countries. Appendix 1 gives the graphical plots of these two series, which show that the series are almost the same. A linear trend line fitted through the scatter of the two series resulted in a R2 value of 0.999 with the coefficient on the explanatory variable very close to one (the restriction that the coefficient was exactly one could not be rejected at the 1 per cent error probability level). 12 These data were accessed from the Commodity Price Bulletin of UNCTAD. For this study the online version of the dataset was used from the website: www.unctad.org
40
Long-Run Trend in the Relative Price 1.5
Cocoa 1.0
Coffee
2.0
1.0
1.5
0.5
1.0
Tea
2.0
Bananas
1.5 0.5
1.0
0.5 1900
1950
2000 1900
1950
2000 1900 3
Sugar
4
2
Rice
1950
2000 1900
Wheat 3
1950
2000
Maize
2 2
2
1 1
1900
1950
2000 1900
1950
3
Cotton
2000 1900 4
Jute
2
3
2
1 1950
Palm Oil
1900 1.00
1950
2000 1900
2000 1900
2000
Copper
1.0
1 1950
2.0
1950
1.5
2 1
1
2000 1900
1950
2000 1900
1950
2000
Tin
0.75 0.50 0.25 1900
1950
2000
Figure 3.1. Relative Prices of 13 Commodities: 1900–2001 Note: The figures correspond to relative commodity prices.
different from zero at the 5 per cent error probability level. For lnRPt1, however, the estimated t-ratios should be compared with those of the critical values computed by Dickey and Fuller (1979) to draw inferences. These critical values are considerably higher than the standard t-ratios. In fact, in order for the ln RPt1 term to be statistically significantly different from zero, the computed t-ratio should be as high as 3.13 (absolutely) at the 10 per cent significance level. Following the usual practice with Dickey-Fuller regressions, the first order lagged dependent variable (i.e. ˜lnRPt1 ) is always retained in the equation irrespective of its statistical significance. In only a few cases additional lags were also included to remove the problem of serial correlation. In a number of equations, regression residuals turn out to be non-normal, which should be considered as a serious problem preventing the drawing of valid inferences. As sudden and precipitous price fluctuations are common, as reflected in Figure 3.1, it is unsurprising that a simple trend equation will fail to explain such movements, resulting in residuals that are not normally distributed. Bleaney and Greenaway (1993) also encountered the problem of
41
The Issue of Declining Commodity Prices non-normality in estimating the trend growth rate in the aggregate commodity price index for which they re-estimated their equation after dropping the first 25 years of data from their sample, arguing that those years were associated with exceptionally violent movements of commodity prices. Figure 3.1, however, does not seem to suggest that at the individual commodity levels the movement in commodity prices prior to 1925 was different from that in the latter period and therefore it was decided not to curtail the sample to tackle the problem of non-normality. Instead, we have used dummy variables to control for the sudden rise(s) and decline(s) in commodity prices. This approach is tantamount to pulling the atypical data points to a normal year, which is defined by the trend equation. All dummies inserted in all equations were found to be highly significant. The estimated equations with the dummies can be considered as the preferred specification and growth rates corresponding to these equations will used for reference. Results reported in Table 3.1 show that for ten out of thirteen commodities the estimated coefficients on the trend equation are negative; only for cocoa, coffee and tin is the sign on the coefficients positive. For eight commodities— tea, sugar, rice, wheat, maize, cotton, jute and palm oil—the estimated trend is negative and statistically significant at the 10 per cent confidence level. Among the three commodities with a positive sign, only the trend rate for tin is significant over the period 1900–87. In all regressions the lagged level dependent variable (lnRPt1 ) is negative and less than zero, as is expected in the case of an error-correction model. In as many as eight cases the T-ratio on lnRPt1 is higher than the Dickey-Fuller critical value (at least at the 10 per cent level), which implies that for these commodities a valid long-run trend growth rate can be estimated irrespective of the order of integration of the real price series. Although for another five commodities the estimated T-ratio on the lagged level term is lower than the critical value, it is always significantly different from zero, considering the standard test of significance for stationary variables. Indeed, if any of these relative price series is TSP, estimation of trend growth rate for it from the regression results can be considered valid.13 In the column for ‘trend rate’, the long-term trend growth rate in relative price (in per cent per annum) has been computed for all commodities for which the coefficient on the trend term appears to be significant at least at the 10 per cent error probability level. For cocoa, coffee, banana, copper and tin the trend term is not significant and the exact interpretation will depend on whether one considers lnRPt1 in those equations to be significant or not.14 13
Note that since there is no pre-testing for unit root, it is not known a priori whether any of these five series are TSP. In some studies, when pre-testing for unit roots is not done, the standard T-ratios are used to make inferences about the statistical significance of the lagged dependent variable (e.g. Athukorala, 2000). 14 As mentioned above, if the statistical significance of lnRPt1 is to be determined on the basis of the Dickey-Fuller critical values, then the variable is significant only in the case of copper.
42
Long-Run Trend in the Relative Price If the coefficient on the lagged level variable is to be considered significant, the real price series of these commodities have no long-term trend but they tend to be pulled back towards their historical mean.15 Negative trend growth rates have been estimated for tea, sugar, rice, wheat, maize, cotton, jute, palm oil and copper. The trend rates lie between 0.79 and 1.43 per cent per annum and the results show that during the past century most commodity prices have fallen at an annual rate of above 1 per cent. This is considerably higher than the estimates of Grilli and Yang (1988) and Bleaney and Greenaway (1993) which were in the range 0.6 to 0.7 per cent per annum.
3.2.2. Estimation for commodities in the UNCTAD database 3.2.2.1. ESTIMATES FOR BROAD COMMODITY GROUPS The commodity price bulletin of UNCTAD provides information on prices for a large number of individual commodities since 1960. It also provides an aggregate commodity price index and price indices for another four broad commodity groups, viz. food and beverages, vegetable oils and oilseeds, agricultural raw materials, and minerals and metals. Before analysing the individual commodities, Table 3.2 gives an estimate for the broad commodity groups. In general, the estimation of the trend equation was affected by the normality problem mainly due to the sudden jump in commodity prices around the mid-1970s, as Figure 3.2 exhibits one clear peak for all broad commodity groups. Therefore, for most equations dummy variables were included to control for these sharp price movements. As with the previous cases, the equations with the dummies are considered to be the preferred specifications. The results reported in Table 3.2 show that for every broad commodity group the trend variable appears to be statistically significant. In every preferred specification, apart from the one for the food and beverage group, the computed T-ratio on the lagged dependent variable (lnRPt1 ) exceeds the DickeyFuller critical values at least at the 90 per cent level.16 Although a firm conclusion about the long-run relationship cannot be made for food and beverages, separate estimates for the ‘food only’ and ‘beverages only’ sub-groups strongly rejected the null hypothesis of statistical insignificance of lnRPt1 , suggesting that irrespective of the order of integration of the dependent variables the estimated trend growth rates are valid. In no regression is lnRPt1 insignificant in comparison with the t-statistics following standard distribution and applicable for drawing inferences in the case of stationary variables.
15
Otherwise, the relative price series form a random walk with zero mean. The equation for food and beverages did not show any residual non-normality problem and therefore no dummy variable was inserted to control for the sharp rise in 1973, as shown in Figure 3.2. 16
43
Table 3.1. Regression Results for 13 Commodities (with Updated Grilli-Yang Series: 1900–2001) DlnRPt Cocoa
Constant
T
lnRPt1
DlnRPt1
0.13* (1.76)
0.00038 (0.044)
0.114 (2.15)
0.15 (1.53)
0.75** (2.98)
.00093 (0.11)
0.86 (1.76)
0.10 (1.13)
Coffee Tea
0.16** (1.90) 0.04 (1.21) 0.92*** (0.3)
0.0014 (0.15) 0.001* (1.68) 0.001** (2.15)
0.19 (2.94) 0.11 (2.24) 0.09 (2.50)
0.05 (0.55) 0.07 (0.1) 0.04 (0.48)
Banana Sugar
0.05*** (3.11) 0.29*** (3.29) 2.82*** (5.19)
0.004 (1.16) 0.004*** (3.02) 0.004*** (4.97)
0.14*** (3.29) 0.40*** (4.84) 0.38*** (6.57)
0.10 (1.02) 0.17* (1.69) 0.16** (2.38)
0.003*** (3.62) 0.003** (3.81) 0.0037*** (4.19) 0.003*** (4.59) 0.004*** (3.80) 0.0032*** (3.46) 0.0025*** (3.94) 0.0017** (2.11) 0.0012* (1.62) 0.0036*** (3.52) 0.003*** (3.11) 0.004 (0.75) 0.0007 (1.02) 0.001 (1.6)
0.25** (4.07) 0.24*** (4.21) 0.32*** (4.84) 0.32*** (5.10) 0.35*** (4.41) 0.22* (3.23) 0.196** (3.89) 0.19** (3.26) 0.16** (3.85) 0.29*** (4.16) 0.26** (3.97) 0.18* (3.21) 0.20** (3.39) 0.16 (2.76)
0.29*** (2.98) 0.29 (3.31) 0.28*** (2.89) 0.27*** (2.95) 0.09 (0.93) 0.084 (0.33) 0.09 (0.76) 0.06 (0.10) 0.05 (0.58) 0.21** (2.10) 0.14 (1.47) 0.11 (1.11) 0.10*** (0.09) 0.153 (1.66)
Rice
0.19*** (3.48) 0.47*** (2.16) Wheat 0.27*** (4.34) 0.82*** (5.23) Maize 0.31*** (3.92) 1.77*** (5.41) Cotton 0.16*** (3.80) Jute 0.11** (2.20) 0.77*** (3.65) Palm Oil 0.18*** (3.11) 0.60*** (2.85) Copper 0.016 (0.49) Tin 0.17** (2.47) 0.76 (4.64)
DlnRPt2 0.28** (2.82) 0.29*** (3.09) — — —
Dummies
Adjusted Serial Functional Heterosce- Trend R2 Correlation Form Normality dasticity rate (%)
None
0.125
0.30
0.60
8.96**
0.06
D47
0.23
0.03
1.14
2.66
0.66
None None D85, D77, D84, D54 — None — None — D20, D21, D63, D65, D74, D80 — None — D73, D82 — None — 0.54*** (3.74) — None — D21, D38, D48 — None — None — D86 — None — D86 — None None 0.032 (0.09) D86
0.06 0.02 0.33
0.02 4.15** 0.72
0.48 0.60 2.38
4.38 11.23** 2.50
0.099 0.37 1.47
1.04 1.26
0.05 0.17 0.64
2.95 2.50 3.22
0.03 79.69** 1.86
3.91 0.64 0.41
White 1.02 3.03
1.02 1.21
0.15 0.34 0.23 0.28 0.15 0.40 0.08 0.07 0.21 0.13 0.24 0.07 0.08 0.19
3.23 0.017 0.09 0.94 1.33 4.40 3.67 1.07 0.17 5.19 1.58 1.94 5.65*** 2.89
0.91 0.57 17.41*** 0.10 1.88 0.006 0.31 1.04 0.39 8.70*** 7.88*** 0.39 3.24 2.04
0.05 0.77 0.18 0.69 0.66 1.08 White 0.99 0.48 3.08 0.22 0.35 0.12 0.48
1.25 1.28 0.92 1.17 1.18 1.43 1.29 0.9 0.79 1.25 1.17
23.97*** 2.04 1.38 5.90* 21.88*** 4.89 0.29 14.67*** 1.13 6.33** 1.00 2.51 9.17*** 2.98
Note : Figures within the parentheses indicate t-ratios. Statistical significance at the 1, 5, and 10 per cent levels is indicated by ***, **, and * respectively. Critical values for the coefficient of lnRPt1 at the 10, 5 and 1 per cent significance levels are, respectively, 3.13, 3.45 and 4.10. Variables with the letter ‘D’ followed by two digits indicate a dummy variable. For example, D73 indicates a dummy variable with 0 for 1973 and 1 for all other years. All dummies are inserted separately and are always significant at the 1 per cent level. ‘White’ indicates that due to heteroscedasticity standard errors are derived from the White’s (1980) heteroscedasticity consistent variance-covariance matrix. implies that the coefficient on the trend term is not significant and hence the trend growth rate is not estimated and can be considered to be zero.
Table 3.2. Regression Results for Broad Commodity Groups as in UNCTAD Commodity Price Bulletin: Annual Data (1960–2002)
(DlnRPt )
Constant
Aggregate 0.19** (2.68) Commodity Price Index 0.09*** (5.96) Food and 0.18** (2.34) Beverages - Food only 0.30*** (3.03) 1.79*** (8.14) -Beverages only Vegetable Oils and Oilseeds
0.05 (0.79)
T
0.007*** (2.82) 0.356 (3.06)
DlnRPt1
DlnRPt2 —
None
0.14
1.49
1.15
4.53*
1.77
1.96
0.007*** (3.71) 0.419*** (4.53) 0.025 (0.19) 0.007** (2.45) 0.30 (2.78) 0.16 (1.05)
— —
D73, D74 None
0.48 0.11
0.85 1.72
1.33 1.05
1.15 3.09
0.27 0.53
1.82 2.37
0.008*** (2.82) 0.40*** (3.51) 0.009*** (4.57) 0.51*** (6.53)
0.32** (2.18) 0.15 (1.41)
— —
0.20 0.65
0.94 0.16
0.19 0.96
8.23*** 2.05
0.08 1.53
2.19 1.85
0.006* (1.94)
0.26** (2.42)
0.14 (0.89)
—
None D80, D73, D74 None
0.07
0.002
0.21
9.30***
0.009
2.63
0.28* (3.33) 0.302 (2.05)
0.002 (0.02) 0.17 (1.16)
—
D76, D77 None
0.42 0.35
0.78 0.61
0.30 5.79**
4.21 1.09
0.42 1.09
2.25 2.92
0.01** (2.84) 0.005** (2.56)
0.49** (3.79) 0.43*** (2.90)
0.31** (2.07) 0.37*** (2.75)
— —
D73 None
0.29 0.37
0.04 0.088
4.42 1.99
0.28 0.04
2.38 1.38
0.331** (2.16) 0.01*** (3.83)
0.295*** (3.14) 0.32*** (3.84) 0.59*** (4.19) 0.24* (1.66)
D73, D76 None
0.76 0.27
0.95 0.05
0.37 5.28
1.29 1.21
1.08 1.75
1.27*** (5.01) 0.006** (2.29) 0.16 (1.57) 0.008* (1.96)
0.70*** (3.48) Agricultural 0.13** (2.17) Raw Materials 0.64*** (8.22) Minerals 0.27*** (3.74) and Metals
lnRPt1
Trend rate Serial Functional HeterosceAdjusted 2 (per cent) Correlation Form Normality dasticity Dummies R
0.18 (1.15)
0.02 77.86***
1.00 0.67
Note: Critical values for the coefficient of lnRPt1 at the 10, 5 and 1 per cent significance levels are, respectively, 3.13, 3.45 and 4.10. Variables with the letter ‘D’ followed by two digits indicate a dummy variable. For example, D73 indicates a dummy variable with 0 for 1973 and 1 for all other years. Figures within the parentheses indicate t-ratios. ‘White’ indicates that due to heteroscedasticity standard errors are derived from the White’s (1980) heteroscedasticity consistent variance-covariance matrix. implies that the coefficient on the trend term is not significant and hence the trend growth rate is not estimated and can be considered to be zero.
The Issue of Declining Commodity Prices Aggregate Commodity Price Index 2.0
3
1.5
2
1.0
1 1960
4
Food and Beverages
1970
1980
1990
1960
2000 2.0
Food Only
3
1.5
2
1.0
1
1970
1980
1990
2000
1980
1990
2000
Beverages Only
0.5 1960
1970
2.0
1980
1990
2000
1960
1.5
1.50 Vegetable Oils and Oilseeds 1.25
1.0
1.00
1970
Agricultural Raw Materials
0.75
0.5 1960
1970
1.50
1980
1990
2000
1960
1970
1980
1990
2000
Minerals, Ores, and Metals
1.25 1.00 0.75 1960
1970
1980
1990
2000
Figure 3.2. Real Prices of Broad Commodity Groups Note: All prices are relative to the unit value of the index of manufactured goods exports of developed market economy countries. Source : Authors’ calculation based on data from Commodity Price Bulletin (UNCTAD).
Vegetable Oils Food and and Oilseeds Beverages
Beverages
Food
Aggregate Minerals, Ores Agricultural Relative Price and Metals Raw Materials
0
per cent per annum
−0.5
−1
−1.5
−2
−2.5
Figure 3.3. Estimated Growth in Relative Prices for Broad Commodity Groups Source : Based on the results associated with the preferred specifications in Table 3.2.
46
Long-Run Trend in the Relative Price According to the estimates in Table 3.2, trend growth rates for prices of broad commodity groups fell by between 1.08 to 2.92 per cent per annum. The rate of decline during the past 40 years was lowest for agricultural raw materials and highest for vegetable oils and oilseeds and food and beverages (Figure 3.3). On the whole, the aggregate relative price for primary commodities has been subject to an annual trend deterioration of 1.82 per cent. It is important to note that our preferred specification does not exaggerate the rate of decline in commodity prices. In fact, in every case the growth rate associated with the preferred specification in Table 3.2 is lower than the equations that do not include any dummy variable to control for residual non-normality. Most dummy variables used to control for a sharp rise in prices fall within the relatively early years of the sample, resulting in a negative effect on the magnitude of the trend growth rate. 3.2.2.2. ESTIMATES FOR INDIVIDUAL COMMODITIES Within each of the broad commodity classifications, it is possible to estimate the growth rate in relative price for several individual commodities. UNCTAD’s Commodity Price Bulletin provides a wealth of information on prices of many commodities which are narrowly defined, and data on them are gathered in a consistent manner. Subject to the availability of data for a reasonable time period, 17 individual food and beverages products, 9 vegetable oils and oilseeds, 17 agricultural raw materials and 17 commodities in the minerals, ores and metals sub-group were used for empirical estimation. The unit value index of manufactured goods exports of developed market economy countries was used to convert the nominal price series in real terms. Despite frequent fluctuations, a close look at the graphical plots of the real prices of the individual commodities, as given in Appendix 3.10–3.12, reveals a declining trend in real prices for most of the commodities. Initial experiments with the estimation of the trend equation revealed problems related to the model diagnostic tests in a number of equations. The main source of the problems could be found to be associated with the commodity price boom of the mid-1970s. Therefore, for some commodities our preferred specification includes dummy variables to control for atypical price rises. One important feature of the specification is that if the dummies were not included, growth rates for real prices of commodities would have been higher (absolutely). Therefore, the estimated models, as presented in Appendices 3.1–3.4, do not exaggerate the trend decline in commodity prices. Among the seventeen products in the food and beverages category, the sign on the trend term for all commodities except for white pepper turns out to be negative (see Appendix 3.1).17 Only for cocoa and white pepper is the trend 17 Among the 17 commodities, four types of coffee and two types of wheat are included. Appendices 3.13 and 3.14 show that prices of different varieties of the two products move quite closely.
47
The Issue of Declining Commodity Prices term found to be not statistically different from zero. The lagged level dependent variable in every equation is correctly signed and is always significant in comparison with the standard t-statistics. When compared with the Dickey-Fuller critical values, the statistical significance of the variable is retained for all commodities except coffee and beef. The estimated trend growth rate varies between 3.27 per cent per annum for tea and 0.92 per cent for bananas. All nine vegetable oil and oilseed commodities have a significant negative trend growth rate along with the statistical significance of lnRPt1 (Appendix 3.2). Turning to agricultural raw materials, Appendix 3.3 only shows a significant positive trend rate for wood items such as non-coniferous woods, sawn wood, tropical logs and plywood. While the estimated trend in the cases of jute and sisal failed to become statistically significant, for cotton (various types), linseed oil, leaf tobacco, cattle hides and rubber there was evidence of significant declining terms of trade. Among the seventeen products covered in the category of minerals, ores and metals, as many as thirteen have a significant lag dependent variable. However, the coefficient on the trend term in the equations for phosphate rock, nickel (cathodes), refined lead, tin (ex-smelter), gold and zinc are not significant. On the whole, the application of the error-correction model in the estimation of trend rate is therefore found to be satisfactory. The correct sign and the significance of the error-correction term even after comparing with the Dickey-Fuller critical values in the overwhelming majority of the equations suggest that the trend growth rates are valid without a priori knowledge about the order of integration of the variables. Figure 3.4 summarizes the trend growth rates in relative prices over 1960–2002 by individual commodities.18 It is found that two minerals, tungsten ore and silver, have witnessed the steepest decline over the past four decades. The trend declining rates for tea and coffee are found to be much higher than estimates using the very long-term data of 1900–2001.19 Among the cereals, the real price decline for rice has been much worse than that for wheat and maize. For eight commodities, cocoa, sugar, white pepper, jute, phosphate rock, tin, gold and zinc, the trend growth rates are not statistically significantly different from zero. While the results for cocoa in both the very long-term sample and the sample beginning from 1960 are qualitatively the same, for tin the positive rate of growth for 1900–2001 has now been turned into one of no significant trend. The results in Figure 3.4 cannot be readily compared with those of Table 3.1, which uses very long time-series data. Given the substantial fluctuation in commodity prices, the estimation of the trend growth rate will be affected by
18 In the case of different varieties, a simple average of the estimated growth rates has been used. 19 For tea, the long-term trend growth over the period of 1900–2001 was found to be 1.25 per cent per annum, while for coffee no significant rate could be found.
48
Cocoa Sugar White Pepper Jute Phosphate Rock Tin Gold Zinc Tropical Logs Plywood Sawn wood Non-Coniferous Wood
Long-Run Trend in the Relative Price 3
2
0
Tea Rice Coconut Oil Coffee Palm Kernel Oil Copra in Bulk Cotton Palm Oil Cattle Hides Rubber Cotton Seed Oil Beef Yellow Maize Yellow Soybean Copper Soybean Meal Crude Soybean Oil Maize Wheat Sunflower Oil Linseed Oil Groundnut Oil Leaf Tobacco Aluminium Lead Fish Meal Banana Iron Ore Nickel Manganese Ore Sisal
growth rate (per cent per annum)
1
−1
−2
−3
−6
Tungsten Ore
−5
Silver
−4
Figure 3.4. Trend (1960–2002) Growth Rates for Individual Commodities with UNCTAD Data
the time span chosen for analysis. The review of the literature in the previous section also highlights this problem. While a very long-term analysis, such as the one covering 100 years, is useful in understanding the evolution of price movements and in studying the pattern and nature of mean reversion in the data, trends emanating from a relatively recent past are probably more informative in understanding the implications for developing countries. There is not much point in arguing about whether to make the starting point of the sample 1940, 1960, or 1970; nevertheless it might be useful to study the trend in the post-war period. However, while 1970 should be avoided as the initial point because of the commodity price boom, a starting point in the 1980s reduces the number of observations that can be worked with.20 On the other 20 Moreover, in the 1980s commodities prices were already very low. Maizels (1992) shows that relative prices in the 1980s were lower than those during the great depression of the 1930s. Bleaney and Greenaway (1993) report a 37 per cent downward jump in commodity prices after 1980 compared to the average for 1925–1991.
49
Maize
Cotton
Rice
Tea
Sugar
Palm oil
Wheat
Jute
Copper
Banana
Coffee
Cocoa
The Issue of Declining Commodity Prices
0
GY 1960–2002
GY 1900–2001
UNCTAD 1960–2002
Figure 3.5. Trend Growth Rates since the 1960s: Grilli-Yang versus UNCTAD Data
hand, the data show that in the 1960s most relative commodity prices were quite stable; therefore, the starting point of the estimates presented should not be inappropriate. One serious concern is whether these results should be considered as evidence for a potential structural break in the very long-run trend equation of 1900–2001. The issue of structural break has been discussed in a number of studies, including Bleaney and Greenaway (1993), where the authors found statistical support for a once-for-all drop in commodity prices after the 1980s. Thus the possibility of a structural break in the very long-run trend equation cannot be overlooked. However, exactly what time frame should be considered for testing such a break will remain an important issue to be resolved if such a debate is to be informative.21 It is also true that the precise point of structural break might be different for different commodities. What will be the magnitude of the trend decline rate in real commodity prices if the Grilli-Yang type long-run data series is restricted to one comparable with the time frame of the UNCTAD databases? To answer this question trend growth rates for 13 commodities using the 1900–2001 dataset were also estimated for a period from 1960 to the end of the sample. In Figure 3.5 the results are compared with those reported in Table 3.1, together with those 21 In the previous section it was found that using the aggregate relative price of primary commodities, Cuddington and Urzua (1989), Sapsford (1985) and Powell (1991) found structural breaks in different years.
50
Long-Run Trend in the Relative Price plotted in Figure 3.4. It now becomes obvious that the estimates from the UNCTAD data and from the Grilli-Yang data for the comparable period beginning in 1960 do not provide very different results. The biggest discrepancy between the two series is for jute. This is because in the UNCTAD data the trend rate for jute appears to be not significant, while using the updated GrilliYang data, results in the trend term are significant only at a somewhat lower level of statistical significance (i.e. at the 10 per cent level). Figure 3.5 also shows that, with the exception of sugar, jute and cocoa, the growth rate over 1900–2001 is much lower than the sample comprising the data for only the post-1960 period.
3.3. Conclusion The empirical evidence presented in this chapter strongly shows the presence of a statistically significant declining trend in the relative price of most individual commodities. When the data spanning the very long period of 1900–2001 are considered, the estimated trend rates lie between 0.79 and 1.43 per cent per annum. Much higher rates of decline are observed for the relatively recent period. Between 1960 and 2002 the aggregate relative price of commodities has fallen at an annual rate of 1.82 per cent, with the corresponding figures for individual commodities ranging from 0.9 to 3.50 per cent. Therefore, the use of very long time-series data considerably undermines the magnitude of the deterioration of relative commodity prices during the recent past.
51
Appendix 3.1. Estimated Trends in Relative Prices for 17 Food and Beverage Products (With UNCTAD Data 1960–2002) Adjusted R2
Normality
Heteroscedasticity
4.06
2.41
0.02
3.07
1960–2002
0.65
1.23
0.06
2.11
1960–2002
0.33
CochraneOrcutt 1.66
2.66
1.90
0.16
3.45
1960–2002
D76
0.30
2.24
2.16
2.19
0.25
2.60
1960–2002
D76
0.28
0.03
0.81
0.54
2.01
1960–2002
—
D77, D84
0.63
3.58
1.68
2.01
0.68
3.27
1960–2002
—
D90, D74
0.44
0.001
0.06
1.33
1.15
1.98
1960–2002
—
D73
0.46
0.33
0.41
5.29*
0.16
1.78
1960–2002
—
D73, D90
0.35
0.22
2.38
0.44
0.55
2.00
1960–1997
—
D73
0.47
3.14
1.45
1.55
0.73
2.98
1960–2002
—
D74, D80
0.64
2.46
0.11
0.92
0.38
1960–2002
—
None
0.11
0.64
5.91**
2.52
2.52
2.22
1960–2002
—
D73
0.39
0.70
0.025
0.43
0.13
2.19
1960–2002
—
None
0.19
1.23
0.02
0.11
0.55
0.92
1960–2000
—
D97
0.63
0.007
2.85
1.81
0.011
1960–2002
0.33*** (3.40) —
D73
0.72
1.07
3.42
1.83
0.29
2.08
1960–2002
D73
0.46
1.46
1.79
1.55
2.15
1.02
1960–2002
Constant
T
lnRPt1
DlnRPt1
DlnRPt2
Coffee-1
0.92*** (3.10) 0.87*** (3.86) 0.82*** (3.42) 0.79*** (3.34) 0.64*** (2.88) 1.49*** (7.46) 0.12 (0.58) 0.65*** (5.11) 0.17 (0.92) 1.06*** (5.78) 2.76*** (7.47) 0.13** (2.11) 0.62*** (4.66) 0.05 (1.40) 0.32* (1.61) 1.11*** (7.23) 1.15*** (5.35)
0.0089** (2.18) 0.0053* (1.94) 0.008** (2.37) 0.0084** (2.10) 0.0043 (1.31) 0.022*** (4.53) 0.0085*** (2.92) 0.0077*** (2.82) 0.008** (2.48) 0.0138*** (3.51) 0.0039 (1.05) 0.0065** (2.44) 0.010*** (3.23) 0.005** (2.45) 0.002 (0.85) 0.0105*** (3.49) 0.007*** (2.38)
0.29 (2.64) 0.25 (2.88) 0.24 (2.97) 0.325 (2.97) 0.26* (3.11) 0.69*** (5.25) 0.43*** (4.30) 0.43** (4.08) 0.41** (3.60) 0.46*** (4.14) 0.47*** (5.74) 0.29 (2.81) 0.466** (3.98) 0.54* (3.31) 0.33*** (5.08) 0.51*** (4.17) 0.71*** (4.97)
0.086 (0.54) 0.30* (1.69) 0.30** (2.06) 0.27* (1.73) 0.45*** (2.98) 0.168 (1.34) 0.34** (2.64) 0.36** (2.79) 0.31 (2.10) 0.29** (2.27) 0.27*** (2.88) 0.14 (0.90) 0.33** (2.48) 0.13 (0.80) 0.69*** (6.92) 0.03 (0.26) 0.18 (1.35)
—
D77
0.23
1.12
—
D76
0.31
—
D76
0.21 (1.44) —
Coffee-2 Coffee-3 Coffee-4 Cocoa Tea Wheat, Argentina Wheat, US Maize Rice Sugar Beef Yellow Maize Bananas White Pepper Soybean Meal Fish Meal
Dummies
Serial Correlation
DlnRPt
Functional Form
Trend rate (per cent)
Sample
Appendix 3.2. Trend Growth Rates in Relative Prices for 9 Vegetable Oils and Oilseed Products (With UNCTAD Data 1960–2002) DlnRPt
Constant
T
lnRPt1
DlnRPt1
Yellow Soybean Crude Soybean Oil Sunflower Oil Groundnut Oil Copra in bulk Coconut Oil
0.81*** (6.10) 0.70** (2.26) 0.19 (0.67) 0.65*** (3.19) 1.09*** (3.50) 1.85*** (4.50) 1.81*** (4.14) 1.24*** (3.69) 0.70*** (3.73)
0.009*** (3.34) 0.0101** (2.73) 0.006* (1.89) 0.008** (2.54) 0.024*** (4.07) 0.02*** (4.29) 0.02*** (3.74) 0.013*** (3.03) 0.10*** (2.86)
0.42*** (4.15) 0.49*** (4.31) 0.37*** (3.44) 0.56*** (4.13) 0.86*** (5.16) 0.80*** (5.33) 0.74*** (4.88) 0.54** (4.09) 0.47** (3.68)
0.08 (0.68) 0.21 (1.63) 0.12 (0.95) 0.18 (1.28) 0.24** (1.55) 0.15* (1.06) 0.10 (0.67) 0.1 (0.97) 0.002 (0.018)
Palm Kernel Oil Palm Oil Cottonseed Oil
Adjusted R2
Serial Correlation
Functional Form
Normality
Heteroscedasticity
Trend rate (per cent)
D73
0.49
0.05
0.25
1.46
0.93
2.18
1960–2002
D73, D74, D86 D74, D86
0.54
2.61
0.61
1.80
1.58
2.04
1960–2002
0.43
0.27
0.26
1.72
2.34
1.86
1960–2002
D74
0.31
0.30
0.09
0.25
1.05
1.55
1960–2002
—
D74
0.41
3.73
3.58
0.018
0.06
2.74
1960–2002
—
D74, D84
0.47
3.02
4.81
1.29
0.016
2.93
1960–2002
—
D74, D84
0.43
2.98
3.01
0.44
0.05
2.75
1960–2002
D74, D84
0.33
3.81
4.36
2.43
0.22
2.55
1960–2002
D74
0.14
3.37
2.11
2.16
0.0003
2.39
1960–2002
Dln RPt2 —
—
Dummies
Sample
Appendix 3.3. Estimated Trends in Relative Prices of 16 Products in Agricultural Raw Materials (With UNCTAD Data 1960–2002) Adjusted R2
Serial Correlation
Functional Form
Heteroscedasticity
Trend rate (per cent)
None
0.15
1.25
0.003
4.36
2.31
2.29
—
None
0.15
0.79
0.001
1.65
2.06
2.10
—
None
0.29
3.35
0.41
2.13
2.26
3.50
0.21 (0.99)
—
None
0.29
0.002
0.58
2.52
0.80
2.81
1960– 2002
0.27 (2.05) 0.41* (3.56)
0.03 (0.29) 0.41** (2.72)
—
D84, D86
0.69
0.23
0.09
1.24
0.85
—
None
0.23
0.14
0.008
1.20
0.04
1960– 2002 1960– 2002
0.47** (3.65) 0.41 (2.28) 0.58** (3.34) 0.80*** (4.55) 0.48 (2.84) 0.76*** (5.59) 0.76*** (5.59) 0.57*** (4.81) 0.51** (3.71) 0.64*** (4.39)
0.36*** (2.32) 0.15 (0.74) 0.29 (1.60) 0.24 (1.50) 0.166 (0.87) 0.17 (1.62) 0.17 (1.62) 0.47*** (3.72) 0.36** (2.26) 0.40** (2.62)
—
None
0.22
0.08
0.71
1.27
1.36
1.23
—
None
0.33
0.26
7.37**
0.47
White
þ1.88
—
None
0.21
0.11
0.002
0.02
0.10
þ1.87
—
None
0.39
0.05
1.05
5.47
2.83
0.69
—
None
0.15
0.36
0.88
0.002
2.13
—
None
0.70
0.001
0.05
0.51
0.77
þ1.35
—
D73, D93
0.70
0.002
0.05
0.51
0.77
þ1.35
—
D74
0.51
0.002
3.23
0.72
0.01
1.79
—
None
0.22
3.70
1.81
3.38
0.55
1.23
—
None
0.29
0.44
0.05
0.28
0.03
2.48
DlnRPt
Constant
T
lnRPt1
DlnRPt1
DlnRPt2
Cotton (US, Memphis) Cotton (US, New Orleans) Cotton (Outlook Index A) Cotton (Outlook Index B) Jute
0.16** (2.03) 0.16** (2.08) 0.73** (2.77)
0.088** (2.52) 0.0088** (2.56) 0.026*** (2.95)
0.38 (2.56) 0.42 (2.66) 0.76* (3.14)
0.06 (0.36) 0.028 (0.16) 0.07 (0.35)
—
0.71** (2.49)
0.024** (2.65)
0.85* (3.31)
0.20 (0.82) 0.06 (0.97)
0.009 (1.51) 0.0037 (1.31)
0.15 (1.76) 0.18** (2.42) 0.26*** (2.12) 0.24*** (3.13) 0.006 (0.10) 0.85 (0.16) 0.85*** (5.17) 0.79*** (3.90) 0.10** (2.15) 0.27** (2.70)
0.005** (1.79) 0.007** (2.77) 0.109** (2.46) 0.005** (2.31) 0.0024 (0.97) 0.10*** (4.15) 0.010*** (4.15) 0.103** (2.73) 0.006** (2.85) 0.015*** (3.38)
Sisal (Tanzania/ Kenya) Sisal (Uganda) Non-coniferous wood Sawn Wood Tropical Logs Tropical Logs (Gabon) Plywood/sheet Plywood/cubic metre Linseed Oil Leaf Tobacco Cattle Hides
Dummies
Normality
Sample 1960– 2002 1960– 2002 1960– 2002
1960– 2002 1972– 2002 1970– 2002 1970– 2002 1970– 2002 1963– 2002 1963– 2002 1960– 2003 1963– 2003 1962– 2002
Rubber in bales Phosphate rock Manganese ore Iron ore Tungsten ore Copper, Grade A Copper, Wire Brass Nickel, LME Nickel, Cathodes Lead, LME Refined Lead Aluminium, high grade Tin, LME Tin, Malaysia Gold Silver Zinc, Special Zinc, Prime Western
0.45*** (3.42) 1.04*** (9.77) 0.11*** (2.51) 0.64 (5.84) 0.19 (1.80) 0.36*** (3.17) 0.23** (2.83) 0.99*** (4.96) 0.84*** (5.04) 0.33*** (2.93) 0.1977 (1.90) 0.29*** (3.42) 0.05 (1.32) 0.04 (0.55) 0.58*** (4.04) 0.26 (1.65) 0.06 (0.93) 0.0077 (0.16)
0.016*** (3.48) 0.0003 (0.02) 0.002* (1.66) 0.003* (1.76) 0.009** (2.14) 0.011** (3.07) 0.007** (2.76) 0.007* (1.71) 0.003 (1.56) 0.009** (2.74) 0.002 (0.99) 0.0094*** (3.22) 0.005** (2.08) 0.006 (1.85) 0.002 (0.98) 0.011** (2.0) 0.0022 (0.87) 0.002 (1.06)
0.66*** (4.25) 0.27*** (4.61) 0.41*** (4.91) 0.33** (3.52) 0.18 (2.38) 0.43** (3.47) 0.40* (3.15) 0.58* (3.40) 0.53*** (4.07) 0.42* (3.27) 0.31 (2.32) 0.80*** (4.47) 0.10 (1.12) 0.14 (1.28) 0.29*** (4.49) 0.29** (2.59) 0.54*** (4.10) 0.60** (3.94)
0.29** (1.87) 0.27*** (4.61) 0.66*** (5.52) 0.26** (2.26) 0.23 (1.46) 0.16 (1.04) 0.12 (0.77) 0.22 (1.51) 0.22* (1.78) 0.13 (0.85) 0.04 (0.28) 0.28** (1.74) 0.14 (0.91) 0.01 (0.07) 0.10 (0.78) 0.20 (1.13) 0.35*** (2.32) 0.28* (1.81)
—
None
0.28
0.76
2.07
2.14
0.07
2.45
—
D74
0.79
0.05
0.008
2.98
0.42
—
None
0.49
1.28
2.40
1.72
2.09
0.63
—
D75, D82
0.60
0.73
1.44
2.23
1.27
0.83
—
None
0.10
0.35
0.24
1.15
0.16
4.96
—
None
0.19
0.17
5.34
0.57
2.02
2.60
—
None
0.15
0.29
0.40
1.03
0.16
1.77
—
D88
0.58
1.11
0.001
2.37
0.45
1.30
—
D88
0.56
0.76
0.002
1.76
0.50
—
None
0.17
3.69
0.54
4.11
0.74
2.29
—
None
0.07
2.42
0.01
0.64
0.18
—
None
0.31
0.04
0.82
6.01**
0.34
1.18
—
None
0.08
0.06
0.013
14.24***
0.77
§
—
None
0.05
0.97
1.26
70.02***
3.18
§
—
D80
0.58
1.49
0.34
2.17
0.43
—
None
0.13
0.03
0.89
6.07**
0.04
3.96
—
None
0.26
0.15
0.09
11.06***
0.51
—
None
0.25
0.88
0.24
0.56
Note : Zinc, Special: The use of dummy for 1973 to control non-normality of errors did not make the coefficient on the trend variable significant.
4.30
1960– 2002 1960– 2002 1960– 2002 1960– 2002 1960– 2002 1960– 2002 1960– 2002 1970– 2002 1960– 2002 1960– 2002 1960– 2002 1960– 2002 1960– 2002 1960– 2002 1970– 2002 1970– 2002 1960– 2002 1960– 2002
Appendix 3.4: Estimated Trends in Relative Prices of 17 Products in Minerals, Ores, and Metals (With UNCTAD Data 1960–2002) DlnRPt
Phosphate rock
Constant
1.04*** (9.77) Manganese ore 0.11*** (2.51) Iron ore 0.64 (5.84) Tungsten ore 0.19 (1.80) Copper, Grade A 0.36*** (3.17) Copper, Wire Brass 0.23** (2.83) Nickel, LME 0.99*** (4.96) Nickel, Cathodes 0.84*** (5.04) Lead, LME 0.33*** (2.93) Refined Lead 0.1977 (1.90) Aluminium , high grade 0.29*** (3.42) Tin, LME 0.05 (1.32) Tin, Malaysia 0.04 (0.55) Gold 0.58*** (4.04) Silver 0.26 (1.65) Zinc, Special 0.06 (0.93) Zinc, Prime Western 0.0077 (0.16)
T
lnRPt1
DlnRPt1
0.0003 (0.02) 0.002* (1.66) 0.003* (1.76) 0.009** (2.14) 0.011** (3.07) 0.007** (2.76) 0.007* (1.71) 0.003 (1.56) 0.009** (2.74) 0.002 (0.99) 0.0094*** (3.22) 0.005** (2.08) 0.006 (1.85) 0.002 (0.98) 0.011** (2.0) 0.0022 (0.87) 0.002 (1.06)
0.27*** (4.61) 0.41*** (4.91) 0.33** (3.52) 0.18 (2.38) 0.43** (3.47) 0.40* (3.15) 0.58* (3.40) 0.53*** (4.07) 0.42* (3.27) 0.31 (2.32) 0.80*** (4.47) 0.10 (1.12) 0.14 (1.28) 0.29*** (4.49) 0.29** (2.59) 0.54*** (4.10) 0.60** (3.94)
0.27*** (4.61) 0.66*** (5.52) 0.26** (2.26) 0.23 (1.46) 0.16 (1.04) 0.12 (0.77) 0.22 (1.51) 0.22* (1.78) 0.13 (0.85) 0.04 (0.28) 0.28** (1.74) 0.14 (0.91) 0.01 (0.07) 0.10 (0.78) 0.20 (1.13) 0.35*** (2.32) 0.28* (1.81)
Dln Dummies Adjusted Serial Functional Normality Heterosced- Trend rate RPt2 R2 Correlation Form asticity (per cent)
Sample
–
D74
0.79
0.05
0.008
2.98
0.42
1960–2002
–
None
0.49
1.28
2.40
1.72
2.09
0.63
1960–2002
–
D75, D82
0.60
0.73
1.44
2.23
1.27
0.83
1960–2002
–
None
0.10
0.35
0.24
1.15
0.16
4.96
1960–2002
–
None
0.19
0.17
5.34
0.57
2.02
2.60
1960–2002
–
None
0.15
0.29
0.40
1.03
0.16
1.77
1960–2002
–
D88
0.58
1.11
0.001
2.37
0.45
1.30
1970–2002
–
D88
0.56
0.76
0.002
1.76
0.50
1960–2002
–
None
0.17
3.69
0.54
4.11
0.74
2.29
1960–2002
–
None
0.07
2.42
0.01
0.64
0.18
1960–2002
–
None
0.31
0.04
0.82
6.01**
0.34
1.18
1960–2002
–
None
0.08
0.06
0.013
14.24*** 0.77
§
1960–2002
–
None
0.05
0.97
1.26
70.02*** 3.18
§
1960–2002
–
D80
0.58
1.49
0.34
2.17
0.43
1970–2002
–
None
0.13
0.03
0.89
6.07**
0.04
3.96
1970–2002
–
None
0.26
0.15
0.09
1960–2002
–
None
0.25
0.88
0.24
1960–2002
11.06*** 0.51 4.30
0.56
Note: Zinc, Special: The use of Dummy for 1973 to control of non-normality of errors did not make the coefficient on the trend variable significant.
Long-Run Trend in the Relative Price Appendix 3.5. Description for Food-Commodities used from UNCTAD Commodity Price Bulletin Name Coffee-1 Coffee-2 Coffee-3 Coffee-4 Cocoa Tea Wheat, Argentina Wheat, US Maize Yellow Maize Rice Sugar Beef Bananas Pepper
Product Description Coffee, Brazilian and other natural Arabicas, ex-dock NY (¢/lb.) Coffee, other mild Arabicas, ex-dock NY (¢/lb.) Coffee, Robustas, ex-dock NY (¢/lb.) Coffee, composite indicator price 1976 (¢/lb.) Cocoa, average daily prices NY/London (¢/lb.) All teas, London auction prices Wheat, Argentina, Trigo Pan Upriver, f.o.b. Wheat, US, n8 2, Hard Red Winter, f.o.b. Gulf ports Maize, Argentina, c.i.f. Rotterdam Yellow maize, n8 3, US, c.i.f. Rotterdam White milled rice, 5% broken, Thailand, f.o.b. Bangkok Sugar in bulk, Caribbean ports, f.o.b. (I.S.A.) (¢/lb.) Frozen and boneless beef (mainly Australia), US ports (¢/lb.) Fresh bananas, Central America and Ecuador, f.o.b. US ports (¢/lb.) White pepper, 100% Sarawak, Singapore, closing quotations
Appendix 3.6. Description of Vegetable Oils and Oilseeds used from UNCTAD Commodity Price Bulletin Name Soybean Meal Fish Meal Yellow Soybean Crude Soybean Oil Sunflower Oil Ground Nut Oil Cora in bulk Coconut Oil Palm Kernel Oil Palm Oil Cottonseed Oil
Product Description Soybean meal 44/45%, Hamburg f.o.b. ex-mill Fish meal 64/65%, any origin, candf, Hamburg Yellow soybeans, n8 2, US, c.i.f. Rotterdam Crude soybean oil, Dutch, f.o.b. ex-mill Sunflower oil, E.U., f.o.b. N.W. European ports Groundnut oil, any origin, c.i.f. Rotterdam Copra in bulk, Philippines/Indonesia, c.i.f. European ports Coconut oil, Philippines/Indonesia c.i.f. N.W. European ports Palm kernel oil, Malaysia, c.i.f. Rotterdam Palm oil, 5% ffa, Indonesia/Malaysia, c.i.f., N.W European ports Cottonseed oil, PBSY, US, f.o.b. Gulf ports
Appendix 3.7. Description of Agricultural Raw Materials used from UNCTAD Commodity Price Bulletin Commodity Name Cotton, US, Memphis Cotton, US, Orleans Cotton Outlook Index A Cotton Outlook Index B Jute Sisal, Tanzania/Kenya Sisal, Uganda Non-coniferous Wood Tropical Logs Tropical Logs, Gabon Sawn Wood Plywood, sheet Plywood, metre Linseed Oil Leaf Tobacco Hides Rubber in Bale
Product Description Cotton, US Memphis/Eastern, Midd.1–3/32’’, c.i.f. (¢/lb.) Cotton, US Orleans/Texas, Midd.1-1/32’’, c.i.f. (¢/lb.) Cotton Outlook Index A (M 1–3/32’’) (¢/lb.) Cotton Outlook Index B (coarse count) (¢/lb.) Jute BWD, Bangladesh, f.o.b. Mongla Sisal, n8 3, long, Tanzania/Kenya, c.i.f. London Sisal UG, East Africa, c.i.f. London Non-coniferous woods, UK Import price index ($ equivalent) [1995¼100] Tropical logs, Sapelli LM, UK import price, f.o.b. ($/m3) Tropical logs, Okoume, LM, f.o.b. Gabon ($/m3) Sawn wood, Dark Red Meranti, Malaysia, select and better, c.i.f. French ports ($/m3) Plywood, S.E. Asian Lauan, 4mm, wholesale price, Tokyo (¢/sheet) Plywood, S.E. Asian Lauan, 4mm, wholesale price, Tokyo ($/m3) Linseed oil, any origin, ex-tank, Rotterdam Leaf tobacco, US import unit value Cattle hides, suspension dried, 8/12 lb. Tanzania ($/100kg) Rubber in bales, Singapore n81 RSS, f.o.b.
57
The Issue of Declining Commodity Prices Appendix 3.8. Description of Minerals, Ores, and Metals used from UNCTAD Commodity Price Bulletin Name Phosphate rock Manganese ore Iron ore Tungsten ore Aluminium Copper Copper wire bars Nickel, LME Nickel, cathodes Lead, LME Refined Lead Zinc, Special high grade Zinc, Prime Western Tin, LME Tin, Malaysia Gold Silver
Product Description Phosphate rock, 70% BPL, Khouribga, f.a.s. Casablanca Manganese ore, 48/50% Mn, c.i.f. Europe Iron ore, Brazilian to Europe, 64.5% Fe, f.o.b. (¢/Fe unit) Tungsten ore, Wo3 > 65%, c.i.f. UK ($/t.Wo3) Aluminium high grade, LME, cash Copper, grade A, LME, cash Copper, wire bars, US producer, f.o.b. refinery (¢/lb.) Nickel, LME, cash Nickel cathodes, New York dealer (¢/lb.) Lead, LME, cash settlement ($/t) Refined lead, North America producer price (¢/lb.) Zinc, special high grade, LME, cash settlement Zinc, Prime Western, delivered, North America (¢/lb.) Tin, LME, cash Tin, ex-smelter price, Kuala Lumpur Gold, 99.5% fine, afternoon fixing London ($/troy ounce) Silver, 99.9%, Handy and Harman, New York (¢/troy ounce)
140
MUVGY
UNCTAD
120
1985=100
100
80
60
40
20
1984
1982
1980
1978
1976
1974
1972
1970
1968
1966
1964
1962
1960
0
Appendix 3.9. MUV of Grilli-Yang Dataset and UNCTAD Unit Value of Exports of Manufactured Goods from Developed Market Economy Countries
58
Coffee, composite (1976)
2.5
Tea
Cocoa
2.0
Wheat, Argentina
2.0
2
2.0
Wheat, US, red hard
2.0 1.5
1.5
1.0
1.0
0.5
0.5
1.5
1.5 1
1.0
1.0 1960 2.0
1980
1960
2000 4
Maize, Argentina
1980
2000
2
1.0
1980
2000
1960
1980
2000
Fresh Bananas
1980
2000
0.5 0.75 1960
1980
1960
2000 3
Crude Soybean Oil
2.0
Beef (boneless)
1960 1980 2.0 Yellow Maize
2000
1.5 1.0
1960 4
1980
2000
3
Soybean Meal
1960
1980
2
1
1 1960
2.0
2
1.5
1
1.0
1980
3
2.0
Fish Meal
1980
2000
Yellow Soy beans
1.5 1.0 1960
2000
Groundnut Oil
1960
2000 2.5
3
2
2000
Sunflower Oil
1980
4
White Pepper
1.00
2.5
2000
0.5 1960
1.0
1980
1.0
5
1 1.25
1960 1.5
10
3
1.5
1960 Sugar
Rice
1980
2000
1960 3
Copra in Bulk
2
2
1
1
1980 Coconut Oil
2000
1.5 1.0
0.5
0.5 1960 3
1980
2000
Palm Kernel Oil 2 1
1960 2.0
1980
2000
Palm Oil
1.5
1.5
1.0
1.0
59
1980
2000
1980
2000
Cottonseed Oil
0.5
0.5 1960
1960 2.0
1960
1980
2000
1960
Appendix 3.10. Real Prices of 23 Food and Beverages Items
1980
2000
1960
1980
2000
1960
1980
2000
60
1.5
1.0
1.5
2.0
Cotton, US, Orleans
Cotton, US, Memphis 1.5
1.0 1960 1980 Cotton Outlook, Index B
2000
Cotton Outlook, Index A
1.5 1.0 1960
1980
2000 3
Jute
2
1960 1980 Sisal, Tanzania / Kenya
2000
2 1.0
1 1 1960
1980
2000
3 Sisal, Uganda
1960 1.5
1980
2000
Woods, non-coniferous
1960 1980 2000 Tropical Logs, UK import price, f.o.b 1.5
2 1.0
1.0
1 1960
1980
2000
1.5 Tropical Logs, Gabon
2.0
1960
1980
2000
Sawn Wood
2
1.5 1.0
1.0 1960
1980
2000
2
Plywood, cubic metre
1980
2000
1980
2000
3
1960
1980
2000
1980
2000
Rubber
2
1
1 1980
1980
2000
1980
2000
Leaf Tobacco
1.0
Cattle Hides
1960
1960 1.5
Linseed Oil
1 1960
2000
2
1 2
1980
Plywood, sheet
1 1960
3
1960
2000
1960
Appendix 3.11. Real Prices of 17 Agricultural Raw Materials
1960
3
2.0
Phosphate Rock
1.25
1960
1980
Tungsten Ore
0.75 1960
2000 1.5
Iron Ore
1.00
1.0
1
3
Manganese Ore
1.5
2
1980
2000
1960 3
Aluminium, high grade
1980
2000
Copper, Grade A
2
2 1.0
1
1 1960
1980
2000
1960 2.0
2.0
Copper, wire bars
1.5
1980
1960
2000
1980
2000
1980
2000
2.0
Nickel
Nickel cathodes
1.5
1.5
1.0
1.0
1.0 1960 3
1980
2000
2 1 1960 1.50
1960 2.5 2.0 1.5 1.0
Lead
1980
2000 1.0
1.25
2000
1960 3
Zinc, special high grade
2 1 1960
Zinc, Prime Western
1980
Refined Lead
1980
2000
Tin, LME
1960
1980
1.0
2000 Tin, ex-smelter price
1.00 0.5
0.75 1960 1.5
1980
2000
0.5 1960
1980
2000
1980
2000
3
Gold
Silver 2
1.0
61
1 0.5 1960
1980
2000
1960
Appendix 3.12. Real Prices of 17 Agricultural Raw Materials
1960
1980
2000
2.50
RCOF1 RCOF3
2.25
RCOF1 = Coffee, Columbian mild Arabicas RCOF2 = Coffee, Brazilian and other natural Arabicas RCOF3 = Coffee, Robustas RCOF4 = Coffee Composite Indicator (1976)
RCOF2 RCOF4
2.00 1.75 1.50 1.25 1.00 0.75 0.50 0.25 1960
1965
1970
1975
1980
1985
1990
1995
2000
Appendix 3.13. Real Price of Four Different Types of Coffee RWHAR
RWHAR = Real Price of Wheat (Argentina) RWHUS = Real Price of Wheat (US,red hard)
RWHUS
2.0 1.5 1.0
1960
1965
1970
1975
1980
1985
1990
1995
2000
RWHAR × RWHUS 2.0 1.5 1.0
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
Appendix 3.14. Real Price of Two Types of Wheat 2.50
RCRSO RGRNO
RSUNO
RCRSO = Real Price of Crude Soybean Oil RSUNO = Real Price of Sunflower Oil RGRNO = Real Price of Groundnut Oil
2.25 2.00 1.75 1.50 1.25 1.00 0.75 0.50 1960
1965
1970
1975
1980
1985
1990
1995
2000
Appendix 3.15. Real Price of Crude Soybean, Sunflower, and Groundnut Oil
Appendix 3.16. Data Set for 13 Commodity Prices (the Updated Grilli-Yang Series: 1900–2001) Year
Cocoa
Coffee
Tea
Bananas
Sugar
Rice
Wheat
Maize
Cotton
Jute
Palm oil
Copper
Tin
MUV
1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933
8.990 8.460 8.510 8.560 8.780 8.620 8.830 10.710 7.440 6.370 5.990 6.320 6.690 7.440 6.640 8.940 7.600 5.990 6.900 9.960 7.280 4.170 4.920 4.070 4.070 5.080 6.160 8.460 6.850 5.570 4.390 2.780 2.360 2.360
4.594 3.598 3.044 3.100 4.317 4.594 4.428 3.598 4.594 4.871 5.757 7.804 8.856 7.196 6.365 5.314 5.867 5.646 7.030 13.727 10.572 5.757 7.915 8.192 11.790 13.616 12.343 10.351 12.841 12.232 7.140 4.871 5.867 5.037
18.814 17.994 19.067 14.521 17.425 16.920 14.837 14.521 13.448 14.710 15.152 15.279 15.468 15.658 15.658 15.152 15.152 19.319 22.602 21.693 17.381 15.092 21.149 27.052 27.656 27.486 29.650 29.206 25.698 25.034 23.370 17.496 10.422 15.675
13.010 13.470 13.940 14.430 14.940 15.470 16.010 16.580 17.164 16.606 17.013 18.100 18.889 18.635 18.965 18.504 19.207 22.065 26.692 24.092 27.067 24.924 23.673 25.037 25.974 29.839 31.406 31.319 30.676 30.858 30.926 29.376 27.847 28.439
33.190 26.857 21.462 22.986 30.609 32.720 24.863 26.153 30.023 29.554 31.900 35.418 30.609 22.869 30.961 38.819 51.250 54.182 49.726 59.343 140.150 36.356 32.838 58.991 44.800 26.270 26.036 30.961 25.567 20.172 14.425 13.018 8.327 11.367
22.771 20.467 19.789 25.035 20.877 23.488 27.608 29.637 30.746 24.195 24.153 31.609 36.468 28.552 26.907 27.253 25.154 21.707 24.719 26.553 31.241 35.767 39.560 37.559 40.847 41.170 44.269 41.005 37.098 37.027 29.171 16.366 14.392 12.232
20.383 19.533 22.364 22.364 26.044 25.478 21.515 24.912 29.441 30.857 27.177 26.894 27.743 22.647 28.309 35.953 39.067 62.280 61.714 61.714 65.960 41.331 34.537 30.008 35.669 46.427 42.464 41.897 38.217 37.934 26.894 16.136 13.588 15.853
16.770 21.465 27.628 19.705 21.130 22.346 19.663 22.262 29.054 28.341 23.813 25.490 28.665 25.990 29.472 30.916 34.823 70.495 68.457 67.692 60.345 23.994 26.287 34.738 40.726 43.783 31.765 36.649 41.448 39.622 34.823 21.955 12.952 16.902
16.990 17.090 17.650 21.520 20.670 19.260 21.240 21.520 20.580 23.510 27.750 24.170 21.900 24.070 20.860 19.730 29.550 46.160 57.210 63.530 50.030 32.850 41.440 52.770 51.260 41.440 32.190 32.190 36.250 32.570 23.980 14.630 12.370 16.990
13.900 12.460 11.970 13.110 13.610 17.960 22.780 20.390 15.060 12.650 14.380 19.710 20.410 25.750 26.660 20.030 29.480 37.460 37.370 44.240 32.760 21.200 27.220 23.730 28.030 47.890 42.560 31.820 32.820 31.020 19.400 14.390 11.260 12.600
16.580 15.700 16.570 16.860 16.570 16.280 18.310 19.770 16.570 17.440 21.220 20.930 20.060 21.510 23.550 24.710 35.470 52.030 97.380 52.330 36.630 20.350 21.510 22.090 23.840 27.030 25.000 23.260 23.550 23.840 18.900 13.950 11.050 11.050
21.724 21.616 14.800 17.765 17.202 20.919 25.870 26.836 17.725 17.417 17.095 16.611 21.925 20.489 18.248 23.186 36.497 36.470 33.049 25.078 23.428 16.773 17.953 19.349 17.470 18.839 18.517 17.336 19.550 24.300 17.417 10.895 7.460 9.419
4.780 2.676 4.283 4.491 4.475 5.014 6.367 6.103 4.710 4.752 5.455 6.759 7.371 7.075 5.484 6.170 6.952 9.881 14.189 10.125 7.717 4.784 5.204 6.821 8.023 9.256 10.437 10.288 8.063 7.220 5.067 3.912 3.521 6.253
14.607 13.858 13.483 13.483 13.858 13.858 14.607 15.356 14.232 14.232 14.232 14.232 14.607 14.607 13.858 14.232 17.603 20.974 25.468 25.966 28.839 24.439 21.723 21.723 21.723 22.097 20.974 19.850 19.850 19.101 18.727 15.356 12.734 14.232 (Continued )
Appendix 3.16. (Continued ) Year
Cocoa
Coffee
Tea
Bananas
Sugar
Rice
Wheat
Maize
Cotton
Jute
Palm oil
Copper
Tin
MUV
1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967
2.780 2.680 3.640 4.500 2.780 2.570 2.730 4.070 4.760 4.760 4.760 4.760 6.160 18.680 21.250 11.560 17.180 19.000 18.950 19.860 30.940 20.070 14.610 16.380 23.710 19.590 15.200 12.100 11.240 13.540 12.530 9.260 13.060 15.580
6.144 4.926 5.258 6.089 4.262 4.096 3.930 6.255 7.417 7.417 7.417 7.528 10.240 14.779 15.000 18.103 25.862 30.517 29.483 29.483 39.828 31.034 35.690 35.172 25.862 22.241 21.207 19.655 18.621 18.103 24.310 23.793 21.724 20.172
21.194 19.996 20.592 23.764 22.264 19.355 19.770 25.300 32.973 29.655 28.964 28.964 27.443 38.020 40.439 43.072 38.370 48.151 40.157 48.057 69.405 65.831 63.668 58.401 60.470 60.000 60.659 58.025 58.684 55.674 56.521 55.110 53.605 54.075
28.188 28.410 27.423 26.327 27.103 28.385 30.986 32.080 33.169 34.676 37.051 38.652 43.921 45.998 46.981 52.050 54.383 54.383 55.126 55.126 56.647 55.903 56.647 59.621 55.126 49.177 48.434 46.947 44.716 56.674 57.391 53.639 52.152 53.639
13.956 18.530 20.289 20.641 17.005 17.709 15.950 19.820 29.671 28.616 28.968 34.480 41.634 56.293 49.608 48.788 58.404 66.496 48.905 39.992 38.233 37.998 40.813 60.515 41.047 34.831 36.825 34.128 34.949 99.686 68.847 24.863 21.814 23.338
15.912 20.785 20.405 21.633 19.850 19.208 23.088 25.814 28.353 29.628 29.628 29.628 34.177 54.719 57.992 50.082 42.235 44.614 48.290 53.975 48.785 43.718 42.297 42.389 43.965 40.844 38.527 42.173 47.209 44.274 42.544 42.111 50.422 63.587
21.515 24.063 26.611 37.934 28.309 17.552 18.684 18.684 20.949 34.254 36.519 39.350 64.545 75.019 67.376 56.028 49.645 57.447 60.283 54.610 47.518 45.886 45.886 44.539 43.759 45.319 44.539 45.106 46.879 47.447 49.787 46.666 49.787 49.220
27.561 34.526 35.502 43.741 23.144 21.233 24.461 29.939 35.375 43.868 48.115 49.559 69.306 87.524 40.738 60.771 65.586 69.342 60.289 57.978 56.148 46.998 49.695 45.843 45.843 44.398 41.702 44.206 49.503 52.681 53.740 52.970 57.207 48.058
21.900 22.660 22.940 20.200 16.340 17.560 19.540 27.090 35.020 36.630 37.950 42.480 54.470 62.020 58.910 56.920 67.680 75.330 66.930 60.890 60.420 60.510 58.620 57.110 57.870 56.070 53.620 56.260 59.190 58.720 55.980 52.490 43.800 36.250
14.180 16.530 17.460 19.430 17.270 23.540 22.360 21.230 19.940 26.620 32.410 31.600 39.560 64.330 77.920 56.710 62.800 93.500 56.020 44.480 52.240 47.330 49.750 59.510 52.010 51.290 74.580 91.500 62.070 61.050 66.860 71.200 79.230 56.560
15.700 22.380 22.670 25.000 19.700 20.350 20.930 28.200 34.590 25.000 25.000 25.000 25.000 51.740 63.370 46.800 42.440 67.150 40.120 35.470 36.340 37.790 43.600 44.190 41.860 42.440 41.280 42.440 40.410 40.700 41.280 45.930 43.900 43.020
11.311 11.607 12.707 17.672 13.418 14.706 15.162 15.833 15.806 15.806 15.806 15.806 18.544 28.124 29.573 25.763 28.500 32.472 32.472 38.644 39.838 50.304 56.114 39.690 34.565 41.837 43.005 40.147 41.059 41.059 42.884 46.990 48.533 51.297
8.344 8.061 7.425 8.688 6.763 8.045 7.967 8.317 8.314 8.314 8.314 8.314 8.720 12.463 15.868 15.883 15.275 20.318 19.261 15.323 14.683 15.147 16.214 15.390 15.209 16.316 16.218 18.116 18.330 18.650 25.197 28.491 26.232 24.531
16.854 16.479 16.479 16.854 17.603 16.105 17.603 18.727 21.723 24.345 27.715 28.464 28.839 34.831 35.581 33.333 30.337 35.955 36.704 35.206 34.457 34.831 36.330 36.704 36.330 36.330 37.079 37.453 37.453 37.453 38.202 38.951 39.700 39.700
1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
18.410 24.460 18.310 14.340 17.290 34.470 52.510 40.040 58.500 108.390 97.410 94.200 74.400 59.410 49.600 60.570 70.110 65.660 60.980 58.760 44.657 35.497 36.355 34.351 31.489 32.061 40.077 40.936 41.794 46.374 48.092 33.779 26.050 30.630
20.172 20.690 26.897 23.276 25.862 32.069 34.138 33.621 73.965 124.660 85.862 89.483 80.690 66.207 72.419 68.048 74.550 75.530 100.520 58.660 72.258 56.996 46.980 44.595 33.625 37.202 78.935 79.412 64.150 99.444 71.066 52.942 45.787 32.671
44.577 41.473 46.740 44.953 44.859 45.141 59.812 59.060 65.549 114.640 93.386 91.975 95.173 86.050 82.447 99.339 147.520 84.420 82.290 72.879 67.634 76.324 76.702 69.523 75.569 70.279 69.145 61.966 62.722 77.836 77.458 68.767 71.035 60.455
51.408 53.639 55.903 47.690 54.383 55.734 62.224 83.382 86.965 92.846 97.104 110.050 128.100 135.640 126.510 144.930 125.000 128.378 129.050 123.310 161.486 184.797 182.770 189.189 159.797 149.662 148.311 150.338 158.784 169.932 166.216 144.932 143.243 196.959
23.221 39.523 43.979 53.010 87.137 112.940 351.360 240.420 135.810 95.229 91.477 113.290 336.240 198.900 99.053 99.534 61.310 48.090 73.030 81.563 118.280 150.538 150.538 107.527 107.527 118.280 145.161 155.914 139.785 134.409 107.527 75.269 96.774 102.151
62.286 57.744 44.490 39.856 45.448 108.140 167.460 112.180 78.630 84.099 113.540 102.360 134.060 149.230 90.532 85.596 77.930 66.740 65.540 71.705 91.238 98.485 89.262 96.509 88.274 77.404 88.274 105.731 111.660 99.802 100.132 83.004 66.535 56.983
47.163 45.603 44.822 45.319 50.567 104.400 148.010 128.580 105.740 82.127 95.602 122.270 135.320 139.290 118.040 120.220 117.310 104.350 88.238 86.703 111.256 129.671 104.350 98.980 115.860 107.420 115.092 135.809 159.595 121.998 96.678 86.703 87.470 97.445
47.287 51.910 56.244 56.244 53.933 94.382 127.130 115.180 108.250 91.782 96.983 111.240 120.670 125.970 105.280 130.970 130.870 108.050 85.710 74.099 102.885 107.692 104.808 102.885 100.000 98.077 103.846 118.269 159.615 112.500 98.077 87.500 85.577 86.538
35.680 35.400 37.850 48.050 49.273 84.825 88.567 72.351 105.408 96.676 97.923 105.408 127.861 115.387 99.794 115.387 111.645 82.330 66.114 102.913 87.320 104.160 113.516 104.784 79.835 79.835 109.774 132.851 110.397 109.150 89.815 75.469 81.083 66.114
61.850 69.160 66.340 72.750 69.430 67.170 83.720 83.140 70.640 89.550 104.740 105.710 98.370 88.520 82.120 86.140 157.370 173.060 79.080 94.500 100.749 101.656 111.186 99.502 76.016 73.860 80.509 99.570 123.690 82.233 70.546 75.063 75.924 89.743
42.150 36.340 46.220 43.900 34.880 48.260 77.910 75.580 59.300 88.370 101.450 110.170 99.130 95.640 73.550 87.210 126.900 87.210 43.520 58.012 73.487 58.857 48.767 57.007 66.256 63.566 88.790 105.607 89.295 91.817 112.838 75.842 52.131 48.095
56.154 63.776 77.422 69.009 67.922 78.992 102.850 85.258 92.343 88.304 87.901 123.790 137.370 112.380 97.791 104.440 89.580 87.980 86.670 112.460 167.439 183.269 171.300 150.515 146.782 123.102 148.456 188.932 147.683 146.525 106.435 98.970 116.667 101.544
Note: All price data are in nominal US dollars with 1977–79 prices. MUV is the prices index of manufactured goods exported by developed countries.
23.686 26.300 27.853 26.756 28.374 36.376 63.356 54.331 60.726 85.472 100.660 113.870 135.260 115.510 104.530 103.070 99.730 94.660 58.560 63.583 79.55 83.85 100.67 72.94 65.57 71.60 60.62 64.05 72.70 72.25 66.17 66.94 63.24 65.20
39.326 40.449 42.697 45.318 48.689 58.801 71.161 79.026 78.652 86.517 98.876 114.610 125.470 119.100 115.730 110.490 108.610 109.590 130.300 147.337 156.486 155.224 170.999 170.999 176.047 166.898 171.315 188.351 182.357 169.422 162.796 156.486 148.283 144.497
Appendix 3.17. UNCTAD Data on Commodity Prices Year
UVXM
Composite/ Aggregate
All Food
Food and Beverages
Food Only
Vegetable oils and oilseeds
Agri raw materials
Minerals and Metals
1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
34.68208 35.4528 35.83815 35.83815 36.41618 37.28324 38.15029 38.15029 38.15029 38.15029 39.49904 42.00382 45.37572 53.46821 64.45087 73.12139 72.83237 80.0578 91.6185 104.0462 115.6069 109.5376 105.7803 102.3121 98.84393 100 119.3642 134.9711
45.20833 43.44167 43.25833 52.04167 51.525 48.36667 49.90833 47.66667 47.23333 51.8 53.575 51.63333 58.98333 95.375 138.9417 109.175 106.3833 117.3583 115.0833 132.7917 168.5167 140.4083 110.6417 117.95 113.1667 100.0083 104.0167 106.625
43.90833 43.10833 43.83333 58.55833 54.28333 45.79167 46.61667 47.475 45.83333 49.875 53.55833 53.075 64.2 107.25 169.0417 127.0417 115.1167 131.4333 125.0917 137.825 188.4083 154.3083 114.5083 122.525 117.6333 100.0083 107.4333 100.8167
30.475 28.05833 27.34167 28.36667 33.1 30.45833 31.05 30.43333 30.49167 32.36667 36.70833 32.26667 36.05 46.35833 54.48333 53.21667 96.125 169.0667 121.8917 125.6583 117.55 96.55 91.675 96.225 109.7167 100 124.2 80.70833
49.95 49.2 51.74167 75.25833 65.53333 51.30833 53.23333 55.71667 53.84167 59.61667 61.11667 62.03333 80.39167 138.8667 226.5167 168.15 130.15 119.3333 128.1417 144.0083 235.4833 189.6833 130.1583 137.7083 115.6083 100 109.475 115.8167
43.975 46.64167 41.75 43.99167 46.34167 52.61667 48.53333 44.90833 40.70833 41.2 53.81667 55.20833 47.75 87.825 141.9667 90.36667 84.375 107.9417 117.5083 134.625 116.825 110.2583 89.11667 106.8333 144.0917 100.0083 61.59167 72.6
54.26667 48.3 45.50833 45.46667 45.725 46.08333 46.1 42.73333 42.79167 45.84167 42.41667 42.2 46.78333 81.54167 84.90833 76.275 97.25 100.875 107.4583 124.8583 137.225 117.95 101.4667 108.3667 110.1417 100 103.5167 121.0917
41.85 40.70833 40.15833 40.19167 48.675 56.65 61.11667 51.75833 54.10833 61.075 61.75833 54.9 54.60833 75.21667 101.6167 87.60833 90.78333 93.50833 95.13333 125.725 140.6083 121.35 107.5083 113.2667 104.0167 100.0083 95.65833 110.8833
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
143.3526 142.1965 156.6474 156.6474 161.2717 152.8902 156.9364 172.5434 167.052 155.2023 149.1329 143.3526 135.8382 132.3699 129.4798
134.8333 135.9583 128.8083 119.7917 115.85 110.3833 130.1667 143.3583 137.0667 136.3667 118.45 101.8583 104.4 101.0333 99.225
125.7333 126.8 117.3667 110.2667 106.575 107.9 129.85 135.9417 137.425 140.075 122.45 98.80833 97.31667 96.825 96.84167
81.725 69.99167 61.55833 56.85833 48.59167 52.31667 91.25 91.925 77.525 103.7 85.525 68.075 58.7 46.10833 50.125
151.8667 161.2667 151.6917 140.875 137.125 137.6833 151.9917 159.5333 169.625 162.475 139.5833 114.225 120.2833 126.3333 121.0917
96.075 84.925 74.04167 79.625 85.975 85.61667 107.4417 118.425 113.05 111.7083 120.3833 91.60833 71.06667 65.45 82.03333
129.825 132.4417 142.3083 133.6667 130.0167 120.8917 140.0917 161.3167 144.525 130.1083 116.3167 104.2 106.3833 104.1833 96.925
161.7417 161.9583 148.1667 133.95 129.175 109.0333 123.7333 149.1667 130.7417 131.4667 109.8083 107.9583 120.9917 109.4667 107.0667
4 Analysis of Movements in the Productivity and Prices of Selected Tropical Commodities in Developing Countries, 1970 to 2002 Euan Fleming, Prasada Rao, and Pauline Fleming
The international community accepts that a secular decline has occurred in the terms of trade of most commodities produced by developing countries, a trend that is confirmed in this study. Yet it has been more or less indifferent to the fate of commodity producers in these countries, particularly since the collapse of international commodity agreements (such as the International Coffee Agreement and the International Cocoa Agreement given our special interest in agricultural commodities). Attempts have been made, albeit not very successfully, to reduce the degree of commodity price fluctuations. But the problem of a secular decline in commodity prices has not been tackled in earnest.
4.1. Background to the Study 4.1.1. The setting of the research problem UNCTAD (2004b, p. 22) observed that the ‘net effect of the secular decline in prices depends on the extent to which world market prices are transmitted to producers and whether higher export volumes (for example, through productivity and yield improvements) make up for falling prices’. In this context, there is a need for evidence of the problem of declining prices faced by commodity exporting developing countries, of which there are many in the African, Caribbean, Pacific, and Asian regions. A pertinent issue is the role of productivity in commodity production and its effects on individual countries. Two related matters concerning productivity
68
Analysis of Movements in Productivity and Prices trends are important. First, the European Commission recently communicated to the Council and the European Parliament that: Commodity prices demonstrate a long-term declining price trend. This trend has been driven mainly by significant productivity gains, which enable producers to accept lower prices for their products. Other factors have also increased production: pressure on countries to earn more foreign exchange but few potential activities with which to do so; devaluation of national currencies of many commodity-producing countries following structural adjustment programmes; entry of new areas into production; and production subsidies in certain countries. The demand for commodities has not kept up with the increase in supply. (Commission of the European Communities 2004, p. 8)
If it is true that commodity export volumes have increased significantly, the next step is to establish whether or not the above statements are true—that (a) ‘This trend has been driven mainly by significant productivity gains’ and (b) producers are able ‘to accept lower prices for their products’. The validity of the first assertion depends on the extent to which export expansion is due to increased input use on the farm or increased productivity in farm production. An empirical study of agricultural productivity growth is needed to confirm this statement. For the second assertion to be valid, economic growth achieved through export expansion should not be immiserizing if productivity growth is high enough. It is of interest to learn about the pattern of productivity improvement across commodity-producing countries in the developing world. In particular, it is important to know whether increases in productivity have compensated for the decline in producer and export prices of commodities in Commonwealth countries that rely considerably on commodity exports to generate economic development. Much of the evidence presented in this report is used to test these two propositions. The greatest and most consistent concern about the ability of the developing world to achieve significant productivity gains in agriculture has been focused on the African continent and, in particular, sub-Saharan Africa. The problems faced by agricultural producers in this region are well known and routinely spelt out. In a recent report to the United Nations on African agriculture, a panel of 18 experts employed by IAC, a Dutch non-government organization, observed that the sector was stagnant (M2 Presswire 2004, p. 1): The panel has learned that, among other things, Africa faced irregular rainfall and irrigation systems; low investment in agriculture; a wide variety of crops, and a lack of knowledge— largely due to brain drain, with some 50 per cent of the people qualified to make decisions and promote innovations towards alleviating food insecurity had left the country.
Many other factors could be added to this list such as declining soil fertility and poor to non-existent infrastructure in many areas, widespread disease and
69
The Issue of Declining Commodity Prices malnutrition in the rural population, and civil disturbances affecting commerce in the rural areas of many countries at different times.
4.1.2. Research objectives The research objectives are to investigate whether: . producers of tropical commodities in developing countries have compensated for falling producer prices by increasing total factor productivity . falling export prices have been compensated by rising total factor productivity of tropical commodities at the national level in developing countries.
4.1.3. Plan of the study 4.1.3.1. INDIVIDUAL COMMODITY STUDIES OR A SECTOR-WIDE ANALYSIS? The research objective entails two strands of estimation: changes in producer prices of selected tropical commodities and changes in total factor productivity in the production of tropical commodities. In this section, two approaches are considered to carry out the estimations required to achieve the second part of the research objective: . A series of individual studies of productivity change in commodity production across developing countries . Sector-wide analyses of productivity change by developing country. The former approach has the advantage of providing numerous specific measures of productivity change by commodity and by country. If enough of these studies were eventually done, a picture could be built up of productivity trends for individual farm enterprises across the developing world. But three major shortcomings make this approach infeasible. First, the data do not exist in enough countries for this approach to be followed for even one commodity let alone a set of commodities. Second, even where data do exist, the time series are unlikely to be long enough to enable a proper longitudinal study of productivity change to be undertaken. Finally, this partial approach ignores factors that influence productivity change on the farm as a whole, such as substitution of factors of production between enterprises. The latter approach of a sector-wide analysis of productivity change largely overcomes the problems outlined above. A sufficiently long data set is available (over three decades) across a wide range of developing countries, and the measures obtained will encapsulate the effects of resource use decisions among enterprises on total factor productivity. The main disadvantage is that productivity measures cannot be obtained for specific enterprises of interest. However, this can be overcome to a large extent by including as an explanatory
70
Analysis of Movements in Productivity and Prices variable the proportion of agricultural output contributed by the commodities of interest when regressing productivity change on a set of explanatory variables. This approach enables a measure of the rate of change in productivity to be made for a one per cent increase in the proportion of agricultural production devoted to each tropical commodity of interest. 4.1.3.2. TASKS UNDERTAKEN The following tasks were undertaken in the study: (a) Calculation of the growth in output and export of selected tropical commodities (coffee, cocoa, copra, palm kernel oil, coconut oil, palm oil, rice, cotton and sugar) in less developed countries from 1970 to 2002, with particular emphasis on Commonwealth and African countries. (b) Estimation of the rates of change in real export unit values of the selected commodities in (a) from 1970 to 2002, examining the movements in these prices and relating them to movements in corresponding world import prices. (c) Estimation of the rates of change in total factor productivity and labour productivity in agriculture from 1970 to 2002 in these less developed countries, with particular emphasis on Commonwealth and African countries producing and exporting the selected tropical commodities. (d) Comparison of the rates of change in productivity in the production of the selected tropical commodities with those for the whole agricultural sector. This is to be done by regressing change in total factor productivity and labour productivity separately on a set of explanatory variables that includes change in the ratio to output of each of the selected commodities in (a). (e) Comparison of the rates of change in productivity in the production of the selected tropical commodities, from (c), and rates of changes in their prices, from (b), for the period from 1970 to 2002. (f) Assessment of the single factoral terms of trade effects for the selected tropical commodities from (a) to test the proposition that the revenueenhancing effects of productivity growth have been outweighed by the revenue-reducing effects of declining commodity prices. (g) Review of the empirical literature on ‘immiserizing growth’ for evidence to test the proposition that output growth in African countries has led to welfare losses. (h) Identify those less developed countries with highest productivity gains in agriculture and compare them with countries achieving lowest (or negative) growth rates. (i) Identify those less developed countries that have managed to achieve agricultural productivity growth rates at least on a par with the rate of decline in real commodity prices.
71
The Issue of Declining Commodity Prices (j) Make policy recommendations where less developed countries have not been successful in realizing productivity gains and the secular decline in commodity prices is having a negative impact on the agricultural economy.
4.2. Review of Commodity Production and Export of Selected Tropical Commodities, 1970 to 2002 4.2.1. Importance of selected tropical commodities in the domestic economy Considerable differences exist in the importance of the selected commodities in the domestic economies of the countries under review. These differences are illustrated in Table 4.1 for countries in which the value of selected commodity exports is at least one per cent of the value of total exports. Proportions for the full list of countries are presented in Appendix 4.1. A number of African ˆ te d’Ivoire, Ghana, Rwanda and Uganda, and countries, notably Burundi, Co Central American countries have relied heavily on the commodities to contribute to both total export earnings and agricultural output. In other countries, such as Nigeria and Indonesia, the commodities have contributed little to export earnings but have been especially important to their large agricultural sectors. The commodities are of little importance for either export earnings or agriculture’s contribution to the economy in a number of countries, shown in Appendix 4.1. In no country is there the situation where the commodities contribute substantially to export earnings but not to agricultural output. We test the assertions made by the Commission of the European Communities (2004) by assembling evidence on the extent to which the quantities of tropical commodity exports have increased. The production and export of selected tropical commodities is reviewed over the period from 1970 to 2002. The focus of our study is a set of commodities of particular relevance to Commonwealth countries, especially to many in Africa. The commodities are coffee, cocoa, lauric oils (comprising copra, palm kernel oil and coconut oil), palm oil, rice, cotton and sugar. Table 4.2 contains a summary of trends in the export quantities of the selected tropical commodities in all countries, Commonwealth countries, African countries and African Commonwealth countries included in the study. Export quantity trends are described separately for indices of the tree crops (coffee, cocoa, palm oil, and lauric oils) and field crops (rice, cotton and sugar). These trends are specified for a period of 33 years, from 1970 to 2002. Quantities are implicit volumes in that they are expressed in values normalized on 1990 international average prices. The indices are calculated using the Fisher index procedure.
72
Analysis of Movements in Productivity and Prices Table 4.1. Contributions by Selected Commodities to Export Earnings and Agricultural Output Export values of selected tropical commodities as a proportion of the total: Country Uganda Burundi Rwanda El Salvador Ghana ˆ te d’Ivoire Co Guatemala Honduras Nicaragua Colombia Costa Rica Kenya Cameroon Papua New Guinea Madagascar Dominican Republic Nepal Sierra Leone Central African Republic Congo, Republic of Togo Ecuador Haiti Malaysia Benin Brazil Peru Indonesia Malawi Bolivia Zimbabwe Guinea Jamaica Nigeria
FOB exports (%)
Agricultural output (%)
79.24 77.72 66.27 41.53 40.15 34.88 26.75 21.38 20.48 20.17 18.91 17.84 15.40 14.55 13.21 12.63 10.10 9.76 8.44 8.17 6.71 6.56 6.47 6.24 5.23 3.93 3.05 2.81 2.69 1.90 1.35 1.09 1.06 1.01
81.50 81.48 70.93 79.93 87.23 62.96 40.44 29.21 28.08 59.85 29.94 28.29 59.11 77.82 23.91 24.33 45.59 80.10 28.01 81.54 28.83 22.64 56.74 41.24 14.71 14.09 36.81 26.92 2.90 8.05 3.35 26.67 5.59 59.99
The output in Table 4.2 was generated with a series of ordinary least squares regression equations where the natural logarithm of the export quantity of interest was regressed on a trend variable. The estimated trend coefficients are of particular interest. Each coefficient reported for the trend can be interpreted as a percentage annual change. For example, in the first section of Table 4.2 for all countries, the coefficient for the trend variable in the second column under the total commodities heading is 0.018. This coefficient means that the index of the export volume of all selected commodities increased on average by 1.8 per cent per annum between 1970 and 2002. The third to fifth columns in Table 4.2 provide evidence that can be used to assess whether the change in export quantity is statistically significant. Figures
73
The Issue of Declining Commodity Prices in the final column showing the p (probability) values are the best ones on which to focus. A p value of 0.01, for instance, indicates that the relevant coefficient in the second column is significantly different from zero at the 1 per cent level (a high probability). Many p values for trend coefficients are less than 0.001, indicating an extremely high probability that they differ from zero. Finally, the R2 values indicate the proportion of the variation in the export quantity that is explained by the regression model including the trend as an explanatory variable. Using the first regression as an example again, the R2 value of 0.839 indicates that 83.9 per cent of the variation in the quantity index of all export commodities is explained by the trend variable. The trends in the export quantities of the selected commodities are discussed for all countries. The discussion is initially based on results for all selected commodities, followed by separate discussions for tree crops and field crops. Similar discussions are then provided for particular sub-groups of countries.
4.2.2. Export quantities of the selected tropical commodities in all countries Movement in the export quantities index of the selected tropical commodities for all countries in the sample for the period from 1970 to 2002 are presented in Figure 4.1. The linear trend lines reported in Table 4.2 are also included for each series. Clear evidence is presented of significantly increasing trends in both tree and field crops over the study period. Tree crop exports display a higher rate of expansion that is to a large extent the outcome of substantial increases in tree plantings in response to a series of spikes in price about a decade apart. The planting response of individual producers was augmented by government encouragement to increase plantings as part of national planning efforts to bring about agricultural development, which tended to be intensified in periods of very high prices. This trend is evident in Figure 4.1 following mid-decade commodity booms. The average annual increase in the quantity index for all selected commodities is 1.8 per cent (Table 4.2). The average annual increase for tree crops of 2.7 per cent is around three times that for field crops at 0.9 per cent. The rates for both sets of commodities and the rate for the index of all commodities are highly significant in statistical terms.
4.2.3. Export quantities of the selected tropical commodities in sub-groups of countries Export quantity indices of the selected tropical commodities for Commonwealth countries in the sample are presented in Figure 4.2. The annual rate of increase in the index for all commodities, reported as 3.3 per cent (Table 4.2), is substantially higher than that for non-Commonwealth countries. The annual
74
Analysis of Movements in Productivity and Prices Table 4.2. Estimates of Trends in Export Quantities of Selected Commodities, 1970 to 2002 Variable
Trend coefficient
p-value
All countries: Total commodities Tree crops Field crops
0.018y 0.027y 0.009y
0, then Xt and Yt will be cointegrated. Thus if Xt and Yt are ~I(1) they will be cointegrated and have a valid long-run relationship if residuals from the OLS regression of Xt on Yt is ~I(0). This is the first step in the Engle-Granger procedure. On the other hand, if variables are cointegrated there will exist an error-correction model (ECM) of that cointegrating relationship, which gives the short-run dynamics in the second step. Assuming that both Yt and Xt are ~I(1) so that DYt and DXt are ~I(0), the short-run error correction model (ECM) can be represented as:
D ln Yt ¼ p0 þ
m X i¼0
pli D ln Xt þ
n X
p2i D ln Yt þ p3 y^t1 þ j
(5)
i¼1
where, y^t1 is the lagged error from the cointegrating relationship and j is the white noise. It is worth noting that the ECM is not a mere regression of the stationary variables; rather it includes y^t1 , the deviation from the long-run relationship. Thus the ECM captures the short-run deviations taking long-run information into account. A valid representation of the ECM will require 0 > p3 $ 1. The usual practice with error-correction modelling is to follow the ‘general to specific’ methodology by constructing a general model in the beginning and subsequently reducing it to a parsimonious form after dropping all the insignificant variables step-by-step. Thus one could employ the Engle–Granger cointegration procedure to estimate equation (2) and test for a valid long-run relationship. However, since the first step of the Engle-Granger procedure is basically an OLS regression involving non-stationary variables, it yields standard errors that do not provide the basis for valid inferences. Thus in estimating equation 2, we cannot be certain whether each of the explanatory variables are individually statistically significant even when the equation turns out to be a cointegrating relationship.43 We propose to handle this problem by using the Phillips-Hansen Fully Modified OLS (PHFMOLS) technique (Phillips and Hansen, 1990). The PHFMOLS is a method of optimal single equation technique, which is asymptotically equivalent to the maximum likelihood procedure. It makes a semi parametric correction to the OLS estimator to eliminate the dependency on the nuisance parameters and provide standard errors that follow a standard normal distribution asymptotically and thus are valid for drawing statistical inferences. Due to its advantages the 43 It might be possible that only one of the explanatory variables is significant resulting in a cointegrating relationship while the other right hand side variable does not have any influence on the model.
218
Marginalization and World Trade use of PHFMOLS has become quite popular in international trade and macroeconometric modelling.44 Yet another problem arising from the estimation of the long-run relationship is that unit root tests of the variables could not confirm the non-stationarity of the lnGLO variable. If lnGLO is indeed stationary on its level, there will be a mixture of I(1) and I(0) variables on the right hand side of the model posing the question whether an I(0) regressor plays a role in determining a dependent variable which is ~I(1). Holden and Perman (1994) considered a model with two I(1) and an I(0) variables. The authors tested for the long-run relationship between the two I(1) variables and included the I(0) variable only in the short-run error-correction model. This procedure thus assumes that the I(0) variable does not play a role in the long-run behaviour of the model even disregarding the economic theory behind it. In contrast, Pesaran et al. (2001) strongly argued that the fact that the variables in the estimating equation have different orders of integration does not necessarily mean that they are unlikely to have any long-run impact. Pesaran et al. also devised a strategy, which tests the existence of a long-run relationship when the regressors are a mixture of I(0) and I(1) variables. For this paper we will use this test to determine the long-run relationship in equation (2). 7.4.4.4. TEST FOR EXISTENCE OF A LONG-RUN RELATIONSHIP First, we apply the Pesaran et al. test to ascertain whether the model in (2) is a cointegrating relationship. This test is based on an OLS estimation of an unrestricted error-correction model, a general specification of which with respect to our model can be written as: ln MARt ¼a þ F1 ln MARt1 þ F2 ln AGXt1 þ F3 ln GLOt1 þ
p X i¼1
pi D ln MARt1 þ
g X i¼0
p2 D ln AGXti þ
g X
p3 D ln GLOti þ qt
(6)
i¼0
where, all the variables are defined as above and the last term on the right hand side is the white noise. Estimation of (5) in itself is not interesting since the existence of a long-run relationship can only be tested by examining the joint null hypothesis that F1 ¼ F2 ¼ F3 ¼ 0 with the help of either a Wald or an F-test. The presence of a long-run relationship requires the rejection of this null. However, the asymptotic distribution of these test statistics is non-standard and Pesaran et al. provide the necessary critical upper (FU ) and lower bound (FL ) values for the tests.45 The FU -statistics are derived under the assumption that all 44 Among others Athukorala and Riedel (1996), Muscatelli (1995) and Senhadji and Montenegro (1998) have used the same technique for trade modelling while Mallick (1999) is an example of the application of the procedure in macroeconometric modelling. 45 Pesaran et al. give both the critical values for Wald-and F-statistics. In this paper we will only consider the F-statistics.
219
The Implications of Declining Commodity Prices variables are ~I(1) and FL consider all of them to be ~I(0). If the computed F-statistic (F ), which is obtained by restricting that F1 ¼ F2 ¼ F3 ¼ 0, is greater than the critical upper value, i.e. F > FU , one can reject the null and conclude that there exists a valid long-run relationship between the variables in the equation. If F < FL , there is no long-run relationship and finally if FL < F < FU the test is inconclusive. Pesaran et al. (p.290) clearly point out ‘[I]f the computed Wald-or F-statistic falls outside the critical value bounds a conclusive inference can be drawn without needing to know whether the underlying regressors are I(1), cointegrated amongst themselves or individually I(0)’. In order to determine the long-run relationship equation (6) was run with p¼1 and g¼0.46 In our case the computed F-test statistic was 5.97 against its critical FU value of 4.85.47 This thus rejects the null hypothesis of non-cointegration and suggests the existence of a valid long-run relationship between the variables, i.e. the share of agriculture exports in world merchandise exports and the measure of globalization do determine the marginalization of the LDCs in world trade. 7.4.4.5. ESTIMATING THE LONG-RUN RELATIONSHIP We now proceed to know the exact nature of the long-run relationship by estimating the model. For reasons discussed earlier, the estimation is done by applying the PHFMOLS procedure, the results of which are given in Table 7.20. The estimated results show that all variables are highly significant at the one per cent level. The coefficient on lnAGX is positively signed as expected. Thus over the long-run a one per cent fall in the share of agricultural products in world exports reduces LDCs’ share by 1.36 per cent. The imposition of a unit coefficient on lnAGX resulted in a Wald-statistic of 17.21 against its 95 per cent critical value 3.84, thereby rejecting the restriction. This suggests that a certain percentage fall in agriculture share will have an even greater impact on the marginalization of the LDCs. The sign on lnGLO is also negative providing support Table 7.20. PHFMOLS Estimates of the Model Regressor
Coefficient
Standard Error
t-ratio
Constant lnAGX lnGLO R2
3.34*** 1.36*** 0.68***
0.34 0.08 0.15 0.91
9.82 15.53 4.61
Note : Statistical significance at the one per cent level is in ***.
46 Since we have a small sample over-parameterization of the model can be very problematic in terms of having fewer degrees of freedom. Such choice of lag length can be rationalized by the use of annual data. 47 This critical value is based on an unrestricted intercept and no trend as reported in Table C1.iii in Pesaran et al. (2001).
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Marginalization and World Trade for our hypothesis. A one per cent rise in the world export-GDP ratio reduces the relative importance of LDCs by 0.68 per cent. The long-run model can explain the 91 per cent variation in the trends in marginalization of the LDCs. 7.4.4.6. SHORT-RUN DYNAMICS The existence of a long-run cointegrating relationship would imply a short-run error correction model, which we model under the framework of error correction modelling strategy. The error-correction model regresses the current value of the dependent variables in stationary form onto its own lagged value, current and lagged values of the stationary form of the independent variables and the lagged error term from the cointegrating equation. The general to specific methodology is used to find a parsimonious representation of the relationship. In initial experiments the model was estimated by taking first order lag of the first differences of the dependent and independent variables and including the lag of the longrun errors (ECMt1 ). Then most insignificant variables were deleted one by one to give the most parsimonious representation of the short-run model. It is to be mentioned that initial runs were confronted by non-normality of errors, which could be detected due to a large unexplained movement in the dependent variable for 1996. A dummy variable for 1996 is thus inserted to overcome the problem. It is now evident from Table 7.21 that in the short-run only the indicator of globalization has a significant influence on marginalization of the LDCs. The sign on lnGLO is also negative suggesting that even in the short-run LDCs cannot take the advantage of global integration and a rise in the level of globalization depresses their relative importance in world exports of merchandise goods. Interestingly, however, the short-run model fails to find a significant effect of share of agriculture on the marginalization of the LDCs. The reason might be that in the short-run potentially there may be many other variables that are likely to affect the export performance of the LDCs, which have not been modelled here. This is also reflected in the somewhat lower size of the explanatory power of the model as the adjusted R2 turns out to be only 0.48. The error-correction term is correctly signed and significant implying that the long-run model is correctly specified as the short-run model converges to the long-run relationship. However, the speed of convergence is slow as only 23 per cent of adjustments are corrected within a year. This slow adjustment mechanism reveals that exogenous shocks (may be natural calamities or other policy factors) in some or in all countries, which cause the share of LDCs as a group to fall will have a considerably long lasting effect before the trends in marginalization can again be explained by the share of agriculture in total global exports and world exports-GDP ratio. Diagnostic tests for the short-run model did not report any problem, as there was no evidence of residual serial correlation, non-normality, heteroscedasticity and functional form problem associated with the model at the 95 per cent level of confidence.
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The Implications of Declining Commodity Prices Table 7.21. Short-Run Error Correction Model DlnMAR ¼
0.49***
0.35***
(S.E.) t-ratio
(0.01) 4.78
(0.12) 2.95
DlnGLOt1
þ 0.19*** (0.05) 3.39
Adjusted R2 ¼ 0.48 Serial Correlation [x2 (1)] ¼ 0:12 Normality [x2 (2)] ¼ 0:82
D96 0.23** ECMt1 (0.08) 2.67 F(3, 24) ¼ 9.27*** Functional Form [x2 (1)] ¼ 0:19 Heteroscedasticity [x2 (1)] ¼ 0:08
Note : *** and ** are for statistical significance at the one and five per cent level respectively. D96 is the dummy variable representing 1 for 1996 and 0 for all other years. For diagnostics Godfrey’s (1978) LM test for serial correlation, Ramsey’s (1969) RESET test for functional form, Jarque-Bera (1987) test for normality of residuals and White’s (1980) test for heteroscedasticity are performed. The critical values for x2 (1) and x2 (2) at the 95 per cent level are 3.84 and 5.99 respectively, which are being used to test the null hypotheses of no serial correlation, no functional form problem, normality of regression residuals and homoscedastic errors. Since in every case the computed statistics are smaller than the corresponding critical values, all the null-hypotheses are maintained or, in other words, the model passes all the diagnostic tests.
7.4.5. Estimation of the model for small states 7.4.5.1. BIVARIATE RELATIONSHIP Estimation for small states will follow the same procedures as described in the case of LDCs. First, we consider the bivariate relationship between marginalization of small states (MARSS), measured by the combined share of small states in world merchandise exports, and share of agricultural exports in world exports (AGX) and between MARSS and globalization, measured by world export-GDP ratio. Figure 7.22 exhibits the scatter plots of logged MARSS (lnMARSS) and lnAGX. A fairly strong positive relationship is found as the estimated R2 is 0.74. In contrast, the relationship between lnMARSS and lnGLO, presented in Figure 7.23, is rather weak; the estimated R2 is only about 0.15. However, we need to estimate the model formally and test for existence of a long-run relationship. 7.4.5.2. TESTS FOR UNIT ROOTS AND COINTEGRATION Since the units roots for lnAGX and lnGLO have already been tested above, such tests are required for only lnMARSS. The DF and ADF regressions for lnMARSS resulted in the following test statistics. Thus it is observed that the DF and ADF test statistics for lnMARSS, both with and without the trend term, are absolutely smaller than the 95 per cent critical values suggesting that lnMARSS contains a unit root on its level. However, when the same tests were performed on the first difference of lnMARSS, DlnMARSS, the null hypothesis of unit root was overwhelmingly rejected at the 95 per cent level. Hence, it can be concluded that lnMARSS is ~I(1). We recall that lnAGX was found to be ~I(1) as well, while lnGLO turned out to be ~I(0). Since the variables are a mixture of I(1) and I(0) variables, the Pesaran et al. test has been carried out to test for the existence of a long-run relationship.
222
Marginalization and World Trade −2.7
−2.5
−2.3
−2.1
−1.9
−1.7
−1.5 −0.6
lnSMARSS = 0.5173 lnAGX + 0.0005 R 2 = 0.7414
−0.7 −0.8
lnMARSS
−0.9 −1 −1.1 −1.2 −1.3 −1.4 −1.5 lnAGX
Figure 7.22. Scatter Plot of lnMARSS and lnAGX for Small States −2.3
−2.2
−2.1
−2
−1.9
−1.8
−1.7
−1.6
lnMARSS = −0.3538 lnGLO −1.7253 R 2 = 0.1464
−1.5
−1.4 −0.6 −0.7 −0.8
lnMARSS
−0.9 −1 −1.1 −1.2 −1.3 −1.4 −1.5 lnGLO
Figure 7.23. Scatter Plot of lnMARSS and lnGLO for Small States
Initial experiments suggested significant unexplained movement in residuals for 1974 and 1993 and thus the existence of a long-run relationship was tested including two dummy variables for those two atypical years.48 The F-test statistic arising out of the Pesaran et al. test was estimated to be 5.01 against its critical upper (FU ) value of 4.85. Since the computed F-statistic exceeds the critical F value we can conclude that the variables are cointegrated and consequently there is a valid long-run relationship between the marginalization of small states, share of agricultural exports in world exports and world export-GDP ratio, as hypothesized in our model. 48 If the dummies are not inserted, the graphical plot of the residuals was found to be non-normal.
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The Implications of Declining Commodity Prices Table 7.22. Unit Root Test for lnMARSS DF-ADF tests without the trend term Variable inMARSS DlnMARSS
DF-ADF tests with the trend terms
DF
ADF
DF
ADF
0.59 5.34*
0.51 4.23*
1.87 5.25*
1.81 4.13*
Note : D implies first difference. The 95 per cent critical values for DF and ADF tests with and without the trend term are respectively 2.97 and 3.57. * indicates rejection of the null hypothesis of unit root at the 95 per cent level.
7.4.5.3. THE LONG- AND SHORT-RUN RELATIONSHIPS Since the long-run relationship was tested by inserting two dummy variables, their inclusion may be justified if they are found to be statistically significant in the long-run model. As for reasons discussed earlier, the long-run relationship is estimated by the procedure of the Phillips-Hansen Fully Modified OLS (PHFMOLS). The long-run model thus estimated is given below: lnMARSS ¼ 5.15*** þ 0.42*** lnAGX 0.18** lnGLO þ 0.17*** D74 þ 0.21*** D93 (s.e.) (0.17) (0.04) (0.07) (0.04) (0.05) t-ratio 30.9 9.89 (2.57) (4.17) (3.91) R2 ¼ 0:85
Therefore, in the long-run agriculture share is positively associated with the marginalization of small states in contrast to an inverse relationship between the dependent variable and globalization. A one percentage point fall in AGX results in 0.42 per cent fall in the share of small states over the long-run, while a rise in GLO by the same magnitude will result in a decline of small states’ share in world merchandise exports by 0.18 per cent. AGX and GLO are statistically significant respectively at the one and five per cent levels. Both the dummies (D74 and D93) are also highly significant at less than one per cent level justifying their inclusion in the model. The long-run model explains 85 per cent variation in lnMARSS. The short-run dynamics are modelled following the error-correction methodology. In the short run, no effect of agriculture share is observed although a negative relationship between globalization and marginalization of small states is maintained. The dummy variables for 1974 and 1993 are also significant in the short run relationship. The error-correction term (ECMt1 ) is correctly signed and significant at the five per cent level and reveals that 44 per cent disequilibrium errors are corrected within a year. The model, however, explains only 38 per cent variation in the dependent variables suggesting that there are other factors explaining most of the marginalization in the short run. The diagnostic test statistics concerning the null-hypotheses of no serial correlation, no functional form problem, non-normality and homoscedasticity of errors are maintained by the statistical tests.
224
Marginalization and World Trade Table 7.23. Short-Run Error Correction Model DlnMARSS ¼ 0.28** 0.25** DlnMARSSt1 DlnGLOt1 D93 0.15*** D74 0.44**ECMt1 0.25** þ0.16*** (S.E.) t-ratio
(0.01) 2.78
(0.10) 2.55
2.02
Adjusted R2 ¼ 0:38 Serial Correlation [x2 (1)] ¼ 0:38 Normality [x2 (2)] ¼ 1:16
(0.12) 3.20
(0.05) 3.78
(0.04) 2.05
(0.22)
F(5, 20) ¼ 7.67*** Functional Form [x2 (1)] ¼ 0:75 Heteroscedasticity [x2 (1)] ¼ 0:25
Note : *** and ** are for statistical significance at the one and five per cent levels respectively. For diagnostics Godfrey’s (1978) LM test for serial correlation, Ramsey’s (1969) RESET test for functional form, Jarque-Bera (1987) test for normality of residuals and White’s (1980) test for heteroscedasticity are performed. The critical values for x2 (1) and x2 (2) at the 95 per cent level are 3.84 and 5.99 respectively, which are being used to test the nullhypotheses of no serial correlation, no functional form problem, normality of regression residuals and homoscedastic errors. Since in every case the computed statistics are smaller than the corresponding critical values, all the null-hypotheses are maintained or, in other words, the model passes all the diagnostic tests.
7.4.6. Conclusion of results In this section an attempt was made to explain the trends in the share of exports of LDCs and small states in terms of agriculture-exports and exports-GDP ratios in the world economy. In particular, we examined whether there was a valid long-run relationship among the variables, as specified in the model. In light of the problems associated with the time series properties of the variables, which might lead to a spurious relationship, careful attention was given by testing the variables for unit roots and using appropriate cointegration methodology. It was found that the variables were integrated on their levels; nevertheless a genuine long-run relationship among the variables in the model, both for LDCs and small states, was confirmed. In the long run, as the share of agriculture in total exports falls and the ratio of world exports to GDP rises, LDCs’ and small states’ relative importance shrinks. These two variables together can explain 91 and 85 per cent variation in marginalization trends respectively for LDCs and small states. The short-run dynamics were modelled following the error-correction methodology where only globalization was found to affect relative importance of the two country groups negatively. The short-run models explained a relatively small variation in the dependent variable and it is possible that other exogenous and policy factors might have contributed to the declining importance of LDCs and small states in the short run.
7.5. Implication for Long-term Trade and Development While the dependence on primary products and increasing globalization in the world economy can explain much of the general trend in declining relative significance of LDCs and small states, there are other factors that aggravate the process, either by inhibiting or by not facilitating the development of dynamic
225
The Implications of Declining Commodity Prices export sectors. The long-term trade and development prospects of LDCs and small states hinge critically upon the interplay of these factors and without addressing them the process of marginalization cannot be checked. To conclude this paper therefore we provide brief discussions on these issues below. First, the existing structure of export trade does not allow LDCs and small states to take full advantage of high income growth in the world economy. One straightforward policy recommendation would be diversification of their export basket by aiming at production and export of manufactured goods. This optionhas, however, so far proved to be a very difficultone. Although most of these countries have a natural comparative advantage in the production of primary products, in the past many of them pursued an inward-looking import-substitution strategy in order to facilitate the formation of a manufacturing industrial base in the domestic economy. The import-substituting industries that were developed under the protective regime remained inefficient and, in the face of severe external and internal imbalances affecting the countries, a policy for trade liberalization and reforms were carried out. Since import-substitution regimes resulted in policy-induced biases against agriculture, a policy reversal to exportpromotion strategy only revived the static comparative advantage of primary commodities. Thus the export structure continues to be dominated by primary commodities, thereby leaving the process of marginalization uninterrupted. Second, due to the small size of the domestic market and low purchasing power of consumers an efficient manufacturing industrial base in LDCs and small states can only flourish if they can engage in international trade.49 Most small states and quite a few LDCs are, however, confronted by natural barriers to trade associated with unfavourable geographical characteristics (such as remoteness and isolation), which increase costs of both export and import trade relative to countries with more favourable geographical characteristics. In particular, small states pay higher transportation costs because of geographical locations, small volume of cargo, bulky low-value products (e.g. agricultural commodities) and lack of equivalent return cargo. The figures quoted in Bernall (2001) show that transportation and freight costs for some small states are as high as 30 per cent of export volume compared to only 4 per cent for large states. Similarly, sub-Saharan Africa’s net insurance freight costs account for 15 per cent of their total exports as against 5.8 per cent for all developing countries (Amjadi and Yeats, 1995).50 When export structure is characterized by a high share of bulky low-value products (e.g. agricultural commodities), countries face much higher freight costs than high-value products with low storage factors (e.g. many manufacturing exports). 49 The small size of the domestic market does not allow firms to exploit either internal economies of scale (i.e. where unit cost is reduced as the size of the firm gets bigger) or external economies of scale (i.e. where unit cost is influenced by the size of the industry). 50 According to the World Bank (1996) net transport and insurance payments average more than 25 per cent of total exports for one-third of sub-Saharan African countries.
226
Marginalization and World Trade These excessive costs alone can serve to make the poor and vulnerable countries’ exports uncompetitive. There is some evidence that increase in transport costs reduces trade volumes (Limao and Venables, 2001) and an ad valorem transport cost of 20 per cent on both final output and intermediate goods reduce the domestic value added (and thus GDP) by 60 per cent when intermediate goods account for 50 per cent of costs (Redding and Venables, 2001).51 As excessive transport costs substantially reduce the domestic value added out of the production of export goods dependent on imported inputs, they not only affect international competitiveness but also discourages foreign firms to relocate their production to these countries even when the wages are low. Third, most small states and LDCs also suffer from a poor state of physical and social infrastructure (human capital), the development of which is considered to be vital for expanding productive capacities and particularly for exporting the manufactured goods that have witnessed rapid growth in world trade.52 However infrastructure development is very expensive and requires long-term investment. Given the current level of income and domestic savings the development of infrastructure and the level of domestic investment in many poor countries will critically depend on the inflow of official development assistance (ODA).53 The data in Table 7.24 show that during the late 1990s the flow of ODA to developing countries, LDCs, and small states declined absolutely. However, while developing countries, on the whole, managed to enjoy increased total financial flows from about $74 billion in 1990 to about $79 Table 7.24. Official Financial Flows ($ million) Total flows
Total ODA
Year
Developing Countries
LDCs
Small States
Developing Countries
LDCs
Small States
1975 1980 1985 1990 1995 1999
21905 42591 41019 74122 70725 79165
4489 9872 10257 17470 17064 11797
982 1693 1730 2872 1792 950
16142 32460 30180 56036 58706 50543
3713 8724 9483 16747 17198 11591
865 1505 1353 2427 1811 1076
Source : UNCTAD (2001).
51 This is compared to a country that faces zero transport costs. Redding and Venables revealed that more than 70 per cent variation in cross-country per capita income could be explained by the geography of access to markets and sources of supply of intermediate inputs. 52 Since most manufacturing exports require a relatively higher input of capital and skill per worker than land per worker, Wood and Mayer (2001) argue that Africa, which includes a number of LDCs, does not have a comparative advantage in exporting labour-intensive manufacturing because of its higher endowment of natural resources (land) to human capital. 53 Gross domestic investment as the percentage of GDP in LDCs has more or less remained unchanged in the 1990s: 22.7 per cent on an average in 1990–94 as against 23.3 per cent in 1997. With such low levels of investment LDCs can hardly create new productive capacities after replacing the depreciation or stock destroyed by factors such as civil war or sheer neglect (UNCTAD, 1999).
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The Implications of Declining Commodity Prices 3
Per cent of global FDI inflow
2.5 LDCs
Small States
2
1.5
1
0.5
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
0
Figure 7.24. Share of LDCs and Small States in Global Inflow of FDI Source: Authors’ estimates from UNCTAD (2002b).
billion in 1999, LDCs and small states saw the flow going down by respectively about 34 and 67 per cent.54 Perhaps the most obvious and important source of financing domestic investment is the inflow of foreign direct investment (FDI). In 2000 total inflow of FDI in the global economy stood at $1271 billion but for LDCs and small states the figures were only $4.4 and $3.7 billion respectively. During 1990–2000 world FDI inflow grew at a trend rate of 14.2 per cent whereas the comparable rates for LDCs and small states were computed to be 11.6 and 9.57 per cent respectively.55 Figure 7.24 shows that while small states’ share of FDI has fallen from a high of about 2.5 per cent of total FDI in 1972 to less than 0.5 per cent, LDCs are down from a peak of over 1.5 per cent in the late 1970s to less than 0.5 per cent by the end of 1990s, both reflecting a clear negative trend. Fourth, for a long time LDCs and small states have benefited from various preferential trading arrangements. The evolving trading system, however, either has reduced the preferential trade margins for these countries or threatens to erode the preferences altogether. For example, in the post-Uruguay Round 54 In the 1990s total ODA contributions from OECD donor countries allocated to LDCs fell by 29 per cent (UNCTAD, 1999) and aid per capita to the developing countries as a whole declined by nearly a third from $32.27 to $22.41 (Stiglitz, 2000). 55 For the period of 1990–2000 the trend growth rates of FDI inflow into developed and developing countries are estimated to be respectively 13.63 and 15.10 per cent.
228
Marginalization and World Trade period average tariffs on industrial goods stood at only 3.9 percent providing a very low margin of preference to the recipient countries. Again, under the Lome´ convention many small states have enjoyed preferential trade margins extended by the EU, which have become incompatible under the WTO regime.56 WTO compatibility of these provisions will require substantial opening-up of the sectors currently protected for the beneficiary countries.57 As a consequence, the net economic effect of the Uruguay Round trade liberalization upon the highly trade preference dependent economies has been found to be negative (Grynberg, 2001). Therefore, it appears that the global trading regime under the WTO will have further consequences on exports and trade of small states and LDCs. Last but not least, factors associated with internal or domestic economy in many LDCs and small states have adversely affected their export trade. Improper interventions resulting in inefficiencies and leading to wastage of resources, social and political unrest creating a domestic environment hostile to investment and production, inefficient and lengthy bureaucratic procedures together with corruption causing high transaction costs, all combine to make the costs of doing business very high; this in turn reduces the competitiveness of tradable activities. Nowhere are social and political stability and good governance more important than in LDCs and in small states that suffer from structural obstacles such as highly concentrated export structure and unfavourable geographical location. It goes without saying that overall competitiveness and export success of these economies in future will critically hinge to a great extent upon the formation of an efficient administrative and institutional framework. Small states and LDCs pose a challenge to the international community in the ongoing process of globalization. Increased integration and rising trade and investment in the world economy may not benefit them substantially. The problem of marginalization in world trade is mostly associated with their inability to diversify exports. Most small states and LDCs have static internal comparative advantage in primary activities, the relative importance of which have shrunk considerably in world trade over the past few decades. Thus, an emphasis on static comparative advantage in the allocation of resources might act as a hindrance to diversification of export structure. One move in the right direction might be
56
Grynberg (2001) provides a detailed discussion on this. Some small states are heavily dependent on various commodity arrangements with the EU. These typically cover exports from small states that would not be competitive in the world market, but are of major economic and social importance to these countries. For example, the Sugar Protocol offers valuable protection to St Kitts and Nevis, where sugar revenue accounts for about 50 per cent of GDP. In light of the problem of WTO incompatibility these arrangements are being renegotiated and revised resulting in considerable erosion of trade preferences to small states (Berthelot, 2001). 57
229
The Implications of Declining Commodity Prices to introduce new products, where possible, in the form of processed primary products or labour intensive light manufacturing exports. It needs to be stressed that although small states can achieve efficiency gains, trade liberalization itself will not remove non-policy barriers to trade imposed by unfavourable geographical locations and undeveloped human and physical infrastructure. While little can be done to overcome geographical locational disadvantages, given the low level of domestic saving and poor capacity of internal resource mobilization for development of infrastructure, an increased inflow of ODA is indispensable. Finally, small states and LDCs should also look into their own domestic economies. Civil unrest, poor law and order, and inefficient, corrupt and lengthy bureaucratic procedures in a large number of LDCs and small states undermine the environment for production and business activities. Only concerted efforts at the domestic fronts of these countries combined with cooperation extended by the international community can help mitigate the problem.
230
Marginalization and World Trade Appendix 7.1. List of LDCs Sl. No.
Country
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 42 43 44 45 46 47 48 49
Afghanistan Angola Bangladesh Benin Bhutan Burkina Faso Burundi Cambodia Cape Verde Central African Republic Chad Comoros Congo, DR Djibouti Equatorial Guinea Eritrea Ethiopia Gambia Guinea Guinea-Bissau Haiti Kiribati Lao People’s Dem. Rep. Lesotho Liberia Madagascar Malawi Maldives Mali Mauritania Mozambique Myanmar Nepal Niger Rwanda Samoa Sao Tome and Principe Senegal Sierra Leone Solomon Islands Somalia Sudan Tanzania Togo Tuvalu Uganda Vanuatu Yemen Zambia
Remark
Commonwealth Member?
Oil Exporter YES
Small State
Small State Small State Small State Data not available Small State
YES
Small State Small State
YES
Small State
YES YES
YES
Small State Small State
Small State
YES
YES YES
YES Data not available Small State
YES YES YES YES
Note : The list of LDCs is from UNCTAD (2002b).
231
The Implications of Declining Commodity Prices Appendix 7.2. List of small states Sl. No.
Country
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
Antigua and Barbuda Bahamas, The Bahrain Barbados Belize Botswana Cape Verde Comoros Cyprus Djibouti Dominica Equatorial Guinea Fiji Gabon Gambia Grenada Guyana Jamaica Kiribati Lesotho Maldives Malta Mauritius Namibia Papua New Guinea Samoa Sao Tome and Principe Seychelles Solomon Islands St Kitts and Nevis St Lucia St Vincent and Grenadines Suriname Swaziland Tonga Trinidad and Tobago Vanuatu
232
Remark
Data Problem OIL
Commonwealth Member? YES YES YES YES YES
LDC LDC LDC YES LDC YES OIL LDC
LDC LDC LDC
Data Problem LDC LDC LDC
OIL LDC
YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES
Marginalization and World Trade Appendix 7.3. Merchandise and Services Exports of Individual LDCs
Merchandise Exports ($ mill) Countries Afghanistan Angola Bangladesh Benin Bhutan Burkina Faso Burundi Cambodia Central Af. Rep. Chad Comoros Congo, DR Eritrea Ethiopia Guinea GuineaBissau Haiti Lao PDR Liberia Madagascar Malawi Mali Mauritania Mozambique Myanmar Nepal Niger Rwanda Senegal Sierra Leone Somalia Sudan Tanzania Togo Tuvalu Uganda Yemen Rep. Zambia
90–94 avg. 1995–2000 avg. 2000
Merchandise share in total exports Exports of Commercial services ($ mill) (per cent) 90–94 avg. 1995–2000 avg.
2000 1995–2000 avg. 2000
328.4 3381.8 2007.9 347.0 70.6 99.5 84.3 267.6 107.0
140.8 3888.2 4646.2 430.7 113.0 257.3 66.8 740.8 164.9
125.0 6646.0 6399.0 376.0 140.0 228.0 50.0 770.0 167.0
4.8 103.2 377.4 117.4 n.a. 38.4 7.0 48.0 16.6
7.2 151.0 293.2 136.8 n.a. 43.3 3.0 130.3 11.5
8.0 150.0 283.0 155.0 n.a. 46.0 2.0 159.0 10.0
95.2 96.3 94.1 75.9 n.a. 85.6 95.7 85.0 93.5
94.0 97.8 95.8 70.8 n.a. 83.2 96.2 82.9 94.4
168.7 19.6 1044.0 n.a. 245.3 629.0 18.8
227.3 9.6 1702.8 n.a. 492.1 845.8 40.2
182.7 12.0 2887.0 n.a. 508.0 940.0 62.0
18.8 15.2 145.2 n.a. 247.0 6.2 6.4
29.7 36.5 132.8 n.a. 345.5 6.7 4.7
25.0 46.0 137.0 n.a. 387.0 10.0 6.0
88.5 20.7 92.8 n.a. 58.8 99.2 89.6
88.0 20.7 95.5 n.a. 56.8 98.9 91.2
112.5 169.9 334.0 306.3 388.9 365.3 384.2 143.2 532.0 314.9 287.0 70.7 726.8 133.1 127.0 387.6 411.8 252.1 n.a. 216.5 703.0 980.1
142.4 331.4 480.3 270.7 470.5 512.2 460.2 222.4 1007.4 502.8 304.2 62.1 960.5 22.0 130.8 716.7 669.0 418.5 n.a. 523.7 2543.6 902.5
163.6 315.0 500.0 250.0 445.0 510.0 355.0 235.0 1391.0 804.5 320.0 52.9 927.0 13.0 110.0 1155.0 663.2 427.0 n.a. 520.0 4200.0 759.0
n.a. 35.4 41.0 146.6 31.4 60.8 15.0 157.2 147.8 277.6 17.4 24.4 313.0 52.6 n.a. 77.8 241.2 80.2 n.a. 42.8 106.0 75.0
n.a. 92.2 46.0 263.8 35.3 65.3 24.7 280.0 481.0 560.5 12.3 27.7 345.7 76.3 n.a. 44.8 568.5 69.2 n.a. 159.7 148.5 77.3
n.a. 111.0 49.0 314.0 31.0 68.0 29.0 325.0 509.0 410.0 13.0 39.0 349.0 86.0 n.a. 24.0 615.0 52.0 n.a. 186.0 161.0 80.0
n.a. 78.2 91.3 50.6 93.0 88.7 94.9 44.3 67.7 47.3 96.1 69.2 73.5 22.4 n.a. 94.1 54.1 85.8 n.a. 76.6 94.5 92.1
n.a. 73.9 91.1 44.3 93.5 88.2 92.4 42.0 73.2 66.2 96.1 57.6 72.6 13.2 n.a. 98.0 51.9 89.1 n.a. 73.7 96.3 90.5
233
The Implications of Declining Commodity Prices Appendix 7.4. Merchandise and Services Exports of Individual Small States
Merchandise Exports ($ mill) Countries Antigua and Barbuda Bahrain Barbados Belize Botswana Cape Verde Comoros Cyprus Djibouti Dominica Equatorial Guinea Fiji Gabon Gambia Grenada Guyana Jamaica Kiribati Lesotho Maldives Malta Mauritius Papua New Guinea Samoa Sao Tome and Principe Seychelles Solomon Islands St Kitts and Nevis St Lucia St Vincent and Grens. Suriname Swaziland Tonga Trinidad and Tobago Vanuatu
234
90–94 avg. 1995–2000 avg. 2000
Merchandise share in total exports Exports of Commercial services ($ mill) (per cent) 90–94 avg. 1995–2000 avg.
2000
1995–2000 avg. 2000
48.3
40.2
38.2
342.6
395.5
406.0
9.2
8.6
3616.1 196.2 113.8 1795.9 5.1 19.6 945.0 19.0 51.7 69.8
4384.9 265.0 161.8 2393.7 12.0 11.1 1147.3 20.7 53.3 354.6
5700.5 272.4 194.3 2250.0 11.0 12.0 953.5 20.0 53.0 425.0
556.8 652.0 101.0 177.2 32.4 15.2 1997.6 33.6 41.6 6.2
715.0 954.2 130.2 251.3 78.3 36.5 2725.5 27.2 78.8 6.7
830.0 1055.0 152.0 353.0 99.0 46.0 2930.0 28.0 86.0 10.0
86.0 21.7 55.4 90.5 13.3 23.2 29.6 43.2 40.3 98.2
87.3 20.5 56.1 86.4 10.0 20.7 24.6 41.7 38.1 97.7
482.6 2234.7 45.6 22.8 333.1 1118.8 4.8 102.8 45.3 1370.1 1267.2 1956.2
613.9 3127.9 15.9 23.2 532.2 1340.4 6.5 181.5 66.0 1904.2 1618.5 2189.8
596.0 3883.0 9.0 23.0 570.0 1296.1 8.0 180.0 75.9 2336.5 1580.0 2096.0
419.8 260.6 65.4 79.2 100.8 1129.8 13.0 32.0 142.8 846.2 552.6 267.2
540.0 210.8 91.8 118.8 143.8 1749.0 19.2 43.7 306.5 1101.3 937.5 332.7
557.0 221.0 116.0 145.0 166.0 1988.0 21.0 36.0 345.0 1086.0 1067.0 280.0
53.2 93.7 14.8 16.4 78.7 43.4 25.3 80.6 17.7 63.4 63.3 86.8
51.7 94.6 7.2 13.7 77.4 39.5 27.6 83.3 18.0 68.3 59.7 88.2
6.2 14.4
13.9 8.2
14.2 8.0
33.4 4.4
56.7 8.7
61.0 12.0
19.7 48.5
18.9 40.0
51.4 105.6
125.6 145.0
180.0 93.0
177.4 30.0
253.7 51.0
305.0 57.0
33.1 74.0
37.1 62.0
26.0
25.5
30.0
75.2
92.2
93.0
21.7
24.4
117.3 67.2
75.6 46.8
60.0 47.2
190.2 52.6
292.8 103.3
307.0 124.0
20.5 31.2
16.3 27.6
572.2 649.6 13.3 1833.0
488.8 932.7 12.0 2767.2
435.0 881.0 17.0 4044.0
43.0 95.6 12.6 364.6
84.5 106.5 14.8 513.2
85.0 72.0 17.0 628.0
85.3 89.8 44.8 84.4
83.7 92.4 50.0 86.6
21.7
29.9
26.0
63.0
95.2
117.0
23.9
18.2
Appendix 7.5. Growth Rates of Exports of LDCs Services exports growth rate (per cent)
Merchandise exports growth rate (per cent)
Afghanistan Angola Bangladesh Benin Bhutan Burkina Faso Burundi Cambodia Central Af. Rep. Chad Congo, DR Eritrea Ethiopia Guinea Guinea-Bissau Haiti Lao PDR Liberia Madagascar Malawi Mali Mauritania Mozambique Myanmar Nepal Niger Rwanda Senegal Sierra Leone Somalia Sudan Tanzania Togo Tuvalu Uganda Yemen Rep. Zambia
90–94
1995
1996
1997
1998
1999
2000
1995–2000
90–94
1995
1996
1997
1998
1999
2000
1995–2000
7.2 1.4 13.4 13.3 1.1 24.3 9.6 51.6 2.8 2.0 17.2 n.a. 17.4 1.5 48.8 5.8 38.2 4.0 7.4 7.4 8.1 2.3 8.6 30.7 19.5 0.5 11.5 3.1 3.1 21.1 0.4 8.3 3.8 n.a. 24.7 13.6 5.8
0.0 20.7 37.2 5.5 24.2 24.9 1.9 42.5 17.9 17.2 4.5 n.a. 13.4 3.8 6.1 34.1 3.7 14.3 9.6 34.3 31.6 22.6 12.0 6.6 5.2 27.1 31.7 22.5 63.5 6.6 6.1 31.4 136.4 n.a. 8.5 108.2 13.1
0.0 39.9 3.9 25.7 20.7 1.3 62.9 12.3 17.4 32.1 35.2 n.a. 1.2 16.6 32.3 18.2 3.5 28.0 18.5 6.6 0.5 1.9 14.9 12.3 11.6 2.1 11.1 1.8 11.9 3.4 11.7 11.3 49.1 n.a. 27.6 37.5 1.0
3.2 1.7 13.5 19.7 19.2 5.5 120.5 15.0 19.0 2.5 10.3 n.a. 40.8 6.1 133.3 33.3 11.5 2.3 7.0 14.0 27.8 3.2 17.1 16.1 4.7 2.9 46.7 8.2 63.8 0.0 4.2 0.8 78.5 n.a. 5.5 6.4 11.8
6.7 29.2 25.9 2.4 9.3 1.2 24.4 4.3 4.0 10.1 13.2 n.a. 4.4 0.5 44.9 45.8 3.1 6.0 15.5 4.3 0.9 0.0 3.5 23.0 17.9 22.8 31.8 7.0 58.8 13.3 0.3 21.8 12.9 n.a. 9.7 40.3 19.1
14.3 45.6 7.9 1.9 23.4 21.4 16.9 197.0 17.6 22.6 18.3 n.a. 20.0 8.8 88.9 12.0 15.9 6.4 0.9 14.0 2.7 16.7 12.4 5.6 27.0 14.1 0.0 6.1 14.3 23.1 30.9 7.8 0.5 n.a. 3.6 63.1 2.6
8.3 52.9 17.2 10.9 6.1 16.1 7.4 35.4 13.3 9.4 8.2 n.a. 7.3 2.7 21.6 16.3 4.0 0.0 11.6 19.7 9.3 19.6 38.4 44.0 33.6 9.4 11.7 2.9 116.7 10.0 131.7 22.1 7.2 n.a. 11.2 67.2 5.4
2.6 21.3 17.6 0.0 14.0 1.2 1.2 45.5 3.2 4.2 2.7 n.a. 6.0 3.3 26.8 15.1 1.6 6.7 6.6 2.8 8.6 3.1 16.4 13.8 14.9 3.6 7.7 4.4 12.0 2.7 29.4 5.7 28.5 n.a. 2.2 38.2 1.8
59.7 13.4 9.6 9.1 n.a. 6.8 5.5 6.5 2.7 0.8 16.2 n.a. 1.3 16.1 15.9 31.9 45.4 4.7 10.4 4.8 3.9 3.1 15.7 48.7 29.9 15.4 6.9 4.5 69.2 — 23.3 29.7 9.4 n.a. — — 0.8
0.0 24.7 11.9 39.5 n.a. 10.5 33.3 128.9 23.5 43.5 116.4 n.a. 16.5 21.4 0.0 1300.0 21.4 2.2 19.7 13.6 33.3 11.8 26.7 36.7 12.3 50.0 50.0 17.8 17.4 — 86.4 37.7 23.1 n.a. 62.5 35.6 1.3
14.3 100.0 52.5 22.6 n.a. 2.4 0.0 47.6 0.0 3.0 0.0 n.a. 3.5 270.6 50.0 6.1 16.2 0.0 15.5 78.9 2.9 21.1 4.5 19.1 14.7 0.0 18.2 13.5 2.8 — 54.9 6.4 59.4 n.a. 39.4 4.3 1.3
25.0 38.5 19.3 17.1 n.a. 2.3 25.0 1.3 23.1 2.9 0.0 n.a. 0.9 11.1 100.0 65.4 1.3 0.0 4.0 38.2 6.1 8.7 10.3 21.8 17.1 0.0 161.5 4.4 2.7 — 18.9 21.9 23.5 n.a. 13.8 14.3 1.3
16.7 11.5 5.3 24.5 n.a. 2.4 0.0 34.7 0.0 25.7 3.8 n.a. 8.5 5.7 16.7 3.5 48.7 2.2 8.6 14.9 1.6 4.0 2.5 20.3 45.5 0.0 5.9 11.2 2.8 — 53.3 13.6 16.7 n.a. 6.7 20.8 3.9
0.0 26.0 5.6 22.0 n.a. 12.2 33.3 22.4 30.0 3.8 7.9 n.a. 13.6 45.5 20.0 2.2 12.9 9.1 9.8 2.5 9.8 16.7 3.1 19.6 4.8 8.3 15.6 4.1 15.1 — 485.7 16.9 16.9 n.a. 3.4 6.0 8.1
14.3 3.2 6.4 0.0 n.a. 0.0 0.0 32.5 23.1 0.0 0.0 n.a. 1.3 0.0 0.0 12.1 9.9 2.1 8.3 24.4 1.5 3.6 10.2 3.7 9.7 0.0 5.4 0.6 2.4 — 70.7 1.4 3.7 n.a. 2.2 14.2 0.0
3.4 8.0 2.4 7.7 n.a. 3.4 15.3 32.6 6.6 3.3 20.1 n.a. 6.7 42.0 25.6 231.6 13.7 1.1 9.7 11.1 5.7 9.6 9.6 13.7 1.0 9.7 24.1 2.6 0.5 — 62.4 8.5 3.6 n.a. 21.3 8.9 0.9
Appendix 7.6. Growth Rates of Exports by Small States Services exports growth rate (per cent)
Merchandise exports growth rate (per cent) 1996
1997
1998
1999
2000
Antigua and Barbuda 20.4 11.1 43.3 Bahrain 5.9 13.7 14.3 Barbados 0.2 31.3 17.6 Belize 6.6 11.8 8.5 Botswana 0.3 14.1 18.4 Cape Verde 5.2 80.0 44.4 Comoros 5.0 0.0 45.5 Cyprus 4.7 27.0 12.9 Djibouti 6.3 0.0 35.3 Dominica 1.5 4.3 13.3 Equatorial Guinea 15.9 92.1 81.8 Fiji 4.6 12.5 20.8 Gabon 9.0 15.4 21.9 Gambia 10.5 54.3 31.3 Grenada 1.6 8.0 8.7 Guyana 15.8 0.2 13.6 Jamaica 5.0 17.7 3.1 Kiribati 10.7 40.0 28.6 Lesotho 19.0 11.9 16.9 Maldives 1.9 8.9 20.4 Malta 14.4 21.9 9.6 Mauritius 2.3 22.6 1.9 Papua New Guinea 15.5 0.2 5.0 Samoa 19.5 125.0 11.1 Sao Tome and Principe 12.0 37.5 20.0 Seychelles 11.3 2.0 92.3 Solomon Islands 14.3 18.3 3.6 St Kitts and Nevis 5.1 13.6 15.8 St Lucia 0.2 17.0 33.9 St Vincent and Grens. 6.3 16.0 9.5 Suriname 1.3 6.2 9.2 Swaziland 9.4 22.2 6.8 Tonga 10.7 0.0 35.7 Trinidad and Tobago 5.6 26.3 4.1 Tuvalu n.a. n.a. n.a. Vanuatu 3.8 12.0 7.1
23.5 6.7 0.7 3.2 12.1 7.7 50.0 9.9 21.7 3.9 114.5 17.1 8.6 28.6 9.5 24.6 0.0 20.0 4.8 23.7 5.2 3.2 13.9 50.0 25.0 13.0 8.0 18.2 19.5 0.0 61.9 7.6 11.1 0.0 n.a. 16.7
15.4 25.4 11.0 2.5 31.5 28.6 0.0 15.1 17.9 18.9 14.9 17.7 36.5 80.0 17.4 24.8 5.1 16.7 1.0 1.4 11.0 0.0 17.9 0.0 20.0 8.0 28.0 7.7 6.1 8.7 37.8 0.6 20.0 11.9 n.a. 2.9
33.3 26.6 4.8 7.1 35.7 20.0 44.4 6.0 13.0 14.3 68.8 11.0 30.2 74.1 70.4 8.1 5.5 14.3 11.3 13.5 8.7 16.7 8.4 33.3 33.3 18.9 15.9 0.0 4.8 2.0 3.2 7.1 50.0 23.9 n.a. 23.5
0.0 37.7 3.0 16.9 1.7 8.3 40.0 4.3 0.0 1.9 31.1 7.8 34.0 0.0 8.7 9.0 4.5 175.0 22.1 18.8 18.0 19.6 8.9 30.0 25.0 24.1 36.3 7.1 1.7 4.1 3.3 9.6 8.3 52.3 n.a. 3.8
Country
90–94
1995
1995–2000 90–94 12.3 10.0 7.7 7.5 7.9 19.2 0.0 0.7 4.4 2.6 62.2 0.3 9.4 7.6 14.9 5.0 1.4 34.8 7.2 9.9 7.5 3.1 3.2 31.6 11.8 26.4 4.3 5.9 7.6 0.6 3.5 1.2 0.5 15.8 n.a. 0.9
8.8 7.4 3.5 13.2 23.9 12.9 29.4 13.2 5.5 16.7 3.5 8.6 3.0 9.2 13.6 10.0 12.2 24.3 2.9 18.6 9.3 3.1 12.3 7.2 21.7 7.4 16.6 13.2 12.1 10.9 16.9 6.0 7.4 5.0 n.a. 13.4
1995
1996
1997
1998
1999
2000
11.0 4.3 10.7 5.5 1.4 5.6 16.6 2.5 4.4 13.8 3.3 10.8 7.5 6.8 3.6 6.6 0.2 5.8 7.3 6.8 0.8 1.6 15.6 7.8 34.9 38.6 29.0 28.9 43.6 2.0 46.2 14.0 21.5 6.3 29.7 3.1 28.6 7.4 13.8 18.2 15.4 2.2 13.2 3.9 2.5 4.4 9.1 1.1 15.2 3.6 0.0 3.7 3.8 3.7 19.6 9.8 19.4 5.0 13.1 9.5 33.3 25.0 20.0 83.3 1300.0 28.6 7.2 8.0 8.8 23.4 7.0 10.3 4.5 7.3 0.0 5.4 28.4 11.2 50.0 131.6 10.2 2.1 14.1 2.7 2.0 7.1 0.0 10.5 24.1 0.7 11.1 2.3 8.3 0.0 1.4 13.7 8.0 0.4 6.2 4.2 11.8 2.0 11.8 10.5 14.3 0.0 0.0 16.7 0.0 13.3 132.4 41.8 19.6 2.7 17.9 24.3 8.0 6.5 3.3 1.5 5.1 2.6 3.5 6.2 3.6 9.4 11.8 21.1 8.7 4.0 16.7 3.6 36.6 34.6 8.1 19.9 22.0 12.9 32.5 17.0 4.8 1.7 19.0 29.8 20.0 16.7 0.0 0.0 85.7 7.7 7.5 11.0 4.1 15.2 12.0 2.3 16.7 34.3 36.2 18.8 1.9 11.8 13.0 7.5 9.3 9.6 5.8 4.1 11.4 0.8 7.9 9.1 2.2 4.1 18.0 33.3 2.1 8.2 17.0 0.0 9.0 16.4 44.3 5.0 11.5 21.2 36.4 34.7 24.5 21.3 5.2 28.7 7.1 0.0 13.3 7.7 7.1 13.3 4.4 35.0 19.7 7.3 1.7 11.3 n.a. n.a. n.a. n.a. n.a. n.a. 7.1 13.3 5.9 35.0 1.9 10.4
1995–2000 0.9 0.7 5.1 5.9 16.6 18.0 14.3 3.6 2.5 9.6 211.1 3.0 2.4 18.4 6.7 6.1 5.4 4.1 13.6 10.3 2.0 9.6 5.7 9.0 19.1 8.7 7.5 0.6 4.5 13.1 5.3 3.1 3.7 12.7 n.a. 9.7
Note : Some of the growth figures might be misleading. For example, in the case of Equatorial Guinea the average growth rate of commercial services exports for 1995–2000 is estimated to be 211 per cent but still its average share in global commercial services exports declined during the same period. This is because the variation in Equatorial Guinea’s exports is very high. It exported 5, 6, 8, 9, 3 millions of services for the periods 1990–94 and 4, 5, 6, 1, 14, 10 for 1995–2000. Thus, average exports for 90–94 and 1995–2000 are 6.2 and 6.66 millions respectively or 1995–2000 average growth over 90–94 exports is 7.41 per cent. On the other hand the growth of world services exports of 1995–2000 over 90–94 was more than 47 per cent.
Marginalization and World Trade 750
Afghanistan
7500
7500
Angola
Bangladesh
600
150
Benin
500
5000
5000
400
100
250
2500
2500
200
50
1980
2000
1980
2000
1980 1000
300
Burkina Faso
150
200
100
100
50
Burundi
2000
1980 200
Cambodia
2000
Central Af.Rep.
150 500
Bhutan
1980 300
2000
Chad
200
100
100
50 1980 3000
2000
Congo, D.R.
2000
1980 1000
Ethiopia
2000
1980 600
Lao PDR
2000
300
200
1980
2000
Mauritania
400
1980 300
2000
Mozambique
Guinea-Bissau
1980
200
2000
Haiti
100
2000
2000
400
200
200
2000
Myanmar
Malawi
400
1980 1000
2000
1980
2000
1980
150
200
2000
100
Tanzania
1980
400
600
2000
Togo
1980
500
1980
Somalia 1000
2000
Sudan
100 500
50 2000
2000
Niger
200
50 1980
Mali
1980 600
Nepal
2000
400
Sierra Leone
Senegal
500
100
1500
1980 600
500
1980 1000
Rwanda
1980
2000
500
100 1980
1980 600
1000
200
200
2000
400
500
100
800
2000
25
1980
300
150
1980
50
Madagascar
Liberia
400
200
Guinea
500
200
600
2000
200 1980
300
600
2000
400
1000
400
1980
2000
1980
2000
1980
Yemen, Republic of 1500
Uganda
2000
Zambia
4000 1000
200
400
250
2000 500
1980
2000
1980
2000
1980
2000
1980
2000
1980
2000
Appendix 7.7. Merchandise Exports of Individual LDCs ($Million), 1970–2000
237
The Implications of Declining Commodity Prices 75
Antigua Barbuda 5000
50
2500
25 1980 20 15
Cape Verde
200 150
200
100
2000
Belize
2000
2000
1980
Djibouti
75
30
1000
2000
Dominica
50
20
500
Botswana
1000 1980
40
Cyprus
3000 2000
50 1980
1500
Comoros
10
5
25
10 1980
2000
1980 750
Eq Guinea
2000
1980
4000
Fiji
3000
500
2000
1980
75
Gabon
2000
1980
Gambia
30
250 1980
Guyana
2000 1500
2000
400
1980
2000
10 2000
Kiribati
1980 200
30
2000
1980
Lesotho
75
2000
Maldives
25
10 1980
2000
50
100
20
500
200
25 1980
40
Jamaica
1000
Grenada
20
1000 1980
2000
50
2000
200
600
1980
20
Barbados
300 100
2000
10
400
400
Bahrain
1980
2000
1980
2000
1980
2000
Sao Tome and Principe 2000
Malta
2000
20
PNGuinea
15
20
1000
10
10
2000
1980
150 100
50
50 1980
Suriname
2000
2000
100
Tonga
10
1980
2000
St Vincent & Grens.
50
1980
15
2000
1980
2000
1980
1980
4000 3000 2000 1000 2000
2000
Vanuatu 40 30 20
1980
2000
1980
Appendix 7.8. Merchandise Exports from Individual Small States (in $Million)
238
2000
50
5 1980
St Lucia
1980
Trinidad and Tobago Swaziland
500
500
2000
100
20 2000
1980 150
40
1980
1000
2000
St Kitts and Nevis
Solomon Islands Seychelles
30
Samoa
2000
5 1980
100
1000
3000
Mauritius
500 1980
150
1500 1000
1000
200
2000
2000
Marginalization and World Trade Afghanistan
250
15
200
10
150
5
100 1980
7.5
1990
2000
Burundi
1980
1990
2000
100
200
50 1980
20
Cambodia
1990
2000
Central African Rep.
2000
1990
2000
2000
1980
1990
2000
Congo, Dem. Rep. of 200
100 1990
2000
1980
50
Lao PDR
40
1990
2000
Liberia
30
50
5 1990
2000
Chad
1980
100 5
1980
20 1990
Guinea-Bissau
10
200
30
10 1980
10
Guinea
Burkina Faso
40
20
10 1990
40
50
Benin
1980
30 15
1980
15
Ethiopia
300
150
300
2000
50
2.5
400
1990
100
5
Bangladesh
400
1980
150
500
Angola
20 1980
1990
2000
1980
1990
2000
1980
1990
2000
1980
1990
2000
Mauritania 300
50
Madagascar
80
Malawi
40
30
Mali
300 200
20
30
40
100
100
20 1980
500
1990
2000
Myanmar
1980
750
1990
2000
1990
2000
200
40
1980
1990
2000
1980
400
1990
2000
Sudan
1990
2000
Yemen, Republic of Uganda
50 1990
2000
Senegal
300 200 1990
2000
1980
125
Tanzania
1990
2000
1990
2000
Togo
75 200 1990
2000
1980
1990
2000
1980
Z am bi a
100
100
100
1980
150
2000
400
1980
150
1990
100
200
1980
1980
400
Rwanda
1980
600
300
Somalia
100 2000
2000
20
20
1990
1990
10
25 1980
1980
20
40
50
2000
30
Sierra Leone 75
1990
30
250 1980
1980
Niger
Nepal
500 250
Mozambique
60
200
50 1980
1990
2000
1980
1990
2000
Appendix 7.9. Commercial Services Exports from Individual LDCs (in $Million)
239
The Implications of Declining Commodity Prices Antigua and Barbuda
400
1000
Bahrain 1000
300 200
150
Belize
750
100
Botswana
500
50
200
500
100
400
Barbados
300
750
100 1980
1990
2000
1980
1990
2000
1980
1990
2000
3000
100 Cape Verde
40
Comoros
1990
2000
2000
1980
1990
2000
100 Djibouti
Dominica
30 50
20
1980
1990
40 Cyprus
2000 50
1980
1000
1980
1990
2000
20
1980
1990
2000
1980
1990
2000
15
1980
1990
2000
150 Equatorial Guinea
10
600
Fiji
300
Gabon
100
1990
Grenada 100
400
200
200
100
50
5
1980
Gambia
2000
1980
1990
2000
1980
1990
2000
50
1980
1990
2000
1980
1990
2000
2000 Guyana
150
20
Jamaica 1500 1000
10
500
5
50 1980
1990
Malta
1000
2000
1980
1000
Kiribati
75
15
100
1990
2000
300
50
1980
1990
2000
400
Maldives
200
25
100 1980
1990
2000
1980
1990
2000
15
Papua New Guinea
Mauritius
Lesotho
50
Samoa
Sao Tome 10
500
500
1980
1990
2000
200
1980
75
1990
2000
1980
Solomon Islands
Seychelles
300
25
1990
2000
St Kitts and Nevis 100
50 50
25
2000
Suriname
1980
150
300
1990
2000
St Lucia
1990
2000
1980
1990
2000
15
Tonga
600
1990
2000
100
100
10
50
1990
2000
2000
Vanuatu
50
200 1980
1990
400
50 2000
2000
St Vincent and the Grenadines
1980
Trinidad and Tobago
100
1990
1990
50
1980
Swaziland
150
1980
1980
100
100
100 1990
1980
200
200
1980
5
1980
1990
2000
1980
1990
2000
1980
1990
2000
Appendix 7.10. Commercial Services Exports from Individual Small States (in $Million)
240
Marginalization and World Trade 1.5
Afghanistan
Angola
1
Bangladesh
Benin
1 1
.75
.5 2000
Burundi
.5
2000
Cambodia
1980
1
2000
Central African Rep.
1
1980
2000
2000
1
1
.5
2000
Guinea
Ethiopia 1.5
.5
1
1980
.5 1980
1
2000
Liberia
1980
1
2000
Guinea-Bissau
.5
2000
2000
2000
1980
2000
Senegal
1
1980
3
2000
2000
Sierra Leone
1 .5
Mauritania
2000
Rwanda
.5
1980
1
Somalia 1
2000
1980
1
Sudan
.5
.5
2000
1
1 1980
1980
Niger
2
.5 1980
2000
.5
.75
.75
2000
1980
1
Mali
1
.5
1980
Lao PDR
1
Nepal
Myanmar 1
.5
2000
.75
1980
Mozambique
2000
.5
1980
Malawi
.5 2000
1980
1
.5
1980
Madagascar
.5
1980
2000
Haiti
1 .5
1980
1
1980
1
2000
Congo, D.R.
.75
.5 1980
1980
Chad
.5
.5
1
1
.5 1980
1.5
1
1
.5
.5 1980
Burkina Faso
1
2000
Tanzania
.5
.5 1980
1.5
2000
1980
3
Togo
1
2
.5
1 1980
2000
2000
Uganda
1980
1.5
2000
1980
Yemen, Republic of
2000
1980
2000
Zambia 1
1 .5 .5 1980
2000
1980
2000
1980
2000
Appendix 7.11. Share of Individual LDCs in World Merchandise Exports, 1980 – 2000 Note: Share of individual LDCs in 1970 has been set to 1.
241
The Implications of Declining Commodity Prices 1.5
Antigua and Barbuda
Bahrain
Barbados
3
1.5
2
1
1
.5
1
2000
1980
Cape Verde
10
1
.5 1980
Belize
2000
1980
1
Comoros
5
.5 2000
Cyprus
1980
1
2000
Dominica
.5
.5
.5
.5 2000
1980
1
Eq Guinea 1 .5
2000
1 .75
1980
1980
Djibouti
1 .5
Botswana
1.5
1
2000
1980
Fiji 2.5
.75
2
.5
1.5
2000
1980
1
Gabon
2000
Gambia
1980
1
.5
2000
Grenada
.5
1 1980
1
2000
Guyana
1980
1
2000
1980
2000
Kiribati
Jamaica
1980
2
1.5 .5
1980
2000
1980
2000
2000
1.5
2.5
1
1.5
2000
Samoa
1
1980
1
2000
Sao Tome and Principe
1
2
2
.5
.5
1 1980
2000
1980
2
Seychelles
3
1.5
2
1
2000
1980
Solomon Islands
2000
St Kitts and Nevis.
1980
2000
Suriname
1.5 1
2000
Swaziland
.75
1980
1
2000
2000
2000
St Vincent & Grens.
2 1 1980
Tonga
2000
Trinidad and Tobago
1980
1
2000
Vanuatu
.5
1
.5
.5 1980
1980
3
St Lucia
.5
1.5 .5
2000
1
1980
1
1980
2
1
1
Maldives
.5
1980
PNGuinea
Mauritius
Malta 3
4
1980
2000
1
1
.5
1980
Lesotho
1.5
1
.5
2000
.5 1980
2000
1980
2000
1980
2000
1980
2000
Appendix 7.12. Share of Individual Small States in World Merchandise Exports, 1970–2000 Note: Share of individual small states in 1970 has been set to 1.
242
Marginalization and World Trade Afghanistan
Bangaladesh
Angola
5
1
1
.5
.5
1980
1990
2000
1980
1990
2000
2.5
Cambodia
1980
1990
2000
2000
.5
1980
1990
1990
2000
1980
2.5
Lao PDR
1990
2000
Liberia
1.5
2
2
1.5
1
1
1
.75
2000
1980
1990
2000
Mauritania
Mali
1
1990
2000
1
2000
Niger
2000
Malawi
1 .5
1990
2000
1980
2
1990
2000
1990
2000
Nepal
2 1
1980
1.5
1990
1.5
.5
.5 1990
1.5
Myanmar
.5
1980
Ethiopia
1980
3 .75
.75
2000
Madagascar
1980
Mozambique
1
1
2000
1 1990
1.25
1980
1990
1.5
1980
3
1980
2
Congo, D.R.
.5 1980
4
Guinea
2000
.5
1 1990
1990
1
1 1.5
1980
.7 1980
1.5
1.5
2 .5
.8
.5
Central African Rep.
Burundi 1
1.25
.9
.75
2.5
1
Burkina Foso
Benin 1
1990
Rwanda
2000
1 1980
1.25
1990
2000
Senegal
1980
1
1990
2000
Somalia
Sierra Leone 1
1 1
.5
.75
.5
1980
.5
.5 .5 1980
1.5
1990
2000
1980
1980
1.5
1
1
.75
.5
.5 1990
2000
Tanzania
Sudan
1980
1990
2000
1990
2000
1980
Togo 10
1990
2000
1980
1
Uganda
1990
2000
Zambia
1
1980
1990
2000
.5
5
.5
1980
1990
2000
1980
1990
2000
1980
1990
2000
Appendix 7.13. Share of Individual LDCs in World Commercial Services Exports, 1980 –2000 Note: Share of individual LDCs in 1980 has been set to 1.
243
The Implications of Declining Commodity Prices Antigua and Barbuda
Bahrain
3
2.5
Barbados
3
1.2 2
2
1
1 1980
1990
2000
1980
1990
1
1.5
.8
1
2000
1980
Cape Verde 3
5
2.5
Comoros
1990
2000
2.5
2000
1980
Eq Guinea 1.25
1
1990
2000
1990
2000
Guyana
2.5
1990
2000
1990
2000
1990
2000
Jamaica
1980
1990
1990
2000
Seychelles
1.2 1
2000
2 1 1980
1990
2000
1990
2000
4
1990
1990
2000
2000
1990
2000
Maldives
3 2
1990
2000
1990
2000
St Vincent and the Grenadines
1.5 1 1980
Tonga
1990
2000
1980
Trinidad and Tobago 1.2
1
2000
Sao Tome
1980
2
St Lucia
1 2000
1990
1 1980
1.5
1990
1980
Samoa
2
1
1990
2000
Vanuatu
1
.75
1980
1990
2000
.8
.5
.5 1990
2000
2
St Kitts and Nevis
1980
Swaziland
1
1980
Grenada
1980
1.5
2000
3
1.5 .5
2000
1 1990
1 1980
Solomon Islands
1980
Suriname
1
2000
1.5
1980
Papua New Guinea
2 2000
1990
Lesotho
3
1
1990
1990
.75
1980
4
.8 1980
1980
2
.5 1990
1
1.5
1980
Kiribati
1
1.5
2000
2000
1
Mauritius
.6
1 1990
1
1980
1.5
2 .8
Dominica
3
1
1980
Malta
2000
1.5
1 1980
4
Gambia
.5
1.2
1
1990
Djibouti
1980
Gabon
1.4
1.5
1980
1.5
1980
1.6
2
2000
2
.75 1980
1990
.5 1980
1
Fiji
1 .5
1980
.75
1 1990
Botswana
.75
1.5
1 1980
1.25 1
1
Cyprus
2
2
Belize
2
1980
1990
2000
1980
1990
2000
1980
1990
2000
Appendix 7.14. Share of Individual Small States in World Commercial Services Exports, 1980–2000 Note: Share of individual small states in 1980 has been set to 1.
244
Marginalization and World Trade Appendix 7.15. Absolute Changes in Merchandise Exports in the 1990s
World United States China Mexico Canada Korea, Rep. United Kingdom Russian Fed. France Taipei, Chinese Netherlands Japan Ireland Hong Kong, China Germany Saudi Arabia Malaysia Spain Philippines United Arab Emirates Iraq Singapore Norway Indonesia Hungary BelgiumLuxembourg Venezuela Thailand Israel Algeria India Iran, Islamic Rep. of Australia Austria Nigeria Vietnam Poland Ukraine Brazil Qatar Czech Rep. Sweden Kazakhstan Turkey Belarus Finland Kuwait Argentina
avg. exports 90–94
avg. change 90–94
avg. change 1995–2000
avg. change 1995–2000 as % of 90–94 avg. exports
3740196
200559
269113
7.20
448177 86338 48473 139956 78353 189361
29759 14729 5043 9437 7749 4962
39435 20103 17376 16888 9442 8577
8.80 23.28 35.85 12.07 12.05 4.53
27360 223025 80441 140555 340300 27894 117497
n/a 4665 6434 6542 27356 2584 17269
8436 7624 7351 7246 7227 6524 5762
30.83 3.42 9.14 5.16 2.12 23.39 4.90
412232 45501 42089 62843 10196 20696
1500 451 7357 4372 1309 258
5762 5509 4844 4728 4456 4260
1.40 12.11 11.51 7.52 43.71 20.58
2874 69220 33975 33132 10099 123835
2968 11019 161 3595 175 5437
4021 3907 3582 3341 3045 2854
139.92 5.64 10.54 10.09 30.15 2.30
15398 33201 13766 11148 20355 16250
352 5548 1201 1013 1762 520
2669 2524 2472 2358 2350 2309
17.33 7.60 17.95 21.15 11.54 14.21
42938 42380 11429 2819 14800 869 36196 3480 8515 56005 265 14944 193 25318 7260 13069
1947 912 1064 413 730 n/a 3036 169 n/a 938 n/a 1287 n/a 736 1055 827
2152 2036 1850 1800 1751 1726 1716 1589 1532 1510 1389 1228 1193 1159 1154 1066
5.01 4.80 16.19 63.85 11.83 198.62 4.74 45.65 17.99 2.70 524.15 8.21 618.13 4.58 15.90 8.16 (Continued )
245
The Implications of Declining Commodity Prices Appendix 7.15. (Continued ) avg. exports avg. change avg. change avg. change 1995–2000 as % 90–94 90–94 1995–2000 of 90–94 avg. exports Oman Libyan Arab Jamahiriya Angola Italy Slovak Rep. Bangladesh Colombia Morocco Lithuania Romania Costa Rica Chile Yemen South Africa Estonia Dominican Republic Trinidad and Tobago Portugal Turkmenistan Congo Sri Lanka Bahrain Yugoslavia Peru Azerbaijan El Salvador Sudan Egypt Latvia Brunei Darussalam Syrian Arab Republic Cambodia Uzbekistan Bosnia and Herzegovina Pakistan Equatorial Guinea Myanmar Denmark Gabon Ecuador Guatemala Macau, China Nepal Botswana Malta Slovenia Tunisia Kyrgyzstan ˆ te d’Ivoire Co Tajikistan Georgia Panama Cameroon Moldova, Rep. of Armenia
246
5330 10473 3574 175627 3701 1985 7290 4118 172 4980 2076 9624 692 24062 147 2823 1910 16872 96 1044 2509 3613 700 3622 75 849 388 3175 97 2338 3385 214 189 92 6731 70 532 39284 2235 3059 1304 1744 320 1807 1366 3918 3941 23 2798 76 8 467 1809 30 11
9 1231 217 5186 n/a 232 413 58 n/a 298 355 808 61 443 n/a 321 32 378 n/a 6 306 36 n/a 331 n/a 167 37 223 n/a 5 291 39 n/a n/a 487 0 118 1383 37 276 90 37 38 24 109 n/a 283 n/a 83 n/a n/a 61 129 n/a n/a
957 890 849 728 658 598 583 542 500 491 482 427 427 425 404 391 361 354 352 343 327 318 299 291 281 258 250 248 234 233 214 197 193 183 170 162 154 140 127 124 108 104 92 92 84 83 75 74 66 62 55 47 46 41 40
17.95 8.49 23.75 0.41 17.78 30.15 8.00 13.16 290.70 9.86 23.23 4.43 61.64 1.76 274.83 13.87 18.91 2.10 366.67 32.85 13.02 8.80 42.64 8.02 374.67 30.35 64.60 7.80 241.24 9.95 6.33 92.14 102.12 197.84 2.53 231.52 28.89 0.36 5.70 4.05 8.30 5.99 28.73 5.07 6.18 2.13 1.90 321.74 2.35 81.58 687.50 10.06 2.53 136.67 363.64
Marginalization and World Trade Mozambique Uruguay Bolivia Jordan Seychelles New Caledonia Iceland TFYR Macedonia Guyana Nicaragua Honduras Guinea Liberia French Polynesia Namibia Mali Albania Ethiopia Bhutan Haiti Belize Lesotho Guam Cuba Barbados Guinea-Bissau Senegal Maldives Grenada Aruba Cayman Islands Congo, Dem. Rep. of Lao PDR St Kitts and Nevis Kiribati Dominica St Vincent and the Grenadines Samoa Cook Islands Djibouti Pacific Islands Cape Verde Uganda British Virgin Islands Montserrat Tuvalu Rwanda Chad Sao Tome and Principe Tonga Vanuatu Comoros St Pierre and Miquelon Bermuda Nauru Central African Republic Gambia Tanzania, United Rep. of Antigua and Barbuda
142 1712 849 1213 51 403 1538 668 336 289 816 675 334 144 1250 339 133 114 71 112 113 102 42 2377 193 19 717 45 23 43 16 608 169 26 4 52 67 6 4 18 18 5 218 3 2 0 75 170 6 13 22 19 28 53 43 131 46 428 38
6 55 27 90 1 24 8 n/a 51 5 3 11 5 29 59 6 23 n/a 2 20 5 21 0 929 7 4 8 2 1 3 1 145 56 2 1 2 8 1 0 2 0 0 69 0 0 0 17 8 1 1 2 2 8 7 7 8 1 26 2
39 38 26 26 26 24 23 23 23 21 20 20 20 20 17 15 12 12 12 11 10 10 8 7 7 6 6 5 5 5 4 3 2 2 2 2 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 4 4
27.64 2.21 3.06 2.11 50.20 5.96 1.52 3.44 6.85 7.28 2.50 2.96 5.99 13.63 1.34 4.54 9.02 10.51 16.43 9.63 9.19 9.78 18.10 0.29 3.42 32.98 0.78 11.95 23.08 12.09 24.05 0.43 1.47 8.46 50.00 3.08 1.49 16.13 23.81 3.26 2.22 7.69 0.09 6.67 0.00 0.00 0.27 0.12 7.14 4.55 2.75 4.12 3.55 1.87 3.72 1.22 3.90 0.89 10.53 (Continued )
247
The Implications of Declining Commodity Prices Appendix 7.15. (Continued )
Togo Afghanistan Niger Sierra Leone Bahamas Korea, DPR Somalia Suriname Benin Paraguay Mauritius American Samoa Ghana Burundi St Lucia Solomon Islands Malawi Burkina Faso Fiji Greenland Lebanon Madagascar Switzerland Jamaica Kenya Swaziland Mongolia Mauritania Greece Zimbabwe Croatia Netherlands Antilles Zambia Cyprus New Zealand Bulgaria Papua New Guinea
248
avg. exports 90–94
avg. change 90–94
avg. change 1995–2000
avg. change 1995–2000 as % of 90–94 avg. exports
219 173 288 133 1363 1265 122 399 347 779 1266 319 1033 82 117 105 384 172 488 349 503 314 64905 1114 1285 658 426 423 8823 1631 2554 1521 980 949 10325 3985 1885
27 20 14 6 5 254 4 6 28 36 38 2 132 8 5 18 22 23 16 36 6 22 1644 19 145 57 76 21 324 40 n/a 103 96 3 674 256 374
4 5 5 6 6 6 7 8 9 10 10 10 11 11 13 15 17 19 19 20 20 21 21 26 29 29 34 34 35 39 40 46 50 55 76 106 110
1.83 2.90 1.81 4.36 0.44 0.51 5.72 2.10 2.54 1.23 0.76 3.13 1.05 13.35 10.92 14.26 4.53 10.81 3.98 5.78 4.02 6.82 0.03 2.35 2.23 4.44 7.97 8.12 0.40 2.37 1.57 3.05 5.06 5.80 0.73 2.66 5.82
Marginalization and World Trade Appendix 7.16. Absolute Changes in Commercial Services Exports in the 1990s
World United States United Kingdom Spain Ireland China India Canada Greece BelgiumLuxembourg Hong Kong, China Korea, Rep. Israel Netherlands Germany Taipei, Chinese Denmark Turkey Sweden Japan Mexico Brazil Australia Malaysia Norway Cuba Croatia Egypt Saudi Arabia Dominican Republic United Arab Emirates Switzerland Hungary Ukraine Morocco Iran, Islamic Rep. of Argentina Bulgaria Costa Rica Kuwait Estonia Chile Lithuania Vietnam South Africa Belarus
avg. exports 90–94
avg. change 90–94
avg. change 1995–2000
avg. change 95–99 as % of avg. 90–94 exports
903144 157568.0 59235.0 30708.8 3669.4 9813.2 5094.4 20470.2 7918.4 28978.2
64086 12270.0 3429.3 1454.0 214.8 2651.5 355.5 1215.0 657.0 2675.5
53298 14700.0 7777.0 2656.2 2367.8 2343.2 2181.4 2172.4 1930.6 1777.8
5.90 9.33 13.13 8.65 64.53 23.88 42.82 10.61 24.38 6.13
24364.8 11607.2 5492.4 35099.2 55879.4 10362.0 13411.6 9309.6 13878.6 48171.4 8323.8 3940.8 11341.8 5685.6 12339.8 801.6 1388.0 6555.2 3207.0 1367.6
3253.5 1769.5 500.8 3002.0 1851.5 1544.5 211.8 710.3 17.5 3848.0 713.3 277.8 983.5 1357.8 134.3 134.0 714.3 720.0 79.0 162.8
1551.4 1355.4 1309.4 1191.6 1059.6 1058.6 1053.4 951.4 935.6 867.4 796.4 568.2 430.4 428.2 367.2 344.4 325.8 285.0 261.0 249.8
6.37 11.68 23.84 3.39 1.90 10.22 7.85 10.22 6.74 1.80 9.57 14.42 3.79 7.53 2.98 42.96 23.47 4.35 8.14 18.27
2480.8
128.0
241.6
9.74
20058.4 2811.6 2450.2 1813.0 548.4
916.5 92.3 133.5 1.5 16.8
232.2 216.4 190.8 166.6 164.8
1.16 7.70 7.79 9.19 30.05
2688.0 947.0 844.2 1059.0 279.6 2266.8 216.4 682.0 3245.2 189.4
233.8 105.0 145.0 33.8 82.3 244.5 35.8 275.3 66.3 22.8
156.6 139.6 139.0 130.0 125.4 118.8 114.0 111.0 103.4 103.2
5.83 14.74 16.47 12.28 44.85 5.24 52.68 16.28 3.19 54.49 (Continued )
249
The Implications of Declining Commodity Prices Appendix 7.16. (Continued )
Panama Latvia Bahamas Qatar Kazakhstan Peru Jamaica Aruba Iceland Ghana Colombia Albania Nigeria Algeria El Salvador Trinidad and Tobago Mauritius Oman Romania Zimbabwe Cyprus Barbados Tunisia Honduras Turkmenistan Myanmar Portugal Bahrain Nicaragua Cameroon TFYR Macedonia Botswana Sri Lanka Maldives Armenia Guatemala Ecuador Haiti Seychelles Madagascar Mozambique Uganda Gambia Ethiopia Namibia Congo Azerbaijan Malta Antigua and Barbuda Cambodia
250
avg. 90–94
avg. change 90–94
avg. change 1995–2000
avg. change 1995–2000 as % of avg. 90–94 exports
1039.6 401.0 1412.6 354.4 424.4 780.2 1129.8 522.8 475.0 110.4 1799.8 42.4 887.6 529.4 310.4 364.6
66.3 96.8 6.3 15.8 22.3 59.0 119.0 51.8 21.8 14.3 6.5 11.8 148.5 14.3 9.3 1.3
101.6 95.0 94.4 94.4 90.4 84.2 84.0 77.8 71.2 70.2 69.8 67.0 66.6 62.0 61.4 59.4
9.77 23.69 6.68 26.64 21.30 10.79 7.43 14.88 14.99 63.59 3.88 158.02 7.50 11.71 19.78 16.29
552.6 33.6 754.2 297.4 1997.6 652.0 1779.4 172.8 61.2 147.8 5830.6 556.8 60.0 364.8 72.2 177.2 579.8 142.8 11.0 519.8 571.8 31.4 177.4 146.6 157.2 42.8 65.4 247.0 167.2 54.6 130.4 846.2 342.6
37.8 13.8 103.3 25.0 159.5 39.5 149.8 21.5 3.0 40.8 411.8 115.0 14.8 15.0 32.0 2.0 76.0 23.5 0.5 86.5 33.0 9.0 6.0 13.5 22.0 16.0 5.8 1.3 34.8 4.0 7.3 63.5 20.8
58.8 54.0 52.8 49.8 43.0 42.2 40.2 38.2 32.6 31.8 31.2 29.4 29.2 28.2 26.8 23.4 23.0 23.0 22.8 21.4 21.2 21.2 21.0 19.0 16.6 16.4 15.6 15.4 14.6 14.6 13.6 12.2 11.6
10.64 160.71 7.00 16.75 2.15 6.47 2.26 22.11 53.27 21.52 0.54 5.28 48.67 7.73 37.12 13.21 3.97 16.11 207.27 4.12 3.71 67.52 11.84 12.96 10.56 38.32 23.85 6.23 8.73 26.74 10.43 1.44 3.39
48.0
0.8
11.2
23.33
Marginalization and World Trade St Vincent and the Grenadines Tanzania, United Rep. of Grenada St Lucia Lao People’s Dem. Rep. Vanuatu Cape Verde Angola Guyana Belize Mongolia Fiji Bolivia Gabon Rwanda Dominica Libyan Arab Jamahiriya Solomon Islands Yemen Guinea Comoros Kyrgyzstan Uruguay Sierra Leone Moldova, Rep. of Uzbekistan St Kitts and Nevis Malawi Mauritania Samoa Lesotho Equatorial Guinea Sao Tome and Principe Congo, Dem. Rep. Liberia Burkina Faso Guinea-Bissau Zambia Tajikistan Paraguay Kiribati Tonga Niger Afghanistan Yugoslavia Czech Rep. Czech and Slovak Fed. Rep., former Djibouti Mali Somalia Burundi
52.6
5.0
10.4
19.77
241.2
70.0
9.8
4.06
79.2 190.2 35.4
9.3 22.0 11.3
9.4 8.6 8.6
11.87 4.52 24.29
63.0 32.4 103.2 100.8 101.0 36.6 419.8 153.6 260.6 24.4 41.6 55.8
3.5 2.3 21.3 8.0 6.5 3.3 28.0 11.0 3.5 2.3 4.5 14.5
8.4 8.4 7.4 7.2 7.0 6.8 6.6 6.6 6.0 5.6 5.0 4.6
13.33 25.93 7.17 7.14 6.93 18.58 1.57 4.30 2.30 22.95 12.02 8.24
30.0 106.0 64.4 15.2 12.8 843.2 52.6 28.8 246.4 75.2 31.4 15.0 33.4 32.0 6.2
5.8 5.5 19.3 3.8 6.5 216.0 10.3 2.0 17.3 9.5 3.8 0.8 1.5 1.0 0.5
4.4 4.0 3.8 3.8 3.6 3.4 3.0 3.0 2.6 2.6 2.4 2.0 1.6 1.2 1.2
14.67 3.77 5.90 25.00 28.13 0.40 5.70 10.42 1.06 3.46 7.64 13.33 4.79 3.75 19.35
4.4
0.5
1.2
27.27
145.2
42.3
1.0
0.69
41.0 38.4 6.4 75.0 41.4 401.6 13.0 12.6 17.4 4.8 1767.8 1959.8 1864.2
2.5 1.0 0.5 4.5 2.8 1.8 2.3 0.8 3.5 1.0 1593.5 1280.0 640.8
0.8 0.8 0.8 0.6 0.6 0.4 0.4 0.4 0.2 0.2 0.0 0.0 0.0
1.95 2.08 12.50 0.80 1.45 0.10 3.08 3.17 1.15 4.17 0.00 0.00 0.00
33.6 60.8 0.0 7.0
0.5 5.0 0.0 0.3
0.0 0.0 0.0 0.4
0.00 0.00 0.00 5.71 (Continued )
251
The Implications of Declining Commodity Prices Appendix 7.16. (Continued ) avg. change 95–99 as avg. 90–94 avg. change 90–94 avg. change 1995–2000 % of avg. 90–94 exports Central African Republic Benin Chad Montserrat Togo Senegal Suriname Papua New Guinea Georgia ˆ te d’Ivoire Co Sudan Jordan Swaziland Bosnia and Herzegovina New Zealand Slovenia Pakistan Kenya Syrian Arab Republic Slovak Rep. Netherlands Antilles Nepal Bangladesh Poland Indonesia Brunei Darussalam Venezuela Macau, China Thailand Russian Fed. Finland Austria France Singapore Philippines Italy
252
16.6
0.0
0.6
3.61
117.4 18.8 21.2 80.2 313.0 43.0 267.2
1.3 0.0 2.3 15.5 11.8 9.8 9.3
0.8 1.6 1.8 2.4 3.0 3.2 8.2
0.68 8.51 8.49 2.99 0.96 7.44 3.07
203.6 445.2 77.8 1460.2 95.6 306.8
12.3 1.5 22.5 28.3 2.0 132.5
11.0 11.4 11.6 13.2 15.6 21.4
5.40 2.56 14.91 0.90 16.32 6.98
2781.4 883.4 1310.4 767.4 1138.0
296.0 451.8 56.5 10.3 219.0
26.0 27.0 29.6 30.0 30.2
0.93 3.06 2.26 3.91 2.65
832.0 1265.6
555.3 63.0
32.0 34.2
3.85 2.70
277.6 377.4 4503.2 3451.8 409.2
90.3 30.8 863.8 548.0 40.8
36.4 37.2 49.4 56.4 77.6
13.11 9.86 1.10 1.63 18.96
1231.8 2137.8 8977.6 7460.0 4577.8 25717.0 71874.8 16797.4 4428.2 51414.4
83.3 312.5 1283.3 447.8 213.3 1184.8 2103.5 2555.0 963.0 1147.8
92.4 113.0 173.4 187.2 266.4 329.8 391.0 519.2 1038.0 1123.0
7.50 5.29 1.93 2.51 5.82 1.28 0.54 3.09 23.44 2.18
Marginalization and World Trade Appendix 7.17. Absolute Changes in Total Exports (Merchandise plus Commercial Services) in the 1990s Countries World United States China Canada Mexico United Kingdom Korea, Rep. Taipei, Chinese Ireland Hong Kong, China Japan Spain Germany Saudi Arabia Malaysia India Russian Fed. BelgiumLuxembourg United Arab Emirates Norway Netherlands Singapore Israel Indonesia France Philippines Turkey Greece Hungary Venezuela Australia Sweden Thailand Brazil Iran, Islamic Rep. of Poland Vietnam Czech Rep. Kuwait Qatar Argentina Denmark Oman Austria Costa Rica Kazakhstan Algeria
avg. exports avg. change avg. exports avg. change avg. change 1995–2000 as 90–94 90–94 1995–2000 1995–2000 % of avg. 90–94 4637277 599514 81458 162231 56796 248470
269602 41303 15412 10381 5756 9525
6883661 902478 205487 259261 129147 366896
297624 54262 26547 20387 18179 16234
6.42 9.05 32.59 12.57 32.01 6.53
89098 90704 31165 141732 380450 94228 467619 48707 46781 25882 67284 141801
9596 7933 2790 20566 29696 6021 6736 371 8381 2165 3731 5827
166030 138230 72391 220417 477397 154249 611378 60678 93206 46729 97934 193571
11619 8329 8170 7339 7027 6985 6176 5770 5726 4560 4343 4130
13.04 9.18 26.21 5.18 1.85 7.41 1.32 11.85 12.24 17.62 6.46 2.91
24808
1279
35990
4101
16.53
46595 168655 88468 19645 36865 286955 14770 24427 13897 11752 16805 54307 69136 41781 40451 19618
42 5954 13365 1626 3902 7573 2287 2051 377 284 296 2916 824 6700 3451 49
61882 246131 149159 33523 58898 372970 36330 47401 19207 24845 24293 76194 102476 72061 56755 21272
3917 3698 3576 3544 3534 2990 2932 2889 2787 2717 2698 2595 2441 2327 2284 2162
8.41 2.19 4.04 18.04 9.59 1.04 19.85 11.83 20.06 23.12 16.06 4.78 3.53 5.57 5.65 11.02
19722 3339 7999 8292 3543 15893 51821 5402 67863 2560 5805 11740
1493 856 5271 1108 157 1151 1629 5 2375 337 227 933
40087 11953 31577 15338 6025 28812 65942 7754 91861 6557 7497 13255
2123 1961 1508 1479 1287 1206 1124 1105 1068 1055 925 773
10.76 58.72 18.86 17.83 36.32 7.59 2.17 20.45 1.57 41.20 15.94 6.58 (Continued )
253
The Implications of Declining Commodity Prices Appendix 7.17. (Continued ) Countries Egypt Finland Colombia Dominican Republic Slovak Rep. Nigeria Chile Romania Belarus Bangladesh Ukraine Estonia Angola Cuba Lithuania South Africa Peru Sri Lanka Bahrain El Salvador Aruba Croatia Yemen Congo Morocco Azerbaijan Equatorial Guinea Sudan Latvia Syrian Arab Republic Guatemala Bahamas Botswana Portugal Honduras Myanmar Ghana Ecuador Trinidad and Tobago Zimbabwe Malta Tunisia Cambodia Cameroon Gabon Iceland Madagascar Bosnia and Herzegovina Haiti Albania
254
avg. exports avg. change avg. exports avg. change avg. change 1995–2000 as 90–94 90–94 1995–2000 1995–2000 % of avg. 90–94 10425 30017 9467 3092
750 1051 489 842
14151 48928 13718 7163
763 763 675 641
7.32 2.54 7.13 20.74
3264 12287 11892 5843 2212 2565 14705 949 3520 3334 2186 27986 4481 3010 4173 1133 1434 3807 1429 1205 6775 583 56
2232 1180 1052 199 201 346 806 284 196 887 81 566 378 415 79 161 337 1779 109 112 330 32 6
12140 14013 19852 10066 6867 5223 18665 3662 4856 3986 4498 35022 7607 5419 5100 2799 2502 8080 2485 1734 9623 1175 544
629 568 546 544 529 496 486 445 403 397 383 376 372 351 347 326 325 313 312 303 276 263 240
19.27 4.62 4.59 9.31 23.91 19.34 3.31 46.86 11.46 11.92 17.51 1.34 8.30 11.67 8.31 28.74 22.66 8.21 21.86 25.12 4.08 45.09 430.47
412 1271 4593
27 171 12
870 2806 5504
239 233 227
57.86 18.34 4.95
1847 1637 1978 22985 1108 646 1147 3721 2105
171 15 18 959 73 199 100 336 47
3230 2030 2732 33416 2126 1616 2049 5485 2979
206 187 182 176 171 169 164 155 146
11.16 11.45 9.21 0.77 15.39 26.17 14.26 4.17 6.94
2005 2279 5708 353 2160 2594 2001 500 589
78 169 432 63 183 35 15 47 254
3026 3052 8235 1037 2306 2992 2661 844 1287
126 118 114 106 105 97 91 82 80
6.30 5.16 2.00 29.88 4.84 3.74 4.53 16.49 13.65
161 186
61 34
386 397
80 77
49.94 41.51
Marginalization and World Trade Brunei Darussalam TFYR Macedonia Nicaragua Slovenia Mauritius Mozambique Guyana Panama Nepal Barbados Uruguay Pakistan Mali Namibia Bolivia Seychelles Guinea Jamaica Bulgaria Netherlands Antilles Afghanistan Armenia Fiji Ethiopia Maldives Lao People’s Dem. Rep. Senegal Mongolia Georgia Kyrgyzstan Jordan ˆ te d’Ivoire Co Grenada Tajikistan Gambia Belize Uganda Lesotho Solomon Islands Antigua and Barbuda Togo Vanuatu Benin St Vincent and the Grenadines Rwanda Guinea-Bissau Tanzania, United Rep. of St Kitts and Nevis Cape Verde Uzbekistan
2747
40
3021
70
2.56
584
263
1386
70
11.95
346 4803 1860 299 496 5860 605 851 2593 7838 416 1405 934 229 655 2361 5241 1584
17 2160 73 28 35 741 128 32 272 485 5 93 50 5 58 208 440 75
748 10604 2571 533 727 7580 1066 1230 3812 9625 607 1746 1331 366 710 3396 6326 1947
66 65 56 56 54 51 51 50 50 47 45 45 44 43 37 36 36 35
18.97 1.35 3.02 18.60 10.90 0.87 8.40 5.92 1.92 0.60 10.81 3.19 4.76 18.62 5.68 1.52 0.68 2.18
361 174 818 487 212 208
45 19 47 21 23 68
524 362 1096 836 399 431
32 31 30 28 28 27
8.86 18.00 3.69 5.75 13.10 12.79
1154 413 412 343 2677 3241
42 23 24 14 118 6
1349 545 591 561 3561 4792
26 24 24 24 23 22
2.22 5.91 5.93 6.99 0.85 0.68
105 350 202 238 276 134 135 397
8 101 9 13 87 20 24 24
167 797 217 320 719 230 216 437
21 20 20 16 15 11 10 9
20.46 5.61 9.70 6.90 5.44 8.48 7.25 2.37
488 81 467 120
62 6 42 4
483 126 582 156
9 9 8 8
1.80 10.89 1.80 7.00
98 25 670
20 3 98
96 47 1229
8 8 6
8.16 30.16 0.96
105
10
138
6
5.88
46 2962
0 167
99 3910
6 5
12.07 0.18 (Continued )
255
The Implications of Declining Commodity Prices Appendix 7.17. (Continued ) Countries Dominica Sierra Leone Comoros Samoa Congo, Dem. Rep. Zambia Tonga Central African Republic Liberia Burkina Faso Kiribati Sao Tome and Principe Niger Czech and Slovak Fed. Rep., former Djibouti Somalia Chad St Lucia Montserrat Mauritania Suriname Malawi Cyprus Burundi Swaziland New Zealand Moldova, Rep. of Kenya Papua New Guinea Turkmenistan Macau, China Italy Libyan Arab Jamahiriya Paraguay Switzerland
256
avg. exports avg. change avg. exports avg. change avg. change 1995–2000 as 90–94 90–94 1995–2000 1995–2000 % of avg. 90–94 94 189 34 40 1669 1276 27 151
3 2 2 0 310 17 3 1
133 115 44 71 1634 1469 36 163
5 3 3 3 2 2 2 1
5.30 1.59 8.72 6.57 0.14 0.14 6.67 0.93
530 284 18 8
17 15 3 1
618 268 26 13
1 1 1 1
0.23 0.35 4.55 9.52
360 8603
69 3550
280 0
0 0
0.11 0.00
89 66 197 310 23 433 573 424 2943 87 747 13006 574 2004 2239
3 2 24 14 3 10 125 15 164 2 62 1043 32 122 378
58 77 270 369 28 454 469 506 3873 71 1000 17636 815 2670 2557
0 0 2 3 4 5 7 10 13 13 27 40 49 60 85
0.00 0.00 0.91 1.10 17.39 1.20 1.15 2.27 0.43 15.24 3.61 0.31 8.61 3.00 3.80
1255 3895 227114 9860
69 354 6427 761
1279 5036 302716 8208
100 108 175 308
7.99 2.78 0.08 3.13
2863 98100
318 2065
3908 119927
369 537
12.88 0.55
Marginalization and World Trade Appendix 7.18. Average Merchandise Export Share of Individual Countries Average Share Millions of US Dollars
per cent of world exports
avg. 1985–89 avg. 1990–94 avg. 1995–2000 1985–89 World Canada United States Greenland Bermuda St Pierre and Miquelon Antigua and Barbuda Argentina Aruba Bahamas Barbados Belize Bolivia Brazil British Virgin Islands Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Mexico Montserrat Netherlands Antilles Nicaragua Panama Paraguay Peru St Kitts and Nevis St Lucia St Vincent and the Grenadines Suriname Trinidad and Tobago Uruguay Venezuela Austria Belgium
2511416 103677 277267 317 40 26 18 8064 21 777 228 92 651 28418 3 11 5670 4810 1183 5711 44 1648 2310 626 29 1044 224 178 825 732 28405 3 1300 273 333 482 2872 26 89 68
3753796 139956 448177 349 53 28 38 13069 43 1363 193 113 849 36196 3 16 9624 7290 2076 2377 52 2823 3059 849 23 1304 336 112 816 1114 48473 2 1521 289 467 779 3622 26 117 67
5563180 222943 677504 304 55 6 16 24537 41 962 265 162 1132 50243 5 26 16116 11284 4901 1678 53 4718 4675 2293 32 2368 532 143 1355 1340 117708 3 1350 608 740 946 6202 26 76 47
384 1600 1238 11446 25970 -
399 1910 1712 15398 42380 -
489 2825 2422 22054 61717 60736
1990–94 1995–2000
100.00000 100.00000 100.00000 4.12822 3.72839 4.00748 11.04026 11.93930 12.17837 0.01263 0.00930 0.00547 0.00158 0.00142 0.00098 0.00104 0.00075 0.00010 0.00071 0.00101 0.00028 0.32111 0.34814 0.44105 0.00083 0.00115 0.00074 0.03095 0.03632 0.01729 0.00908 0.00515 0.00477 0.00366 0.00302 0.00291 0.02591 0.02262 0.02034 1.13155 0.96426 0.90314 0.00011 0.00008 0.00010 0.00042 0.00042 0.00046 0.22578 0.25639 0.28969 0.19153 0.19420 0.20283 0.04711 0.05531 0.08809 0.22739 0.06332 0.03016 0.00174 0.00139 0.00096 0.06564 0.07520 0.08481 0.09199 0.08150 0.08404 0.02494 0.02263 0.04122 0.00117 0.00062 0.00057 0.04155 0.03474 0.04256 0.00891 0.00895 0.00957 0.00708 0.00299 0.00256 0.03286 0.02175 0.02435 0.02914 0.02968 0.02409 1.13102 1.29130 2.11584 0.00010 0.00006 0.00006 0.05175 0.04052 0.02426 0.01088 0.00769 0.01093 0.01327 0.01245 0.01329 0.01918 0.02075 0.01701 0.11436 0.09649 0.11148 0.00104 0.00069 0.00046 0.00356 0.00312 0.00136 0.00270 0.00178 0.00084 0.01529 0.06370 0.04929 0.45575 1.03406 0.00000
0.01064 0.05088 0.04560 0.41021 1.12898 0.00000
0.00878 0.05077 0.04353 0.39643 1.10939 1.09176 (Continued )
257
The Implications of Declining Commodity Prices Appendix 7.18. (Continued ) Average Share Millions of US Dollars
per cent of world exports
avg. 1985–89 avg. 1990–94 avg. 1995–2000 1985–89 1990–94 1995–2000 BelgiumLuxembourg Bosnia and Herzegovina Croatia Denmark Finland France Germany Greece Iceland Ireland Malta Netherlands Norway Portugal Slovenia Spain Sweden Switzerland Turkey United Kingdom Albania Armenia Azerbaijan Belarus Bulgaria Czech Rep. Estonia Georgia Hungary Kazakhstan Kyrgyzstan Latvia Lithuania Moldova, Rep. of Poland Romania Russian Fed. Slovak Rep. Tajikistan Turkmenistan Ukraine USSR, former Uzbekistan Algeria Angola Benin Botswana Burkina Faso
258
79560
123835
114727
92
596
— 24427 19011 144438 277236 5939 1219 15681 612 90631 21813 9207 — 34096 42705 42526 9778 127422
2554 39284 25318 223025 412232 8823 1538 27894 1366 140555 33975 16872 3918 62843 56005 64905 14944 189361
— — — — 15362 — — — 9378 — — — — — 12640 10461 — — — — — 102192 — 9256 2255 218 1300 109
133 11 75 193 3985 8515 147 8 10099 265 23 97 172 30 14800 4980 27360 3701 76 96 869 21640 189 11148 3574 347 1807 172
—
3.16795 3.29892
2.06225
—
0.00246
0.01071
4432 50002 42171 303474 532984 11055 1921 59930 1904 207326 47533 24135 8564 104450 84052 79887 25410 269215
— 0.97263 0.75699 5.75124 11.03905 0.23647 0.04853 0.62439 0.02437 3.60877 0.86856 0.36661 0.00000 1.35762 1.70043 1.69332 0.38936 5.07373
0.06804 1.04651 0.67447 5.94133 10.98174 0.23505 0.04098 0.74309 0.03640 3.74435 0.90509 0.44948 0.10439 1.67413 1.49195 1.72904 0.39809 5.04452
0.07966 0.89880 0.75804 5.45505 9.58057 0.19871 0.03453 1.07727 0.03422 3.72675 0.85443 0.43383 0.15394 1.87753 1.51086 1.43600 0.45675 4.83923
214 230 838 5789 4714 24492 2501 209 20630 5794 453 1535 3175 582 26728 8596 82725 9979 675 1378 12228 0 3240 13525 5055 431 2452 289
0.00000 0.00000 0.00000 0.00000 0.61169 0.00000 0.00000 0.00000 0.37341 0.00000 0.00000 0.00000 0.00000 0.00000 0.50330 0.41654 0.00000 0.00000 0.00000 0.00000 0.00000 4.06910 0.00000 0.36854 0.08980 0.00869 0.05176 0.00435
0.00354 0.00029 0.00200 0.00514 0.10616 0.22684 0.00392 0.00021 0.26903 0.00706 0.00061 0.00258 0.00458 0.00080 0.39427 0.13267 0.72886 0.09859 0.00202 0.00256 0.02315 0.57648 0.00503 0.29698 0.09520 0.00924 0.04813 0.00458
0.00385 0.00413 0.01507 0.10406 0.08474 0.44025 0.04495 0.00376 0.37083 0.10415 0.00815 0.02759 0.05707 0.01047 0.48045 0.15451 1.48701 0.17938 0.01213 0.02476 0.21979 0.00000 0.05824 0.24311 0.09087 0.00774 0.04408 0.00519
Marginalization and World Trade Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Congo, Dem. Rep. of ˆ te d’Ivoire Co Djibouti Egypt Equatorial Guinea Ethiopia Ethiopia, former Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Libyan Arab Jamahiriya Madagascar Malawi Mali Mauritania Mauritius Morocco Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Sudan Swaziland Tanzania, United Rep. of Togo Tunisia Uganda Zambia Zimbabwe Bahrain Cyprus Iran, Islamic Rep. of Iraq Israel Jordan Kuwait Lebanon Oman
114 984 6 110 114 17 900 1080 3047 22 2155 37 0 407 1460 41 897 534 14 1032 45 416 8750 302 266 194 391 792 2871 92 975 284 8389 126 6 615 27 130 95 19992
82 1809 5 131 170 19 1044 608 2798 18 3175 70 114 131 2235 46 1033 675 19 1285 102 334 10473 314 384 339 423 1266 4118 142 1250 288 11429 75 6 717 51 133 122 24062
67 1732 12 167 218 8 1703 516 4247 22 3715 488 486 0 2802 16 1719 749 40 1915 187 480 9427 279 460 514 417 1604 6763 241 1335 287 14609 63 5 975 119 22 131 28527
478 370 307
388 658 428
825 914 665
225 2192 337 940 1405 2554 598 10236 11872 8538 916 9054 553 3758
219 3941 218 980 1631 3613 949 16250 2874 13766 1213 7260 503 5330
346 5670 514 883 2165 4385 1146 19535 7663 23725 1825 14038 756 7439
0.00452 0.03917 0.00022 0.00440 0.00453 0.00069 0.03582 0.04301 0.12133 0.00088 0.08580 0.00146 0.00000 0.01622 0.05815 0.00162 0.03571 0.02128 0.00055 0.04109 0.00180 0.01655 0.34840 0.01203 0.01058 0.00774 0.01555 0.03155 0.11432 0.00367 0.03881 0.01132 0.33402 0.00502 0.00023 0.02450 0.00107 0.00516 0.00377 0.79604
0.00220 0.04819 0.00014 0.00348 0.00454 0.00052 0.02781 0.01620 0.07453 0.00049 0.08459 0.00186 0.00304 0.00350 0.05953 0.00123 0.02752 0.01798 0.00050 0.03422 0.00272 0.00890 0.27899 0.00835 0.01023 0.00904 0.01128 0.03374 0.10969 0.00378 0.03329 0.00767 0.30445 0.00200 0.00015 0.01909 0.00136 0.00354 0.00326 0.64101
0.00120 0.03113 0.00021 0.00300 0.00392 0.00014 0.03061 0.00928 0.07634 0.00039 0.06677 0.00878 0.00874 0.00000 0.05037 0.00028 0.03090 0.01346 0.00072 0.03442 0.00335 0.00863 0.16946 0.00501 0.00827 0.00925 0.00750 0.02882 0.12157 0.00434 0.02400 0.00515 0.26259 0.00112 0.00008 0.01753 0.00213 0.00040 0.00235 0.51278
0.01903 0.01033 0.01475 0.01752 0.01224 0.01141
0.01484 0.01643 0.01195
0.00896 0.08729 0.01343 0.03743 0.05594 0.10169 0.02382 0.40758 0.47273 0.33998 0.03646 0.36052 0.02204 0.14964
0.00621 0.10193 0.00924 0.01588 0.03891 0.07881 0.02061 0.35115 0.13775 0.42646 0.03281 0.25233 0.01358 0.13372
0.00583 0.10498 0.00582 0.02611 0.04344 0.09625 0.02527 0.43290 0.07656 0.36672 0.03231 0.19339 0.01340 0.14199
(Continued )
259
The Implications of Declining Commodity Prices Appendix 7.18. (Continued ) Average Share Millions of US Dollars
per cent of world exports
avg. 1985–89 avg. 1990–94 avg. 1995–2000 1985–89 1990–94 1995–2000 Qatar Saudi Arabia Syrian Arab Republic United Arab Emirates Yemen Afghanistan American Samoa Australia Bangladesh Bhutan Brunei Darussalam Cambodia China Cook Islands Fiji French Polynesia Guam Hong Kong, China India Indonesia Japan Kiribati Korea, Dem. People’s Rep. of Korea, Rep. of Lao PDR Macau, China Malaysia Maldives Mongolia Myanmar Nauru Nepal New Caledonia New Zealand Pacific Islands Pakistan Papua New Guinea Philippines Samoa Singapore Solomon Islands Sri Lanka Taipei, Chinese Thailand Tonga Tuvalu Vanuatu Vietnam
260
2430 24725 1726 13370 526 452 284 28430 1124 51 2052 28 39594 4 390 66 62 50340 11802 18685 231599 4 1544
3480 45501 3385 20696 692 173 319 42938 1985 71 2338 214 86338 4 488 144 42 117497 20355 33132 340300 4 1265
6251 56445 3744 30398 2523 142 315 58694 4646 113 2563 604 185124 6 598 254 49 182489 35038 51385 426920 8 708
0.09675 0.98450 0.06873 0.53237 0.02094 0.01801 0.01130 1.13205 0.04477 0.00204 0.08172 0.00111 1.57654 0.00018 0.01551 0.00261 0.00248 2.00444 0.46992 0.74400 9.22185 0.00014 0.06148
0.09270 1.21212 0.09019 0.55134 0.01845 0.00460 0.00850 1.14386 0.05287 0.00188 0.06228 0.00570 2.30002 0.00011 0.01299 0.00383 0.00112 3.13009 0.54226 0.88263 9.06550 0.00010 0.03371
0.11236 1.01461 0.06731 0.54642 0.04535 0.00255 0.00566 1.05504 0.08352 0.00203 0.04608 0.01086 3.32766 0.00010 0.01074 0.00457 0.00088 3.28029 0.62981 0.92366 7.67402 0.00014 0.01272
47070
78353
139867
1.87425 2.08731
2.51416
59 1290 18659 33 716 234 66 160 365 7291 18 3905 1182 5963 13 31593 71 1380 50082 12736 7 — 21 1065
169 1744 42089 45 426 532 43 320 403 10325 18 6731 1885 10196 6 69220 105 2509 80441 33201 13 — 22 2819
333 2214 81146 66 372 1046 37 502 494 13328 19 8751 2186 28094 14 121775 145 4558 121451 58583 11 — 30 9540
0.00237 0.05135 0.74298 0.00131 0.02853 0.00933 0.00264 0.00638 0.01454 0.29031 0.00071 0.15551 0.04706 0.23743 0.00053 1.25798 0.00284 0.05495 1.99416 0.50711 0.00026 — 0.00084 0.04241
0.00598 0.03980 1.45863 0.00118 0.00668 0.01879 0.00067 0.00902 0.00887 0.23957 0.00034 0.15729 0.03929 0.50500 0.00025 2.18894 0.00261 0.08192 2.18313 1.05305 0.00019 — 0.00053 0.17148
0.00451 0.04646 1.12123 0.00120 0.01136 0.01418 0.00115 0.00853 0.01073 0.27504 0.00048 0.17932 0.05021 0.27162 0.00017 1.84399 0.00280 0.06683 2.14293 0.88447 0.00035 — 0.00058 0.07510
Marginalization and World Trade Appendix 7.19. Average Share of Individual Countries in Exports of Commercial Services Exports of Commercial Services ($ Mill.)
Share (per cent)
avg. 85–89 avg. 1990–94 avg. 1995–2000 1985–89 1990–94 1995–2000 World Australia Austria Belgium-Luxembourg Canada Denmark Finland France Germany Greece Iceland Ireland Israel Italy Japan Netherlands New Zealand Norway Portugal South Africa Spain Sweden Switzerland United Kingdom United States Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina Armenia Aruba Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belize Benin Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic
523406 6223 14298 15521 12949 7668 3185 47476 32646 3973 362 1879 3676 26225 29894 19091 2016 8746 2893 2183 20069 8500 13487 39318 86477 4 25 496 123 203 1768 7 221 78 1251 834 221 519 109 39 65 113 0 74 2180 216 1114 21 3 27 433 22 16
903144 11342 25717 28978 20470 13412 4578 71875 55879 7918 475 3669 5492 51414 48171 35099 2781 12340 5831 3245 30709 13879 20058 59235 157568 5 42 529 103 343 2688 11 523 130 1413 557 377 652 189 101 117 154 307 177 3941 409 947 38 7 48 365 32 17
1327809 17106 30744 36730 31239 16338 6676 82111 79600 12288 764 10818 9676 62115 64816 49504 4224 13986 8277 4884 47110 17815 25490 98503 234893 7 172 892 151 396 4181 98 840 238 1659 715 293 954 823 130 137 210 597 251 6458 410 1626 43 3 130 347 78 12
100 100 100 1.1890 1.2558 1.2883 2.7318 2.8475 2.3154 2.9653 3.2086 2.7662 2.4739 2.2665 2.3526 1.4651 1.4850 1.2305 0.6085 0.5069 0.5028 9.0705 7.9583 6.1839 6.2372 6.1872 5.9948 0.7590 0.8768 0.9254 0.0692 0.0526 0.0575 0.3589 0.4063 0.8147 0.7023 0.6081 0.7287 5.0105 5.6928 4.6780 5.7114 5.3337 4.8814 3.6475 3.8863 3.7282 0.3852 0.3080 0.3181 1.6709 1.3663 1.0533 0.5527 0.6456 0.6234 0.4172 0.3593 0.3678 3.8342 3.4002 3.5480 1.6241 1.5367 1.3417 2.5768 2.2210 1.9197 7.5120 6.5588 7.4184 16.5219 17.4466 17.6903 0.0008 0.0005 0.0005 0.0047 0.0047 0.0130 0.0948 0.0586 0.0671 0.0235 0.0114 0.0114 0.0387 0.0379 0.0298 0.3377 0.2976 0.3148 0.0013 0.0012 0.0074 0.0421 0.0579 0.0633 0.0150 0.0144 0.0179 0.2390 0.1564 0.1250 0.1593 0.0617 0.0538 0.0423 0.0418 0.0221 0.0992 0.0722 0.0719 0.0209 0.0210 0.0620 0.0075 0.0112 0.0098 0.0124 0.0130 0.0103 0.0216 0.0170 0.0158 0.0000 0.0340 0.0449 0.0142 0.0196 0.0189 0.4164 0.4363 0.4864 0.0412 0.0453 0.0309 0.2129 0.1049 0.1225 0.0040 0.0043 0.0033 0.0006 0.0008 0.0002 0.0051 0.0053 0.0098 0.0827 0.0404 0.0261 0.0042 0.0036 0.0059 0.0031 0.0018 0.0009 (Continued )
261
The Implications of Declining Commodity Prices Appendix 7.19. (Continued ) Exports of Commercial Services ($ Mill.)
Share (per cent)
avg. 85–89 avg. 1990–94 avg. 1995–2000 1985–89 1990–94 1995–2000 Chad Chile China Colombia Congo Congo, Dem. Rep. Costa Rica ˆ te d’Ivoire Co Croatia Cuba Cyprus Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Fiji Gabon Gambia Georgia Ghana Guinea Guinea-Bissau Hong Kong, China Hungary India Indonesia Iran, Islamic Rep. of Jordan Kazakhstan Kenya Kiribati Korea, Rep. of Kuwait Kyrgyzstan Lao People’s Dem. Rep. Latvia Lesotho Liberia Libyan Arab Jamahiriya Lithuania Macau, China Madagascar Malawi Malaysia Maldives Mali Malta Mauritania Mauritius
262
19 1036 3989 1132 68 191 348 394 0 448 999 21 17 830 415 3458 246 5 124 222 228 151 39 119 60 48 2 11761 921 3497 1199 254 1224 263 528 5 6230 934 4 11 184 21 31 79 124 786 81 29 2193 69 57 484 20 258
19 2267 9813 1800 55 145 844 445 1388 802 1998 34 42 1368 572 6555 310 6 280 247 420 261 65 204 110 64 6 24365 2812 5094 3452 548 1460 424 767 13 11607 1059 13 35 401 32 41 56 216 2138 147 31 5686 143 61 846 15 553
30 3736 23949 1925 90 133 1267 468 3588 2324 2726 27 79 2446 679 8872 492 7 1290 346 540 211 92 250 301 48 5 37782 5736 10935 5405 1001 1726 813 735 19 24954 1417 48 92 1030 44 46 32 920 2950 264 35 13125 307 65 1101 25 938
0.0036 0.1979 0.7620 0.2164 0.0130 0.0365 0.0666 0.0754 0.0000 0.0856 0.1908 0.0040 0.0032 0.1586 0.0794 0.6606 0.0469 0.0010 0.0236 0.0424 0.0436 0.0289 0.0075 0.0227 0.0114 0.0091 0.0003 2.2470 0.1759 0.6682 0.2292 0.0485 0.2338 0.0502 0.1008 0.0010 1.1902 0.1785 0.0008 0.0020 0.0352 0.0040 0.0059 0.0151 0.0237 0.1501 0.0154 0.0055 0.4189 0.0132 0.0109 0.0925 0.0039 0.0493
0.0021 0.2510 1.0866 0.1993 0.0060 0.0161 0.0935 0.0493 0.1537 0.0888 0.2212 0.0037 0.0046 0.1514 0.0633 0.7258 0.0344 0.0007 0.0310 0.0273 0.0465 0.0289 0.0072 0.0225 0.0122 0.0071 0.0007 2.6978 0.3113 0.5641 0.3822 0.0607 0.1617 0.0470 0.0850 0.0014 1.2852 0.1173 0.0014 0.0039 0.0444 0.0035 0.0045 0.0062 0.0240 0.2367 0.0162 0.0035 0.6295 0.0158 0.0067 0.0937 0.0017 0.0612
0.0022 0.2814 1.8036 0.1450 0.0068 0.0100 0.0954 0.0353 0.2702 0.1750 0.2053 0.0020 0.0059 0.1842 0.0511 0.6682 0.0371 0.0005 0.0971 0.0260 0.0407 0.0159 0.0069 0.0188 0.0227 0.0036 0.0004 2.8454 0.4320 0.8235 0.4070 0.0754 0.1300 0.0612 0.0553 0.0014 1.8793 0.1067 0.0036 0.0069 0.0776 0.0033 0.0035 0.0024 0.0693 0.2221 0.0199 0.0027 0.9885 0.0231 0.0049 0.0829 0.0019 0.0706
Marginalization and World Trade Moldova, Rep. of Mongolia Morocco Mozambique Myanmar Namibia Nepal Niger Nigeria Oman Pakistan Papua New Guinea Paraguay Peru Philippines Poland Qatar Romania Russian Fed. Rwanda Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovak Rep. Slovenia Solomon Islands Somalia Sri Lanka St Kitts and Nevis St Lucia St Vincent and the Grenadines Sudan Suriname Swaziland Syrian Arab Republic Taipei, Chinese Tajikistan Tanzania, United Rep. of TFYR Macedonia Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Uganda Ukraine United Arab Emirates Uruguay Uzbekistan Vanuatu Venezuela Vietnam Yemen Yugoslavia Zambia Zimbabwe
17 75 1284 78 60 68 138 24 341 22 795 87 232 692 2111 2402 248 776 4481 32 19 2 2697 250 128 17 6411 0 0 15 2 295 38 100 31 183 64 49 528 4600 25 109 0 3374 84 11 246 1202 4293 39 7 1524 1367 388 143 34 821 119 0 4363 53 194
29 37 1813 157 148 167 278 17 888 34 1310 267 402 780 4428 4503 354 754 7460 24 33 4 3207 313 177 53 16797 832 883 30 0 580 75 190 53 78 43 96 1138 10362 41 241 72 8978 80 13 365 1779 9310 61 43 2450 2481 843 246 63 1232 682 106 1768 75 297
141 62 2478 280 481 341 561 12 822 225 1379 333 584 1404 8960 9794 574 1467 11501 28 57 9 4234 346 254 76 26622 2161 1998 51 0 856 92 293 103 45 85 107 1472 17020 57 569 177 14729 69 15 513 2571 17524 189 160 4029 3459 1330 332 95 1306 2455 149 0 77 635
0.0032 0.0143 0.2454 0.0148 0.0114 0.0130 0.0263 0.0046 0.0652 0.0043 0.1520 0.0167 0.0443 0.1321 0.4033 0.4588 0.0475 0.1483 0.8560 0.0062 0.0036 0.0003 0.5153 0.0477 0.0245 0.0033 1.2249 0.0000 0.0000 0.0029 0.0003 0.0563 0.0073 0.0191 0.0059 0.0349 0.0122 0.0094 0.1010 0.8789 0.0047 0.0209 0.0000 0.6445 0.0160 0.0022 0.0469 0.2297 0.8201 0.0074 0.0013 0.2911 0.2611 0.0741 0.0273 0.0065 0.1569 0.0228 0.0000 0.8337 0.0101 0.0371
0.0032 0.0041 0.2007 0.0174 0.0164 0.0185 0.0307 0.0019 0.0983 0.0037 0.1451 0.0296 0.0445 0.0864 0.4903 0.4986 0.0392 0.0835 0.8260 0.0027 0.0037 0.0005 0.3551 0.0347 0.0196 0.0058 1.8599 0.0921 0.0978 0.0033 0.0000 0.0642 0.0083 0.0211 0.0058 0.0086 0.0048 0.0106 0.1260 1.1473 0.0046 0.0267 0.0080 0.9940 0.0089 0.0014 0.0404 0.1970 1.0308 0.0068 0.0047 0.2713 0.2747 0.0934 0.0273 0.0070 0.1364 0.0755 0.0117 0.1957 0.0083 0.0329
0.0106 0.0046 0.1866 0.0211 0.0362 0.0257 0.0422 0.0009 0.0619 0.0169 0.1039 0.0251 0.0440 0.1058 0.6748 0.7376 0.0432 0.1105 0.8661 0.0021 0.0043 0.0007 0.3189 0.0260 0.0191 0.0057 2.0050 0.1628 0.1505 0.0038 0.0000 0.0645 0.0069 0.0221 0.0078 0.0034 0.0064 0.0080 0.1109 1.2818 0.0043 0.0428 0.0133 1.1093 0.0052 0.0011 0.0386 0.1936 1.3198 0.0142 0.0120 0.3034 0.2605 0.1002 0.0250 0.0072 0.0983 0.1849 0.0112 0.0000 0.0058 0.0478
263
The Implications of Declining Commodity Prices Lao PDR
LDCs: Net Shifts in 1995–2000 in comparison with 1980–85
700
Myanmar
300 200
Tanzania
Guinea
Senegal Togo Congo, Dem. Rep.of
Central African Rep.
Ethiopia Mauritania Malawi
Madagascar
Sudan
Somalia Zambia Liberia
Burundi Haiti Niger
−400
Rwanda
−300
Afghanistan Sierra Leone
−200
Mozambique Uganda Angola
0
−100
Chad Guinea-Bissau Mali
100
Bangladesh Bhutan
400
Nepal
500
Burkina Faso Yemen, Republic of Benin
600
Appendix 7.20. Average net shift in exports of individual LDCs in 1995–2000 in comparison with 1980–85
Maldives
Mauritius
Dominica
Malta
Botswana
Lesotho
Cyprus
Seychelles
St Lucia
Grenada
Cape Verde
St Kitts and Nevis
Swaziland
Samoa
Comoros
Papua New Guinea
Jamaica
Belize
Solomon Islands
Guyana
Fiji
Kiribati
Barbados
Vanuatu
Tonga
Djibouti
Gambia
Sao Tome and Principe
0.00
Gabon
50.00
Bahrain
100.00
Suriname
per cent of average 1980–85 exports
150.00
Trinidad and Tobago
200.00
St Vincent and the Grenadines
250.00
Antigua and Barbuda
Note: Cambodia was found have the highest average net positive shifts in 1995–2000. On the basis of its 1980–85 average share in world exports, Cambodia’s average exports for 1995–2000 were predicted to be about $65 million. In reality, its exports in the late 1990s averaged at $806 million resulting in a net positive gain of 3807 per cent. Since gains/losses of individual countries are measured on a single scale in the figure, inclusion of Cambodia make inter-country graphical comparison for others indistinct.
−50.00 −100.00 −150.00 −200.00 −250.00
Appendix 7.21. Average net shift in exports of small states in 1995–2000 in comparison with 1980–85
264
Marginalization and World Trade Relationship Between GDP and Export Growth Rates LDCs 12
GDP growth rate (per cent)
10
y = 0.3043x + 1.0236 R 2 = 0.4734 Maldives
8 Solomon Islands 6
Bhutan
Cape Verde
4
Gambia
2 Djibouti
0 DR Congo 0
2 4 6 8 Exports growth rate (per cent)
10
12
14
16
Appendix 7.22. Exports and GDP Growth Rates in LDCs
Relationship Between GDP and Export Growth Rates LDCs 12 y = 0.3943x + 1.1521 R 2 = 0.4537
GDP growth rate (per cent)
10
Botswana Maldives
8 Cape Verde 6
St Kitts and Nevis
4
Gambia
2 Djibouti 0 0
Suriname 2 4
6 8 10 12 Exports growth rate (per cent)
14
16
18
Appendix 7.23. Exports and GDP Growth Rates in LDCs
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PART III Mitigating the Impacts for Commodity Dependent Countries
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8 Instruments for Addressing Commodity Price Behaviour Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
While volatility in commodity prices triggers the problem of export earnings instability, the long-term trend decline in relative prices has caused sustained foreign exchange losses for commodity-dependent LDCs, SVs, and HIPCs. There have been a number of initiatives at the international level either to ensure price stability in commodity markets or to help producing countries maintain export earnings stability. The basic objective of this chapter is to provide a brief review of the instruments employed to intervene in commodity markets, as well as of other support mechanisms which deal with export shocks in poor countries, and to examine whether they are useful in dealing with the secular decline in the relative prices of primary commodities. The idea of intervening in the operations of commodity markets can be traced to the first half of the twentieth century, when British and Dutch officials employed export restraints under various international agreements to regulate prices of rubber, tea, tin, sugar and wheat produced in their colonies (Herrmann et al., 1993). Following World War II, private stock-holding in Britain proved inadequate in reducing commodity price volatility, and Lord Keynes proposed the creation of international buffer stock arrangements to reduce price fluctuations in commodity markets. What was envisaged was the creation of a third Bretton Woods institution, ‘Commod Control’, to stabilize world commodity prices. Keynes’ scheme was never put into practice, but other intervention tools have been employed as part of international commodity policy. For the post-war period, one can identify four main instruments used in international commodity policy: 1. International commodity agreements (ICAs); 2. External Compensatory Finance;
269
Mitigating the Impacts of Dependent Countries 3. Preferential trade arrangements; 4. Market-based commodity risk management instruments. Of these, market-based commodity risk instruments are relatively recent, and are currently being considered and experimented with. The three other instruments have a long history of international policy negotiations, which have been closely scrutinized in the economic literature.1 In general, all the instruments were designed to provide export earnings stabilization, but some of the ICAs also implicitly raised absolute price levels. Addressing the problem of secular decline in relative prices was never an explicit objective of any of the schemes; they have generally focused on price volatility. The majority of the instruments are now effectively defunct or under-utilized, so this review is essentially historical.
8.1. International Commodity Agreements Supply management of commodities in international markets was a dominant strategy in the management of international commodity prices in the second half of the twentieth century. The objective was to stabilize prices by the use of export quotas or buffer stock (national or international) arrangements. Most commodity-producing countries maintained individual reserves but these hardly affected world market prices. Having witnessed the success of OPEC countries in controlling world oil prices, developing countries realized the benefits of stronger international producer associations in securing favourable commodity prices. Following the specifications of the Havana Charter in 1948, ICAs were to be negotiated between producing countries and consumers, with agreements lasting no more than five years, after which repeated renewals were expected.2 Agreements were initially designed to mitigate unexpected price fluctuations and ensure long-run equilibrium between forces of supply and demand, thus precluding the raising of prices above market trends. With support from UNCTAD, various international commodity agreements were negotiated, renewed or strengthened—the most prominent ones being the International Sugar Agreement (ISA), the International Tin Agreement 1 More detailed historical overviews are provided by World Bank (1999) and Maizels (1992). Other programmes proposed in the past to provide supplementary finance include the Olano Mutual Insurance Scheme (1953), Development Insurance Fund (1961), the Organization of American States Proposal (1962) and proposals by various national governments—France (1963), USA (1975), Sweden (1977), and Germany (1978). 2 The Havana Charter established the Interim Coordinating Committee for International Commodity Arrangements (ICCICA), under which agreements for coffee, sugar, tin and wheat were established. ICCICA was subsequently absorbed into UNCTAD in 1964.
270
Table 8.1. Salient Features of Five Important International Commodity Agreements
ICAs
Time of Establishment
Mode of Operation
Main Problems
Time of Suspension/ Collapse
International Sugar Agreement (ISA)
1954
Export controls
The first major problem occurred in 1962 when supplying countries were unwilling to accept Cuba’s demand for increased quota allocation as it faced an export restriction in the US market. This resulted in the collapse of the Agreement in 1963. ISA was later renegotiated. In the 1980s the EU emerged as the single largest net exporter of sugar and the US also supported domestic production with a stringent import quota regime. Since the EU and the US remained outside the cartel, the ISO had very little capacity to influence prices.
1983
International Tin Agreement (ITA)
1954
Buffer stock and export controls
In the early 1980s the US failed to renew its membership of the ITA, triggering a situation where there was insufficient finance to hold stock. Futures market operation in the London Metal Trade became very expensive due to the depreciation of the US dollar, making the International Tin Council insolvent. At the same time increased supply from low-cost non-members, such as Brazil, combined with increased competition from aluminium to cause a rapid fall in prices.
1985
International Coffee Agreement (ICoA)
1962
Export controls
Despite ICoA’s success in maintaining high and stable prices, there were disagreements among members over distribution of quotas. Consumers’ taste shifted to ‘arabica beans’ and other ‘milds’ but the quota allocation favoured large robusta producers, such as Brazil. Because of political changes, Brazil, which played a central role in the operation of ICoA, lacked a clear coffee policy and therefore failed to enforce an agreement on other producing countries, causing the break-up of ICoA.
1989
International Cocoa Agreement (ICCA)
1972
Buffer stock
During the first two phases of the Agreement prices were maintained above the ˆ te ceiling, encouraging excessive planting in non-member countries such as Co d’Ivoire, Brazil, Indonesia and Malaysia, which resulted in prices lower than the stabilization range in the next two rounds of ICCA. The third and fourth rounds of ICCA were chronically under-financed. Total stock at the end of the 1986–87 crop year was 650,000 tons, but a buffer stock of 150,000 tons only could be operated, making the agreement ineffective in influencing prices. The low marginal cost of harvesting beans and the long life of trees make the management of cocoa production and buffer stock difficult.
1988
International Natural Rubber Agreement (INRA)
1980
Buffer stock
The Agreement attempted an active market intervention in terms of a daily market indicator price, which was the average of the Kuala Lumpur, London, New York and Singapore cash prices. Producing countries disagreed on the use of rounded versus unrounded price. The price range of intervention also created tensions among members. Finally, the two main producers, Malaysia and Thailand, withdrew in 1999, leading to the transformation of the agreement into a mere study group.
1999
Source: Compiled from Gilbert (1987 and 1996) and Page and Hewitt (2001).
Mitigating the Impacts of Dependent Countries (ITA), the International Coffee Agreement (ICoA), the International Cocoa Agreement (ICCA) and the International Natural Rubber Agreement (INRA). ICAs, as employed in international commodity policy, aimed at achieving the two simultaneous goals of reducing variability in prices and raising depressed price levels. An Integrated Programme for Commodities (IPC) was negotiated during UNCTAD IV in 1976. The IPC envisaged the establishment of a Common Fund for Commodities (CFC) as an umbrella institution for negotiating additional commodity agreements, and providing financial economies of scale compared with the establishment of separate commodity funds. The CFC had two accounts—the first aimed at financing buffer stocks, and the second promoting research on crop production techniques. The two accounts of the CFC became active only in the late 1980s. By the late 1990s, most commodity agreements had disintegrated, as a result of unfavourable market conditions and, in some cases, disagreement among member countries (see Table 8.1).3 The breakdown of the sugar and cocoa agreements was triggered by unfavourable market conditions, while disagreement among producing members ended the coffee and rubber agreements. At present, most international commodity organizations no longer attempt to intervene in the markets with a view to influencing prices or supplies, i.e. even price stabilization measures have been abandoned. A large number of them have been converted into ‘study groups’ providing market information on production and prices. The CFC has been transformed into a grant-making institution, primarily supporting agronomic research on individual commodities (CFC, 2002).
8.2. External Compensatory Finance In the post-war period, shortfalls in export earnings threatened to disrupt the development plans envisaged by many developing countries. Supplementary finance was conceived in response to this, which aimed at supporting export earnings stabilization of commodity-dependent poor countries. Direct external compensation was an attractive policy tool, in contrast to other commodity market instruments, as it provided the least distortionary means of intervention. The two main schemes of this type are: 1. IMF Compensatory Financing Facility (CFF) 2. EC-ACP Programmes: STABEX, SYSMIN, and COMPEX 3 A full discussion of the operation and breakdown of commodity agreements is provided by Gilbert (1996). The spectacular collapse of the International Tin Agreement is also discussed in Andersen and Gilbert (1988).
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Instruments for Addressing Commodity Price Behaviour
8.2.1. Compensatory (and contingency) financing facility (CCFF) Established in 1963, the IMF Compensatory (and Contingency) Financing Facility is the sole initiative undertaken by Bretton Woods institutions to provide some form of compensatory finance. The facility operates as a concessional loan arrangement that helps recipient countries to mitigate balance of payments difficulties in the face of export earnings shortfalls caused by unexpected exogenous factors such as unfavourable movements in prices and adverse production shocks (for example drought). The CCFF considers compensation for net export earnings, rather than on a commodity-by-commodity basis and consequently captures portfolio effects present in a country’s entire export basket. Since its inception, the financial architecture of the CCFF has been subject to periodic modification. The credit quota for members was increased from 25 per cent in 1963 to 75 per cent in 1975. The definition of balance of payment shortfalls has also been frequently revised. In 1979, compensation was extended beyond traditional export earnings to include deficits arising from low receipts from tourism or workers’ remittances. Coverage was further extended to include compensation for increases in cereal import costs in 1981 and, finally, to include all services where adequate data were available (World Bank, 1999). A significant modification was made to the scheme in 1987 to include a contingency facility that permitted member countries to borrow, ex ante, in anticipation of balance of payment shortfalls. The new Compensatory and Contingency Financing Facility provided up to 122 per cent of national quota drawings, but required recipient countries to agree to a series of IMF-led programmes aimed at addressing balance of payments problems.4 In recent years, however, financing from the CCFF has rarely been used by LDCs and HIPCs, partly because of the high rates of interest charged (about 3.52 per cent) compared with other IMF sources of credit, such as the Poverty Reduction Growth Facility (PRGF). For most of the 1990s, credit facilities from the CCFF have been used by a few countries, such as Russia and Algeria, to support their crude oil export earnings.
8.2.2. EC-ACP programmes: STABEX, SYSMIN and COMPEX STABEX (the French acronym for STABilization des recettes d’EXportation) was established in 1975 by the European Community (EC) as part of the Lome´ arrangements with ACP member countries, and has served as a major delivery mechanism for EC aid (about one-eighth of the EU aid budget). STABEX was 4 Specific financing available is distributed as: 20 per cent of the national quota for export shortfall, 20 per cent of the quota for external contingency and 10 per cent of the quota for import costs of cereal, plus an optional 15 per cent.
273
Mitigating the Impacts of Dependent Countries initially conceived as a concessional loan agreement, but evolved into a grantbased program.5 The STABEX scheme was essentially in operation under the four Lome´ Conventions spanning 1975–2000 and was discontinued under the Cotonou Agreement. As an aid instrument, STABEX provided periodic export earnings compensation to many ACP countries, disbursing a total ECU4.4 billion over the ˆ te d’Ivoire and Senegal being major beneficiaries period 1975–1998, with Co (World Bank, 1999). The required trigger was a loss of export earnings (to the EU) relative to a six-year trend. STABEX funds were relatively small: the most recent funding ceiling of ECU1.8 billion over the five-year period 1996–2000 implied an average disbursement of ECU360 million per annum. This sum represented the net resources for which some fifty or so states were eligible. It is important to note that STABEX focused on a restricted set of countries, and provided compensation for only a limited number of agricultural exports supplied to EU markets. In Lome´ IV an ‘all destinations’ derogation was granted to certain ACP countries exporting commodities to countries other than the EU. Under the scheme, a total of 50 raw and processed agricultural, fishery and forestry products were covered for export compensation, with nearly 80 per cent of transfers made to a shortlist of commodities: coffee, cocoa, groundnuts, cotton, and copra. The STABEX scheme therefore provided compensation on a commodity-by-commodity basis, in contrast to the CCFF which provided general compensation for shortfalls in net export earnings. In the course of its operation, the STABEX scheme was criticized for two main reasons. First, significant time lags often existed between approval of compensation for member countries and the actual disbursement of funds. These frequent delays in delivering supplementary finance implied that STABEX disbursements tended to be pro-cyclical, rather than responding countercyclically to adverse shocks (Herrman et al., 1993). Second, there have been marked shifts in policy conditionalities attached to STABEX transfers (Hewitt, 1993). Initial compensation under the scheme was untied, and provided extra budgetary support to recipient governments. However, by the 1980s, the STABEX scheme was projectized, with strict conditionalities imposed on the use of transfers, discouraged diversification and required programmes to promote recovery of the particular commodity in distress (at least under articles of Lome´ IV). This tying of aid to the recovery of ailing commodities was prone to the fallacy of composition argument, as it encouraged further dependence on weak commodities, the prices of which were already in secular decline. 5 Original articles under Lome´ required designated funds as grants for least developed countries in the ACP, repayable under concessional terms by other member states. However, in 1991, outstanding reimbursements due from Senegal, Gabon, Madagascar, and Jamaica were cancelled; in return, ACP countries were required not to pursue compensation for outstanding STABEX claims from 1980–1 and 1987–8.
274
Instruments for Addressing Commodity Price Behaviour By the mid-1980s, following demands by commodity-dependent countries not adequately covered by STABEX transfers, two other complementary programs were created by EC donors—SYSMIN and COMPEX. SYSMIN was established during Lome´ II (1980–85) and was designed to focus on minerals and mining (in contrast to the predominant focus on agriculture in STABEX). The specific list of commodities covered included copper, cobalt, phosphates, manganese, bauxite, alumina, tin, iron ore, uranium, and gold. SYSMIN receipts were intended to be partly supplementary financing, and also to support technical and financial assistance for exploratory (geological and mining) research. In the period 1995–2000, ECU575 million was allocated, and the scheme was expected to have transferred a total of ECU1.7 billion over its 20-year lifespan. Transfers of SYSMIN funds were triggered either by adverse external shocks which threatened the viability of important components of the mining industry or by shortfalls in export earnings which were likely to derail existing development projects. The effectiveness of SYSMIN aid has been questioned, as transfers were often made to corrupt governments or to mining sectors run by foreign organizations which often did not need external aid.6 COMPEX emerged in the late 1980s, and was established to extend the benefits of STABEX to non-ACP LDCs. The scheme proved to be largely symbolic with very few states benefiting. With the EC lacking the necessary administrative structure, and the absence of a treaty document, it was difficult for LDCs to request funding (Page and Hewitt, 2001). In the current review, it is found that compensatory payments are mostly non-existent. Existing EU-ACP agreements within the Cotonou framework are vague about the establishment of other compensatory finance schemes. Article 68 of the Cotonou Agreement recognizes the problem of commodity dependence, and aims to ‘mitigate the adverse effects of any instability in export earnings’. Consequently, a new scheme, FLEX (an acronym for FLuctuations in EXport earnings), was suggested under the EU-ACP Cotonou Agreement.
8.3. Preferential Trade Agreements A third channel through which donors have indirectly supported commodity prices has been through the use of various preferential trade arrangements. The case of sugar under EU-ACP trade cooperation is the best example. Under the terms of the Lome´ Agreement, a scheme granting preferential prices for sugar supplied by the ACP countries was established. A number of ACP countries received special quota arrangements that ensured preferential prices 6 Page and Hewitt (2001) argue that there is inconclusive evidence that SYSMIN transfers supported weak mining sectors in Zambia and the Democratic Republic of Congo.
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Mitigating the Impacts of Dependent Countries for their sugar exports, equivalent to domestic sugar beet prices in the EU. Apart from sugar, commodity protocols also operated for bananas, beef/veal, and rum.7 For those ACP countries that received quotas, the commodity protocols have proved to be an invaluable source of foreign exchange earnings from the commodities in question. Moreover, given that the commodity arrangements linked export prices to domestic EU intervention prices, this provided high and stable prices for ACP farmers. However, given the expansion of the EU, the possible results of the Doha Round and the internal pressures for reform of the EU Common Agricultural Policy, these arrangements will be phased out by the end of the current decade, leaving affected ACP states to make substantial market adjustments. For developing countries that have not been beneficiaries of the commodity protocols, the overall economic impact of EU preferential pricing arrangements is mixed.
8.4. Market-Based Instruments for Commodity Risk Management and Insurance Schemes At present, significant donor attention (including from the World Bank and the EU) is aimed at providing financial market derivatives which assist developing countries to manage commodity price risks. Smoothing of income streams may be achieved using a number of tools, including futures, options, swaps and commodity-linked notes.8 Unlike ICAs or other compensatory financing schemes, commodity risk management tools do not intend to provide external resources to stabilize export earnings. In addition, under these schemes price risks are envisaged as increasingly allocated to private traders and farmers rather than absorbed by governments. However, since farmers do not generally have direct access to these instruments, the role of intermediaries is crucial in the functioning of the schemes.9 The goal of managing risk and liability in commodity-dependent countries is, however, fraught with difficulty. Such difficulties include the needed regulation reform, the identification of appropriate intermediaries, developing local trading exchanges, and the ability of the emerging private sector to make full use of the available range of modern commodity marketing, price 7 Only the protocols for sugar and beef/veal provided a guaranteed price for fixed quantities of exports from beneficiary countries. The banana, rum and sugar protocols granted duty-free access to fixed quantities supplied by ACP countries, while the arrangement for beef/veal considered guaranteed price, subject to a reduced level of duty, for a pre-determined quantity. 8 UNCTAD (1999) and Page and Hewitt (2001) provide discussions on these instruments. 9 Large private traders and banks are usually considered to be in the best position to act as intermediaries.
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Instruments for Addressing Commodity Price Behaviour risk management and financing instruments. Donor funding may be relevant in this regard in paying insurance premiums and covering producers in developing countries for their exposure to commodity-related risks. Tools like insurance schemes have also been proposed for ex ante price risk management. In 1999, the World Bank (ITF) considered a market-based international commodity price insurance mechanism consisting of price floor guarantees for producers/exporters and price ceiling guarantees for consumers/ importers. Under the scheme it is thought that international intermediaries would bridge the gap between private providers of insurance and other parties in developing countries. It would also provide local entities with the core services and technical assistance needed to extend the market to them. Market-based instruments for commodity risk management and insurance schemes are, however, concerned solely with the problem of revenue fluctuations due to commodity price volatility, clearly distinguishing this goal from that of addressing long-term price weaknesses, as ITF (1999: 17) observes: The scheme focuses on short-term price fluctuations, not price trends . . . The negative impact of the long-term deterioration of commodity terms of trade faced by many developing countries, needs to be dealt with by broader macroeconomic policies and development strategies. (ITF, 1999:17)
8.5. Conclusion From the above discussion, it can be concluded that there does not currently exist any policy instrument that explicitly addresses the problem of weakness in relative commodity prices. While price stabilization was the principal motive of the international commodity agreements, they also made some attempt to raise depressed price levels absolutely. However, no consideration was made of the trends in real prices (i.e. commodity prices relative to manufactured goods). On the other hand, external compensatory financing mechanisms (such as IMF, CFF, and STABEX) focused only on the shortfalls in absolute export earnings; export earnings from commodities and commodity prices were not specifically targeted. Various commodity protocols under EU-ACP trade arrangements guaranteed preferential prices for specific commodities. This has eased the problem of low prices of relevant commodities supplied by the recipient countries. The coverage of commodities in these arrangements has been quite limited. Finally, the relatively recent attempts with market-based instruments and insurance programmes are not designed to address the problem of trends in real prices. Rather, these schemes are intended to smooth out price fluctuations through the participation of the private sector.
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9 Commodity Prices and the Debt Relief Initiative Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
Over the past two decades a number of low-income countries have amassed large stocks of external debt. Among the primary reasons usually cited for the debt crisis are the economic shocks of the 1970s, declining terms of trade, highly volatile and declining commodity prices, heavy reliance on foreign aid and borrowing, and weak fiscal management and governance.1 The lack of diversification in export structure, together with excessive dependence on commodities facing price and income inelastic demand in the world market, has accentuated the process of accumulation of debt in an overwhelming majority of the heavily indebted poor countries. In fact, these countries are eventually caught in a vicious circle: commodity price declines exerting terms of trade shocks causing reduced export earnings and hence reduced capacity to import, leading to lower economic growth and necessitating more and more borrowing.2 The late 1990s witnessed some genuine and serious efforts to mitigate the debt problems of these countries. This section of the study argues that while the latest round of debt relief initiatives, known as the Enhanced HIPC Initiative, is laudable, the crucial factors of secular decline and sharp movements in commodity prices pushing most poor countries into the debt trap have not been adequately taken into
1 It should be noted here that the debt problem of heavily indebted poor countries is quite different from that of the middle-income countries of Latin America. In most cases, lowincome countries borrowed from bilateral donors and multilateral agencies such as the World Bank and IMF at concessional rates of interest. On the other hand, the middle-income countries borrowed mainly from the private commercial financial institutions at market interest rates. 2 Despite implementing extensive policy reforms to overcome the problem of internal and external imbalances, rates of external indebtedness continued to increase in the countries labelled as the highly indebted poor countries.
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Commodity Prices and Debt Relief consideration. This omission threatens the credibility of the initiative if the poverty reduction strategies formulated to enable countries to qualify for the assistance fail to energize exports in poor countries. We emphasize the importance of establishing a support mechanism for the HIPC countries so that, at least in the short run, they are covered against falling commodity prices while implementing their poverty reduction strategies. In the long run, however, their permanent exit from the debt problem will depend on their success in diversifying their export structures and achieving a more favourable share of international trade and finance.
9.1. Commodity Prices and Debt The overall debt of low-income poor countries increased substantially in the 1980s and 1990s. In 1970 the total debt stock of the 42 heavily indebted poor countries stood at US$6.7 billion, rising to US$59 billion over the following 10 years.3 It was in the 1980s that debt stocks exploded with a volume of outstanding payments of US$190 billion. Since by the late 1980s the situation for HIPCs had already become unsustainable with persistent debt-overhang, in the last decade there has been only a small increase of about US$15 billion in the overall debt burden.4 As a proportion of GDP, the total outstanding debt of HIPCs in the 1990s reached 95 per cent, in comparison with about 35 per cent for other lower-middle-income countries. The rapid and concomitant accumulation of arrears of low-income countries undoubtedly reflects their inability to make regular debt service payments. Weak macroeconomic management of the economy supposedly affecting growth performance, together with the inability to make necessary fiscal adjustments in the face of exogenous shocks, is often given much emphasis in explaining the debt default phenomenon.5 However, it cannot be 3 Total external debt here includes both long-term and short-term outstanding loans together with the use of IMF credit. 4 Also, in the 1990s under various initiatives the low-income countries received some debt forgiveness extinguishing a portion of their outstanding debt stock. 5 According to Easterly (2001), the growth slow-down played an important role in the debt crisis of HIPCs in the 1980s and 1990s. The author attributes the poor growth performance of HIPCs to lower investment in infrastructure, such as transport and communication, overvalued exchange rates, lower primary and secondary enrolments, and lower monetization in the economy as measured by the ratio of broad money to GDP. The terms of trade shock is, however, not considered and therefore its impact on economic growth is overlooked. There is evidence of declining commodity prices translating into terms of trade shocks (Grilli and Yang, 1988; Bleaney and Greenaway, 1993) resulting in an immediate negative and significant impact on growth (Dehn, 2001; Dehn et al., 2003; Singer and Edstrom, 1993). For African countries, Deaton (1999) provides further stronger evidence of a positive relationship between commodity prices and growth: for a country whose commodity exports are a third of GDP, a commodity price increase of 1 per cent will directly increase national income by 1 per cent plus another half of 1 per cent. With regard to bad governance and increased government
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Mitigating the Impacts of Dependent Countries overlooked that as most poor countries rely heavily on primary commodities for exports, the secular decline in commodity prices has certainly undermined their capacity to service debt.6 In all the World Bank country case studies on HIPCs, falling commodity prices have been identified as one of the principal reasons for the debt crisis (Gautam, 2003).7 Figure 9.1 presents the outstanding stock of debt of HIPCs (in real terms and measured on the left vertical axis) vis-a`-vis the relative price of commodities (measured on the right vertical axis). It can be seen that the mounting debts of HIPCs in the 1980s were accompanied by a sharp fall in the real commodity price index. A simple regression of the logarithm of real debt on a constant and the logarithm of relative commodity price showed that a 1 per cent fall in commodity price was associated with a 0.91 per cent increase in outstanding stock.8 The statistical association and Figure 9.1, therefore, seem strongly to suggest that commodity prices have been central in the debt crisis of HIPCs. In order better to appreciate the severity of the impact of the secular decline in commodity prices, we juxtapose the cumulative foreign exchange losses (1985–2000) for a number of countries from some primary commodities and the respective countries’ outstanding debt (Table 9.1).9 Note that the foreign exchange losses from the commodities as considered are not exhaustive for all the countries listed in the table; in most cases, countries will have other exportable primary commodities that have not been taken into account. expenditure followed by a positive shock in commodity prices, it must be acknowledged that the weak state of management is a general characteristic of poor countries. Nevertheless, it has also been argued that growth of public spending was not always reckless and in many cases concentrated on the development of infrastructure and other long-term investment (Geda, 2002). 6 Easterly (2002) finds the growth of terms of trade in HIPCs to be not significantly different from that of a set non-HIPC developing countries, leading him to suggest that adverse movement in terms of trade cannot explain the debt problem. However, it should be understood that the classification of a country as a non-HIPC does not necessarily mean that it does not have a debt problem. The criteria according to which countries are classified as HIPCs are not strictly scientific and the classification is considered to be a ‘rule of thumb’ only. More importantly, similar movements in terms of trade of countries with a different composition of exports can have different consequences. For example, countries exporting only primary commodities will find their revenues falling in the case of a decline in price as the demand for commodities is price inelastic in nature. Manufacturing exporters can have rising exports despite falling prices as they have a fairly elastic demand curve. 7 ˆ te d’Ivoire, EthiThe country case studies include Bolivia, Burkina Faso, Cameroon, Co opia, Ghana, Malawi, Mozambique, Tanzania, Togo, Uganda, Zambia, and Yemen. A summary of these case studies is given in Gautam (2003). 8 Due to the potential problem of the non-stationarity of data, the regression was estimated using the Phillips-Hansen Fully Modified Ordinary Least Squares (PHFMOLS) procedure. The PHFMOLS technique yields standard errors that are valid for inference and the resultant ‘t’-ratio on the relative commodity price confirmed the statistical significance of the elasticity coefficient at less than the 1-per-cent level. Short size of the sample did not allow us to use the more powerful procedure of the Johansen cointegration test. 9 In Chapter 6 we estimated the cumulative foreign exchange losses in 1984–86 prices; but for comparing with outstanding debt stocks, they have been converted into 1999 dollar prices.
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Commodity Prices and Debt Relief 170
140000 130000
150 130
US$ Million
110000 100000
110 90000 90
80000
Index: 1985=100
120000
70000 70 60000 Debt stock in 1985 Prices
Real Commodity Prices 1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
50 1980
50000
Figure 9.1. Real Commodity Price Index and Real Outstanding Debt in HIPCs Note : (1) Debt stocks are measured on the left vertical axis and the real commodity price index is on the right vertical axis. (2) The US consumers’ price index (CPI) has been used to convert debt stock in nominal US dollars into real terms. (3) The real commodity price index is the UNCTAD composite price index for primary goods relative to manufactures as used in Chapter 3.
Table 9.1. Foreign Exchange Losses from Commodities and Outstanding Debt
Countries Benin ˆ te d’Ivoire Co Ghana Honduras Mali Papua New Guinea Sao Tome and Principe Solomon Islands Togo Uganda Vietnam
Commodities considered Cotton Cocoa, Cotton, Rubber, Coffee, Palm Oil Cocoa Coffee Cotton Cocoa, Rubber, Palm Oil, Coconut Oil Cocoa Palm Oil Cotton Cotton, Coffee Rubber, Tea, Coffee, Coconut Oil
Outstanding Debt (US$ Million)
Cumulative Foreign Exchange Loss (1985–2000) (US$ Million)
Foreign Exchange Loss as % of debt stock
1589 11582
355 19456
22.3 167.9
6240 4366 2956 2500
6330 2482 248 1590
101.4 56.85 8.4 63.6
225
83
36.8
141
88
62.4
1485 3544 12578
262 2545 6004
17.6 71.8 47.7
Note: Cumulative foreign exchange losses in 1984–86 prices have been estimated in Chapter 4 but in the above table they have been adjusted upward to current prices in 1999. Source: The data on debt are in most cases from the UNCTAD Handbook of Statistics CD-ROM 2001 and represent the outstanding stock in 1999. Data from Global Development Finance (World Bank, 2003) have been used for 2001.
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Mitigating the Impacts of Dependent Countries Nevertheless, the comparison of loss with the outstanding debt stock is striking. By the end of the 1990s, Ghana had accumulated a debt stock of US$6240 million, which appeared to be equivalent to the amount it had lost in foreign exchange as a result of the declining price of cocoa alone. The calculation of forgone foreign exchange covered a set of commodities that happened to ˆ te d’Ivoire (cocoa, coffee, comprise the five most important export items of Co cotton, rubber, and palm oil)—cumulative losses from which amount to 168 per cent of the country’s outstanding debt stock. In the case of Uganda, lost revenues from coffee and cotton equal about 72 per cent of its debt stock. Among others, the small state of Sao Tome and Principe, which acquired a debt stock-GNI ratio of 735 per cent (World Bank, 2003), lost an amount equivalent to 37 per cent of its arrears because of the falling cocoa price. On the whole, the information presented in Table 9.1 suggests that the loss in foreign exchange earnings because of secular decline in commodity prices has significantly contributed to the debt problem of poor countries.
9.2. Debt Relief Initiatives There have been several attempts to relieve poor countries of their external debts (see Table 9.2). Traditional debt relief initiatives have included: (1) concessional flows and rescheduling; (2) stock-of-debt operations; (3) bilateral forgiveness of ODA claims by Paris Club creditors; and (4) private commercial debt relief and buy-back operations (Daseking and Powell, 1999). These debt restructuring, rescheduling and forgiveness schemes have proved to be insufficient to resolve the debt problem of poor countries. Indeed most donors have frequently resorted to a ‘defensive lending’ strategy—involving debt rescheduling and fresh loans and grants to prevent default by borrowing countries.10 In 1996 the World Bank and the IMF proposed the Heavily Indebted Poor Countries (HIPC) Initiative. Its main objective was to bring down the external debt burdens of the eligible countries to a ‘sustainable level’ and to eliminate the problem of debt overhang, thereby paving the way for a permanent exit from the process of debt rescheduling. In its design it was recognized that a precondition of any effective debt relief programme should be broad and equitable participation by all creditors—bilateral, multilateral, and commercial, and that the debt relief programme should be complemented by additional resource flow to enable the poor countries to initiate an effective poverty reduction strategy. The Initiative was reviewed and enhanced (and
10 Despite the debt problem the net transfer in the form of fresh loans, grants and other transfers has been positive and relatively high in HIPC countries (Birdsall et al., 2002; Gautam, 2003). UNCTAD (2002a) shows that throughout the 1990s for the group of LDCs gross aid disbursements were strongly correlated with debt service payments.
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Commodity Prices and Debt Relief Table 9.2. Debt Relief Initiatives Initiative
Main Feature
UNCTAD, 1977–79
Led to official creditors writing off US$6 billion in debt to 45 poor countries in terms of elimination of interest payments, the rescheduling of debt service, local cost assistance, untied compensatory aid and new grants to reimburse old aid.
Venice G7 Summit, 1987
Called for interest rate relief on debt of low-income countries.
Special Programme of Assistance (SPA) to Africa, 1987
Initiated by the World Bank to help African countries (African IDA-only borrowers with a ratio of debt service to exports above 30 per cent) to service their official debt. The IMF complemented the SPA with an enhanced structural adjustment facility. Agreed a menu of options including partial forgiveness, longer maturities and lower interest rates. The level of reduction was defined as 33.33 per cent. World Bank and IMF initiatives to help mainly middle-income countries to service and reduce debts to commercial bank creditors. Available to low-income countries—the main objective was to restructure and buy back commercial debt with IDA credit. Considered extended repayment periods for lower-middle-income countries and rescheduling of ODA debt at a concessional rate. A proposal by the UK at the Commonwealth Finance Minister’s Meeting in Trinidad that would increase the grant element of debt reduction by up to 67 per cent from 20 per cent under the ‘Toronto terms’. Also known as ‘Enhanced Toronto’—it agreed to reduce debt service by 50 per cent on non-concessional bilateral debt with a 12-year grace period and 30 years maturity.
Paris Club—Toronto Terms, 1988 Brady Plan, 1989 IDA Debt Reduction Facility, 1989 Paris Club—Houston Terms, 1990 Trinidad Terms, 1990
London G7 Summit, 1991 Paris Club—Naples Terms, 1995
HIPC, 1996
Paris Club—Lyon Terms, 1996 Enhanced HIPC, 1999
Paris Club—Cologne Terms, 1999
A consensus about the UK proposal on Trinidad terms emerged—eligible countries would receive additional debt relief and debt service would be reduced by 67 per cent on non-concessional bilateral debt with a 16-year grace period and 40 years maturity. Debt stock reduction to bring the debt-to-export ratio under 200 per cent for 41 heavily indebted poor countries. The multilateral creditors for the first time would take the initiative to forgo claims on their credits to help countries. Along with HIPC 1996, the Paris Club agreed to provide 80 per cent debt reduction in net present value terms. Increased stock reductions to bring the debt-to-export ratio down to below 150 per cent for a number of the 42 HIPC countries. Debt relief facility was made conditional on the formulation of comprehensive poverty reduction strategy papers (PRSPs). To support the Enhanced HIPC initiative, non-ODA credits were cancelled to a level of up to 90 per cent or more if necessary.
Source: Birdsall et al. (2002); Daseking and Powell (1999); Easterly (2002); and UNCTAD (2000).
hence called the Enhanced HIPC) in 1999 to provide faster and deeper debt relief to heavily indebted poor countries.11 Currently, there are 42 countries under the HIPC-II initiative (see Table 9.3); 27 of them have reached the ‘decision point’—when it is determined whether 11 The initiative is currently open to those poor countries that: (i) are eligible for highly concessional assistance such as from the World Bank’s International Development Association (IDA) and IMF’s Poverty Reduction Growth Facility (PRGF); (ii) face an unsustainable debt situation; and (iii) have a proven track record in implementing structural adjustment and poverty reduction strategies.
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Mitigating the Impacts of Dependent Countries Table 9.3. Debt Relief for HIPC Countries
Countries
Total Reduction in Debt in NPV Terms (US$ million)
Nominal Debt Service Relief (US$ million)
Date of Completion Point/Decision Point
Countries that have reached their completion points (8) (as of 1 August 2003) Benin 265 460 Bolivia 1302 2060 Burkina Faso 553 930 Mali 539 895 Mauritania 622 1100 Mozambique 2023 4300 Tanzania 2026 3000 Uganda 1003 1950 Total for 8 countries
8333
14695
Countries that have reached decision points (19) Cameroon 1260 Chad 170 Congo, D.R.* 6300 Ethiopia 1275 Gambia 67 Ghana 2186 Guinea 545 Guinea-Bissau* 416 Guyana 585 Honduras 556 Madagascar 814 Malawi 643 Nicaragua 3267 Niger 521 Rwanda* 452 Sao Tome and Principe 97 Senegal 488 Sierra Leone* 600 Zambia 2499
2000 260 10000 1930 90 3700 800 790 590 900 1500 1000 4500 900 800 200 850 950 3850
Total for 19 countries
22741
35610
2224
3900
31074
50305
Countries still to be considered Burundi* ˆ te d’Ivoire Co Central African Republic Comoros Congo, Rep* Lao PDR Liberia* Myanmar* Somalia* Sudan* Togo Total Debt Relief Committed
Mar-03 Jun-01 Apr-02 Mar-03 Jun-02 Sep-01 Dec-01 May-00
Oct-00 May-01 Aug-03 Nov-01 Dec-00 Feb-02 Dec-02 Dec-00 Nov-00 Jul-00 Dec-00 Dec-00 Dec-00 Dec-00 Dec-00 Dec-00 Jun-00 Mar-02 Dec-00
Note: Four countries, Angola*, Kenya, Vietnam and Yemen, are considered as potentially sustainable cases without the HIPC assistance. * indicates conflict-affected countries. Source: Various World Bank and IMF Documents as available at www.worldbank.org/hipc
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Commodity Prices and Debt Relief the level of debt is sustainable and, if unsustainable, how much debt relief will be required. Of the ‘decision point’ countries, eight have also attained the ‘completion point’—when countries receive their full package of debt relief benefits.12 Under the Extended HIPC (HIPC-II) programme the external debt burden is deemed sustainable if, in general, the net present value (NPV) of debt does not exceed 150 per cent of exports.13 A highly open HIPC with a debt-toexport ratio of less than 150 per cent can qualify for the relief when its NPV of debt-to-revenue ratio reaches 250 per cent, with two further conditions: if it has (i) an export-GDP ratio of at least 30 per cent and (ii) a revenue-GDP ratio with a minimum threshold of 15 per cent.14 An important feature of HIPC-II is its conditionality, linking debt relief to policies for poverty alleviation. Countries benefiting from the scheme are required to establish a good record of implementing economic and social policy reforms and to prepare a poverty reduction strategy paper setting out ways and means of tackling the problem of poverty. PRSPs are supposed to envisage pro-poor complementary policies and compensatory expenditures.15 It is believed that savings out of the written-off outstanding stock and additional resource flows will allow the beneficiary countries to devote more resources to social sectors such as health and education.16 As can be seen in Table 9.3, the currently eligible HIPCs are mostly from the continent of Africa—a total of 34. Of the remaining eight, Lao PDR, Vietnam, Myanmar and Yemen are Asian, and the rest—Bolivia, Guyana, Honduras and Nicaragua—are in Central and South America. Around three-quarters of these countries are LDCs (four—Comoros, Gambia, Guyana, and Sao Tome and Principe—are also small states) and countries classified as neither LDCs nor small states comprise about 20 per cent of the target sample (Bolivia, Cameroon, Republic of Congo, Ghana, Honduras, Kenya, Nicaragua, and Vietnam). 12 The HIPC initiative involves two stages—in the first, the country builds a track record in implementing policies under the support and supervision of the World Bank and IMF for approximately three years and then it is decided whether or not its debts are sustainable. Based on performance of further policy reforms and structural adjustment, the relief package is delivered. In HIPC-I it was stipulated that countries would have to demonstrate their capacity for implementing reforms for another three years after the decision point. HIPC-II introduced the concept of the ‘floating completion point’, effectively making it possible to obtain debt relief earlier. Differences between the original and enhanced version of the HIPC schemes are discussed in detail in Gautam (2003) and UNCTAD (2000). 13 Since a significant portion of the external debt stock is received on concessional terms, the NPV rather than the absolute amount is a better measure of the burden. The NPV is defined as the sum of all future debt service obligations (interest and principal) on existing debt, discounted at the market interest rate. Since the interest rate is lower than the market rate, the NPV of debt for these poor countries is smaller than its face value. 14 Of the 27 decision point countries, assistance has been granted to 22 countries on the basis of the debt-to-export ratio condition. Five countries, Ghana, Guyana, Honduras, Mauritania, and Senegal, have been considered on the basis of the fiscal criteria (Dodhia, 2002). 15 Issues relating to pro-poor conditionality in HIPC may be found in Morrissey (2002). 16 According to the World Bank and IMF (2003), the decision point HIPCs are now spending approximately four times more on social services than on servicing debt.
285
Mitigating the Impacts of Dependent Countries The four HIPCs that are considered to be potentially viable without the debt relief package are Angola, Kenya, Vietnam, and Yemen. Table 9.3 shows that nominal debt reduction for 27 decision/completion point countries is estimated at about US$50 billion; in NPV terms the comparable figure is slightly over US$31 billion. The HIPC initiatives produce a marked improvement in the debt indicators of the recipient countries. After the relief operation, the mean NPV of debt-to-export ratio for 23 HIPCs for which information is available fell from a level much higher than that of the developing countries to a level at par with them (see Table 9.4). Similarly, the average NPV of debt-to-GDP ratio for the beneficiaries has fallen from as high as 60 per cent to 29 per cent. The burden of debt service in terms of export earnings has also declined substantially; the debt service-to-export ratio for HIPCs is estimated to be only 8 per cent in comparison with 21 per cent for the developing country group. The cost of debt relief under HIPC initiatives (up to March 2003) in 2002 NPV terms amounts to US$39.2 billion, which is almost equally divided between bilateral and multilateral creditors.17 Of the US$19.4 billion attributable to bilateral donors, US$14.5 billion is due to the Paris Club donors. On the other hand, the World Bank is to bear a cost of US$8.7 billion out of a total multilateral cost of US$18.8 billion; the IMF and the African Development Bank account for about US$3 billion each. In order to help multilateral lenders meet the cost, a special HIPC Trust Fund has been established with contributions from donor governments.18 Table 9.4. Debt Indicators in Developing Countries and HIPCs, 1999 (%) Decision point HIPCs (23)*
Indicators NPV of debt-to-exports ratio NPV of debt-to-GDP ratio Debt service-to-exports ratio
Developing country average
Non-HIPC developing countries
All HIPCs before HIPC relief
Before HIPC Relief (1999)
After HIPC Relief (2003)
133
128
249
259
127
38
36
84
60
29
20
21
14
16y
8*
Note: (1) Liberia and Somalia are always excluded from the group of HIPCs due to data unavailability; (2) *D R Congo, Ethiopia, Ghana and Sierra Leone are excluded; (3) y Average for 1998–99 based on debt service paid. Source: Abrego and Ross (2002). 17 The cost of the HIPC initiative is computed in NPV terms at the time of decision point. The cost is then increased every year after the decision point by a factor which is estimated as the average interest rate applicable for relief to be committed (IDA, 2003). 18 The Trust Fund will also comprise the lenders’ own resources. The IMF’s contribution to debt relief programmes will be funded in part from interest on the original subscriptions (in
286
Commodity Prices and Debt Relief
9.3. Commodity Prices and the HIPC Initiative The HIPC initiative has been subject to criticism ranging from the argument that ‘debt relief and other forms of aid are too great and too easy to get’ to that of ‘debt reduction being too small and tied to conditionality that is onerous and misguided’. Birdsall (2002) identifies three other critiques relating to the structure under which the programme operates: (1) that the eligibility criteria of a country’s stock of debt as a share of its exports is inappropriate; (2) that as forgiving unpayable loans is an accepted reality, most of the debt reductions are to be treated as a loss (bad debt) rather than the relief provided; and (3) that debt sustainability analysis as carried out in the programme has been unrealistic. For the purpose of the present section of the report, criticisms related to (3) above deserve the most serious attention since, for reasons discussed below, this issue threatens to undermine the basic objective of the HIPC Initiative. As mentioned earlier, under the latest debt relief scheme emphasis is given to the issue of the sustainability of debt. Explicit attention has been given to determining the magnitude of debt stock needed to be written off to make any outstanding debt and new borrowing viable. The decision on the amount of relief that is thought to be sufficient to achieve debt sustainability is made at the decision point and is updated at the completion point. While debt indicators such as NPV debt-to-exports and debt-to-revenue ratios are useful guidelines in assessing the severity of the burden for a country, reducing the ratios to a predetermined ‘sustainable’ level does not provide any automatic guarantee of debt sustainability in the medium to long run (UNCTAD, 2000). The medium-term prospective DSA is done on the basis of various assumptions about a number of key macroeconomic variables. These variables include real GDP growth, the income elasticity of demand for imports, growth in the volume and value of exports, and flows of grants and foreign direct investment (FDI) and debt-creating flows and conditionality attached to them. To attain sustainability it is required that the current account deficit be covered by nondebt creating or concessional debt flows so that the building up of debt stock can be avoided. Being projections, the DSA is sensitive to assumptions regarding the value of parameters. For example, UNCTAD (2000) shows that in the case of the United Republic of Tanzania, a small change in the export growth
gold) by members at the time of its inception. The balance will come from bilateral contributions. In the case of the World Bank, the earnings on its loans to middle-income countries will be used in the first few years; after that it has to be supported by donor contributions. Of the others, because of lack of resources the African Development Bank will have to rely heavily on donors’ contributions; currently it can support only about 20 per cent of its contribution to the debt relief programme from its own resources.
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Mitigating the Impacts of Dependent Countries
export growth (%)
200 10 150
5 0
100
50
250
Bolivia
12 200
10 8
150
6 100
4 2
50
Decision Point Projection: Export Growth
Updated Projection: Export Growth
Updated Projection: Export Growth
Decision Pt. Project.: NPV Debt/Exports (%)
Decision Pt. Project.: NPV Debt/Exports (%)
Updated Project.: NPV Debt/Exports (%)
Updated Project.: NPV Debt/Exports (%)
200
5 150 0 100
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
50 0
Mali
15
250
200
10 150 5 100 0 50
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
export growth (%)
250
10
20
300
export growth (%)
Burkina Faso
15
288
0
Decision Point Projection: Export Growth
NPV debt to export ratio (%)
20
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
0
NPV debt to export ratio (%)
15
14
0
Decision Point Projection: Export Growth
Decision Point Projection: Export Growth
Updated Projection: Export Growth
Updated Projection: Export Growth
Decision Pt. Project.: NPV Debt/Exports (%)
Decision Pt. Project.: NPV Debt/Exports (%)
Updated Project.: NPV Debt/Exports (%)
Updated Project.: NPV Debt/Exports (%)
NPV debt to export ratio (%)
250
export growth (%)
Benin
NPV debt to export ratio (%)
20
Commodity Prices and Debt Relief 350
200 5 150 0
100 50
80
30
60
20 10
40
0
20 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
100
40
0
Decision Point Projection: Export Growth
Updated Projection: Export Growth
Updated Projection: Export Growth
Decision Pt. Project.: NPV Debt/Exports (%)
Decision Pt. Project.: NPV Debt/Exports (%)
Updated Project.: NPV Debt/Exports (%)
Updated Project.: NPV Debt/Exports (%)
150
8 100
6 4
50
2008 2009 2010
2005 2006 2007
2003 2004
2 0
Uganda
15
export growth (%)
10
NPV debt to export ratio (%)
200
12
2001 2002
20
250
Tanzania
2000
120
50
0
14
export growth (%)
140
60
Decision Point Projection: Export Growth
16
0
160
70
NPV debt to export ratio (%)
10
180
80
300 250
10 200
5
150
0
100 50
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
export growth (%)
250
90
NPV debt to export ratio (%)
300
15
Mozambique
export growth (%)
Mauritania
NPV debt to export ratio (%)
20
0
Decision Point Projection: Export Growth
Decision Point Projection: Export Growth
Updated Projection: Export Growth
Updated Projection: Export Growth
Decision Pt. Project.: NPV Debt/Exports (%)
Decision Pt. Project.: NPV Debt/Exports (%)
Updated Project.: NPV Debt/Exports (%)
Updated Project.: NPV Debt/Exports (%)
Figure 9.2. Projected Export Growth and NPV Debt-to-Export Ratio for Countries that have Reached Completion Point Note : The NPV debt-to-export ratio is measured on the right vertical axis while the export growth rates are measured on the left vertical axis. Source : Data on decision point and updated projections are taken from IMF and IDA (2002).
289
Mitigating the Impacts of Dependent Countries target could have substantial effects.19 In fact, the assumptions that are crucial to the outcomes are the level and terms of new financing and an assessment of future economic and export performance. Already, there is some evidence that assumptions with regard to future growth rates of exports and GDP may have been too optimistic. Figure 9.2 gives the decision point projected export growth rate and net debt-to-exportratios vis-a`-vis the comparable updated projections at the completion point for eight HIPC countries. As can be seen, the updated figures for export growth for 2000–03, which more closely reflect reality, differ quite significantly from the original projections. For Benin, the export growth rate for 2000 was projected to be about 5 per cent, but actually proved to be 12.5 per cent; Bolivia’s 10 per cent export growth projection in 2001 compares with an actual performance of 0.9 per cent. Similar contrasts between projected and updated figures also appear for Mauritania (2002), Mozambique (2002), Tanzania (2000, 2002 and 2003) and Uganda (2000, 2001 and 2002). These figures suggest that the decision point assumptions do not even match the actual performance in the near future, making the basis for projections for the distant and long-time periods (e.g. 2005–2017) less than informative. The unweighted average projected export growth rate during 2000–2010 for a number of 24 HIPCs is envisaged to be 8.6 which is just about the half the rate achieved by these countries in the decade of the 1990s (see Figure 9.3). Figure 9.3 shows that in order to achieve the projected growth, given their recent performance, HIPCs need to demonstrate unusually robust performance in the next decade. The unweighted average export growth for the 24 countries as shown in Figure 9.3 in 2000–01, estimated at 5.4 per cent, appears to be significantly lower than the 9.4 per cent projected at the decision point (IMF and IDA, 2002). Lower than projected exports in 2001 were observed for 16 out of 24 countries, while only six recorded a better than projected export performance. According to estimates by the IMF and World Bank, the shortfalls in export revenue were particularly large for Uganda (27 per cent), Guinea, Senegal and Zambia (16–18 per cent), Burkina Faso and Guinea Bissau (20 per cent), Benin, Honduras and Mauritania (10–13 per cent). Lower exports for these countries reduced the basis for export projections in the medium term, thus shifting downwards the level of projected exports and, with other factors remaining constant, moving the projected NPV of debt-to-exports ratios upwards. An evaluation by the IDA and the IMF, therefore, observes that the recent global economic slowdown, coupled with a significant decline in many primary commodity prices, has weakened the HIPCs’ growth and export performance 19 In the UNCTAD sensitivity analysis, the projected growth of exports of 8.3 per cent per annum for 2000–2018 was reduced by only 10 per cent to generate a financial gap 120 per cent higher than the baseline forecast.
290
Commodity Prices and Debt Relief
Weighted average
Zambia
Simple average
Tanzania Senegal
Rwanda
Nicaragua Niger
Mozambique Mauritania
Malawi Mali
Honduras
Madagascar
Guinea-Bissau Guinea
Burkina Faso
Gambia
Guyana
5
Cameroon
per cent
10
Benin Bolivia
15
Uganda
Projected for 2000–10 Sao Tome and Principe
1990–99 actual average 20
0
−5
Figure 9.3. Average 1990–99 Actual Export Growth Rate vis-a`-vis Projected Growth Rate for HIPCs Note : Actual growth rates are based on nominal US dollars for exports of goods and non-factor services using the World Bank data. Projected growth rates are from World-IMF documents on HIPCs.
in the last two years and led to a deterioration of the external debt indicators for many of these countries (IMF and IDA, 2002). The importance of export performance to debt sustainability of HIPCs is best illustrated by the experience of Uganda. Uganda was the first country to qualify for debt relief under HIPC-I and it also reached completion point under HIPC-II before any other country. In 2000 Uganda’s export growth rate was approximately 14 per cent and this was followed by a further decline of 4 per cent in 2001, as against a strong recovery projection of about 15 per cent. The updated growth for 2002 is two and a half times lower than the decision point projection. As a result, in 2002 Uganda’s NPV of debt-to-export rose to 254 per cent—considerably more than the debt sustainability target of 150 per cent—and, more detrimentally, much higher than the decision point projected ratio of 97 per cent. This weak performance cannot be attributed to poor economic management. Rather, it was due mainly to the secular decline in coffee prices, which resulted in the loss of significant export earnings to Uganda (Birdsall, 2002). It follows from the above that exogenous external shocks resulting from a secular fall, together with frequently volatile movements in commodity
291
Mitigating the Impacts of Dependent Countries prices, are likely to remain as the most important threat to countries maintaining a sustainable debt target in the future. While differences in the evolution of the debt indicators among HIPCs might reflect differences in the implementation of various reform programmes, the external debt sustainability outlook for most of these countries critically hinges on their export structure and performance. As most of these countries have a degree of dependence on commodities for exports, they are most likely to be subject to adverse commodity price shocks. Downward trends in the prices of primary commodities would also imply that in the absence of diversification in the composition of their exports, these countries will continue to be marginalized in world export trade.20 This is further accentuated by the fact that due to low income elasticity, the share of primary commodities in world merchandise is in secular decline.21 In fact, it appears that one of the most important omissions in the formulation of the HIPC Initiative has been the lack of recognition of the problem posed by trends in commodity prices and of a policy framework to address it. Although the linking of debt relief to poverty reduction is desired, the problem of debt sustainability is ultimately tied to a country’s potential foreign exchange earning capacity. The policy conditionality of HIPC–PRSP, however, has only an indirect influence by aiming to provide a stable macroeconomic environment. Although the principal objective of growth in a low-income country is poverty alleviation, in the short run the link between poverty reduction efforts and debt sustainability is obscure, while the impact of export earnings on debt indicators is immediate. In an extreme case, a country can borrow to invest in the non-tradable sector only to alleviate poverty effectively, but future debt servicing will be difficult if exports do not expand, even though the poverty situation may improve.22 Allowing for sufficient time, poverty alleviation and economic growth can energize exports, but the problem is that if the debt is not serviced immediately and continuously a country may well slip back into the trap of new borrowing to service the accumulated debt. This is all the more likely, because despite the HIPC relief, the absolute amount of debt service payment for HIPCs in the medium term will be higher than in the pre-HIPC period. Table 9.5 compares the information on actual and projected debt service in absolute terms, as well as in terms of proportion of exports and GDP. It is observed that for Chad, Niger, Sierra Leone, and Zambia, debt service payments in 2005 will be higher than the corresponding actual payments made in 1998. For Burkina Faso, Cameroon, 20 Grynberg and Razzaque (2003) provide evidence of statistical association between LDCs’ and small states’ marginalization in world export trade and the diminishing share of agricultural commodities in world merchandise trade. 21 In 1980 agricultural products constituted about 16 per cent of world merchandise exports, whereas they now account for about 7 per cent. 22 It has also been argued that the debt relief initiative may not even be able to meet the poverty reduction targets (Serieux, 2001). In the context of Kenya, Kiringai (2002) argues that the process of the preparation of a PRSP only enhances the stakeholders’ expectations, but cannot deliver.
292
Commodity Prices and Debt Relief Honduras, Mali, Rwanda, Senegal, Tanzania and Uganda, these payments will comprise substantial proportions of their actual payments in the pre-HIPC period. If overall exports and resource flow grow as projected, the debt indicators will not be worrying, but how far export growth targets can be maintained remains a big question. For commodity-dependent countries, the role of trade policy in development should be well integrated into its poverty reduction strategy. Understandably, most HIPCs have a natural static comparative advantage in the primary sector, and in the past many pursued inward-looking import-substitution strategies to develop a domestic industrial base. The import-substituting industrialization strategy developed under the protective regime remained inefficient, and in the face of severe external and internal imbalances a policy of trade liberalization and reform was carried out. Since the import-substitution regime resulted in policy-induced biases against agriculture, a policy reversal to a strategy of export promotion served to revive and enhance the static comparative advantage of primary commodities. The export structure remained undiversified and, in most cases, has not allowed poor countries to take full advantage of high income growth in the world economy. The challenge for the HIPCs is to formulate an effective strategy that enables them to reduce dependence on commodities through diversification with the help of policy and incentive mechanisms in a market-friendly way and not in the way of traditional import-substitution. Do PRSPs address the issue of trade policy and diversification? Reviewing the strategies for a number of countries, Hewitt and Gillson (2003) find that the PRSPs designed by different countries do not give adequate attention to trade policy. In general, there has been some emphasis on the promotion of exports, which may not have any significant effect on the objective of diversification. For countries in the interim period, the enhanced HIPC initiative allows some flexibility in exceptional cases to top-up debt relief at the completion point where exogenous factors have caused fundamental changes in their economic circumstances (known as ‘topping-up’) (IMF and IDA, 2002).23 But it does not contain any provision for addressing this problem beyond completion point. It is rather difficult to conceive that the effect of exogenous shocks can be mitigated in the post-completion stage. The bottom line of our argument is: while there is no doubt that linking debt relief with enhanced poverty reduction efforts and increased social expenditure is commendable, the importance of foreign exchange earnings, which is fundamental to the countries’ long-term external debt sustainability, has not been sufficiently emphasized.24 23 Under this provision Burkina Faso was granted an additional US$128 million at completion point. 24 As Ranis and Stewart (2001: 2) also stress, ‘ . . . in a sense it [the HIPC Initiative] is answering the wrong question—the problem is not debt, nor insufficient flows of externally unearned resources, but lies elsewhere, in the failure of countries to earn foreign exchange, and in deep structural and political problems that are not addressed by HIPC’. Similarly, Geda
293
Table 9.5. Actual and Projected Debt Service Indicators for HIPCs that have Reached Decision Point Debt Service (in US$million) Actual
Debt service/exports (in per cent)
Projection
Actual
Debt service/GDP (in per cent)
Projection
Actual
Projection
Countries
1998
2002
2003
2005
1998
2002
2003
2005
1998
2002
2003
2005
Benin Bolivia Burkina Faso Cameroon Chad Ethiopia Gambia Ghana Guinea Guinea-Bissau Guyana Honduras Madagascar Malawi Mali Mauritania Mozambique Nicaragua Niger Rwanda Sao Tome and Principe Senegal Sierra Leone Tanzania Uganda Zambia
64.1 390 60 401 26 101 26.1 560.1 128.2 7 130.8 311.2 166.1 90.1 74 88 104 231.4 17 18 6.6 207 8.9 224 110 147.3
33 270 22.5 228.9 25.8 149 16.1 267 85.6 2.2 59 220.5 50.5 47.2 69.5 39.2 40.2 158 25.4 13 2 141 19.7 106.9 60 137.7
30.9 290 19.1 225.2 32.7 88 16.1 164.2 81.6 5.3 46 315.2 53.6 79.4 63.4 35.1 47.3 11 26 13.7 2.1 146.4 27.6 157.7 64.3 177.8
33.5 277 25.1 253.6 36.4 88 12 111.6 68.4 3.3 37 202.4 72.7 50.7 64.9 36.5 61.9 8.3 29 15.7 1 138.9 13.5 166.3 78.1 215.1
16.1 28.6 16.5 18 8 9.7 12.4 22.1 15.5 23.5 19 12.6 20.6 15.6 11.5 22 41 8.2 5 30 55 15 9.4 20.7 15 16
10 16.1 7.4 9.2 11 15.1 12.5 10.2 10.8 3.7 8.8 9.1 6.6 9.7 6.7 10.6 3.8 16.9 7.2 9.6 8.7 9.2 14.1 6.9 8.6 12.7
6.8 17.3 5.2 7.7 12 9.2 11.9 5.6 9.4 8.1 6.8 13.3 5.2 15 6.2 9.1 4.3 11 6.7 9.1 8.6 8.8 18.2 8.8 8 14.2
6 14.1 5.9 8.9 2.1 9.3 8.2 3.4 7 3.8 5.5 8.3 5.9 8.6 5.3 8.1 2.6 8.3 6.8 9 3.4 7.3 5.7 7.8 8.2 13.5
2.8 4.5 2.3 4 2.2 1.5 6.2 7.5 3.6 3.4 18.2 5.9 4.3 5.1 2.8 10 2.5 10.5 1.3 2 16.3 4 1.3 2.8 1.7 4.5
1.2 3.5 0.8 2.5 1.3 2.4 5.8 4.3 2.7 1 8.8 3.3 1.1 2.5 2.2 3.9 1 6.1 1.2 0.8 3.7 2.8 2.5 1.2 1 3.7
1 3.8 0.6 2.1 1.4 1.3 6.1 2.4 2.5 2.1 6.2 4.5 1.1 4.5 1.8 3.2 1.1 4.3 1.1 0.8 3.4 2.5 3 1.7 1.1 4.8
0.9 3.5 0.6 2 0.9 1.2 4 1.3 1.8 1.1 4.7 2.5 1.3 2.5 1.6 3 1.2 3.5 1 0.8 1.4 2 1.3 1.6 1.2 5.2
2250.5
2296.6
2169.1
17.5
9.9
9.2
7
4.1
2.4
2.3
1.9
All (26 Countries)
3673
Note: For all 26 countries the debt service-export and debt service-GDP ratios are a weighted average. Source: Authors’ compilation from various World Bank and IMF documents available at www.worldbank.org/hipc
Commodity Prices and Debt Relief
9.4. Incorporating a Real Price Adjustment Mechanism in the HIPC Initiative to Address the Problem of Weakness in Commodity Prices It follows from the above discussion that the debt relief programmes for primary producing HIPC members can only be effective if they are complemented by external support in the event of external shocks. While unfavourable production shocks cannot be predicted far in advance, in the light of historical experience, it is very likely that a secular downward trend in prices, together with sharp price fluctuations, will tend to persist. Therefore, it is important to have a support mechanism for the HIPCs to compensate for an adverse trend in commodity prices. Gilbert and Tabova (2003) outline a number of modalities for linking commodity prices with countries’ ability to service debt using various financial derivatives.25 These modalities appear to be complex and may require further motivations and refinements. The nature of the empirical operation of the schemes also appears to be complicated. Despite the attractive features of Gilbert and Tabova’s modalities, the authors’ simulated results provide only mixed results and the authors themselves have refrained from making any recommendation on the adoption of such a scheme. The area of commodities is one where many different schemes have been tried in the past, but they have all virtually collapsed without providing any clear indication about corrective measures which would have made them viable. Excessive distortionary measures in commodity markets may also be untenable, especially as under the current WTO trade regime there is enormous pressure for liberalization of trade in agriculture. Rather than emphasizing any new initiatives, it appears more feasible to consider the impact of commodity prices on the debt burden of the predominantly commodity-exporting countries under the existing HIPC Initiative. Birdsall, Williamson and Deese (2002) propose such a provision, calling for additional debt relief for decision-point HIPCs in the face of adverse exogenous shocks.26 It is suggested that if in any year any of the HIPCs’ (2002) concludes that the African debt problem is essentially a trade problem and consequently the long-run solution to debt points to the importance of addressing trade and traderelated structural problems. 25 Gilbert and Tabova (2003) considered two schemes: commodity swaps and commodity swaptions. Commodity swaps are similar to loans taken by mining companies where both borrowing and repayment take place in terms of gold. There can be a gold interest and since transactions are made in terms of the quantity of gold, the arrangement combines financing and hedging. A gold loan is to be considered as a normal currency loan plus a fixed-for-floating gold swap. In the case of countries dependent on other commodities for exports, their debt service is independent of commodity prices and hence the countries might benefit from swapping out this fixed rate exposure for a floating rate which matches their floating commodity price exposure. Commodity swaptions, on the other hand, are schemes with the principle of coping with exceptional price movements only. 26 Birdsall et al. (2002) also propose that other exogenous shocks, such as weather, should be considered.
295
Mitigating the Impacts of Dependent Countries debt service to GNP ratio exceeds 2 per cent, the World Bank/IMF would examine whether the unfavourable trend could be attributable to exogenous factors.27 In the case of a positive assessment, compensation might be provided to the countries to reduce their debts to a sustainable level. Modifying the Birdsall, Williamson, and Deese (2002) proposal, we propose that there should at least be a compensatory mechanism for providing additional debt relief to decision point HIPCs when they are subjected to export revenue shortfall due to adverse trends in commodity prices. Establishment of such a support mechanism is justified given the secular decline and volatile nature of commodity prices and the resulting loss of purchasing power of exports, as well as earnings instability, of the commodity-dependent poor countries. The proposed extension to the HIPC Initiative is found to be relatively low cost, as estimated in the next section, providing further justification for this additional support to beneficiaries.
9.4.1. Hypothetical cost of a real commodity price adjustment mechanism under the HIPC initiative Ideally, using the trend rate of decline in commodity prices and some quantity of output considered to be the export of a normal year, one can estimate the effect of weakness in commodity prices and work out the compensation needed by individual decision point countries to keep their debt service at a sustainable level. However, trend rates are sensitive to the choice of sample period and are frequently accompanied by a much sharper fall in price than the average rate.28 Estimation based on the trend rate will also require the consideration of several export commodity prices for each of the individual countries, making it an extremely data intensive and complicated procedure. Furthermore, changes in export quantity might allow a country to maintain a sustainable debt burden for some period despite weakness in prices. Taking all these factors into account, we calculate the cost of a compensatory debt relief mechanism for HIPCs based on their export earnings from commodities.
27 The ‘2 per cent of GNP’ threshold comes from the argument of inappropriateness of the NPV of debt-to-export ratio as an adequate indicator of a country’s debt-servicing burden. It is suggested that a country should not be expected to spend more than 10 per cent of government revenue on debt service. Since most HIPCs in 1999 collected about 20 per cent of their GNP in tax revenue, the sustainable debt-servicing ratio can then be calculated as 2 per cent of GNP. 28 Therefore, a compensation based on the long-term trend rate can result in lower debt relief for a country in periods with much bigger adverse shocks in prices than the one suggested by the trend.
296
Table 9.6. Hypothetical Cost of Compensation for HIPCs Share of Primary Export Projection for HIPCs (US$ million) Exports Primary in Total Exports Exports Actual export Avg: 1996– in 2001 growth rate: (US$ 2000 million) 2001 2010 1990–99 (%)b (ratio)c a
Countries (1) Benin Bolivia Burkina Faso Cameroon Chad Ethiopia Gambia Ghana Guinea Guinea-Bissau Guyana Honduras Madagascar Malawi Mali Mauritania Mozambique Nicaragua Niger Rwanda Sao Tome and Principe Senegal Sierra Leone Tanzania Uganda Zambia
Primary Exports in 2010 if Grown at a Rate of 1990–99 (US$ million)
Projected Primary Exports in 2010 (US$ million)d
Shortfall in Primary Exports in 2010e
Revised Total 2010 Exports Debt in 2010f Stockg
Revised Debt-toExport Ratio in 2010
Total Exports Required for Debt-toExport Compensatory Ratio to be Debt Relief 150 per centh (US$ million)i
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
392 1442 305 2586 242 952 128 2416 860 71 718 2673 1046 480 662 433 805 932 279 126 18
791 3108 751 4248 1978 1815 233 4597 1647 181 1037 5456 1811 763 1190 528 3451 1570 484 367 42
2.5 3.6 2.6 0.0 0.6 2.6 2.7 11.1 1.0 7.5 5.0 8.5 6.5 2.1 2.3 2.5 6.8 10.0 4.5 3.0 5.0
0.4 0.7 0.6 0.6 0.6 0.9 0.9 0.6 0.6 0.7 0.7 0.5 0.8 0.8 0.5 0.6 0.6 0.8 0.7 0.8 0.8
156.8 980.6 180.0 1474.0 133.1 866.3 115.2 1425.4 481.6 51.1 473.9 1283.0 868.2 384.0 357.5 251.1 458.9 708.3 203.7 97.0 13.5
191.8 1310.1 146.6 1482.4 140.4 1073.5 143.8 3328.3 446.9 91.8 704.8 2476.0 1448.8 457.1 431.1 206.3 781.1 1529.9 141.9 76.6 20.1
316.4 2113.4 443.1 2421.4 1087.9 1651.7 209.7 2712.2 922.3 130.3 684.4 2618.9 1503.1 610.4 642.6 306.2 1967.1 1193.2 353.3 282.6 31.5
124.6 803.4 296.5 938.9 947.5 578.2 65.9 616.0 475.4 38.5 20.3 142.8 54.4 153.3 211.5 100.0 1186.0 336.7 211.4 206.0 11.4
666.4 2304.6 454.5 3309.1 1030.5 1236.8 167.1 5213.0 1171.6 142.5 1057.3 5313.2 1756.6 609.7 978.5 428.0 2265.0 1906.7 272.6 161.0 30.6
795.0 3333.0 1024.0 4248.0 934.0 2439.0 301.0 3503.0 1565.0 248.0 736.0 3323.0 1929.0 1148.0 1520.0 656.0 1611.0 1712.0 768.0 541.0 59.0
1.2 1.4 2.3 1.3 0.9 2.0 1.8 0.7 1.3 1.7 0.7 0.6 1.1 1.9 1.6 1.5 0.7 0.9 2.8 3.4 1.9
530.0 2222.0 682.7 2832.0 622.7 1626.0 200.7 2335.3 1043.3 165.3 490.7 2215.3 1286.0 765.3 1013.3 437.3 1074.0 1141.3 512.0 360.7 39.3
1692 121 1194 801 1038
2765 330 2274 1953 2207
1.0 5.0 7.9 11.5 3.0
0.5 0.4 0.7 0.9 0.7
778.3 49.6 871.6 688.9 737.0
721.5 33.0 1613.1 1659.8 581.7
1271.9 135.3 1660.0 1679.6 1567.0
550.4 102.3 46.9 19.8 985.3
2214.6 227.7 2227.1 1933.2 1221.7
2364.0 127.0 3525.0 1320.0 2575.0
1.1 0.6 1.6 0.7 2.1
1576.0 84.7 2350.0 880.0 1716.7
Total Compensatory Debt Relief (2001–2010)
(14)
228.1
389.2 33.5
22.8
155.6 34.8 9.3
239.4 199.6 8.7
122.9 494.9 1939.0
Note: a Projections made during decision point; b exports including both primaries and fuels; c estimated from UNCTAD database; d based on the assumption that share of primary exports in total exports remains unchanged; e is the difference between columns (8) and (7); f (3) less (10); g projected at the decision point; h such that figures in column 13 divided by 11 come out to be 150 per cent; i compensation is estimated for only those countries with revised debt-to-export ratio higher than 150 per cent.
Mitigating the Impacts of Dependent Countries Table 9.6 gives the details of the hypothetical cost estimation procedure. Following Birdsall, Williamson and Deese (2002), the present proposal considers the operation of a real commodity price adjustment mechanism for a period spanning over ten years, i.e. 2001–10.29 Column 6 of Table 9.6 gives the export earnings from primary commodities for individual decision point HIPCs in 2001. Since the DSA export growth projections have been too optimistic, as argued above, we estimate export earnings from primary commodities for each of the HIPCs in 2010 based on their actual export growth rate during 1990–99 (column 7). The assumption of unchanging share of commodities in total exports allows the computation of the value of primary exports in 2010 (column 8) based on the World Bank/IMF projection for total export earnings (column 3). The shortfall (column 9) in primary exports is then estimated as the difference between the projections based on DSA growth rate and those based on actual growth rate in the 1990s. The shortfall in export earnings is deducted from the projected exports of 2010 to obtain the figure for revised total exports shown in column (10).30 With the help of the projected total debt stock (column 9), it is now possible to obtain the debt-to-export ratio (column 12). Column (13) then calculates the export earnings required to make the debt burden just sustainable, which is then compared with the revised total exports in column (10) to obtain the adjustment to debt relief. Naturally, the adjustment is to be made only for those countries with a debt-toexport ratio higher than 150 per cent. The total solely due to deteriorating commodity export earnings for the 26 decision point countries involving a time horizon of 2001–10 is estimated at approximately US$2 billion, i.e. about US$0.2 billion a year.31
9.5. Conclusion While poverty alleviation should be the prime target of growth and development in poor countries, the role of trade and particularly enhanced export earnings cannot be overemphasized if any debt relief effort is to be made sustainable in the long run. The current HIPC scheme does not consider how the objective of rising exports will be achieved, given the nature of HIPC dependence on commodity exports and the past trends associated with their prices which has resulted in instability of export earnings and declining real export prices.
29
The extension over any time period is quite straightforward. This, in essence, assumes that non-primary exports grow at a rate as depicted in the DSA. The comparable computation by Birdsall, Williamson and Deese (2002), who consider shocks in total exports rather than the earnings from primary exports only, appears to be US$5.2 billion. 30 31
298
Commodity Prices and Debt Relief Currently, the HIPC scheme does not consider the possibility of postcompletion point beneficiary countries’ exposure to terms of trade shocks due to commodity price collapse and the resultant unsustainable debt burden. It is, however, realistic and feasible to consider an adjustment mechanism for adverse shocks in commodity export earnings so that the HIPC graduates can receive additional debt relief in cases where falling prices result in unsustainable debt. Finally, two other issues are of utmost importance. First, given the problems associated with commodities and the excessive dependence of LDCs and small states on primary commodities, the permanent solution to the problem of debt crisis lies in a structural shift in the composition of the export basket of these countries. Second, the debt relief programme should not be considered as a replacement for donor aid flows. Without additional aid, the enhanced fiscal capacity needed for social and other expenditures may not be achieved.
Appendix 9.1. Debt Indicators for 11 HIPCs that are still to be considered in the Debt Relief Programme COUNTRY
NPV of Debt stock 2001 (US$ Million)
PV/GNI
PV/XGS
EDT/GNI
1065 10647 536 177 4232 1295 1928 4032 2531 14547 999 41989
95 102 55 82 218 82 472 55 n.a. 148 79 —
1090 220 499 294 182 263 1679 174 n.a 673 205 —
156 111 84 82 231 157 487 78 n.a. 156 111 —
Burundi ˆ te d’Ivoire Co Central African Rep. Comoros Congo Rep. Lao PDR Liberia Myanmar Somalia Sudan Togo Total Debt
Note: PV stands for present value. GNI is gross national income; XGS is exports of goods and services; EDT is total external debt including short-term and the use of IMF credit. Source: Global Development Finance, 2003.
299
Mitigating the Impacts of Dependent Countries Appendix 9.2. Debt Indicators for non-HIPC LDCs COUNTRY
NPV of Debt Stock 2001 (US$ million)
PV/XGS
EDT/GNI
PV/GNI
n.a. 9712 245 2301 235 817 1567 n.a. 231 177 193 n.a. 406 177 142 112 37
n.a 113 151 159 82 154 92 n.a. 97 72 8 n.a. 74 38 148 68 21
n.a 33 53 85 61 32 49 n.a 63 46 54 n.a 55 43 85 58 31
n.a 21 49 72 35 21 28 n.a 41 31 44 n.a 37 32 59 40 17
Afghanistan Bangladesh Bhutan Cambodia Eritrea Haiti Nepal Tuvalu Cape Verde Djibouti Equatorial Guinea Kiribati Lesotho Maldives Samoa Solomon Islands Vanuatu Total
16352
Note : PV stands for present value. GNI is gross national income; XGS is exports of goods and services; EDT is total external debt including short-term and the use of IMF credit. Source: Global Development Finance, 2003.
Appendix 9.3. Debt Indicators for non-HIPC Small States COUNTRY
NPV of Debt Stock 2001 (US$ million)
PV/XGS
EDT/GNI
PV/GNI
n.a 739 765 307 231 177 181 193 175 190 5361 n.a. 406 177 1357 1658 2188 142 212 112 n.a. 297 42 2609 37
n.a. 49 191 9 97 72 125 8 15 79 120 n.a. 74 38 32 60 98 148 45 68 n.a. 27 45 59 21
n.a. 29 102 8 63 46 87 54 11 59 68 n.a 55 43 43 40 80 85 37 58 n.a. 22 42 33 31
n.a. 30 110 6 41 31 76 44 10 52 73 n.a 37 32 38 38 70 59 37 40 n.a. 22 28 35 17
Antigua and Barbuda Barbados Belize Botswana Cape Verde Djibouti Dominica Equatorial Guinea Fiji Grenada Jamaica Kiribati Lesotho Maldives Malta Mauritius PNG Samoa Seychelles Solomon Islands Suriname Swaziland Tonga Trinidad and Tobago Vanuatu
Note: PV stands for present value. GNI is gross national income; XGS is exports of goods and services; EDT is total external debt including short-term and the use of IMF credit. Source: Global Development Finance, 2003.
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10 Aid Flows and Commodity Prices Mohammad A. Razzaque, Philip Osafa-Kwaako, and Roman Grynberg
The persistent weakness of real commodity prices presents serious challenges for export earnings and domestic incomes in predominantly commodity exporting developing countries, as indicated by the estimates provided in Chapter 6 of substantial foreign exchange losses for these countries. In most of these countries, the budgetary position of national governments or monetary authorities is highly sensitive to their primary commodity export earnings and aid flows.1 In principle, the effects of declining commodity prices may be offset by increased aid flows, and following the effective collapse of most international commodity agreements and the end of compensatory finance arrangements (such as STABEX), there has been renewed interest in developing alternative concessionary aid-supported schemes to compensate for some of the terms of trade losses incurred by commodity-dependent countries.2 In this chapter, issues related to aid flows and commodity prices are considered from two perspectives. First, the pattern of recent aid flows to developing countries that are heavily dependent on primary commodities is examined to assess whether the secular decline in commodity prices has been accompanied by any commensurate increases in aid flows. Second, we propose the establishment of a multilateral aid instrument that would provide additional funds to commodity-dependent countries. In contrast to the risk management tools designed to address the problem of price volatility, the prolonged and secular decline in real commodity prices requires policies aimed at long-term structural diversification. Our present proposal is not to create an export earnings stabilization fund, but to provide additional 1 This is partly the result of the low tax revenues obtained in most developing countries (Agenor and Montiel, 1999). 2 Proposals for the establishment of new STABEX-like compensatory arrangements have been suggested under the ‘Chirac Initiative’, and in a report to the European Parliament Research Directorate prepared by the Overseas Development Institute (Page and Hewitt, 2001).
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Mitigating the Impacts of Dependent Countries resources for diversification projects in the low-income and highly vulnerable developing countries that rely predominantly on commodities for domestic production and export. The design of such a scheme may be envisioned in a number of ways, with different burden-sharing arrangements among donors, and different implications for recipient countries. What is proposed is one option for addressing the many issues involved in establishing such a scheme and is not intended to be definitive, but rather a basis for subsequent debate. The underlying principles in the proposal are outlined, together with some preliminary assessment of the cost of putting such a scheme into operation.
10.1. Aid Flows and Declining Commodity Prices For most developing countries, aid flows, in real terms, fell in the 1990s (White, 2002).3 Figures 10.1–10.3 show the relationship between the composite relative commodity price index and aid flows to LDCs, HIPCs, and small states respectively. The left vertical axis in these graphs measures the aid flows in real terms (in 1985 prices), while the right vertical axis is the scale of measurement for the composite relative commodity price index. A cursory assessment of the plots indicates that declining commodity prices have not been compensated for by commensurate increases in aid flows. Although real aid flows to HIPCs and LDCs showed modest growth in the 1980s, in the 1990s they fell, thereby compounding the economic impact of the simultaneous weakness in relative commodity prices. The pattern of declining aid flows is most prominent in the case of small vulnerable states (Figure 10.3). Apart from a temporary increase in the early 1990s, aid flows to SVs have experienced a sustained decline for most of the period since 1980. As an analysis based purely upon a composite index of export prices might miss individual price trends in the group of LDCs, HIPCs and SVs, we examine, for expository purposes, three country-specific cases. Figures 10.4–10.6 give a graphical exposition of aid flows to Mali, Papua New Guinea and Togo, and the real prices of significant national export commodities, viz. cotton, cocoa and phosphate respectively.4 In the case of Mali, cotton contributed an annual 3 We use the OECD definition of aid as ‘official development assistance’, referring to the sum of grants, concessional loans (with a grant element of more than 25 per cent), food aid and technical cooperation (Cassen et al., 1994). Other measures of aid such as ‘effective development assistance’ (EDA) and ‘official development finance’ (ODF) exist in the development finance literature. The World Bank’s most recent measure of EDA refers to the sum of grants and the grant element present in concessional loans disbursed within a given time period (Chang et al., 1999). On the other hand, ODF refers to the sum of EDA and the non-grant component of official loans. Compared with EDA, both ODA and ODF tend to overestimate aid flows to recipient countries as the face value of concessional loans is included in these measures. 4 A more rigorous exercise would require the construction of country-specific aggregate relative price indices reflecting individual countries’ export baskets.
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Aid Flows and Commodity Prices 12000
1.4
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Composite Commodity Price Index
0 1980
1985
Relative Commodity Price Index
1.6
1990
1995
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Figure 10.1. Composite Relative Commodity Prices and Aid Flows to LDCs, 1980-2000.
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1 8000 0.8 6000 0.6 4000
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0.2 HIPC Aid Flows
Relative Commodity Price Index
Note : The left vertical axis measures aid flows, while the right vertical axis measures the aggregate relative price index. Aid flows are in terms of 1985 real prices. Source : Real aid flows are authors’ estimates based on data from UNCTAD (2002b), while the relative commodity price index is as constructed in Chapter 3.
Composite Commodity Price Index
0
0 1980
1985
1990
1995
2000
Figure 10.2. Composite Relative Commodity Prices and Aid Flows to HIPCs, 1980–2000. Note : The left vertical axis measures aid flows, while the right vertical axis measures the aggregate relative price index. Aid flows are in terms of 1985 real prices. Source : Real aid flows are authors’ estimates based on data from UNCTAD (2002b), while the relative commodity price index is as constructed in Chapter 3.
average of 62 per cent of national export earnings over the period 1995–2000. Real cotton prices have, however, declined markedly since 1980, resulting in substantial foreign exchange losses for the country.5 Aid flows to Mali, 5 The trend decline rate in the real cotton price is estimated in Chapter 3, while estimates of foreign exchange losses are provided in Chapter 6.
303
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Small States Aid Flows
0 1980
Realtive Commodity Price Index
Mitigating the Impacts of Dependent Countries
Composite Price Index 0
1985
1990
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2000
Figure 10.3. Composite Relative Commodity Prices and Aid Flows to Small States, 1980–2000. Note : The left vertical axis measures aid flows, while the right vertical axis measures the aggregate relative price index. Aid flows are in terms of 1985 real prices.
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1990
Real ODA Flows: US$ Million
Source : Real aid flows are authors’ estimate based on data from UNCTAD (2002b), while the relative commodity price index is as constructed in Chapter 3.
0
Figure 10.4. Aid Flows to Mali and Cotton Prices Note : The left vertical axis measures real ODA flows, while the right vertical axis measures the relative commodity price index.
however, have not responded favourably to offset the decline in the relative cotton price and thus compensate for the resultant fall in the purchasing power of exports (see Figure 10.4). Indeed, Figure 10.4 seems to suggest that aid flows to Mali have been positively associated with the real price of cotton.
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Real Cocoa Price Index
Real Aid flows: US$ Millions
Aid Flows and Commodity Prices
Cocoa Price Index
0
0 1980
1985
1990
1995
2000
Figure 10.5. Aid Flows to Papua New Guinea and the Real Cocoa Price.
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Phosphate Price Index
Real ODA Flows (US$ millions)
Note : The left vertical axis measures real ODA flows, while the relative commodity price index is measured on the right vertical axis.
0.2
Phosphate Prices
0
0.0 1980
1985
1990
1995
2000
Figure 10.6. Aid Flows to Togo and the Real Phosphate Price. Note : The left vertical axis measures the real ODA flows while on the right vertical axis relative commodity price index is measured.
In the second case, that of Papua New Guinea, the cocoa price and aid flows are shown in Figure 10.5. With the sustained decline in cocoa prices since the mid-1980s, Papua New Guinea incurred a cumulative loss of US$562 million during the period 1985–2000 (as estimated in Chapter 6). However, real aid flows to Papua New Guinea declined over the same period—falling from $257.2 million in 1985 to an annual average of US$176.7 million in 1999– 2000. Finally, real ODA flows to Togo, together with the real price of phosphate, the country’s primary commodity export, are plotted in Figure 10.6.6 6 Real prices for other primary commodity exports from Togo—cotton and coffee—have also been persistently weak.
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Mitigating the Impacts of Dependent Countries Aid flows seem to have no relationship to phosphate price, but in the late 1990s some recovery in the price was accompanied by a substantial decrease in aid inflows. While the cases illustrated above might not represent the trends in all individual LDCs, HIPCs, and SVs, there is a general recognition that weakness in relative commodity prices has never been a criterion for aid allocation and there appears to be little evidence that aid flows in recent years have tended to compensate for decreased real commodity prices.7
10.2. A Proposal for the Establishment of an Aid-Financed Diversification Fund Declining commodity prices have serious implications for most LDCs, HIPCs and SVs, as it was estimated in Chapter 6 that they caused large foreign exchange losses in terms of reduced purchasing power of primary exports. Increased aid flows to these countries are one way of mitigating this problem. With the end of the EU STABEX scheme, a programme of pure export earnings stabilization has received mixed reviews from donors (Page and Hewitt, 2001).8 Pure compensatory schemes are criticized for two main reasons. First, donors point out that if the goal of a compensatory scheme is temporary stabilization of export revenues, then this may be best attained through the use of the IMF Compensatory Finance Facility (CFF), designed to resolve the temporary balance of payments disequilibria of countries. Second, critics argue that to the extent that grant compensation supports countries for their export earnings, they create adverse incentives and a moral hazard. The moral hazard results as compensatory transfers might discourage developing countries from embarking on the long-term adjustment of their economies given the sustained decline in the real prices of their dominant commodity exports. The present study takes the position that in the face of a secular decline in relative commodity prices, any compensatory scheme for commoditydependent poor countries should differ markedly from the functioning of STABEX (designed as an export earnings stabilization scheme) or from that of the CFF (designed to resolve temporary balance of payment disequilibria). Indeed, the pragmatic policy option in light of the secular declining trend would call for an intensification of support measures for export diversification and structural change. Our proposal is, therefore, for the establishment of a Joint Diversification Fund ( JDF), which would make additional grant transfers available to LDCs, HIPCs, and SVs to enable them to diversify their exports 7 In general, aid flows have focused on growth enhancing and poverty alleviation schemes. But there is also evidence that donors have given aid to ensure that the recipient countries can service their debts (UNCTAD 2002a). 8 Part of the theoretical argument, derived from Newbery and Stiglitz (1981), suggests that stabilization schemes may have very limited welfare benefits.
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Aid Flows and Commodity Prices away from a few primary commodities. According to the proposal, the JDF would be funded annually by OECD donors working within a multilateral framework. Developing countries satisfying a number of eligibility requirements could subsequently present well-defined diversification projects to the scheme for funding consideration. A number of choices and questions emerge in the operationalization of such a compensatory scheme, intended to strengthen the diversification process. What is outlined below is meant purely for the purposes of discussion and should not be seen as a definitive proposal. Clearly, the development of such a programme is beyond the intended scope of this paper and numerous issues would need to be resolved should such a proposal be accepted. Some hypothetical costs to developed countries in supporting the proposed Joint Diversification Fund are also calculated, along with the potential burden-sharing arrangement among donors.
10.2.1. Principles in scheme design The proposal below outlines several aspects of the JDF as envisaged by the authors. The implicit assumptions underlying this particular proposal are: . The fund would operate on the principle that contributions and access to the fund should be based upon the net transfer of resources from developing to developed countries that stems from the past change in real commodity prices. The actual resource requirements needed for economic diversification in HIPCs, LDCs, and SVs will almost certainly prove to be significantly larger than the loss of real purchasing power and hence the fund could only make a partial contribution to the process of diversification. . The fund would be aimed at providing resources for coherent private sector projects as well as infrastructural projects for export diversification. As diversification often requires both public and private sector investments, two windows should ideally be created for such a fund. 10.2.1.1. TIMING OF FUND DISBURSEMENTS The effectiveness of earlier instruments such as STABEX was partly hampered by inefficiencies in administration and disbursement of funds from the scheme (Page and Hewitt, 2001; Radetzki, 1990). There were significant time lags in the disbursement of funds, so that their release cycle resulted in pro-cyclical, instead of the anticipated counter-cyclical, shocks to recipient economies (Radetzki, 1990). The JDF would not be based upon price volatility but rather upon secular decline in price and would therefore be neutral in terms of price volatility. In our assessment, the disbursement of funds from any compensatory scheme, including the one proposed here, may be designed as ‘retrospective’ or ‘prospective’.
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Mitigating the Impacts of Dependent Countries A scheme is retrospective in its disbursement cycle if compensation is calculated at the end of each fiscal year, and is based on past movements in commodity prices. Under a ‘retrospective’ scheme, compensation is calculated each year based on country-specific composite commodity price indices, or on a commodity-by-commodity basis (as in the case of STABEX). This is in contrast to ‘prospective’ compensatory schemes where a given volume of funds is committed by donors, and released annually over a defined time span. The proposed JDF combines elements of both retrospective and prospective schemes in that the obligations are known to donor governments a priori but are based upon past price movements. 10.2.1.2. ELIGIBILITY REQUIREMENTS AND GRADUATION CRITERIA The intended beneficiaries of any JDF must be clearly specified if disbursements are to be effectively targeted. Previous compensatory schemes targeted various groups based on the objectives of the scheme—whether it aimed at redressing balance of payments difficulties (e.g. CFF) or ensuring export earnings stabilization (e.g. STABEX). In cases where commodity-specific stabilization is the goal, transfers to each country are made on a sectoral basis and are not tied to overall primary commodity receipts or to the export performance of other sectors of the economy (such as manufacturing or services). Such a scheme presents mixed blessings as the eligibility requirements may predict disbursements to a country experiencing an unusual adverse shock in one commodity, when its net primary commodity export earnings are fairly stable (or even increasing). Disbursements from such a fund may, therefore, be heavily skewed, with disproportionate shares being allocated to a few countries.9 As presently envisaged, the eligibility criteria for the JDF must be such as to result in the inclusion of a set of poor countries relying on primary commodities for a large fraction of their total export earnings. In the design of the JDF, we restrict our analysis to LDCs, HIPCs and small vulnerable states. Countries would have to decide whether all commodity-exporting countries in the group would be eligible to take part in the programme, or only those that had achieved a threshold dependence ratio on commodities. Should countries choose any above zero threshold of commodity dependence for the JDF, as presented in Table 10.1 below, it would also be necessary to devise an appropriate graduation arrangement for those entering or exiting the included group, e.g. LDCs, HIPCs, and countries with a 25 per cent dependence threshold.
9 ˆ te d’Ivoire, Ghana and This was the curse of STABEX where four countries (Senegal, Co Sudan) alone received over 40 per cent of fund disbursements for the period 1975–84 (Hewitt, 1987).
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Aid Flows and Commodity Prices 10.2.1.3. CALCULATING THE ANNUAL CONTRIBUTIONS According to our proposal, the JDF is to be initially capitalized by donor contributions and will require annual replenishments equivalent in total to the net transfers to developed countries from the real price decrease of exports over the reference period, which in the case illustrated below is six years. Ultimately, the actual size of an individual country’s contributions will remain a political decision subject to negotiation, though an attempt to develop a country-specific approach is considered below. There exist two main choices for calculating the contribution to the JDF scheme. First, one might calculate annual commodity earning losses of eligible countries based on trend or average decline in commodity prices.10 However, as discussed in Chapter 6, computed trend or average rates are sensitive to the choice of the base year in the sample, and to the reference period examined. Calculations of payments based on trend estimates are also tedious in practice, as they require detailed knowledge of a country’s export volumes.11 An alternative method is to base contributions to the fund on real export earning losses by countries with reference to a set of past real prices. This is the same approach as that adopted in Chapter 6 in quantifying terms of trade losses of commodity-dependent countries. A practical question arises as to which set of real prices should be chosen as the reference year for calculation. A set of (relatively) recent prices may be required, but a scheme sensitive only to annual movements in real prices may be undesirable.12 The desired payments into the JDF must therefore be calculated relative to a past base year. The choice of a base year in the mid-1980s may be desirable as it precedes the major commodity price collapse of the late 1980s. Other suggestions may be to construct price indices based on a moving average index of real commodity prices, calculated over a number of years. 10.2.1.4. POOLED OR COUNTRY-SPECIFIC ENVELOPES Rather than individual country allocations, we recommend that the scheme operates as a common pool arrangement (Kanbur et al., 1999), and is annually replenished by donors based on past known commodity price trends and resulting 10 Besides, it might be argued that for practical implementation, the trend rate for the most recent sample (for example, since the 1980s) is more relevant. Estimation of such a recent trend decline rate is likely to be very low, as real prices for most commodities had already fallen to a very low level by the 1980s. 11 It was also pointed out in Chapter 6 that calculation of losses based on the trend rate of decline would require finding out a representative volume of exports, which might not be a straightforward task. 12 A compensation scheme based solely on annual movements in prices is undesirable for two reasons. First, a temporary upward bulge in commodity prices may not disburse any returns from the fund although current prices may be severely depressed compared with prices over a broader time period (for example, since the 1960s). Second, when the current prices are already so depressed, a scheme triggered solely by year-to-year price changes is likely severely to underestimate the export earning shortfalls.
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Mitigating the Impacts of Dependent Countries loss of purchasing power of exports incurred by LDCs, HIPCs and SVs. Beneficiaries’ maximum envelopes could be based upon share of total real losses in the commodities covered. However, this would imply a country-specific envelope and a homogeneous capacity to develop viable and coherent projects. Where individual countries are unable to generate projects, the pooled fund nature would allow individual LDCs, HPICs, and SVs to draw on the fund in amounts over and above individual country losses in the reference period. 10.2.1.5. AID FUNGIBILITY AND MORAL HAZARD A contentious debate exists on the fungibility of aid (Cassen et al., 1994) and its consequences for aid effectiveness (Devarajan and Swaroop, 1998). Critics argue that to the extent that aid is fungible, specific development goals may not be attained as additional resources only permit recipient governments to reallocate budget expenditure in favour of immediate consumption needs or other lower priority projects. Untied aid is fully fungible, as it is readily absorbed by the recipient government’s most pressing social expenditure needs, or used to accomplish balance of payments needs such as debt servicing. Even in cases when aid is tied, critics argue that aid is fungible as additional donor finance simply ‘crowds out’ similar investments which would have been made by recipient governments. The basic argument is that aid relaxes a government’s budget constraint, enabling it to reallocate resources. While these arguments may be valid for large-donor projects targeting social expenditure, they may not be prominent in the case of the present proposal for a diversification fund, if only because the country-specific resources in the JDF are unlikely to be of such an order of magnitude as to induce crowding out. The problem of the fungibility of untied aid can be addressed by setting stringent requirements on resource use. We propose that the JDF grants should be tied solely to diversification projects, and disbursed to recipient governments presenting coherent strategies for funding with a view to reducing dependence on a limited range of commodities. To the extent that fungibility issues arise, they are more correctly of concern with regard to donor contribution rather than to recipient use of resources in such a programme. Indeed, if the JDF were accepted, it would be essential for funding to come from additional resources as real aid budgets are generally not expanding and hence the programme could be developed at the expense of other potentially important programmes, such as those combating HIV/AIDS. The JDF may present adverse incentives for diversification. In principle, this may also mean that countries which have reduced their dependence on primary exports via successful diversification schemes might be discouraged from continuing their efforts. There was certainly some evidence of this under STABEX when it operated as a scheme involving the transfer of public funds
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Aid Flows and Commodity Prices to the coffers of recipient beneficiaries. However, we find moral hazard concerns to be insignificant in the present case. As argued in the case of aid fungibility, the relatively small size of annual transfers, and the tying of funds to specific diversification projects, greatly minimize the scope for moral hazard behaviour. In the proposed design, triggering compensation into the joint fund does not guarantee automatic fund disbursements, as countries need to present planned diversification projects.
10.2.2. Hypothetical costs to developed countries of the diversification fund 10.2.2.1. COSTS OF SCHEME Let us consider an aid-financed scheme to compensate for foreign exchange losses incurred by LDCs, HIPCs, and SVs due to declining commodity prices but tied to diversification projects. The diversification fund can be based on either a full or partial adjustment for primary commodity export earning losses. Another factor is the determination of the threshold level of dependence on commodities for export earnings that will make countries eligible for the diversification support. We simulate the potential costs of a number of schemes on the basis of 50 per cent compensation and various threshold dependence levels, assuming that they had been in operation for the period 1995–2000. Table 10.1 summarizes the results of hypothetical cost estimates. The size of the proposed fund varies significantly depending on the commodity dependence threshold and rate of contribution for commodity-related foreign exchange losses. The second column shows that a scheme which is aimed at making up for 50 per cent real foreign exchange loss by individual LDCs, HIPCs, and SVs, but without needing any dependence threshold, would have required US$1.9 billion (in 1984–86 prices) in 1995.13 However, if the size of the fund is to depend on foreign exchange losses of those countries that receive at least 50 per cent of their export earnings from commodities, the comparable figure would be about US$1.3 billion (in 1984–86 prices) in 1995, which increases to approximately US$2 billion in 2000 (column 4 in Table 10.1). Estimation of the fund size based on 25 per cent and 75 per cent dependence on commodities is also presented in Table 10.1. Corresponding to aggregate results as summarized in Table 10.1, details of the size of grants available to each country are presented in Appendix 10.6, paras. 10.1–10.4.
13 Note that this figure is higher than 50 per cent of the foreign exchange loss estimated for LDCs, HIPCs and SVs in Chapter 6. This is because the net loss calculated in Chapter 6 was obtained by summing over both negative (losses) and positive (terms of trade gains) figures. But, for the exercises in Table 10.1, the compensation is based on countries that have incurred only foreign exchange losses.
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Mitigating the Impacts of Dependent Countries Table 10.1. Cost Estimates for a Joint Diversification Fund for LDCs, HIPCs, and Small States (US$ million in 1984–86 prices) Size of Fund with 50% Adjustment for Foreign Exchange Loss from Commodities
Year
No dependence threshold
25% dependence threshold
50% dependence threshold
75% dependence threshold
1995 1996 1997 1998 1999 2000
1932.06 2666.40 3212.79 3899.73 4413.30 4385.20
1790.68 2562.96 3007.87 3458.47 3990.70 3965.00
1292.22 1815.98 1944.22 2152.61 2391.86 2053.54
530.23 722.31 704.52 636.34 694.99 712.46
20509.47
18775.67
11650.42
4000.84
TOTAL
Note: The dependence threshold in columns 2, 3 and 4 implies that countries are only considered for the fund if their earnings from commodities account for, respectively, 25, 50, and 75 per cent of their merchandise exports. Estimates in column 1 (i.e. with no dependence threshold) does not consider individual countries’ dependence on commodities for exports. Source: Authors’ estimates.
10.2.2.2. BURDEN SHARING AMONG DONORS The establishment of the JDF will involve costs for donor countries. For the sixyear period examined, 1995–2000, estimated costs for the scheme total approximately US$20 billion in 1984–86 prices, i.e. about US$25 billion in 2000 nominal prices (under the no dependence threshold condition as presented in column 2 of Table 10.2).14 This figure drops to US$11.6 billion in real terms or US$14 billion in nominal 2000 dollars if a 50% threshold is employed. We now consider hypothetical burden-sharing arrangements among OECD’s 22 Development Assistance Committee (DAC) members. In some recent studies, donor contributions are calculated according to their ability to pay (Addison et al., 2003), which is computed as the relative share of an individual country’s GDP (or GNI) of the combined DAC total. However, as many DAC members do not meet the UN stipulated ODA target, a scheme requiring more contributions from donors with relatively low ODA/GNP ratios may be desirable. Consequently, we illustrate a funding arrangement where half the required funds are contributed by donors based on the relative sizes of their GNI, and the other half depending on ODA/GNP ratios. For the second half of the contribution, following Berlage et al. (2003), individual country contributions, Ci , to the fund can be computed as: Ci ¼ (0:7 Łi )Gi
14 The conversion from 1984–86 real prices to 2000 current US dollars is based on the changes in the manufacturing export unit value index of developed market economy countries over the same period.
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Aid Flows and Commodity Prices where Gi is the GNI of donor i, with f chosen such that Ci contributions sum up to $25 billion (in nominal US dollars).15 Results for this hypothetical burden-sharing arrangement are presented in Tables 10.2 and 10.3. From Table 10.2 it is observed that based on the share of combined DAC GNI, the US has to make the largest contribution, followed by the EU. Since the US ODI/ GNI ratio of 0.1 is much lower than the UN stipulated targets, it will also have to make a relatively high contribution to the JDF. Denmark, Luxembourg, the Netherlands, Norway and Sweden have negative figures for share based on ODA targets. This is because the ODA that these countries currently provide amounts to more than the UN target of 0.7 per cent of their GNI. On the whole, about 75 per cent of the assistance should come from the US and the EU.16 Table 10.3 estimates the hypothetical annual contribution of the donor countries, had the programme been operated in 1995–2000. The US, the EU and Japan would have been required to provide, respectively, US$1.9 billion, US$1.2 billion and US$0.8 billion. These costs fall to US$1 billion, US$0.65 billion and US$0.5 billion with a 50% dependence threshold. Even after the contribution to the diversification fund, for most countries the ODI/GNI share would be much lower than the UN target. In the case of the US and the EU, the ODI/GNI ratio would increase only marginally—by just 0.2 percentage points. On the whole, funding of the scheme would require an annual increase of donor ODA flows of 9.31 per cent. It might be of interest to compare the estimated EU funding under the current scheme with that under the STABEX programme. Total contributions from EU members in this hypothetical arrangement are estimated at US$7.2 billion for the six-year period, implying an annual average contribution of about US$1.2 billion (or about !1.07 billion). Under the Lome´ IV negotiations, STABEX funds amounted to !1.8 billion for the final five-year period of 1995–2000—i.e. annual payments of !360million (Page and Hewitt, 2001). Therefore, for EU member countries, the current scheme of 50 per cent compensation for LDCs, HIPCs and SVs with no commodity dependence threshold requires three times the support provided under STABEX. With a 50% dependence threshold, the EU contribution would be halved. Another way of calculating the donors’ contribution to the JDF might be on the basis of some combination of their GNI and their imports of primary commodities from LDCs, HIPCs and SVs. Data on imports of primary commodities from the recipient countries by DAC members are not available, so it was not possible to undertake such an exercise.17 However, given the general 15 In this present exercise f was calculated as 0.0013 for the set of estimates presented in Tables 10.2 and 10.3. 16 The share of the US in the JDF fund is calculated to be 46 per cent, while the corresponding figure for the EU is 29 per cent. The third largest contribution is estimated for Japan, which is about 19 per cent. 17 Data on DAC member countries’ total imports of primary commodities are, however, available. But a large portion of these imports are supplied by other countries not included in the sample of LDCs, HIPCs, and SVs.
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Mitigating the Impacts of Dependent Countries Table 10.2. Hypothetical Burden Sharing among Donors Contribution to Joint Diversification Fund (US$ million) Share of Share based GNI in 2000 Combined ODA in 2000 Share based on ODA (US$ million) DAC GNI (US$ million) ODA/GNI on GNI targets Total Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States TOTAL DAC Of which : EU Members
370209.09 186121.01 229121.48 684489.93 156876.5 119307.19 1284764.47 1862363 111916 79335.36 1074281.77 4807580.71 17314.07 374644.53 45017.01 158010 103795.03 552060 224515.64 258364.53 1417780.81 9928500
0.015 0.008 0.010 0.028 0.007 0.005 0.053 0.077 0.005 0.003 0.045 0.200 0.001 0.016 0.002 0.007 0.004 0.023 0.009 0.011 0.059 0.413
987.14 423.27 819.66 1743.6 1664.18 370.84 4104.71 5030 226 234.82 1376.26 13507.96 122.97 3134.78 113.22 1263.56 270.62 1194.82 1798.95 890.37 4501.26 9954.89
0.267 0.227 0.358 0.255 1.061 0.311 0.319 0.270 0.202 0.296 0.128 0.281 0.710 0.837 0.252 0.800 0.261 0.216 0.801 0.345 0.317
192.45 96.75 119.10 355.82 81.55 62.02 667.86 968.11 58.18 41.24 558.44 2499.12 9.00 194.75 23.40 82.14 53.96 286.98 116.71 134.31 737.00
175.01 95.95 85.54 332.47 61.75 50.65 533.27 873.38 60.80 34.96 670.18 2197.51 0.19 55.88 22.02 17.18 49.74 291.21 24.80 100.16 591.58
367.45 192.70 204.65 688.29 19.80 112.67 1201.13 1841.50 118.98 76.21 1228.62 4696.63 8.81 138.87 45.43 64.96 103.69 578.19 91.91 234.46 1328.59
0.100
5161.12
6495.36
11656.48
24046368.13
1.000
53 734
0.223
12500.00
12500.00
25000.00
7794196.86
0.324
25 273
0.324
4051.65
3194.66
7246.31
Note: The compensation scheme as presented in this table is based on 50 per cent compensation for the foreign exchange loss incurred by all LDCs, HIPCs, and SVs. The estimates of foreign exchange loss in 1984–86 prices come from Chapter 4, but have been converted into nominal dollars. The present hypothetical exercise considers a funding arrangement where half the required funds are contributed by donors on the basis of the relative size of their GNI, and the other half depend on ODA/GNP ratios. Source: Authors’ estimates based on data on GNI and ODA flows from World Bank (2002).
illustrations in Tables 10.2 and 10.3, the burden-sharing arrangement among donors under alternative scenarios can be readily undertaken when the information becomes available.
10.3. Conclusions The secular decline in relative commodity prices requires commoditydependent countries to pursue growth strategies that would reduce their reliance on a narrow set of primary commodity exports. Therefore, export diversification provides a useful forward-looking growth strategy. Our proposal in this chapter is for the establishment of a multilateral fund that provides a means of supporting diversification projects in commodity-dependent LDCs, HIPCs, and SVs.
314
Aid Flows and Commodity Prices Table 10.3. ODA/GNI Positions of Donors after the Hypothetical Contributions to the Joint Diversification Fund Annual Percentage Increase in Annual ODA (based ODA (2000) Contribution on 2000 values) Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States TOTAL DAC Of which EU Members
987.14 423.27 819.66 1743.6 1664.18 370.84 4104.71 5030 226 234.82 1376.26 13507.96 122.97 3134.78 113.22 1263.56 270.62 1194.82 1798.95 890.37 4501.26 9954.89 53733.88 25 273
61.24 32.12 34.11 114.71 3.30 18.78 200.19 306.92 19.83 12.70 204.77 782.77 1.47 23.15 7.57 10.83 17.28 96.36 15.32 39.08 221.43 1942.75 4166.67 1207.72
6.20 7.59 4.16 6.58 0.20 5.06 4.88 6.10 8.77 5.41 14.88 5.79 1.19 0.74 6.69 0.86 6.39 8.07 0.85 4.39 4.92 19.52 7.75 4.78
Current Additional Ratio of increase Total ODA/GNI in ODA/GNI ODA/GNI (expressed (expressed (expressed in percent- in percentage in percentage age points) points) points) 0.267 0.227 0.358 0.255 1.061 0.311 0.319 0.270 0.202 0.296 0.128 0.281 0.710 0.837 0.252 0.800 0.261 0.216 0.801 0.345 0.317 0.100 0.223 0.324
0.017 0.017 0.015 0.017 0.002 0.016 0.016 0.016 0.018 0.016 0.019 0.016 0.008 0.006 0.017 0.007 0.017 0.017 0.007 0.015 0.016 0.020 0.017 0.015
0.283 0.245 0.373 0.271 1.063 0.327 0.335 0.287 0.220 0.312 0.147 0.297 0.719 0.843 0.268 0.807 0.277 0.234 0.808 0.360 0.333 0.120 0.241 0.340
Source: Authors’ estimates.
Before concluding the chapter, it is worth pointing out two important issues. First, the fund for diversification should not be considered as a substitute for other aid flows to the recipient countries. In fact, it must be supplemented by regular and increased aid flows, which are critical for supporting overall growth and development in poor countries. Last, but not least, setting up a diversification fund does not guarantee success in attracting local or foreign investment in diversification projects; a host of internal economic reforms are also needed in many commodity-dependent countries to establish an appropriate investment climate. An export diversification fund cannot be a panacea for the problems of commodity-dependent developing countries. However, if it is based on additional aid resources and is combined with sound economic policies, it would make a valuable contribution to redress the negative development impact of the secular decline in real commodity prices.
315
Appendix 10.1. Country Allocations from JDF with No Dependence Threshold (with Compensation 50 per cent of Foreign Exchange Loss from Commodities) (Dependence Threshold 0.0)
COUNTRY Afghanistan Antigua and Barbuda Angola Bangladesh Barbados Belize Benin Bhutan Bolivia Botswana Burkina Faso Burundi Cambodia Cameroon Cape Verde CAR Chad Comoros Congo ˆ te D’Ivoire Co DRC
Dep. Index
1995
1996
1997
1998
1999
2000
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Dep. Index
Dep. Index
Dep. Index
Dep. Index
Dep. Index
0.56 0.04
8.93
0.82 0.07
11.07
0.60 0.06
9.37
0.48 0.06
6.53
0.70 0.05
7.42 0.09
0.53 0.07
0.01 0.14 0.30 0.92 0.48 0.18 0.64 0.14 0.52 0.93 0.26 0.67 0.27 0.49 0.61 0.59 0.12 0.71 0.91
4.81
0.01 0.13 0.37 0.90 0.36 0.19 0.68 0.12 0.50 0.93 0.25 0.64 0.25 0.42 0.55 0.27 0.07 0.67 0.78
4.15
0.01 0.12 0.35 0.84 0.51 0.15 0.74 0.10 0.62 1.00 0.17 0.54 0.24 0.44 0.64 0.59 0.10 0.62 0.76
5.45
0.01 0.12 0.32 0.89 0.47 0.16 0.68 0.15 0.67 1.00 0.10 0.57 0.23 0.66 0.56 0.74 0.10 0.64 0.88
10.32
0.01 0.11 0.30 0.96 0.32 0.15 0.67 0.11 0.53 1.00 0.08 0.59 0.22 0.58 0.46 0.57 0.08 0.58 0.68
0.84
0.01 0.11 0.29 0.88 0.35 0.12 0.64 0.12 0.62 0.78 0.06 0.52 0.42 0.60 0.51 0.41 0.05 0.57 0.64
2.75 216.35 5.23 28.78 32.13 0.19 12.88 8.04 1.27 14.02 386.23 4.63
4.56 187.16 4.73 25.69 37.85 18.51 0.16 15.63 6.49 0.93 3.74 495.49 20.79
5.53 201.50 17.80 65.20 40.09 0.25 27.28 8.06 1.18 17.48 510.25 28.77
6.47 205.01 34.04 57.03 34.17 16.16 0.18 57.44 12.54 0.84 41.24 522.14 64.43
6.98 7.11 201.60 47.52 78.92 34.23 113.85 0.20 66.16 9.85 0.87 15.92 474.49 29.72
8.18 0.14
17.84 8.25 2.70 6.70 211.73 42.92 80.14 31.57 0.42 89.81 13.00 0.74 419.23 24.83
Djibouti Dominica Equatorial Guinea Eritrea Ethiopia Fiji Gabon Gambia Ghana Grenada Guinea Guinea-Bissau Guyana Haiti Honduras Jamaica Kenya Kiribati Lao PDR Lesotho Liberia Madagascar Malawi Maldives Mali Mauritania Mauritius Mozambique Myanmar Nepal Nicaragua Niger Papua New Guinea Rwanda Samoa Sao Tome and Principe Senegal
0.28 0.55 0.31
0.26
0.32 0.97 0.51 0.12 0.92 0.49 0.80 0.74 0.92 0.66 0.34 0.50 0.71 0.66 0.95 0.47 0.08 0.06 0.86 0.89 0.74 0.61 0.59 0.30 0.81 0.91 0.17 0.82 0.72 0.61
0.22 23.18 71.38 63.12
9.10 0.46 11.81 29.05
21.07 0.95 25.51 57.71 2.95 6.75
20.88 112.79 8.71 22.58 45.54
0.20 0.48 0.26
0.35
0.18 0.95 0.44 0.10 0.91 0.69 0.81 0.64 0.91 0.71 0.34 0.45 0.74 0.61 0.96 0.38 0.05 0.08 0.95 0.80 0.83 0.73 0.61 0.30 0.62 0.91 0.17 0.92 0.58 0.58
0.37 31.22 7.05 43.45
0.74 0.86 0.73
0.30
0.84 0.91 0.88
0.44
8.23
0.46
0.17 0.53 0.13
0.31
0.22 35.02 11.45 56.83
40.98 158.55 15.55 50.16 55.30 31.54
0.06 0.90 0.40 0.12 1.00 0.58 0.79 0.61 0.57 0.55 0.28 0.44 0.77 0.62 0.95 0.19 0.05 0.11 0.88 0.94 0.77 0.53 0.65 0.29 0.74 0.73 0.34 0.83 0.66 0.56
0.20 0.26 5.58
95.32
10.12 11.40 54.42
0.13 28.64 1.91 14.77 51.99 2.45 7.43
0.30 0.38 0.14
0.76
0.32 59.57 8.36 188.42
51.11 203.86 44.92 63.36 59.63 55.47
0.12 0.95 0.41 0.20 0.57 0.47 0.79 0.52 0.71 0.72 0.20 0.53 0.76 0.73 0.91 0.15 0.04 0.12 0.88 0.72 0.76 0.56 0.52 0.28 0.60 0.61 0.15 0.62 0.74 0.59
0.65 0.93 0.66
0.64 0.36
0.84 0.94 0.50
0.47
3.57
0.46
76.98
8.83 11.64
0.27 21.86 2.26 15.13 51.67 4.35 13.46
0.69 0.43 0.14
3.87 1.01
0.62 0.44 0.08
0.80 78.16 6.90 199.92
43.20 241.97 25.93 42.62 79.38 42.24
0.32 0.90 0.37 0.20 0.99 0.54 0.57 0.53 0.77 0.63 0.13 0.42 0.81 0.64 0.81 0.20 0.04 0.16 0.92 0.77 0.61 0.43 0.52 0.26 0.51 0.58 0.13 0.74 0.85 0.52
44.71 267.74 32.86 65.98 84.26 33.15
0.39 0.84 0.42 0.15 0.96 0.68 0.49 0.51 0.69 0.70 0.16 0.55 0.81 0.64 0.68 0.19 0.03 0.29 0.53 0.82 0.54 0.47 0.60 0.25 0.48 0.41 0.08 0.72 0.83 0.51
0.71 0.39
0.80 0.92 0.74
0.62 0.72
0.80 0.91 0.95
0.32 1.12
0.45
12.36
0.48
33.32
20.62
12.95 0.12 10.96
0.26 21.00 2.58 16.40 85.75 4.35 7.79
73.69 1.11 23.51 18.16 8.94 2.06 43.76 0.27 25.20 0.56 5.93 16.45 58.47 3.16 26.04
1.12
5.57 214.49 112.00 1.19 35.87 20.00 9.76 25.44 50.58 0.21 24.94 0.69 16.20 12.57 67.08 3.72 25.26 4.99 37.11 260.26 24.78 78.96 98.07 26.59
(Continued )
Appendix 10.1. (Continued )
COUNTRY Seychelles Sierra Leone Solomon Islands Somalia St Kitts and Nevis St Lucia St Vincent Sudan Suriname Swaziland Togo Tonga Trinidad and Tobago Tuvalu Uganda United Rep. of Tanzania Vanuatu Vietnam Yemen Zambia TOTAL
Dep. Index
1995
1996
1997
1998
1999
2000
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
0.46 0.77 0.98
1.97
0.59 0.86
5.30
0.52 0.99 0.91 0.96 0.34 0.52 0.81 0.08 0.48 0.89 0.69 0.73 0.38 0.05 0.80
Dep. Index 0.30 0.52 0.97 0.56 0.61
19.41
0.72 0.80 0.92 0.95 0.42 0.50 0.81 0.08
125.08 100.73
27.84 26.60 13.18 24.70
304.37 14.12 1932.06
2.09 4.14 6.29
Dep. Index 0.63 0.58 0.99 0.70 0.76
42.44 22.74 23.46 34.13 0.21 6.73
0.61 0.76 0.96 0.79 0.44 0.51 0.92 0.10
0.32 0.84 0.69
0.01 218.81 115.02
0.79 0.33 0.03 0.77
0.51 559.93 17.73 56.03 2666.40
5.49 9.85 5.52
Dep. Index 0.74 0.34 1.00 0.48 0.57
6.93 6.40 3.61
Dep. Index
10.48
3.44
0.65 0.31 0.91
0.62 0.45
6.50 0.51
0.63 0.39
9.86 0.56
1.66 1.64 66.58 30.56 31.77 14.57 0.36 9.51
0.84 0.81 0.21 0.91 0.49 0.48 0.87 0.06
1.84 1.84
0.76 0.32 0.94
8.94
Dep. Index
2.51
37.93 20.17 27.83 28.60 0.46 10.00
0.62 0.85 0.86 0.92 0.49 0.62 0.88 0.11
56.82 22.25 36.62 18.68 0.27 33.57
0.68 0.78 0.46 0.88 0.45 0.49 0.88 0.09
0.71 0.83 0.78
0.02 219.51 78.60
0.67 0.85 0.89
0.01 209.05 137.44
0.25 0.90 0.69
0.01 257.85 102.38
0.49 0.91 0.58
0.01 367.27 114.94
0.86 0.32 0.03 0.96
1.40 936.96 26.83 72.17
1.00 0.35 0.06 0.56
1.36 1235.88 32.05 113.58
0.95 0.30 0.03 0.57
0.81 1426.40 33.76 179.95
0.91 0.26 0.03 0.69
0.57 1470.40 46.36 123.65
3212.79
3899.73
4413.30
32.59 31.77 19.84 0.28
4385.19
Appendix 10.2. Country Allocations from JDF with No Dependence Threshold (with Compensation 50 per cent of Foreign Exchange Loss from Commodities) (Dependence Threshold 0.25)
COUNTRY
Dep. Index
Afghanistan Antigua and Barbuda Angola Bangladesh Barbados Belize Benin Bhutan Bolivia Botswana Burkina Faso Burundi Cambodia Cameroon Cape Verde CAR Chad Comoros Congo ˆ te D’Ivoire Co DRC Djibouti
0.56 0.04 0.01 0.14 0.30 0.92 0.48 0.18 0.64 0.14 0.52 0.93 0.26 0.67 0.27 0.49 0.61 0.59 0.12 0.71 0.91 0.28
1995
1996
1997
1998
1999
2000
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
8.93
216.35
28.78 32.13 0.19 12.88 8.04 1.27 386.23 4.63 0.26
Dep. Index 0.82 0.07 0.01 0.13 0.37 0.90 0.36 0.19 0.68 0.12 0.50 0.93 0.25 0.64 0.25 0.42 0.55 0.27 0.07 0.67 0.78 0.20
11.07
187.16
25.69 37.85 18.51 15.63 6.49 0.93 495.49 20.79
Dep. Index 0.60 0.06 0.01 0.12 0.35 0.84 0.51 0.15 0.74 0.10 0.62 1.00 0.17 0.54 0.24 0.44 0.64 0.59 0.10 0.62 0.76 0.17
9.37
201.50
65.20
27.28 8.06 1.18 510.25 28.77
Dep. Index 0.48 0.06 0.01 0.12 0.32 0.89 0.47 0.16 0.68 0.15 0.67 1.00 0.10 0.57 0.23 0.66 0.56 0.74 0.10 0.64 0.88 0.30
6.53
205.01
57.03 16.16 57.44 12.54 0.84 522.14 64.43 0.76
Dep. Index 0.70 0.05 0.01 0.11 0.30 0.96 0.32 0.15 0.67 0.11 0.53 1.00 0.08 0.59 0.22 0.58 0.46 0.57 0.08 0.58 0.68 0.69
7.42
6.98
201.60
78.92 113.85 66.16 9.85 0.87 474.49 29.72 3.87
Dep. Index 0.53 0.07 0.01 0.11 0.29 0.88 0.35 0.12 0.64 0.12 0.62 0.78 0.06 0.52 0.42 0.60 0.51 0.41 0.05 0.57 0.64 0.62
8.18
8.25 2.70 211.73
80.14
0.42 89.81 13.00 0.74 419.23 24.83
(Continued )
Appendix 10.2. (Continued )
COUNTRY
Dep. Index
Dominica Equatorial Guinea Eritrea Ethiopia Fiji Gabon Gambia Ghana Grenada Guinea Guinea-Bissau Guyana Haiti Honduras Jamaica Kenya Kiribati Lao PDR Lesotho Liberia Madagascar Malawi Maldives Mali Mauritania Mauritius Mozambique Myanmar
0.55 0.31 0.32 0.97 0.51 0.12 0.92 0.49 0.80 0.74 0.92 0.66 0.34 0.50 0.71 0.66 0.95 0.47 0.08 0.06 0.86 0.89 0.74 0.61 0.59 0.30 0.81 0.91
1995
1996
1997
1998
1999
2000
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
0.22 23.18
63.12
9.10 0.46 11.81 29.05
21.07
25.51 57.71 2.95 6.75
20.88 112.79
Dep. Index 0.48 0.26 0.18 0.95 0.44 0.10 0.91 0.69 0.81 0.64 0.91 0.71 0.34 0.45 0.74 0.61 0.96 0.38 0.05 0.08 0.95 0.80 0.83 0.73 0.61 0.30 0.62 0.91
31.22 7.05
95.32
10.12 11.40 54.42
0.13 28.64
14.77 51.99 2.45 7.43
40.98 158.55
Dep. Index 0.53 0.13 0.06 0.90 0.40 0.12 1.00 0.58 0.79 0.61 0.57 0.55 0.28 0.44 0.77 0.62 0.95 0.19 0.05 0.11 0.88 0.94 0.77 0.53 0.65 0.29 0.74 0.73
35.02 11.45
76.98
8.83 11.64
0.27
15.13 51.67 4.35 13.46
51.11 203.86
Dep. Index 0.38 0.14 0.12 0.95 0.41 0.20 0.57 0.47 0.79 0.52 0.71 0.72 0.20 0.53 0.76 0.73 0.91 0.15 0.04 0.12 0.88 0.72 0.76 0.56 0.52 0.28 0.60 0.61
59.57 8.36
20.62
12.95 0.12
0.26
16.40 85.75 4.35 7.79
43.20 241.97
Dep. Index 0.43 0.14 0.32 0.90 0.37 0.20 0.99 0.54 0.57 0.53 0.77 0.63 0.13 0.42 0.81 0.64 0.81 0.20 0.04 0.16 0.92 0.77 0.61 0.43 0.52 0.26 0.51 0.58
1.01 0.80 78.16 6.90
73.69 1.11 23.51 18.16 2.06 43.76 0.27
16.45 58.47 3.16 26.04
44.71 267.74
Dep. Index 0.44 0.08 0.39 0.84 0.42 0.15 0.96 0.68 0.49 0.51 0.69 0.70 0.16 0.55 0.81 0.64 0.68 0.19 0.03 0.29 0.53 0.82 0.54 0.47 0.60 0.25 0.48 0.41
1.12
5.57
112.00 1.19 35.87 20.00 25.44 50.58 0.21
16.20 12.57 67.08 3.72 25.26 4.99 37.11 260.26
Nepal Nicaragua Niger Papua New Guinea Rwanda Samoa Sao Tome and Principe Senegal Seychelles Sierra Leone Solomon Islands Somalia St Kitts and Nevis St Lucia St Vincent Sudan Suriname Swaziland Togo Tonga Trinidad and Tobago Tuvalu Uganda United Rep. of Tanzania Vanuatu Vietnam Yemen Zambia TOTAL
0.17 0.82 0.72 0.61 0.74 0.86 0.73 0.44 0.46 0.77 0.98 0.59 0.86 0.52 0.99 0.91 0.96 0.34 0.52 0.81 0.08 0.48 0.89 0.69 0.73 0.38 0.05 0.80
22.58 45.54
0.30 8.23 1.97
5.30
27.84 26.60 13.18 24.70
125.08 100.73 304.37
1790.68
0.17 0.92 0.58 0.58 0.84 0.91 0.88 0.46 0.30 0.52 0.97 0.56 0.61 0.72 0.80 0.92 0.95 0.42 0.50 0.81 0.08 0.32 0.84 0.69 0.79 0.33 0.03 0.77
50.16 55.30 31.54 0.20 0.26 5.58 2.09 4.14 6.29
42.44 22.74 23.46 34.13 0.21 0.01 218.81 115.02 0.51 559.93 56.03 2562.96
0.34 0.83 0.66 0.56 0.65 0.93 0.66 0.47 0.63 0.58 0.99 0.70 0.76 0.61 0.76 0.96 0.79 0.44 0.51 0.92 0.10 0.71 0.83 0.78 0.86 0.32 0.03 0.96
44.92 63.36 59.63 55.47 0.64 0.36 3.57 5.49 9.85 5.52
37.93 20.17 27.83 28.60 0.46 0.02 219.51 78.60 1.40 936.96 72.17 3007.87
0.15 0.62 0.74 0.59 0.84 0.94 0.50 0.46 0.74 0.34 1.00 0.48 0.57 0.62 0.85 0.86 0.92 0.49 0.62 0.88 0.11 0.67 0.85 0.89 1.00 0.35 0.06 0.56
42.62 79.38 42.24 0.71 0.39 6.93 6.40 3.61
56.82 22.25 36.62 18.68 0.27 0.01 209.05 137.44 1.36 1235.88 113.58 3458.47
0.13 0.74 0.85 0.52 0.80 0.92 0.74 0.45 0.76 0.32 0.94 0.62 0.45 0.68 0.78 0.46 0.88 0.45 0.49 0.88 0.09 0.25 0.90 0.69 0.95 0.30 0.03 0.57
65.98 84.26 33.15 0.62 0.72 12.36 8.94 3.44 6.50 0.51 1.66 1.64 66.58 30.56 31.77 14.57 0.36
257.85 102.38 0.81 1426.40 179.95 3990.69
0.08 0.72 0.83 0.51 0.80 0.91 0.95 0.48 0.65 0.31 0.91 0.63 0.39 0.84 0.81 0.21 0.91 0.49 0.48 0.87 0.06 0.49 0.91 0.58 0.91 0.26 0.03 0.69
78.96 98.07 26.59 0.32 1.12 33.32 10.48 2.51 9.86 0.56 1.84 1.84 32.59 31.77 19.84 0.28 0.01 367.27 114.94 0.57 1470.40 123.65 3965.00
Appendix 10.3. Country Allocations from JDF with No Dependence Threshold (with Compensation 50 per cent of Foreign Exchange Loss from Commodities) (Dependence Threshold 0.50)
COUNTRY
Dep. Index
Afghanistan Antigua and Barbuda Angola Bangladesh Barbados Belize Benin Bhutan Bolivia Botswana Burkina Faso Burundi Cambodia Cameroon Cape Verde CAR Chad Comoros Congo ˆ te D’Ivoire Co DRC Djibouti Dominica Equatorial Guinea Eritrea
0.56 0.04 0.01 0.14 0.30 0.92 0.48 0.18 0.64 0.14 0.52 0.93 0.26 0.67 0.27 0.49 0.61 0.59 0.12 0.71 0.91 0.28 0.55 0.31 0.32
1995
1996
1997
1998
1999
2000
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
8.93
216.35
28.78
8.04 1.27 386.23 4.63
Dep. Index 0.82 0.07 0.01 0.13 0.37 0.90 0.36 0.19 0.68 0.12 0.50 0.93 0.25 0.64 0.25 0.42 0.55 0.27 0.07 0.67 0.78 0.20 0.48 0.26 0.18
11.07
187.16
25.69 18.51
6.49
495.49 20.79
Dep. Index 0.60 0.06 0.01 0.12 0.35 0.84 0.51 0.15 0.74 0.10 0.62 1.00 0.17 0.54 0.24 0.44 0.64 0.59 0.10 0.62 0.76 0.17 0.53 0.13 0.06
9.37
201.50
65.20
8.06 1.18 510.25 28.77
Dep. Index 0.48 0.06 0.01 0.12 0.32 0.89 0.47 0.16 0.68 0.15 0.67 1.00 0.10 0.57 0.23 0.66 0.56 0.74 0.10 0.64 0.88 0.30 0.38 0.14 0.12
6.53
205.01
57.03 16.16 57.44 12.54 0.84 522.14 64.43
Dep. Index 0.70 0.05 0.01 0.11 0.30 0.96 0.32 0.15 0.67 0.11 0.53 1.00 0.08 0.59 0.22 0.58 0.46 0.57 0.08 0.58 0.68 0.69 0.43 0.14 0.32
7.42
6.98
201.60
78.92 113.85 66.16 0.87 474.49 29.72 3.87
Dep. Index 0.53 0.07 0.01 0.11 0.29 0.88 0.35 0.12 0.64 0.12 0.62 0.78 0.06 0.52 0.42 0.60 0.51 0.41 0.05 0.57 0.64 0.62 0.44 0.08 0.39
8.18
8.25
211.73
80.14
89.81 13.00
419.23 24.83
Ethiopia Fiji Gabon Gambia Ghana Grenada Guinea Guinea-Bissau Guyana Haiti Honduras Jamaica Kenya Kiribati Lao PDR Lesotho Liberia Madagascar Malawi Maldives Mali Mauritania Mauritius Mozambique Myanmar Nepal Nicaragua Niger Papua New Guinea Rwanda Samoa Sao Tome and Principe Senegal Seychelles Sierra Leone
0.97 0.51 0.12 0.92 0.49 0.80 0.74 0.92 0.66 0.34 0.50 0.71 0.66 0.95 0.47 0.08 0.06 0.86 0.89 0.74 0.61 0.59 0.30 0.81 0.91 0.17 0.82 0.72 0.61 0.74 0.86 0.73 0.44 0.46 0.77
23.18
9.10 0.46
25.51 57.71 2.95 6.75
20.88 112.79 22.58 45.54
0.30
0.95 0.44 0.10 0.91 0.69 0.81 0.64 0.91 0.71 0.34 0.45 0.74 0.61 0.96 0.38 0.05 0.08 0.95 0.80 0.83 0.73 0.61 0.30 0.62 0.91 0.17 0.92 0.58 0.58 0.84 0.91 0.88 0.46 0.30 0.52
31.22
95.32
10.12
0.13
14.77 51.99 2.45 7.43
40.98 158.55 50.16 55.30 31.54 0.20 0.26
0.90 0.40 0.12 1.00 0.58 0.79 0.61 0.57 0.55 0.28 0.44 0.77 0.62 0.95 0.19 0.05 0.11 0.88 0.94 0.77 0.53 0.65 0.29 0.74 0.73 0.34 0.83 0.66 0.56 0.65 0.93 0.66 0.47 0.63 0.58
35.02
76.98
8.83
0.27
15.13 51.67 4.35 13.46
51.11 203.86 63.36 59.63 55.47 0.64 0.36 5.49
0.95 0.41 0.20 0.57 0.47 0.79 0.52 0.71 0.72 0.20 0.53 0.76 0.73 0.91 0.15 0.04 0.12 0.88 0.72 0.76 0.56 0.52 0.28 0.60 0.61 0.15 0.62 0.74 0.59 0.84 0.94 0.50 0.46 0.74 0.34
59.57
12.95 0.12
0.26
16.40 85.75 4.35 7.79
43.20 241.97 42.62 79.38 42.24 0.71 0.39 6.93
0.90 0.37 0.20 0.99 0.54 0.57 0.53 0.77 0.63 0.13 0.42 0.81 0.64 0.81 0.20 0.04 0.16 0.92 0.77 0.61 0.43 0.52 0.26 0.51 0.58 0.13 0.74 0.85 0.52 0.80 0.92 0.74 0.45 0.76 0.32
78.16
73.69 1.11 23.51 18.16
43.76 0.27
16.45 58.47 3.16
44.71 267.74 65.98 84.26 33.15 0.62 0.72 8.94
0.84 0.42 0.15 0.96 0.68 0.49 0.51 0.69 0.70 0.16 0.55 0.81 0.64 0.68 0.19 0.03 0.29 0.53 0.82 0.54 0.47 0.60 0.25 0.48 0.41 0.08 0.72 0.83 0.51 0.80 0.91 0.95 0.48 0.65 0.31
112.00
35.87 20.00 25.44 50.58 0.21
12.57 67.08 3.72
78.96 98.07 26.59 0.32 1.12 10.48
(Continued )
Appendix 10.3. (Continued )
COUNTRY
Dep. Index
Solomon Islands Somalia St Kitts and Nevis St Lucia St Vincent Sudan Suriname Swaziland Togo Tonga Trinidad and Tobago Tuvalu Uganda United Rep. of Tanzania Vanuatu Vietnam Yemen Zambia
0.98 0.59 0.86 0.52 0.99 0.91 0.96 0.34 0.52 0.81 0.08 0.48 0.89 0.69 0.73 0.38 0.05 0.80
TOTAL
1995
1996
1997
1998
1999
2000
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
5.30
27.84 26.60 24.70
125.08 100.73
1292.22
Dep. Index 0.97 0.56 0.61 0.72 0.80 0.92 0.95 0.42 0.50 0.81 0.08 0.32 0.84 0.69 0.79 0.33 0.03 0.77
4.14 6.29
42.44 22.74 34.13 0.21
218.81 115.02 0.51
56.03 1815.97
Dep. Index 0.99 0.70 0.76 0.61 0.76 0.96 0.79 0.44 0.51 0.92 0.10 0.71 0.83 0.78 0.86 0.32 0.03 0.96
9.85 5.52
37.93 20.17 28.60 0.46 0.02 219.51 78.60 1.40
72.17 1944.22
Dep. Index 1.00 0.48 0.57 0.62 0.85 0.86 0.92 0.49 0.62 0.88 0.11 0.67 0.85 0.89 1.00 0.35 0.06 0.56
6.40
56.82 22.25 18.68 0.27 0.01 209.05 137.44 1.36
113.58 2152.61
Dep. Index 0.94 0.62 0.45 0.68 0.78 0.46 0.88 0.45 0.49 0.88 0.09 0.25 0.90 0.69 0.95 0.30 0.03 0.57
3.44 6.50 1.66 1.64 30.56
0.36
257.85 102.38 0.81
179.95 2391.86
Dep. Index 0.91 0.63 0.39 0.84 0.81 0.21 0.91 0.49 0.48 0.87 0.06 0.49 0.91 0.58 0.91 0.26 0.03 0.69
2.51 9.86 1.84 1.84 32.59
0.28
367.27 114.94 0.57
123.65 2053.54
Appendix 10.4. Country Allocations from JDF with No Dependence Threshold (with Compensation 50 per cent of Foreign Exchange Loss from Commodities) (Dependence Threshold 0.75)
COUNTRY
Dep. Index
Afghanistan Antigua and Barbuda Angola Bangladesh Barbados Belize Benin Bhutan Bolivia Botswana Burkina Faso Burundi Cambodia Cameroon Cape Verde CAR Chad Comoros Congo ˆ te D’Ivoire Co DRC Djibouti Dominica
0.56 0.04 0.01 0.14 0.30 0.92 0.48 0.18 0.64 0.14 0.52 0.93 0.26 0.67 0.27 0.49 0.61 0.59 0.12 0.71 0.91 0.28 0.55
1995
1996
1997
1998
1999
2000
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
28.78
4.63
Dep. Index 0.82 0.07 0.01 0.13 0.37 0.90 0.36 0.19 0.68 0.12 0.50 0.93 0.25 0.64 0.25 0.42 0.55 0.27 0.07 0.67 0.78 0.20 0.48
11.07
25.69
20.79
Dep. Index 0.60 0.06 0.01 0.12 0.35 0.84 0.51 0.15 0.74 0.10 0.62 1.00 0.17 0.54 0.24 0.44 0.64 0.59 0.10 0.62 0.76 0.17 0.53
65.20
28.77
Dep. Index 0.48 0.06 0.01 0.12 0.32 0.89 0.47 0.16 0.68 0.15 0.67 1.00 0.10 0.57 0.23 0.66 0.56 0.74 0.10 0.64 0.88 0.30 0.38
57.03
64.43
Dep. Index 0.70 0.05 0.01 0.11 0.30 0.96 0.32 0.15 0.67 0.11 0.53 1.00 0.08 0.59 0.22 0.58 0.46 0.57 0.08 0.58 0.68 0.69 0.43
6.98
78.92
Dep. Index 0.53 0.07 0.01 0.11 0.29 0.88 0.35 0.12 0.64 0.12 0.62 0.78 0.06 0.52 0.42 0.60 0.51 0.41 0.05 0.57 0.64 0.62 0.44
8.25
80.14
(Continued )
Appendix 10.4. (Continued )
COUNTRY
Dep. Index
Equatorial Guinea Eritrea Ethiopia Fiji Gabon Gambia Ghana Grenada Guinea Guinea-Bissau Guyana Haiti Honduras Jamaica Kenya Kiribati Lao PDR Lesotho Liberia Madagascar Malawi Maldives Mali Mauritania Mauritius Mozambique
0.31 0.32 0.97 0.51 0.12 0.92 0.49 0.80 0.74 0.92 0.66 0.34 0.50 0.71 0.66 0.95 0.47 0.08 0.06 0.86 0.89 0.74 0.61 0.59 0.30 0.81
1995
1996
1997
1998
1999
2000
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
Foreign Exchange Loss from Commodities (1984–86 prices)
23.18
9.10
25.51 57.71
20.88
Dep. Index 0.26 0.18 0.95 0.44 0.10 0.91 0.69 0.81 0.64 0.91 0.71 0.34 0.45 0.74 0.61 0.96 0.38 0.05 0.08 0.95 0.80 0.83 0.73 0.61 0.30 0.62
31.22
10.12
0.13
14.77 51.99 2.45
Dep. Index 0.13 0.06 0.90 0.40 0.12 1.00 0.58 0.79 0.61 0.57 0.55 0.28 0.44 0.77 0.62 0.95 0.19 0.05 0.11 0.88 0.94 0.77 0.53 0.65 0.29 0.74
35.02
0.27
15.13 51.67 4.35
Dep. Index 0.14 0.12 0.95 0.41 0.20 0.57 0.47 0.79 0.52 0.71 0.72 0.20 0.53 0.76 0.73 0.91 0.15 0.04 0.12 0.88 0.72 0.76 0.56 0.52 0.28 0.60
59.57
0.26
16.40 4.35
Dep. Index 0.14 0.32 0.90 0.37 0.20 0.99 0.54 0.57 0.53 0.77 0.63 0.13 0.42 0.81 0.64 0.81 0.20 0.04 0.16 0.92 0.77 0.61 0.43 0.52 0.26 0.51
78.16
23.51
43.76 0.27
16.45 58.47
Dep. Index 0.08 0.39 0.84 0.42 0.15 0.96 0.68 0.49 0.51 0.69 0.70 0.16 0.55 0.81 0.64 0.68 0.19 0.03 0.29 0.53 0.82 0.54 0.47 0.60 0.25 0.48
50.58
67.08
Myanmar Nepal Nicaragua Niger Papua New Guinea Rwanda Samoa Sao Tome and Principe Senegal Seychelles Sierra Leone Solomon Islands Somalia St Kitts and Nevis St Lucia St Vincent Sudan Suriname Swaziland Togo Tonga Trinidad and Tobago Tuvalu Uganda United Rep. of Tanzania Vanuatu Vietnam Yemen Zambia TOTAL
0.91 0.17 0.82 0.72 0.61 0.74 0.86 0.73 0.44 0.46 0.77 0.98 0.59 0.86 0.52 0.99 0.91 0.96 0.34 0.52 0.81 0.08 0.48 0.89 0.69 0.73 0.38 0.05 0.80
112.79 22.58 45.54
27.84 26.60
125.08
530.23
0.91 0.17 0.92 0.58 0.58 0.84 0.91 0.88 0.46 0.30 0.52 0.97 0.56 0.61 0.72 0.80 0.92 0.95 0.42 0.50 0.81 0.08 0.32 0.84 0.69 0.79 0.33 0.03 0.77
158.55 50.16
0.20 0.26
4.14
42.44 22.74
0.21
218.81 0.51
56.03 722.30
0.73 0.34 0.83 0.66 0.56 0.65 0.93 0.66 0.47 0.63 0.58 0.99 0.70 0.76 0.61 0.76 0.96 0.79 0.44 0.51 0.92 0.10 0.71 0.83 0.78 0.86 0.32 0.03 0.96
63.36
0.64
9.85
37.93 20.17
0.46
219.51 78.60 1.40
72.17 704.52
0.61 0.15 0.62 0.74 0.59 0.84 0.94 0.50 0.46 0.74 0.34 1.00 0.48 0.57 0.62 0.85 0.86 0.92 0.49 0.62 0.88 0.11 0.67 0.85 0.89 1.00 0.35 0.06 0.56
0.71
6.40
56.82 22.25
0.27
209.05 137.44 1.36
636.34
0.58 0.13 0.74 0.85 0.52 0.80 0.92 0.74 0.45 0.76 0.32 0.94 0.62 0.45 0.68 0.78 0.46 0.88 0.45 0.49 0.88 0.09 0.25 0.90 0.69 0.95 0.30 0.03 0.57
84.26
0.62
8.94 3.44
1.64 30.56
0.36
257.85 0.81
694.99
0.41 0.08 0.72 0.83 0.51 0.80 0.91 0.95 0.48 0.65 0.31 0.91 0.63 0.39 0.84 0.81 0.21 0.91 0.49 0.48 0.87 0.06 0.49 0.91 0.58 0.91 0.26 0.03 0.69
98.07
0.32 1.12
2.51
1.84 1.84 32.59
0.28
367.27 0.57
712.46
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Index
adding-up problem 11 Addison, T. 312 Afghanistan 189–96 African Development Bank 286 Agenor, P. R. 301 agriculture: causes of stagnation 69–70 global export share 10–11 output index 93–4 productivity change 81–7 surge in production 28 aid 4, 163, 299 and commodity prices 302–6 fungibility 310–11 ODA 227, 230 Akiyama, T. 173 Alauddin, M. 86, 93, 105 alumina 275 Amin, A. A. 108 Amjadi, A. 226 Andersen, R. W. 272 Andrews, D. 23, 25, 27 Angola 198, 286 Antigua 205, 206 Appleyard, D. R. 87 Ardeni, P. G. 25 Argentina 105 Athukorala, P. C. 18, 42, 219 Atkins, J. P. 176 Australia 144 Australia-US, farm gate-to-retail spread 148, 149, 150, 158 Baanante, C. 109 Bahrain 178, 206 Balasubramanyam, V. N. 17 Balat, J. F. 109 bananas 39–67, 276 Bangladesh 104–5, 111, 115, 117–18, 189–96, 198, 202, 208 Barbados 79, 107–8, 115 Barbuda 205–6 bauxite 275 beef/veal 276
Belgium 198 Benin 104, 173, 189–96, 288 Bera, A. K. 222, 225 Bergevin, J. 20–1 Berlage, L. 312 Bernall, R. 226 Beveridge, S. 23, 25 Bhagwati, J. 77–8 Bhutan 189–96 Birdsall, N. 282, 287, 291, 295–6, 298 Bleaney, M. F. 28, 35–8, 41, 43, 49–50, 279 Bloch, H. 29 Bolivia 81, 171, 288 Borensztein, E. 28 bottlenecks 141 Boue¨t, A. 100 Braga, C. P. 183 Brazil 105, 144 Brazil-US, farm gate-to-retail spread 148, 149, 150, 157 buffer stocks 269, 270 Burkina Faso 104, 109, 288, 292 Burundi 72, 171, 189–96 Cambodia 202, 205 Cameroon 101, 107, 108, 122, 143, 292 Cameroon-UK, farm gate-to-retail spread 146, 151 Canada 196, 198 Cape Verde 207 Cashin, P. 26 Cassen, R. 302, 310 catch up 86 Central African Republic 123–4, 171, 173 Chad 104, 123, 124, 173, 292 Chang, C. 302 Chile 89, 103, 105 China 196, 198 Choraria, J. 79, 101 civil unrest 107–8, 129, 189, 196, 230 cobalt 275 cocoa 39–67, 72, 130–4, 274, 282, 302 and aid 302, 305
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Index cocoa (cont.) farm gate-to-retail spread 147, 153 foreign exchange loss 173 prices: fob 140; producer 101, 144; trends 39–67 coconut oil 173 Coelli, T. 82–3, 93 coffee 39–67, 72, 79, 101, 107, 130, 274, 282 farm gate-to-retail spread 145–6, 147, 151–3 prices: fob 140; producer 101, 143; trends 9–67; UK retail 144 value chain 138–9 cointegration techniques 217–8, 222–3 Colombia 123, 125 commercial services: by country 239–40, 243–44, 249–52, 261–3 exports 183–6, 198, 205 commodities: declining use of 28, 208 dependence on 7–10 mix 109–10 commodity prices: and aid flow 302–6 classical view of 18 and debt 279–82 and Heavily Indebted Poor Countries (HIPC) Initiative 287–303 see also prices; relative prices commodity value chain: data 142–5 definition 137 the literature 137–42 and market power 141–2 Common Fund for Commodities (CFC) 272 Commonwealth countries 69, 72, 74–7, 105–9 Comoros 81 Congo DR 198 consumption shocks 139 convergence variable 112 copper 39–67, 275 copra 274 Cororaton, C. B. 100 Costa Rica 126 costs: changes in 139 transportation 226–7 ˆ te d’Ivoire 72, 104, 171, 173, 274, 282 Co Cotonou agreement 115, 274–5 cotton 39–67, 72, 85, 274, 282, 302–13 foreign exchange loss 173 productivity 109–12 relative price trends 39–67 Cuddington, J. T. 22–5, 27–8, 50 Cypher, J. M. 78–9 Cyprus 184
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Daseking, C. 282 Deaton, A. 29, 39, 165, 279 debt 3 and commodity prices 279–82 net present value (NPV) 285, 286, 287, 290 service payments 292–4 sustainability 282, 285, 287, 291–2 top-up relief 293, 295–8 see also Heavily Indebted Poor Countries Deese, B. 295–6, 298 DEFRA 140–1 Dehn, J. 279 demand 69 income elasticity of 11, 18, 28, 78, 210 Denmark 313 Devarajan, S. 310 Diakosavvas 19–21 Dickey, D. A. 39, 41 Dietz, J. L. 78–9 Djibouti 206 Dodhia, D. 285 Dominica 173 Douya, E. 108 Duncan, R. C. 114 Dutch disease 11, 106, 108 Easterly, W. 279–80 Eastern Europe 28 Economist’s index of industrial commodity prices 26 economy: importance of selected commodities 72–4 inefficiencies 229–30 edible oils 173 Edstrom, J. 279 Engel’s Law 208, 210 Engle, R. F. 23, 26, 37, 140, 215 Equatorial Guinea 178, 198, 205, 207 error correction model 36, 140 Ethiopia 143 Ethiopia-UK, farm gate-to-retail spread 146 European Commission 69, 72, 78 European Union (EU) 229 ACP Programmes 3, 115, 147–8, 275–6; COMPEX 272, 275; STABEX 3, 272–4, 306–7, 313; SYSMIN 272, 275 Common Agricultural Policy (CAP) 142, 276 Common Market Organization (CMO) 147 Special Preferential Sugar (SPS) Agreements 148 export growth 71 by country 75–7, 194–5, 235–6 long-term 189–96 projected 288–90 trend coefficients 73–4
Index export unit value 71 across country groups 94–100 index 94 and TFP 113–22 and world import unit values 98–100 exports: by country 89–91, 190–1, 233–4 commercial services 183–6, 198, 205 prices 70, 142, 144–5 quantities 74–7 quotas 270 of services 163, 183–6, 194–5, 198, 205, 233–4 shares 192–3 structural shift 299 total 186–7, 198–200, 253–6, 264–5 value 171 volume 69, 167, 177 see also merchandize exports External Compensatory Finance 269, 272–5, 306
Gillson, I. 293 globalization 211, 229 Godfrey, L. G. 222, 225 gold 275 governance structures 137 government role 74 Granger, C. W. J. 23, 26, 37, 140, 215 Greece 198 Greenaway, D. 17, 28, 35–8, 41, 43, 50, 279 Grenadines 207 Grilli, E. R. 19, 21–6, 28, 36, 39–40, 43, 279 Grilli-Yang dataset 40–4, 50–1, 57, 63–5 gross domestic product (GDP), per capita 85, 111–12 groundnuts 274 Grynberg, R. 136, 229, 292 Guatemala 123, 125 Guinea Bissau 171, 288 Gujarati, D. N. 26 Guyana 81
Fagerna¨s, S. 105, 119 Field, A. J. 87 field crops 72, 74, 75 Fiji 79, 107–8, 115, 144, 173, 207 Fiji-EU, farm gate-to-retail spread 149, 156 Fiji-US, farm gate-to-retail spread 148–50, 156 finance, external compensatory 269, 272–5, 306 financial market derivatives 276 Fitter, R. 139, 141, 146 foreign direct investment 228 foreign exchange loss 2–3 by commodities 172–3 by country 174 methodology 163–7 results 167–71 France 196 Fuglie, K. O. 103 Fuller, W. A. 39, 41
Hadass, Y. 26 Haiti 189–96, 208 Hall, S. 216 Hansen 218 Harris, R. 26, 216 Harvey, A. C. 25 Havana Charter (1948) 270 Headey, D. 86 Healy, S. 139 Heavily Indebted Poor Countries (HIPC) Initiative 3–4, 278, 282–6 commodity export dependence 7–10 and commodity prices 287–93 conditionality 285, 292 costs 286, 296–8 countries listed 13–14 criticised 287 leading exports 15–16 real price adjustment mechanism 295–8 Henao, J. 109 Hendry, D. F. 22 Herrmann, R. 269, 274 Hertel, T. W. 100 Hewitt, A. 274–6, 293, 306–7, 313 high-performing Asian economies (HPAE) 181 Hoekman, B. 183 Holden, D. 219 Honduras 81, 173, 288, 293 Hong Kong 196, 198 Hopkins, T. K. 137
Gabon 104, 115, 117, 126, 171, 178, 206 Gallup, J. L. 85–6, 89 Gambia 81, 107, 108, 189–96 Gautam, M. 280, 282, 285 Geda, A. 280 General Agreements of the Trade in Services (GATS) 183 Gereffi, G. 137 Germany 196 Ghana 72, 79, 101, 107–8, 118–19, 173 Ghana-UK, farm gate-to-retail spread 147, 153 Gibbon, P. 137 Gilbert, C. L. 272 Gilbert, R. 295 Gillis, M. 88
IAC 69 IADB 107 IDA 290 illiteracy rate 86, 111
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Index immiserizing growth 71 evidence of 79–80 mechanics of 77–9 see also poverty import-substitution strategy 226, 293 income distribution, commodity value chain 137 income elasticity of demand 11, 18 low 28 manufactures 78, 210 indebtedness see debt India 119, 198 Indonesia 72, 103, 123, 125, 196 infrastructure, social and physical 227–8 institutional framework 137 insurance 226, 277 Integrated Programme for Commodities (IPC) 272 International Cocoa Organization (ICCO) 144 International Coffee Agreement (ICoA) 272 International Coffee Organization (ICO) 138, 143–4 International Commodity Agreements (ICAs) 69, 139, 269–72 International Monetary Fund (IMF) 3, 30, 79, 114, 119, 212, 282, 286, 290 Compensatory Financing Facility (CFF) 272–3, 306 International Natural Rubber Agreement (INRA) 272 International Sugar Agreement (ISA) 270 International Tin Agreement (IAT) 40 Ireland 196, 198 iron ore 275 Jamaica 107, 123, 124, 184 Japan 196, 313 Jarque, C. M 222, 225 Joint Diversification Fund (JDF) 306–14 costs of 311–4 country allocations 316–27 design of 307–11 jute 39–67 Kanbur, R. 309 Kaplinsky, R. 139, 141, 146 Karugia, J. T. 119 Kellard, N. 27 Kenya 81, 101, 104, 105, 119, 129, 143, 173, 286 Kenya-UK, farm gate-to-retail spread 146, 152 Keynes, J.M. 269 Kiribati 189–96 Kiringai, J. 292 Knapp, R. 134 Korzeniewicz, M. 137 Kuhnen, F. 78
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labour absorption 105 labour productivity 85–6 changes in; causes 111–13; rate of 71, 101–2 definition 81 and TFP 104–5 see also productivity Lao PDR 81, 189–96, 202 Laroque, G. 29 Larson, D. F. 140 lauric oils 72 Leon, J. 24–5 Lesotho 205 less developed countries (LDCs), list of 13–14, 231 Lewis, W. A. 21, 29 Limao, N. 227 Lipsey, R. E. 114 literature review 17–34 econometric models 21–8 structural models 28–9 to mid-1980s 20–1 LMC International 144 Lome´ Conventions 115, 147, 229, 274–5, 313 Lumsdaine, R. 27 Luxembourg 313 McCorriston, S. 79 McCulloch, N. 100 McDermott, C. J. 26 McKay, A. 100 Madagascar 124, 125, 208 maize 39–67 Maizels, A. 49, 164–5, 270 Malawi 111, 126 Malaysia 104, 107, 119–20, 196 Maldives 205 Mali 104, 126, 173, 293, 302–3 Mallick, S. K. 219 Malta 184, 198 manganese 275 manufacturing base 226 marginalization 176, 196 avoidance 199–205 by country 197 data 212–3 and development 225–30 estimation of 205–9 LDCs and SVSs 181–3 long- and short-run relationships in 219–21, 224 and merchandise export trade 208–25 model estimation 213–24 simple model 211 market power 141–2 market structure 18 Mauritania 104, 290 Mauritius 101, 107, 126, 130, 144, 184
Index Mauritius-EU, farm gate-to-retail spread 149, 155 Mauritius-US, farm gate-to-retail spread 148, 149, 150, 155 Mayer, J. 227 Mbeaoh, A. 108 Mengistu, T. 100 merchandise exports: by country 237–8, 241–2, 245–8, 257–60 change in 196 LDCs and SVSs 176, 178, 180–3 marginalization in 208–25 see also exports merchandise imports 178–9 Mexico 196 Miller, R. 165 Montenegro, C. 219 Montiel, P. J. 301 moral hazard 310–11 Morgan, C. W. 17 Morisset, J. P. 136, 138–42, 146 Morrissey, O. 285 Mozambique 107–8, 122, 126, 288 Mundlak, Y. 140 Muscatelli, V. A. 219 Myanmar 81, 171, 189–96, 198, 202 National Statistics Offices 144 natural rubber 173 Nelson, C. R. 22–3, 25 Nepal 104–5, 189–96, 198, 202, 206 Netherlands 196, 313 Newbery, D. 306 Newbold, P. 23, 27 Nicaragua 81 Nicita, A. 100 Niger 104, 171, 189–96, 292 Nigeria 72, 106, 119, 124 Nkamleu, G. B. 82–3, 86 Norway 313 Nyangito, H. O. 119 oil-exporters 178, 180, 181, 188 Okonski, K. 129 Okunmadewa, F. 106 Olayemi, J. K. 106 openness index 111 Orden, D. 100 Osafa-Kwaako, P. 136 Page, S. 275–6, 306–7, 313 palm oil 39–67, 72, 173, 282 Papell, D. 27 Papua New Guinea 79, 107, 111, 124–5, 143, 173, 206, 302, 305 Papua New Guinea-UK, farm gate-to-retail spread 146, 152
Paris Club 286 Perkins, D. H. 88 Perman, R. 219 Perron, P. 23, 25, 27 Pesaran, M. H. 36–8, 219–20, 223 Philippines 196 Phillips, P. C. B. 218 phosphates 275, 302, 305–6 physical conditions 86, 112–13 Plosser, C. I. 22 policy change 139 policy instruments 3, 269–77 Ponte, S. 137, 139, 141 population, rural proportion 86, 111 Porto, G. G. 109 poverty: alleviation 292, 293 extreme 12–13 see also immiserizing growth Poverty Reduction Growth Facility (PRGF) 273 Powell, A. 25, 27, 37, 50 Powell, R. 282 Prebisch, R. 1, 7, 17–20, 22, 28, 35, 78–9 preferential trade agreements 270, 275–6 price elasticity 11, 18 price index 94, 100–1, 109 price shocks 11, 74, 189 prices 109, 210 adjustment mechanism 295–8 asymmetric transmission 140–1 commodity-retail spread 138–9, 145–7, 149–50 exports 70; fob 142, 144–5 producer 70, 94, 100–1, 142, 144–7 and productivity real 82 see also commodity prices; relative prices producer price index production data 91–4 productivity 210 cross country and sectoral 70–1 and prices 71, 78, 85, 87–8 role in production 68–9 see also labour productivity; total factor productivity purchasing power index 167, 171 purchasing power parity (PPP) 11 quality improvements 19 Radelet, S. 88 Radetzki, M. 307 Ramsey, J. K. 222, 225 Ranis, G. 293 Rao, D. S. P. 82–3, 86, 93 Razzaque, M. A. 136, 292 Redding, S. 227
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Index Reinhart, C. M. 25, 28, 210 relative prices 1–2, 11, 17–34 for commodity groups 43–7 growth rate 40–3 individual commodities 47–51 methodology 36–9 model 37–9 trends 39–67 see also commodity prices; prices remittances from abroad 163 Republic of Korea 196 rice 72, 85, 110–11, 39–67 Riedel, J. 219 risk management instruments 270, 276–7 Roberts, J. 105, 119 Roemer, M. 88 rubber 282 rum 276 Russian Federation 196 Rwanda 72, 189–96, 293 Sachs, J. D. 85–6, 89 St Kitts and Nevis 206 St Lucia 173, 206 St Vincent 206 Samoa 173 Sao Tome and Principe 189–96, 206, 282 Sapsford, D. 17, 20, 24, 27–9, 50, 173 Sarkar, P. 18–21 Saudi Arabia 196 Sawada, Y. 78–9 Scandizzo, P. L. 19–21 Schlote, W. 21, 24 Senegal 274, 288, 293 Senhadji, A. 219 Serieux, J. 292 Sexton, R. J. 79 Sheldon, I. M. 79 Shepard, B. 139–40 Shikwati, J. S. 133 shocks: consumption 139 external 275, 291, 295 price 11, 74, 189 supply 189 Sierra Leone 107–8, 122, 129, 189–96, 208, 292 Singapore 196 Singer, H. 1, 7, 17–20, 22, 28, 35, 78–9, 173, 279 small vulnerable states (SVS), listed 13–14, 232 Snodgrass, D. R. 88 soil quality 112–13 Solomon Islands 107, 115, 117, 126, 127, 173 Soto, R. 24–5 South Africa 89, 106 South Korea 196 Soviet Union 28 Spain 196, 198
350
Spraos, J. 19–20 Sri Lanka 107, 119, 126, 128 stability, social and political 229 Stewart, F. 293 Stiglitz, J. E. 228, 306 structural break 50 sub-Saharan Africa 69 Sudan 173, 198 sugar 72, 85, 110–12, 130–4, 275–6 farm gate-to-retail spread 154–7 policies 147–8 prices: farm gate 144; fob 144–5, 148; retail 145; trends 39–67 Sugar Protocol 2, 147, 148, 150 supply shock 189 Suriname 189–96, 206 Swaroop, V. 310 Swaziland 79 Sweden 313 Tabova, A. 295 Taiwan 196 Talbot, J. 138–40 Tanzania 79, 107, 119, 143, 171, 287–8, 293 Tanzania-UK, farm gate-to-retail spread 146, 153 tea 39–67 technical progress 18, 81, 208, 210 technological change differentials 78 terms of trade 17, 19 circular deterioration 78 net barter (NBTT) 18, 23, 114 single factoral 71, 87–8, 122–34 Thailand 144, 196 Thailand-US, farm gate-to-retail spread 148, 149, 150, 157 Thirwall, A. P. 20–1 tin 39–67, 275 Tisdell, C. 105 Togo 104, 124, 173, 302, 305–6 Tonga 107, 108, 122, 206 total factor productivity (TPF) 2 between Commonwealth countries 105–9 change: causes of 83–7, 109–11; rate of 71, 102–3 definition 80–1 and export unit values 113–22 and labour productivity 104–5 model 82–3 national level 70 see also productivity tourism 184, 189 trade barriers 141, 226 trade policy, import-substitution 226, 293 trade transactions by country groups 187–8 trading arrangements, preferential 228–9 tree crops 72, 74–5, 85, 109–11, 122
Index Trinidad and Tobago 79, 107, 115, 122, 130, 178, 189–96 Turkey 89 Uganda 72, 107, 122, 171, 173, 205, 282, 288, 291, 293 unit root tests 36, 214–7, 222–3 United Arab Emirates 196 United Kingdom 18–19, 147, 196, 198 United Nations 69, 114 Food and Agriculture Organization (FAO) 88, 93–4 Commodity Yearbook 172, 212 UNCTAD 2, 68, 164–5, 270, 287 Commodity Price Bulletin 47–51 database 40, 43, 45–7, 51–7, 66–7 United States 196, 198, 313 services supplier 184 sugar export 147 Tariff Rate Quota (TRQ) 148 uranium 275 Uruguay Round 229, 183 Urzua, C. M. 22–5, 28, 50 Vanuatu 107, 108, 122 Venables, A. J. 227 Venezuela 115, 117
Vietnam 81, 170–1, 173, 286 von Braun, J. 100 Vorley, B. 139, 141 Vougas, D. 23, 27 Wallerstein, I. 137 wheat 39–67 White, H. 222, 225, 302 Wickham, P. 25, 28, 210 Williamson, J. 26, 295–6, 298 Wilson, T. 20 Winters, L. A. 100 Wohar, M. E. 27 Wood, A. 227 World Bank 30, 79, 119, 138, 212, 277, 282, 286, 290 World Trade Organization (WTO) 229, 295 Wright, B. 25 Yabuki, N. 173 Yang, M. C. 19, 21–6, 28, 36, 39–40, 43, 279 Yeats, A. 226 Yemen 81, 198, 205, 286 Zambia 107, 108–9, 171, 189–96, 288, 292 Zivott, E. 23, 25, 27
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