Gains from Trade Versus Adjustment Costs: the Economic Partnership Agreement and the Pacific Agreement on Closer Economic Relations

Gains from trade versus adjustment costs: The Economic Partnership Agreement and the Pacific Agreement on Closer Economic Relations

Uwe Kaufmann

A thesis submitted in fulfillment of the requirements for the degree of Masters of Commerce

School of Economics Faculty of Business and Economics The University of the South Pacific

June, 2008

This thesis contains my own work except where otherwise indicated.

Uwe Kaufmann

June 2008

Acknowledgements I would like to thank my Supervisor, Dean of the Faculty of Business and Economics of the University of the South Pacific, Professor Biman Chand Prasad for his advice, the stimulating discussions, his support and understanding during my studies of the Masters of Commerce program. Thank you also for making my studies at the University of the South Pacific successful. I am further grateful to my advisors Professor Ronald C. Duncan, Professor B. Bhaskara Rao, Dr. Haruo Nakagawa and Rup Singh.

Without the encouragement of Professor Ron Duncan, I would have never thought about commencing my studies in the Pacific region. I am very grateful for his support throughout my studies at the University of the South Pacific, his help and introduction to the important areas of research in the Pacific Islands. Professor Duncan made me understand the problems of development economics, the obstacles to sustainable growth of developing countries and pointed out the importance of the aspects of this thesis. Thank you for all the interesting and helpful discussions, your advice and much more! I am very grateful for all your help!

Professor Rao was my Professor and supervising Professor in three courses of my postgraduate studies at the University of the South Pacific and helped me in the field of international economics and econometrics and supported me whenever I was in need for clarification or any kind of help (including Sunday afternoons). He encouraged me to write research papers to improve my writing skills, therefore I am very grateful.

Dr. Nakagawa deserves a lot of thanks for his ongoing support in econometric areas, questions and help and for always providing me with necessary data for my economic research – Thank you for this, Haruo, as well as for your friendship!

Rup Singh taught me applied econometrics and introduced me to several econometric methods and time series econometrics. He called up my interest in the fields of econometric research on the PICs.

iii I also would like to thank the staff and students of the Pacific Institute of Advanced Studies in Development and Governance at the University of the South Pacific for their support throughout my studies at the University of the South Pacific: Seraseini Colata, Dr. Frank Thomas, Feue Tipu, and all the others.

I would also like to thank Professor Mahendra Reddy, Marjorie Bola and all the others of the Faculty of Business and Economics.

I appreciate the help of Dr. Prakash of the Faculty of Arts and Law of the University of the South Pacific by giving me editorial advice. Thank you!

Last but not least, thank you also Nicola Schwaebel for all your unconditional support and your friendship. Thank you so much!

This thesis is dedicated to my parents for their ongoing and unconditional support – thank you.

iv Abstract

The Pacific Island Countries (PICs) are finally in the wake of trade liberalization. In December 2007, Fiji and Papua New Guinea signed an Interim Partnership Agreement (IPA) with the European Community (EC), which promises gradual trade liberalization between the two Pacific countries and the EC. The IPA triggers the Pacific Agreement on Closer Economic Relations (PACER). PACER, similar to IPA promises gradual trade liberalization between PICs and Australia and New Zealand.

One of the best known issues of trade liberalization is the reduction and/or elimination of tariffs. The resulting tariff revenue losses of the developing countries are used, more and more often, to criticize advanced, developed countries for their demand of freer trade. Instead, the real impact of trade liberalization, of IPA (EPA) and PACER, remains undetected and also underestimated. This may have several reasons. For once, because it serves the protected industries to build up a negative picture of the agreements due to the threat of tariff revenue loss. Also, NGOs simply do not understand the obstacles to trade many PICs face. Therefore, both agreements IPA and PACER are analyzed. It is found that they precisely address all common barriers to trade identified by the World Trade Organization. This thesis argues that trade liberalization offers the possibility for these Pacific countries to overcome trade and economic constraints, which hinder sustainable development.

Existing empirical studies have found the Pacific nations as being trade open economies, making the goal of trade liberalization questionable. If the PICs are open to trade, no need for trade liberalization would exist, and the PICs would perform well. Unfortunately, the PICs have one of the lowest average growth rates of all developing countries and therefore, the thesis argues that the common used trade openness measurements are inappropriate. It is claimed that the PICs are not as open to trade as often predicted. If the trade openness measurement is used inappropriate, the results in an empirical study will be biased.

v The thesis analyzes the trade relationship between the Pacific ACPs (PICs) and the European Community (Australia and New Zealand) over the last 30 years, using actual trade data and recent advancements in time series econometrics. The demand for trade offers both the determination of a continuing trade relationship between two nations or trading blocks and secondly gives the opportunity for trade policies; regarding the price and income responsiveness. It is found that the PICs have a strong trade relationship with both the EC and Australia & New Zealand. Additionally, Fiji’s demand for trade with the EC, and Australia and New Zealand is investigated.

To overcome the obstacles to trade, adjustment and adjustment costs are unavoidable. Several reports are discussed for two reasons, once to address possible costs but also to identify the areas in which costs appear. These are areas where major reforms are inevitable (e.g. fiscal adjustment costs, costs for trade facilitation, costs for export diversification).

In the end, the thesis wishes to show what IPA and PACER really mean for the PICs; what the major challenges are to comply with the agreements (including the potential adjustment costs), and in which way the PICs can gain or lose.

vi Table of Content

Declaration…………………...………………………………………………… ...ii Acknowledgements……………………...……………………………………... ..iii Abstract……..………………………………………………………………….. ...v Table of Content………..……………………………………………………… .vii List of Tables……………...…………………...………………………………....xi List of Charts……..……………………………………………………………. xvi List of Figures…..…………………………………………………...…………. xvii Abbreviations.…..…………………………………………………...…………. xviii

1. Introduction…………………………………………………………………. …1 1.1 Purpose and scope of the study……………………………………... …1 1.2 Outline of the study…………………………………………………. …4 2. Trade – Theory and Empirical Discussion……………………………………6 2.1 The Comparative Advantage………………………………………... …7 2.2 Obstacles to trade…………………………………………………… ..10 2.2.1 Trade Policy: Protectionism through import substitution (tariffs and quotas) and export promotion and the infant industry argument..10 2.2.1.1 Import Substitution……………………………………………… ..10 2.2.1.2 Export Promotion……………………………………………….. ..13 2.2.1.3 Non-tariff barriers to trade………………………………………. ..16 2.3 The World Trade Organization……………………………………... ..18 2.3.1 Some of the “more important” agreements in detail……………… ..20 2.4 Environment and trade……………………………………………… ..23 2.5 Conclusion of Chapter 2…………………………………………….. ..27 3. The economies of the Pacific Island Countries and their trade relationship…………………………………………………………………....29 3.1 The economies of the Pacific Island Countries……………………... ..29 3.1.1 PNG and Fiji………………………………………………………. ..38 3.1.1.1 Papua New Guinea……………………………………………… ..38 3.1.1.2 Fiji Islands………………………………………………………. ..43 3.1.2 Summary of Section 3.1…………………………………………... ..47

vii 3.2 The Pacific Island Countries and their trade relationship with the world…………………………………………………………………..48 3.2.1 Who are the major trading partners? What is being traded?...... 48 3.2.1.1 Papua New Guinea……………………………………………… ..49 3.2.1.2 Fiji Islands………………………………………………………. ..51 3.2.1.3 “The others”……………………………………………………... ..53 3.2.2 Aggregated trade with the European Community, Australia and New Zealand………………………………………………………. ..56 3.2.2.1 Fiji Islands and its major trading partners in the EC, Australia and New Zealand………………………………………………... ..59 3.2.3 Summary of Section 3.2…………………………………………... ..62 3.3 Conclusion of Chapter 3…………………………………………….. ..64 4. Trade openness ………………………..…………………………………...... 66 4.1 Literature review….…………...…………………………………….. ..66 4.2 The measurement(s) of trade openness……………………………… ..85 4.2.1 Trade openness – a cross country analysis of 210 countries for 2006…………………………………………………………… ..88 4.2.2 Trading across borders – a measurement for trade openness?...... 90 4.3 Conclusion of Chapter 4……...……………………………………... ..94 5. Demand for Trade...…………………..…………………………………...... 97 5.1 How strong is the PICs’ demand for trade?...... 98 5.2 Demand, income and price elasticity………...……………………… ..99 5.2.1 Income elasticity……...…………………………………………… 101 5.3 Literature review on demand for trade in the Pacific……...………... 102 5.4 Model used…………………………………………………………... 105 5.4.1 Data used………………...………………………………………... 107 5.5 Results……...………………………………………………………... 111 5.5.1 Results of unit root tests…………..………………………………. 111 5.5.2 Results of cointegation tests……..………………………………... 111 5.5.3 Estimation results………..………………………………………... 112 5.6 Conclusion of Chapter 5…………………………………………….. 123

viii 6. EPA, PACER, Tariff loss and Adjustment Costs…………………………. 127 6.1 Economic Partnership Agreement and the Pacific Agreement on Closer Economic Relations…………………………………………. 128 6.1.1 The European Union and the Pacific Island Countries – a historical overview: From Pre-Lomé to Cotonou ………………...128 6.1.2 The Cotonou Agreement………………………………………….. 131 6.1.2.1 The Pacific Island Countries and the Cotonou Agreement: The Pacific Strategy…………………………………………………..132 6.1.2.2 The Economic Partnership Agreement (EPA)………………….. 134 6.1.3 The Interim Partnership Agreement between certain Pacific ACP countries and the European Community………………………….. 136 6.1.4 The Pacific Agreement on Closer Economic Relations…………... 143 6.1.5 Summary of Section 5.1………………………………...………… 146 6.2 Tariff revenue loss and adjustment costs……………………………. 150 6.2.1 Revenue structure ………………………………………………… 150 6.2.2 Tariff revenue loss………………………………………………… 152 6.2.3 Adjustment costs………………………………………………….. 155 6.2.4 What needs to be done? How to adjust for trade liberalization?...... 164 6.2.4.1 Excise tax………………………………………………………... 164 6.2.4.2 Personal income and corporation tax…………………………… 165 6.2.4.3 Consumption tax………………………………………………… 166 6.2.4.4 Complementary reforms………………………………………… 167 6.2.5 Summary of Section 5.2…………...……………………………… 169 6.3 Conclusion of Chapter 5…………………………………………….. 171 7. Conclusion…………………………………………………………………… 174 7.1 Major findings and policy implications……………………………... 174 7.2 Limitations and direction of future research……………………..…. 180 Appendices – overview………………………………………………………… 182 Appendix A: Description of variables…...………………………...…………. 183 A.1 Variables for trade openness analysis………………………………. 183 A.2 Variables for demand for trade analysis……………………………. 183 Appendix B: Results of unit root tests for individual variables………..…… 185

ix Appendix C: Tables and Charts……………………………………………….191 C.1 Growth Forecast for several Pacific Island Countries……………… 191 C.2 Doing Business 2008 – Enforcing contracts……………………….. 192 C.3 Trade openness measurements 1990, 1995, 2000 and 2006……….. 193 C.4 Short-run dynamics……………………………………………….… 218 C.5 Johansen cointegration test (JCT) results…………………………... 239 C.6 Stability test results – plots of cumulative sum of recursive residuals and cumulative sum of squared residuals…………………244 Appendix D: Definitions and details………………………………………….. 259 D.1 Mode 4…..…………………………………………………………. 259 D.2 Article 5 Agreement on Agriculture...……………………………… 260 D.3 GATT 1994 Article VII: Valuation for Customs Purposes………… 260 D.4 GATT 1994 Article XXIV: Territorial Application…………..……. 260 D.5 GATT 1994 Article XIX: Emergency Action on Imports of Particular Products…..…………………………………………... 261 List of References……………………………………………………………… 262

x List of Tables

Table 3.1: GDP growth, GDP per capita and structure of output for PICs (1996-2006)…………………… ……………………………………. ..31 Table 3.2: World Bank’s Doing Business 2008: Starting a business…………… ..35 Table 3.3: Trade balance (goods and services) of the PICs……………………... ..37 Table 3.4: Pacific Island Countries’ trade with the European Community in 2006……………………………………………………………….. ..56 Table 3.5: Pacific Island Countries’ trade with Australia in 2006…...…………....57 Table 3.6: Pacific Island Countries’ trade with New Zealand in 2006………….. ..58 Table 4.1: Corruption and openness…………………………………………….. ..83 Table 4.2: Trade openness measurements for 210 countries – PICs rankings in 2006……………………………………………………………….. ..89 Table 4.3: Change in trade openness rankings of PICs (1990 – 1995 – 2000 – 2006) ……………………………………….....90 Table 4.4a: Doing Business 2008 - Trading across borders……………………… ..92 Table 4.4b: Doing Business’ trading across borders vs. TI vs. RWTI vs. CTI…... ..93 Table 5.1: Estimates of long-run demand for exports of Pacific ACPs to the EC for 1976 - 2006……………………………………………. 112 Table 5.2: Estimates of long-run demand for Pacific ACPs’ imports from the EC for 1976 - 2006………………………...……………..... 113 Table 5.3: Estimates of long-run demand for exports of PICs to Australia & New Zealand for 1975 - 2006………………………………………... 114 Table 5.4: Estimates of long-run demand for imports from Australia & New Zealand to PICs for 1975 - 2006…………………………………….. 114 Table 5.5: Estimates of long-run demand for exports from Fiji to the United Kingdom for 1976 - 2004……………………………………. 115 Table 5.6: Estimates of long-run demand for Fijian imports of products from the United Kingdom for 1976 - 2004…………………………...115 Table 5.7: Estimates of long-run demand for exports from Fiji to Germany for 1976 - 2004………………………………………………………. 116 Table 5.8: Estimates of long-run demand for imports from Germany to Fiji for 1976 - 2004...…………………………….………………...116

xi Table 5.9: Estimates of long-run demand for exports from Fiji to Belgium 117 for 1976 - 2004………………………………………………………. Table 5.10: Estimates of long-run demand for imports from Belgium to Fiji for 1976 - 2004………………………………………………………. 117 Table 5.11: Estimates of long-run demand for exports of Fiji to the Netherlands for 1976 - 2004.……………………………………. 118 Table 5.12: Estimates of long-run demand for imports from the Netherlands to Fiji for 1976 - 2004………………………...………... 118 Table 5.13: Estimates of long-run demand for exports of Fiji to France for 1976 - 2004………………………………………………………. 119 Table 5.14: Estimates of long-run demand for imports from France to Fiji for 1976 - 2004………………………………………………………. 119 Table 5.15: Estimates of long-run demand for imports from Denmark to Fiji for 1976 - 2004………………………………………………...120 Table 5.16: Estimates of long-run demand for imports from Italy to Fiji for 1976 - 2004………………………………………………………. 120 Table 5.17: Estimates of long-run demand for exports from Fiji to Australia for 1975 - 2006………………………………………………………. 121 Table 5.18: Estimates of long-run demand for imports from Australian products to Fiji for 1975 - 2006……….……………………………... 122 Table 5.19: Estimates of long-run demand for exports from Fiji to New Zealand for 1975 - 2006………………………………………... 122 Table 5.20: Estimates of long-run demand for imports of New Zealand products to Fiji for 1975 - 2005…………….……..…………………. 123 Table 6.1: Total revenue to GDP and customs to total revenue ratios in % (2006) ………………………………………………………….. 151 Table 6.2: Tariff revenue losses – summary of three reports…………………… 152 Table 6.3: Total tariff losses as percent of total revenue – three tariff lines……..154 Table 6.4: Milner (2005) - adjustment costs for the Pacific ACPs……………… 157 Table 6.5: Adjustment costs: Smith’s re-modified Milner approach…………….157 Table 6.6: Adjustment costs: Smith’s “refined” report based on Milner (2005)... 159 Table 6.7: Smith (2006) - total adjustment costs by category…………………... 160 Table 6.8: Adjustment cost measurements: comparison of Smith and Milner….. 161

xii Table 6.9: Fiscal Adjustment costs - Watergall (2007) ………………………… 163 Table B.1: Unit root tests for trade variables of the PACPs and the EC………… 186 Table B.2: Unit root tests for trade variables of the PICs and Australia & New Zealand………………………………………………………… 186 Table B.3: Unit root tests for trade variables of Fiji and the United Kingdom….. 187 Table B.4: Unit root tests for trade variables of Fiji and Germany……………… 187 Table B.5: Unit root tests for trade variables of Fiji and Belgium………………. 188 Table B.6: Unit root tests for trade variables of Fiji and the Netherlands………..188 Table B.7: Unit root tests for trade variables of Fiji and France………………… 189 Table B.8: Unit root tests for import variables of Fiji from Denmark and Italy… 189 Table B.9: Unit root tests for trade variables of Fiji and Australia……………… 190 Table B.10: Unit root tests for trade variables of Fiji and New Zealand…………. 190 Table C.1: Growth Forecast for several Pacific Island Countries……………….. 191 Table C.2: Doing Business 2008 – Enforcing Contracts…………………………192 Table C.3: Trade openness measurements for 210 countries – rankings 1990….. 193 Table C.4: Trade openness measurements for 210 countries – rankings 1995….. 198 Table C.5: Trade openness measurements for 210 countries – rankings 2000….. 203 Table C.6: Trade openness measurements for 210 countries – rankings 2006….. 208 Table C.7: Change in trade openness rankings (1990 – 1995 – 2000 – 2006)…... 213 Table C.8: Dynamic adjustment estimates for exports demand of Pacific ACPs to the EC for 1976 - 2006………….…………………..219 Table C.9: Dynamic adjustment estimates of demand for Pacific ACPs imports from the EC for 1976 - 2006…………………………..……. 220 Table C.10: Dynamic adjustment estimates for exports demand of Pacific ACPs to Australia & New Zealand for 1975 - 2006……....… 221 Table C.11: Dynamic adjustment estimates of demand for Pacific ACPs imports from Australia & New Zealand for 1975 - 2006……….…… 222 Table C.12: Dynamic adjustment estimates of demand for exports of Fiji to the United Kingdom for 1976 - 2004……………………………... 223 Table C.13: Dynamic adjustment estimates of demand for Fiji’s imports from the United Kingdom for 1976 - 2004……………….…………..224 Table C.14: Dynamic adjustment estimates of demand for exports of Fiji to Germany for 1976 - 2004…………………………………………. 225

xiii Table C.15: Dynamic adjustment estimates of demand for imports from Germany to Fiji for 1976 - 2004……………………………………...226 Table C.16: Dynamic adjustment estimates of demand for exports of Fiji to Belgium for 1976 - 2004………………………………………….. 227 Table C.17: Dynamic adjustment estimates of demand for imports from Belgium to Fiji for 1976 - 2004………….…………………….. 228 Table C.18: Dynamic adjustment estimates of demand for exports of Fiji to the Netherlands for 1976 - 2004…………………….…………….. 229 Table C.19: Dynamic adjustment estimates of demand for imports from the Netherlands to Fiji for 1976 - 2004………………………………….. 230 Table C.20: Dynamic adjustment estimates of demand for exports of Fiji to France for 1976 - 2004……………………………………………. 231 Table C.21: Dynamic adjustment estimates of demand for imports from France to Fiji for 1976 - 2004………………….……………………..232 Table C.22: Dynamic adjustment estimates of demand for imports from Denmark to Fiji for 1976 - 2004……….……………………………..233 Table C.23: Dynamic adjustment estimates of demand for imports from Italy to Fiji for 1976 - 2004………………………………………….. 234 Table C.24: Dynamic adjustment estimates of demand for exports of Fiji to Australia for 1976 - 2004………………………….…………….... 235 Table C.25: Dynamic adjustment estimates of demand for imports from Australia to Fiji for 1975 - 2006…….……………………………….. 236 Table C.26: Dynamic adjustment estimates of demand for exports of Fiji to New Zealand for 1976 - 2004….…………………………………..237 Table C.27: Dynamic adjustment estimates of demand for imports from New Zealand to Fiji for 1975 - 2006………………………………… 238 Table C.28: JCT for demand for Pacific ACPs’ exports to the EC……………….. 239 Table C.29: JCT for demand for Pacific ACPs’ imports from the EC……………. 239 Table C.30: JCT for demand for PICs’ exports to Australia & New Zealand……. 239 Table C.31: JCT for demand for PICs’ imports from Australia & New Zealand… 240 Table C.32: JCT for demand for Fiji’s exports to United Kingdom……………… 240 Table C.33: JCT for demand for Fiji’s imports from the United Kingdom………. 240 Table C.34: JCT for demand for Fiji’s exports to Germany……………………… 240

xiv Table C.35: JCT for demand for Fiji’s imports from Germany…………………... 241 Table C.36: JCT for demand for Fiji’s exports to Belgium…...………………….. 241 Table C.37: JCT for demand for Fiji’s imports from Belgium…………………… 241 Table C.38: JCT for demand for Fiji’s exports to Netherlands…………………… 241 Table C.39: JCT for demand for Fiji’s imports from the Netherlands……………. 242 Table C.40: JCT for demand for Fiji’s exports to France………………………… 242 Table C.41: JCT for demand for Fiji’s imports from France……………………... 242 Table C.42: JCT for demand for Fiji’s imports from Denmark…………………... 242 Table C.43: JCT for demand for Fiji’s imports from Italy………………………... 243 Table C.44: JCT for demand for Fiji’s exports to Australia……………………….243 Table C.45: JCT for demand for Fiji’s imports from Australia………………...… 243 Table C.46: JCT for demand for Fiji’s exports to New Zealand………………..… 243 Table C.47: JCT for demand for Fiji’s imports from New Zealand……………….244

xv List of Charts

Chart 2.1: Static welfare effect of a tariff…………………………………………… ..11 Chart 2.2: Static welfare effect of subsidy based export promotion…………………..14 Chart 5.1: The elasticity of a linear demand curve………………………………….. 100

xvi List of Figures

Figure 3.1: Distribution of GDP of PICs………………………………...…………... ..39 Figure 3.2: Inflation rate (change in consumer price index in %) – PNG and Fiji...... 41 Figure 3.3: PNG’s merchandise trade (imports and exports) 1976-2006……………. ..49 Figure 3.4: Fiji’s merchandise trade (exports and imports) 1955 to 2007…………… ..51 Figure 3.5: Fiji’ merchandise trade (exports and imports) with EC (1975-2004)…...... 59 Figure 3.6: Major exports partners: Fiji’s exports to the EU………………………… ..60 Figure 3.7: Fiji’s merchandise exports to the EC, Australia and New Zealand……… ..61 Figure 3.8: Fiji’s merchandise imports from the EC, Australia and New Zealand….....62

Figure C.1: Stability test for exports demand of PACPs to EC………………………. 245 Figure C.2: Stability test for PACPs’ imports demand of EC………………………... 245 Figure C.3: Stability test for exports demand of PICs to Australia & New Zealand….246 Figure C.4: Stability test for PACPs’ imports demand of Australia & New Zealand... 247 Figure C.5: Stability test for exports demand of Fiji to the United Kingdom………... 247 Figure C.6: Stability test for Fiji’s imports demand of the United Kingdom………… 248 Figure C.7: Stability test for exports demand of Fiji to Germany……………………. 249 Figure C.8: Stability test for Fiji’s imports demand of Germany…………………….. 249 Figure C.9: Stability test for exports demand of Fiji to Belgium…………………….. 250 Figure C.10: Stability test for Fiji’s imports demand of Belgium……………………... 251 Figure C.11: Stability test for exports demand of Fiji to the Netherlands……………... 251 Figure C.12: Stability test for Fiji’s imports demand of the Netherlands………………252 Figure C.13: Stability test for exports demand of Fiji to France………………………. 253 Figure C.14: Stability test for Fiji’s imports demand of France……………………….. 253 Figure C.15: Stability test for Fiji’s imports demand of Denmark…………………….. 254 Figure C.16: Stability test for Fiji’s imports demand of Italy….…………………….....255 Figure C.17: Stability test for exports demand of Fiji to Australia……………………. 255 Figure C.18: Stability test for Fiji’s imports demand of Australia…………………….. 256 Figure C.19: Stability test for exports demand of Fiji to New Zealand………………...257 Figure C.20: Stability test for Fiji’s imports demand of New Zealand………………... 257

xvii Abbreviations

ACP African Caribbean Pacific Group ADA Anti-Dumping Agreement ADB Asian Development Bank AILP Agreement of Import Licensing Procedures ASCM Agreement on Subsidies and Countervailing Measures AUS Australia BoP Balance of Payments CRP Comprehensive Reform Program CTI composite trade intensity EC European Community e.g. Example given EPA Economic Partnership Agreement EU European Union FMOLS Philip Hanson’s Fully Modified Ordinary Least Squares F$ Fiji Dollars FSEC Pacific Island Forum Secretariat FSM Federal States of Micronesia GATS General Agreement on Trade in Services GATT General Agreement on Tariffs and Trade GDP Gross Domestics Product GETS LSE Hendry’s General to Specific IFS International Financial Statistics IMF International Monetary Fund IPA Interim Partnership Agreement JML Johansen Maximum Likelihood method KI Key Indicators LDC Least Developed Countries M Merchandise Imports MFN Most-favored-nation NZ New Zealand PACER Pacific Agreement on Closer Economic Relations

xviii PACPs Pacific African Caribbean Pacific Countries PICs Pacific Island Countries PICTA Pacific Island Countries Trade Agreement PNG Papua New Guinea PSRP Public Sector Reform Program RMI Republic of the Marshall Islands ROA Rules of Origin Agreement RWTI Relative World Trade Intensity SPARTECA South Pacific Regional Trade and Economic Cooperation Agreement SPS Sanitary and Phytosanitary Measures Agreement TBT Technical Barriers to Trade Agreement TI Trade Intensity TO Trade Openness TRIPS Trade-Related Aspects of Intellectual Property Rights UK United Kingdom UNSD United Nations Statistics Division US$ United States Dollars US / USA United States of America VAT Value Added Tax WB World Bank WTO World Trade Organization X Merchandise Exports

xix 1. Introduction

1.1 The purpose and scope of the study In today’s globalized world trade plays an important role for a country’s economic performance. Since the late 1970s and early 1980s, a change in general economic policies introduced by the then Prime Minister of the United Kingdom, Margaret Thatcher and US-President Ronald Reagan with spillovers to trade policies was noticeable. This meant the movement away from government control and “inward orientation” towards structural reforms and privatization and “outward orientation” in the sense of trade being welfare and growth promoting. Since then many countries have joined the pro-trade movement with bilateral and multilateral trade agreements and trade liberalization and often experienced most remarkable economic progress. The 1990 Washington Consensus adopted the policies of pro-trade and claimed that major structural reforms for developing countries need to be conducted by pointing out that trade liberalization helps a country’s economic performance (liberalization of imports, elimination of quantitative restrictions and in general that any trade protection should be provided by low and relatively uniform tariffs). Even though John Williamson (1990), the proclaimer of the Washington Consensus, focused on Latin America, NAFTA (North American Free Trade Agreement) and DR-CAFTA (Dominican Republic-Central America Free Trade Agreement), it was seen as a general call for not just trade liberalization throughout the developing world but also for structural reforms. In the end, the Washington Consensus had a global perspective.

Already in 1948, the General Agreement on Tariffs and Trade (GATT) was established, doing both setting up a main consensus on international trade and tariffs and having the objective of reducing barriers to international trade. The so- called Uruguay Round of 1986-1994 addressed the changes to international trade, reformed GATT and in 1995 transformed it into the World Trade Organization (WTO). The WTO is seen as the main international institution that deals with the rules of global trade and follows the objective of GATT to reduce and eliminate the obstacles to international trade.

1 Economic theory says that trade barriers, tariffs and non-tariff barriers to trade are an impediment for economic growth and development. Free trade is seen as most welfare enhancing.

This thesis focuses on the trade liberalization movement of the PICs by analyzing the trade agreements of the Interim Partnership Agreement (IPA) with the European Community (EC), and the Pacific Agreement on Closer Economic Relations (PACER) with Australia and New Zealand, whereas the IPA will be the focus of the discussions.

The purpose of the study is to investigate whether the trade agreements will improve the economic performance of the PICs or not. The question that the thesis tries to answer is whether PICs will gain or lose from the trade liberalization movement.

As a first step to analyze the problem, the thesis takes a closer look at the basic trade theory and identifies and discusses the obstacles to trade. The second step concentrates on the economies of the PICs and its trade relationship with a focus on the trade flow between the PICs on the one side and the EC, Australia and New Zealand, on the other side, over three decades.

In various empirical studies the PICs are earmarked to be open economies. Furthermore, empirical studies claim that the more open an economy, the better its economic performance will be. The PICs belong to some of the slowest growing developing countries in the world, which means that they are either not as open as many econometric studies state or that the claim of the trade openness and economic performance hypothesis is incorrect. As a consequence, the thesis concentrates on trade openness (TO) and its various ways of measurements in empirical studies. To the statements above, a substantial literature review on empirical studies over the recent years shows that no common ground on the correlation between TO and economic growth can be found. The study examines four different TO measurements in a 210 (177) cross-country comparison and

2 finds inconsistent results. Inconsistent results indicate inappropriate measurements, which indicate biased interpretation of empirical results.

In an empirical analysis the demand for trade of the PICs towards the EC, Australia and New Zealand is investigated. The relationship is analyzed by the application of time series techniques conducted with three different econometric methods, namely Philip Hanson’s Fully Modified Ordinary Least Squares (FMOLS), LSE Hendry’s General to Specific (GETS-NLLS), and the Johansen Maximum Likelihood method (JML). Most of the existing empirical studies focus on a country’s elasticities of the export and import demand with the rest of the world or the major trading partners. For the PICs, there is a dearth of empirical work on the topic and the few studies available follow the research methodology above.

This empirical study focuses on the relationship between trade blocks (PICs/PACPs, EC, Australia & New Zealand), and Fiji with its major European trading partners, Australia and New Zealand, whereas for Fiji, the exchange rate is included and offers additional trade political discussion on the basis of the Marshall-Lerner condition.

To fully analyze the possible impact and outcome of IPA and PACER, the adjustment to trade liberalization has to be addressed. This thesis discusses possible tariff losses and adjustment costs and points out major reforms that should be conducted to adequately prepare the PICs for IPA and PACER.

3 1.2 Outline of the study This study is organized in seven chapters. Chapter 2 discusses the basic trade theory. The comparative advantage as the best pro-trade liberalization argument is addressed. The obstacles to trade, trade policy including the topics of import substitution, export promotion and non-tariff barriers to trade, the World Trade Organization (WTO) and the relationship between trade and environment are further issues of the second chapter.

The economies of the PICs and their trade relationship are topic of Chapter 3. The economic performance of the PICs is summarized by concentrating on economic variables such as GDP growth, the structure of output, GDP per capita and the trade balance. The focus is upon Papua New Guinea (PNG) and Fiji, the two dominating economies in the Pacific. Aside from the aggregated trade relationship with the world, the chapter analyses the PICs trade link with the European Community (EC), Australia and New Zealand.

Chapter 4 reviews empirical literature on trade openness and its nexus with a country’s economic performance. Emphasis is placed on the common measurements of TO and its “inappropriateness” to determine a country’s openness to trade. Therefore, a cross-country examination for 210 (177) countries with four different possible TO measurements is conducted, achieving inconsistent results, which leaves room to question the findings of empirical studies using these measurements to determine, e.g. a country’s trade impact to GDP.

In Chapter 5 the demand for trade linkage between the trade blocks of the PICs (PACPs)1 on the one side and the EC and Australia & New Zealand on the other side is discussed. A “case study” also investigates Fiji’s demand for trade relationship with certain European countries, Australia and New Zealand.

the historical trade and aid relationship between the PACPs, the Lomé Convention and the Cotonou Agreement is topic of Chapter 6. The Interim

1 Pacific “African Caribbean and Pacific group” member countries.

4 Partnership Agreement (IPA) and the Pacific Agreement on Closer Economic Relations (PACER) are introduced.

The second part of Chapter 6 debates the potential tariff losses and adjustment costs of the trade liberalization movement. It further emphasizes major reforms which should be implemented to keep, on the one hand, the adjustment costs as low as possible and, on the other hand and more important, achieve as much welfare gains as possible.

The final chapter, Chapter 7, summarizes the major findings of the thesis, presents possible policy implementation, examines future research opportunities, and discusses limitations.

5 2. Trade – Theory and Empirical Discussion International trade is the exchange or buying and selling of goods and services beyond national or across international borders. In most countries international trade, export and import of commodities represents a significant share of GDP. Therefore, international trade is not just a part of the economy and its performance but, together with international finance, forms the larger branch of international economics.2 The words borders, international, GDP and economics imply that politics and policies of international trade are not far away. Trade policies govern and control the international relations and trade in the broader sense and define the way the exchange of commodities is handled.

In theory, countries trade because they benefit by specializing in the production of those goods and services which they can produce most efficiently in terms of higher quality and lower costs, and exchange these for goods and services other countries can produce at a more efficient level. Exchange of the more efficient production increases the welfare of a nation by increasing the consumers’ choice, by giving access to lower cost and higher quality variety of goods and services. The efficiency promotion increases competition and motivates businesses to innovate and to find new ways of production processes and technologies to better serve customers and to increase and advance knowledge and know-how.

These are the general assumptions of why nations trade with each other. In reality trade barriers are not uncommon and mostly have the opposite effect by promoting inefficient domestic industries, raising the costs for products, decreasing the consumer’s choice by reducing the goods and services offered. This may lead to a decline in innovations and slower economic growth.

Simply put, trade policies can be differentiated as either free trade policies or protectionism; in other words, open and closed trade policies. A more closed economy with closed trade policies is defined by protectionism, trade barriers, high tariffs and quotas.

2 http://www.apparelsearch.com – Definition of International Trade.

6 The questions that have to be asked are by looking at the theoretical introduction of why countries trade and the predominant claim that trade without barriers increases the overall welfare of a nation, why trade barriers exist, what kind of trade barriers exist and what these do.

This chapter discusses the basic trade theory. It starts with the sources of the comparative advantage (section 2.1), followed by the general discussion of the obstacles to trade including the issues of trade policy (section 2.2).

Section 2.3 introduces the World Trade Organization and its role in the world economy. Over the last years and especially due to the trade liberalization movement, the relationship between trade and the environment has become more important. Section 2.4 discusses this relationship and 2.5 closes with final remarks.

2.1 The Comparative Advantage Ricardo (1817)3 established the theory of comparative advantage which offers one of the best arguments in favor of free trade.

A country has a comparative advantage in the production of a particular good or service if it can domestically transform other commodities into this good or service more easily than other countries can.

There are different resources of a comparative advantage identified. Technology or technical know-how is considered as one of the greatest sources of comparative advantage of developed countries over developing countries. Another, and probably one of the most important sources of comparative advantage overall, is the endowment of factors. For example, a country may have certain factors of production that makes it profitable to produce a certain commodity. These factors may be land, soil of a special quality, or an abundance

3 Similar ideas of the comparative advantage were expressed by James Mill and Robert Torrens.

7 of labor force. Therefore, there is no need for a technical advantage – very often the example of South Africa is given, whereas even without technological advantage, South Africa would still be one of the largest exporters of diamonds due to its large diamond factor endowment, combined with its labor force.

Apart from that, even if technological advantage may be the same among countries, they could still be trading with each other. The Heckscher-Ohlin Model of international trade is based on the assumptions that even though countries have the same technological advantage compared to a third country, they will still trade with each other due to the fact that they have different factor endowments (e.g. in capital, labor or land). Thus the essence of the Heckscher-Ohlin model is that the comparative advantage is not bound by technological advantage and that even with an assumption of similar know-how, countries will tend to export more of the commodity in which they are intensive in factor possessions compared to other countries in relative abundance.

But still, technological variety makes a difference in trade. Both Heckscher-Ohlin and the Ricardian models predict a huge trade among advanced and developing countries due to difference in development of technological levels.

The relationship between capital and labor is one of the most common relationships used to explain trade between developed and developing countries. These two factors can perfectly be put in the concept of the Heckscher-Ohlin model where they represent different levels of factor endowments – developing countries have a relatively low capital per person ratio compared to developed countries; therefore relatively high labor-intensive exports from developing countries and relatively high capital intensive exports from developed countries could be expected.

Preferences are another main source of trade flows. Simply put, when countries differ in preferences, they may trade with each other. It may be argued that this is a senseless statement because a preference-based trade flow might assume that

8 individuals among countries have to differ completely when aggregated. But this fact is true when considered different per capita income levels. Preferences between developing and developed countries vary significantly. Low per capita income societies prefer more food products, compared to technologically capital intensive preferences of developed countries. In other words, different per capita GDP levels refer to different demands for goods. When per capita GDP increases, the preferences change as well – developing countries may demand more of those goods that they were unable to consume (purchase) before. But an increase in per capita GDP may also lead to a phenomenon which in the end tends to dampen trade between developed and developing countries. This happens when, for example, an increase in per capita GDP raises the demand for the domestically produced commodity in which they have a relative advantage – thus, the domestic consumption of this commodity increases and trade among countries decreases when the country fails to supply more of the commodity to meet foreign demand.

This does not have to be the way because the preference of variety plays another major role. Even though countries may produce similar commodities, this does not imply that the countries do not trade these commodities with each other. The trade for similar products thus depends on the demand for varieties of goods and services. This is an interesting topic since it shows that there is a chance of trade among countries with similar products, but is mostly found in developed countries (e.g. trade of cars between Germany and France). Country identification is another preference – leading to trade. One of the best examples is trade of German steel, Japanese cars, French wine, Fijian tourism etc.

The presence of increasing returns to scale is an additional source of international trade, even though the countries may have similar levels of technology, similar preferences and even similar factor endowments. Economies of scale in production mean that the average cost of production declines when the production is expanded. This may lead to specialization of countries’ export markets in the areas where economies of scale exist. Hence, trade may here be

9 viewed as a way of concentration of production of industries in some countries to maximize their economies of scale.

For the world market this signifies that, when countries trade similar but specialized products, the countries produce with maximizing economies of scale which reduces the world market price and increases the consumers’ affordable income; thus they can consume more of the good or service, which leads to a positive welfare effect. Another explanation of trade in similar products is provided by the work of Brander (1981) which argues that two countries trade with each other even though they have identical industries producing homogenous products. Furthermore, Brander assumes that these industries are run by oligopolistic or monopolistic power, which can lead to an increase in trade when these countries and its industries try to compete with each other for consumers, causing price reduction.

2.2 Obstacles to trade Obstacles to trade can be identified by two major groups, namely tariff and non- tariff barriers to trade. This part of the chapter discusses the trade policies of protectionism and the obstacles of non-tariff barriers to trade.

2.2.1 Trade Policy: Protectionism through import substitution (tariffs and quotas) and export promotion and the infant industry argument

2.2.1.1 Import Substitution The concept of import substitution, also called inward orientation, can be seen in various different forms such as a ban of imports on certain products or the discriminatory treatment of foreign products in favor of domestic ones, thus protecting the domestic industry. Import substitution is often aimed to promote domestic competition by treating imported goods differently by imposing quotas or tariffs or both in a mix. A tariff is a percentage applied to the value of an imported commodity and raises the effective price above international levels. By

10 substituting the actual potential power of imports to the domestic industry, a tariff serves as an instrument of protectionism. Resulting, the domestic industry can produce at higher inefficient price levels. The tariff has to be used wisely to promote the domestic industry, “lowering” the price of the domestically produced goods below the price to pay for the foreign goods plus tariff. A quota sets out a maximum number of foreign products that may be imported. Often tariffs and quotas go hand in hand; after reaching the quota, a high tariff is set into place to protect and promote the domestic industry.

The simple static welfare effect of a tariff system can be seen in Chart 2.1.4 It shows the domestic demand and supply curves of a commodity. p* is the international price of the commodity, pt the international price plus a tariff t,

Chart 2.1 Static welfare effect of a tariff

Supply

E F pt G p* J I H

Demand

O C A D B q

4 Two assumptions are made in this welfare accounting, first equal weights to all market participants, and second a domestic-independent international price.

11 which shows that the implementation of a tariff increases the domestic price. On the one hand, the domestic supply climbs from OA to OC, since the domestic producers, with the knowledge of the tariff and the higher domestic price, are encouraged to produce more of the now more expensive good. On the other hand, the higher price reduces the domestic demand for the good (from OB to OD) as well as the imports, which are always determined by the gap of the domestic supply and demand curves from quantity JG at international price to EF at tariff price level. Just the description of the chart introduces the welfare discussion.

Under welfare perspective, the higher price makes the difference. The domestic consumers pay a higher price and lose; the domestic producers gain as does the government by collecting producers surplus and tariff revenue intakes, respectively: The producers gain by increasing their producers surplus which is captured by the area of p*ptEJ. The consumers lose consumer surplus, captured by p*ptFG and the government gains tariff revenue captured by EFHI.

An overall welfare accountancy of gains in tariff revenue and producers surplus and loss in consumers surplus leaves the two triangles EIJ and FGH, which are the so-called deadweight efficiency losses, incurred from moving from a free market to a tariff protected market.

Many developing countries argue that tariffs and quotas are inevitable to develop and increase the internationally competitiveness of their domestic industries. And indeed the trade theory offers (in the dynamic welfare perspective) explanations and support for protectionism. One is the so-called learning by doing and increasing returns. A protected industry might be encouraged to learn and assimilate new technologies and new techniques of production. The protectionism and the higher producers surplus might encourage the producers to invest in new technologies and in training staff to produce more efficiently by reducing the costs of production and becoming international competitive.

12 The theory further offers support of beneficial spillovers of protectionism, e.g. when an industry is encouraged to increase production and needs to increase domestic inputs such as labor. Thus, spillovers are to be seen as positive externalities, not lowering production costs but encouraging the acquisition of human capital, provoke the provision of adequate infrastructure and generate new income.

2.2.1.2 Export Promotion Another way of promoting the domestic industry’s trade capacity is the so-called export promotion which increases trade beyond the market-determined limits. Export promotion subsidizes exports in various ways. One of the most common is the promotion of non-primary export goods such as light manufactures (e.g. footwear and garments) or sophisticated items (e.g. technological commodities). In very rare cases, developing countries promote primary goods (e.g. Fijian sugar industry).5

The decision of what product is to be supported depends highly on whether or not a comparative advantage exists; the comparative advantage is discussed in the following section. Another way of export promotion is the strategy of reducing import tariffs or quotas on imported raw materials and inputs needed for the domestic production. Therefore, exporters are allowed to import those inputs at preferential prices.

Preferential credits also play an important role as an export promoting instrument by directing banks to provide credits, especially to exporting firms. The conditions of the credits are mostly subsidized by lower interest rates or larger loans.

5 Usually developing countries are found to have a comparative advantage in primary goods and have no need for further promotion.

13 Chart 2.2 Static welfare effect of subsidy based export promotion

Demand G H p s Supply E F p* J I p** K L

O C A B D q

Notes: p* is the international price of the commodity, ps is the international price plus the subsidy paid by the government. The subsidy increases domestic supply (from B to D), lowers domestic demand (from A to C) and increases exports (from originally EF to GH). Domestic consumers lose welfare (psp*EG), whereas producers gain welfare (psp*FH), and the government loses due to the payment of the subsidy (psp*JH). In the end, the grey triangles (EGI & FHJ) represent the dead weight loss that occurs from moving from a free market to an export subsidized market. If the export subsidies cause an oversupply, the international price may even fall to p** which would increase the overall welfare loss.

Together, import substitution and export promotion lead to the so-called and well known infant industry argument6 in developing countries, where the idea of protecting an infant industry from the global importers is to increase the industry’s international competitiveness. The protected industry needs to invest their gains in producer surplus actively (e.g. in new technologies). Without political good-will and active pro-international-competitive policies, this might not happen but will lead to the opposite effect (e.g. using the producer surplus to further expand production without actually investing in pro-efficient

6 Even though it is not originated by John Stuart Mill makes several recommendations in his book Principles of Political Economy (1848).

14 technologies) and might result in an inefficient way to promote the domestic industry whereas the costs are to be borne by the consumers due to welfare loss.

The best strategy, if an infant industry protection is politically unavoidable, would be to temporarily limit protectionism by letting it expire after a given time. This gives the domestic industry the chance to overcome its inefficiency and reach international competitiveness and/or specialize in a field where a comparative advantage exists. The success of the strategy depends on the credibility of its temporariness and its limitations. The government and the industry have to, on the one hand, keep their promises by lowering the protectionism and investing into new technologies. On the other hand, they have to be fully prepared for the changes in trade policies (e.g. international competitiveness and overcome the loss in tariff revenue losses).

Smith (1776) makes several comments on international trade, trade restrictions and welfare. Smith discusses the balance of trade for determining the welfare. The goal of achieving a trade surplus was to be achieved through import restrictions (import substitution) to decrease imports, and export subsidies (export promotion) to increase exports. Smith argues against both actions by pointing out that on the one hand, the procedure of exports subsidies is nothing other than paying someone to pay the domestic good and that, on the other hand, import substitutions would only benefit a small number of domestic industries and would certainly not encourage competition. Further, in a letter Smith (1783) wrote it "may, I think, be demonstrated to be in every case a complete piece of dupery, by which the interests of the State and the nation is constantly sacrificed to that of some particular class of traders". Smith criticized trade restrictions by emphasizing that it only benefits some but not the nation on the whole. Any obstacles to trade would diminish the gains from trade itself and therefore, harm the economy. On the one hand, Smith and the classical economists made a powerful case for liberalizing trade from government restrictions (such as import tariffs and quotas) and moving toward free trade, but on the other hand, the same economists recognized that there may be situations in

15 which a government might wish to sacrifice economic gains for other political objectives (e.g. Smith argued in favor of the British Navigation Acts, which restricted trade and promoted British shipping).7

2.2.1.3 Non-tariff barriers to trade Non-tariff barriers to trade are any measures other than tariffs that restrict or disturb trade among countries in one way or another. These obstacles can be summarized into five major categories:

(1) Quantitative restrictions and similar specific limitations. These are most widely implemented through import and export quotas as well as import and export licenses.

(2) Customs procedures and administrative practices. Customs surcharges, customs classification and classification procedures, customs clearance procedures, minimal customs value, excise and special customs formalities are summarized under this category.

(3) Non-tariff charges and related policies affecting trade. Non-tariff charges are sales taxes, border taxes and border tax adjustments, consumption taxes (e.g. VAT) but also measures such as anti-dumping and countervailing measures. Furthermore, the country specific rules of origin are part of this rubric.

(4) Government participation in trade, restrictive practices and general policies. This category combines the government participation in the form of subsidies paid for export promotion, tax exemptions for critical imports, the introduction of single or limited channels for imports.

7 Book 4, Chapter 2, An Inquiry into the Nature and Causes of the Wealth of Nations, by Adam Smith, 1776.

16 (5) Technical barriers to trade. These can be defined as “standards and regulations adopted by countries to meet the needs of the world-wide increasing demand for safe and high-quality products.”8 These include quality standards such as health and sanitary regulations, safety and industrial standards and regulations, packaging and labeling as well as advertising and marketing regulations.

Obviously, the first and the fourth point, quantitative restrictions and similar specific limitations have similar results as do import substitution by imposing tariffs.

The other three points essentially cause obstacles to trade, when they vary among countries; for example, when customs classifications and procedures differ among trading countries, disturbances in trade are identified due to time and cost inefficiency of reclassification or information costs of the different classification systems (e.g. SITC versus HS classification).9 Furthermore, a huge disturbance of trade flow can occur when the customs administration works inefficiently itself, for example, by lack of capacity very often given in developing countries.

Non-tariff barriers to trade can also be used as instruments of protectionism by a country’s government to support the domestic industry by slowing the process of the customs procedures or increasing the documents needed, thus increasing regulations. These regulations can go beyond customs procedures. For example, different taxation of domestic and foreign goods and services whereas foreign goods are being discriminated against due to higher taxation, which in the end is nothing else but a tariff used under a different name and procedure with similar effects.

The most widely used measurements for protectionism are the technical barriers to trade. High standards and regulations disturb trade flows and support certain domestic industries. But even if the government does not favor protectionism,

8 World Trade Organization – www.wto.org, 2008. 9 SITC equals Standard International Trade Classification, HS is Harmonized System.

17 different standards among countries make trade for producers, exporters and importers much harder.

A last major obstacle to trade which addresses all the above is the lack of transparency. When countries trade with each other not having enough or the correct information on customs procedures makes trade uncertain, longer and more expensive and inefficient and means a loss of welfare.

2.3 The World Trade Organization “The World Trade Organization (WTO) is the only international organization dealing with the global rules of trade between nations. Its main function is to ensure that trade flows as smoothly, predictably and freely as possible.”10

Thus, the WTO deals with both, tariff and non-tariff barriers to trade and tries to set basic rules to overcome obstacles to trade implied. The WTO was established in 1995, created by the so-called Uruguay Round negotiations (1986-1994) and is based on the General Agreements on Tariffs and Trade (GATT) (1948). The functions of the WTO can be summarized by the following: - administering WTO trade agreements - forum for trade negotiations - handling of trade disputes - monitoring national trade policies - technical assistance and training for developing countries - cooperation with other international organizations (e.g. IMF, ADB, WB)11

Therefore, the WTO is a framework that allows governments to overcome trade differences to be solved by the general legal ground-rules for international

10 World Trade Organization – www.wto.org, 2008. 11 IMF = International Monetary Fund, ADB = Asian Development Bank, WB = World Bank.

18 commerce.12 These are essential contracts to bind governments to keep their trade policies within limits of agreement – the WTO therefore works as a dispute settlement instrument due to the fact that trade relations very often involve conflicting interests.

The most important agreements are found in the two largest areas of goods and services and are namely GATT (for goods), and the General Agreement on Trade in Services (GATS). Trade-Related Aspects of Intellectual Property Rights (TRIPS) comprises as the name indicates, the rules of the intellectual property of international trade. Furthermore, extra agreements and annexes deal with the special requirements of specific sectors or issues of different countries (e.g. the group of the Least Developed Countries). Detailed and lengthy schedules (or lists) of commitments made by individual countries allow specific foreign products or service providers access to their markets. For GATT, these take the form of binding commitments on tariffs for goods in general, and combinations of tariffs and quotas for some agricultural goods. For GATS, the commitments state how much access foreign service providers are allowed for specific sectors, and include lists of types of services where individual countries have the opportunity of not applying the “most-favored-nation” principle of non- discrimination.

The major principles of the WTO are trade without discrimination and the so- called national treatment – treating foreigners and locals equally. By using those two principles the goal of the WTO is to help not just producers of goods and services, exporters and importers to conduct their business but also to improve the welfare of the people of their 156 member countries.13

12 The heart of the WTO is the multilateral trading system (general legal ground-rules for international commerce) which was signed by a large majority of the world’s trading nations. 13 On 16 May 2008, Ukraine became the WTO’s 156th member after starting its accession negotiations in 1993.

19 2.3.1 Some of the “more important” agreements in detail a.) Sanitary and Phytosanitary Measures Agreement (SPS) SPS is an agreement that sets out the basic rules on food safety and animal and plant health standards.14 TO install SPS measures, the WTO encourages both to make food, animal and plant products more safe and to use international standard, guidelines and recommendations to harmonize SPS measures themselves. The agreement further includes all provisions on control and inspections as well as on approval procedures. It asks the member countries for transparency, thus changes or the implementation of new SPS regulations have to be reported to any member country and to the WTO. Thus, with the SPS, the WTO addresses possible unnecessary obstacles to trade, discussed above, and tries to overcome those. b.) The Technical Barriers to Trade Agreement (TBT) Technical regulations and standards can become obstacles to trade when they vary from country to country and make trade more difficult for producers, exporters and importers. The WTO recognizes that technical regulations and standards are important and necessary to protect the environment, to assure safety and national security to consumers. The problem that occurs is that these technical standards and regulations can be used as instruments of protectionism in the form of creating a situation of overregulation.

“The Technical Barriers to Trade Agreement (TBT) tries to ensure that regulations, standards, testing and certification procedures do not create unnecessary obstacles to trade.”15 On the one hand, similar to the SPS, the WTO supports the countries’ rights to implement standards appropriate to protect human, animal and plant life or health safety. On the other hand, the WTO also supports the usage of international standards and the implementation of the “code of good practice”16 to achieve the goal of harmonization. The TBT also

14 Rules must be based on science. The introduction of higher rules scientifically justifiable. 15 World Trade Organization – www.WTO.org, 2008. 16 Set of more than 200 voluntary standards for both the government and non-government sectors.

20 introduces non-discrimination between domestic and foreign goods and further asks for transparency including possible changes of technical barriers.17 c.) General Agreement on Trade in Services (GATS) Services represent the fastest growing global economic sector. The WTO finds it to account for more than two thirds of global output and employs one third of the world’s total employees. Further, it makes up nearly 20% of total trade. “GATS is the first and only set of multilateral rules governing the international trade in services.”18 All services are covered by GATS. The total coverage is made up by four different modes: (1) “cross-border supply”, which is defined by the service supply from one country to another (2) “consumption abroad” by domestically using the services of a foreign country (3) “commercial presence” through support of branches (e.g.) to provide services abroad (4) “presence of natural persons” where individuals travel to another country to supply services19

One of the most important sub-agreements of GATS is the Most-favored-nation (MFN) treatment which says that if a country favors another country, it has to favor all trading partners. Under GATS, if a country provides market access to one nation, it has to offer the same market access and the same opportunities to all other members of the WTO. MFN further applies to all services and allows temporary exemptions.20

Furthermore, GATS includes international regulations and obligations to commitments on market access and national treatment, a transparency rule which

17 Around 900 changes are reported every year. 18 World Trade Organization – www.WTO.org, 2008. 19 “Mode 4” is one of the most commonly discussed issues when it comes to trade agreements between developed and developing countries. A detailed discussion is offered in Appendix D. 20 See General Agreement on Trade in Services (GATS).

21 commits all WTO members to make all information accessible and/or publish all necessary and relevant laws and regulations, whereas regulations are supposed to be objective and reasonable. GATS also discusses international payments and transfers and states that “services are not all the same” by differentiating into the movement of natural persons, the financial services, the telecommunication sector, and air transport services. d.) Anti-Dumping Agreement (ADA)21 The practice of “dumping” is not uncommon in international trade. Dumping is defined as exporting a product at a lower price level than the one that is considered normal at the domestic market. Since dumping of commodities from country A can have serious influence on the domestic market of country B, the WTO’s ADA defines how countries may react towards dumping. Article 6 of GATT allows a country to take actions against dumping, which is expanded by ADA. A typical and most common measure to address dumping is an extra import duty on the “dumped” commodity in order to bring its price level to normality. Important to remember is that “anti-dumping measures must expire five years after the date of imposition, unless an investigation shows that ending the measure would lead to injury.”22 e.) Agreement on Subsidies and Countervailing Measures (ASCM) ASCM regulates the usage of subsidies and the actions that countries may take to counter other countries’ use of subsidies and its adverse effects. The WTO encourages its members to remove subsidies and thus to free the economy from its adverse effects. ASCM identifies two different kinds of subsidies, the so- called prohibited subsidies and actionable subsidies. The prohibited subsidies are aimed in favor of the domestic market and support protectionism. The actionable subsidies react to the usage of subsidies by another country.

21 ADA is officially the Agreement on the implementation of Article VI of the General Agreement on Tariffs and Trade, 1994. 22 World Trade Organization – www.WTO.org, 2008.

22 f.) Non-tariff barriers agreements Bureaucratic and/or legal issues can lead to obstacles to trade. These are covered by the non-tariff barriers, as discussed above. The WTO recognizes import licensing, rules for the valuation of goods at customs, pre-shipment inspections and further checks on imports, rules of origin, and investment measures as non- tariff barriers or other issues that need to be addressed.

The Agreement of Import Licensing Procedures (AILP) states that licenses have to be simple, transparent and predictable. The agreement gives strong recommendations to the WTO members on how to handle new or existing import licenses.

The Rules of Origin Agreement (ROA) determines where a product was made. Since a number of countries use different measurements on how to estimate in which country which product was made, the ROA ensures that the country- specific rules of origin are transparent, have no restricting, disturbing, nor any disruptive effect on the international trade flow. In the long-term, the WTO’s goal is a harmonized rules of origin understanding to overcome possible misuse of the rules of origin in favor of domestic industries.

2.4 Environment and trade Trade and trade liberalization can have a large impact on the relative prices and the economic income of small open economies. Furthermore, trade liberalization can also have a large impact on the environment.

Many environmentalists are concerned that trade liberalization may shift pollution from rich to poor countries by simply shifting and outsourcing polluting industries. This so-called dirty-industry migration is a serious problem because poor and less developed countries simply bear the burdens of pollution of rich countries’ consumption. A competition for dirty industries among poor countries

23 might even lead to an “environmental showdown.”23 The claim is that this happens in countries unable to specialize in clean goods production or countries that are simply not interested in clean production and therefore are targeted by polluting industries that leave highly regulated countries and move towards countries with low environmental regulations.

And indeed, the arguments are not taken out of the blue as examples in China or India show where big companies of the developed world outsourced manufacturing bases to developing countries often having much lower standards than those of their countries of origin (e.g. VW, Daimler Chrysler). Hence, most environmentalists ask for strict environmental policies and encourage the developed world to accept responsibility when negotiating for trade liberalization movements or oppose trade liberalization right from the beginning.

Often the relationship between trade liberalization and environmental issues is misunderstood. It is not the trade liberalization, as often claimed, that causes environmental problems but the environmental policies of the political actors. Thus, policies play the major role when it comes to trade liberalization and environmental issues.

On the one hand, it is correct that international trade leads to greater scale of economic activity (e.g. due to an increase in service transactions and in the production of goods and services as well as due to higher consummation) and that this may lead to higher pollution. On the other hand, trade liberalization can increase environmental friendliness due to income gains and specialization in environmental friendly production. This shows that strict environmental policies may actually harm the economy.

Besides the important political role, the comparative advantage determines whether the political decision makers should introduce strict regulations or not.

23 Copeland and Taylor (2003).

24 In the end, the trade liberalization’s effect on the environment is determined by a country’s comparative advantage, whether the country is a dirty goods importer or a dirty goods exporter, and by its environmental policies. For example, if a country has a comparative advantage in exporting clean goods then weak environmental policies can increase the export of those because of a shift from dirty inefficient production towards clean goods production in which the economy has an opportunity. In such an economy stricter policies would increase the pressure on the clean producers and would lower substitution of dirty goods producers towards the production of clean goods. This implies that a more regulated economy harms the exporting industries, which may be the country’s comparative advantage.

But if the economy is in a state where weak policies stimulate trade in the polluting sector by having a comparative advantage in dirty goods, the income gains from freer trade can be compensated by the costs of the increased environmental degradation.

An increase in income can increase the demand for environmental friendly productivity due to usage of new technology but only if the service sector is included in the trade liberalization movement since most technological improvements accrue from trade in services.

If the income elasticity of demand for environmental quality is not too high, trade will improve the environment in countries with clean goods production and worsen it in countries with a comparative advantage in dirty goods.

This leads directly to determining whether an economy should have strict regulations on environmental issues or not – the source of increasing income. A neutral technological progress will both raise real incomes and improve environmental quality by not favoring any particular industry. Hence, the environmental quality improves through its role of raising incomes and tightening production techniques. If the economic growth is fueled by capital accumulation

25 only, the environmental conditions will be worse off since it raises income and scale and thus favors the productivity of dirty capital intensive productions.

Copeland and Taylor (2006) estimated that if trade liberalization increases the scale of economic activity by 1% the pollution concentration will increase by 0.25-0.5% due to the scale effect. The authors further find that when accompanied by the increase in per capita income the concentration actually decreases by 1.25-1.5% caused by the technique effect.

Political decisions and willingness of governments to promote environmentally friendly economic activity remain the main issue.

Trade often occurs between countries that have imperfect environmental policies even in the long run. If no environmental policy reform is undertaken, trade liberalization may well complicate the environmental problems already existing. Common political instruments for environmental policies are the pollution tax and emission licenses. The political choice of which instrument is most efficient to address the environmental issues remains a governments’ responsibility. It may even be that governments are motivated to use environmental policies as an instrument of protectionism by sheltering favored groups from competition pressure of trade (e.g. by tightening environmental rules for one group but not for another).

In the end it is probably correct to put the developed countries into the spotlight and ask them to take environmental responsibility when negotiating for trade liberalization. But it is also essential that flexible pollution policies are in place to respond to the changes that trade liberalization brings, to identify the sources of income and the comparative advantage of a country as well as to understand how international trade affects the environment before making a final environmental political decision.

26 2.5 Conclusion of Chapter 2 In this chapter of the thesis the general trade theory has been discussed. It started with the topic of the comparative advantage which is seen to be the best argument in favor of trade liberalization. The discussion analyzed different sources of comparative advantage such as technology and technical know-how, factor endowments, preferences and increasing returns to scale. Free trade is not just found to be welfare promoting but also to increase competition and specialization. In the second part, the obstacles to trade were addressed by looking at trade policy, recognizing import substitution and export promotion as the major policy driven obstacles to trade. The infant industry argument is found to be an argument in favor of trade barriers, but many economists see the threat of promoting inefficient economies; therefore if an infant industry protection is in place, several limitations should be addressed so that the infant industry is forced to reform to become internationally competitive. Additionally, non-tariff barriers to trade are identified and outlined, including the technical barriers to trade.

The WTO “as the only organization dealing with the global rules of trade between nations” is introduced as an institution based and on several trade agreements designed to overcome the obstacles to trade by addressing and firmly identifying them in the first place. Harmonization and general consensus are seen as the main instruments to eliminate barriers to trade. It is discussed that especially the variation of the non-tariff barriers to trade are meant to be the major obstacles to trade and cause major welfare reduction.

The last part of the chapter discussed the increasing importance of the topic of environmental issues in trade agreements and in the relationship between trade and the environment on the whole. The common view of many environmentalists has been discussed – that trade worsens the environment. In conclusion, it was shown that trade is not responsible for the environmental conditions but policies. Therefore, the correct trade and environmental policies should be in place to

27 assure both that environmental policies are not an obstacle to trade and that they protect the environmental in a decent way. To do so, two key issues need to be identified: (1) the country’s comparative advantage, and in which field it occurs (dirty or clean goods production) and (2) the source of increasing income and whether it is due to neutral technological progress or due to capital accumulation.

28 3. The economies of the Pacific Islands Countries and their trade relationship This chapter gives an overview of the PICs’ economic structures and their trade relationship with the world. The chapter is build up as follows. Section 3.1 discusses the economies of the PICs, including real GDP growth rates, the structure of output, the GDP per capita and the trade balance averaged over a ten year period. In section 3.1.1 the two largest economies of the PICs, PNG and Fiji, are surveyed. The first part of the chapter is concluded by section 3.1.2. The second part of chapter 3 investigates the PICs’ trade relationship with the world (3.2.1), while focusing on PNG (3.2.1.1) and Fiji (3.2.1.2). The PICs aggregated trade with the European Community, Australia and New Zealand is discussed in section 3.2.2. Fiji’s trade relation with the above is discussed in section 3.2.2.1. Section 3.2.3 summarizes the second part and 3.3 concludes the chapter.

3.1 The economies of the Pacific Islands Countries The Pacific island countries (PICs) can be described in various ways: small, vulnerable to natural disasters, underdeveloped (least developed), aid dependent, dependent on trade and agricultural markets, having a negative trade balance, being low to middle-income countries, being mismanaged, having good governance to bad governance and being politically unstable. And indeed, all these characteristics mentioned above can be found in the Pacific. Most of the PICs are small island states which differ widely in size, population and resources. The countries are spread over 30 million square kilometers of the Pacific Ocean but the total land area is less than 600,000 square km. The Pacific group is divided into Polynesia, Micronesia and Melanesia. The Melanesian group (e.g. Fiji, PNG, Solomon Islands and Vanuatu) is larger in size in comparison to the Polynesian (e.g. Cooks Islands, Samoa and Tonga) and Micronesian group (e.g. FSM). The land size varies between 21 to 26 square kilometers in Nauru and Tuvalu, respectively, all the way to Papua New Guinea (PNG) with a total land area of 462,000 square kilometers, which makes it the

29 largest of the PICs in size and population – PNG’s land size equals approximately 85% of the PICs total land size and with a population of 6.2 million, 65% of the total population of the Pacific group.

Compared to the general problems most developing countries face, the PICs have additional difficulties, making them unique in the developing world. These include diseconomies of scale, remoteness from major ports, export markets and trading partners, and high vulnerability to natural disasters.

Due to limited resources and production facilities, PICs concentrate on the production of a few primary commodities (mostly agriculture based). Thus, the PICs depend highly on imports of intermediate and finished goods. Exports diversification is found to be limited.

Table 3.1 gives an overview of economic indicators of the PICs. Besides the fact that their growth rates have shown large variations, most PICs have experienced, on average, low growth in output over the period of 1996-2006.

On average, real GDP growth over the period of 1996-2006 ranges from more than 3% in Kiribati, Samoa, and Cook Islands to a shrinking economic performance in countries as the Federal States of Micronesia (FSM), the young independent democracy of Timor-Leste, or the Republic of the Marshall Islands (RMI). The structure of output varies widely across these 15 island states, but indications can be found that the higher the GDP per capita, the higher is the share of services in structure of output (e.g., Cook Islands, Nauru, or Palau), or in states with a higher increase in GDP per capita than in actual GDP (e.g. Samoa and Tonga). The PICs can be described as low-income or middle-income countries. The GDP per capita varies from only US$ 319 per year in Timor Leste, which makes it one of the poorest countries in the Pacific region up to more than US$ 13,000 in the Cook Islands. Timor-Leste, Kiribati, Samoa, the Solomon Islands, Tuvalu, and Vanuatu are identified as Least Developed Countries (LDC) by the United Nations. The recent political turmoil in Timor-Leste and the

Table 3.1 GDP growth, GDP per capita and structure of output for PICs (1996-2006)

Real GDP Structure of Output (% of GDP) GDP per capita Growth Rate (average 1996-2006) 2006 Growth rate in % Countries average 1996-2006 1 Agriculture Industries Services (US $) average 1996-2006 1 Cook Isl. 3.1* (3.1) 13.3** 8.3** 81.5** 13,005 7.6 (9.4) Fiji 2.4* (2.4) 16.4 23.2 64.4 3,724 2.4 (3.1) FSM -0.4 (-0.5) … … … 2,212 1.5 (1.4) Kiribati 3.3 (3.9) 7.1 7.4 72.6 801 2.7 (2.3) Nauru … (-1.0) 7.2** 9.2*** 66.0** 2,742 3.2 (3.0) Niue … … Palau ... (2.2) 3.5** 14.5** 80.8** 7,698 1.9 (2.0) PNG 2.6 (2.8) 35.3 37.4 27.3 989 -3.2 (-0.7) RMI 0.3* (-1.3) ...... 2,204 1.0 (1.5) Samoa 3.5 (3.6) 15.8 26.6 58.7 2,348 7.2 (5.9) Solomon Isl. 0.3 (0.1) ...... 860 -3.7 (-2.3) Timor-Leste -0.7 (-0.7) 31.7 16.9 51.4 319 -2.1 (-3.1) Tonga 2.0 (2.0) 29.5 15.8 58.0 2,328 2.8 (2.5) Tuvalu … (1.8) … … … 2,441 … (6.8) Vanuatu 2.2* (2.0) 15.7 8.8 75.2 1,635 2.2 (1.6)

1. Growth rates in parenthesis are from United Nations Statistics Division. Notes: * Data used for derivation: 1996-2005 - Asian Development Bank - Key Indicators 2007, 2006: United Nations Statistic Division.

** Data used for the years 1996-2005.

Source (s): ADB Key Indicators 2007; United Nations Statistics Division, 2008.

Solomon Islands is just one of the reasons for the poor economic performance that can easily be seen in the per capita GDP growth rates which actually declined over the 10-year period of 1996-2006 in these two island states.

Kiribati’s relatively good performance is driven by government expenditure of trust fund earnings, fishing license revenue, remittances, and foreign aid, which also explains their relatively high share in services in total output. In the year 2005, aid and grants accounted for more than 48% of Kiribati’s total revenues, which underlines the hypothesis that PICs are aid dependent. Development in the fisheries sector plays a major role for future economic performance in Kiribati; resource management and the implementation of a remodeled marine tenure system are just one part of the priorities that need to be taken into account to

31 make the fisheries development sustainable. Kiribati’s private sector can be seen as underdeveloped or partially non-existing.24

On the one hand, with a GDP per capita of more than US$ 13,000 the Cook Islands is probably the wealthiest country (in terms of GDP per capita) within the 15 countries of interest, but on the other hand, with US$ 14.1 million, aid and grants made up 21.5% of the country’s total revenues in 2006.25 Other highly aid dependent countries are Tuvalu with ca. 50% of its revenue income accounting to grants, Palau with 56%, FSM with US$ 84.5 million of total revenues or in percentage terms 63.1%, and RMI which leads the highly aid-dependent countries with more than 66% of its revenues coming from grants (US$ 71.4 million). Grants as a source of revenue income has been rapidly increased in Tuvalu since 2000 when it accounted for less than 1% of its total revenues. In countries such as Timor-Leste, Samoa, Fiji, or Vanuatu, aid as a source of revenue income has been decreasing over the period of 2005-2006. In the case of Samoa, one reason for the decline of the share of grants in total revenue is the country’s successful implementation of necessary reforms in the mid 1990s.26 The ADB-based comprehensive reform program (CRP) was adopted after the fiscal crisis in the early 1990s, when Samoa faced a public debt of ca. 100% of GDP (1992) and the fiscal deficit averaged 12.4% of GDP (1990-94). The CRP of 1996/97 removed credit controls, controls on interest rate, and capital account, included a public sector reform with corporization and privatization of government owned businesses, further introduced the value added tax, and opened its telecommunication and postal sector. Even though the reform steps slowed down in the recent years, its successful implementation makes the country one of the best performers with an average growth in GDP of approximately 3.5% (1996-2006). The country’s external debt has declined significantly to less than 26% of GDP in 2006, which is another indicator of the positive implementations of the CRP. Samoa’s deposit rate with 4.6% (2006)27 is

24 See Duncan (2007) and Thomas (2003). 25 Asian Development Bank’s Key Indicators 2007. 26 Presentation given by Prof. Ron Duncan on “Binding Constraints to Economic Growth in the Pacific” to the Fiji Island Reserve Bank, 2007. 27 IMF’s IFS 2007: Deposit rate 3-6 months.

32 relatively high among PICs and prevents the flow of savings. Samoa’s success can also be seen in growth of GDP per capita over the same period, where Samoa places second with an annual increase of 7.2%, being higher than its GDP increase which can be explained by high emigration and remittances of emigrants working overseas, providing access to overseas savings. Spillovers of the CRP are also noted in the form of infrastructure development which has assisted development in tourism. In 2006, commerce, transportation and communication, public administration, and finance and business services are the leading sectors, accounting for more than 53% of total GDP. Even though the economy is considered to be one of the best in the Pacific region, Samoa faces challenges such as a shrinking of the country’s agriculture sector (from 15.4% of GDP in 1994 to 6.7% in 2006) and a declining manufacturing industry (from 16.9% of GDP in 1994 to 8.8% in 2006) which increases the pressure on lost employment opportunities already facing a high youth unemployment and thus increasing the risk of expanding the population living below the acceptable standard and under the national poverty line (20.3% of the population were considered to live below the poverty line in 2002).28

Furthermore, the drop in those two sectors are noted to be significantly responsible for the relative “slowdown” of growth from 5.2% in 2005 to 3.4% in 2006. It also undermines the national food security and might lead to an increased dependency on food imports, which again might add pressure to the balance of payments (BoP). The BoP structure can be described as having a persistent trade deficit (ca. 45% of GDP in 2006)29 and high dependency on current account receipts from tourism and private remittances, and grant aid receipts. Other problems the economy faces are its aging and diabetes-prone population, adding fiscal pressure on the health sector, adversely affecting labor productivity, and a possible resource flow constraint due to a graduation out of the LDC status by 2010 are considered to be medium-term risks.30 Nonetheless, the CRP and the

28 Household income and expenditure survey for Samoa, 2002. 29 ADB Key Indicators 2007: Balance of Payments in % of GDP, Balance on Goods. 30 Samoa economic update 2007, KVA Consult Ltd.

33 government’s further commitment to public sector reforms show success and make Samoa a role model within the Pacific of how to reform.

The opposite of Samoa’s progress can be found in the case of FSM’s Compact I (1987-2003) economic reform program and the Public Sector Reform Program (PSRP) of 1997. Compact I was designed to reform budget transfers, to create fewer restrictions and accountability. The PSRP focused on the reduction in government departments due to retirement of public servants and a reduction in work hours. Both reform programs failed to reduce the high costs of business in FSM, thus its dependency on mainly US aid and its government owned businesses are assiduous. The financial market is characterized by falling interest rates (from 5.3% in 1995 to 2.035% in 2006) on deposits and an increasing interest rate spread (11.83% in 1995 to 14.11% in 2006).31

Similar to Samoa, Tonga also experienced a more rapid increase in GDP per capita than in GDP growth (2.0% GDP growth against 2.8% per capita GDP increase) “as the result of their high level of emigration—which keeps population growth at very low levels—and the high level of remittances from emigrants working overseas”.32 Only PNG, the Solomon Islands, and Timor-Leste show a decline of per capita GDP. As mentioned above, the political turmoil is significantly responsible for the poor performances in Timor-Leste and the Solomon Islands.

A cross-country analysis of 178 countries on the subject of “Doing Business”33 shows that Fiji performs best of the PICs by achieving the 36th place, ahead of Portugal, Spain, Luxembourg, and most of the new twelve EU-27 members (e.g. Czech Republic 56th, Poland 74th). On the other hand, the analysis also shows that Timor-Leste is placed 168th on the rank of ease of doing business. On a low- income perspective, two out of three PICs being considered as low-income34 and are placed under the Top 5. In the category “lower middle income”, 6 countries

31 Data derived from IMF’s IFS 2007 32 Ron Duncan and Carmen Voigt-Graf (2007). 33 World Bank Group, 2008. 34 Solomon Islands (4th), PNG (5th) and Timor-Leste (43rd).

34 are placed relatively well – Fiji 3rd, followed by Tonga 7th and Samoa, and Vanuatu on 10th and 11th places, respectively. Kiribati 16th, RMI 23rd, and FSM 34th are still quite well off in this 57 country perspective.

In Table 3.2 the costs, procedures and time it takes to start a business in the PICs and, for comparison, the world leaders in starting a business (Australia, Canada,

Table 3.2 World Bank’s Doing Business 2008: Starting a business

Country Starting a business GDP per % of GDP per Cost (US$) Procedures Time Capita (US$) Capita (US$) Fiji 1,124.48 8* 54 3,724 30.20 FSM 132.00 7* 16 2,212 5.98 Kiribati 698.44 6* 28 801 97.20 Palau 375.00 8* 28 7,698 4.87 PNG 202.83 8* 55 989 20.51 RMI 530.004 5* 17 2,204 24.05 Samoa 947.00 9* 39 2,348 40.33 Solomon Isl. 400.82 7* 57 860 46.61 Timor-Leste 100.00 9* 84 319 31.35 Tonga 236.93 4* 32 2,328 10.18 Vanuatu 1,029.44 8* 40 1,635 63.96

Singapore 321.29 5 4 30,159 1.07 Australia 301.21 2 2 37,924 0.79 Canada 317.36 2 3 39,004 0.81 New Zealand 38.91 2 12 25,603 0.15

Notes: 1. Cost is measured as the average fees and duties in U.S. dollars (average exchange rate for the period of 2006, ADB Key Indicators 2007) including all fees associated with completing the procedures of “starting a business (e.g. fees for registration, application, license(s), and lawyers, and stamp duties).

2. Procedures comprise all required steps and registration requirements. * indicates that some of the procedures take place simultaneously with another procedure.

3. Time is recorded in calendar days required for the procedures and governments requirements to start a business, until completed.

4. The representative fee schedule for business licenses depend on the kind of business. In the costs here, the business license fees for a retail business (US$ 105) are calculated. Others are subdivided in Banks: US$ 5,000. - Professional: US$ 3,000. - Hotels: US$ 500.

5. The data of GDP per Capita is obtained by the United Nations Statistical Division for the period of 2006.

6. * indicates that some procedures take place simultaneously with another.

Source: Doing Business 2008. The World Bank Group.

35 New Zealand), and the overall leader in terms of ease of doing business (Singapore) are presented. It further derives the costs as share of GDP per capita. It shows that the leaders’ share of GDP per capita to start a business hardly exceeds 1% and that Kiribati with 97.2% is worst and placed, in the international cross-country comparison with 178 countries, 86th due to the fact that it takes relatively less time and “only” 6 procedures. On average it costs approximately US$ 525, takes 41 days and 7 procedures to start a business in the 11 PICs reported, exceeding the world leaders by far. One reason is that governments in the Pacific region need to collect revenues due to their limited resources and revenue capacity.

Another source of revenue are taxes on imports. Taxes on imports are found to be 30.2% of the total tax revenues in FSM in 2005. In 2001, tax revenues on import duties and levy accounted for ca. 60% of Kiribati’s total tax revenue. In RMI, import duties are 19.7% of total revenues in 2004. In Samoa, taxes on international trade give 14.7% of total tax revenue or 10% of total revenue and grants. Vanuatu’s taxes on international trade account for 33% of total direct taxes or 26.8% of total revenue. All these numbers show how important trade is for the PICs. As instruments for economic development, the PICs have introduced several trade liberalization agreements, implemented in regional (e.g. PICTA or SPARTECA)35 and international trade agreements (e.g. PACER and EPA).36

Narsey (2004) sees those agreements as instruments for creating faster economic development, employment, increased incomes as well as the standard of living. Therefore, these agreements are likely to further increase trade.37

35 PICTA: Pacific Island Countries Trade Agreement, SPARTECA: South Pacific Regional Trade and Economic Cooperation Agreement. 36 PACER: Pacific Agreement for Closer Economic Relations, EPA: Economic Partnership Agreement. 37 EPAs and PACER will be discussed in chapter 5.

Table 3.3 Trade balance (goods and services) of the PICs

Trade Balance Trade Balance Average growth in Average 1996-2006 in % of GDP trade in % in million US$ in % of GDP 2006 (1996-2006) Cook Islands 11.27 9.22 10.54 7.82 Fiji -154.92 -5.78 -14.19 4.26 FSM -177.17 -80.94 -80.94 1.65 Kiribati -26.06 -45.85 -41.65 2.93 Nauru -18.64 -45.85 -41.65 1.86 Niue ...... Palau -42.39 -36.40 -7.93 11.22 PNG -42.43 -1.13 -4.35 0.43 RMI -107.52 -102.08 -102.03 2.73 Samoa -71.49 -24.46 -23.71 6.17 Solomon Isl. -0.18 -0.05 -0.04 0.22 Timor-Leste … … … … Tonga -73.55 -43.45 -40.93 1.92 Tuvalu -17.01 -101.44 -101.44 7.44 Vanuatu -36.95 -13.23 -15.82 3.94

TOTAL -875.14 -10.01 -12.59 2.36 Singapore 20,848.75 20.71 31.66 6.86

Notes: 1. Trade balance refers to exports minus imports in goods and services.

2. There is no reliable consistent data available for Timor-Leste.

3. Total refers to the aggregated numbers of the 13 Pacific Island Countries reported above.

4. Average growth in trade reports the average change of the sum of imports and exports

in goods and services per year.

Source: United Nations Statistics Division, 2008.

Table 3.3 shows that most PICs manage a negative trade balance, widely varying from just negative 0.04% of GDP in the Solomon Islands all the way to more than negative 100% of GDP share in Tuvalu and RMI. Of all the 13 island states reported, Cook Islands is the only country with a positive trade balance. On average, the trade balance of all PICs is negative 10% of total share of GDP. The table also indicates that trade has been increasing rapidly with an overall average of more than 2.3% per year during the period of 1996-2006. In 2006, the Cook Islands remain the leading trade performer in the Pacific with a positive trade balance of more than 10% of total GDP. Compared to the total PICs average

37 negative trade balance of the ten year period, the table indicates, that the situation is worsening with more than negative 12.5% of total GDP share of the PICs in 2006. The biggest contributors are Fiji, RMI and FSM with each having a negative trade balance with more than US$ 100 million.

3.1.1 PNG and Fiji Papua New Guinea and Fiji are the dominating economies in the Pacific, both making up more than 86% of the total 2006 GDP accomplished by the PICs. Further, PNG contributes, with a population of more than 6.2 million, 65% of the total PICs’ population. Together, Fiji and PNG contribute about 90% of the total land area of the PICs.

3.1.1.1 Papua New Guinea PNG can be viewed from two different angles: (1) with about 66% of the total GDP contributing to the PICs, PNG is the largest economy of the PICs, having had a fourth successive year of GDP growth equal or above the population growth and (2) with more than 50% of the population living below the poverty line and an adult literacy rate of less than 60% to mention just two examples, shows PNG’s need for sustainable development effort. The economy and its performance are characterized by an average economic growth of 2.6% (2.8%) and average per capita growth of –3.2% (-0.7%)38 over the period of 1996-2006. The latter can be explained due to high population growth and a decline in GDP in 2000 and 2002, when the economy was defined by stagnation and a decline of -0.2%, respectively. With a per capita GDP of US$ 989, PNG is at the lower end of PICs. Grants make up 14.5% of PNG’s total revenues in 2006, down from 24% in 2005,39 indicating that foreign aid and donor support in economic development helps stabilize PNG’s economy. The structure of output indicates that the agriculture sector’s average share of GDP is 35.3% making it the highest agricultural share of all the PICs (see Table 3.1).

38 Asian Development Bank’s Key Indicators 2007 (United Nations Statistics Division). 39 Data derived from the Asian Development Bank’s Key Indicators 2007.

38 Figure 3.1 Distribution of GDP of PICs

50 40 30 20

PICs GDP of total % of 10 0

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Source: Data derived from the United Nations Statistics Division 2008.

The large and labor-intensive traditional economic sector (e.g. fishing, forestry, coffee, copra, and palm oil production) together with the capital- intensive oil and minerals sector (e.g. gold, silver, copper and nickel, petroleum and natural gas) dominates PNG’s domestic economy. Duncan (2007) and Chand (2007) point out that improvement in macroeconomic stability and management is one of the latest achievements in PNG. This can be seen in low inflation and reduced government debt. The “mineral boom” increased revenues and helped to establish a budgetary surplus over the last years. Unfortunately, despite the idealistic management of surpluses to be run to buy down possible debts caused by changes in mineral prices, PNG’s government expenditures have increased dramatically over the last years (from 10.7 billion kina in 2000 to more than 17 billion kina in 2006); thus even though the Treasury of PNG talks about fiscal discipline, the recent budgets do not show any evidence. In 2006, tax revenues from mining and petroleum added up 31% of the total revenue. This shows how sensitive the budget reacts to changes in mineral prices, and due to the fact of a further decline in petroleum output extreme pressure is put onto the agriculture sector. Compared to many other island countries, PNG’s GDP share of the primary sector has increased over the last decades and so has

39 not changed much since its independence. The agriculture sector's “sustainable” output has been achieved due to an increase of forestry and fisheries production over the recent years. These two are marked by large, foreign-owned enterprises. Still, the main agriculture sector is dominated by small farmers and has been “hurt” by the deterioration of physical infrastructure and weak law and order situation.

Chand (2007) finds that 85% of PNG’s population live in the rural sector and draw their livelihood from the primary sector, also representing PNG’s informal sector, and therefore, being substantial for subsistence to the main population. Chand further sees the economy’s formal sector, with mining, petroleum, and logging industries, the cash-crop production, and the import-substituting manufacturing sector to remain small in the medium term.

Chand sees the main problem of PNG’s economy in the inconsistent reform policies, failing to promote the secondary and tertiary sectors which would have caused a drop in employment in the primary sector, followed by an increase in output and rise in labor productivity, further caused by a growth in the second and tertiary sector drawing resources out of the primary sector. Chand points out that a number of developing countries (e.g. Vietnam or China) have grown rapidly due to labor movement from the low productivity subsistence primary sector, agriculture, into higher productivity sectors of the economy. And hence, they achieved an increase in effective labor by an increase in labor force participation in higher secondary and tertiary sectors, by better rewarding work in manufacturing and service sectors. Instead, in PNG the opposite can be seen. The non- agriculture sectors are found to be stagnant, leaving the primary sector as the “reservoir for excess and often unskilled labor.”40 The lack of labor opportunities has skimmed investment in “skilled areas” causing further problems of low investment in the accumulation of human capital. Therefore, PNG needs an increase in investments in the primary sector, which would increase labor mobility due to investment in human, physical, and financial capital. Therefore,

40 Chand (2007).

40 one aim of a future development strategy should be investments in primary education, especially in the primary rural sector, and to provide access to basic health care.

Overall, Chand finds that the investment to GDP ratio has remained 13% during the last decade being too low to sustain GDP growth rates at 5% or higher. Nevertheless, public and foreign investors continue to play a major role in PNG’s economy. The largest source of foreign investment in both, mining and non- mining sectors is Australia, followed by investors from the US, UK, and Canada. The mining sector is mostly foreign-owned and accounted for 13% of the share of GDP in 2003-2004, down from more than 30% in the early 1990s.

Despite being low, compared to international standards, public investment had a stimulating impact on private (and foreign) investment in the construction sector.

On the other hand, investments in the manufacturing sector (e.g. food, soft drinks, food canning, tobacco processing, and small furniture) being concentrated in some protected industries, have been decreasing. Faal (2007) finds that the recent

Figure 3.2 Inflation rate (change in consumer price index in %) – PNG and Fiji

20 PNG 18 Fiji 16 14 12 10 8 6 Rate % Inflation in 4

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Source: Data derived from the World Bank’s World Development Indicators 2007.

41 reduction in growth is mostly accounted for by a significant slowdown in capital inputs and lower total factor productivity. The author claims that PNG should push forward in the implementation of structural reforms to improve governance and to remove barriers to private sector investments, and further, to maintain strong capital formation by increasing public investment with the main focus on crowding-in of private investment in the non-mineral sector. This would increase the productivity growth of PNG. The service sector, accounting for more than 40% during the 1970s and 1980s, has been declining to less than 28% on average over the period of 1996-2006. It is another example of low investment structure “hurting” PNG’s economy. The service sector includes transportation and communication activities, having deteriorated over time, and community and personal services which are sensitive to weaknesses of law and order.

Duncan (2007) shares Faal’s opinion by highlighting the fact that, compared to PNG’s macroeconomic performance, its microeconomics performance is unsatisfactory. Like Faal (2007), Duncan sees institutional reforms as necessary to overcome the high levels of insecurity (e.g. problematic law and order situation with ongoing corruption) which downsizes the future broadening potential of PNG’s domestic and foreign investments. Instead these will be concentrated on the resource sector with low value to the general population.

The export sector is one of PNG’s main sources of income. The vastness of its exports is to be found in the minerals and oil sector accounting for more than 70% of its total exports. This, due to high international prices, makes it PNG’s major engine for economic growth. In 2006, 30% of its total exports were to Australia, 9% to Japan, 6% to China, and 4% to Germany. Australia is also its largest import partner with 52% of PNG’s total import, followed by Singapore and China with 13% and 6%, respectively. Machinery, transport equipment, and prepared foodstuff are the major import classifications.

42 3.1.1.2 Fiji Islands Fiji’s economic performance has been hampered by its political instabilities. The 2006 coup, which is the fourth since 1987, caused the economy to decline by ca. 4% in 2007.41 The overall influence of the coup has not been seen yet. For 2008 the growth forecast indicates an economic progress of about 1.8%.42

In the period of 1996-2006, Fiji’s economy grew by an average of 2.4%. The growth rate could have been higher if it was not for the cyclone caused decline in 1997 and coup 2000 by 2.2% and 1.7%, respectively. Fiji’s average 1996-2006 structure of output in percent of GDP is dominated by a large service sector, making up 64.4%, followed by the industrial sector (23.2%) and the agriculture sector (16.4%). With an average growth rate of 2.4% (3.1%) of GDP per capita,43 Fiji achieved the 4th highest GDP per capita of the PICs in 2006. The higher per capita GDP increase measured by the use of the data provided by UNSD can be explained as a result of a higher emigration rate caused by the 1987 and 2000 coups. Chand (2007) sees emigration as one of the major problems that the economy of Fiji has faced since the first coup in 1987. The author finds evidence that instead of investments into the domestic industry (e.g. in the garment sector) investments into mobile (human) capital have been conducted. Thus, the government has to guarantee future stability to ensure faith, not just for foreign investors but for the Fijian population to support investments and growth in the domestic economy on the whole. The main problem, the economy faces is to rebuild an investor friendly environment to reinsure investor confidence.

Furthermore, Chand points out that the 2006 coup has worsened the external and internal imbalances of Fiji’s economy. Direct foreign investment, the form of investment most sensitive to political uncertainty, was negative in 1999 (-1.78%), 2000 (-1.08%) and 2005 (-0.14%),44 is likely to have been negative in 2007 and to be negative in 2008, since many investment projects have been stalled since

41 Reserve Bank of Fiji. 42 Appendix C, Table C.1 offers the GDP growth forecasts for several PICs. 43 Asian Development Bank Key Indicators 2007 (United Nations Statistics Division). 44 World Bank’s World Development Indicators 2007: Foreign direct investment, net inflows (% of GDP).

43 the 2006 coup. Chand further points out that each of the coups cost Fiji a minimum of 3 years of economic progress, lowering the rate of growth of income by slowing down private and foreign investment and encouraging capital flight. All these facts can be seen in Fiji’s less than satisfactory macroeconomic performance. Since 1972, the economy grew at an average of less than 1.4% per year, the population living in poverty increased from 8% in 1977 to more than 30% (United Nations Development Program 1996, Abbott 2007, 2008). Chand sees poverty to increase further over the next couple of years, until “real” necessary reforms are undertaken.

Fiji’s domestic industry is characterized by being rich in forest, mineral, and fish resources, and even though the labor force still depends on the agriculture sector, the tourism, sugar, clothing, and gold industries make the economy to be one of the largest and most developed of the PICs. As mentioned before, on average 64% of the structural output over the period of 1996 to 2006 was in services, dominated by the tourism sector, which has become one of the major sectors of economic growth and foreign exchange. The sector has been expending, except over the short times of the coups. Tourism arrivals are extremely sensitive to perceptions of security in the host nation. The 2006 coup had a significant rebound but the numbers are likely to return to normalcy, as the history of the coup culture in Fiji has shown. Australia has been the major source of visitors to Fiji, accounting for some 35% of the total 550,000 arrivals in 2005, followed by New Zealand with 21% and USA 13%. On the one hand, the sugar cane industry employs about 13% of Fiji’s labor force, contributes 9% of the GDP and generates ca. 30% of the total exports, which makes the sugar industry the main agricultural sector. On the other hand, the Fijian sugar industry faces a future loss in “revenue” due to a price reduction of the European Union (EU). The decline in sugar production that has been experienced over the last few years is not helpful either. The interim government plans to increase the sugar production which will be difficult due to the domestic and international changed conditions. Leased land continues to be vacated, which means a decline in the source of production of Fijian sugar. On the international

44 side, the EU is reducing its price by 36% over a spread of three years (the EU has promised to pay some F$ 133 million in sugar-adjustment over the period of 2007-2011 and a similar amount over 2011-2013).45

Duncan (2007) sees Fiji’s economy characterized by obstacles to economic growth caused by the fact that the economic structure serves some particular political or economic interest, instead of the society on the whole. The driving force of economic growth is to be found as private sector investment. To have a stronger private sector, property rights as an institution need to be secured. This leads to the ongoing issue of land rights, especially in the just mentioned agriculture sector, where political inefficiency and irresolution over the future of the well serving agricultural land lease system over the recent years has severely damaged the overall economic performance. Drop in investment due to a drop in “non-indigenous farmers” have led to a declining production in the sugar industry, which has had its spillovers caused by rising uncertainty of investment in general.

Duncan (2007) points out that corruption is one of the major problems that Fiji faces and which has been costly to the economy; therefore it is very important to promote the fight against corruption. Corruption in terms of nepotism is found to be a general problem in the Pacific, being defined by the fact that the best person available for a specific job is not been hired, with the result, that the economy is worse off and not as well served as it could be. In Fiji this kind of corruption can be found all over the economy, which is another reason for the economy’s poor performance. As mentioned above, Chand (2007) finds that poverty has been increasing rapidly in Fiji; while Duncan (2007) concludes that the high level of corruption must be one of the reasons for the increase of poverty. This leads to a simple assumption – the elimination of corruption leads to economic progress. Inappropriate institutions and bad governance favor corruption, therefore efforts should be put into the development of appropriate institutions.

45 Chand (2007).

45 Enforcement of contracts is another constraint on the economic performance, because if contracts are made in good faith and cannot be effectively enforced, the result is a reduction in investment and business activity. The rule of thumb says that investors will go to countries where contracts are effectively enforced.46

Government-owned enterprises have a poor reputation in Fiji but are important for the private sector, depending on the infrastructure provided by the state- owned businesses. Most government businesses run deficits, and therefore privatization of those seems to be reasonable. Duncan (2007) sees that adopting high-cost, monopolistic arrangements for the telecommunication sector has locked Fiji out of the IT-revolution that is sweeping through the world. Spillovers of the benefits of a low-price communication sector provided would be recognizable in the economic performance. The best would be to change the subsidized economic policies to a neutral regime, which would, with respect to economic growth of individual industries, offer the best chances for the emergence of international competitive industries.

With about 20% of customs and other import duties of total tax revenue,47 trade is one of the major sources of income in Fiji. Fiji faces a high and persistent trade deficit (see Table 3.3) and even the latest increase in tariffs and the existence of quotas on imports, which might have been introduced to reduce import, are a very economically distorting action, serving only those interests of the favored protected industries, reducing the economic performance. Duncan (2007) draws attention to the fact that restrictions and tariffs on imports act as a tax on exports, since they increase the costs of production and make the domestic economy less internationally competitive. The best example is the increased tariffs on agricultural imports, increasing the price of the tourism industry, making it less international competitive. In general high tariffs and quotas protect the domestic markets, forming the establishment of inefficient enterprises, depending on import barriers. Instead of tariffs and trade barriers, Fiji as the second largest economy needs to deepen trade links, and relax their capital controls, and thus

46 See Appendix C, Table C.2 – World Bank Group’s Doing Business 2008, Enforcing contracts. 47 World Bank’s World Development Indicators 2007.

46 return to “normalcy”, as Chand (2007) claims. Fiji faces a high loss of foreign reserves due to the worsening of the trade balance.

Fiji’s exports sector is dominated by prepared foodstuff (fish and sugar), mineral products (gold), and textiles and textile articles (garments). The major export partners are the USA with 17.2%, Australia 14.3% and the United Kingdom with 13.8% of the total exports of Fiji in 2006, respectively. The major importing countries in 2006 were made up by Australia, Singapore and New Zealand, with 24%, 29% and 17% of the total imports in 2006, respectively. Fiji’s imports are a perfect example of the main imports of all PICs. The imports are dominated by mineral products and fuels, machinery, mechanical appliances and electrical equipment, and transportation equipment.

3.1.2 Summary of Section 3.1 PNG and Fiji are the leading economies in the Pacific region contributing about 86% of the total GDP of the PICs. The first part of the chapter has shown that the PICs have experienced negative GDP growth rates all the way to an average of positive 3.5% over the period of 1996-2006. Compared to other developing countries in the world, this makes the PICs to be one of the slowest growing regions. The structure of the PICs varies widely, whereas the largest economy PNG is divided in about “one third” for agricultural, industrial and service sector output. Over the ten year period, the PICs have experienced a negative trade balance. The exception is the Cook Islands also being the country with the highest per capita GDP ratio.

The first part of the chapter has also addressed several issues of obstacles to economic growth. Bureaucracy and institutional inefficiency do not just mean inefficiency on the whole but they also induce severe negative spillovers. Corruption, crime and the general problem of the rule of law create an investor unfriendly environment. The World Bank Group’s Doing Business Reports have shown that the PICs lack of appropriate institutional rights, for example when it

47 comes to enforce contracts, explains the rate of investments being below the average investment rate of developing countries (domestic and foreign direct). Without certainties, an improvement of the law enforcement agencies and a general safe environment, investments will hardly increase and thus the PICs will hardly achieve sustainable development.

3.2 The Pacific Island Countries and their trade relationship with the world The Pacific Island Countries (PICs) trade is characterized by their economies, their remoteness and their political trade policies. Generally speaking, the PICs trade structure can be summarized by having a consistent negative trade balance (with the exceptions of PNG and the Cook Islands); with PNG lacking trade diversification, the main exports are of agricultural nature, whereas the imports are dominated by technical and mechanical equipment, petrol and oil, and food stuff; see Table 3.3. Furthermore, direct foreign investments in the Pacific are lower than the average in developing countries, not helping the service sector either. This Section of the chapter analyses the PICs trade with the rest of the world. It further investigates the aggregated trade relationship (exports and imports) between the PICs (PACPs) and the EC, and Australia and New Zealand, with a focus on Fiji and PNG. The last part of this section investigates Fiji’s trade with the major European trading partners, Australia and New Zealand in more detail.

3.2.1 Who are the major trading partners? What is being traded? The following section gives an overview of the trade structure of the PICs. It gives the major trading partners and explains the major trading goods. The data for the sub-sections are derived from the ADB Key Indicators 2007, United Nations Statistics Division, Eurostat, Australian Bureau of Statistics, World Bank’s World Development Indicators, IMF’s IFS, New Zealand Statistics, the Fiji Islands Bureau of Statistics and the Fiji Islands Trade and Investment Bureau.

48 3.2.1.1 Papua New Guinea PNG, as the strongest economy, contributing more than 65.5% of the total GDP of the Pacific FSEC members, also contributes the majority of total imports and exports. PNG exports 65% and imports 59.9% of the total PICs exports and imports, respectively. PNG is special due to the fact that it is not considered to be remote. Furthermore, it is rich in natural resources such as petroleum, oil, or minerals, which characterize its exports structure. The majority of its exports are crude petroleum, oil, gold and copper. The destination of 30% of PNG’s total exports is Australia, making the “neighbor” its major exports partner followed by Japan with 9%, China 6%, and Germany with 4% of PNG’s total exports. Australia is also the largest imports partner with 52% of PNG’s total imports, followed by Singapore and China with 13% and 6%, respectively. PNG further imports ca. 4% of its total imports from Japan. The imports are characterized and classified by machinery and transport equipment, manufactured goods, and prepared foodstuff.48

Figure 3.3 PNG’s merchandise trade (imports and exports) 1976-2006

3.5 1.8

Exports Imports

1.6 3.0 US$ per Kina 1.4 2.5 1.2 2.0 1.0

1.5 0.8

billion US$ billion 0.6 US$ Kina per 1.0 0.4 0.5 0.2 0.0 0.0

1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Notes: In billion US dollars.

Source: Data derived from IMF’s IFS 2007.

48 Data derived from ADB’s Key Indicators 2007.

49 It is difficult to make a clear statement towards the trade balance of PNG over the last couple of years due to the fact that statistical sources such as UNSD, ADB’s KI 2007, IMF’s IFS and the WB’s WDI show different results. Over the last 36 years, UNDS indicates a trade balance with “high times” in both directions. The early 1970s were characterized by a record trade deficit of ca 680 million US$ or 47.1% of GDP, stabilizing over a short period of time to a positive trade balance, until the early 1980s, when the trade deficit averaged ca. 20% of GDP. The boom in exports resources resulted in a high positive trade balance in the early 1990s, hitting a record with US$ 1.94 billion positive trade balance or 37.9% of GDP in 1994.

The UNDS data set indicates that PNG experienced a consistent negative trade balance since 1999, being negative 8.2% of GDP in 2005 and 2006, up from negative 5.3% in 2004.

IMF’s IFS data tells a slightly different story. Even though the data shows similar results for the period of the 1970s till the late 1990s (as do ADB’s KI and WB WDI’s), it indicates a positive trade balance of ca. 2.2% of GDP in 2004, down from 20% in 1993. The data indicates a strong increase of exports in goods and services in 2005, leading to an increase in the trade balance by ca. 290% compared to 2004, to 6.2% of GDP.49

The outcome of PNG’s high devaluation of its currency (kina) in the late 1990s towards a stabilization of the country’s trade balance depends on the data. Table 3.3 shows a comparison of the current merchandise trade balance compared to its exchange rate measured in US Dollars per Kina. It can be argued that the continuing devaluation of the Kina led to a significant increase in exports. What all data sets confirm is that PNG experienced a decline in exports (in goods and services) since its record in the mid-1990s. The mid-1990s were characterized by the mineral and petroleum boom. The decline of production in this sector caused

49 Compared to UNDS, IFS shows no negative trade balance since 1991.

50 a decline in exports of minerals and petroleum, which caused an overall decline.50 In 2005 trade in services contributed 23.4%, trade in goods 76.6% using IMF’s IFS data.

3.2.1.2 Fiji Islands Fiji as the second strongest economy contributes approximately 19.2% of total GDP in 2006. In the same year, its exports share was 24.4% compared to a 23.7% share of total imports in the Pacific.51 Fiji’s exports sector is dominated by prepared foodstuff (fish and sugar), beverages, spirits and tobacco, contributing to 33.2% of total exports. Mineral products (gold) with 24.7%, live animals and animal products with 12.3% and textiles and textile articles (garments) with 9.5% of total exports share follow. Pearls, precious, semi-precious stones and metals contribute 4.2% of exports. The major 2006 export partners are Australia 17.0%, the USA with 14.1%, the United Kingdom with 10.9%, followed by Japan 7.9% and New Zealand with 5.7% of the total exports of Fiji.

Figure 3.4 Fiji’s merchandise trade (exports and imports) 1955 to 2007

3.5 1.4

3 1.2

2.5 1.0

2 Exports 0.8

1.5 Imports 0.6 US$ per F$ per US$

in billion F$ US$ per Fiji $ 1 0.4

0.5 0.2

0 0.0

1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006

Notes: In million Fiji dollars.

Source: Data derived from Fiji Island Bureau of Statistics 2008.

50 The latest statistical indicators provided by the National Statistics Office of PNG are for the year 2002, thus the statistical dilemma in form of different results provided by different sources cannot be solved by using national statistical trade data. 51 Pacific Island Pacific Island Forum Secretariat members (excluding Australia and New Zealand).

51 In 2006, the major importing countries were made up by Singapore, Australia, New Zealand, China and Japan making up 34.4%, 22.4%, 15.9%, 3.6% and 3.5% of the total imports, respectively. The classification of the imports is dominated by mineral products and fuels (33.4%), machinery and mechanical and electrical appliances and parts thereof (14.9%), vehicles, aircraft and associated transport equipment (7.1%), base metals and articles thereof (6.1%) and chemicals and allied products (5.4%).52 A closer look at the major domestic exports of the Fiji Islands shows that sugar dominates with US$ 124.25 million (25.78%) followed by fish and garments each with approximately US$ 55 million (11.63%). Mineral water was exported to the value of US$ 50 million (10.43%), followed by gold with US$ 24.9 million (5.17%).53

Compared to the inconsistencies in the statistic data sets in the PNG example discussed above, for Fiji, the statistic data sources give similar results. The data provided by UNSD and IFS give same results and show a relatively high negative trade balance of 17.5% in 2005 and 14.2% in 2006. The Fiji Islands Bureau of Statistic’s data for 2007 indicates a negative trade balance of goods and services of 32%, which indicates a continuously persistent increase of the negative trade balance since 1999 when it was only negative 0.5% of GDP, caused by a stagnant export market and increasing imports especially by New Zealand, Australia and Singapore.54 In 1999, the last year IFS data for derivation was available, trade in services made up 43.4% of total Fijian trade. The Fiji Islands Bureau of Statistics gives trade in services a share of only 27.23 % in 2006 which can be explained by the huge increase in merchandise imports.55

52 Numbers in parenthesis as share of total imports. 53 Numbers in parenthesis as share of total domestic exports. 54 Again the huge difference between 2006 and 2007 lets one wonder about the accuracy of statistical data but does not change the trend of the persistent increase of a negative trade balance. 55 Exports in services made up 43.74% of total exports, driven by the tourism sector. Imports in services contributed to 17.96% of total imports. Thus Fiji had a positive imports in services balance of US$ 145.21 million.

52 3.2.1.3 “The others”56 The export sector of the Cook Islands is dominated by exports to New Zealand and Japan, making up 30% and 35% of the total exports in 2006, respectively. New Zealand also dominates the imports with 69% of the Island country’s total imports. Imports from the Fiji Islands contribute 17% of the total. The major export goods are copra, papayas, fresh and canned fruit, coffee and fish (thus prepared foodstuff) and pearls. Further, the Cook Islands also export basic manufactures. The major imports are prepared foodstuff, textiles, machines and transport equipment, minerals and fuels, and basic manufactures.

The Federal States of Micronesia trades mostly with Japan (21% of total exports, 11% of total imports) and the USA (24%, 50%). The major exports are fish and garments compared to machinery, prepared foodstuff, and mineral products and fuels on the import side.

Kiribati’s export sector is led by exports in fish and copra. The USA with 23%, Belgium with 21% and Japan with 15% of the total exports of the island country of Kiribati are the main directions of exports which are made up by copra, coconut, seaweed and fish products. Importing countries are Australia (33% of total imports), Fiji (27%), Japan (18%) and New Zealand (7%). Prepared foodstuff, minerals and fuels, machinery equipment, and basic / miscellaneous manufactured goods are the leading import products.

Nauru’s trade contributes about 15% of the total GDP by industrial origin. The leading import partners are Australia with 36% and South Korea 43% of total imports. Canada and South Korea are the leading trading partner on the export side with 20% and 23% of total exports, respectively.

Palau imports vastly machinery and transport equipment, and minerals and fuels, and exports mostly foodstuff in terms of fish. The overall 20% that trade account

56 Export and Import data is derived from several statistical sources; see 3.2.1.

53 to the 2004 total GDP by industrial origin were made up by trade with the USA and Singapore.

The Republic of the Marshall Islands imports mainly manufactured goods, and minerals and fuels, vastly from the USA and Australia with shares of 54% and 13% of total imports, respectively. The major exports are made up by copra and coconut oil.

Samoa imports and exports goods of the classification of machinery and transport equipment. Australia with 76% of the total exports is the major recipient of goods of Samoa. Other export goods can be summarized in the group of foodstuff (fish, beer and coconut products – oil and cream). New Zealand (21%), Fiji (14%), Singapore (13%), Australia (11%), and Japan (9%) are the main importing partners. Further to the above mentioned, industrial supplies and prepared foodstuff are also major import classifications. Trade (exports and imports of goods and services) is one of the major sectors contributing 24% to the total GDP by industrial origin.

The Solomon Islands export prepared foodstuff (fish) and crude materials excluding fuels (timber). Minerals and fuels, machinery and transport equipment, and food and live animals make up the main import goods. China and South Korea with 46% and 13% of the total exports direction, and Australia with 25%, Singapore and New Zealand with 24% and 8% of the total imports, are the major trading partners. In 2002, trade made up 10% of the total GDP by industrial origin and 14.57% of nominal GDP in 2004, indicating an increase in trade to GDP ratio.

Trade accounts only 6.5% of the total GDP in Timor-Leste. Australia and the USA contributed 54% and 24% of total exports. One of the major export goods is coffee. The imports are dominated by machinery and transport equipment, mineral products and fuels, prepared foodstuff, and vegetables. The major importing countries are Indonesia with 21% and Singapore contributing 7% of

54 the total imports. The negative trade balance is improving from US$ 257 million in 2001 to US$ 133 million in 2006, significantly caused by decreasing imports. With total exports of only US$ 8 million in 2006, the exports market does more or less not exist.

Tonga’s main exports are squash, fish and vanilla beans. The USA with about 41% and Japan with 31% of the total exports are the country’s major export partners followed by New Zealand with ca. 10% of total exports in 2006. Fiji and New Zealand contribute as the leading import partners with 29% and 28% of total imports followed by Australia with approximately 10%. Prepared foodstuff, minerals and fuels, and machinery and transport equipment are the dominant import classifications.

Even though machinery, mechanical appliances and electrical equipment, and transport equipment belong to both, the export and import side, the trade deficit is huge in those categories. Other imports are foodstuff, and minerals and fuels. Germany is Tuvalu’s main export partner with 60% of total exports going to the European country, mainly fish and coconut products. The main imports are received from Fiji (46% of total imports), Japan (19%), China (18%) and Australia (10%).

Thailand (60% of total exports), India (16%), and Japan (12%) are the main export partner of Vanuatu, exporting copra, beef, timber, and cacao. On the import side, Australia, Japan and Taiwan are the leading countries with each contributing about 20% to the total imports, followed by Singapore with 12% and New Zealand 9%. The imports can be classified by machinery and equipment, minerals and fuels, food and live animals, and manufactures. Vanuatu’s trade accounted to more than 30% of the total GDP by industrial origin and therefore is one of the major accounting sectors in 2006.

55 3.2.2 Aggregated trade with the European Community, Australia and New Zealand Tables 3.4 to 3.6 capture the trade relationship between the European Community and Australia and New Zealand on the one side, with the Pacific ACPs / Pacific Island Forum Secretariat members on the other side. The tables present exports and imports of the PICs to/from its partners as well as its specific trade balance and share of total exports and imports for the year 2006.

Table 3.4 shows that Timor-Leste, PNG, Fiji and the Solomon Islands are the major exports partners to the EC in terms of their share of total exports. Timor- Leste leads the list with exporting more than 73% of their total exports to the EC. Fiji and PNG follow with ca. 20% of their exports going to the EC. Furthermore, even though it was not possible to achieve exports data of Tuvalu for 2006, the export share to the EC exceeds 60% as we know that Tuvalu exports 60% of its total exports to the EC-member Germany.

Table 3.4 Pacific Island Countries’ trade with the European Community in 2006

Exports % of total Imports % of total Balance Exports Imports with EC Cook Isl. 0.38 0.29 5.19 4.64 -4.26 Fiji Islands 144.34 20.80 60.81 3.37 83.53 FSM 0.08 1.00 0.51 0.23 -0.43 Kiribati 0.67 2.96 3.47 6.44 -2.79 Nauru 0.36 2.15 2.01 5.04 -1.65 Niue 0.00 n.a. 3.96 n.a. -3.95 Palau 0.12 1.00 0.96 0.73 -0.84 PNG 500.68 20.28 58.97 2.16 441.71 RMI n.a. n.a. n.a. n.a. n.a. Samoa 0.45 0.34 7.55 3.22 -7.11 Solomon Isl. 14.29 5.81 9.55 3.88 4.74 Timor-Leste 5.79 73.29 12.39 8.59 -6.61 Tonga 0.30 0.61 15.45 10.71 -15.15 Tuvalu n.a. n.a. 5.38 18.44 n.a. Vanuatu 9.05 5.85 9.23 4.36 -0.18

Notes: Export and import numbers in million US$.

Source: Data derived from Eurostat 2008.

56 On the import side, Tuvalu also leads the list of major importing nations in percent of total imports with more than 18% being imported from the EC. Tonga imports 10% of their total imports from the EC. In the overall balance, PNG with US$ 441.71 million, Fiji with US$ 83.53 million and the Solomon Islands with US$ 4.74 million are the only Pacific ACP countries having a positive trade balance with the EC.57 The country worst off is Tonga with a negative trade balance of US$ 15.15 million.

Table 3.5 gives an overview of the PICs trade structure with Australia. Australia is by far PNG’s strongest export partner with exports to the continent state contributing to some 70% of PNG’s total exports. Samoa and Fiji are also considered to be strong exporters to Australia by exporting 43.54% and 16.97% of their total exports to Australia, respectively. The other PICs’ exports to

Table 3.5 Pacific Island Countries’ trade with Australia in 2006

% of total % of total Balance Exports Imports Exports Imports with AUS Cook Islands 0.00 0.00 4.52 4.04 -4.52 Fiji Islands 117.78 16.97 403.45 22.36 -285.67 FSM 0.00 0.00 6.02 2.92 -6.02 Kiribati 0.00 0.00 20.33 37.76 -20.33 Nauru 0.00 0.00 14.31 35.90 -14.31 Niue 0.00 0.00 0.00 0.00 0.00 Palau 0.00 0.00 0.00 0.00 0.00 PNG 1719.91 69.68 1127.28 41.21 592.63 RMI 0.00 0.00 3.77 2.58 -3.77 Samoa 57.23 43.54 26.36 11.23 -30.87 Solomon Isl. 0.75 0.31 60.24 24.49 -59.49 Timor-Leste 0.00 0.00 22.59 15.99 -22.59 Tonga 0.00 0.00 9.79 6.79 -9.79 Tuvalu 0.00 0.00 0.75 2.57 -0.75 Vanuatu 0.75 0.48 50.45 23.81 -49.70

Notes: 1. Export and import numbers in million US$.

2. AUS = Australia.

Source: Data derived from Australian Bureau of Statistics 2008.

57 It can be assumed that Tuvalu has a positive trade balance.

57 Australia are marginal. Again a different story can be told by looking at the PICs imports from Australia. PNG, Nauru and Kiribati are all importing more than 35% of their total imports from Australia. PNG is the only nation having a positive trade balance with Australia, exporting US$ 592 million more to Australia than it imports. Fiji on the other hand is worst off by importing more than US$ 285.67 million of Australian goods than it exports.

Table 3.6 shows that the exports of the PICs to New Zealand are only marginal. Fiji leads with exporting 5.72% of its total exports to New Zealand followed by Tonga with 2.93% and PNG with 2.42% of its total exports. The imports side shows that New Zealand is a strong trading partner. 43.7% of the Cook Islands total imports are contributed by New Zealand. Tonga and Samoa import 24.21% and 25.95% of their total imports from New Zealand, respectively. 15.88% of Fiji’s total imports are from New Zealand.

Table 3.6 Pacific Island Countries’ trade with New Zealand in 2006

% of total % of total Balance Exports Imports exports imports with NZ Cook Islands 0.74 0.57 48.89 43.70 -48.15 Fiji Islands 39.70 5.72 286.65 15.88 -246.95 FSM 0.00 0.00 1.50 0.73 -1.50 Kiribati 0.03 0.13 4.14 7.69 -4.11 Nauru 0.04 0.24 0.08 0.20 -0.04 Niue 0.14 n.a. 7.48 n.a. -7.34 Palau 0.00 0.00 0.20 0.15 -0.20 PNG 59.66 2.42 74.08 2.71 -14.42 RMI 0.07 0.44 2.63 1.80 -2.56 Samoa 1.24 0.94 60.89 25.95 -59.65 Solomon Isl. 1.08 0.44 10.13 4.12 -9.05 Timor-Leste n.a. n.a. n.a. n.a. n.a. Tonga 1.44 2.93 34.93 24.21 -33.49 Tuvalu 0.00 0.00 1.44 4.94 -1.44 Vanuatu 0.44 0.28 19.68 9.23 -19.24

Notes: 1. Export and import numbers in million US$.

2. NZ = New Zealand.

Source: Data derived from New Zealand Statistics 2008.

58 Since the exports of New Zealand are only small to marginal but imports are high, no country has a positive trade balance with New Zealand. Again, Fiji has by far the highest negative trade balance by importing goods in the value of US$ 247 million more than it exports to New Zealand.

3.2.2.1 Fiji Islands and its major trading partners in the EC, Australia and New Zealand Figure 3.7 describes Fiji’s exports to the EC disaggregated by major European trading partners in percentage of total trade to the EC. Exports to the EC contribute 20.80% to the total exports of the Fiji Islands of which, as the Figure 3.7 indicates, the vast is contributed by exports to the United Kingdom (UK) (in 2006 more than 96% of total exports to the EC). The major commodity exported was sugar under the so-called sugar protocol which will be further discussed in Chapters 5 and 6. The domination of the exports to the UK and the domination of the commodity sugar, driven by preferential trade offered by the sugar protocol, shows that Fiji’s trade relationship with the EC can be considered as weak and lacking diversification. Other European countries importing from Fiji are Italy,

Figure 3.5 Fiji’ merchandise trade (exports and imports) with EC (1975-2004)

300 Exports 250 Imports 200

150

million F$ 100

1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

Notes: Export and import numbers in million Fiji dollars.

Source: Data derived from Fiji Island Bureau of Statistics 2008.

Figure 3.6 Major exports partners: Fiji’s exports to the EC

100%

90%

80%

70% Unite d Kingdom 60% Germany 50% other EC members 40% 30%

to EC of exports total % 20%

10% 0%

1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

Notes: Exports in percent of total Fijian exports to the EC.

Source: Data derived from Fiji Island Bureau of Statistics 2008.

Germany, France, Belgium and the Netherlands. In 2006, Germany imported Fijian ginger worth F$ 2 million, followed by frozen fish (F$ 0.645 million), Coral (F$ 0.25 million) and Garments (F$ 0.21 million). France was an even stronger export market for Fijian products in 2006. Goods exported to France can be summarized by folding cartons (F$ 1.8 million), tissues and towels (F$ 1.2 million), prepared foodstuff (F$ 1.2 million) and flour (F$ 1.1 million). This underlines that that exports with Germany and France are only marginal compared to the total exports to the UK (total ca. F$ 185 million). Fijian imports of commodities from the EC show the opposite, in the form of import diversification at least in terms of EC exporting countries to Fiji. The UK, Germany, France, Italy, Denmark, Belgium and the Netherlands are the major exporting countries to Fiji; a figure such as Figure 3.5 is not useful. The major commodities imported from the EC are machinery, mechanical and electrical, and other technical appliances or equipment (e.g. Germany F$ 31.68 million, France F$ 12.2 million). Other major import commodities from the EC are prepared foodstuff, spirits and tobacco (e.g. Denmark F$ 2.5 million) and ceramic products

60 (e.g. F$ 0.74 million). Even though an imports relationship with EC member countries exists compared to the major importing partners the trade of EC members is only marginal.

Figure 3.7 shows the export shares of Fijian exports to the EC, Australia and New Zealand to total Fijian exports over the period of 1975-2004. Clearly, exports to the EC have been declining continuously from more than 55% of total exports in 1975 to less than 18% in 2004. The opposite is true for Australia where the exports share increased from less than 10% to more than 30% in 2003.

Exports to New Zealand have remained constant by approximately 8%. In 2006, the major commodities exported to Australia are gold (F$ 43.1 million), garments (F$ 39.8 million) and biscuits (F$ 15.1 million).

Australia’s, the EC’s and New Zealand’s imports shares of total Fijian imports show a similar picture; see Figure 3.8. Over time, Australia is by far the most significant import partner of Fiji, even though imports have been declining since

Figure 3.7 Fiji’s merchandise exports to the EC, Australia and New Zealand

60% EC 50% Australia

40% New Zealand

30%

20% in % of total exports 10%

1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

Notes: Exports in percent of total Fijian exports.

Source: Data derived from Fiji Island Bureau of Statistics 2008.

61 Figure 3.8 Fiji’s merchandise imports from the EC, Australia and New Zealand

50% 45% EU Australia New Zealand 40%

35% 30% 25% 20%

in % of totalin imports 15%

10%

1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

Notes: Imports in percent of total Fijian imports.

Source: Data derived from Fiji Island Bureau of Statistics 2008.

2002. Imports from the EC have become smaller whereas New Zealand’s imports have increased significantly over the last couple of years. Imports from Australia are made up of cereal and wheat (F$ 55.6 million), milk products (F$ 10.1 million) and machinery and mechanical equipment, and technical equipment including cars (F$ 25.5 million).

3.2.3 Summary of Section 3.2 Apart from PNG the PICs lack trade diversification, especially in their export sector, mostly being agriculture based. Over a ten year period it was shown that almost all PICs experienced on average a negative trade balance. The major trading partners of the PICs can be summarized by Australia, New Zealand, the USA, several Asian countries (e.g. Japan, Singapore, South Korea, and China), the United Kingdom, Germany and France. For trade with the EC, the countries of Fiji, PNG, Timor-Leste and Tuvalu have a significant exports relationship, whereas for the other countries the relationship is insignificant or marginal. With the EC, three countries are found to have a positive trade balance, namely PNG

62 (US$ 441.71 million), Fiji (US$ 83.53 million) and the Solomon Islands (US$ 4.74 million). For Tuvalu it can be assumed that a positive trade balance with the EC exists.

A similar picture can be drawn for the trade between the PICs and New Zealand. The exports are mostly small to marginal, whereas the imports from New Zealand show just the opposite. Compared to the other PICs, the Cook Islands, Solomon Islands, Tonga and Fiji experience the highest imports from New Zealand in percentage of the country’s total imports. Fiji leads the importing countries with more than US$ 205 million.

Trade with Australia tells a completely different story. Trade is found to be important on both sides, for imports and exports. PNG and Samoa export ca. 70% and 43% of their total exports to Australia, respectively. On the imports side, Australia is even more important for several PICs and therefore is a very significant trading partner.

For the case study of Fiji several issues can be addressed. First, Fijian exports to the EC have been declining over the last 30 years, Australia as a market has become more important and the exports to New Zealand remain constant at approximately 5%.

Secondly, a similar picture can be seen on the import side, whereas the Australian and over the last year products from New Zealand have become very significant. Even though Fiji experienced a positive trade balance with the EC, the high imports from Australia and New Zealand contribute to its persistent negative trade balance.

In the end, Fiji’s trade is a perfect example of the trade of most PICs. High imports from developed countries, especially products of the classification of

63 machinery and petrol, and exports of products of agricultural nature are characteristically.58

3.3 Conclusion of Chapter 3 The economic performance of the PICs over the period of 1996 - 2006 has been less than successful. With average growth rates of -1.3% to 3.9% per annum the PICs belong to the slowest growing group of the developing world.

There are many reasons for the poor showing (e.g. political instability, corruption, poor institutions) and all indicate that economic reforms are necessary. Other reasons could be the mostly high negative trade balance and the lack in export diversification, whereas the latter one is more significant because imports are necessary to overcome the limited natural resources of the PICs. Imports of intermediate goods used as inputs to transform these into final goods like light manufacturing commodities are inevitable. But due to the fact that the manufacturing sector is largely underdeveloped or non-existing, the PICs exports are concentrated on goods of agricultural basis. The relatively low investment structure is not helpful either but explains the missing of an export significant domestic manufacturing industry. Poor institutions, especially in the fields of enforcing contracts (rule of law), insecurity of land tenure, limited access to credits and political instability are unlikely to create an investment-friendly environment in the near future.

The PICs trade relationship with the EC, Australia and New Zealand shows that trade with the EC is found to be small to marginal compared to trade with Australia and New Zealand. This fact is especially significant on the import side and indicates that the PACER agreement is likely to be more important than the IPA/EPA when it comes to trade flows. On the one hand, this reveals that the potential tariff revenue losses are more significant under PACER; on the other

58 PNG is the exception having diversified exports due to its richness in natural resources which shows why PNG experiences a high positive trade balance with the EC and Australia and only a small negative trade balance with New Zealand.

64 hand, trade liberalization with Australia and New Zealand is likely to be more beneficiary for the PICs than the trade liberalization movement with EC.

In the case of Fiji, in volume, merchandise exports with the EC have been constant over the last few years, driven by preferential trade. Imports from the EC have been declining over the last 30 years and are marginal compared to imports from Australia and New Zealand, underlining the above assumption that the trade liberalization movement will be more significant with Australia and New Zealand, especially in the field of tariff revenue. Exports to Australia and New Zealand are not as high as one might assume, indicating that there is still a huge potential for Fiji, notably for exports to New Zealand. Therefore, PACER is likely to be more welfare-enhancing for Fiji than the IPA/EPA.

65 4. Trade openness Chapter four concentrates on the topic of trade openness and discusses in specific how open the PICs economies are.

Trade openness plays an important role in trade economics, not just since Romer’s step towards endogenous growth models that give the opportunity to include further variables to reduce the residual and explain more of the actual sources of growth. In a recent study Squalli and Wilson (2006) criticize many studies that used openness measurement of trade intensity (TI) by questioning its sensibility when it constantly “determines the world’s largest trading countries” as closed from international trade and small states such as the PICs as most open to trade. The authors introduce a new measurement of trade openness – the so-called composite trade intensity (CTI) – which combines trade intensity and the relative world trade intensity (RWTI). Additional to this new measurement, the World Bank Group’s Doing Business Reports offer another approach for trade openness by discussing “trading across borders”. In the next section (4.1) a literature review on the usage of TO in empirical economics is given. Section 4.1.2 discusses the widely used measurements of TI and RWTI and introduces Squalli and Wilson’s CTI. These measurements are used in a 210 cross-country investigation on trade openness (4.1.3) and together with the Doing Business trading across borders show that trade openness measurements need to be used with caution in empirical studies (4.1.4). Section 4.1.5 closes the first part of the chapter with concluding remarks.

4.1 Literature review The empirical literature on openness varies widely. Simple economic growth discussions and whether open economies are more vulnerable and the relations between openness and corruption are discussed in the following sections.

66 a.) Does trade openness cause growth? In the last decade, financial and trade openness have become a central dimension of globalization in most industrial and developing economies and are claimed to be the reason for successful economic performance in East Asia, for instance. Trade openness (TO) be defined as total trade of goods and services (exports plus imports) as share of GDP. TO has grown from 26% in 1970 to 61% in 2000 and to about 75% in 2006 underlining the increasing dimension of trade in today’s globalized world.59 TO is seen as an important factor contribution to successful economic progress of a country. Various studies argue that a more open country performs better than a closed economy. Many empirical studies have provided a strong positive relationship between TO and growth and thus justify that TO is inevitable for sustainable development. In one of the earlier studies on the relationship between trade and growth, Trued and Micksell (1955) investigated the bilateral payments arrangements used as a measurement for outward and trade orientation of a country and found that the agreements could be seen as important steps towards freer trade and payments regimes which could favor economic progress. The countries with bilateral payment agreements were seen as being more open and further enjoyed stronger economic progress than those without. Referring to Bretton Woods, Triffin (1976) argues that “paradoxical it may seem, the bilateral payments arrangements served the essential function, through their mutual credit provisions, of avoiding or at least postponing the danger of a strict bilateral balancing of exports and imports on a barter basis”.

The bilateral payments arrangements were seen to be welfare improving for the countries involved, which underlines the theoretical assumption that TO or in this case trade itself improves a country’s welfare. Auguste (1997) analyses the welfare of the bilateral payments arrangements in the context of customs unions and finds that they are discriminatory measures against non-member states but improves the welfare of the countries inside the arrangements. The author says that “since bilateral trade is financed via central bank clearing accounts, the

59 Data derived from United Nations Statistics Division 2008.

67 bilateral payments arrangements also reduce the need to hold transactions balances of foreign exchange to finance imports from partner countries.” Thus, on the one hand, the transaction balances needed for imports are seen as a barrier to trade and are overcome by the agreements and therefore are seen to be welfare- enhancing; on the other hand, Yanikkaya (2003) argues that the direction of trade which is controlled by the arrangements with special countries is welfare- reducing.

Merota (1990) provides evidence that the arrangements are still growth improving due to an increase in trade. He gives the example of India, where he claims that bilateral payments arrangements increased trade dramatically over the 1970s and 1980s and implied a positive relationship with growth. Apart from that, it may be argued that the bilateral trade arrangements are actual trade barriers because of “third country” discrimination but provide a measurement of TO and show that an increase in trade improves a country’s economic progress. Furthermore, these arrangements were commonly used during times when inward orientation was the common trade political view; thus the arrangements can be used, as mentioned as a measurement of outward orientation and thus as trade openness.60

The most commonly used measurement of TO is the usage of simple trade shares, the exports plus imports divided by GDP, which will be referred to as trade intensity (TI) measurement of TO. Numerous studies show a strong positive relationship between TI and growth. Frankel and Romer (1999) included geographical variables controlling the endogeneity of trade. Irwin and Tervio (2002) investigated a cross country income on trade regression using IV and OLS estimates and found that the latter underestimates the effect of trade on GDP. Further, other studies use export shares and import shares to GDP in growth regressions and find a positive relationship between those (e.g. Rodrik (1999) sees the main benefits of openness on the import side, rather than on the export

60 For a longer discussion of the bilateral payments arrangements see Yanikkaya (2002).

68 side and focuses on four main imports namely ideas, goods and services, capital, and institutions).61

In recent economic studies, the relationship of tariffs and growth is seen as very important when discussing TO, even though by ignoring the non-existent theoretical evidence of growth effects of trade restrictions, see Yanikkaya (2003). Lee (1993), Harrison (1996) and Edwards (1998), to mention only a few, have found a significant negative relationship between average tariffs and growth; thus tariffs are seen to hinder economic progress and to make a country more closed to trade.

Another measurement includes financial openness and the so-called black market premium which indicates the success of the function of prices in foreign exchange markets and is frequently used to show the severity of restrictions of trade (Yanikkaya, 2003).

Nonetheless of the different methods of measuring TO, Krueger (1997) says: “It is now widely accepted that growth prospects for developing countries are greatly enhanced through an outer-oriented trade regime and fairly uniform incentives (primarily through the exchange rate) for production across exporting and import-competing goods… It is generally believed that import substitution at a minimum outlived its usefulness and that trade liberalization of trade and payments is crucial for both industrialization and economic development … the current consensus represents a distinct advance over the old one, in terms both of knowledge and of the prospects it offers for rapid economic growth.”62

Even though in an earlier version Edwards (1992) stated that the negative significant relationship between average tariffs and growth is weak63 it was not

61 See Harrison (1996) for a large literature review. 62 Krueger 1997 63 See also Sala-i-Martin (1997) and Clemens and Williamson (2001) who confirm weakness of the negative relationship between average tariff rates and growth.

69 until a paper written by Rodriguez and Rodrik (2001) in which the most often stated positive relationship of TO and economic growth was questioned.

In their paper Rodriguez and Rodrik (2001) find a positive relationship between average tariffs and total factor productivity. Thus, the authors criticize the evidence of Krueger’s 1997 statement saying that openness indicators are not appropriately used in terms of the openness measurement. Further, the authors question the usage of the methodologies many economists have used in their empirical studies. In detail they raise issues against the results of Dollar (1992), Sachs and Warner (1995), Harrison (1996), Edwards (1998), and Frankel and Romer (1999).

Sachs and Warner (1995) constructed an indicator of TO that turned out to be very robust in most growth specifications and therefore was a standard used measurement for showing the relationship between TO and economic performance. The Sachs and Warner 1995 openness index put together information on average tariffs, non-tariff barriers, the adoption of central planning, state monopolies of exports, and the black market premium. In their results, the authors argue that outward-oriented economies have consistently outperformed inward-oriented countries. Sachs and Warner claim that they have found “a strong association between openness and growth, both within the group of developing and the group of developed countries”.

Rodriguez and Rodrik (2001) criticize Sachs and Warner (1995) e.g. by pointing out that their export-marketing board dummy excluded two of the ten fastest growing economies, namely Mauritius and Indonesia, which are considered to be closed economies in 1994 and therefore the researchers results are considered to be biased.

Frankel and Romer (1999) constructed an indisputable exogenous variable, the amount of trade caused by geographical factors, used as an instrument for trade/GDP ratios in a regression in which income levels are the dependent

70 variable. The results of their studies show that, when instrumented with the predicted trade share, trade ratios maintain a strongly significant coefficient in these regressions. Rodriguez and Rodrik (2001) see the predicted trade share as a proxy for geography’s direct effect on growth, which could work through the effect of climate on disease, international transmission of technology and institutional patterns of specialization. Rodriguez and Rodrik (2001) included other geographical measures (e.g. distance from equator) and found that the coefficient on trade becomes insignificant. Therefore, they question the results of Frankel and Romer (1999). Rodriguez and Rodrik corrected the measurements and methodologies, used the same data sets and found that the significance of TO for growth vanishes or becomes very small. Furthermore, the authors do not find any significant negative correlation between trade policies (e.g. tariffs or non-tariff barriers) and economic growth. As stated above, surprisingly, a positive significant relationship between import duties and total factor productivity was determined.

“There is a significant gap between the message that the consumers of this literature derived and the “facts” that the literature has actually demonstrated. The gap emerges from a number of factors. In many cases, the indicators of “openness” used by researchers are problematic measures of trade barriers or are highly correlated with other sources of poor economic performance. In other cases, the empirical strategies used to ascertain the link between trade policy and growth have serious shortcomings, the removal of which results in significantly weaker findings.”64

Since then, a huge discussion has arisen of whether the methodology used or the measurement of TO is appropriate. Rodriguez (2007) offers an interesting summary of the “results” of the Rodriguez and Rodrik (2001) paper.

64 Rodriguez and Rodrik (2001).

71 Several economists such as Warner (2003), Jones (2001), Dollar and Kraay (2002), Wacziarg and Welch (2003), just to mention some, have added oil to the fire of the discussion of the correlation of trade and growth.

Warner (2003) criticizes the findings of Rodriguez and Rodrik (2001) by showing that the un-weighted tariff rate on capital and intermediate goods displays a negative relation (at least after dropping India from the sample), thus he rejects the findings of Rodriguez and Rodrik (2001). Warner argues that export marketing boards and exchange controls are important since they limit the access to international trade. Rodriguez (2007) claims that he tried to reconstruct Warner’s 2003 results but failed, receiving different results and further states that even if he would trust the Warner’s findings, it would mean that the tariff rates on intermediate inputs and capital goods are significant coefficients in the growth regression or, as Rodriguez puts it, a policy protecting consumer goods industry is not harmful for growth but that protecting the intermediate and capital goods industry is. Further, he claims that this would be an interesting conclusion but even more nuanced than the Sachs and Warner (1995) results that showed a significant linear effect of openness on trade exists.

Dollar and Kraay (2002) find that countries that have increased openness have shown stronger growth rates compared to those that did not open up. For open economies they find growth rates of 2.9% for the 1970s and 5.0% for the 1980s compared to 3.3% and 1.4% of closed economies over the same periods. The authors characterize two different openness measurements, namely trade to GDP shares, which are found to be significantly positive, and tariff rates. In their conclusion it is argued that TO has a positive impact on economic growth. Thus, the Dollar and Kraay results are consistent with the findings of Frankel and Romer (1999).

Nye, Reddy and Watkins (2002) criticize the findings of Dollar and Kraay (2002). The authors change the assumptions of open and closed economies by using tariff reduction data to re-classify open and closed countries. The authors

72 find that closed economies actually outperformed open economies on average growth rates. Closed economies grew by 1.7% over the periods of 1985-1989 and 1995-1997, whereas open economies grew only by 1.3%.

It is not surprising to see why Nye, Reddy and Watkins (2002) have achieved different results. Rodriguez (2007) summarizes it by pointing out that comparing tariff reduction data and trade to GDP ratios is not very meaningful. The reason why Rodriguez only mentions the comparison of tariff reduction and trade to GDP shares is because Dollar and Kraay (2002) fail to report their other TO measurement of tariff rates. Wacziarg and Welch (2003) used the critics of Rodriguez and Rodrik (2001) and revised the Sachs and Warner (1995) approach and extended the data set. In their results, the authors “revised the evidence on cross-country effects of Sachs and Warner’s (1995) simple dichotomous indicators of outward orientation on economic growth, confirming the pitfalls of this indicator first underlined by Rodriguez and Rodrik (2001).” Furthermore, it is stated that the indicator “effectively separates fast growing ones in the 1980s and to a lesser extent in the 1970s, fails to do so in the 1990s.”65

Additionally, a time-dependent trade liberalization index is added which is based on a country’s trade liberalization and by doing so, the authors show that the index has a significant growth, investment and openness impact. Rodriguez (2007) uses the same critical view of Rodriguez and Rodrik (2001).66

Rodriguez (2007) closes by pointing out that in his regressions he does not find evidence between the linkage of greater integration in the form of TO and economic growth. “Some of the fastest growing economies since the 1990s, such as Lebanon and Lesotho, have applied restrictive trade policies, whereas some of the most open economies in the world, such as Moldova and Mongolia, have experienced considerable growth collapses.” Further, he adds that new

65 Wacziarg and Welch (2003) 66 For a more detailed discussion, see “Openness and Growth: What Have We Learned” by Francisco Rodriguez, 2007.

73 developments in the growth theory have shown that the complex relationship of economic growth cannot be simplified by a growth-openness nexus. “There is little in the cross-national data that can be used as evidence of a strong link between openness and growth.” b.) Recent literature on trade and growth Besides the given discussion of the “eventually existing” relationship between trade, trade openness and growth, this thesis presents some of the recent studies on trade openness (and financial openness) and its correlation to growth and volatility (e.g. Cavalloi and Frankelii (2004), Calderon, Loayza and Schmidt- Hebbel (2005), Bejan (2006), Sarkar (2003), Yanikkaya (2003), Billmeier and Nannicini (2007), Giovanni and Levchenko (2006)). Other authors have used the discussion of trade openness to investigate its relationship with budget deficits in developing countries (Combes and Saadi-Sedki (2006)), the size of government (Epifaniy and Ganciaz (2008)) and corruption (Neeman, Paserman and Simhon (2004)). The investigation of trade openness is by far un-exhausted67 which underlines the importance of the topic.

Sarkar (2003) discusses the linkage between trade openness (trade-GDP ratio) and growth by cross-country panel data analysis of a sample of 51 developing countries over the period of 1981-2002 and finds that a higher level of openness leads to higher real GDP growth. The author further investigates the long-term relationship between openness and growth for the period of 1961-2002 and shows that there is no positive correlation. Thus, Sarkar questions the “neo-liberal paradigm of the Washington Consensus – the policy of trade openness to promote growth”.68

Cavalloi and Frankelii (2004) investigated whether TO makes a country more vulnerable, especially to sudden stops in financial capital flows or not using the

67 Just the word combination “trade openness” gives more than 78,400 results in Google of which are 23,900 pdf files. 68 Sarkar 2003, for further evidence against the Washington Consensus see Sarkar 2007 (a & b).

74 gravity approach to establish causality. The authors discuss three of the most common assumptions why TO makes a country more vulnerable to sudden stops in financial flows: (1) a sudden stop in capital flows weakens the country’s export markets, so that a high-trade country is more vulnerable; (2) sudden stops in financial flows often trigger the trade performance, leading to a loss in trade credit and that thus, the larger the share of the economy, the larger the negative effect; and (3) TO and FO are interconnected, therefore changes in financial capital also influence trade (e.g. multinational corporations move money across national borders).

In their study, the authors use capital account (financial account) and current account data for 141 countries to identify sudden stops in capital flows statistically for the time period of 1970-2002. A sudden stop is defined as the period in which a noticeable reduction in the current account deficit occurs in a country, driven by a disruptive, in example recessionary, reduction in foreign capital inflows.69 Trade openness is defined as total trade as a share of GDP. For instrumental variables Cavalloi and Frankelii use the Frankel and Rose (2002) dataset to compute gravity estimates for each country in the sample, which are by definition of the authors the key variables needed to test the relationship between TO and sudden stops.

The authors find strong evidence that economies that are less open and thus trade less with other nations are actually more sensible to sudden stops in financial flows and to currency crashes. Therefore, openness has a positive effect on reducing the probability of a currency crisis. The authors find a causal linkage between lack of TO and the instability of financial flows and state that TO and the size of before-shock current account deficit appear as significant predictors of sudden stops. Furthermore, TO, foreign debt, reserves and the nominal exchange rate rigidity also appear as significant predictors of other forms of external crises analyzed. Further findings are on the one hand, a large stock of foreign debt as a

69 Following Calvo, Izquierdo, and Mejia (2003).

75 percentage of GDP a “peg system”70 in place increases the probability of a crisis; on the other hand, having a large stockpile of reserves reduces the probability of being hit by a crisis. The most interesting finding is that the effect of TO on the probability of a sudden stop of capital flows is not just qualitatively but also quantitatively significant. Estimation yields that, all else being equal, a 10% increase in trade to GDP ratio (e.g. going from Argentina’s current trade share to Australia’s average trade share) reduces the probability of a sudden stop by approximately 32%. Not surprisingly, the authors further find evidence that more openness reduces the output cost associated with crises.

In one of the most recent works on trade and financial openness, Calderón, Loayza and Schmidt-Hebbel (2006) investigate whether international integration increases a country’s external vulnerability or not by asking two key questions: (1) Does openness by itself influence the macroeconomic performance negatively by decreasing economic growth and increasing its volatility?, and (2) does openness lead to the impact of adverse foreign shocks and external volatility? In their study, the authors find a strong indication that FO and TO lead to growth and reject the assumption that FO and TO lead to a decline in economic performance.71 The authors further find that the larger and more advanced a country is, the larger is the positive impact of FO and TO on growth. It indicates that FO helps a country to reduce its volatility, whereas TO tends to have a small impact to increase volatility, especially in low to middle income developing economies. In those, FO and TO present more trade-offs than in advanced countries, but are still mostly in favor of international integration. The more a country moves towards development, the more beneficial the impacts of FO and TO become. In their concluding remarks, the authors point out that even though there might be some negative aspects, FO and TO are engines of growth and stability.

70 See Cavalloi and Frankelii (2004). 71 Trade openness is measured as total trade over GDP, financial openness as stock equity-related foreign liabilities to GDP.

76 Billmeier and Nannicini (2007) study the impact of trade openness on growth based on “transparent econometric methods”, a classic pooled cross-country regression, “drawn from the treatment evaluation literature to make the comparison between treated (e.g. open) and control (e.g. closed) countries explicit while remaining within a unified statistical framework.”72 After “appropriately“ restricting the sample, the authors confirm a positive and significant relationship of openness and growth, a positive effect of openness on economic growth. Billmeier and Nannicini point out the importance of control for continent or macro-region dummies which make cross-country comparisons much more sensible.

In a second step, synthetic control methods to countries that liberalized their trade regime are applied, and it is found that “trade liberalization has often had a positive effect on growth.”

The authors further state that the simple open and closed economy classification may not be evenly distributed across regions. Therefore, the simple openness indicator73 should not be used for developed countries because in that case all countries are considered to be open; thus a within group variation fails.

Giovanni and Levchenko (2006) investigate the relationship between trade openness measured by actual trade in a sector, rather than by trade barriers, and volatility using an industry-level panel dataset of manufacturing production74 and trade for 59 countries and 28 manufacturing sectors over the period of 1970 to 1999/2000.

Main findings are that the higher the trade in a sector is, the higher becomes its volatility, and the sector becomes less correlated with the rest of the country’s economy. Furthermore, the higher the overall TO, the higher is the degree of specialization in the economy. Moving from 40% to 80% in the distribution of

72 Billmeier and Nannicini (2007). 73 Referring to the relative world trade intensity of a country’s total trade over total world trade. 74 This includes data on production, quantity indices, employment, and prices for the manufacturing sector.

77 TO measured by TI, the volatility of the aggregate manufacturing sector increases by about 20%. The authors point out that the marginal impact of openness on volatility has doubled over the last thirty years, implying that trade exerts a larger influence on volatility over time but that the effect is much higher in a developing country than it is in a typical developed country.

Bejan (2006) investigates the relationship between TO, measured as total trade share over GDP and output volatility and finds that trade openness increases output volatility using data of 111 countries for the period of 1950 to 2000. The author finds that the correlation between openness and volatility was more significant during 1950-1975 than during 1975-2000 and that developing countries’ openness increased volatility (even though the effect became weaker over the last decade), while it helped the output in developed countries. Financial openness tends to decrease the effect of volatility of output due to TO over time, thus financial openness is of importance to overcome volatility of output.75

Bejan also states that the size of the government may increase volatility in less developed countries, which he claims to be explained by the positive relation between openness and government size. He further argues that “the degree of specialization and the volatility of the terms of trade do a good job in explaining why openness increases output volatility.”

Rodrik (1998) finds that more open economies tend to have a larger government size in attempting to deal with increased volatility. The so-called ”compensation theory” says that governments play an important role to compensate the exposure to external risk. Bejan tested the “compensation theory” and found a positive relationship for developed countries and that the relationship does not hold for developing countries. Even after controlling for the degree of openness, government size continues to reduce output volatility. However, this is no longer true for developing countries. Under further specifications, Bejan finds that larger governments in poorer countries lead to increased volatility.

75 Bejan (2006) gives the black market premium as an example as a proxy of financial development.

78 c.) Linkage of trade openness and government size In several studies, TO is also linked to government size. It is argued that the more open a country is, the larger is its government size due to externalities of trade. Market integration should reduce the influence of domestic policies and increase the international pressure to lower the activities of the government. A large public sector may cause a loss in international competitiveness and lower the demand for exports and employment in favor of the outflow of mobile factors76 which suggests that with more economic integration, tax rates tend to decrease, which could further lead to smaller government size as well as the size of the welfare state.77

Cameron (1978) and Rodrik (1998) show that TO is associated with larger governments whereas the empirical studies indicate that the higher the level of openness to trade, the larger is the size of the government.78 Rodrik (1997, 1998) argues that the increase in public expenditure can be explained as a form of insurance provided for the economic subjects to the risks of international markets because the risks of exposure increase with the higher level of TO, thus the demand for insurance increases as well. Alesina and Wacziarg (1998) find that small countries tend to have large public sectors and to be open (in TI definition) anyway and therefore question the assumption of TO being related to government size.

Epifaniy and Ganciaz (2008) investigate the relationship between TO and the size of government theoretically and empirically. The authors use a 143 cross-country observation over the period of 1950-2000 and find that the correlation between government size and trade openness is strong and positively significant, and secondly, that there is no indication of TO and government transfers of social security and welfare. In their results Epifaniy and Ganciaz argue that trading countries tend to have a large sized government due to their benefits from terms

76 Alesian and Perotti (1997), Persson and Tabellini (1992). 77 Epifanyi and Ganciaz (2008) in conclusion of Gordon (1983) and Wilson (1987). 78 Countries experienced significant increase in public expenditure.

79 of trade externalities shifting part of the taxation abroad.79 The authors justify their results by discussing that governments may have incentives to “abuse” tariffs to manipulate terms of trades in their favor and that even the simplest form of domestic taxation may lead to similar terms of trade effects. This leads to an interesting discussion of whether the WTO rules are adequate to deal with nations that abuse domestic taxation. The authors claim that the WTO does not address the issue, because politicians may see this as a matter of national sovereignty and thus justify the issue of being beyond WTO jurisdiction, which may in the end lead to discrimination and disturbances of trade. Furthermore, trade integration has occurred more in the sense of market integration instead of political coordination on fiscal issues. d.) Trade openness and its impact on a country’s budget Combes and Saadi-Sedik (2006) investigate the influence of trade openness on budget deficits in 66 developing countries over the period of 1974-1998. In their econometric analysis the GMM system estimator, a panel data model, is used. The authors analyze the effect of TO on budget balances in regards to instabilities of government revenues and further examine additional variables in relation to TO and their influence on budget balances. The authors test two openness measurements, whereas the conventional TO measurement of total exports and imports as share of GDP is decomposed. Natural trade openness is measured as the “level of trade openness a country should have based on its structural factors” and trade policy induced openness is computed “as the difference between actual and natural openness”. The authors results show that TO increases a country’s exposure to external shocks and reinforces the adverse effects of the instability of the terms of trade on budget balances. The authors further find that several other channels due to TO influence budget balances (e.g. corruption, income inequalities). Additionally, the results point out that the trade policies induced TO improves budget balances compared to natural openness.

79 Fiji and Papua New Guinea are to be found of having a relatively weak relationship between openness and government size.

80 e.) Trade openness and corruption Budget deficits are also very often related to bad governance and institutional failure which promote corruption.

The World Bank (2001) has identified corruption as “the single greatest obstacle to economic and social development”. In 2004 the World Bank estimated that payments to bribes made up more than US$ 1 trillion each year and that those countries that would fight corruption and improve institutions, promote good governance and the “rule of law” could actually increase per capita GDP by about 400%.

In which way and how corruption harms an economy was topic in several studies of economists, historians and politicians. Leff (1964) and Huntington (1968), among the first to investigate the effects of corruption on the economic performance, found that corruption might increase growth by bypassing inefficient bureaucratic systems and thus increase the business process. Nowadays, the most common view, as shared by the World Bank, is that corruption harms the economy most due to disruption in the efficient allocation of resources. Some of the recent studies on corruption, growth and openness include Mauro (1995), Treisman (2000),80 Fisman and Gatti (2002),81 Neeman, Paserman and Simhon (2004), Gokcekus and Knörich (2006) and Dreher, Kotsogiannis and McCorriston (2004).

Mauro (1995) analysis the relationship between corruption and growth by using nine indicators of institutional efficiency of the Business Indices of Corruption and Institutional Efficiency.82 In the restriction to those nine indicators, Mauro concentrates on independency of macroeconomic variables and interests on the domestic business circle, rather than interests of foreign businesses.83 The author

80 Investigates the causes of corruption. 81 Study the links between political structure and corruption. 82 The Economists Intelligence Unit (former Business International) provides indices on 30 country risk factors for 57 countries for the period of 1971-1979 and on 56 risk factors for 68 countries for 1980-1983. 83 For a detailed description of the indicators see Mauro’s 1995 paper.

81 states that economic variables and institutions evolve and affect each other jointly in one way or the other. To address the issue of endogeneity the author uses a ethno-linguistic fractionalization which measures the probability of two person not belonging to the same ethno-logistic group, whereas Mauro finds the index highly correlated with corruption.

Summarizes the findings the author points out that there is a negative significant correlation between corruption and investment. Corruption lowers private investments and thus reduces economic growth.

Neeman, Paserman and Simhon (2004) use the works of Mauro (1995) and Dollar and Kraay (2003) to investigate the relationship between corruption and openness of a country. TO is measured in different ways, by TI, continuous TI, openness as a binary variable taking the value of 1 if the level of tariff is below 20 % and openness as a measurement as being one minus the average tariff. The authors argue that corruption and TO (all four different measurements) are strongly negatively correlated in open economies, thus the more open an economy is, the less corruption occurs.

Using a fifth measurement to explore a country’s relationship between corruption and FO (measured by the black market premium) whereas open and closed is characterized by the black market premium being below or above 20%, the results tend to be similar to the four above: open economies have a negative significant relationship with corruption, but closed economies are found to have no relationship at all. Furthermore, it is found that the main channel through which corruption affects output is capital drain. Therefore, corruption is seen as affecting the economy primarily by the degree of financial openness.

Table 4.3 summarizes Neeman, Paserman and Simhon’s results. All “western hemisphere” countries are classified as open and having low corruption. Comparing the results with the Doing Business Report of Enforcing Contracts it may be assumed that even though TO and FO might influence the degree of

82 corruption in an economy, institutions are crucial for the well being of an economy. The report underlines the importance of the judicial system to enforce contracts and thus of well functioning institutions and low corruption; see also Duncan (2007) and North (1990).

North (1990) focuses on efficient judicial systems to enforce contracts and finds low security of property rights on physical capital, profits, and patents as main obstacles for incentives and opportunities for investments, innovations and obtaining necessary foreign technology.

Table 4.1 Corruption and openness

Low Corruption Medium Corruption High Corruption Closed Estonia Bangladesh, Belarus, China, Croatia, Algeria, Angola, Burundi, Central Total: 1 country Ethiopia, Guyana, India, Nepal, African Republic, Chad, Congo, Congo Romania, Rwanda, Senegal, Democratic Republic (Zaire), Gabon, Zimbabwe. Haiti, Iran, Kazakhstan, Malawi, Total: 12 countries Nigeria, Pakistan, Papua New Guinea, Russia, Sierra Leone, Syria, Tanzania, Togo, Turkmenistan, Ukraine, Uzbekistan, Zambia. Total: 24 countries Open Australia, Austria, Belgium, Albania, Argentina, Benin, Brazil, Armenia, Azerbaijan, Bolivia, Botswana, Canada, Chile, Costa Bulgaria, Burkina Faso, Cape Verde, Cameroon, Ecuador, Georgia, Rica, Cyprus, Czech Republic, Colombia, Cote d’Ivoire, Dominican Guatemala, Guinea-Bissau, Denmark, Finland, France, Germany, Republic, Egypt, El Salvador, The Honduras, Indonesia, Kenya, Greece, Hong Kong, Hungary, Gambia, Ghana, Guinea, Jamaica, Kyrgyzstan, FYR Macedonia, Moldova, Iceland, Ireland, Israel, Italy, Japan, Jordan, Latvia, Lesotho, Lithuania, Mozambique, Nicaragua, Niger, South Korea, Luxembourg, Madagascar, Mali, Mauritania, Paraguay, Tajikistan, Uganda, Malaysia, Malta, Mauritius, Mexico, Morocco, Panama, Venezuela. Netherlands, New Zealand, Peru, Philippines, Sri Lanka, Norway, Poland, Portugal, Swaziland, Thailand, Turkey, Total: 21 countries Singapore, Slovak Republic, Yemen. Slovenia, South Africa, Spain, Total: 33 countries Sweden, Switzerland, Trinidad and Tobago, Tunisia, United Kingdom, United States, Uruguay. Total: 43 countries

1. Countries are defined to have low, medium, or high corruption based on the Kaufmann et al. (2003) graft index.

2. Countries in the bottom third of the corruption distribution are defined as low corruption, countries in the middle third are defined as medium corruption, and countries in the top third are defined as high corruption.

3. The openness dummy is taken from Wacziarg and Welch (2003).

Source: Neeman, Paserman and Simhon (2004)

83 A comparison of the results given in the Table and the World Bank’s Doing Business 2008 Enforcing Contracts ranking underlines the importance of the judicial system to enforce contracts and thus of well-functioning institutions and low corruption.

Gokcekus and Knörich (2006) constructed a quality of openness index and demonstrated its impact on corruption. For their quality of openness index the authors have used two components, namely a quality of exposure measurement, which is defined as the level of corruption of a country’s trading partners, and secondly, a quantity of exposure index defined as “the trading partner’s importance of a specific trading partner in relation to other trading partners”.

The corruption indicator is derived from the Transparency International’s Corruption Perception Index. In their results Gokcekus and Knörich find that quality and the openness level can have possible significant impacts on the level of corruption. Furthermore, the authors find that the interactions with more corrupt trading partners increase a country’s corruption. Therefore, openness has a significant impact on the losses of per capita GDP due to trade with corrupt nations.

Dreher, Kotsogiannis and McCorriston (2004) address the problem of how much economic loss is attributed to corruption. The authors treat corruption as a variable directly related to its underlying causes, using a cardinal index of corruption derived from about 100 countries, allows to compute a measure of the losses caused by corruption as a percentage of GDP per capita. By doing so, the results provide a more direct measure of how significant corruption is as a policy issue. Furthermore, by the usage of this approach, it is possible to estimate the model for different time periods to see whether a policy that was for example introduced in the early 1970s actually reduced corruption in the 1980s or not. In their results Dreher, Kotsogiannis and McCorriston find open and developed countries such as Switzerland, Japan, Norway, Denmark, Germany and the Netherlands as being the least corrupt nations of the sample. The most corrupt

84 countries are in Africa, namely Zambia, Ghana and the Central African Republic. Further, it is confirmed that corruption is a significant problem and causes high losses of per capita GDP, especially in developing countries.

For example, the authors estimate a loss due to corruption of 11.2% of GDP per capita for Norway while the loss for Nigeria was at 66% of GDP per capita. For East Asia and the Pacific, the economic loss caused by corruption averaged 39.7%. This shows that corruption is a common policy issue for developing countries. In developed countries, the losses due to corruption as a percentage of per capita GDP are comparably low (22.26%) in the period 1991-97; in developing and transition countries, the losses are considerably higher, particularly in sub-Saharan Africa where the average loss amounts to over 60% of per capita GDP.

Acemolgu (2001) and Rodrik (2002) point out that the impact of economics, politics and reforms in these sectors on corruption is a fruitful avenue of research that has received some attention for those who correctly identify the importance of the role of institutions as a determinant of economic development.

4.2 The measurement(s) of trade openness Trade openness as discussed in Section 4.1 enjoys a large field of interest in empirical studies. Most of the research investigate the relationship of TO and growth or output volatility. Others investigate if TO and the government size and the budget deficit are correlated, all the way to the issue of the relationship between TO and corruption and the question whether corruption declines or increases when a country opens up. Trade openness of a country can be measured in many different ways. Different measurements surely imply different results. The most widely used measurement is the so-called trade intensity (TI), which measures trade openness as total trade (imports plus exports) over GDP. Another measurement is the so-called relative world trade intensity (RWTI). RWTI measures the country’s total trade as share of the world’s total trade (imports plus

85 exports). Even though both measurements are used in empirical studies, their accuracy has to be questioned due to the fact that they give inconsistent results. A recent study by Squalli and Wilson (2006) draws attention to this by estimating the TI and RWTI for several different data sets and with the result of inconsistency. Even more, the TI measurement of all data sets used specifies the world’s largest trading nations (e.g. the United States, Germany, Japan or China) as closed from international trade. As shown in the literature review, studies including the measurement of trade openness to determine if trade leads to GDP growth came up with inconsistent results, too. Romer and Frankel (1999) say that “despite the great effort that has been devoted to studying the issue, there is little persuasive evidence concerning the effect of trade on income”, which Squalli and Wilson (2006) use to justify their statement of inappropriate measurement of trade openness. If trade openness is measured inappropriately, then surely the results of empirical studies, including inappropriate measurements, will be biased.

TI is measured as follows

X M TI (4.1) GDP where X is nominal exports, M nominal imports and GDP real income.

Alcala and Ciccone (2004) suggest the usage of nominal trade data and real GDP by saying that otherwise TI can yield a biased downward estimate on income caused by the impact of non-tradable commodities on productivity. This study follows Alcala and Ciccone approach. The definition of TI shows that it is a one- dimensional measurement of the relative trade performance compared to its domestic income or, in other words, TI focuses “on the question of how large is the proportion of a country’s income associated with international trade.”84 TI is

84 Squallli and Wilson (2006).

86 measured in the range of 0 TI ,85 where the closer to 1, the more open is an economy.

RWTI gives the country’s relative importance to world trade and how open it is to the world’s total trade and is measured as

X M RWTI i i n (4.2) B (X M ) j1 j

where RWTIi represents country i 's relative world trade intensity. X M i are

n 86 country i 's exports plus imports in nominal terms and B (X M ) j is the j1 total trade of the set of countries including country i . RWTI operates in the range of 0 RWTI 0.5, 87 the closer to zero, the more closed an economy.

Both RWTI and TI fail to appreciate either the country’s income or the country’s relative world trade share, which is one of the reasons why they give inconsistent results. Squalli and Wilson (2006) developed a measurement which captures both TI and RWTI and thus appreciates the importance of income and relative world’s share. Therefore, the composite trade intensity (CTI) is a two-dimensional measurement determined by both TI and RWTI and is expressed as

CTI 1 Dr TIi (4.3) where RWTI Dr (4.4) (x 1)

85 In rare cases TI is greater than 1; this can be justified by either performing minimal value adding on imports, thus re-exports, or specializing in one product, while extensively importing from the rest of the world. 86 j={1,2,3,…,n}. 87 Proof given by Squalli and Wilson (2006).

87 and represents a distance ratio of RWTI’s deviation from x . x is the mean of all countries’ RTWI ratios build up under the hypothetical possibility that all countries share the same proportion of international trade, thus implying that TI is fixed. Furthermore, on the one hand, a positive distance from x indicates the extent to which a country derives income benefits due to its larger than average share of world trade. On the other hand, a negative distance from x indicates the gains the country could achieve by increasing its relative share in world trade.

F V X M B n G i W j 1 G n W (4.5) B X M j x H j 1 X 1 n n

With the knowledge of the above, CTI can be rewritten as follows

1 CTI i x (RWTIi TI i ) (4.6)

CTI i nRWTIi TIi (4.7)

nX M 2 CTI i i n (4.8) GDPi B X M j1 j adjusting TI by the relative importance of RWTI.

4.2.1 Trade openness – a cross country analysis of 210 countries for 2006 A cross country analysis of 210 countries on the topic of trade openness measurement underlines the findings of Squalli and Wilson (2006). The widely used TI marks countries such as Singapore, Hong Kong and Malaysia as most trade open countries putting them under the top 10, which can surely be justified. On the other hand, countries such as San Marino, Luxembourg, Swaziland and Iraq are considered to be most open economies, compared to Germany, the United States or the United Kingdom which place 111th, 150th and 206th, respectively, and are considered to be semi-closed or closed economies. Under

Table 4.2 Trade openness measurements for 210 countries – PICs rankings in 2006

TI (%) TI - Rank RWTI (%) RWTI - Rank CTI CTI - Rank Cook Islands 136.68 39 0.00082 197 23.4820 192 Fiji Islands 126.75 49 0.01327 138 353.3133 121 FSM 87.70 106 0.00072 198 13.3338 198 Kiribati 102.13 84 0.00026 205 5.5357 204 Nauru 102.13 83 0.00019 207 4.0985 207 Palau 160.57 17 0.00084 196 28.4436 187 PNG 84.81 112 0.01756 129 312.7375 131 RMI 126.90 48 0.00055 202 14.5779 197 Samoa 84.10 113 0.00124 192 21.8183 193 Solomon Isl. 118.20 57 0.00166 188 41.1909 180 Timor-Leste 41.95 199 0.00050 203 4.4346 206 Tonga 83.25 116 0.00065 200 11.4106 201 Tuvalu 126.56 50 0.00011 209 2.9050 208 Vanuatu 101.57 85 0.00124 191 26.3854 188

Source: Data derived from the United Nations Statistics Division 2008.

TI, the PICs are open all the way to closed countries. RWTI on the other hand indicates that the PICs are semi-closed to closed economies. The CTI- measurement gives a similar result and puts the PICs in the lower half of trade open nations.88

Table 4.2 shows the trade openness measurement results for the PICs for the year 2006. The complete result of the cross country analysis of the 210 countries is given in Appendix C., together with the results for the years 1990, 1995 and 2000.

The Table indicates that TI is very high in the small island states due to the fact that the PICs have limited resources and thus depend on imports of inputs which are partially re-exported. This fact is especially high in Palau, the Cook Islands, RMI, Fiji and Tuvalu. On the other hand, ignoring the income of the countries,

88 See equations of TI, RWTI and CTI in Section 4.1.2.

89 RWTI indicates that their relative world share of international trade is, relatively insignificant. CTI confirms this and puts countries that are considered to be open economies such as the Cook Islands or Palau into the lower end of the observation, thus identifying them as closed economies, which can easily be justified by their remoteness compared to other countries and to the high non- tariff barriers to trade.

Table 4.3 compares the rankings of the trade openness measurements of TI, RWTI and CTI of the years 1990, 1995, 2000 and 2006. The changes in rankings of the results of the TI over time tell an interesting story of the country’s trade performance. For example, PNG’s increase in TI in 1995 can easily be explained by its boom in exports of minerals and petroleum, as can its drop be described by the decreasing exports in this sector. Palau’s increase, on the other hand, can be described by a huge increase in trade over the time from US$ 55.5 million in 1990 to US$ 250 million in 2006.

Table 4.3 Change in trade openness rankings of PICs (1990 – 1995 – 2000 – 2006)

2006 2000 1995 1990 TI RWTI CTI TI RWTI CTI TI RWTI CTI TI RWTI CTI Cook Isl. 39 197 192 12 198 186 51 200 197 89 207 204 Fiji Isl. 49 138 121 32 134 120 46 129 115 35 132 114 FSM 106 198 198 96 196 192 85 193 193 80 193 193 Kiribati 84 205 204 86 207 204 57 206 204 21 208 199 Nauru 83 207 207 85 209 207 56 207 205 20 201 189 Palau 17 196 186 53 199 194 103 204 201 102 204 203 PNG 112 129 131 105 121 122 62 99 95 78 115 108 RMI 48 202 197 40 200 195 32 197 189 22 197 181 Samoa 113 192 193 107 194 193 79 194 190 72 198 196 Sol. Isl. 57 188 180 54 183 174 42 179 167 57 184 178 T.-Leste 199 203 206 123 192 191 210 210 207 210 210 208 Tonga 116 200 201 130 201 201 81 196 195 64 194 191 Tuvalu 50 209 208 41 210 208 31 209 208 19 209 205 Vanuatu 85 191 188 75 191 187 65 190 183 38 189 176

Source: Data derived from the United Nations Statistics Division, 2008.

90 4.2.2 Trading across borders – a measurement for trade openness? The World Bank Group’s Doing Business reports of comparing regulations in 178 countries have become famous not only because of their wide range of data. This section concentrates on trading across borders of the Doing Business Report 2008.

The report compiles the procedural requirements for trading a standardized cargo of goods, whereby every official procedure required for trading goods has been recorded, beginning from the contractual agreement between the two trading partners, to the delivery of goods. Furthermore, the time necessary and the costs for completion as well as all documents required for the clearance of the cargo across the borders are taken into consideration.

For exporting, this includes procedures from packing goods at the factory to their departure, the port where goods exit and across the border. For importing, the procedures start at the entry of the cargo at the port all the way to the final destination, the factory warehouse.

The information is derived by several sources such as shipping lines, customs brokers and port officials. The Doing Business reports inform that assumptions had to be made so that the information derived is comparable across countries. This includes assumptions about the business (more than 200 employees, locate in most populous city, a private or limited liability company with non special import or export privileges of any kind, domestically owned and exporting more than 10% of its sales) and about the traded goods (traded products in dry cargo, 20-foot container, full container load, excluding hazardous or military products as well as products requiring refrigeration or any other special environment).

The number of documents includes all required documents to import or export goods (e.g. bank documents, customs declaration, clearance documents, licenses etc.). Time is recorded in calendar days and compiles the moment the procedure of trade starts until it is completed, where the fastest legal procedures are chosen

91 and assumptions are made that nobody is interested in wasting any time. Furthermore, the waiting time between procedures is included. Costs are measured in US dollars and include all fees and charges that are necessary to complete the procedures of import and export (e.g. fees for customs clearance, costs for documents, terminal handling charges and inland transport).

Table 4.4a introduces the World Bank Group’s trading across borders indicators for the PICs and the world leaders Singapore, Denmark and Hong Kong by summarizing the costs, time and documents necessary for exports and imports. It shows that especially the costs to trade and the time necessary are major constraints to trade in the Pacific.

Table 4.4a Doing Business 2008 - Trading across borders

Country Import Export Cost (US$) Documents Time Cost (US$) Documents Time Fiji 573 13 25 570 13 25 FSM 1,145 3 30 1,145 6 30 Kiribati 1,550 7 21 1,550 6 21 Palau 1,110 10 35 1,160 6 29 PNG 642 9 29 584 7 26 RMI 765 5 33 765 5 21 Samoa 1,375 7 31 1,010 7 27 Solomon Isl. 1,238 4 21 1,056 7 24 Timor-Leste 995 7 26 990 6 25 Tonga 620 6 25 545 7 19 Vanuatu 2,225 9 30 1,815 7 26

Singapore 367 4 3 416 4 5 Denmark 540 3 5 540 4 5 Hong Kong 525 4 5 525 4 6

Notes: 1. Cost is measured as the fees levied on a 20-foot container in U.S. dollars including all fees associated with completing the exports / imports procedures (documents, handling charges, inland transfer). Tariffs or taxes on trade are not included.

2. Documents comprise all required documents to export or import. This includes bank documents, customs clarification, licenses for import / export and others.

3. Time is recorded in calendar days required for the procedure of imports and exports from the moment it initially runs until it is completed.

Source: The World Bank Group Doing Business 2008.

92 The costs of exports (imports) vary widely between US$ 545 (US$ 573) for Tonga (Fiji Islands) and US$ 1,815 (US$ 2,225) for exports (imports) in Vanuatu (Vanuatu). Miserable and shocking is the time it takes to actually trade with the PICs. Tonga with 19 days for export exceeds the world leading nations Denmark and Singapore by two weeks. Micronesia is even worse by exceeding the leader by more than three weeks in time to export. Similar indications can be seen on the imports side. In comparison, in Palau it takes 32 days longer, costs US$ 733 and takes 6 documents more to import than it is the case in Singapore.

The following survey this thesis conducts is adjusted to 177 countries to be consistent when comparing it with the results of TI, RWTI and CTI.

Table 4.4b presents the results for 11 of the 15 PICs included in the Doing Business report 2008 ranking compared to adjusted measurements of TI, RWTI and CTI.89

Table 4.4b Doing Business’ trading across borders vs. TI vs. RWTI vs. CTI

TI - Rank RWTI - Rank CTI - Rank Doing Business Fiji 39 126 111 110 Kiribati 65 177 175 96 FSM 85 171 171 84 Palau 13 170 163 120 Papua New Guinea 91 118 119 81 RMI 38 174 170 45 Samoa 92 167 166 107 Solomon Islands 43 163 158 73 Timor-Leste 170 175 177 77 Tonga 94 172 174 43 Vanuatu 66 166 164 141

Source: Data derived from the United Nations Statistics Division, 2008.

89 Adjusted to 177 countries.

93 The comparison of the results shows that there is no consistency at all. Most of the results of TI show that the PICs are considered to be open economies. RWTI gives the opposite results and bookmarks most of them as closed economies. The results of CTI give similar results with the exception of Fiji and PNG which are considered to be semi-closed.

The Doing Business 2008 report indicates completely different results and shows that PICs are considered to be open (e.g. Tonga and RMI) but also closed (e.g. Vanuatu). Fiji as the second largest country in the Pacific is considered to be semi-closed and shows some consistency with the results of RWTI and CTI. PNG on the other hand, ranks between the 81st and the 119th place and thus could be characterized as a semi-open or semi-closed economy, respectively. Timor- Leste’s results are the most interesting. Even though TI, RWTI and CTI consider the nation as one of the most closed economy in the world, the Doing Business report places the country into the upper half and therefore sees it as a semi-open economy. A similar inconsistency can be seen in the example of Tonga, which is under the Doing Business 2008 report the country easiest to trade with.

4.3 Conclusion of Chapter 4 The chapter started with a literature review of the topic of trade openness and its relationship with GDP growth. It shows that the empirical literature varies widely on both the definition of the appropriate openness measurements and the methodology applied to analyze the relationship between openness and economic performance. Until Rodriguez and Rodrik (2001), the common language of many economists was that trade openness and GDP growth are positively related. Rodriguez and Rodrik openly criticized not just the openness measurements used (e.g. TI and tariff barriers) but also the econometric methodologies. Rodriguez and Rodrik (2001) and Rodriguez (2007) point out that trade openness is too complex a relationship to simply estimate its linkage with GDP by using tariffs or trade ratios. Rodrik (1999) discusses that the topic of openness includes institutions and good governance. The question is how economists are able to

94 include country specific data on those two “variables” (of many others) to appreciate their existing influence on the trade and economic performance of a country itself.

The literature review further discussed the relationship between openness and the size of governments (Epifaniy and Ganciaz, 2008) and budget deficits (Combes and Saadi-Sedik, 2006). Another important subject is the impact of openness on corruption. Several authors have discussed the relationship using different openness measurements and variables to capture corruption but find common ground in the fact that the more developed and the more open an economy is, the lesser corruption occurs; see Dreher, Kotsogiannis and McCorrison (2004). Furthermore, it was found that the main level of corruption affecting an economy is determined by the level of financial openness; see Neeman, Paserman and Simhon (2004).

The second part discussed trade openness rankings by introducing three different trade openness measurements, namely TI, RWTI and CTI and estimated trade openness rankings for 210 countries. Additionally, the World Bank Group’s Doing Business report “trading across borders” as another trade openness indicator was reviewed. A comparison of all those showed that there is no constant indication for the measurement of trade openness which leaves the question of which openness measurement is appropriate to fully capture the a country’s openness to trade.

In an interesting statement, Esterly and Kraay (2000) point out that “small states, no different from large states in income and growth, should receive the same policy advice as large states do. Because of their great openness, they may be vulnerable to volatility in terms of trade shocks--but their openness pays off in growth”.

In that sense, small states as the PICs do not have any disadvantages compared to countries such as the USA or China. With respect to the authors, this remark is

95 not just questionable but simply wrong; compared to a strong economic power of the USA, the PICs have many disadvantages, not just in terms of remoteness. Prasad and Singh (2006, 2007), Sampson (2003) and Winters and Martin (2004) support the criticisms of the Esterly and Kraay statement.

Prasad and Singh (2006, 2007) point out that the dynamics behind the economic performances of small states are simply different compared to large countries. The cultural, social and structural as well as environmental problems that small island states face are pure disadvantages compared to large economies. The question whether the remoteness is an obstacle to economic growth is a different one but still plays a significant role and cannot be ignored.

This discussion leads strait back to the question of openness and openness measurements. It shows that trade and trade openness is indeed a much wider and complex topic than to simply measure the trade openness of a country with the TI or RWTI as still many economists do; see Bejan (2006) or Billmeier and Nannicini (2007). A real appropriate openness measurement needs to include more than just the trade performance, tariffs and the income of a country. Country specific variables such as culture and structure play a major role as do institutions, corruption and geographic variables such as remoteness, to mention just some. These are just a few areas necessary to consider in a country’s trade openness measurement next to its non-tariff barriers to trade, which mean the highest obstacles to trade, especially in developing countries. Quarantine restrictions are more significant to hinder or disturb trade than actual tariff barriers and need to be appreciated in one way or another in a TO measurement. Another question that needs to be raised is as follows: If the PICs are open to trade, why is there a need for trade liberalization and opening the economies up to the globalized world?

Simply comparing the most remote countries in the world, the PICs with countries such as the USA or Great Britain, does not make much sense unless further determinants of the complexity of openness are included. This may even

96 lead to country specific trade openness measurements for example for estimating endogenous growth equations to determine the impact of trade on GDP growth.

97 5. Demand for Trade There are several interesting reasons to investigate the demand for trade relationship. First of all it determines whether a continuing trade relationship between two trading partners exists or not. Secondly and for trade policies very important, it offers the chance to investigate price and income responsiveness of both the demand for exports and imports. Thus, Chapter 5 investigates the demand for trade of the PICs.

Studies on the demand for trade vary in the forms of the subject of interest (e.g. sector or product specific demand versus total demand for trade) and in model specifications. Unfortunately, not many studies have been conducted for the Pacific region. A short literature review is given in section 5.3.

The thesis concentrates on the trade relationship between the trading blocks of the PICs on the one side and the EC, and Australia and New Zealand on the other side. Furthermore, it investigates Fiji’s demand for trade with several member countries of the European Union (e.g. United Kingdom, Germany and France), and Australia and New Zealand. Section 5.4 explains the model and the econometric methods used, followed by an analysis of the results in Section 5.5. Concluding remarks are made in section 5.6.

5.1 How strong is the PICs demand for trade? The PICs demand for exports and imports is another way to analyze its trade relationship with trade partners and trade blocks. This thesis concentrates on the trade agreements of PACER and EPA, therefore the demand for trade between the PICs and the European Community and the PICs and Australia & New Zealand is estimated.

There are several interesting reasons to investigate the demand for trade relationship. First of all, it offers an opportunity to investigate whether a strong continuing trade relationship between two trading partners over time exists or

98 not. Secondly, it provides a chance to investigate price and income responsiveness of both the demand for exports and imports.

This thesis concentrates on the aggregate trade relationship between the trading blocks of the PICs on the one side and the EC, and Australia & New Zealand on the other side. Furthermore, it investigates Fiji’s demand for aggregated trade with several member countries of the European Union (e.g. United Kingdom, Germany, France), as well as with Australia and New Zealand.

5.2 Demand, income and price elasticity The slope of a demand function is defined as follows

q slope of the demand function p (5.1)

and can easily be used as a measurement of responsiveness. The problem of the usage of the slope of a demand curve is its dependency on units, which makes it inconvenient. A unit-free measurement of responsiveness is the so-called elasticity.

The price elasticity of demand is the percentage change in quantity divided by the percentage change in price and is defined by the following equation:

C q S D T E q U p q C p S q p (5.2) D T E p U where is the price elasticity, p is price, p is the change in price, q is quantity and q is the change in quantity.

Chart 5.1 The elasticity of a linear demand curve

a 1 2b

a q 2

Thus, the price elasticity of demand can be expressed as price divided by quantity multiplied by the slope of the demand curve.

Chart 5.1 shows an example of a linear demand curve which can be defined by

q a bp (5.3) and thus, the slope of the demand curve is a constant b .

Using the just discussed formula (5.3), the price elasticity of demand can be written as bp bp q a bp (5.4)

100 Therefore, when p 0 , the price elasticity of demand is 0 and when q 0 it becomes infinite. bp 1 a bp (5.5)

Solving for p gives a p (5.6) 2b

In the case of an elastic demand curve a 1 % price increase means a more than 1% reduction of demand of quantity.

5.2.1 Income elasticity The income elasticity is defined as the percentage change in quantity divided by the percentage change in income.

The rule of thumb indicates that the income elasticities are close to unity. Using the following budget constraint for two different income levels gives an idea why. p x p x m 1 '1 2 '2 ' (5.7) p x p x m 1 1 2 2 (5.8)

By subtracting (5.8) from (5.7) and including the change variable gives:

m p x p x 1 1 2 2 (5.9)

x Multiplying and dividing price i by i and further dividing both sides by m : xi

m p x x p x x 1 1 1 2 2 2 m m x m x (5.10) 1 2

101 m p x Now, dividing both sides with and defining s i i , the share of m i m expenditure of good i , gives the following equation

C x S C x S D 1 T D 2 T E x U E x U 1 s 1 s 2 (5.11) 1 C m S 2 C m S D T D T E m U E m U which is equal to 1, thus the weighted average of the elasticity of income is 1 whenever the expenditure shares are the weights. Luxury goods, inferior goods and normal goods have, not surprisingly, different income elasticities, whereas luxury goods are seen as goods where a 1 % increase in income leads to a more than 1 % increase in demand – the income elasticity is greater than 1. The inferior goods are characterized by a declining demand when income increases, whereas normal goods have positive income elasticities, when the income increases, then the demand for the good increases as well.

5.3 Literature review on demand for trade in the Pacific90 Several studies have been conducted on the topic of demand for trade but not many for the Pacific region. Senhadji and Montenegro (1999) estimated the export demand function for 75 developing and advanced countries with the IMF’s demand for export equation approach using OLS and FMOLS. In their results, the authors find an average long-run price elasticity of -1 and an average long-run income elasticity of 1.5. Further, the authors found that instead of the general assumption that developing countries show lower price elasticities Asian countries had significantly higher price elasticities. Additionally, the findings show that Asian countries benefit from higher income elasticities than the rest of the developing world, underlining that in the Asian region trade has a significant influence in growth. Africa on the other hand faces the lowest income elasticities.

90 For a wider literature review see Singh (2007).

102 Reddy (1997) estimated the demand for trade (exports and imports) of Fiji with the OLS. In the study the author used the general equation of demand for exports and imports. For the demand for Fijian exports, Reddy estimated a relative price elasticity of -0.778 and an income elasticity of 0.761. The relative price elasticity for the demand for imports was 1.530 and the income elasticity 2.385. The results of Reddy have to be seen with caution since he failed to investigate his variables for unit roots, thus failing to follow a crucial rule of time series estimations.

The imports demand equation for Fiji using annual data for the period of 1968- 1998 was estimated by Rogers (2000). The author found an income elasticity of 1.8 and a price elasticity of 0.6.

Prasad (2000) estimated the demand for exports for Fiji using Rogers (2000) dataset and found an income elasticity of 2.45 which would imply that Fiji’s exports should be treated like luxury goods for which there is no reason, with respect to the Fiji Islands.

The demand for Fijian trade (imports and exports) was the topic of the studies conducted by Narayan and Narayan (2004, 2005). For exports demand, using annual data for 1972-1999, 0.80 income elasticity was estimated. For imports demand (1970-2002), the income elasticity ranged between ca. 1.05 and 1.9.91

Rao & Singh (2006) argue that the relative price variable needs to be appropriately specified by included E as the exchange rate as the price of a unit of foreign currency in domestic currency. The authors estimated both approaches (1) including and (2) excluding the exchange rate using three different econometric methods, namely GETS, JML and FMOLS. Their results for income and relative price elasticities are as follows: (1) The income elasticities are 1.164 (GETS), 1.147 (JML) and 0.995 (FMOLS), and relative price elasticities of -0.862 (GETS), -2.578 (JML) and -1.018 (FMOLS).

91 Information taken from Singh (2007), price elasticity was not annotated.

103 (2) The income elasticities are found to be 1.652 (GETS), 1.641 (JML) and 1.379 (FMOLS), and relative price elasticities of -1.202 (GETS), -1.357 (JML) and -1.024 (FMOLS).

Therefore, ignoring the exchange rate leads to indications of underestimation of the absolute value of the relative prices (e.g. -2.578 against -1.357) and to overestimation of the income elasticity (e.g. 1.47 against 1.641). Their income elasticity was close to unity in all their estimates, which underlines their hypothesis that misspecifications of relative prices lead to an over-estimation of the income elasticity of 40-60%;92 see Singh and Rao (2006) and Singh (2007).

Singh (2007) expanded the work of Rao and Singh (2006) by estimating exports and imports demand equation for Fiji using annual data from 1970-2002 with three econometric methods, namely GETS-NLLS, FMOLS and JML. For exports Singh found income elasticities of 1.066 (GETS), 0.995 (FMOLS) and 1.147 (JML) and a relative price elasticities of -1.248 (GETS), -1.018 (FMOLS) and - 2.578 (JML), whereas the relative price elasticity of GETS and JML are found to be 10% insignificant. The income elasticities for imports are reported to be 0.837 (GEST, 1.162 (FMOLS) and 1.153 (JML) and the relative price elasticities 0.436 (GETS), 0.383 (FMOLS) and 0.504 (JML), whereby the relative price elasticity of GETS was 10 % insignificant.

In an earlier study I have estimated the demand for exports for four Pacific Island nations, namely Fiji, PNG, Solomon Islands and Tonga and using GETS-NLLS. In the estimation I included the exchange rate using the Rao and Singh (2006) approach which will be discussed in more detail in the following section. For Fiji, the author found an income elasticity of 1.0935 and a relative price elasticity of - 0.75. For PNG the income elasticity of the demand for exports is relatively low with 0.67 whereas the relative price elasticity was estimated with -1.0975. For the Solomon Islands and Tonga, the income elasticities are 1.0279 and 0.88353 and the relative price elasticities -1.0396 and -0.58901, respectively.

92 See section 5.4 for the specifications of the relative price variable.

104 5.4 Model used The most common estimated export demand equation for a country’s exports

X t and the demand for imports M t in the log-linear form is as follows:

F P V ln X lnG Dt W lnY t 0 1 P 2 F t t (5.12) H F t X

F P V ln M lnG Dt W lnY t 0 1 P 2 Dt t (5.13) H F t X

where PD is the domestic price of exports, PF the price level of the major export partners, YF the income of the major trading partners and YD domestic income. The estimates of 0 , 1 and 2 give the results for the constant term, the relative price elasticity and the income elasticity, respectively. t and t are the error terms.

Rao and Singh (2006) challenged Sendhadji and Montenegro (1999), saying that by ignoring the exchange rate in the relative price variable the price elasticity might be under-estimated and the income elasticity over-estimated. Thus, Rao and Singh used the following relative price variable specification

F P V G Dt W E P (5.14) H t F t X where the relative price variable includes E as the exchange rate, in form of one unit of foreign currency in local currency.

The inclusion of E is justified because a 1% decline in relative prices could be due to a 1% decline of the domestic price of exports PD or a 1% increase of the price level of the major export partners PF or a depreciation of the exchange rate E ; see Rao and Singh (2006).

105 Nevertheless, in this thesis, it is found that by estimating the “general approach” for the demand for exports and imports for the trading blocks of the PICs (PACPs) and the EC and Australia & New Zealand, thus ignoring the exchange rate, an indication of an over estimation of the income elasticity or an under estimation of the relative price elasticity is not given; see results 5.5.3 a.) and b.). Furthermore, this indicates that the exchange rate does not have a significant impact on the demand for trade estimates for the trading blocks. Therefore, for the trading blocks, the “general approach” is applied.

On the other hand, the “Rao & Singh approach” by substituting the relative price variable (5.14) into (5.12) given by country’s exports X t in the log-linear form F P V ln X lnG Dt W lnY t 0 1 E P 2 F t t (5.15) H t F t X for the demand of exports and similarly (5.14) into (5.13) for the demand for imports constructed as follows

F P V ln M lnG D t W lnY t 0 1 E P 2 Dt t (5.16) H t F t X is used to estimate the demand for exports and imports of Fiji with several EC member countries and Australia and New Zealand.

For the estimations three methods are used, namely Philip Hanson’s Fully Modified Ordinary Least Squares (FMOLS), LSE Hendry’s General to Specific (GETS-NLLS) and the Johansen Maximum Likelihood method (JML) with annual data for the PICs trade relationship with Australia, EC and New Zealand, from 1975/1976 until 2006. For the case study of Fijian trade, the data is used from 1975/76 to 2004/06. Details for the data used are given in the following section.

106 5.4.1 Data used Most studies on demand for exports and imports use data of real GDP, nominal exports, domestic and foreign price levels, and the nominal exchange rate and this study follows this tradition.

All the data used in this investigation on demand for trade are compiled from several publicly available sources, namely the World Bank’s World Development Indicators, IMF’s IFS, the National Accounts data base of the United Nations Statistics Division, Fiji Islands Bureau of Statistics, Australia Bureau of Statistics, Eurostat and New Zealand Statistics. In the estimations there is an attempt to use the data as accurately as possible but one has to keep in mind that the different sources very often give different statistical results for the same data of interest. Therefore, to be consistent, data on real GDP as well as the components of the relative price variables are used from the UNSD’s National Accounts data base. Further details are given in the following sub-sections. a.) Membership in the PACP – Membership in the EU In this study, the demand for the Pacific ACPs exports and imports to and from the European Community (EC) is derived.

And hence, for the PACP countries, data of Fiji, Tonga and Samoa is used from 1976 onwards. Kiribati, PNG, Solomon Islands and Tuvalu became members of the ACPs in 1979, thus their data is used from 1979-2006. Vanuatu’s data was added to the total PACPs data from 1984 onwards. In 2000, Cook Islands, FSM, Nauru, Palau and RMI joined the ACP countries by ratifying the Cotonou Agreement, thus their data was included from 2000-2006. The data of Timor Leste is used from 2003-2006, appreciating their membership of the PACPs in 2003.

For the EC’s data set, the data from the six founding members (Belgium, France, Germany, Italy, Luxembourg and Netherlands) and Denmark, Great Britain and Ireland becoming members of the EC in 1973 were used from 1976 onwards. The

107 data of Greece (1981), Portugal (1986), Spain (1986), Austria (1995), Sweden (1995) and Finland (1995) are included from the moment the countries became members of the EC, year given in parenthesis.

In 2004, data of the countries of Cyprus, the Czech Republic, Estland, Hungary, Latvia, Lithuania, Malta, Poland, Slovenia and Slovakia are further added to the total data set of the EC. b.) Price deflator data Instead of the often used unit value index, in an interview Professor B. Bhaskara Rao, Research Fellow within the School of Economics & Finance at the University of Western Sydney suggested and explained the option of the usage of the exports and imports deflators. This study follows this suggestion, by using imports and exports price deflators derived from the United Nations Statistics Division using current and constant exports and imports.

i. Demand for PACPs’ exports to the EC For the demand for exports of the PACPs to the EC, the export weighted exports price deflator of the members of the PACPs is used, where the export weights are calculated as the country’s share of exports of total PACPs exports multiplied with the country’s export price deflator. The PACPs price deflator is the sum of the country’s export-weighted price deflators and is considered to be the domestic price deflator.

The foreign price deflator is the import-weighted exports deflator of the EC’s member countries derived as described for the PACPs, whereas here the country’s imports share of total EC imports is multiplied with the country’s exports deflator.

ii. Demand for EC’s exports to the PACPs The domestic price deflator is considered to be the export-weighted exports deflator of the EC members and the foreign price deflator is the import-weighted exports deflator of the PACPs; both indexes are derived following the approach explained in 5.4.1 b.) i.

108 iii. Demand for PICs exports to Australia and New Zealand The domestic price deflator is the export-weighted exports deflator of the PICs and the foreign price deflator is the import weighted exports deflator of Australia and New Zealand; the indexes are both derived following the approach explained in 5.4.1 b.) i.

iv. Demand for Australia’s and New Zealand’s exports to PICs The domestic price deflator is the export-weighted exports deflator of Australia and New Zealand’s exports to the PICs. The foreign price deflator is the import- weighted exports deflator of PICs exports, both derived indexes following the approach explained in 5.4.1 b.) i.

v. Demand for imports The domestic price deflator is the import-weighted imports deflator of this trade block, the demand for imports is of interest (PACPs, PICs, AUSNZ, EC) and the foreign price deflator is the export-weighted imports deflator of the trading partner block. The data on the deflators is derived following the approach explained 5.4.1 b.) i. to iv.

vi. Fiji’s demand for imports and exports The construction follows the explanations of 5.4.1 b.) i. to v. with country specific data. c.) Relative Price Index The relative price index is derived using the data on the price deflators explained in Section 5.4.1 b.).

109 d.) Trade data The data on imports (CIF)93 and exports (FOB)94 between the countries and trade blocks of interest are derived from national bureaus of statistics and several sources of international statistic. The trade data between the EC and the Pacific ACP countries is provided by the Eurostat. The data is provided for the years of 1976 – 2006, disaggregated by each Pacific ACP country and the EC. The trade data, total exports and total imports data, for the PACPs used in the estimation is derived by considering the member status of the PACPs.

The trade data between the Pacific Island Countries and Australia and New Zealand is derived by the usage of two sources – the Australian Bureau of Statistics and the New Zealand Statistics provided from 1976-2006, disaggregated by PICs.

Both data sets are provided in nominal values – exports in FOB and imports in CIF – and are deflated with the specific export price index and import price index, given in Section 5.4.1. Further, both data sets are provided in national currency – Euro, Australian Dollars and New Zealand Dollars. The IMF’s IFS data on exchange rate was used to make the data sets US Dollars based. e.) GDP data The GDP data used for the trading blocks is derived from the United Nations Statistics Division using real 1990 data. The real GDP data for the EU and the PACPs is derived considering the membership as described in Section 5.4.1.

93 Cost, Insurance and Freight (CIF), seller owns goods until they are loaded on vessel; selling price includes all costs so far plus cost of ocean marine insurance. 94 Free On Board or Freight on Board (FOB), transportation term that indicates that the price for goods includes delivery at the seller’s expense to a specified point and no further.

110 5.5 Results

5.5.1 Results of Unit Root Test One of the major rules of empirical studies including time series estimation is to test for possible cointegration by testing whether the individual time series variables contain unit roots or not, thus, whether the variables are non-stationary or stationary.

For the empirical studies used in this thesis, two different unit root tests are examined:95 1. The Augmented Dickey Fuller test (ADF) 2. The Phillips Perron Test (PP) The unit root tests indicate that all level variables are non-stationary and that their first differences are stationary with the exemption of a few variables being non- stationary in first differences in ADF but stationary in PP. Nakagawa (2003) finds that even though “PP tests have poorer size properties”, they “have greater power to reject a false null hypothesis of a unit root than the DF/ADF tests”.96 Furthermore, the rule of thumb indicates that variables being non-stationary in level are stationary in first difference. Therefore, the ADF results showing non- stationary in first difference are being rejected.97

5.5.2 Results of Cointegration Tests Three cointegration tests are implemented to determine whether the estimation equation is cointegrated or not: 1. Engle Granger Cointegration Test 2. Johansen Cointegration Test 3. Erickson-MacKinnon Test for Cointegration In most of the cases the cointegration tests for the three methods were found to be positive in terms of confirming existing cointegration.98

95 See Appendix B for results. 96 Nakagawa (2003, pp. 211-212); see also Enders (1995, pp. 239-243) and Phillips and Peron (1988). 97 Due to limited resources the widely considered stronger unit root tests, the Kwiatkowski- Philips-Schmidt-Shin test and the Elliott-Rothenberg-Stock Point-optimal could not be tested. 98 See Appendix B for results.

111 5.5.3 Estimation results The econometric methods of GETS-NLLS, FMOLS and JML are used to estimate the demand for trade in this study. The error correction terms are stationary in all FMOLS results reported. The coefficients all have the correct expected negative (positive) signs for the export’s relative price elasticity (import’s relative price elasticity) and positive signs for the income elasticities of exports and imports demand estimations.

This section describes the estimation results based on GETS-NLLS, FMOLS and JML including the long-run estimates of income elasticities and relative price elasticities of demand for exports and imports of the PICs to/from the EC, Australia & New Zealand. Further it presents the case study results for Fiji and its major trading partners in the EC (United Kingdom, Germany, France, Belgium and Netherlands), Australia and New Zealand. In Appendix C., the dynamic adjustment equations of the long run equations on the significant lagged variables of income elasticity and relative price elasticity are reported. This includes 2 information on the adjusted R2 (Adjusted R ), the standard error of regression 2 (SER), and the diagnostic tests of Serial Correlation (X sc), Functional Form 2 2 2 (X ff), Normality (X n), and Heteroscedasticity (X hs).

a.) Long-run estimates of demand for imports/exports of PACPs to/from the EC:

Table 5.1 Estimates of long-run demand for exports of Pacific ACPs to the EC for 1976 - 2006 Variable GETS-NLLS FMOLS JML -0.02110* -1.33452** -0.57728* Constant [2.2166] [1.7722] [1.9777]

0.74309 0.83544 0.67612 lnY F [82.6391] [13.4349] [68.9127] F P V lnG Dt W -0.64315* -0.66325 -0.63592* P H F t X [2.1949] [5.0969] [2.0121] Notes: 1. The absolute values of the t-ratio are given in the parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

112

Table 5.2 Estimates of long-run demand for Pacific ACPs’ imports from the EC for 1976 - 2006 Variable GETS-NLLS FMOLS JML -2.4413 -1.4854 -1.6357 Constant [5.2420] [4.9722] [5.0035]

1.1794 1.1419 1.1607 lnY D [35.6247] [32.8322] [34.8814] F P V lnG Dt W 0.50743 0.51217 0.50369 P H F t X [4.5619] [3.1820] [5.2301] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

Table 5.1 presents the long-run estimates of the demand for Pacific ACP’s exports to the EC. The results show an income elasticity of between 0.67612 and 0.83544, signifying that a 1% increase of income of the EC, the demand for PACP’s exports increase by between 0.67612 and 0.83544 %. The relative price elasticity is almost consistent and in between -0.63592 and -0.66325. These results signify that a 1% decrease in prices of the PACP’s exports increases the demand for exports to the EC by approximately 0.65%.

Table 5.2 presents the long-run estimates of Pacific ACP’s imports of products from the EC. The results indicate an income elasticity of approximately 1.16 and a relative price elasticity of ca. 0.5.

113 b.) Pacific Island Forum Secretariat Members and Australian & New Zealand:

Table 5.3 Estimates of long-run demand for exports of PICs to Australia & New Zealand for 1975 - 2006 Variable GETS-NLLS FMOLS JML -3.4844 -3.6844 -3.2159 Constant [5.0150] [5.1295] [4.8813]

0.93006 1.3099 0.93211 lnY F [8.9661] [17.5255] [10.4620] F P V lnG Dt W -1.1700 -0.87983 -0.89950 P H F t X [4.6399] [3.9094] [4.3759] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

The results show an income elasticity of in between 0.93 (GETS-NLLS and JML) and 1.31 (FMOLS) for the exports demand of Pacific Island exports to the trading block of Australia & New Zealand. For imports, the income elasticity is between unity and 1.15. The relative price elasticity for exports ranges from -0.88 to -1.17, whereas for imports it is as low as 0.267 and goes up to 0.42. All estimates are found to be 1% significant. The “most significant” results are given by FMOLS for imports, even though the income elasticity seems too high, and JML for exports.

Table 5.4 Estimates of long-run demand for imports from Australia & New Zealand to PICs for 1975 - 2006 Variable GETS-NLLS FMOLS JML -1.73796 -1.7388 -1.28169 Constant [4.6199] [4.5504] [3.5862]

1.0858 1.1569 1.0037 lnY D [14.2219] [28.3641] [12.7055] F P V lnG Dt W 0.42046 0.32769 0.26735 P H F t X [4.4603] [5.8561] [4.1833] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

114 c.) Estimates results of Fiji’s imports / exports demand (merchandise): Fiji’s demand for trade with several members of the EC, Australia and New Zealand …

I. Fiji and the United Kingdom

Table 5.5 Estimates of long-run demand for exports of Fiji to the United Kingdom for 1976 - 2004 Variable GETS-NLLS FMOLS JML -2.2870 -3.6849* -3.4693 Constant [3.0632] [2.2860] [2.5714]

0.96546 1.0393 1.0228 lnY F [10.4771] [7.1590] [8.8141] F P V D -0.78696 -0.84941* -0.82641 lnG W H E PF X [15.0933] [10.6357] [11.9375] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

Table 5.6 Estimates of long-run demand for Fijian imports of products from the United Kingdom for 1976 - 2004 Variable GETS-NLLS FMOLS JML -2.78684* Constant - [2.4211]

0.95216* 0.92458** lnYD - [2.2166] [2.0021] F P V D 0.61218* 0.58278* lnG W - H E PF X [3.1121] [2.1876] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance. 4. FMOLS did not give significant results.

The income elasticity of both the demand for exports and imports are around unity. The price elasticity of the demand for exports is between -0.79 and -0.85. For imports, the price elasticity is at approximately 0.60. The three methods used show similar results, thus indicate consistent significant results. For imports demand due to endogenous problems, it was not possible to obtain significant results with FMOLS.

115 II. Fiji Islands and Germany

Table 5.7 Estimates of long-run demand for exports from Fiji to Germany for 1976 - 2004 Variable GETS-NLLS FMOLS JML -2.4647 -3.2189 -1.9426* Constant [6.3409] [27.8430] [2.1666]

0.95961 0.98160 0.84472 lnY F [62.8026] [78.6647] [32.8434] F P V D -0.29809 -0.27874 -0.39782* lnG W H E PF X [11.0737] [8.3828] [2.2080] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

Table 5.8 Estimates of long-run demand for imports from Germany to Fiji for 1976 - 2004 Variable GETS-NLLS FMOLS JML -2.11215* Constant - [2.1153]

0.91221** 0.93071* lnYD - [1.9924] [2.1231] F P V D 0.79221 0.76291 lnG W - H E PF X [3.7740] [4.2755] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance. 4. FMOLS did not give significant results.

The income elasticity for the demand of exports equation ranges from 0.84 (JML) to 0.98 (FMOLS) whereas the closer it gets to unity, the more significant the elasticity variable becomes. The price elasticity is between -0.28 and -0.39 where FMOLS with -0.278 gives the highest significant variable. For the demand for import, FMOLS did not give any significant results which are due to endogeneity. The price elasticity is approximately 0.78. The income elasticity is found to be 10% significant for GETS-NLLS at 0.91 and 5% significant for JML at 0.93, which indicates that the closer the income elasticity moves towards unity, the more significant it becomes.

116 III. Fiji Islands and Belgium

Table 5.9 Estimates of long-run demand for exports from Fiji to Belgium for 1976 - 2004 Variable GETS-NLLS FMOLS JML -4.0577 -3.8934 -3.3082 Constant [3.3669] [6.4395] [7.7670]

0.67626 0.71512 0.66853 lnY F [10.5422] [14.3262] [9.8041] F P V D -0.36313 -0.31512 -0.36069 lnG W H E PF X [5.8485] [5.7999] [5.8408] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significances.

Table 5.10 Estimates of long-run demand for imports from Belgium to Fiji for 1976 - 2004 Variable GETS-NLLS FMOLS JML 1.8301** 0.45780X Constant [1.8397] [0.4884]

0.87739 0.81702 0.91238 lnY D [6.7498] [5.4306] [7.8673] F P V D 1.0856* 1.2274* 1.0039 lnG W H E PF X [2.3534] [2.1114] [3.5564] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance 3. ** 10% significance 4. X: Constant term in FMOLS is found to be not significant.

The income elasticity for the demand for exports ranges between 0.668 and 0.715, and for the demand for imports between 0.817 and 0.912. In both the higher the elasticity becomes, the more significant it is. The highest significant price elasticity is given by JML and indicates ca. -0.36 for the demand for exports and unity for the demand for imports, where it also goes up to 1.2274 at 5% significance for FMOLS.

117 IV. Fiji Islands and Netherlands

Table 5.11 Estimates of long-run demand for exports from Fiji to the Netherlands for 1976 - 2004 Variable GETS-NLLS FMOLS JML -7.1308 -6.7379 -7.1340 Constant [4.4853] [21.2338] [5.2233]

0.80271 0.93072 0.96181 lnY F [10.8238] [37.1273] [44.0696] F P V D -0.41489* -0.42066 -0.34070* lnG W H E PF X [2.4113] [4.5206] [2.1655] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

Table 5.12 Estimates of long-run demand for imports from the Netherlands to Fiji for 1976 - 2004 Variable GETS-NLLS FMOLS JML 1.8697** Constant - [1.9534]

0.88703 0.92323 lnYD - [2.7498] [3.0147] F P V D 0.34670* 0.35527 lnG W - H E PF X [2.3259] [2.6690] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance. 4. FMOLS did not give significant results.

Similar to some results discussed above, the closer the income elasticity gets to unity, the more significant the result becomes. This is true for both the income elasticity of the demand for exports (ranging between 0.8 and 0.96) and the income elasticity for imports (0.88 to 0.92) for Fiji’s trade relationship with Belgium. The price elasticity of exports demand varies from -0.34 for JML to approximately -0.42 for FMOLS and GETS-NLLS, where the FMOLS result is the most significant one. For imports, the relative price variable is approximately 0.35 (GETS-NLLS and JML); due to endogeneity there was no significant FMOLS result obtained.

118 V. Fiji Islands and France

Table 5.13 Estimates of long-run demand for exports from Fiji to France for 1976 - 2004 Variable GETS-NLLS FMOLS JML -0.73341 -1.9892 -1.8859 Constant [3.5252] [9.8891] [7.0337]

1.0059 1.0013 0.99223 lnY F [22.0401] [36.7035] [3.7006] F P V D -0.37306 -0.65860 -0.83408 lnG W H E PF X [4.1233] [6.2917] [3.1108] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

Table 5.14 Estimates of long-run demand for imports from France to Fiji for 1976 - 2004 Variable GETS-NLLS FMOLS JML 3.4322 0.38145 Constant [5.8885] [0.47069]

0.42689 0.85257 0.90825 lnY D [5.5761] [7.1377] [12.0445] F P V D 1.2244 0.74488* 1.1333** lnG W H E PF X [4.9764] [2.3152] [1.7490] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

For France, the results show an income elasticity of around unity and a relative price elasticity which ranges from -0.37 to -0.83 for GETS-NLLS and JML, respectively. Even though both are 1% significant, the FMOLS result with -0.658 indicates the highest t-ratio with 6.2917 which might lead to the conclusion that the relative price elasticity is closer to the FMOLS result. For imports, the relative price variable ranges from 0.7488 to 1.2244 whereas the latter one also indicates the most significant result. The income elasticity is most significant at 0.91 estimated with JML.

119 d.) Other results In some cases it was not possible to obtain results for the demand for Fijian exports due to the fact that the export relationship is too seen to be too weak. Still it was possible to obtain import demand estimates for those, since a “strong enough” import-trade relationship between Fiji and, in the following cases, Denmark and Italy exists.

VI. Fiji’s import demand for imports from Denmark

Table 5.15 Estimates of long-run demand for imports from Denmark to Fiji for 1976 - 2004 Variable GETS-NLLS FMOLS JML -1.6657** 1.3722* 1.8272** Constant [1.8364] [2.7843] [1.7654]

0.68799 0.67125 0.59805 lnY D [8.7498] [8.0565] [7.7549] F P V D 0.84236 0.84588 0.84305 lnG W H E PF X [3.3259] [2.9625] [3.2214] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

VII. Fiji’s import demand estimates for imports from Italy

Table 5.16 Estimates of long-run demand for imports from Italy to Fiji for 1976 - 2004 Variable GETS-NLLS FMOLS JML 3.4322 0.66423 Constant [5.8885] [.83126]

0.42689 0.78321 0.90825 lnY D [5.5761] [6.7169] [12.0445] F P V D 1.2244 1.1583** 1.1333** lnG W H E PF X [4.9764] [1.7552] [1.7490] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

120 GETS-NLLS gives the strongest results for the income elasticity and price elasticity for Danish products traded to Fiji with 0.842 and 0.688, respectively. All three methods report similar price elasticities, but vary in income elasticities, where JML indicates 0.598, being most significant.

For Italian goods imported to Fiji, the income elasticity ranges from only 0.427 for GETS-NLLS to 0.908 estimated using JML. The closer the result moves towards unity, the more significant it is, as the JML result shows. GETS-NLLS reports the most significant price elasticity with 1.2244, whereas FMOLS and JML report 10% significant price elasticities of 1.15 and 1.13, respectively.

e.) Fijian export / import demand for trade with Australia and New Zealand

VIII. Fiji and Australia

Table 5.17 Estimates of long-run demand for exports from Fiji to Australia for 1975 - 2006 Variable GETS-NLLS FMOLS JML -1.7239 -2.8460 -3.4684 Constant [4.0220] [12.8464] [16.5797]

1.0139 1.0159 1.0774 lnY F [33.1040] [47.3908] [5.1501] F P V D -0.10391* -0.10803** -0.094097* lnG W H E PF X [2.7897] [1.7377] [2.8965] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

The income elasticity of the demand for Fijian exports to Australia is found to be around unity. For imports of Australian products to Fiji the income elasticity is between 0.78 and unity.

121

Table 5.18 Estimates of long-run demand for imports from Australian products to Fiji for 1975 - 2006 Variable GETS-NLLS FMOLS JML 2.2546 4.0029 8.4524 Constant [3.2970] [3.1771] [3.6179]

0.87303 0.99790 0.78822* lnY D [4.6050] [4.9422] [2.5127] F P V D 0.44857 0.49123 0.54531* lnG W H E PF X [3.8154] [3.3839] [2.2114] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

The relative price elasticity for the demand for exports is approximately -0.1 for all three econometric methods used by at least 10% significance. The demand for imports indicates a relative price elasticity of 0.44 to 0.54 whereas the GETS- NLLS indicates the highest significance with a t-ratio of 3.8154 meaning 1% significance.

IX. Fiji and New Zealand

Table 5.19 Estimates of long-run demand for exports from Fiji to New Zealand for 1975 - 2006 Variable GETS-NLLS FMOLS JML 1.5511* 1.5717** 1.5453 Constant [2.4713] [1.7985] [3.9044]

0.73235* 0.73723 0.74979 lnY F [2.9245] [5.9852] [6.0006] F P V D -0.44251 -0.46705 -0.53535** lnG W H E PF X [4.1013] [4.0697] [1.7526] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

122

Table 5.20 Estimates of long-run demand for imports from New Zealand products to Fiji for 1975 - 2006 Variable GETS-NLLS FMOLS JML 2.1449* 5.2920 4.8717 Constant [2.2715] [6.2227] [17.5862]

1.1410 0.99315 0.74508* lnY D [5.9222] [10.1697] [2.6897] F P V D 0.39825** 0.31757 0.37908* lnG W H E PF X [2.1009] [3.1214] [2.7519] Notes: 1. The absolute values of the t-ratio are given in parenthesis. 2. * indicates 5% significance. 3. ** 10% significance.

The relative income elasticity for the demand for exports of Fijian products to New Zealand tends to be approximately 0.74 for all three methods used. The relative price elasticity is between -0.44 and -0.53 whereas -0.44 indicates 1% significance compared to an only 10% significance of -0.53.

For imports, the income elasticity ranges from 0.745 with JML to 1.14 with GETS-NLLS. FMOLS indicates the highest significance with a t-ratio of 10.16 an income elasticity of unity. The relative price elasticity is found to be between 0.31 and 0.40.

5.6 Conclusion of Chapter 5 Income and price elasticities play an important role for trade policy implications in terms of price and income responsiveness. Therefore, the chapter started with a theoretical discussion of the demand curve and price and income elasticities. A review on demand for trade literature for the Pacific was conducted introducing the discussion of the appropriateness of the specification of the relative price variable. In case a researcher is interested in a country’s demand for trade to imply political recommendations based on exchange rate policies, the exchange rate should be included. Otherwise, when interested in trading blocks, the exchange rate is not included. Therefore, in the estimation of the demand for

123 trade, two different approaches were used. For the demand for trade of the trading blocks of the PICs (PACPs) and Australia & New Zealand, and the EC, the “general approach” (excluding the exchange rate) was estimated. In the “case study” of Fijian demand for trade with several European countries, Australia and New Zealand, the exchange rate was included, thus the Rao and Singh (2006) approach was used for the estimations of the demand for trade regression. The demand for the PICs exports to the EC indicates an income elasticity of ca. 0.75 (using the strongest significant estimate) and a relative price variable of ca. -0.65. For imports, the relative price elasticity is estimated to be ca. 0.5 and an income elasticity 1.15. These numbers say that a 10% increase in income of the EC increases demand for exports of PACPs by approximately 7.5%. A domestic increase of income by 10% increases demand of goods from the EC by ca. 11.5%. A 10% decrease in domestic prices increases demand for exports by 6.5%, whereas a 10% decreasing imports prices increases imports demand from the EC by 5%. The results indicate that the high income elasticity implies that exports are a strong engine for growth in the Pacific but that in the case of a 10% world income increase, the PACPs would demand more imports than the EC would demand exports. Secondly, a reduction in prices by 10% would increase the demand for exports from the PACPs to the EC only marginally.

For the demand for exports between the PICs and the trading block of Australia & New Zealand, the results are similar. The FMOLS result of 1.31 as an income elasticity has to be questioned since it would indicate that goods from the PICs are considered to be luxury goods to Australia and New Zealand; therefore, the results of JML and GETS-NLLS are more appropriate. A 10 % increase in income in Australia & New Zealand would increase the demand for exports by approximately 9.3%. A domestic income increase in the same margin of percentage would increase the demand for imports by more than 10% (10.03% to 11.57%). A reduction in domestic prices (foreign prices) would increase the demand for exports by 8.7% to 11.7% (increase imports demand by 0.27% to 0.42%).

124 A comparison of both trading blocks, EC and Australia & New Zealand shows that the demand for PICs exports to Australia & New Zealand is stronger than the one to the EC, which indicates that trade with Australia & New Zealand is more welfare increasing for the PICs than trade with the EC. It further shows that European products are seen more as luxury goods. There are two possible reasons for this; first, European products are much scarcer than Australian or New Zealand products, and secondly the product varieties of Australian & New Zealand imports differs from the imports from the EC. Products from the EC are mostly technical equipment and of a mechanical nature whereas products from Australia & New Zealand are mostly foodstuff based.

The exports and imports demand results for Fiji and the EC members show that Fiji has, besides the United Kingdom, a strong trade relationship with Germany, France, Belgium, the Netherlands and also a consistent import relationship with Denmark and Italy.

As discussed in Chapter 3, more than 96% of Fiji’s total exports to the EC are directed to the United Kingdom. Even though Fiji has a continuous trade relationship with other members of the EC, it is only marginal compared to the one with the United Kingdom (UK). Therefore, demand for trade with the UK is of main interest for Fiji. The demand for exports indicates that a 10% increase in GDP in the UK leads to an 8.45% to 9.82% increase in demand for exports. Further, a price reduction (or deprecation of the Fiji Dollar) leads to a 2.78% to 3.97% increase in demand for Fijian exports.

A look at the Marshall-Lerner condition for Fijian trade shows that the condition is, with the exception of the Netherlands, satisfied to all other EC member countries with which Fiji has a strong export and import trade relationship. This means that a depreciation of the currency or domestic price reduction increases demand for exports and leads to a stronger trade balance.

125 This fact becomes untrue for Fiji’s trade relationship with Australia and New Zealand. In both cases, the sum of relative price variables in absolute terms is smaller than one, indicating that a real depreciation worsens the trade balance. Furthermore, an increase in income of Australia has a stronger impact on exports than an increase in New Zealand’s income. The opposite is true for an increase in domestic income – in that case, Fiji would demand relatively more imports from New Zealand than from Australia. This finding can be underlined by the recent development in increasing imports which shows a consistent relatively higher increase of imports from New Zealand. Figures 3.7 and 3.8 show that trade with the EC is, compared to trade with Australia and New Zealand only small, thus a devaluation would help the performance of the trade balance with the EC but worsen the one with Australia and New Zealand. A look at Tables 3.3, 3.5, and 3.6 also signify that a devaluation would have severe consequences for the total trade balance, under assumptions of the Marshall-Lerner condition.

126 6. EPA, PACER, Tariff loss and Adjustment Costs The new trade agreements, the Economic Partnership Agreements (EPA) and the Pacific Agreement on Closer Economic Relations (PACER) mean a huge change of foreign exchange policies for many Pacific Island states and therefore are an important issue for their foreign as well as their domestic policies. The EPA is the successor of the preferential trade agreements implemented in the so-called Lomé Convention between the European Community (EC) on the one side and the African, Caribbean, and Pacific nations on the other side. In 2000, the Cotonou Agreement became the legal successor of the Lomé IV Convention and offered a transition period until December 31st 2007, when the EPA was supposed to replace the preferential trade agreements of Lomé IV. In 2007 the EC recognized the need for more adjustment time for the Pacific ACPs and offered a so-called Interim Partnership Agreement (IPA) which will be replaced by a full EPA on December 31st 2008. On November 27th Papua New Guinea (PNG) and in December 2007 Fiji signed the IPA.

PACER on the other hand is a new trade framework agreement amongst the Pacific Island Forum Secretariat members (FSEC), compared to PICTA,99 including Australia and New Zealand, providing them with equal first negotiating rights. The agreement further provides for technical assistance and capacity building, in the area of trade facilitation from Australia and New Zealand to the Pacific FSEC. PACER entered into force on 3rd October 2002 after the necessary minimum of 7 FSEC members ratified it. Until today, 11 FSEC have ratified PACER.100

One of the major criticisms of the trade liberalization movement is very often based on the loss in tariff revenue and the claimed “enormous” costs of adjustment that PICs “will” face to comply with the EPAs and PACER.

99 Pacific Island Free Trade Agreement among FSEC, excluding Australia and New Zealand. 100 These are Australia, Cook Islands, Fiji, Kiribati, Nauru, New Zealand, Niue, Papua New Guinea, Samoa, Solomon Islands and Tonga.

127 This chapter has two main parts, namely the trade relationship between the PICs and the EC, and Australia and New Zealand (including the trade agreements of IPA and PACER), and the tariff revenue loss and adjustment cost discussion.

Section 6.1 starts with a historical overview of the trade relationship between the PICs and the EC (Section 6.1.1) followed by a discussion of the Cotonou Agreement (6.1.2), including the Pacific Strategy (6.1.2.1) and the general EPAs (6.1.2.2). Section 6.1.3 introduces the Interim Partnership Agreement, 6.1.4 PACER, and 6.1.5 closes the first part with conclusions and comparison of IPA and PACER.

The second part of the chapter starts with a discussion of the revenue structure of the PICs (6.2.1), the tariff loss estimates (6.2.2) and adjustment costs discussion (6.2.3). Section 6.2.4 discusses the reforms that need to be undertaken to keep the adjustment costs discussed in section 6.2.3 as low as possible. Section 6.2.5 closes with summarizing remarks on the second part and 6.3 with political recommendations and major findings of the chapter.

6.1 Economic Partnership Agreement and the Pacific Agreement on Closer Economic Relations

6.1.1 The European Union and the Pacific Island Countries – a historical overview: From Pre-Lomé to Cotonou The history of “diplomatic negotiations” between the Pacific Island Countries and the European Community (EC),101 including its trade and aid relationships, go back to 1957, when the European Economic Community (EEC) was founded and signed the European Treaty of Rome in which they expressed their solidarity with their existing and former colonies and overseas countries and territories. The expressed solidarities ended up in the first association between the EEC and the African, Caribbean and the Pacific (ACP) group in the first European

101 Representing the European Union and the former European Economic Community.

128 Development Fund (EDF) (1959) and in 1963 in the so-called Yaoundé I (1963- 1969), which was renewed in Yaoundé II (1969-1975). By that time the EEC- ACP associations were mainly focused on financial support to French-speaking Africa to build up infrastructure in the wake of decolonization. Still, the Yaoundé Association was the first big milestone on the way to the so-called Lomé Convention, which came into force in 1976. The Convention resulted from the pressure of the EC membership of the United Kingdom in 1973, which wanted to ensure special trading preferences for its former colonies beyond the existing bilateral support.

Another landmark was the establishment of the above-mentioned EDF in 1957. The EDF is the main aid instrument for development cooperation for the ACP and the overseas countries and territories group. It is funded by the EC member states and is not included in the general budget of the European Union (at least not until 2013). The Development Fund is providing community aid for development cooperation, and so finances projects or programs which contribute to economic, social and (or) cultural development. The Fund has several instruments such as grants, risk capital and loans to the private sector. Each EDF follows the cycle of the partnership agreement and is set for 5 years.

In 1975, the most important turning point for ACP-EC “diplomatic negotiations” took place with the establishment of the Lomé I Agreement, which was signed by 46 ACP countries, including the Fiji Islands, Samoa and Tonga as founding members. Papua New Guinea became a member in 1978.

The main feature of the Lomé I Convention was the non-reciprocal preference for most of the exports of the ACP Countries to the EC. This fact is very important to understand because it takes up most of today’s discussions between the EC and the PACPs. Other features of the Lomé 1 Convention were the equality between the partner countries and its respect for sovereignty, their mutual interests and their interdependence. Furthermore, each member is responsible to determine its own development strategy.

129 Beyond the agreement, separate “trading protocols” were introduced: Bananas, Sugar, and Beef and Veal. The Sugar Protocol is the most important protocol for the PACPs. Under the Sugar Protocol, the EC agreed to buy a fixed quantity of sugar yearly from ACP countries (especially Barbados, Fiji, Guyana and Mauritius) at a guaranteed price which is aligned to the EC’s internal sugar price. The EC domestic sugar price, set under the Common Agricultural Policy, is usually two to three times higher than the international sugar market price. The establishment of the quotas has been valuable to the economic development of above-mentioned countries and therefore for Fiji as the sole Pacific producer.

In 1984 when Lomé III came into being, a great change was introduced by shifting the main intention from the promotion of industrial development to self- reliant development on the basis of self-sufficiency on the one hand and food security on the other.102 This was the first major change in the development strategy of the EU and was maintained by Lomé IV (1989-1999), which will be remembered as another key turning point in the EU’s negotiations with the ACP states. Lomé IV introduced a structural adjustment program as a means towards economic growth by embarking on dialogue with the World Bank and the IMF. Furthermore, several ACP states agreed on a balance of payments support, and sectoral and general import programs were introduced that raised money for health and education projects by the sale of goods in short supply. Other significant changes included the banning of toxic waste movements between ACP states and EC members and an increase of EDF financial support for decentralized cooperation and diversification of the economies of ACP states. Article 5 of the Lomé IV Convention included human rights as an “essential” element of cooperation. Therefore, Lomé IV became the first development agreement to incorporate a human rights clause as a fundamental part of cooperation, in the sense that any violation could lead to partial or total suspension of development aid.

102 “Self sufficiency” and “food security” are being both recognized, whereas “Self sufficiency” means producing all one’s own food and “food security” means having sufficient income to be able to purchase sufficient food, etc, to live a healthy life, the food may be produced domestically or imported.

130 In the year 2000 the Cotonou Agreement became the legal successor of the Lomé IV Convention, which will be discussed in more detail in the next section.

6.1.2 The Cotonou Agreement The successor to the Lomé IV Convention, the Cotonou Agreement (CA), was signed in 2000 by 79 ACP countries. The CA has the goal of reducing and eventually eradicating poverty, while contributing to sustainable development and to the gradual integration of the ACP states into the world economy.

The CA is based on four main principles: First, the equality of partners and ownership of development strategies, which leaves the determination of economic development up to the member countries. Second, participation, inviting, additional to the central role of the governments of the member countries, other actors such as civil society and the private sector. Third, dialogue and mutual obligations, which means that member countries have to meet obligations which will be monitored through the continuing dialogue and evaluations. Fourth, differentiation and regionalization. Cooperation agreements will be set up, depending on the member’s level of development, its needs, performance, and its long-term development strategy. Least developed or vulnerable countries will be given special treatment.

The CA introduced two major reforms – in aid delivery and in trade: The first reform concerns the old aid entitlements, which were fixed allocations regardless of the countries’ performance. The EU aid allocations are now based on the assessment of a country’s needs and performance. This opens the possibility of adjustment of financial resources in regard to those assessments; which means that more money can be “channeled” to “good performers” and the share of bad performers can be reduced.

131 The second and probably most important reform in the CA is the change in trade cooperation. The CA is not a trade agreement but a framework in which objectives in terms of the development of democracy and civil society are set up and there is provision of aid through the EDF and the European Investment Bank (EIB). The agreement only makes provision for the negotiation of the new trading arrangements; known as the Economic Partnership Agreements (EPAs). These EPAs will replace the non-reciprocal trade preferences of the Lomé Agreement and will come into effect by 2008. The EPAs will be reciprocal trade agreements, with the consequence that not only the EU provides “duty free” access to its markets but that the ACP countries also provide free entry to EU companies and exports.103

6.1.2.1 The Pacific Island Countries and the Cotonou Agreement: The Pacific Strategy The Cotonou Agreement includes the Pacific Strategy as its development strategy of and for the Pacific Island Countries.

The EU Pacific Strategy consists of three dimensions: The first dimension can be called the political dimension. Herein the EU’s goals are stronger political relations on matters of common interest such as global political security, trade, and social development and the environment. The second component is the regional development dimension, which has a more focused development action, with greater emphasis on regional cooperation, enhanced regional governance. The aid dimension is the third part of the Pacific Strategy and reaches out for a more efficient aid delivery, including greater use of direct budget support and closer coordination with other partners, in particular Australia and New Zealand.

103 Based on the Cotonou Agreement 2000.

132 While taking into account that many of the PICs are small and vulnerable economies that are remote and prone to natural disasters, the EU puts concentration on sound management and protection of the environment. Since the political and economic importance has increased, not least because of a growing demand for substantial natural resources like fish, timber, minerals, oil and gas, the EU faces the challenges of state fragility and weak governance through its political dimension.

The second dimension includes three priorities: governance, regionalism and sustainable management of natural resources. With regard to regionalism, the Pacific Island Pacific Island Forum Secretariat (FSEC), the main regional institution for political issues, is recognized as the official counterpart.

The EU will also provide help in prevention of conflict and in post-conflict situations to establish good governance by strengthening credible institutions, institutions that become very important for a sustainable development strategy. The EU also encourages the fight against corruption, money laundering and terrorist financing.

In their second and third dimensions of the Pacific Strategy, the EU points out that the regional integration is crucial for effective development and aid delivery. The EU sees a regional integration and regional development dimension as a more efficient use of small-country allocations and greater use of budget support, donor coordination to avoid overlap or inconsistency between those seeking to achieve common goals. The so-called Economic Partnership Agreement becomes of major importance since it is seen as having a “catalytic” effect on economic cooperation and integration in the Pacific region as it stimulates the PACP countries to negotiate as a group, open up to each other and improve regional governance. Moreover, the EPA negotiations are closely coordinated with the programming and implementation of the development assistance provided by the European Union; assistance in managing the ocean and coastal resources in a

133 sustainable manner through initiatives that could combine the use and conservation of fisheries and marine biodiversity.

The next section takes a closer look at the Economic Partnership Agreements as based on Cotonou 2000. Later, the Interim Partnership Agreement 2008 signed by Fiji and PNG will be discussed in detail.

6.1.2.2 The Economic Partnership Agreement (EPA) An EPA, in the view of the European Union, is an instrument to achieve “the objectives that have not been met in the past”. But what are those objectives and how does the EU want to achieve them?

The EU is concerned at not having prevented the marginalization of the ACP countries in world trade, whereby the ACP group’s share in world exports fell from 3.4 % to just 1.1 %.104 Furthermore, it did not prevent the share of the ACP members in total EC imports declining constantly nor the ACP members’ share in total EC FDI from falling even lower.

Because of those reasons the EC/EU moved away from the Lomé objective of economic and trade co-operation “to promote trade between the ACP States and the Community” as it was defined in Art. 167 (1) in the 4th Lomé Convention. “To foster the smooth and gradual integration of the ACP States into the world economy thereby promoting their sustainable development and contributing to poverty eradication in the ACP countries” (Art. 34 (1) Cotonou Agreement) is the new definition of the Cotonou Agreement.

The EU also moved away from the primary aim of Art. 167 (2) of Lomé IV, “securing effective additional advantages for ACP States’ trade with the Community, and (at) improving the conditions of access for their products to the markets”.

104 Based on Cotonou 2000.

134 The CA defines therefore in Art. 34 (2) and (3) “enhancing the production, supply and training capacity of the ACP countries as well as their capacity to attract investment” and “creating a new trading dynamic between the Parties” with a view to “facilitate (their) transition to the liberalized global economy”.

The EPA as an instrument shall meet these objectives by “removing progressively all barriers to trade between (the Parties) and enhancing cooperation in all areas relevant to trade” (Art. 30 (1) Cotonou Agreement). This means that an EPA consolidates existing regional integration initiatives but also leads to the creation of largely integrated economic areas between the ACP and EU with the goal of free movement of goods and services. The integration will be governed by a stable, predictable and transparent framework and enlarge the markets of the ACP members themselves. Furthermore, the EPAs allow for economies of scale, to improve the level of specialization to increase competitiveness of ACPs and attract investment. In the eyes of the European Union, the EPA will lead to an increase in trade flows in the regions of the EPA, with the European Union and with the rest of the world. That implies that the EPA will ensure the promoting of sustainable economic and social development of the ACP members. But the European Union also makes clear that this will only happen if appropriate policies are implemented by the ACPs.

The European Union sees EPAs also as an objective, which can only be achieved if all barriers to trade are removed. Therefore a free trade area as the primary block for the achievement of this objective must be accomplished and progressively all tariffs between the parties as well as all non-tariff measures, such as quotas and measures having equivalent effect, have to be substantially abolished. This implies a reciprocal trade agreement and means the move away from the non-reciprocal trade agreements of the Lomé Convention. Thus, this includes the simplification of all requirements and procedures related to imports and exports, based on the highest international standards. Further on, free trade should not only be extended to trade in goods but also in trade in services to completely capture the welfare effects of free trade.

135 6.1.3 The Interim Partnership Agreement between certain Pacific ACP countries and the European Commission The Interim Partnership Agreement (IPA) is titled as an agreement between certain Pacific States and the European Community and is based on fundamental principles of the CA,105 acts as a provisional agreement until the EPA negotiations have been accomplished by the end of 2008, when the final or "full EPA” shall replace it.106

Compared to the assumptions made in former EPA negotiations between the EC and the PACPs, the IPA is a goods only agreement, thus excluding services. The agreement comprises five Parts and three Annexes. The Annexes are very important because they differentiate the IPAs of the different PACPs from each other. Unfortunately up to this date, there are no detailed information on the Annexes for the Fijian and PNG IPAs available.

The first part is titled “Trade Partnership for Sustainable Development” and introduces the IPA as enabling the PACPs to benefit from improved market access offered by the EC. The conclusion of this agreement is seen as to avoid trade disturbances due to the expiry of the trade preferences of Lomé IV and to promote the gradual integration of the PACPs into the world economy by establishing a free trade area between the PICs and the EC. Further, the first part introduces the importance of the IPA concluding progressive trade liberalization to comply with WTO rules and the principles of asymmetry, common interests to special needs, to capacity constraints, as well as international based institutional and dispute settlement arrangements. Part 3 of the agreement implements the just mentioned “Dispute Avoidance and Settlement” arrangement based on international standards (WTO and IMF) and is ruled by PART IV installed “Institutional Provisions” and with Article 68 established so-called “Trade Committee” (TC), represented by all members and co chaired by one EC and one PICs representative.

105 Article 30 (1) Cotonou Agreement. 106 Interim Partnership Agreement, Part IV: General and final provision. Conclude final EPA negotiations by 31st December 2008.

136 Centered on the human person who is the “main beneficence” of development, sustainable development, Article 3, is one of the major statements of the first part of the agreement, seen as an integral part of the provision, with the goal of reducing and eventually eradicating poverty in a consistent way of sustainable development, by accepting human, social, health and environmental best interest of today’s and the future generation.

Regional integration shall be an instrument to fulfill the goal of sustainable development; thus, the agreement is seen as deepening the regional integration on the one hand and not to prevent any member from engaging into other agreements (e.g. PACER or PICTA). On the other hand, close work with existing regional organizations and programs whenever useful to implement the agreement is seen as an important step towards the goal of sustainability.

The second part, “Trade in Goods” defines the agreement on goods. Chapter one of this part discusses the elimination of tariffs, charges and other fees. Article 9 states that the goods are classified in the Harmonized Commodity Description and Coding System (HS), thus products originated in EC and PACPs falling within chapters 1-97, which determines the full HS are included in the trade agreement. Exceptions are made and addressed in the annex of each of the separate IPAs.

In the period following the entry of this agreement, the WTO’s rules of origin are to be reformed to be simpler in concept and method in light of the purpose of development of the PACPs and to give certainty for investors and the development of technologies and production processes. Article 10 assures the elimination of export duties, taxes, fees, or “other fees and charges imposed on or in connection with the exportation of goods to the other Party” as well as “any other internal taxes, fees and charges on” export goods. The agreement allows exception, e.g. to ensure fiscal solvency of the PACPs, to protect the environment, or to simply justify the specific protection of the development of infant industries. Here the Article makes clear that the export

137 taxes are to be temporary, based on most favored nation (MFN)107 status, and only admitted on a limited number of products to EC after mutual agreement with the EC for a limited period of time. The customs duties on products from the EC to the PACPs and vice versa are to be eliminated, if not said so otherwise in Annex I. and II.108

Article 11 discusses customs duties on products originated in PICs. These shall be free of any duties, if not said otherwise in Annex I. One exception is Chapter 93 (heading 1006 [rice]) where the MFN temporary customs and taxes are imposed. Customs on Chapter 93 of HS classification will be eliminated by January 1st 2010.109 Further, Article 11 discusses the important Protocol 3 – the so-called sugar protocol which remains applicable until September 30th 2009. After that, the sugar protocol will totally expire.110 Until its expiry the guaranteed price, provided for in Article 5(4), the EC offers to assist the countries benefiting from the sugar protocol with EDF adjustment payments.

Customs duties on products originated in EC party are discussed in Article 12. Annex II. gives the details for reduction or elimination on duties and the time frame for doing so. As mentioned above, no information for the Annexes are available as of today.

Article 13 states that the PACPs can address the under this agreement established TC, to review the specific time frames which are discussed in Annex II, when the Pacific Countries feel that they would be facing serious problems and/or difficulties with imports of EC products, according to Annex II. Under any circumstances, the regulations made after the review of the TC have to be in accordance with Art. XXIV GATT 1994.111 There will be no new duties imposed from the moment this agreement enters into force (see Article 14), and Article 16 includes a non-discrimination clause that prevents discrimination of any other

107 See Section 2.3.1 c.). 108 See Article 11 and 12. 109 Subheading 1006 10 10 are to be set free of any duties immediately. 110 Customs duties on sugar - Heading 1701. 111 Appendix D. offers a short description of Art. XXIV GATT 1994.

138 member or third party, thus preventing discrimination of domestic and international goods.

In the second chapter, “Trade defense instruments” are discussed. These range from anti-dumping and countervailing measurements, which are based on WTO rules on anti-dumping and non-preferential rules of origin, whether the member of the IPA is a member of the WTO or not, over multilateral safeguards, which states that nothing may interfere with adopting GATT 1994 Art XIX,112 Agreement on Safeguards, and Article 5 of the Agreement on Agriculture.113 In relation to the above, the EU may consider excluding imports of agriculture products in references to Article 5 of the Agriculture Agreement by considering the small economies of the PICs, as long as these are WTO compatible, to bilateral safeguards.

“Non-Tariff Measures”, the topic of chapter three of Part II, state that all limitations if not said otherwise shall be eliminated immediately. This includes quotas, import and export licenses, and other measures. Further, no new measures are to be introduced (except customs and other duties). The chapter also states that there are no internal taxation etc. charges and fees allowed to protect domestic products - no less favorable treatment is allowed for imported products over domestic ones either, thus same treatment for imported products as for domestic ones and no discrimination in one way or another, including no internal quantity restrictions. This does not apply to subsidies etc. which may still be imposed; thus the agreement does not interfere with domestic subsidies policies to “help” the domestic industries.

Article 24 “Agriculture export subsidies” states that the EC commits themselves to put no more subsidies on agriculture exports (products) to PICs, if the PICs promise to erase duties on that agriculture product (under WTO-compatible, see Annex I of WTO Agreement on Agriculture).

112 See Appendix D. for more details on GATT 1994 Art XIX. 113 Article 5 of the Agreement on Agriculture is explained in Appendix D.

139 Chapter four calls on customs and trade facilitation and is meant to assist the Pacific states to integrate into the international economy. The goal is to be achieved by reinforcing cooperation on customs issues, due to ensuring the relevance of legislative procedures, the administrative capacity, enable effective and efficient administration of customs. Further, the importance of the relationship with other existing programs and assistance to endeavor and coordinate trade facilitation and promotion with other actors is pointed out. This includes national, regional and international bodies and organizations with the goal to avoid duplication and to maximize the benefits from efficient resource allocation. To promote the achievement in the development of establishment and implementation of international agreements on harmonizing standards and procedures and/or the establishment of new regional organizations and/or further participation of others in the trade facilitation and trade promotion programs, several instruments can be used. For example, sharing information among the above mentioned and utilization of existing resources as well as cooperation.114 The harmonization of customs standards at regional level is to be established in a common language in terms of legislation, procedures, requirements, and is to be put in line with international standards to help to improve and promote the PACPs integration into the international economy.115

“The EC Party and the Pacific States agree that their respective customs legislation, provisions and procedures shall draw upon the international instruments and standards applicable in the field of customs and trade, including the substantive elements of the revised Kyoto Convention on the Simplification and Harmonization of Customs Procedures, the WCO Framework of Standards to Secure and Facilitate Global Trade, the WCO data set and the Convention on the Harmonized Commodity Description and Coding System.”116

114 See Article 25, 26 and 27. 115 Article 31 Interim Partnership Agreement. 116 Article 28 IPA.

140 Relationship of customs and business community is to be determined by total transparency to maximize the benefits and to minimize the costs of trade.117 Article 30, customs valuation apply GATT Art. VII.118 WTO members need to apply as soon as agreement enters into force, whereas non-WTO members have to apply and implement the above within 5 years.

Chapter five – “Technical barriers to trade and sanitary and phytosanitary measures” is based on the WTO Agreement on Technical Barriers to trade and the WTO Agreement on Sanitary and Phytosanitary (SPS).119 Fully appliance of WTO rules, whether the country is a member of the WTO or not, is required. The EC hereby takes full account of capacity constraints in the short term of non- WTO member countries to comply.120 The main goal is to facilitate and increase trade in goods by identifying, preventing and eliminating obstacles to trade arising by the above, and to cooperate facilitation compliance applying to exports. Safeguarding human, animal and plant safety and health, by capacity building of public and private sector, as well as regional integration and promotion of public and private sector to comply with the agreements are of major importance. The priority products are defined in the Annexes.

Article 40 can be called the transparency article since it assures total transparency between the members of the agreement as well as its business cycles and others due to the exchange of information on a regular basis (if not said otherwise in Chapter six).121 The progress of the implementation of the agreement itself as well as the different parts (partial progress) is going to be monitored and reviewed on a constant basis (e.g. to assure that the implementation of Chapter four takes no more than 3 years after the ratification).122

117 Article 29 IPA. 118 See Appendix D. for more details on GATT 1994 Art. VII. 119 See Section 2.3.1.a.). 120 Article 36 Nr.3 IPA. 121 In combination with Article 26 IPA. 122 Article 31 IPA.

141 Chapter six includes exceptions, with Article 42, a “General Exception Clause”: the necessity to protect public safety and public morals or public order, to protect human, animal or plant life, or to secure compliances with laws and regulations not being inconsistent with provisions of the agreement. This includes privacy protection of individuals, safety, customs enforcements, the protection of intellectual property rights. Other exception are whenever related to import/export of gold and silver, the protection of natural artistic, historic or archeological value, the conservation of exhaustible natural resources (e.g. domestic restrictions on production or consumption of goods), domestic supply or consumption of services and on domestic investors. The exception clause is further valid if the reason is inconsistent with Art. 23 on “National Treatment” provided that difference in treatment is and to ensure effective or equitable imposition or collection of direct tax in respect of economic activities, investors or service suppliers of EC or PICs. Article 43 further gives security exceptions in terms of national security. No information has to be released, whenever essential security interests occur (e.g. in times of war, state of emergency), the agreement may rest, as long as the TC is informed immediately. The agreement is built not to interfere with the fiscal legislation of the members. Article 45 complies with the fear of most of the PACPs, “Balance of Payments (BoP) difficulties” and states that if serious BoP problems occur, the PACP state may increase or impose tariff for the minimum period necessary to prevent serious decline in reserves / to protect fiscal position. These have to be reported to the TC123 under full transparency to other member countries, business cycle etc. in consideration with Article 40. The TC needs to agree to the measurements undertaken which have to be consistent with WTO rules and IMF obligations, including non-discrimination. Furthermore, it has to be assured that the measurements avoid unnecessary damage to commercial / economic interest of any other member country.

In terms of “Food Security”, “The Parties acknowledge that the removal of barriers to trade between the Parties, as envisaged in this Agreement, may pose significant challenges to producers in the agricultural and food sectors and agree

123 Immediately, including a time frame of the imposed measurements, consultation on IMF based, including and accepting IMF-statistics.

142 to consult with each other on these issues”, and “where compliance with the provisions of this Agreement leads to problems with the availability of, or access to, foodstuffs or other products essential to ensure food security of a Party or Pacific State and where this situation gives rise or is likely to give rise to major difficulties for such a Party or Pacific State, that Party or Pacific State may take appropriate measures in accordance with the procedures laid down in Article 21, Paragraph 2c.”

6.1.4 Pacific Agreement on Closer Economic Relations PACER is a framework agreement among the Pacific Island Forum Secretariat (FSEC) member countries including Australia and New Zealand, provided with equal first negotiating rights with the Pacific FSEC members. PACER is made up by 5 parts and one annex on the topic of trade facilitation.

The first part – definitions and objectives – characterizes PACER as an establishment of a framework of free trade that gradually integrates the Pacific Island member countries of FSEC into world trade and global economic markets. The objective of the agreement is the development of a single regional market with effective trade arrangements to increase trade and thus economic opportunities and competitiveness. The agreement shall further minimize disruptive effects and adjustment costs by providing technical and economic assistance for the integration of the FSEC into the world market. Thus, the implementation of trade liberalization and economic integration, by securing benefits from trade liberalization, based on the WTO framework. Part two of the agreement is headed by “Pacific Economic Integration Initiative”. Article 3 states that the gradual and progressive integration of the trade agreement shall fulfill the needs of sustainable development. The free trade area being established in this agreement should facilitate trade and not raise any barriers between the FSEC by recognizing the special needs of the Least Developed Countries (LDC) and offering them a different structure and time frame which are to be discussed in separate country specific agreements.

143 The article further discusses that the agreement is not meant to be a free trade area to be notifiable under GATT Article XXIV, neither an agreement to be notifiable under GATS Article 5 and shall not interfere with other existing agreements, obligations or treaties. Article 4 points out that the FSEC members must have the desire to achieve trade liberalization and economic integration among themselves first, which is supposed to be undertaken by the PICTA agreement. 8 years after PICTA has entered into force, unless earlier agreed as part of the general review or otherwise triggered by Article 6, which will be discussed shortly, the parties (FSEC and Australia and New Zealand) will establish a reciprocal free trade arrangement among each other which may be different from PICTA. Until that day when the agreement enters into force, Australia and New Zealand will maintain all existing arrangements (e.g. market access) or may conclude new arrangements as long as they provide equal or better market access.

Article 6 defines the “free trade arrangement” to be consistent of GATT Article XXIV:8 and point 3 a) states that if an FSEC member commences formal negotiations for free trade arrangements which would include at least one non- forum developed country, the FSEC member should offer to undertake consultations with Australia and New Zealand as soon as practicable to establish free trade arrangements.

The same is to be offered from the moment that Australia and/or New Zealand undertakes negotiations for free trade arrangements with any other non-FSEC member; thus Australia and New Zealand have to offer FSEC negotiations e.g. to provide advanced market access if Australia and New Zealand should offer better market access than the status quo to any other non FSEC member.

The establishment of PICTA is excluded from the trigger, as long as rules of origin do not discriminate Australia, New Zealand or any other developed nations or between any FSEC members who wish to undertake free trade arrangements with Australia and New Zealand.

144 The second part of the agreement also points out that trade negotiations are independent of other aspects of the relationship between the parties, including the provision of technical assistance provided under this agreement.

Further, it is discussed that Australia and New Zealand are forced not to give any less favorable treatment to FSEC than they give to any other country. Australia and New Zealand recognize the different levels of development among the FSEC for assistance under the principles of non-discriminatory measurements. Exemptions are to be made for special treatment of WTO and UN recognized LDC and small island states. The parties also acknowledge the importance of gradual tariff reduction as part of their overall trade strategy and set a periodical review of tariff schedules as unilateral reduction in tariffs.

Part three of the agreement discusses “Trade Facilitation and Economic and Technical Assistance” of the PACER agreement. The Parties agree that the development of appropriate, efficient and transparent trade facilitation will maximize the effectiveness and benefits of trade liberalization; therefore the parties establish a detailed program for the development, establishment and implementation of trade facilitation measurements in accordance to Annex I, which has to be consistent with international standards of the WTO.

Article 10 states, that “each Party which is a member of the WTO undertakes to provide no less favorable treatment in relation to sanitary and phytosanitary matters, customs procedures, and standards and conformance to all other parties than it is required to provide to other members of the WTO by virtue of its membership in that organization.”

In terms of financial and technical assistance, mutual assistance in the integration in the international trade and economic framework while following WTO standards shall be provided. Further, Australian and New Zealand pledge to continue to support FSEC to become active members of international trade and

145 economic organizations by developing necessary capacity to negotiate, to participate effectively in, and monitor and implement PACER itself.

In part the “General Provisions” of PACER, Article 13, states that PACER shall not affect any other agreement or be exempt from other obligations and should not prevent the FSEC to enter any other agreements, as long as it is consistent with this agreement itself.

The integration of trade facilitation into the regional framework should include the Oceania Customs Organization and the Pacific Plant Protection Organization. The goal of trade facilitation cooperation among the regional and international organization is to avoid unnecessary duplication of existing programs and to maximize the resources and thus the benefits by sharing information, utilization of experiences and resources, cooperation with international and regional bodies and organizations itself as well as to achieve harmonization.

6.1.5 Summary of Section 6.1 In the first part of this section, the history of the trade relationship between the Pacific ACPs and the European Community is summarized, including the major changes of the movement from the Lomé Convention to the Cotonou Agreement. The two major changes are to be found in the aid delivery and trade. The aid delivery is based on the assessment of a country’s needs and performance and can be adjusted in regards to those assessments. Since the CA is not a trade agreement, the Economic Partnership Agreements are to replace the non- preferential trading agreements installed under Lomé agreement, where the Pacific Strategy and the general EPAs are explained further.

The legal texts of the Interim Partnership Agreement and the Pacific Agreement on Closer Economic Relations are the topics in the second part of this section.

146 Both agreements offer/ask for gradual/progressive integration of the PICs into the world economy by removing tariff and also non-tariff barriers to trade based on sustainable development, following the Brundtland reports definition that sustainable development "meets the needs of the present without compromising the ability of future generations to meet their own needs”.

To achieve the integration of the PICs into the world economy, the agreements have different strategies. PACER wants to achieve the goal by installing a regional trade agreement among the PICs first (PICTA), whereas the EU wants a complete liberalization among all ACP countries and the EC which includes the PICs thus would include PICTA in terms that an EPA among all PACPs is equal to a regional agreement.

Article 6 (3) a of PACER, the so-called “Trigger-Article” “overruns” the PICTA agreement by “forcing” this PIC into direct negotiations with Australia and New Zealand should the country start trade negotiations with another developed country besides Australia and New Zealand. Further, should this PIC sign a trade agreement, then the PIC has to offer Australia and New Zealand the same deal, should the agreement offer better in example market access than the status quo does.

Thus, a completion and ratification of an IPA/EPA does not just mean a trading agreement between the PACPs and the EC but also in the end between the PACPs and Australia and New Zealand (depending on the PACPs ratifying the IPA/EPA). Therefore, one can blame the EC for triggering negotiations with Australia and New Zealand by “forcing” PACPs, which depend on trade with the EU such as Fiji and PNG to sign an IPA but on the other hand it only accelerates the trade negotiations with Australia and New Zealand from originally year 2012 to 2008. With respect to a welfare perspective, the sooner the PICs start trade liberalization negotiations, the better it will be. Since it is a gradual and not abrupt liberalization, going beyond simple tariff elimination, it offers a lot to overcome the basic and most common obstacles to trade. For example, technical

147 barriers and other non-tariff barriers to trade. Further, it is a gradual integration into the WTO and international standards, offering support in form of aid and technical assistance by the EC (EDF), Australia and New Zealand.

Besides the gradual tariff elimination, trade facilitation is one of the main objectives of both agreements. The facilitation of trade shall be achieved by addressing the above mentioned non-tariff barriers to trade. Harmonization plays a key role - by harmonizing the customs classification form several different classification systems used in the Pacific to the HS definitely means an improvement in trade of goods since it reduces adjustment time and costs to re- classify from one coding system to another. Several PICs have already adopted the HS; therefore the countries that have not signed the IPA and might not do so in future times should still go forward to change their customs classification to the international HS system, since it increases trade efficiency.

An increase in trade efficiency and standardization and harmonization go hand in hand as long as the customs authorities have the capacity to assure the ability of the usage of a “harmonized system”. Therefore, both agreements offer support in capacity building in the form of technical assistance and aid. Furthermore, the capacity building process as well as the general trade facilitation shall be done under terms of maximizing resource allocation, avoidance of duplication and resource utilization and thus, should include the work of national, regional and international bodies and organizations. This means the inclusion of the FSEC which is already recognized by Australia and New Zealand as the “regional body” but also by the EC/EU due to its Pacific Strategy as its official counterpart. Thus, the “inclusion” of the FSEC also means an inclusion of the Regional Trade Facilitation Program which is coordinated and managed by the FSEC, where the customs and quarantine components are being delivered by the Oceanic Customs Organization and the Secretary of the Pacific Community. The World Health Organization delivers and coordinates the Food Standard Project. For resource allocation purposes, the EU, Australia and New Zealand state to work with all of the above to assure that the scarce resources are used efficiently. On the other

148 hand, this means a boost in recognition and resources for the above mentioned and by the FSEC managed and organized projects.

The last point mentioned is one of the most important issues in the agreement. The SPS measurements are included and have the purpose of ensuring that imports of products and commodities from country A to country B follow the international standards on food safety. Additionally, they assure that the SPS measurements are not used as an option to create an environment of protectionism and thus that it does not create trade barriers. The inclusion of the SPS measurements in the agreement offers a great chance for further market access of PICs’ products to the EU, Australia and New Zealand as well as among all PICs. Due to its international WTO based standards, it also offers easier access to all markets of WTO and ACP members and therefore gives an option for trade increase.

One major difference between the PACER framework and the IPA is that the EC offers some exceptions and recognizes the possibility of a BoP crisis. BoP difficulties are antagonizes with the statement that many globalization opponents and NGOs use that the EPA causes a fiscal disaster. Australia and New Zealand should see the exception clause of the IPA as a model and address this article in the PACER Plus negotiations to overcome the fear of “huge” fiscal difficulties of the PICs.

The exceptions as well as the time period of the IPA are discussed in the appendixes of the agreements. Unfortunately, these were not available up to date. In a March 2008 newsletter the EC states that it will allow a liberalization of goods of 88% for PNG by value and 80% for Fiji over a time period of 15 years. It will cover 100% of EU tariff lines, 82% and 80% for PNG and Fiji, respectively. It further states that certain agriculture, forestry products and non agricultural processed goods are excluded in order to protect infant industries and to maintain fiscal revenues. Detailed information on these are not available up to date.

149 6.2 Tariff Revenue loss and adjustment costs

6.2.1 Revenue Structure From the beginning of trade negotiations of EPAs and PACER, the topic of adjustment costs has been enormous. A simple look at the PICs revenue structure shows why. Therefore, before discussing the costs of adjustment towards the agreements, it is necessary to discuss the basic revenue structure of the PICs. The following table (Table 6.1) gives the ratio of total revenue to GDP and the customs revenue to total revenue GDP.124 Furthermore, the value added tax (VAT),125 if in place, is reported as well as its share of total revenue.

The results indicate that the revenue to GDP ratio is extremely high in Kiribati, Niue and Tuvalu where the ratio tops 90%. The lowest ratio was derived for the Marshall Islands. This can easily be explained by the fact that aid and grants are excluded - making up one of the biggest contributors of total revenue with more than 66% to GDP.126

But even more interesting and important for the topic of trade liberalization is the fact that in many countries of the Pacific, the customs to total revenue still makes up more than 20%, in some cases nearly or even more than 30% (e.g. RMI and Vanuatu). The Table also shows that in countries such as Fiji, Nauru, Palau and PNG the ratio of customs to total revenue is lower than 8% and just 2.1 % in Fiji, which indicates a much broader diversification of the revenue structure and weakening dependency on customs revenues. This fact is underlined by the average customs duty which is, not surprisingly, lower in those countries where the customs to total revenue ratio is low. Furthermore, the Table shows that some countries have already adopted the value added tax (VAT) which contributes a lot to the overall revenue (PNG excluded) and offers an option of diversification away from customs revenue dependency.

124 The total revenues of the PICs excludes aid and grants. 125 Summarizing all sales taxes under VAT, e.g. Tonga’s consumption tax. 126 Asian Development Bank’s Key Indicators 2007.

150 Table 6.1 Total revenue to GDP and customs to total revenue ratios in % (2006)

Total Average Customs to VAT to revenue to customs VAT total revenue total revenue GDP ratio duty ratio ratio Cook Islands 31.8 7 12.5 20.0 42.00 Fiji Islands 24.2 3 12.5 2.1 33.23 FSM 11.2 7 - 15.8 - Kiribati 91.0 20 - 19.4 - Nauru 29.2 6 - 6.3 - Niue 90.0 19 - 21.4 - Palau 27.6 3 - 6.7 - PNG 23.1 1.2 – 1.6 10.0 7.6 5.00 RMI 23.7 9 - 29.7 - Samoa 26.4 7 12.5 n.a. n.a. Solomon Isl. 47.0 10 - 7.6 - Timor Leste n.a. n.a. - n.a. - Tonga 38.0 15 15.0 n.a. n.a. Tuvalu 96.0 15 - 27.6 - Vanuatu 20.0 15 12.5 31.0 35.16

Notes: 1. Tonga: 0-30% rest tariffs, cars 45%, petroleum 35% 2. FSM: food 3%, general 4%, others much higher 3. Kiribati: food 5%, others 8%, cars, alcohol and tobacco much higher 4. Palau: general rate 3%, food stuff, medical equipment and medicine 0% 5. PNG: 75% of all imports incl. consumable goods 0%, rest 5%, 15%, 40%, sugar 75% on temp. basis until 2011, canned mackerel from 100% to 40%, luxury goods from 200% in 1998 to 40% 6. RMI: food 5% flat rate, others 8%, variable higher rates on tobacco, alcohol and fuel 7. Samoa: import duties are declining gradually 8. Solomon Islands: 10% is top 9. Tonga: now 15% was 20% (fell in 2007) 10. Vanuatu: VAT replaced the turnover tax, tariffs used to be 20%, changed to 15% in 2000 11. Samoa, Fiji and Vanuatu have already the 6 digit HS system for customs classifications. 12. The Solomon Islands are using the HS 2002 system and want to change to 6 digit HS system. 13. Numbers given in %.

Source (s): IMF, WDI, ADB KI, UNSD, Country Statistics, Watergall (2007).

151 6.2.2 Tariff revenue loss The potential loss in tariff revenue caused by the implementing of the IPAs/EPAs and PACER has been subject of a few studies. Narsey (2003) and Filmer and Lawson (1999) measure the cumulative revenue losses from potential free trade agreements, including EPAs and PACER. Scollay (1998) estimated tariff revenue losses from free trade agreements including PACER. In a recent study Watergall (2007), the potential losses of revenue as percentage of total tax revenue from free trade agreements including EPAs and PACER are estimated.

Table 6.2 summarizes the findings of Scollay (1998), Filmer and Lawson (1999) and Narsey (2003). The three studies show partially significant different results (e.g. PACER’s impact on Fiji) but also agree on the impact of the EPAs. Filmer and Lawson (1999) and Narsey (2003) mostly agree that an EPA’s influence on

Table 6.2 Tariff revenue losses – summary of three reports

Scollay (1998) Filmer & Lawson (1999) Narsey (2003) EPA PACE EPA PACER EPA PACER R Cook Islands --- 46 2 22 n.a. n.a. Fiji Islands --- 66 1 15 1 52 FSM --- 5 0 1 n.a. n.a. Kiribati --- 58 14 52 13 82 Nauru --- 75 2 95 n.a. n.a. Niue --- 29 0 38 0 98 Palau --- 18 0 0 n.a. n.a. PNG --- 52 0 7 n.a. n.a. RMI --- 7 0 2 0 8 Samoa --- 72 3 17 14 67 Solomon Isl. --- 48 0 7 n.a. n.a. Tonga --- 67 3 44 n.a. n.a. Tuvalu --- n.a. 17 50 n.a. n.a. Vanuatu --- 41 9 32 13 73

Source / 1. Scollay (1998) reports the loss of tariff revenue in % of total tariff revenue. Notes: 2. Narsey (2003) calculated the loss in % of the total cumulative tariff revenue. 3. Filmer and Lawson (1999) report the percentage loss of total cumulative revenue.

152 tariff revenue loss will be marginal. Two exemptions are made, namely Kiribati and Vanuatu where tariff revenue loss is considered to be relatively high.127 For PACER, there is no obvious consensus. For example for Fiji, Filmer and Lawson (1999) predict a 15 % tariff revenue loss, whereas Narsey estimated 52% and Scollay even 66%. Samoa and Vanuatu show similar inconclusive results. On the one hand, one reason is simply that the studies were undertaken for different time periods, another one that different assumptions and methods were used, so the measurements of tariff revenue loss rest on three different bases: in percentage of total tariff revenue (Scollay), in percentage of total cumulative tariff revenue (Narsey), and in percentage of total cumulative revenue (Filmer and Lawson). On the other hand, this does not explain why Narsey and Filmer and Lawson agree on the EPAs but not on PACER. For Kiribati, Scollay reports for the PACER agreement, a revenue loss of 58%. Filmer and Lawson find the loss to be 52%, thus not too far away from Scollay’s estimates, even though these rest on different base measurements. For Kiribati, Narsey finds the loss at even 82%. Thus, it points out that the different measurements report different results. But interestingly, the results are similar when it comes to the outcome of the EPAs. This indicates that the results of the three studies have to be questioned and cannot be taken too seriously. Further, it shows that there is need for additional research on the topic of tariff revenue loss. Still, what the three studies indicate is that the loss in tariff revenue will be more significant for PACER than it will be for the EPAs, which can be justified by the findings of Chapter 3: trade between Australia & New Zealand and the PICs is much more intense than the trade flow of the EC and the PICs.

Table 6.3 gives the results of the recent Watergall (2007) study. Watergall (2007) finds that there will be a revenue loss in the short term. The investigations show a worst case scenario of 10 years until the countries regain the pre-liberalization tax revenue to GDP ratios.

127 Narsey further indicates Samoa with 14 % loss of total cumulative tariff revenue loss.

153 Table 6.3 Total tariff losses as percent of total revenue – three tariff lines

EPA PACER Tariff line 100% 80% 67% 100% 80% 67% Cook Islands 0 0 0 6 6 5 Fiji Islands 0 0 0 2 2 2 FSM 0 0 0 1 0 0 Kiribati 0 0 0 13 12 9 Nauru 0 0 0 5 n.a. n.a. Niue 0 0 0 5 4 4 Palau 0 0 0 0 0 0 PNG 0 0 0 1 1 1 RMI 0 0 0 5 n.a. n.a. Samoa 0 0 0 9 9 9 Solomon Is. 0 0 0 3 2 2 Tonga 0 0 0 15 10 10 Tuvalu 0 0 0 4 3 3 Vanuatu 1 1 1 17 15 11

Notes: 1. The 100%, 87% and 67% scenarios represent revenue losses assuming substantially all trade means either 100% of all tariff lines or 80% and 67% by volume. 2. Assumptions: no change in trade flows, no externalities, oligopolistic business environment.

Source: Watergall (2007)

The table shows the losses for three different scenarios – for a 100%, 80% and 67% liberalization of the tariff lines by volume.

Impressively it shows that there are almost no tariff losses to be expected from trade liberalization with the EC and even though a 17% reduction or loss of tariffs revenue in Vanuatu seems high, it is relatively low compared to 32% estimated by Filmer and Lawson (1999) using the same base of measurement.128 The Watergall estimates for Fiji, Nauru, Kiribati and Tonga (to just mention some) show similar declining results. Thus, this shows that many PICs already have begun to adjust their fiscal systems towards trade liberalization by diversifying their revenue system away from tariff revenue dependency.

128 Even though Filmer and Lawson (1999) measure the loss in percentage of total cumulative revenue.

154 6.2.3 Adjustment Costs Milner (2005) for the Commonwealth Secretariat and the European Research Office, and Smith (2006) regional adviser for the United Nations Economic and Social Commission for Asia and the Pacific, Pacific Operations Centre (UN- EPOC) estimated the potential total adjustment costs for EPAs.

The so-called Milner report disaggregated the adjustment costs caused by a goods-EPA (excluding services of the non-government sector) for the ACP countries by fiscal adjustment, trade facilitation and export diversification, employment adjustment and by skills and productivity enhancement.

One of the major problems faced is measuring the costs of the implementation of an EPA due to the lack of knowledge of the final structure of the tariff reduction and the nature and scope of the financial instruments which vary widely. This includes the extent of product coverage, the time frame for implementation, and the handling of sensitive products, and not at least the technical and financial assistance offered by the European Development Fund (EDF). The Milner report takes all of these uncertainties into account, to fully assess the actual impact of an EPA, but also states that “no matter what” the final EPA will look like, how much adjustment time it will provide, what the tariff structure will look like (e.g. tariff elimination 90% of all imports or 60% over a time frame of less or more than 10 years), the economic challenges associated with implementing an EPA will be enormous.

The report further points out that the less the time and the higher the tariff reduction, the higher will be the economic challenges for the PACPs because of lesser time for restructuring the government revenue collection away from import dependent customs duties and import taxes.

Due to those uncertainties, the Commonwealth Secretariat has developed an alternative approach measuring the “costs of EPAs” by focusing “on the cost of

155 implementing remedial measures designed to minimize the adjustment costs incurred”, which includes four main types of adjustments: 1. fiscal adjustment (e.g. costs arise as a need to replace tariff revenue losses, includes the development and reform of the fiscal sector); 2. trade facilitation and export diversification (e.g. redeployment of assets (capital, labor, skills and land) away from import-competing sectors towards new export activities (under the stimulus of greater competition on the home market from EU exporters).; 3. production and employment adjustment (e.g. reallocation of displaced resources from current (pre-EPA) activities to export sectors); 4. skills development and productivity enhancement (e.g. costs of adjustment contraction of import-substitution activities and expansion of export sectors, increasing competitiveness and productivity).

Furthermore, Milner classified all ACPs by country size based on population (micro, small, medium, large, very large) and the degree of adjustment (low, medium, high, others). Using this classification, the actual costs of programs to minimize the effects of adjustment for each of the four types are calculated from existing adjustment programs being financed by the World Bank in countries of similar sizes and facing similar problems.

For the Pacific region, the Milner report comes up with the following figures (in million US$ at 2005 prices) shown in Table 6.4. The results indicate that the total adjustment costs in PNG are highest with more than US$ 160 million and lowest in Samoa. It has to be kept in mind that the adjustment costs are measured over a 10-year period and therefore the additional row “as % of real 2005 GDP” cannot be taken seriously as a measurement of the total costs over GDP, but it shows that the two larger economies Fiji (1.95% of2005 GDP) and PNG (2.5% of 2005 GDP) will not be affected as much as smaller island economies such as Kiribati where the adjustment costs would be equal to 77% of their 2005 GDP. The Table also indicates that the highest form

156 Table 6.4 Milner (2005) - adjustment costs for the Pacific ACPs

Fiscal Trade Facilitation Employment Skills and in % of Adjust- & Export Adjustment Productivity Total GDP Country ment Diversification Enhancement (2005) Fiji Islands 16.1 4.0 4.8 12.1 37.0 1.95 Kiribati 16.1 8.0 4.9 12.1 41.0 76.98 FSM 16.1 8.0 4.10 12.1 41.0 24.27 Palau 16.1 8.0 3.2 12.1 39.4 46.73 PNG 40.2 52.3 32.2 36.2 160.8 2.48 Samoa 16.1 4.0 4.8 8.0 33.0 19.22 Solomon Is. 16.1 24.1 4.8 12.1 57.1 22.67 Tonga 16.1 24.1 3.2 12.1 55.5 30.74 Vanuatu 16.1 8.0 3.2 24.1 51.5 22.72 TOTAL 168.9 140.7 65.9 140.7 516.2 5.43

Notes: 1. Numbers in million US dollars (2005 prices).

Source: Milner (2005), United Nations Statistics Division, 2008.

Table 6.5 Adjustment costs: Smith’s re-modified Milner approach

Trade Fiscal Skills and Total costs in Facilitation & Employment Adjust- Productivity Total % of GDP Export Adjustment ment Enhancement (2005) Country Diversification Cook Islands 16.1 4.0 3.2 8.0 31.4 33.21 Fiji Islands 16.1 4.0 4.8 8.0 33.0 1.73 FSM 16.1 4.0 3.2 12.1 35.4 20.94 Kiribati 16.1 24.1 3.2 12.1 55.5 104.15 Nauru 16.1 4.0 3.2 8.0 31.4 103.09 Niue 16.1 4.0 3.2 8.0 31.4 n/a Palau 16.1 24.1 3.2 12.1 55.5 65.80 PNG 32.2 52.3 32.2 36.2 152.8 2.36 RMI 16.1 8.0 3.2 8.0 35.4 55.03 Samoa 16.1 4.0 4.8 8.0 33.0 19.22 Solomon Islands 16.1 24.1 3.2 12.1 55.5 22.03 Tonga 16.1 24.1 3.2 8.0 51.5 28.52 Tuvalu 24.1 4.0 3.2 8.0 39.4 245.66 Vanuatu 16.1 8.0 3.2 24.1 51.5 22.72 TOTAL 249.3 193.0 77.2 172.9 692.4 7.13

Notes: 1. Numbers in million US dollars (2005 prices).

Source: Smith (2006), United Nations Statistics Division, 2008.

157 of adjustment costs lies in the sector of fiscal adjustment which reflects the still high dependence on import and fiscal duties as mentioned above.

In the approach used by Smith (2006), non-government services are included; thus, the author takes total trade into consideration. Smith further assumes that the FTA includes all PACPs and sets the adjustment period up to at least 10 years after the EPA enters into force. He also assumes that the EPA will significantly increase imports from the EU but fails to justify his assumption. The author re- modifies Milner (2005) approach by considering the smallness of the PICs compared to other ACP members. He also includes a separate adjustment category for productivity enhancements costs additionally to Milner’s four categories and considers the existing measures and reforms undertaken in the PACPs.

Smith used the Milner’s methodology and adjusted it to a broader country base. Smith included Cook Islands, Nauru, Niue, Tuvalu and RMI, and applied deeper accuracy in the degree of increase in per capita costs. With this he appreciated the smallness of the island states and came up with the following figures, presented in Table 6.5, showing that the total adjustment costs increase from US$ 516.2 million to US$ 692.4 million.129

Smith’s refined Milner’s 2005 methodology by classifying the Pacific ACPs into different PACPs country sizes and by changing the indicators used to divide the countries into high, medium or low adjustment for each of the cost and benefit categories. Smith further uses 25 Pacific ACP projects or model projects and comprehensive country data to assess the degree of adjustment; thus he focuses much more onto the Pacific and not onto the ACP countries as a whole.

As already mentioned, Smith includes the non-government service sector and provides numbers for both, trade facilitation and export diversification in trade of goods (further disaggregated by trade facilitation and fishing sector) and services (with a main interest in tourism).

129 In 2005 prices; rounded for convenience.

158 Table 6.6 Adjustment costs: Smith’s “refined” report based on Milner (2005)

Trade Total costs Fiscal Skills and Facilitation & Employment Productivity in % of Adjust- Productivity Total Export Adjustment Enhancement GDP ment Enhancement Country Diversification (2005) Cook Islands 1.0 0.2 1.9 1.2 0.5 4.7 5.02 Fiji Islands 2.0 0.0 2.3 4.2 2.4 10.9 0.58 FSM 0.6 0.2 2.6 1.7 2.0 7.1 4.19 Kiribati 0.5 0.1 2.4 1.3 1.1 5.3 9.96 Nauru 1.5 0.1 1.2 5.2 0.5 8.4 27.76 Niue 1.5 0.1 2.0 1.6 0.5 5.7 n.a. Palau 1.0 0.2 1.9 1.6 0.4 5.1 6.01 PNG 2.5 11.7 5.8 2.7 3.8 26.4 0.41 RMI 1.0 0.1 1.6 1.6 1.1 5.3 8.25 Samoa 0.6 1.5 4.3 1.3 1.4 8.9 5.20 Solomon Islands 2.0 1.3 3.6 7.2 5.7 19.8 7.85 Tonga 1.6 1.5 2.6 1.7 1.4 8.7 4.81 Tuvalu 0.5 0.2 2.0 1.6 0.5 4.8 30.08 Vanuatu 3.3 1.3 4.3 1.8 4.7 15.4 6.81 TOTAL 19.5 18.4 38.4 34.5 25.8 136.6 1.75

Notes: 1. Numbers in million US dollars (2005 prices).

Source: Smith (2006), United Nations Statistics Division, 2008.

The author’s underlying assumptions are that the FTA with the EC comes into force with all Pacific ACPs, that all sectors are included, that the adjustment period is at least 10 years, and that the Pacific Island nations have a commitment to the policies necessary to adjust for the FTA. Again as already mentioned, he assumes that the FTA with the EC will increase imports significantly due to the tariff preference compared to existing tariffs on goods from the US, for example. Given those assumptions, Smith’s adjustment costs are summarized in Table 6.6.

Smith’s Report also provides a summary of the total costs in the different adjustment cost categories which can be seen in Table 6.7. Table 6.8 compares Smith’s with Milner’s (2005) results.

159 Compared to the Milner’s 2005 study, Smith’s data set provides figures for more countries, namely Cook Islands, Nauru, Niue, RMI and Tuvalu. Additionally the study includes trade in services of non-government organizations. Because of that, the results of both studies are different. Smith’s projections are with a total US$ 107.75 million,130 much lower than the US$ 516.2 million that Milner projected.131

Tuvalu with approximately 31% of adjustment costs share of 2005 GDP followed by Nauru (28%) and Vanuatu (15.5%) face the highest economic adjustments.132 The two studies differ completely when it comes to the adjustment costs of Kiribati – US$ 5.4 million versus US$ 41 million. Other main differences can be seen in the compliance costs (fiscal adjustment & production/employment adjustment) where Milner’s study estimated ca. US$ 235 million versus “just” ca.

Table 6.7 Smith (2006) - total adjustment costs by category

Adjustment Cost Category Total (US$) Fiscal Adjustment 19.5 Production & Employment 18.4 Trade Facilitation & Goods Trade Facilitation 20.0 Export Diversification Fishing Sector 7.3 Services Tourism 11.2 Skills Development 34.6 Productivity Enhancement & Competition Authority 11.9 Investment Climate2 SME advisory 14.1 Sub Total 136.6

IPPA+ Facility3 4.9 HRD Facility4 6.0 Total Adjustment Costs 147.6

Notes: 1. Total costs in million US dollars (2005 prices). 2. Includes regional regimes and national competition authorities as components. 3. Designed to raise investments in the PICs. 4. To enable Mode 4 service providers to increase access to skilled and semi skilled segments of the labor market.

Source: Smith (2006)

130 Adjusted by the countries that Milner investigated. 131 In 2005 prices. 132 Again, the assumptions made by Smith are that the adjustment will take place over a time frame of at least 10 years, therefore the % of GDP row cannot be taken too seriously.

160 Table 6.8 Adjustment cost measurements: comparison of Smith and Milner

Smith (2006) Milner (2005) Country total costs in % of GDP total costs in % of GDP Cook Islands 4.75 5.02 ...... Fiji Islands 11.02 0.58 37.0 1.95 Kiribati 5.39 10.11 41.0 76.98 Micronesia 7.08 4.19 41.0 24.27 Nauru 8.52 28.02 ...... Niue 5.79 ...... Palau 5.07 6.01 39.4 46.73 PNG 26.29 0.41 160.8 2.48 RMI 5.39 8.38 ...... Samoa 8.93 5.20 33.0 19.22 Solomon Is. 19.78 7.85 57.1 22.67 Tonga 8.68 4.81 55.5 30.74 Tuvalu 4.91 30.58 ...... Vanuatu 15.52 15.52 51.5 22.72 TOTAL 137.10 1.41 516.2 5.43

Notes: 1. Notes: total costs are measured in million US$ (in 2005 prices) 2. % of GDP reflects to 2005 GDP

Source: Smith (2006), Milner (2005), United Nations Statistics Division, 2008.

US$ 38 million estimated by Smith. A similar difference shows the benefit- related policy adjustment costs (trade facilitation/export diversification & skills/productivity enhancement) with US$ 281 million versus US$103 million.

The Watergall (2007) study also has investigated the fiscal adjustment. Here static data was used, which has been composed of customs duty data base by 8- digit-HS, using average 2003-2005 customs duties, in some cases updated to 2006. For VAT and income tax effects assumptions were made at oligopolistic and fully competitive structures. Thus, not all price reductions will be passed onto the consumers but stay in companies or get simply lost through the market process as well. Thus, it is found that the potential impacts depend highly on the price elasticity assumptions.

Watergall states that the data provided can only serve as an indication since it was not possible to accurately model externalities due to trade flow or broader economic effect to an extent where they were of sufficient quality to be

161 published. This includes rules of origin which may reduce revenue losses compared to an increasing revenue loss effect by the substitution effect.

Previous studies show that the gradual trade liberalization process already takes place. Therefore, the fiscal adjustment for PNG, Fiji and Palau are very small and can be “corrected” very easily by simple changes in the VAT system for example. Watergall further investigated the potential costs of fiscal adjustment towards the trade agreements. By doing so the study takes recent fiscal reforms undertaken into account. The study gives three different examples namely PNG, Vanuatu and Samoa, by focusing on the introduction of the VAT and comparing it to the fiscal reforms undertaken in New Zealand which can be summarized by:133 - removing wide ranging income based tax (turnover tax) - reducing tariffs - replacing with VAT - public service right sizing

For PNG the study summarizes the reform with “good design, poor implication, long recovery period” and a missing of the “NZ-Style”. PNG has rationalized the tariff regime into three brands: (1) removing exemptions, (2) reducing tariffs and (3) introducing VAT, thus changing the basis for the duty drawback. Furthermore, another change was the move forward to modernization of the customs system. Summarizing, one can say that the implementation of the reform was poorly done caused by bureaucratic obstructions and time inefficiencies. Vanuatu’s reforms are summarized by “poor design, good implementation, long recovery period” and is found to be “NZ Style”. Watergall (2007) finds that even though the way of Vanuatu’s fiscal reform was corrected by replacing the only direct tax base with VAT as well as tariff reductions, the country struggled in between the reforms, the post reform period was very successful due to long term institutional support by all major donors.

The best fiscal system reform is found in Samoa – “good design, gradual implementation, gradual and immediate positive economic input” as well as “NZ

133 Will be referred to as NZ-Style.

162 Style”. The fiscal reforms have focused on replacing the sales tax with the VAT, introduction of the new public finance act as well as program budgeting. Furthermore, state owned enterprises were sold or privatized. The success of the reforms was due to the long-term donor support.

Watergall (2007) finds that changes in the fiscal system should be well thought, not rushed through, a major problems most PICs face. Many administrations simply cannot handle the reforms due to the lack of skills and lack of capacity. Furthermore, a fiscal reform should target a broad tax base and should have long term donor support and include other reforms such as public services. With these assumptions, Watergall (2007) came up with the following figures for the fiscal adjustment which are summarized in Table 5.9. Compared to Milner’s (2005) and Smith’s (2006) findings, the Watergall (2007) fiscal adjustment costs are in between US$ 78.55 – 137.1 million lower than the Milner report but higher than Smith’s estimates.

Table 6.9 Fiscal Adjustment costs - Watergall (2007)

Fiscal adjustment costs of total revenue (%) % of GDP (2005) Cook Islands 2.11 – 4.93 4 – 9 2.23 – 5.22 Fiji Islands 13.01 – 26.61 2 – 4 0.68 – 1.40 FSM 3.00 – 4.00 6 – 8 1.78 – 2.37 Kiribati 8.02 – 13.36 14 – 24 15.05 – 27.08 Nauru 0.61 – 0.69 6 – 7 2.00 – 2.27 Niue 0.35 – 0.63 5 – 8 n.a. Palau 1.00 3 1.19 PNG 8.06 – 16.12 1 0.12 – 0.25 RMI 6.00 24 9.33 Samoa 10.33 – 20.66 14 – 18 6.02 – 12.04 Solomon Is. 2.39 – 4.12 4 – 6 0.95 – 1.64 Tonga 9.78 – 20.59 19 – 38 5.42 – 11.41 Tuvalu 1.07 – 1.91 10 – 17 6.67 – 11.91 Vanuatu 12.82 – 16.48 20 – 25 5.66 – 7.27 Total 78.55 – 137.1 n.a. 8.05 – 14.05

Notes: 1. Fiscal Adjustment in million US dollars (in 2005 prices). 2. In percent of total revenue refers to 2005/2006 revenue reports.

Source: Watergall (2007), United Nations Statistics Division, 2008.

163 Table 6.9 describes that Tonga faces the highest fiscal adjustment costs in terms of percentage of total revenue, followed by Kiribati and Samoa. The two larger countries, Fiji and PNG, face relatively low costs, in between 2-4 and 1% of total revenue, respectively.

6.2.4 What needs to be done? How to adjust for trade liberalization? Watergall (2007) suggests that the gradual tariff reduction should be accompanied by tariff and thus by fiscal reforms where tariff exemptions should be reduced as far as possible. Further, a tariff and fiscal reform will also affect the increasing equity and will minimize administrative costs for customs if the reforms are done efficiently and correctly. To overcome the tariff losses, a range of revenue raising measures should be considered. These include excise tax measurements, general consumption taxes (VAT), income tax and corporate tax. The main goal of the PICs by reforming their systems should be an improvement of the inefficient customs and tax collection agencies towards more efficiency as well as to reducing expenditures and improve compliances.

6.2.4.1 Excise tax An excise tax is a tax levied on the producers of certain goods and commodities or even activities and is different from VAT which is consumer based. One may argue that this is not entirely true since an excise tax may be passed from the producers to the consumers by price increase; still in definition the excise tax is producer based.

Watergall suggests that if the PICs use the excise tax to overcome the tariff revenue loss, then they should introduce excise taxes only on few products. The excise tax can be based on imports or on domestic processing. Important to keep in mind is that the IPA and PACER prohibit the discriminatory measurement of taxes on foreign goods. As IPA and PACER state, any taxes, duties etc. have to be applied in a non-discriminatory way, thus be applied on domestic and foreign goods.

164 A third type of excise good could be the so-called sin-tax. A sin tax can be seen as a negative externality since consumers have a cost to the society broadly. Possible sin tax bases are cars, tobacco, petrol or alcohol since those goods are usually not very price sensible and thus are considered to have an inelastic demand. Therefore, the sin tax on goods with an inelastic demand can be recognized as an easy way to generate “profit” in terms of tax revenue.

Again, it is important that the administrative costs are kept as low as possible and that only a few products are targeted because otherwise imposing excise taxes simply in form reassigning for tariff cuts may be contra productive in welfare aspects.

Even though most countries believe that an excise tax is the easiest way to reduce the losses in tariff revenues, one has to keep in mind that the WTO-rules on which both the IPA/EPA and PACER trade liberalizing agreements are based state that any domestic tax or duty has to be imposed on both domestic and foreign products in order to comply with the non-discriminatory rules and to ensure that foreign goods and commodities are not discriminated against compared to domestic ones. Thus, imposing an excise tax means also imposing a new domestic tax.

6.2.4.2 Personal income and corporation tax Personal Income tax and corporation tax are important, too, but in this discussion not too much time is spent on those. It has to be kept in mind that the personal income tax adds only little to the total tax revenue of the PICs. An increase in corporation taxes can be seen as a counter-productive step since the goal of trade liberalization is to ensure greater domestic and international competition. On the one hand, an increase in corporate tax means decreasing the competitiveness of the domestic industry. On the other hand, because it is common that corporations use a corporate tax as a turnover tax and will in the end slim the welfare effect of the tariff reductions and trade liberalization towards another “goods tax”. This leads to a price increase through corporations simple to avoid tax increases.

165 6.2.4.3 Consumption tax The easiest consumption tax is probably the VAT, which is already in place in some PICs, as Table 6.1 in section 6.2.1 shows. The problem with the VAT is that in some cases it is seen as being incomplete in one way or another (e.g. some important sectors such as services, wholesale, retail have been left out of the VAT tax base).134

When introducing or imposing a VAT the production sector has rarely any chances to get credits for inputs for VAT. Those credits are extensively restricted which can also be seen in the general way for credits, thus a VAT can be a negative externality and have negative spillovers if other sectors are not fully prepared for the reforms.

For the PICs, a VAT imposition should have different rates for different products similar to the OECD countries (e.g. two different rates in Germany, 7% on food products, 19% on other commodities and services). Even though multiple rates may not be essentially efficient, they are politically attractive and serve as an equity objective and avoid targeting the lower income base.

In the more developed Pacific Island nations such as Fiji or the Cook Islands and the largest economy PNG, VAT systems are already in place; the administrative costs of changing and adjusting the VAT to the needs of the fiscal system should be, even though they might be higher than in developed countries, relatively low compared to those countries such as Kiribati which do not have VAT systems.

By adjusting or introducing a VAT system, the governments and politicians/political agents should be aware that the tax rates do not “hurt” the lower and middle class already suffering from the high unemployment rates and poverty. Thus, a multiple tax incentive seems to be the best way by keeping the rates on food and basic products, goods and commodities needed and consumed

134 See Watergall (2007).

166 by the lower and middle class as low as possible and to “increase (impose)” a VAT for luxury goods as discussed in the section on excise taxes.

6.2.4.4 Complementary reforms The customs and tax collection agencies need to undergo reforms. Since the customs collection will remain an important part of the overall government revenue collection, the administrations need to be efficient. Due to a lowering in tariffs and non-tariff barriers, the chances are good that trade will increase and therefore will also increase the demand of efficient authorities. Therefore, the collection of customs should be made as easy as possible: Increasing transparency, minimizing documents needed, introduction of further ITS to lower the costs and processing time (especially for the private sector).

Corruption is one of the main problems in the customs sector, thus a transparent system would do both simplify the process of revenue collection and decrease the high level of corruption itself.

IMF based programs such as Walsh (2006) who recommends four major changes should be used as a framework for complementary reforms: 1. Establish coherent trade policy establishment and clear legislative support 2. adopt modern and simple, transparent procedures to minimize abuse in form of corruption to maximize efficiency 3. shift to substantial reliance on tax payer self-assessment 4. introduce incentive and organizational structures that promote integrity and effectiveness in customs administration. Customs administrations must be given a clear mandate and be free of political interference and have adequate resources to carry out its tasks, which are promotion of trade flows and increase in government revenue collection.

The Pacific Island Forum Secretariat should develop a common strategy which could be imposed on every member country to achieve the goal of harmonization, not just among the PICs, because if carried out in an international based way, it

167 would not just increase efficiency and improve welfare due to trade increase between the PICs, New Zealand and Australia and the EU but also between all other WTO members and the PICs themselves. This would increase the welfare due to lower costs and time and would lower the burden of bureaucracy.

Besides customs authorities and harmonizing the customs systems, other barriers of trade have to be addressed. GATS has identified and therefore asks for the targeting of the following four principles which are, namely, transparency, non- discrimination, market access and national treatment.

Transparency should include all policies and regulations imposed on the trade in services. The transparency should be clear and easily accessible to foreign service exporters, thus transparency will increase efficiency and welfare by reducing information costs which would be necessary to identify all burdens on trade in services without transparency. Non-discrimination and national treatment go hand in hand. Again, as already mentioned, the non-discrimination rule guarantees the same treatment for domestic and foreign services and overcomes the obstacle of, as the name says, discrimination of foreign services but also of inefficient domestic services which are protected by the imposed discrimination itself. The national treatment assures the same treatment for all services and service providers, irrespective of their origin.

Market access includes the free market access of foreign as well as domestic services and service providers under conditions that are seen to be non trade disturbing.

A PDP (2007)135 study on trade in services has shown that regulations in the Pacific hinder trade in services and further constraints foreign investors. The reason can easily be found by the missing compliance to the principles of GATS and therefore contributes to uncertainties in the business environment and discourages domestic and foreign direct investments.

135 PDP Australia Pty Ltd.

168

Labor law issues are another interesting topic that may not be forgotten. A simple labor law liberalization can create an increase in highly skilled workers which can fill the gaps in the government and business world and can lead to capacity building and worker transfer and mean spillovers in increasing investments.

Under-regulation is another problem that many PICs face and is severe in the financial sector, as the recent example of the collapse of the Pacific Savings Bank in Palau has shown. Under-regulation means inefficiency not just in the financial sector but in general. Further, it gives inadequate service provision and leads to un-profitability and unpredictability and thus to uncertainty. Another result of under regulation can be identified as poor institutional performance e.g. in form of poor policy coordination and lack of accountability.

6.2.5 Summary of Section 6.2 The topic of the second part of the chapter was the potential impact of EPAs and PACER on tariff revenues and the adjustment costs. Four studies have been discussed, showing that the results of the tariff revenue losses due to trade liberalization vary from study to study. This is caused by the fact of different assumptions made and measurements resting on different bases and secondly the time period the studies were conducted in. Nevertheless, compared to the results for PACER the potential losses for EPAs are in similar margins. Therefore, one needs to be careful and cannot take the tariff loss estimates too seriously. The latest available study on tariff revenue losses from Watergall (2007) indicates similar results for the EPAs in terms of marginal to insignificant tariff revenue losses. For PACER the study shows, compared to Filmer and Lawson (1999),136 that potential tariff revenue losses have been declining significantly over the last few years. This indicates that the PICs are already started to adjust their fiscal systems toward trade liberalization. One of the steps six countries have taken is the introduction of the VAT. It can easily be used to overcome the tariff revenue

136 Both studies basically using the same measurement base.

169 loss by imposing multiple rates, for example a lower rate for foodstuff and a higher for “sin” goods such as alcohol, tobacco or general luxury goods. Similarly the excise good can be used.

The adjustment costs have been the topic in the second section whereas three different studies were discussed. The Milner report estimated US$ 516.2 million in total adjustment costs for nine PICs. Smith (2006) re-modified Milner’s approach by extending it to all PICs (excluding Timor-Leste) which led to US$ 692.4 million. Smith further “refined” the Milner approach by concentrating at the PICs instead of all ACPs and found total costs of US$ 147.6 million. Watergall (2007) evaluated fiscal adjustment costs to be between US$ 78.55 million and US$ 137.1 million. All three reports are based on different assumptions (e.g. Milner excludes services, Smith includes them) and time frames for the adjustment period. Not surprisingly, the reason for the different assumptions is that the reports are based on the EPA negotiations rather than on the actual final Pacific IPA.

The Milner report is probably the most questionable report even though it is based on a pure goods EPA and comes close to the actual IPA. The report is not just purely unreasonable but it is also senseless to classify all ACPs by some kind of population size and a degree of adjustment based on existing programs of the World Bank and to claim that e.g. 8 out of 9 PACPs will have the same fiscal adjustment costs. It becomes even more pointless when a country that has a VAT system in place (e.g. Tonga or Fiji) is argued to have the exact same fiscal adjustment costs as a country without VAT. Moreover, PACPs are assumed to have similar or the exact same adjustment costs as some African or Caribbean ACPs which underlines the uselessness of the Milner report, at least to predict adjustment costs for the PICs.

The reports of Smith and Watergall are much more reasonable, especially because they concentrate on the PICs and therefore give an idea of how massive the impact of trade liberalization could be. Watergall finds the “fiscal” adjustment

170 costs (including costs for trade facilitation etc.) in between 8.05% – 14.05% of total GDP137 of the PICs. Smith on the other hand sees the adjustment costs at 1.75% of the total PICs’ GDP. Both reports find that the larger island nations will have the lowest adjustment costs and that Kiribati and Tuvalu the highest (in % of GDP). But more important than the actual costs, which have to be taken with some caution due to the fact that the assumptions of the reports are based on the EPA negations rather than the actual IPA, the reports show which areas of the economy will be affected. These areas are besides the often mentioned fiscal adjustment costs, trade facilitation and export diversification, employment adjustment, skills development, and productivity enhancement. This reveals that the adjustment will go beyond the fiscal revenue adjustment and thus, the trade liberalization will affect the economies on the whole. The Smith study finds that the costs that occur due to WTO compatibility in terms of trade facilitation and skills development are highest. Hence, countries need to take action in those fields by addressing comprehensive reforms to keep the final adjustment costs as low as possible.

6.3 Conclusion of Chapter 6 The Pacific Island Countries are in the wake of trade liberalization. The IPA/EPA and PACER as the agreements of carrying out the trade liberalization are more than simple “tariff lowering and elimination” agreements. As the chapter has shown, tariff revenues from trade conducted with the EC and Australia & New Zealand are already low; therefore the gradual reduction or elimination of customs duties are not to be seen as the major change the PICs face and can easily be overcome by an introduction or adjustment of the value-added tax or an excise tax with similar results.

IPA and PACER are agreements that go beyond tariff barriers to trade by addressing and targeting directly all common known non-tariff barriers to trade which are the real obstacles to growth the PICs face. This includes the technical

137 Based on 2005 GDP.

171 barriers to trade – standards and regulations asking for human safety and product quality. Quarantine restrictions are market access restrictions and main constraints many developing countries and PICs face. These can be overcome by addressing the technical barriers to trade and SPS and harmonizing the standards and safety requirements to international norms. For a successful implementation of the trade agreements, reforms are inevitable and therefore adjustment costs will arise. The real impact of these cannot be shown for certain due to the fact that many studies have had wrong assumptions of the trade agreements; nevertheless, the studies point out that the adjustment costs go beyond simple fiscal adjustment. The just mentioned trade facilitation and harmonization to international standards mean a huge challenge for many PICs due to capacity constraints. Other areas affected by the trade liberalization movement of the PICs are the whole export sector in terms of export diversification, employment adjustment, skills development to overcome the capacity constraints, and adjustment costs in the field of productivity enhancements.

In the short term the reforms can be seen with a negative point of view due their immense challenges for the PICs economies; but with the help of the PACER and IPA offered adjustment payments in form of aid and technical assistance, the short term challenges can be easily overcome, especially by harmonizing the PICs trade to international norms. Therefore, the IPA/EPA and PACER are more than simple trade agreements but instruments of promoting sustainable development. Due to their interconnection, both agreements, IPA/EPA and PACER, will in the end be similar, which reduces the adjustment challenges. Secondly, the agreements have a wider perspective for the PICs, going beyond simple trade relationship improvements between the PICs and the EC and Australia and New Zealand. By adopting international standards based on WTO rules, the PICs are also increasing potential market access to all members of the WTO, increasing trade efficiency among the PICs themselves and also to all other ACP countries due to the fact that all EPAs are based on the same core principles.

172 Additionally, complementary reforms need to be addressed to completely capture the welfare enhancement of the trade liberalization movement. Efficient institutions, the rule of law, enforcing contracts, access to credits and property rights are only some areas that are constraints to be overcome; thus the PICs should target both make sure that they do not limit the welfare increase and to keep the adjustment costs as low as possible.

173 7. Conclusion

7.1 Major findings and policy implications Whether PICs are found to be open to trade or not is a question that cannot be answered from a simple trade and GDP perspective, as the discussion in Chapter 4 has shown. Different TO measurements show different results but it is wishful thinking to consider the PICs as open to trade. Studies have shown that tariff revenues from trade with the EC and Australia & New Zealand are not as high as claimed by many opponents of trade liberalization; see bilaterals.org (2007), Oxfam New Zealand (2007), Kelsey (2005, 2006, 2007). In Chapter 6 four studies on tariff revenue losses due to free trade agreements with the EC, Australia and New Zealand have been discussed and reveal that potential tariff revenue losses have been declining over the recent years, which implies that the PICs are already adjusting their fiscal systems away from tariff revenue dependency. The much bigger problem the PICs face, whether being member of the WTO or not, is the high non-tariff barriers to trade; see Section 2.2.1.3. The World Bank Group’s Doing Business Report on trading across borders is a good example for non-tariff barriers to trade but that is only the tip of the iceberg. First of all, it points out that there is no harmonization among PICs at all (e.g. documents required to trade) and secondly, compared to the world leaders, the trade procedures are an obstacle on its own; see Table 4.4a in Section 4.1.4. This makes trade not just time inefficient and increases the costs but also uneconomical on the whole.

Both trade agreements covered in this thesis, the Interim Partnership Agreement (PACPs and EC) and the Pacific Agreement on Closer Economic Relations (PICs and Australia & New Zealand), address specifically all common known tariff and non-tariff barriers to trade discussed in section 2.2.1.3. Therefore, the agreements ask not just for adjustment to international standards but also for harmonization among the PICs to international standards and procedures (e.g. customs) to increase the efficiency and the welfare of conducting trade. Due to the exports structure of the PICs, being mostly of agricultural nature compliance to WTO

174 based standards means a huge increase in trade potential by overcoming e.g. the quarantine restrictions on agricultural exports outlining one of the major obstacles to trade between developing and developed countries. Furthermore, a harmonization will reduce the costs to trade significant and offers a chance to overcome the limitations of market access to both exporters and importers.

The IPA, being the starting point for future PACER-Plus negotiations, is a pure goods agreement and uses, compared to PACER, a different strategy to introduce the PICs into the global economy. The “PACER strategy” sees the achievement of a regional trade agreement among the PICs, namely PICTA the base for further negotiations with Australia & New Zealand. Even though PICTA was ratified and entered into force by 2003, so far it has to be proclaimed a failure because no trade under this agreement has been conducted so far. Furthermore, the establishment of a regional trade agreement among the PICs is questionable in the first place because it is unlikely to be welfare improving; see Section 2.1.

“In fact, it is more likely to set back the promotion of free trade in the FICs138 because of the great propensity for trade diversion and tariff and investment diversion to the more advanced states. This results in income divergence and increased antagonism against free trade.”139 The risks of the above will be reduced by trade agreements between developing and developed economies. The Pacific IPA/EPA offers an interesting development strategy. At first, it calls for a harmonized trade agreement with the EC based on international standards of the WTO to reduce the obstacles to trade. Secondly, it has the goal of building a regional trade agreement among the PICs in the form of PICTA based on the IPA. Therefore, the EC is pushing the PACPs to agree to one harmonized Pacific IPA (the PNG IPA) instead of many country specific IPAs. By doing so, the EC’s strategy is to build up one harmonized trade agreement with the PACPs, which can also be used as a regional trade agreement among the PACPs, as just outlined, to promote regionalism and to use the IPA as a development instrument. The interconnection between PACER and the IPA (due to Article 6 PACER; see

138 FIC: Forum Island Countries, Pacific FSEC. 139 ADB Pacific Trade Issues (2008).

175 Section 5.1.4) will harmonize both agreements and offers the PICs standardized trade with the EC, Australia & New Zealand based on WTO rules. In a wider perspective, these agreements increase the export potential not just to the EC and Australia & New Zealand but also with all other WTO members and ACP countries due to the fact that the EPAs are based on the same principles to overcome the common barriers to trade.

The trade relationship between the PICs and the EC and Australia & New Zealand has been discussed in Chapter 3. Obviously PICs’ trade with Australia & New Zealand is more significant than trade with the EC. Therefore, Duncan (2008) sees trade with Australia and New Zealand under PACER likely to be more welfare enhancing, with generating greater benefits for the PICs due to its more intensive trade relationship than with the EC (preferential trade agreements excluded); see Section 3.2.2.

As already discussed earlier, the trade liberalization movement asks for structural reforms of the PICs in a wider field both being triggered by and going beyond trade facilitation and harmonization of barriers to trade addressed in IPA and PACER. As pointed out in Chapter 5, the adjustment will take place in the fiscal sector due to gradual reduction of customs duties. Other sectors addresses are areas of production and employment enhancements, trade facilitation and export diversification and skills development due to the changed environment of WTO compliance. Additionally, well defined property rights are essential for creating a secure and investor friendly environment. Inefficient and insecure land tenure systems and the limited access to credits are constraints that need to be overcome.140

Section 5.2.4 addressed possible solutions to keep the adjustment costs as low as possible. The adjustment or introduction of the value added tax or an excise tax are identified as the best strategies to manage the tariff revenue losses caused by IPA/EPA and PACER. Caution needs to be taken that the implementation does

140 See Duncan (2008).

176 not harm the lower or middle income class. A good implementation sees a multi- rate VAT or an excise tax on “sin” goods such as alcohol and tobacco and can result in beneficial social effects; see Duncan (2008).

Both agreements IPA/EPA and PACER offer development assistance to overcome the adjustment costs in one way or another. To this date no approved development funds for assistance to the adjustment allocations are available. Unconfirmed reports offer an amount of about F$ 180 million of adjustment payments to Fiji by the EC’s European Development Fund. Important to understand is that the Cotonou Agreement as the successor of the Lomé Convention sets aid delivery and trade negotiations and agreements apart; see Section 5.1.2. The IPA has no direct relation to the development and adjustment payment funds which explains why, even though the IPA has been signed by Fiji and PNG, the European Commission's Delegation for the Pacific is unable to confirm any financial assistance up to this day.

For Fiji, the expiry of the sugar protocol means a huge challenge for the economy; not only because it accounts for more than 95% of total exports to the EC. The gradual 36% price reduction until 2015 will be accompanied by sugar market adjustment payments and programs to reform the sugar market (€ 124 million are earmarked, € 60 million are allocated). Estimates of the European Commission's Delegation for the Pacific indicate both a 36% reduction of price and a reduction in production costs caused by the reform of the sugar market.141 “From 1st October 2009 to 30th September 2012, the EU importers will be required to purchase ACP sugar at a minimum price not lower than 90 % of the EU reference price.”142 After 30th September 2012 there will be no guarantee for purchases of Fijian Sugar from the EC. On the other hand, the EC offers a F$ 16 million agriculture diversification program to promote farmers to turn away from sugar.

141 For 2008, Production costs (PC) estimates are F$40 per ton, sold for F$62.60 per ton. For 2012, PC equals F$31 per ton, price F$46.80 per ton. Estimates for 2015 show a PC of F$29 per ton and the accepted price by the EC will be F$39.30. The estimates are best case scenario. 142 Xavier Canton-Lamousse, Sugar Programme Coordinator Trade, Regional Economic Integration Sugar Protocol, Delegation of the European Commission for the Pacific.

177 The strategic goals of both programs have to be questioned because they are contradicting each other: The EC promotes the inefficient sugar market sector with a reform program to lower the costs of Fijian sugar production: This is based on the gradual declining price, which is likely to lead to an expansion of the sugar production to overcome the price decline; thus a move away from other agriculture production such as ginger or rice are justified with best case scenario production cost reductions which seem to be overestimated (the Fiji Development Bank estimated production costs of F$52/ton for 2008). But on the other hand, the EC tries to restructure and diversify the agriculture sector to promote the movement of sugar farmers to alternative agricultural production such as ginger or rice. The insecurity of land tenure and poor access to credits are not addressed – the same is true for existing agricultural export relations between Fiji and the EC (e.g. ginger exports to Germany). The same is true for existing non- agricultural relations (e.g. export of folding cartons, tissues and towels to France) which have certainly potential for expansion by following international rules, lowering the costs of trade and making it more competitive; see Section 3.2.2.1. These are areas where the EC could promote Fiji but instead is concentrating on further promoting of the inefficient sugar market. The worst is that, even if the sugar market reform program should be successful and leads to a radical decline of the sugar production, there will be no guaranteed quotas for Fiji sugar exports to the European Community.143 It is uneconomical to assume that the Fijian sugar industry is able to compete with leading sugar producers such as Brazil. For example, transportation costs from Fiji to the EC are much higher than those from Brazil to the EC, which leaves the question whether the Fijian sugar industry has a future on the whole. The real question that needs to be addressed is what the EC’s interest in Fijian sugar is, what the goal of Fiji is and whether these are, under international competitive markets, justifiable.

What neither the EC nor Australia & New Zealand should do is to generalize the PICs when it comes to policy implications. The demand for trade estimates have

143 Xavier Canton-Lamousse, Sugar Programme Coordinator Trade, Regional Economic Integration Sugar Protocol, Delegation of the European Commission for the Pacific.

178 shown that the PICs have a strong trade relationship with both trading blocks the EC and Australia & New Zealand but have also indicated that the policy implications of the aggregated demand for trade differs significant from country specific demand for trade relationships (e.g. aggregated trade block specific estimates have concluded that the Marshall-Lerner condition is satisfied compared to Fiji’s demand for trade relationship where it is only satisfied for trade with several European countries but not for Australia & New Zealand); see Section 4.2.4. The EC, Australia and New Zealand should investigate the country specific structures of the economies, societies and cultures and appreciate those by addressing policies and structural programs for the PICs.

In the end, the success of trade liberalization will be determined by at least two major issues. First, in which way the PICs can use their comparative advantage and secondly how fast they can, with the help of the financial assistance, achieve harmonization and international standards, WTO compliance. In regards to the comparative advantage it is argued that the smallness and remoteness of the PICs are two major obstacles to economic growth making it impossible for them to adjust to the globalized world and that therefore trade liberalization should be either set out, or that the PICs should be relieved from major WTO obligations. Under such an argument, the comparative advantage is seen as not being sufficient enough to overcome the adjustment towards trade liberalization. Winters and Martin (2004) argue that therefore the major income source to keep the economies running “must be external”. Chand (2004) strongly disagrees with such assumptions and sees this argumentation as invitations for rent seeking. Chand argues precisely the opposite. Small island states can maximize their gains from trade by lowering trade barriers and border controls and by following WTO obligations. Relieving the countries from trade liberalization and international standardization would only benefit and strengthen the opponents of trade liberalization, namely protected industries. This would harm the economy by welfare losses. High transportation costs due to its remoteness are seen as the biggest disadvantages and are used to justify the hypothesis that the comparative advantage is not enough; see Winters and Martin

179 (2004). Chand (2004) argues that the relatively high transportation costs can mean a positive externality. Falling costs in the telecommunication sector and can imply specialization of trade in the service sector, such as an increase in tourism and the outsourcing of call centers. On the one hand, the remoteness promotes the technology sector and on the other hand, these trends lead to an erosion of the remoteness over time.

This discussion underlines that a simple goods agreement as the IPA is, is not enough and will not be as welfare enhancing. Trade in services need to be included to fully benefit from trade liberalization. Without services trade the main comparative advantage of the PICs, namely tourism, is unlikely to create positive spillovers in terms of job creation and increase in demand for efficient telecommunication services such as internet or infrastructure; see also Duncan (2008). To achieve this, major reforms need to be introduced to create an investor friendly environment. Targeting the rule of law and the enforcement of contracts as discussed above are just two areas. Therefore, the IPA is a major stepping stone into the right direction but further negotiations, especially PACER Plus (labor movement under Mode 4; see Appendix D.), will determine whether the trade liberalization will be successful in terms of improving welfare and promoting sustainable development and to overcome the adjustment costs or not.

7.2 Limitations and directions of future research This thesis places a major interest in the relationship of the EC with the PICs and into IPA/EPA which might lead to the conclusion that it undervalues PACER. The main gains due to trade will be through the trade negotiations with PACER Plus. Therefore, the IPA, as a role model or starting point, offers further areas of research, especially for PACER Plus negotiations. I believe that the findings of this thesis are helpful for PACER Plus negotiations and thus offer room for further research.

180 Fiji’s trade relationship with certain EC members, Australia and New Zealand is estimated, ignoring other PICs. This is caused by data limitations. With a wider budget, further data could have been extracted by visiting the countries of the Pacific. Most of the statistical data is available on hard but not soft copy. Thus, a wider data base would allow further research.

Current account persistence and exchange rate variability and the exchange rate pass through are further research topics to be conducted to capture the whole affects of trade policies.

A unified PIC currency further harmonizes and improves trade efficiency in the Pacific, leading to another interesting field of research.

181

Appendices

Appendix A: Description of Variables

Appendix B: Results of unit root tests for individual variables

Appendix C: Charts and tables

Appendix D: Definitions and further details

182 Appendix A: Description of variables The data source and the construction of the variables used in chapter 4 are described in this appendix.

A.1 Variables for trade openness analysis

Data source The variables used for the 210 (177) cross-country analysis are extracted from the United Nations Statistics Division National Account data base (2008): (1) nominal values of exports and imports of goods and services in US$ (2) the 1990 based constant GDP

A.2 Variables for demand for trade analysis

Data source (1) the 1990 based constant GDP from the United Nations Statistics Division National Account data base (2008) (2) nominal values of exports and imports of goods and services in US$ from the United Nations Statistics Division National Account data base (2008) (3) constant 1990 values of exports and imports of goods and services in US$ from the United Nations Statistics Division National Account data base (2008) (4) implicit 1990 GDP deflator from the United Nations Statistics Division National Account data base (2008) (5) market exchange rate U.S. dollar per national currency from the International Financial Statistics (IFS) of the International Monetary Fund (IMF)

183 (6) nominal merchandise export and import data a. Demand for trade (imports and exports) of Pacific ACPs to the EC i. from Eurostat’s external trade data, COMEXT (2008) b. Demand for trade (imports and exports) of Pacific ACPs to the Australia and New Zealand i. from Statistics New Zealand and Australian Bureau of Statistics c. Demand for trade (imports and exports) from Fiji to several EU member countries, Australia and New Zealand i. from Fiji Island Bureau of Statistics, Eurostat, New Zealand Statistics, Australian Bureau of Statistics

Data construction (1) domestic price of exports (imports) Implicit domestic export (import) deflator (Index numbers 1990=100) derived from nominal and real (1990) exports (imports) data.

(2) foreign price of exports (imports) Trade weighted implicit price deflator (Index numbers 1990=100) derived from nominal and real (1990) exports (imports) data.

(3) domestic real GDP ( lnYD ) Logarithm of the real 1990 GDP.

(4) foreign real GDP ( lnYF ) Logarithm of the real 1990 GDP trade weighted GDP.

184 Appendix B: Results of unit root test for individual variables

Definitions

LYF log variable of real foreign income LYF: First difference of log variable of real foreign income LY: log variable of real domestic income LY: First difference of log variable of real domestic income LRX log variable of real exports LRX: First difference of log variable of real exports LRM log variable of real foreign imports LRM: First difference of log variable of real foreign imports LRP: log variable of relative price variable LRP: First difference of log variable of relative price variable LRPE log variable of relative price variable with exchange rate LRPE: First difference of log variable of relative price variable with exchange rate

The regressions with the log-variable include an intercept and a linear trend, whereas the change variable-regressions include an intercept but not a trend.

185

Table B.1 Unit root tests for trade variables of the PACPs and the EC

Exports Imports ADF (lag) PP ADF (lag) PP 2.8883 3.4608 0.83524 3.17455 LRX (1) LRM (1) [3.5943] [4.2949] [3.5943] [4.2949] 3.1172 4.4042 3.1172 7.07211 LRX (1) LRM (1) [2.9850] [3.6752] [2.9850] [3.6752] 2.1195 3.49942 0.79989 2.81981 LYF (1) LY (1) [3.5943] [4.2949] [3.5943] [4.2949] 3.8854 5.82369 4.4470 6.12298 LYF (1) LY (1) [2.9850] [3.6752] [2.9850] [3.6752] 2.7014 2.80524 0.64369 4.06961 LRP (1) LRP (2) [3.5943] [4.2949] [3.5943] [4.2949] 5.4995 6.10311 5.4995 7.48427 LRP (1) LRP (1) [2.9850] [3.6752] [2.9850] [3.6752]

Notes: -

Table B.2 Unit root tests for trade variables of the PICs and Australia & New Zealand

Exports Imports ADF (lag) PP ADF (lag) PP 1.6558 1.77638 2.4237 2.53922 LRX (1) LRM (1) [3.5867] [4.2826] [3.5867] [4.2826] 2.6907x 3.64607* 2.9524x 3.46012* LRX (1) LRM (1) [2.9798] [2.9627] [2.9798] [2.9627] 1.3310 1.54593 3.9764 2.22475 LYF (1) LY (3) [3.5867] [4.2826] [3.5867] [4.2826] 3.4116 4.2419 3.6445 4.24929 LYF (1) LY (4) [2.9798] [3.6661] [2.9798] [3.6661] 3.0680 2.88574 2.2162 3.49054 LRP (1) LRP (1) [3.5867] [4.2826] [3.5867] [4.2826] 3.7133 4.67757 2.9162x 4.62738 LRP (1) LRP (1) [2.9798] [3.6661] [2.9798] [3.6661]

Notes: 1. * signifies 5% significance. 2. x indicates that the change variable is found to be I(1).

186

Table B.3 Unit root tests for trade variables of Fiji and the United Kingdom

Exports Imports ADF (lag) PP ADF (lag) PP 2.1538 2.5407 2.9049 2.3535 LRX (1) LRM (2) [3.6027] [4.3082] [3.6027] [4.3082] 4.0581 6.6080 2.9418 4.24996 LRX (1) LRM (1) [2.9907] [3.6852] [2.9907] [3.6852] 2.1677 3.0619 3.5303 5.51199 LYF (1) LY (2) [3.6027] [4.3082] [3.6027] [4.3082] 4.1602 7.42917 5.6157 11.8509 LYF (1) LY (1) [2.9907] [3.6852] [2.9907] [3.6852] 2.8993 2.27659 2.9837 2.66357 LRPE (2) LRPE (1) [3.6027] [4.3082] [3.6027] [4.3082] 3.8071 4.15942 4.0681 3.81996 LRPE (4) LRPE (1) [2.9907] [3.6852] [2.9907] [3.6852]

Notes: -

Table C.4 Unit root tests for trade variables of Fiji and Germany

Exports Imports ADF (lag) PP ADF (lag) PP 2.2068 6.16315 2.3068 2.15486 LRX (4) LRM (2) [3.6027] [4.3082] [3.6027] [4.3082] 5.6263 14.4267 2.4756 5.19459 LRX (4) LRM (1) [2.9907] [3.6852] [2.9907] [3.6852] 2.1500 6.15368 2.4277 3.62564 LYF (4) LY (1) [3.6027] [4.3082] [3.6027] [4.3082] 5.5858 14.61 4.7723 8.98311 LYF (4) LY (1) [2.9907] [3.6959] [2.9907] [3.6852] 1.3975 1.96838 2.5967 2.34275 LRPE (1) LRPE (1) [3.6027] [4.3082] [3.6027] [4.3082] 2.9459x 4.97356 3.3229 3.92598 LRPE (1) LRPE (1) [2.9907] [3.6852] [2.9907] [3.6852]

Notes: 1. x indicates that the change variable is found to be I(1).

187

Table B.5 Unit root tests for trade variables of Fiji and Belgium

Exports Imports ADF (lag) PP ADF (lag) PP 2.8529 3.53288 2.2966 2.63992 LRX (3) LRM (1) [3.6027] [4.3082] [3.6027] [4.3082] 4.7816 6.32559 4.4442 7.49443 LRX (1) LRM (1) [2.9907] [3.6852] [2.9907] [3.6852] 2.9256 4.20212 2.8543 4.74042 LYF (4) LY (1) [3.6027] [4.3082] [3.6027] [4.3082] 5.0273 9.01632 5.4401 12.3601 LYF (1) LY (1) [2.9907] [3.6852] [2.9907] [3.6852] 3.1913 1.98414 3.1057 2.50307 LRPE (3) LRPE (1) [3.6027] [4.3082] [3.6027] [4.3082] 2.7724x 4.82456 3.1026 3.84315 LRPE (1) LRPE (1) [2.9907] [3.6852] [2.9907] [3.6852]

Notes: 1. x indicates that the change variable is found to be I(1).

Table B.6 Unit root tests for trade variables of Fiji and the Netherlands

Exports Imports ADF (lag) PP ADF (lag) PP 2.1157 6.7777 3.4198 4.04106 LRX (3) LRM (1) [3.6027] [4.3082] [3.6027] [4.3082] 2.6384x 16.9826 5.0393 8.137 LRX (3) LRM (1) [2.9907] [3.6852] [2.9907] [3.6852] 2.1662 7.0036 3.8075 3.85501 LYF (3) LY (2) [3.6027] [4.3082] [3.6027] [4.3082] 2.5944x 16.7403 4.4588 7.93719 LYF (3) LY (2) [2.9907] [3.6852] [2.9907] [3.6852] 2.0078 2.17015 2.8073 2.71242 LRPE (1) LRPE (1) [3.6027] [4.3082] [3.6027] [4.3082] 2.9375x 5.02271 3.3474 4.05723 LRPE (1) LRPE (1) [2.9907] [3.6852] [2.9907] [3.6852]

Notes: 1. x indicates that the change variable is found to be I(1).

188

Table B.7 Unit root tests for trade variables of Fiji and France

Exports Imports ADF (lag) PP ADF (lag) PP 2.5131 3.81825 2.6772 3.65323 LRX (4) LRM (1) [3.6027] [4.3082] [3.6027] [4.3082] 3.6199 8.19698 5.1859 9.1697 LRX (4) LRM (2) [2.9907] [3.6852] [2.9907] [3.6852] 2.3889 3.79977 2.6685 5.13536 LYF (4) LY (1) [3.6027] [4.3082] [3.6027] [4.3082] 4.1992 8.20945 5.8904 12.6284 LYF (1) LY (2) [2.9907] [3.6852] [2.9907] [3.6852] 1.4670 2.10126 2.6447 2.46543 LRPE (1) LRPE (1) [3.6027] [4.3082] [3.6027] [4.3082] 2.6559 x 5.18048 3.6430 3.95531 LRPE (1) LRPE (2) [2.9907] [3.6852] [2.9907] [3.6852]

Notes: 1. x indicates that the change variable is found to be I(1).

Table B.8 Unit root tests for import variables of Fiji from Denmark and Italy

Imports - Denmark Imports - Italy ADF (lag) PP ADF (lag) PP 1.1283 3.79581 4.0541 5.67777 LRM (1) LRM (1) [3.6027] [4.3082] [3.6027] [4.3082] 3.6116 12.2094 6.1742 12.7346 LRM (1) LRM (1) [2.9907] [3.6852] [2.9907] [3.6852] 2.2758 2.95741 3.5859 5.78294 LY (1) LY (2) [3.6027] [4.3082] [3.6027] [4.3082] 4.7686 7.47041 4.1985 13.2156 LY (1) LY (1) [2.9907] [3.6852] [2.9907] [3.6852] 3.4252 2.24943 3.0976 2.32014 LRPE (1) LRPE (1) [3.6027] [4.3082] [3.6027] [4.3082] 3.1017 3.80047 4.3963 3.94835 LRPE (1) LRPE (2) [2.9907] [3.6852] [2.9907] [3.6852]

Notes: -

189

Table B.9 Unit root tests for trade variables of Fiji and Australia

Exports Imports ADF (lag) PP ADF (lag) PP 3.0242 2.52367 2.9143 2.75864 LRX (1) LRM (1) [3.5867] [4.2826] [3.5867] [4.2826] 5.5297 4.96 4.2972 4.30998 LRX (1) LRM (1) [2.9798] [3.6661] [2.9798] [3.6661] 3.2778 2.57025 2.2051 4.25936 LYF (1) LY (1) [3.5867] [4.2826] [3.5867] [4.2826] 5.8767 -5.00031 5.1895 10.5879 LYF (1) LY (1) [2.9798] [3.6661] [2.9798] [3.6661] 2.7211 2.44274 2.5015 2.13413 LRPE (1) LRPE (1) [3.5867] [4.2826] [3.5867] [4.2826] 3.4880 5.94067 2.9919 4.20605 LRPE (1) LRPE (1) [2.9798] [3.6661] [2.9798] [3.6661]

Notes: -

Table B.10 Unit root tests for trade variables of Fiji and New Zealand

Exports Imports ADF (lag) PP ADF (lag) PP 2.9554 3.01488 3.3876 2.73675 LRX (1) LRM (2) [3.5867] [4.2826] [3.5867] [4.2826] 7.5890 6.22606 2.9667 x 3.79462 LRX (1) LRM (1) [2.9798] [3.6661] [2.9798] [3.6661] 3.1757 3.41309 4.1554 6.47566 LYF (1) LY (1) [3.5867] [4.2826] [3.5867] [4.2826] 9.8558 6.88104 6.0575 13.6877 LYF (1) LY (1) [2.9798] [3.6661] [2.9798] [3.6661] 2.4129 1.78311 1.7444 1.55835 LRPE (1) LRPE (1) [3.5867] [4.2826] [3.5867] [4.2826] 2.8377 x 5.10319 2.5925 x 4.11977 LRPE (1) LRPE (1) [2.9798] [3.6661] [2.9798] [3.6661]

Notes: 1. x indicates that the change variable is found to be I(1).

190 Appendix C: Tables and Figures C.1 Growth Forecast for several Pacific Island Countries The following Table C.1 shows growth forecasts for PICs using several statistical sources.

Table C.1 Growth forecast for several Pacific Island Countries

Countries 2006 2007 2008 2009 2010 2011 Fiji 3.6 -4.o(a) 1.9(a) 2.8(a) 2.9(a) - Kiribati 5.8(b) 2.5(c) - - - - PNG 3.7 4.3 3.7 4.4 4.7 4.0 RMI 4.0 3.5 3.0 2.5 2.3 1.8 Samoa 3.0 3.5 3.0 3.5 3.5 3.5 Solomon Islands 6.1 5.4 4.2 2.8 1.6 1.5 Timor-Leste -1.6 32.1 3.5 - - - Tonga -3.5 0.8 1.3 1.3 1.2 - Vanuatu 5.5 5.0 4.1 3.0 3.0 -

Source(s): PNG, RMI, Samoa, Solomon Islands: IMF 2006 Article IV Consultation Staff Report

Fiji Islands: Republic of Fiji, Ministry of Finance and National Planning, Economic and Fiscal Update, Supplement to the 2007 Budget Address, Nov. 2006 (a) Reserve Bank of Fiji Quarterly Bulletin, June 2007

Timor-Leste: Country Update: Timor-Leste - IMF, Economics@ANZ Kiribati: Compiled by the Market Information and Analysis Section, Australian Government Department of Foreign Affairs and Trade, using the latest data from the ABS, the IMF and various international sources. (b) All recent data subject to revision; (c) IMF estimate

Vanuatu: Oxford Economics - Vanuatu - Highlights and Key Issues, 02. August 2007

191 C.2 Doing Business 2008 – Enforcing contracts Table C.2 shows the results of the World Bank Group’s Doing Business report 2008 for the category “Enforcing Contracts” for several PICs in a cross country comparison of 178 countries. Also shown the four world leaders.

Table C.2 Doing Business 2008 – Enforcing Contracts

Country Rank Cost (% of claim) Duration Procedures Fiji 62 38.9 397 34 FSM 139 66.0 965 34 Kiribati 71 25.8 660 32 Palau 142 35.3 885 38 PNG 162 110.3 591 43 RMI 60 27.4 476 36 Samoa 80 19.7 455 44 Solomon Isl. 104 78.9 455 37 Timor-Leste 178 163.2 1,800 51 Tonga 58 30.5 350 37 Vanuatu 66 74.7 430 30

Hong Kong 1 14.5 211 24 Luxembourg 2 8.8 321 26 Latvia 3 12.9 279 27 Singapore 4 17.8 120 22

Notes: 1. Number of procedures from the moment the plaintiff files a lawsuit in court until the moment of payment.

2. Duration in calendar days to resolve the dispute.

3. Cost in % of claim are in court fees and attorney fees.

Source: The World Bank Group Doing Business 2008.

192 C.3 Trade openness measurements 1990, 1995, 2000 and 2006 The following tables show the detailed results for the trace openness measurements of TI, RWTI and CTI as well as their rankings for the 210 cross- country analysis for the years 1990, 1995, 2000 and 2006. Additionally Table C.??? shows a change in trade openness measurements over time.

Table C.3 Trade openness measurements for 210 countries – rankings 1990

TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Afghanistan 25.87 195 0.011 153 57.2 173 Albania 37.11 180 0.009 160 71.0 168 Algeria 48.47 150 0.337 43 3434.4 51 Andorra 35.52 184 0.005 171 40.4 182 Angola 62.66 117 0.073 80 954.9 91 Anguilla 155.33 23 0.001 200 31.0 187 Antigua & Barbuda 175.93 11 0.008 163 286.3 130 Argentina 14.91 204 0.237 52 742.8 104 Armenia 81.35 88 0.020 130 337.2 129 Aruba 165.11 15 0.015 138 533.2 113 Australia 32.58 190 1.170 19 8001.7 33 Austria 74.58 96 1.384 18 21678.7 15 Azerbaijan 83.10 85 0.061 91 1062.6 87 Bahamas 110.13 47 0.039 108 907.0 94 Bahrain 182.22 10 0.088 75 3367.2 54 Bangladesh 18.97 201 0.068 87 270.6 133 Barbados 100.83 61 0.020 131 413.1 125 Belarus 89.61 76 0.190 58 3572.1 49 Belgium 136.99 32 3.123 9 89846.6 4 Belize 121.59 41 0.006 170 141.6 152 Benin 48.58 149 0.010 155 102.8 158 Bermuda 91.19 74 0.021 129 397.3 126 Bhutan 61.85 122 0.002 191 25.2 194 Bolivia 46.70 151 0.026 125 250.8 136 Bosnia & Herzegovina 98.68 65 0.075 79 1551.2 75 Botswana 106.49 54 0.042 105 934.6 92 Brazil 13.97 205 0.689 28 2019.6 68 British Virgin Islands 187.23 8 0.002 188 86.9 164 Brunei Darussalam 100.14 62 0.039 110 815.3 98 Bulgaria 70.63 105 0.165 64 2442.7 63 Burkina Faso 34.84 185 0.012 147 89.5 163 Burundi 34.19 187 0.004 174 31.7 186 Cambodia 10.84 206 0.002 190 4.7 207 Cameroon 39.07 175 0.063 89 516.0 117

193 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Canada 51.50 141 3.376 8 36504.1 10 Cape Verde 85.63 82 0.003 181 53.4 177 Cayman Islands 122.54 39 0.010 158 251.1 135 Central African Rep. 42.39 168 0.006 166 55.0 175 Chad 43.81 165 0.008 164 69.9 169 Chile 62.44 118 0.235 53 3086.0 57 China 33.39 189 1.519 16 10650.1 29 Colombia 34.60 186 0.186 61 1349.9 81 Comoros 48.99 148 0.001 195 13.8 201 Congo 89.31 77 0.028 121 527.3 115 Cook Islands 80.71 89 0.001 207 9.0 204 Costa Rica 68.92 110 0.056 93 813.9 99 Côte d'Ivoire 53.37 139 0.071 82 800.1 100 Croatia 111.56 45 0.311 47 7284.3 34 Cuba 63.79 115 0.220 55 2949.6 60 Cyprus 108.49 51 0.070 85 1606.2 71 Czech Republic 82.92 86 0.345 42 6015.3 38 D. P. Rep. of Korea 19.28 200 0.032 116 129.1 154 D. Rep. of the Congo 165.00 16 0.155 65 5370.9 40 Denmark 69.71 108 1.065 21 15591.8 20 Djibouti 190.67 6 0.010 157 392.4 127 Dominica 125.33 37 0.002 186 62.1 172 Dominican Republic 90.89 75 0.072 81 1380.3 79 Ecuador 59.99 127 0.076 78 956.3 90 Egypt 61.23 124 0.271 49 3490.8 50 El Salvador 49.78 146 0.027 123 281.1 131 Equatorial Guinea 98.53 66 0.001 192 30.5 188 Eritrea 94.24 70 0.008 162 158.5 147 Estonia 91.83 73 0.059 92 1131.5 86 Ethiopia 15.76 202 0.019 133 63.8 171 Fiji 129.49 35 0.019 132 529.6 114 Finland 46.43 152 0.729 25 7106.3 35 France 44.03 163 6.137 4 56738.9 8 French Polynesia 41.11 171 0.014 143 117.0 156 Gabon 76.90 92 0.047 97 766.7 102 Gambia 100.85 60 0.004 176 80.1 167 Georgia 85.48 84 0.082 76 1472.6 77 Germany 49.66 147 9.578 2 99890.1 3 Ghana 39.41 174 0.028 122 228.4 137 Greece 40.58 172 0.494 37 4206.8 43 Greenland 88.03 79 0.010 154 186.5 143 Grenada 116.23 44 0.002 187 56.5 174 Guatemala 63.47 116 0.049 96 649.0 106 Guinea 58.35 130 0.018 134 226.6 138 Guinea-Bissau 51.00 144 0.001 196 14.3 200 Guyana 133.55 34 0.006 167 166.9 144

194 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Haiti 39.80 173 0.012 150 97.8 161 Honduras 77.15 91 0.026 124 428.6 122 Hong Kong SAR 254.10 3 2.170 11 115817.2 1 Hungary 51.03 143 0.211 57 2260.9 65 Iceland 66.00 112 0.047 98 655.5 105 India 15.60 203 0.575 32 1884.2 70 Indonesia 45.78 156 0.647 29 6223.8 37 Iran (Islamic Rep. of) 42.20 169 0.429 38 3801.5 48 Iraq 184.27 9 0.356 41 13771.4 24 Ireland 108.48 52 0.584 31 13302.8 25 Israel 65.93 113 0.422 39 5844.1 39 Italy 38.25 177 4.877 6 39177.0 9 Jamaica 107.46 53 0.052 95 1165.0 84 Japan 20.04 198 6.802 3 28617.1 11 Jordan 154.65 24 0.070 86 2271.1 64 Kazakhstan 38.22 178 0.128 67 1023.5 88 Kenya 44.93 160 0.056 94 526.3 116 Kiribati 158.87 21 0.001 208 16.9 199 Kosovo 57.35 132 0.014 145 162.7 146 Kuwait 102.61 58 0.213 56 4593.8 42 Kyrgyzstan 78.74 90 0.006 168 98.1 160 Lao P.'s Dem. Rep. 36.53 181 0.004 177 27.3 192 Latvia 96.73 68 0.096 74 1957.5 69 Lebanon 121.42 42 0.038 111 979.1 89 Lesotho 137.73 31 0.010 159 277.0 132 Liberia 62.05 121 0.003 183 35.0 184 Libyan Arab Jam. 70.80 104 0.230 54 3422.1 52 Liechtenstein 70.11 106 0.011 151 165.1 145 Lithuania 111.17 46 0.127 68 2972.8 58 Luxembourg 189.76 7 0.270 50 10777.5 28 Macao SAR of China 193.94 5 0.065 88 2656.7 61 Madagascar 42.72 167 0.015 139 132.8 153 Malawi 61.52 123 0.012 148 156.7 148 Malaysia 146.89 27 0.727 26 22437.8 13 Maldives 168.57 14 0.004 175 144.4 151 Mali 51.03 142 0.014 142 154.4 149 Malta 162.72 17 0.047 100 1601.9 72 Marshall Islands 158.28 22 0.001 197 40.7 181 Mauritania 101.27 59 0.012 149 254.3 134 Mauritius 141.02 30 0.041 106 1215.5 83 Mexico 38.31 176 1.132 20 9106.0 30 Micronesia (F. S. of) 87.70 80 0.001 193 26.5 193 Monaco 44.03 164 0.003 180 29.4 190 Mongolia 62.17 119 0.009 161 114.7 157 Montenegro 97.27 67 0.023 127 477.2 118 Montserrat 110.03 48 0.001 202 19.2 198 Morocco 57.38 131 0.186 60 2244.9 66

195 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Mozambique 44.38 162 0.014 144 126.0 155 Myanmar 5.58 209 0.003 178 3.8 209 Namibia 119.85 43 0.032 117 794.0 101 Nauru 158.87 20 0.001 201 30.5 189 Nepal 31.63 191 0.013 146 83.2 166 Netherlands 109.22 49 3.658 7 83893.1 5 Netherlands Antilles 175.53 12 0.039 109 1441.2 78 New Caledonia 57.32 133 0.016 136 196.3 142 New Zealand 53.44 138 0.264 51 2961.6 59 Nicaragua 71.81 103 0.029 120 438.4 120 Niger 37.65 179 0.011 152 83.9 165 Nigeria 122.00 40 0.849 24 21754.2 14 Norway 74.31 97 0.971 22 15146.3 22 Occ. Palestinian Ter. 109.21 50 0.024 126 545.4 111 Oman 74.83 95 0.098 72 1545.5 76 Pakistan 29.04 194 0.187 59 1139.0 85 Palau 72.22 102 0.001 204 9.5 203 Panama 148.36 26 0.101 71 3159.4 56 Papua New Guinea 88.44 78 0.033 115 607.0 108 Paraguay 72.74 99 0.040 107 612.9 107 Peru 29.55 193 0.097 73 603.8 109 Philippines 60.80 126 0.303 48 3869.4 46 Poland 45.86 155 0.333 44 3206.4 55 Portugal 69.07 109 0.585 30 8482.6 32 Puerto Rico 149.43 25 0.543 34 17029.0 19 Qatar 85.57 83 0.071 83 1273.1 82 Republic of Korea 56.98 134 1.691 14 20231.9 17 Republic of Moldova 99.66 63 0.044 103 931.1 93 Romania 41.55 170 0.180 62 1570.8 74 Russian Federation 36.11 182 2.314 10 17544.6 18 Rwanda 19.77 199 0.006 169 23.5 195 Saint Kitts and Nevis 134.89 33 0.002 185 68.4 170 Saint Lucia 146.60 28 0.007 165 211.2 141 St Vincent & Grenad. 142.62 29 0.003 179 95.2 162 Samoa 92.33 72 0.001 198 22.6 196 San Marino 537.57 1 0.034 113 3853.8 47 S.Tome and Principe 86.94 81 0.001 205 10.3 202 Saudi Arabia 72.23 101 0.948 23 14372.9 23 Senegal 52.23 140 0.036 112 392.1 128 Serbia 44.42 161 0.170 63 1587.7 73 Seychelles 129.24 36 0.005 172 145.4 150 Sierra Leone 46.22 154 0.005 173 47.5 180 Singapore 358.99 2 1.490 17 112336.3 2 Slovakia 59.07 129 0.110 70 1365.9 80 Slovenia 169.29 13 0.331 45 11766.8 26 Solomon Islands 103.23 57 0.002 184 52.4 178

196 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Somalia 6.60 208 0.001 203 1.0 210 South Africa 43.00 166 0.542 35 4892.2 41 Spain 35.52 183 2.081 12 15526.7 21 Sri Lanka 66.81 111 0.062 90 865.0 95 Sudan 10.00 207 0.018 135 37.7 183 Suriname 54.04 137 0.003 182 33.1 185 Swaziland 162.26 18 0.016 137 541.6 112 Sweden 59.49 128 1.621 15 20244.0 16 Switzerland 70.11 107 1.860 13 27383.2 12 Syrian Arab Republic 56.29 136 0.071 84 834.7 96 Tajikistan 105.27 56 0.034 114 751.0 103 TFYR of Macedonia 64.79 114 0.031 118 423.2 123 Thailand 75.73 94 0.727 27 11564.8 27 Timor-Leste -31.07 210 -0.001 210 4.1 208 Togo 74.30 98 0.014 141 225.2 139 Tonga 98.97 64 0.001 194 28.6 191 Trinidad and Tobago 82.66 87 0.047 99 817.9 97 Tunisia 94.16 71 0.130 66 2579.0 62 Turkey 30.85 192 0.523 36 3387.3 53 Turkmenistan 234.82 4 0.081 77 3997.3 45 Turks and Caicos Isl. 94.34 69 0.001 199 22.3 197 Tuvalu 158.87 19 0.000 209 5.7 205 Uganda 24.54 196 0.010 156 51.9 179 Ukraine 56.35 135 0.572 33 6765.4 36 U. Arab Emirates 106.24 55 0.404 40 9005.7 31 United Kingdom 50.55 145 5.635 5 59820.4 6 U. R. Tanzania: ML 45.31 159 0.023 128 221.8 140 United States 20.54 197 13.304 1 57391.8 7 Uruguay 46.27 153 0.044 104 423.1 124 Uzbekistan 76.70 93 0.127 69 2044.4 67 Vanuatu 122.99 38 0.002 189 54.6 176 Venezuela 61.05 125 0.323 46 4140.1 44 Vietnam 62.08 120 0.045 101 589.1 110 Yemen 33.43 188 0.014 140 101.6 159 Zambia 72.47 100 0.031 119 464.2 119 Zanzibar 45.31 158 0.001 206 5.3 206 Zimbabwe 45.66 157 0.045 102 431.7 121

Notes: See Section 4.1.2 for definitions.

Source: Data derived from the United Nation Statistics Division 2008.

197 Table C.4 Trade openness measurements for 210 countries – rankings 1995

TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Afghanistan 69.15 116 0.017 130 254.1 134 Albania 45.96 168 0.009 150 86.0 165 Algeria 57.31 147 0.189 54 2269.0 59 Andorra 44.77 172 0.005 168 51.1 180 Angola 154.40 19 0.060 84 1955.0 63 Anguilla 176.11 10 0.001 198 38.1 184 Antigua & Barbuda 171.46 13 0.007 163 238.3 137 Argentina 19.77 204 0.399 41 1656.7 71 Armenia 86.11 90 0.009 154 156.7 146 Aruba 171.32 14 0.018 127 636.5 99 Australia 38.77 186 1.164 22 9479.5 30 Austria 70.42 114 1.319 19 19507.0 20 Azerbaijan 85.89 91 0.021 122 372.9 118 Bahamas 104.45 58 0.028 111 614.4 102 Bahrain 152.52 20 0.070 76 2234.0 60 Bangladesh 29.78 199 0.096 67 601.2 104 Barbados 113.19 44 0.017 132 393.6 116 Belarus 103.72 60 0.112 65 2445.6 56 Belgium 131.25 28 2.918 10 80420.8 5 Belize 97.19 68 0.005 172 96.1 162 Benin 63.80 133 0.011 145 145.0 151 Bermuda 81.45 96 0.016 133 280.7 128 Bhutan 82.73 94 0.002 188 33.1 186 Bolivia 49.74 162 0.026 114 272.8 132 Bosnia & Herzegovina 91.88 74 0.015 134 283.2 127 Botswana 88.44 83 0.031 105 568.0 107 Brazil 17.21 205 0.948 24 3426.0 48 British Virgin Islands 173.55 11 0.005 169 196.3 138 Brunei Darussalam 108.88 50 0.043 97 973.2 88 Bulgaria 90.92 77 0.093 68 1778.9 69 Burkina Faso 36.38 191 0.008 160 57.9 176 Burundi 40.21 182 0.003 181 26.6 192 Cambodia 76.61 105 0.020 124 319.0 125 Cameroon 41.37 180 0.029 106 255.0 133 Canada 71.46 112 3.299 8 49507.8 10 Cape Verde 83.10 92 0.003 180 55.6 179 Cayman Islands 122.21 35 0.011 144 278.3 129 Central African Rep. 41.01 181 0.003 178 29.2 188 Chad 59.97 139 0.007 162 85.3 166 Chile 56.10 151 0.316 43 3723.5 45 China 37.32 189 2.209 13 17311.5 21 Colombia 35.50 192 0.257 50 1913.9 64 Comoros 64.33 132 0.001 195 15.8 198 Congo 127.78 30 0.021 120 567.3 108

198 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Cook Islands 106.38 51 0.001 200 17.3 197 Costa Rica 77.92 104 0.071 75 1168.0 81 Côte d'Ivoire 69.15 117 0.060 85 871.9 92 Croatia 88.06 84 0.130 61 2394.7 57 Cuba 26.32 201 0.067 77 370.8 120 Cyprus 100.70 61 0.072 74 1526.6 73 Czech Republic 105.81 52 0.457 37 10157.0 28 D. P.'s Rep. of Korea 9.46 207 0.004 176 7.1 206 D. Rep. of the Congo 52.22 158 0.023 118 252.7 135 Denmark 71.19 113 1.013 23 15142.6 23 Djibouti 94.99 71 0.004 175 75.5 170 Dominica 117.98 40 0.002 186 50.1 181 Dominican Republic 98.46 64 0.092 69 1899.8 66 Ecuador 53.99 156 0.085 71 966.5 89 Egypt 47.39 166 0.255 51 2540.9 54 El Salvador 59.40 140 0.044 95 550.3 109 Equatorial Guinea 145.13 21 0.002 189 57.5 177 Eritrea 105.04 55 0.005 171 109.0 156 Estonia 139.59 26 0.049 94 1433.0 75 Ethiopia 30.36 197 0.017 131 111.5 155 Fiji 113.00 46 0.018 129 417.3 115 Finland 65.14 129 0.665 29 9099.0 32 France 44.40 173 5.451 4 50832.8 9 French Polynesia 39.58 185 0.012 143 96.4 161 Gabon 91.37 76 0.035 102 679.6 98 Gambia 96.02 70 0.003 183 57.4 178 Georgia 42.48 179 0.009 149 80.6 168 Germany 47.44 165 9.357 2 93223.2 3 Ghana 57.31 148 0.029 107 348.3 123 Greece 37.56 188 0.444 38 3501.5 47 Greenland 66.62 121 0.006 164 88.1 164 Grenada 105.71 53 0.002 187 42.8 182 Guatemala 65.17 128 0.067 78 911.2 91 Guinea 40.09 184 0.012 140 103.5 159 Guinea-Bissau 46.81 167 0.001 199 9.1 203 Guyana 164.52 18 0.008 158 276.3 130 Haiti 43.89 177 0.008 157 73.8 171 Honduras 91.78 75 0.028 110 547.8 110 Hong Kong SAR 290.85 3 3.280 9 200344.4 1 Hungary 86.24 87 0.309 45 5604.4 38 Iceland 67.41 120 0.037 101 523.9 111 India 22.95 203 0.665 30 3203.1 50 Indonesia 50.28 160 0.873 25 9218.9 31 Iran (Islamic Rep. of) 34.26 194 0.293 47 2110.8 61 Iraq 36.64 190 0.010 147 76.7 169 Ireland 140.59 24 0.738 27 21779.6 16

199 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Israel 66.37 122 0.491 34 6841.8 36 Italy 47.67 164 4.198 6 42022.9 11 Jamaica 111.63 48 0.051 91 1186.0 79 Japan 16.93 206 6.942 3 24676.7 13 Jordan 124.58 34 0.066 79 1715.6 70 Kazakhstan 82.52 95 0.133 59 2297.6 58 Kenya 55.80 152 0.051 90 598.2 105 Kiribati 104.59 57 0.000 206 8.2 204 Kosovo 57.33 146 0.006 167 68.7 172 Kuwait 96.57 69 0.201 52 4066.1 44 Kyrgyzstan 71.82 111 0.008 156 126.4 153 Lao P.'s Dem. Republic 60.55 137 0.008 155 107.0 158 Latvia 86.18 89 0.033 104 604.5 103 Lebanon 75.90 107 0.065 81 1037.2 85 Lesotho 141.43 23 0.010 146 306.4 126 Liberia 64.73 131 0.001 202 9.3 202 Libyan Arab Jamahiriya 51.60 159 0.103 66 1116.5 82 Liechtenstein 65.72 123 0.012 139 172.2 142 Lithuania 109.49 49 0.056 88 1277.4 78 Luxembourg 191.58 7 0.310 44 12460.7 24 Macao SAR of China 119.88 38 0.065 80 1638.9 72 Madagascar 55.48 153 0.014 136 159.7 144 Malawi 79.73 98 0.009 151 148.5 149 Malaysia 192.11 6 1.334 18 53834.9 8 Maldives 169.86 15 0.005 170 189.0 140 Mali 57.78 145 0.012 141 149.1 148 Malta 177.82 9 0.050 92 1866.0 67 Marshall Islands 126.54 32 0.001 197 27.7 189 Mauritania 79.70 99 0.009 152 147.6 150 Mauritius 119.68 39 0.038 100 950.7 90 Mexico 58.17 144 1.302 20 15901.8 22 Micronesia (Fed. St. of) 87.70 85 0.001 193 26.3 193 Monaco 44.40 174 0.003 182 26.9 191 Mongolia 90.79 78 0.009 153 166.2 143 Montenegro 97.34 66 0.009 148 189.0 139 Montserrat 142.36 22 0.001 203 20.0 196 Morocco 55.32 154 0.159 58 1852.7 68 Mozambique 64.95 130 0.012 142 158.7 145 Myanmar 2.54 208 0.002 191 0.8 209 Namibia 105.16 54 0.029 108 636.0 100 Nauru 104.59 56 0.000 207 7.3 205 Nepal 58.83 142 0.019 125 240.0 136 Netherlands 113.05 45 3.703 7 87918.4 4 Netherlands Antilles 166.74 17 0.034 103 1173.8 80 New Caledonia 45.22 169 0.013 138 121.8 154 New Zealand 57.29 149 0.275 49 3302.3 49 Nicaragua 53.79 157 0.013 137 151.3 147

200 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Niger 47.89 163 0.006 165 63.0 173 Nigeria 88.54 82 0.304 46 5648.4 37 Norway 69.70 115 0.807 26 11805.7 26 Occ. Palestinian Ter. 83.08 93 0.021 121 365.0 121 Oman 79.59 100 0.086 70 1435.7 74 Pakistan 31.42 195 0.200 53 1320.8 77 Palau 78.02 103 0.001 204 9.5 201 Panama 178.30 8 0.126 63 4720.2 41 Papua New Guinea 100.39 62 0.038 99 801.1 95 Paraguay 86.21 88 0.054 89 984.4 87 Peru 30.70 196 0.129 62 830.3 93 Philippines 80.54 97 0.467 36 7894.4 33 Poland 44.25 175 0.481 35 4470.4 42 Portugal 63.61 134 0.562 32 7504.5 35 Puerto Rico 121.42 36 0.430 40 10976.5 27 Qatar 87.67 86 0.056 87 1027.0 86 Republic of Korea 58.75 143 2.375 11 29306.8 12 Republic of Moldova 128.02 29 0.018 128 475.1 112 Romania 58.89 141 0.165 57 2034.3 62 Russian Federation 55.19 155 1.722 15 19962.6 18 Rwanda 34.94 193 0.004 177 25.7 194 Saint Kitts and Nevis 126.41 33 0.002 185 60.3 174 Saint Lucia 139.95 25 0.006 166 177.7 141 S. Vincent & Grenad. 117.67 41 0.002 184 60.0 175 Samoa 90.54 79 0.001 194 27.0 190 San Marino 469.48 1 0.025 115 2503.9 55 Sao Tome and Principe 120.71 37 0.000 205 10.9 199 Saudi Arabia 65.45 127 0.728 28 10005.8 29 Senegal 67.75 119 0.025 116 360.2 122 Serbia 43.69 178 0.062 83 572.8 106 Seychelles 113.51 43 0.005 173 107.5 157 Sierra Leone 40.12 183 0.004 174 33.3 185 Singapore 359.41 2 2.359 12 178028.2 2 Slovakia 112.63 47 0.174 55 4106.2 43 Slovenia 104.20 59 0.165 56 3617.4 46 Solomon Islands 116.51 42 0.003 179 81.3 167 Somalia 2.03 209 0.000 208 0.1 210 South Africa 44.87 170 0.530 33 4994.7 40 Spain 44.77 171 2.089 14 19636.9 19 Sri Lanka 78.74 101 0.082 72 1360.5 76 Sudan 27.91 200 0.025 117 144.0 152 Suriname 171.80 12 0.008 159 274.6 131 Swaziland 168.09 16 0.018 126 633.1 101 Sweden 71.92 110 1.410 17 21290.6 17 Switzerland 65.72 124 1.618 16 22326.2 15 Syrian Arab Republic 68.98 118 0.073 73 1057.7 84 Tajikistan 233.24 5 0.022 119 1099.4 83

201 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank TFYR of Macedonia 75.78 108 0.027 113 422.0 114 Thailand 90.43 80 1.188 21 22560.8 14 Timor-Leste -31.07 210 -0.001 210 5.0 207 Togo 62.29 136 0.008 161 98.3 160 Tonga 89.39 81 0.001 196 20.4 195 Trinidad and Tobago 93.01 73 0.039 98 757.0 96 Tunisia 93.71 72 0.132 60 2600.0 53 Turkey 44.24 176 0.586 31 5442.1 39 Turkmenistan 287.43 4 0.049 93 2968.9 52 Turks and Caicos Isl. 98.90 63 0.001 192 30.6 187 Tuvalu 126.56 31 0.000 209 3.1 208 Uganda 30.30 198 0.014 135 91.2 163 Ukraine 97.23 67 0.370 42 7545.5 34 United Arab Emirates 131.99 27 0.442 39 12245.1 25 United Kingdom 57.15 150 5.076 5 60925.1 7 U. R. Tanzania: ML 65.58 125 0.027 112 371.1 119 United States 23.37 202 13.416 1 65838.2 6 Uruguay 38.10 187 0.057 86 459.9 113 Uzbekistan 60.25 138 0.063 82 802.7 94 Vanuatu 97.76 65 0.002 190 38.1 183 Venezuela 49.99 161 0.293 48 3072.4 51 Vietnam 74.72 109 0.121 64 1901.1 65 Yemen 63.53 135 0.029 109 383.7 117 Zambia 75.94 106 0.021 123 328.6 124 Zanzibar 65.58 126 0.001 201 10.5 200 Zimbabwe 78.70 102 0.044 96 727.4 97

Notes: See Section 4.1.2 for definitions.

Source: Data derived from the United Nation Statistics Division 2008.

202 Table C.5 Trade openness measurements for 210 countries – rankings 2000

TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Afghanistan 112.44 62 0.021 119 491.8 113 Albania 54.88 163 0.013 137 146.6 146 Algeria 62.81 143 0.215 52 2837.5 54 Andorra 61.20 147 0.005 170 66.9 172 Angola 152.46 24 0.087 74 2785.6 55 Anguilla 179.13 13 0.001 195 45.5 181 Antigua & Barbuda 145.10 27 0.006 165 183.9 140 Argentina 22.53 205 0.400 42 1894.5 70 Armenia 73.92 121 0.009 150 137.1 150 Aruba 145.45 26 0.017 129 516.2 111 Australia 45.05 178 1.125 19 10644.7 33 Austria 89.49 93 1.084 22 20379.4 22 Azerbaijan 78.55 111 0.026 109 427.1 116 Bahamas 110.58 65 0.035 104 803.2 96 Bahrain 153.82 23 0.077 78 2475.7 59 Bangladesh 36.88 192 0.112 67 868.3 91 Barbados 107.51 69 0.017 128 388.3 121 Belarus 132.94 36 0.087 75 2416.9 61 Belgium 166.35 17 2.412 12 84255.2 6 Belize 125.59 43 0.007 159 172.2 141 Benin 55.19 162 0.008 151 94.3 160 Bermuda 81.68 108 0.018 127 304.4 127 Bhutan 81.14 109 0.002 187 38.6 185 Bolivia 45.60 176 0.024 114 229.2 133 Bosnia & Herzegovina 88.29 95 0.028 108 516.4 110 Botswana 103.20 72 0.032 106 683.5 102 Brazil 22.84 204 0.859 25 4119.9 47 British Virgin Islands 182.91 10 0.009 148 344.3 125 Brunei Darussalam 103.17 73 0.039 99 838.5 94 Bulgaria 116.76 55 0.092 72 2254.8 63 Burkina Faso 37.04 191 0.006 167 43.5 182 Burundi 29.03 200 0.001 193 7.8 203 Cambodia 111.56 64 0.026 110 599.3 105 Cameroon 42.91 182 0.025 112 224.5 134 Canada 85.40 106 3.870 6 69409.1 7 Cape Verde 78.20 113 0.003 180 43.3 183 Cayman Islands 123.01 46 0.013 135 344.4 124 Central African Rep. 35.47 193 0.002 189 15.0 197 Chad 62.70 144 0.005 168 71.5 169 Chile 61.33 146 0.288 46 3712.8 49 China 44.45 180 3.315 8 30941.4 15 Colombia 40.90 184 0.214 53 1839.4 73 Comoros 42.06 183 0.001 203 4.7 205 Congo 122.18 47 0.025 113 631.0 104

203 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Cook Islands 179.19 12 0.001 198 34.1 186 Costa Rica 94.39 84 0.094 71 1865.0 72 Côte d'Ivoire 73.60 122 0.049 90 759.5 97 Croatia 99.39 78 0.114 63 2389.4 62 Cuba 29.06 199 0.059 84 362.4 123 Cyprus 109.87 67 0.064 81 1472.7 78 Czech Republic 129.77 37 0.460 39 12537.9 29 Dem. P.'s Rep. of Korea 14.25 207 0.009 147 28.3 189 Dem. Rep. of the Congo 13.28 208 0.004 173 12.2 200 Denmark 87.15 97 0.872 24 15961.6 24 Djibouti 99.73 76 0.003 175 72.2 168 Dominica 120.77 50 0.002 188 51.9 177 Dominican Republic 99.72 77 0.123 61 2581.0 57 Ecuador 68.06 135 0.068 80 968.9 87 Egypt 43.78 181 0.273 47 2505.5 58 El Salvador 69.83 129 0.057 86 840.8 92 Equatorial Guinea 196.08 6 0.014 133 594.2 106 Eritrea 75.13 118 0.003 178 47.2 180 Estonia 174.40 15 0.061 83 2247.0 64 Ethiopia 37.25 190 0.018 126 142.7 149 Fiji 135.25 32 0.014 134 404.9 120 Finland 76.89 114 0.586 33 9458.2 34 France 56.23 159 4.668 5 55113.0 10 French Polynesia 46.92 174 0.010 146 93.7 161 Gabon 102.69 74 0.032 105 694.8 101 Gambia 98.01 79 0.003 182 53.1 176 Georgia 62.66 145 0.012 140 157.6 145 Germany 66.40 137 7.887 2 109984.6 3 Ghana 116.57 56 0.036 102 887.9 90 Greece 54.38 164 0.496 37 5665.2 41 Greenland 59.68 152 0.004 174 49.9 178 Grenada 133.28 35 0.003 179 78.1 167 Guatemala 71.60 124 0.077 77 1157.1 82 Guinea 40.70 185 0.008 152 68.2 171 Guinea-Bissau 67.86 136 0.001 197 13.0 199 Guyana 144.65 28 0.006 161 195.7 139 Haiti 45.54 177 0.010 144 95.7 158 Honduras 96.56 82 0.036 101 737.3 98 Hong Kong SAR 282.69 3 2.982 10 177036.1 1 Hungary 147.72 25 0.443 40 13737.2 27 Iceland 74.92 120 0.040 98 636.6 103 India 27.31 201 0.801 27 4592.4 45 Indonesia 71.44 125 0.737 29 11055.0 32 Iran (Islamic Rep. of) 39.50 187 0.254 48 2108.1 68 Iraq 155.51 22 0.204 55 6656.9 39 Ireland 183.24 9 1.103 21 42457.7 11

204 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Israel 76.70 115 0.580 34 9344.4 35 Italy 53.18 166 3.648 7 40732.7 13 Jamaica 97.60 80 0.048 91 986.5 86 Japan 20.60 206 5.986 3 25889.6 17 Jordan 109.86 68 0.058 85 1340.4 80 Kazakhstan 105.70 70 0.121 62 2682.8 56 Kenya 52.81 167 0.042 97 461.5 114 Kiribati 93.67 86 0.000 207 5.9 204 Kosovo 58.03 155 0.007 156 81.6 166 Kuwait 86.62 101 0.204 54 3715.0 48 Kyrgyzstan 89.43 94 0.008 154 143.8 148 Lao P.'s Dem. Republic 64.57 141 0.007 155 94.9 159 Latvia 90.32 91 0.044 96 838.8 93 Lebanon 50.64 170 0.053 88 561.4 109 Lesotho 121.75 48 0.007 158 167.8 143 Liberia 56.39 157 0.002 190 23.4 190 Libyan Arab Jamahiriya 50.58 171 0.108 68 1150.5 83 Liechtenstein 85.65 104 0.013 136 239.2 132 Lithuania 95.90 83 0.068 79 1378.6 79 Luxembourg 278.99 4 0.353 43 20710.8 21 Macao SAR of China 165.05 18 0.063 82 2182.1 66 Madagascar 68.68 132 0.017 130 240.1 131 Malawi 60.39 151 0.007 157 83.5 165 Malaysia 228.87 5 1.292 18 62108.5 9 Maldives 161.10 20 0.006 162 212.7 137 Mali 59.17 153 0.010 145 122.0 153 Malta 193.47 7 0.047 92 1923.3 69 Marshall Islands 126.91 40 0.001 200 20.9 195 Mauritania 91.60 89 0.006 163 118.4 154 Mauritius 123.33 45 0.035 103 915.0 89 Mexico 63.94 142 2.321 13 31172.5 14 Micronesia (F. S. of) 87.70 96 0.001 196 21.9 192 Monaco 56.23 158 0.002 185 29.0 188 Mongolia 134.20 34 0.008 153 224.0 135 Montenegro 91.67 88 0.005 169 103.9 157 Montserrat 169.60 16 0.000 205 13.1 198 Morocco 61.15 149 0.142 59 1819.3 74 Mozambique 50.55 172 0.012 139 128.5 151 Myanmar 1.08 210 0.000 204 0.1 209 Namibia 96.79 81 0.021 120 419.8 117 Nauru 93.67 85 0.000 209 3.8 207 Nepal 55.71 160 0.019 124 217.5 136 Netherlands 134.62 33 3.240 9 91605.5 4 Netherlands Antilles 163.66 19 0.029 107 1005.5 85 New Caledonia 45.02 179 0.009 149 84.2 164 New Zealand 69.33 131 0.228 50 3323.2 52 Nicaragua 74.99 119 0.018 125 290.7 128

205 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Niger 46.58 175 0.005 172 47.5 179 Nigeria 71.38 126 0.301 45 4505.3 46 Norway 76.05 116 0.793 28 12672.4 28 Occ. Palestinian Ter. 87.05 98 0.022 117 409.4 119 Oman 91.14 90 0.113 65 2166.2 67 Pakistan 30.37 197 0.149 58 950.2 88 Palau 118.26 53 0.001 199 21.5 194 Panama 142.39 30 0.103 69 3093.0 53 Papua New Guinea 85.41 105 0.021 121 370.0 122 Paraguay 86.90 99 0.039 100 703.4 100 Peru 34.01 195 0.113 64 809.7 95 Philippines 110.18 66 0.517 36 11956.7 30 Poland 60.65 150 0.650 31 8273.1 36 Portugal 70.41 128 0.496 38 7330.7 38 Puerto Rico 138.23 31 0.598 32 17358.6 23 Qatar 89.61 92 0.099 70 1872.2 71 Republic of Korea 78.49 112 2.510 11 41378.3 12 Republic of Moldova 126.16 42 0.010 143 269.2 129 Romania 71.37 127 0.165 57 2475.5 60 Russian Federation 68.09 134 1.106 20 15808.3 25 Rwanda 29.71 198 0.003 177 20.3 196 Saint Kitts and Nevis 121.12 49 0.002 184 63.4 173 Saint Lucia 116.18 57 0.005 171 125.2 152 St. Vincent & Grenad. 113.26 60 0.002 186 56.4 175 Samoa 84.51 107 0.001 194 21.6 193 San Marino 393.10 1 0.019 123 1569.8 77 Sao Tome and Principe 114.45 59 0.000 206 8.0 202 Saudi Arabia 68.55 133 0.808 26 11625.8 31 Senegal 65.14 140 0.019 122 260.7 130 Serbia 26.73 202 0.015 132 84.7 163 Seychelles 157.37 21 0.006 164 200.9 138 Sierra Leone 57.53 156 0.003 176 40.0 184 Singapore 377.68 2 2.189 15 173616.2 2 Slovakia 143.07 29 0.183 56 5494.0 42 Slovenia 114.64 58 0.138 60 3332.1 51 Solomon Islands 118.19 54 0.002 183 62.0 174 Somalia 2.01 209 0.000 208 0.1 210 South Africa 52.79 168 0.438 41 4860.4 44 Spain 61.20 148 2.221 14 28546.7 16 Sri Lanka 86.68 100 0.091 73 1648.9 76 Sudan 33.00 196 0.024 115 165.1 144 Suriname 118.95 52 0.006 166 143.9 147 Swaziland 176.59 14 0.015 131 568.1 108 Sweden 85.98 102 1.301 17 23487.1 20 Switzerland 85.65 103 1.317 16 23696.1 19 Syrian Arab Republic 65.33 139 0.080 76 1101.1 84 Tajikistan 192.57 8 0.010 142 418.9 118 TFYR of Macedonia 112.15 63 0.025 111 592.3 107

206 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Thailand 124.92 44 0.958 23 25141.2 18 Timor-Leste 73.25 123 0.001 192 22.3 191 Togo 80.36 110 0.007 160 109.7 156 Tonga 69.73 130 0.001 201 9.5 201 Trinidad and Tobago 104.56 71 0.053 87 1170.4 81 Tunisia 92.73 87 0.113 66 2194.9 65 Turkey 55.58 161 0.692 30 8080.6 37 Turkmenistan 179.65 11 0.047 93 1761.1 75 Turks and Caicos Isl. 129.36 39 0.003 181 70.2 170 Tuvalu 126.56 41 0.000 210 2.6 208 Uganda 34.30 194 0.012 138 88.6 162 Ukraine 119.86 51 0.234 49 5895.5 40 United Arab Emirates 129.59 38 0.571 35 15546.3 26 United Kingdom 58.18 154 5.245 4 64080.4 8 U. Rep. Tanzania: ML 37.66 189 0.021 118 169.3 142 United States 26.34 203 16.078 1 88937.6 5 Uruguay 40.29 186 0.051 89 427.9 115 Uzbekistan 53.24 165 0.046 94 512.0 112 Vanuatu 99.75 75 0.002 191 31.9 187 Venezuela 47.86 173 0.350 44 3522.1 50 Vietnam 112.53 61 0.219 51 5181.7 43 Yemen 75.60 117 0.046 95 722.9 99 Zambia 52.47 169 0.011 141 117.1 155 Zanzibar 37.66 188 0.001 202 4.4 206 Zimbabwe 65.63 138 0.023 116 318.3 126

Notes: See Section 4.1.2 for definitions.

Source: Data derived from the United Nation Statistics Division 2008.

207 Table C.6 Trade openness measurements for 210 countries – rankings 2006

TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Afghanistan 113.82 64 0.032 108 762.7 98 Albania 69.83 141 0.022 118 321.0 129 Algeria 72.82 135 0.285 52 4357.3 56 Andorra 58.68 162 0.007 161 81.4 166 Angola 121.04 55 0.193 61 4907.1 52 Anguilla 154.33 21 0.001 194 34.0 184 Antigua and Barbuda 132.01 44 0.004 172 118.8 161 Argentina 37.95 201 0.277 53 2207.3 71 Armenia 56.04 176 0.012 144 142.6 158 Aruba 138.45 35 0.011 145 323.3 127 Australia 43.03 197 1.130 22 10213.7 35 Austria 105.40 75 1.144 21 25327.6 21 Azerbaijan 111.26 67 0.075 80 1741.2 75 Bahamas 103.44 80 0.022 119 470.6 116 Bahrain 164.15 16 0.089 75 3067.9 62 Bangladesh 43.52 196 0.100 72 915.5 94 Barbados 110.51 68 0.013 139 298.2 134 Belarus 124.12 51 0.155 65 4033.2 58 Belgium 177.73 13 2.355 13 87904.1 6 Belize 127.54 47 0.005 170 140.2 159 Benin 46.33 191 0.007 153 71.4 170 Bermuda 83.54 114 0.015 137 256.9 140 Bhutan 105.01 77 0.003 176 72.1 169 Bolivia 59.13 160 0.021 124 255.2 142 Bosnia & Herzegovina 83.35 115 0.032 109 557.5 112 Botswana 82.74 118 0.025 113 428.6 118 Brazil 26.37 207 0.950 27 5262.2 49 British Virgin Islands 186.07 10 0.006 163 253.7 143 Brunei Darussalam 101.10 86 0.039 98 831.6 95 Bulgaria 146.20 29 0.150 66 4609.6 54 Burkina Faso 33.07 203 0.007 159 46.3 176 Burundi 48.59 184 0.002 190 15.6 196 Cambodia 147.58 27 0.032 107 992.5 91 Cameroon 53.28 178 0.033 104 372.7 120 Canada 70.67 138 3.030 9 44965.9 12 Cape Verde 68.16 145 0.003 179 36.8 183 Cayman Islands 123.22 53 0.010 149 263.3 138 Central African Rep. 34.12 202 0.002 189 11.7 200 Chad 83.18 117 0.019 126 325.4 125 Chile 76.28 128 0.375 43 6013.3 46 China 73.63 131 6.626 3 102456.0 4 Colombia 46.83 189 0.207 58 2034.7 73 Comoros 42.84 198 0.001 201 5.2 205 Congo 137.32 36 0.033 106 959.0 93

208 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Cook Islands 136.68 39 0.001 197 23.5 192 Costa Rica 106.43 74 0.080 78 1777.5 74 Côte d'Ivoire 88.65 104 0.054 90 1003.2 89 Croatia 100.56 87 0.143 68 3027.0 64 Cuba 29.60 205 0.052 91 325.3 126 Cyprus 104.20 78 0.064 87 1401.8 80 Czech Republic 145.84 30 0.695 33 21288.0 25 D. P.'s Rep. of Korea 16.92 208 0.007 158 24.6 191 Dem. Rep. of Congo 74.01 130 0.021 123 320.6 130 Denmark 102.44 82 0.959 26 20623.7 27 Djibouti 97.07 92 0.002 180 50.6 175 Dominica 105.12 76 0.001 193 24.7 190 Dominican Republic 73.50 132 0.078 79 1209.5 85 Ecuador 66.79 149 0.092 74 1292.4 84 Egypt 59.73 157 0.222 56 2782.6 65 El Salvador 72.66 136 0.045 93 686.6 103 Equatorial Guinea 132.82 43 0.043 95 1187.5 86 Eritrea 47.42 188 0.002 183 18.6 194 Estonia 175.85 14 0.095 73 3525.7 60 Ethiopia 47.73 186 0.021 120 214.5 145 Fiji 126.75 49 0.013 138 353.3 121 Finland 82.53 119 0.584 35 10118.7 37 France 56.45 170 4.256 6 50460.7 10 French Polynesia 47.55 187 0.009 150 90.4 165 Gabon 103.79 79 0.033 105 724.9 101 Gambia 121.40 54 0.002 182 53.3 174 Georgia 89.79 102 0.023 116 442.3 117 Germany 84.88 111 8.273 2 147469.9 3 Ghana 87.14 107 0.036 101 658.9 105 Greece 45.65 192 0.474 37 4546.2 55 Greenland 57.29 166 0.003 177 38.5 182 Grenada 114.10 63 0.002 187 40.6 181 Guatemala 66.97 148 0.069 85 966.9 92 Guinea 62.27 152 0.006 166 78.5 168 Guinea-Bissau 84.89 110 0.001 195 16.4 195 Guyana 206.63 6 0.006 164 272.5 136 Haiti 57.36 165 0.009 151 107.7 162 Honduras 107.27 73 0.034 103 758.4 99 Hong Kong SAR 400.05 2 2.559 10 214942.5 1 Hungary 155.08 19 0.586 34 19086.0 28 Iceland 81.80 121 0.043 94 741.6 100 India 48.70 183 1.484 17 15176.8 30 Indonesia 56.94 167 0.700 32 8373.9 39 Iran (Islamic Rep. of) 56.16 175 0.459 39 5410.8 48 Iraq 183.22 12 0.290 51 11169.3 33 Ireland 150.42 25 1.107 24 34965.5 14

209 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Israel 88.52 105 0.419 42 7789.9 40 Italy 56.50 169 3.523 7 41799.8 13 Jamaica 98.00 90 0.034 102 702.1 102 Japan 30.41 204 4.551 5 29066.8 18 Jordan 146.54 28 0.071 84 2181.3 72 Kazakhstan 96.04 94 0.250 54 5048.0 50 Kenya 61.04 153 0.049 92 627.1 108 Kiribati 102.13 84 0.000 205 5.5 204 Kosovo 58.14 163 0.007 160 81.2 167 Kuwait 94.35 100 0.322 48 6379.5 44 Kyrgyzstan 115.72 59 0.011 146 267.4 137 Lao People's Dem. Rep. 61.00 154 0.007 155 91.0 164 Latvia 108.61 71 0.074 81 1680.3 77 Lebanon 50.27 182 0.037 100 395.1 119 Lesotho 128.53 46 0.006 165 169.3 151 Liberia 80.50 122 0.002 184 31.5 186 Libyan Arab Jamahiriya 113.65 65 0.193 60 4614.5 53 Liechtenstein 100.52 88 0.012 143 256.6 141 Lithuania 135.38 40 0.134 69 3802.8 59 Luxembourg 336.02 4 0.459 38 32418.7 15 Macao SAR of China 149.55 26 0.072 82 2265.0 70 Madagascar 67.79 146 0.013 141 179.3 148 Malawi 79.61 124 0.006 167 100.0 163 Malaysia 221.02 5 1.111 23 51555.7 9 Maldives 188.51 8 0.006 169 228.4 144 Mali 63.26 151 0.013 140 169.2 152 Malta 154.42 20 0.031 110 999.3 90 Marshall Islands 126.90 48 0.001 202 14.6 197 Mauritania 115.71 60 0.011 148 259.7 139 Mauritius 133.67 42 0.029 111 811.9 97 Mexico 67.15 147 1.880 15 26505.9 20 Micronesia (Fed. St. of) 87.70 106 0.001 198 13.3 198 Monaco 56.45 171 0.002 181 26.0 189 Mongolia 158.67 18 0.015 136 500.0 113 Montenegro 95.94 95 0.007 154 146.8 157 Montserrat 152.07 24 0.000 206 7.6 203 Morocco 72.42 137 0.160 64 2429.2 67 Mozambique 79.77 123 0.020 125 329.7 124 Myanmar 0.19 210 0.000 210 0.0 210 Namibia 86.16 109 0.018 127 332.0 123 Nauru 102.13 83 0.000 207 4.1 207 Nepal 56.28 174 0.015 135 179.8 147 Netherlands 142.73 33 3.198 8 95847.2 5 Netherlands Antilles 185.29 11 0.021 122 815.5 96 New Caledonia 44.28 194 0.007 156 65.9 172 New Zealand 59.67 158 0.213 57 2674.4 66 Nicaragua 89.18 103 0.016 133 302.6 133

210 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank Niger 50.51 180 0.006 168 61.2 173 Nigeria 50.38 181 0.226 55 2386.9 68 Norway 72.87 134 0.821 30 12563.5 32 Occ. Palestinian Ter. 75.98 129 0.011 147 173.5 149 Oman 109.15 70 0.133 70 3038.4 63 Pakistan 40.49 200 0.201 59 1706.5 76 Palau 160.57 17 0.001 196 28.4 187 Panama 69.27 143 0.040 96 581.8 110 Papua New Guinea 84.81 112 0.018 129 312.7 131 Paraguay 69.16 144 0.021 121 308.8 132 Peru 48.25 185 0.147 67 1485.7 79 Philippines 94.94 97 0.375 44 7467.7 41 Poland 82.52 120 0.935 28 16197.2 29 Portugal 69.82 142 0.452 40 6624.9 43 Puerto Rico 143.00 32 0.438 41 13146.7 31 Qatar 94.71 98 0.168 63 3350.8 61 Republic of Korea 86.76 108 2.555 11 46554.4 11 Republic of Moldova 141.11 34 0.016 134 473.5 115 Romania 79.59 125 0.327 47 5457.3 47 Russian Federation 55.07 177 1.830 16 21165.2 26 Rwanda 44.11 195 0.003 175 31.6 185 Saint Kitts and Nevis 112.05 66 0.002 185 43.3 177 Saint Lucia 134.83 41 0.004 173 120.2 160 S. Vincent & Grenad. 116.04 58 0.002 186 42.8 178 Samoa 84.10 113 0.001 192 21.8 193 San Marino 369.15 3 0.018 128 1363.2 82 Sao Tome and Principe 153.82 23 0.000 204 12.5 199 Saudi Arabia 96.34 93 1.182 20 23921.9 24 Senegal 70.10 140 0.022 117 322.9 128 Serbia 73.40 133 0.089 76 1369.9 81 Seychelles 201.75 7 0.005 171 203.9 146 Sierra Leone 56.51 168 0.003 174 41.4 179 Singapore 473.51 1 2.112 14 209962.1 2 Slovakia 164.60 15 0.306 49 10572.3 34 Slovenia 137.29 37 0.171 62 4928.5 51 Solomon Islands 118.20 57 0.002 188 41.2 180 Somalia 2.01 209 0.000 208 0.1 209 South Africa 59.52 159 0.498 36 6221.4 45 Spain 58.68 161 2.426 12 29894.4 17 Sri Lanka 76.96 127 0.071 83 1148.9 87 Sudan 50.65 179 0.060 88 640.3 107 Suriname 115.39 61 0.007 157 171.7 150 Swaziland 187.31 9 0.017 130 676.0 104 Sweden 94.42 99 1.220 19 24182.7 23 Switzerland 100.52 89 1.271 18 26820.7 19 Syrian Arab Republic 77.55 126 0.082 77 1334.9 83 Tajikistan 131.04 45 0.012 142 342.3 122

211 TI (%) TI - Rank RWTI (%) RWTI-Rank CTI CTI - Rank TFYR of Macedonia 114.54 62 0.024 115 586.1 109 Thailand 143.56 31 0.999 25 30121.6 16 Timor-Leste 41.95 199 0.001 203 4.4 206 Togo 95.29 96 0.007 152 147.0 156 Tonga 83.25 116 0.001 200 11.4 201 Trinidad and Tobago 107.83 72 0.066 86 1495.2 78 Tunisia 102.67 81 0.106 71 2291.0 69 Turkey 60.36 155 0.799 31 10127.4 36 Turkmenistan 118.39 56 0.026 112 645.6 106 Turks & Caicos Islands 123.78 52 0.003 178 70.3 171 Tuvalu 126.56 50 0.000 209 2.9 208 Uganda 46.57 190 0.016 132 158.9 154 Ukraine 97.25 91 0.349 46 7135.8 42 United Arab Emirates 137.15 38 0.843 29 24287.2 22 United Kingdom 64.62 150 5.173 4 70204.3 8 Un. Rep. Tanzania: ML 56.37 172 0.024 114 289.8 135 United States 28.00 206 12.465 1 73298.4 7 Uruguay 60.16 156 0.039 97 495.2 114 Uzbekistan 70.40 139 0.038 99 566.6 111 Vanuatu 101.57 85 0.001 191 26.4 188 Venezuela 57.60 164 0.351 45 4240.3 57 Vietnam 154.07 22 0.301 50 9753.1 38 Yemen 92.16 101 0.058 89 1116.0 88 Zambia 45.49 193 0.017 131 160.9 153 Zanzibar 56.37 173 0.001 199 8.1 202 Zimbabwe 110.46 69 0.007 162 152.6 155

Notes: See Section 4.1.2 for definitions.

Source: Data derived from the United Nation Statistics Division 2008.

212 Table C.7 Change in trade openness rankings (1990 – 1995 – 2000 – 2006)

2006 2000 1995 1990 TI RWTI CTI TI RWTI CTI TI RWTI CTI TI RWTI CTI Afghanistan 64 108 98 62 119 113 116 130 134 195 153 173 Albania 141 118 129 163 137 146 168 150 165 180 160 168 Algeria 135 52 56 143 52 54 147 54 59 150 43 51 Andorra 162 161 166 147 170 172 172 168 180 184 171 182 Angola 55 61 52 24 74 55 19 84 63 117 80 91 Anguilla 21 194 184 13 195 181 10 198 184 23 200 187 Antigua & Barbuda 44 172 161 27 165 140 13 163 137 11 163 130 Argentina 201 53 71 205 42 70 204 41 71 204 52 104 Armenia 176 144 158 121 150 150 90 154 146 88 130 129 Aruba 35 145 127 26 129 111 14 127 99 15 138 113 Australia 197 22 35 178 19 33 186 22 30 190 19 33 Austria 75 21 21 93 22 22 114 19 20 96 18 15 Azerbaijan 67 80 75 111 109 116 91 122 118 85 91 87 Bahamas 80 119 116 65 104 96 58 111 102 47 108 94 Bahrain 16 75 62 23 78 59 20 76 60 10 75 54 Bangladesh 196 72 94 192 67 91 199 67 104 201 87 133 Barbados 68 139 134 69 128 121 44 132 116 61 131 125 Belarus 51 65 58 36 75 61 60 65 56 76 58 49 Belgium 13 13 6 17 12 6 28 10 5 32 9 4 Belize 47 170 159 43 159 141 68 172 162 41 170 152 Benin 191 153 170 162 151 160 133 145 151 149 155 158 Bermuda 114 137 140 108 127 127 96 133 128 74 129 126 Bhutan 77 176 169 109 187 185 94 188 186 122 191 194 Bolivia 160 124 142 176 114 133 162 114 132 151 125 136 Bosnia & Herzeg. 115 109 112 95 108 110 74 134 127 65 79 75 Botswana 118 113 118 72 106 102 83 105 107 54 105 92 Brazil 207 27 49 204 25 47 205 24 48 205 28 68 British Virgin Isl. 10 163 143 10 148 125 11 169 138 8 188 164 Brunei Darussalam 86 98 95 73 99 94 50 97 88 62 110 98 Bulgaria 29 66 54 55 72 63 77 68 69 105 64 63 Burkina Faso 203 159 176 191 167 182 191 160 176 185 147 163 Burundi 184 190 196 200 193 203 182 181 192 187 174 186 Cambodia 27 107 91 64 110 105 105 124 125 206 190 207 Cameroon 178 104 120 182 112 134 180 106 133 175 89 117 Canada 138 9 12106 6 7 112 8 10 141 8 10 Cape Verde 145 179 183 113 180 183 92 180 179 82 181 177 Cayman Islands 53 149 138 46 135 124 35 144 129 39 158 135 Central Afr. Rep. 202 189 200 193 189 197 181 178 188 168 166 175 Chad 117 126 125 144 168 169 139 162 166 165 164 169 Chile 128 43 46 146 46 49 151 43 45 118 53 57 China 131 3 4 180 8 15 189 13 21 189 16 29 Colombia 189 58 73 184 53 73 192 50 64 186 61 81 Comoros 198 201 205 183 203 205 132 195 198 148 195 201

213 2006 2000 1995 1990 TI RWTI CTI TI RWTI CTI TI RWTI CTI TI RWTI CTI Congo 36 106 93 47 113 104 30 120 108 77 121 115 Cook Islands 39 197 192 12 198 186 51 200 197 89 207 204 Costa Rica 74 78 74 84 71 72 104 75 81 110 93 99 Côte d'Ivoire 104 90 89 122 90 97 117 85 92 139 82 100 Croatia 87 68 64 78 63 62 84 61 57 45 47 34 Cuba 205 91 126 199 84 123 201 77 120 115 55 60 Cyprus 78 87 80 67 81 78 61 74 73 51 85 71 Czech Republic 30 33 25 37 39 29 52 37 28 86 42 38 D. P.'s Rep. Korea 208 158 191 207 147 189 207 176 206 200 116 154 Dem. Rep. Congo 130 123 130 208 173 200 158 118 135 16 65 40 Denmark 82 26 27 97 24 24 113 23 23 108 21 20 Djibouti 92 180 175 76 175 168 71 175 170 6 157 127 Dominica 76 193 190 50 188 177 40 186 181 37 186 172 Dominican Rep. 132 79 85 77 61 57 64 69 66 75 81 79 Ecuador 149 74 84 135 80 87 156 71 89 127 78 90 Egypt 157 56 65 181 47 58 166 51 54 124 49 50 El Salvador 136 93 103 129 86 92 140 95 109 146 123 131 Equatorial Guinea 43 95 86 6 133 106 21 189 177 66 192 188 Eritrea 188 183 194 118 178 180 55 171 156 70 162 147 Estonia 14 73 60 15 83 64 26 94 75 73 92 86 Ethiopia 186 120 145 190 126 149 197 131 155 202 133 171 Fiji 49 138 121 32 134 120 46 129 115 35 132 114 Finland 119 35 37 114 33 34 129 29 32 152 25 35 France 170 6 10159 5 10173 4 9 163 4 8 French Polynesia 187 150 165 174 146 161 185 143 161 171 143 156 Gabon 79 105 101 74 105 101 76 102 98 92 97 102 Gambia 54 182 174 79 182 176 70 183 178 60 176 167 Georgia 102 116 117 145 140 145 179 149 168 84 76 77 Germany 111 2 3 137 2 3 165 2 3 147 2 3 Ghana 107 101 105 56 102 90 148 107 123 174 122 137 Greece 192 37 55 164 37 41 188 38 47 172 37 43 Greenland 166 177 182 152 174 178 121 164 164 79 154 143 Grenada 63 187 181 35 179 167 53 187 182 44 187 174 Guatemala 148 85 92 124 77 82 128 78 91 116 96 106 Guinea 152 166 168 185 152 171 184 140 159 130 134 138 Guinea-Bissau 110 195 195 136 197 199 167 199 203 144 196 200 Guyana 6 164 136 28 161 139 18 158 130 34 167 144 Haiti 165 151 162 177 144 158 177 157 171 173 150 161 Honduras 73 103 99 82 101 98 75 110 110 91 124 122 Hong Kong SAR 2 10 1 3 10 1 3 9 1 3 11 1 Hungary 19 34 28 25 40 27 87 45 38 143 57 65 Iceland 121 94 100 120 98 103 120 101 111 112 98 105 India 183 17 30 201 27 45 203 30 50 203 32 70 Indonesia 167 32 39 125 29 32 160 25 31 156 29 37 Iran (Islamic Rep.) 175 39 48 187 48 68 194 47 61 169 38 48 Iraq 12 51 33 22 55 39 190 147 169 9 41 24

214 2006 2000 1995 1990 TI RWTI CTI TI RWTI CTI TI RWTI CTI TI RWTI CTI Ireland 25 24 14 9 21 11 24 27 16 52 31 25 Israel 105 42 40 115 34 35 122 34 36 113 39 39 Italy 169 7 13166 7 13164 6 11 177 6 9 Jamaica 90 102 102 80 91 86 48 91 79 53 95 84 Japan 204 5 18206 3 17206 3 13 198 3 11 Jordan 28 84 72 68 85 80 34 79 70 24 86 64 Kazakhstan 94 54 50 70 62 56 95 59 58 178 67 88 Kenya 153 92 108 167 97 114 152 90 105 160 94 116 Kiribati 84 205 204 86 207 204 57 206 204 21 208 199 Kosovo 163 160 167 155 156 166 146 167 172 132 145 146 Kuwait 100 48 44 101 54 48 69 52 44 58 56 42 Kyrgyzstan 59 146 137 94 154 148 111 156 153 90 168 160 Lao P.'s Dem. Rep. 154 155 164 141 155 159 137 155 158 181 177 192 Latvia 71 81 77 91 96 93 89 104 103 68 74 69 Lebanon 182 100 119 170 88 109 107 81 85 42 111 89 Lesotho 46 165 151 48 158 143 23 146 126 31 159 132 Liberia 122 184 186 157 190 190 131 202 202 121 183 184 Libyan Arab Jam. 65 60 53 171 68 83 159 66 82 104 54 52 Liechtenstein 88 143 141 104 136 132 123 139 142 106 151 145 Lithuania 40 69 59 83 79 79 49 88 78 46 68 58 Luxembourg 4 38 15 4 43 21 7 44 24 7 50 28 Macao SAR 26 82 70 18 82 66 38 80 72 5 88 61 Madagascar 146 141 148 132 130 131 153 136 144 167 139 153 Malawi 124 167 163 151 157 165 98 151 149 123 148 148 Malaysia 5 23 9 5 18 9 6 18 8 27 26 13 Maldives 8 169 144 20 162 137 15 170 140 14 175 151 Mali 151 140 152 153 145 153 145 141 148 142 142 149 Malta 20 110 90 7 92 69 9 92 67 17 100 72 Marshall Islands 48 202 197 40 200 195 32 197 189 22 197 181 Mauritania 60 148 139 89 163 154 99 152 150 59 149 134 Mauritius 42 111 97 45 103 89 39 100 90 30 106 83 Mexico 147 15 20 142 13 14 144 20 22 176 20 30 Micronesia (F. S.) 106 198 198 96 196 192 85 193 193 80 193 193 Monaco 171 181 189 158 185 188 174 182 191 164 180 190 Mongolia 18 136 113 34 153 135 78 153 143 119 161 157 Montenegro 95 154 157 88 169 157 66 148 139 67 127 118 Montserrat 24 206 203 16 205 198 22 203 196 48 202 198 Morocco 137 64 67 149 59 74 154 58 68 131 60 66 Mozambique 123 125 124 172 139 151 130 142 145 162 144 155 Myanmar 210 210 210 210 204 209 208 191 209 209 178 209 Namibia 109 127 123 81 120 117 54 108 100 43 117 101 Nauru 83 207 207 85 209 207 56 207 205 20 201 189 Nepal 174 135 147 160 124 136 142 125 136 191 146 166 Netherlands 33 8 5 33 9 4 45 7 4 49 7 5 Neth. Antilles 11 122 96 19 107 85 17 103 80 12 109 78 New Caledonia 194 156 172 179 149 164 169 138 154 133 136 142

215 2006 2000 1995 1990 TI RWTI CTI TI RWTI CTI TI RWTI CTI TI RWTI CTI New Zealand 158 57 66 131 50 52 149 49 49 138 51 59 Nicaragua 103 133 133 119 125 128 157 137 147 103 120 120 Niger 180 168 173 175 172 179 163 165 173 179 152 165 Nigeria 181 55 68 126 45 46 82 46 37 40 24 14 Norway 134 30 32 116 28 28 115 26 26 97 22 22 Oc. Palestinian T. 129 147 149 98 117 119 93 121 121 50 126 111 Oman 70 70 63 90 65 67 100 70 74 95 72 76 Pakistan 200 59 76 197 58 88 195 53 77 194 59 85 Palau 17 196 187 53 199 194 103 204 201 102 204 203 Panama 143 96 110 30 69 53 8 63 41 26 71 56 Papua New Guinea 112 129 131 105 121 122 62 99 95 78 115 108 Paraguay 144 121 132 99 100 100 88 89 87 99 107 107 Peru 185 67 79 195 64 95 196 62 93 193 73 109 Philippines 97 44 41 66 36 30 97 36 33 126 48 46 Poland 120 28 29 150 31 36 175 35 42 155 44 55 Portugal 142 40 43 128 38 38 134 32 35 109 30 32 Puerto Rico 32 41 31 31 32 23 36 40 27 25 34 19 Qatar 98 63 61 92 70 71 86 87 86 83 83 82 Republic of Korea 108 11 11 112 11 12 143 11 12 134 14 17 Rep. of Moldova 34 134 115 42 143 129 29 128 112 63 103 93 Romania 125 47 47 127 57 60 141 57 62 170 62 74 Russian Federation 177 16 26 134 20 25 155 15 18 182 10 18 Rwanda 195 175 185 198 177 196 193 177 194 199 169 195 St. Kitts & Nevis 66 185 177 49 184 173 33 185 174 33 185 170 Saint Lucia 41 173 160 57 171 152 25 166 141 28 165 141 S. Vincent & Gren. 58 186 178 60 186 175 41 184 175 29 179 162 Samoa 113 192 193 107 194 193 79 194 190 72 198 196 San Marino 3 128 82 1 123 77 1 115 55 1 113 47 S. Tome & Prin. 23 204 199 59 206 202 37 205 199 81 205 202 Saudi Arabia 93 20 24 133 26 31 127 28 29 101 23 23 Senegal 140 117 128 140 122 130 119 116 122 140 112 128 Serbia 133 76 81 202 132 163 178 83 106 161 63 73 Seychelles 7 171 146 21 164 138 43 173 157 36 172 150 Sierra Leone 168 174 179 156 176 184 183 174 185 154 173 180 Singapore 1 14 2 2 15 2 2 12 2 2 17 2 Slovakia 15 49 34 29 56 42 47 55 43 129 70 80 Slovenia 37 62 51 58 60 51 59 56 46 13 45 26 Solomon Islands 57 188 180 54 183 174 42 179 167 57 184 178 Somalia 209 208 209 209 208 210 209 208 210 208 203 210 South Africa 159 36 45 168 41 44 170 33 40 166 35 41 Spain 161 12 17 148 14 16 171 14 19 183 12 21 Sri Lanka 127 83 87 100 73 76 101 72 76 111 90 95 Sudan 179 88 107 196 115 144 200 117 152 207 135 183 Suriname 61 157 150 52 166 147 12 159 131 137 182 185 Swaziland 9 130 104 14 131 108 16 126 101 18 137 112 Sweden 99 19 23 102 17 20 110 17 17 128 15 16

216 2006 2000 1995 1990 TI RWTI CTI TI RWTI CTI TI RWTI CTI TI RWTI CTI Switzerland 89 18 19 103 16 19 124 16 15 107 13 12 Syrian Arab Rep. 126 77 83 139 76 84 118 73 84 136 84 96 Tajikistan 45 142 122 8 142 118 5 119 83 56 114 103 TFYR of Maced. 62 115 109 63 111 107 108 113 114 114 118 123 Thailand 31 25 16 44 23 18 80 21 14 94 27 27 Timor-Leste 199 203 206 123 192 191 210 210 207 210 210 208 Togo 96 152 156 110 160 156 136 161 160 98 141 139 Tonga 116 200 201 130 201 201 81 196 195 64 194 191 Trinidad & Tobago 72 86 78 71 87 81 73 98 96 87 99 97 Tunisia 81 71 69 87 66 65 72 60 53 71 66 62 Turkey 155 31 36 161 30 37 176 31 39 192 36 53 Turkmenistan 56 112 106 11 93 75 4 93 52 4 77 45 Turks & Caicos Isl. 52 178 171 39 181 170 63 192 187 69 199 197 Tuvalu 50 209 208 41 210 208 31 209 208 19 209 205 Uganda 190 132 154 194 138 162 198 135 163 196 156 179 Ukraine 91 46 42 51 49 40 67 42 34 135 33 36 Un. Arab Emirates 38 29 22 38 35 26 27 39 25 55 40 31 United Kingdom 150 4 8 154 4 8 150 5 7 145 5 6 U. R. Tanzania: ML 172 114 135 189 118 142 125 112 119 159 128 140 United States 206 1 7 203 1 5 202 1 6 197 1 7 Uruguay 156 97 114 186 89 115 187 86 113 153 104 124 Uzbekistan 139 99 111 165 94 112 138 82 94 93 69 67 Vanuatu 85 191 188 75 191 187 65 190 183 38 189 176 Venezuela 164 45 57 173 44 50 161 48 51 125 46 44 Vietnam 22 50 38 61 51 43 109 64 65 120 101 110 Yemen 101 89 88 117 95 99 135 109 117 188 140 159 Zambia 193 131 153 169 141 155 106 123 124 100 119 119 Zanzibar 173 199 202 188 202 206 126 201 200 158 206 206 Zimbabwe 69 162 155 138 116 126 102 96 97 157 102 121

Notes: See Section 4.1.2 for definitions.

Source: Data derived from the United Nation Statistics Division 2008.

217 C.4 Short-run dynamics

Definitions

ECMt-1: Error Correction Term

LYFt: First difference of log variable of real foreign income

LYt: First difference of log variable of real domestic income

LRXt: First difference of log variable of real exports

LRMt: First difference of log variable of real foreign imports

LRPt: First difference of log variable of relative price variable

LRPEt: First difference of log variable of relative price variable with exchange rate

DUMn: Dummy variables

SER: Standard error of regression

Diagnostic tests of: 2 X sc: Serial correlation 2 X ff: Functional form 2 X n: Normality 2 X hs: Heteroscedasticity

218

Table C.8 Dynamic adjustment estimates for exports demand of Pacific ACPs to the EC for 1976 - 2006

Variable GETS FMOLS JML 0.018869* Intercept [2.7856] -0.12638 -0.38938 -0.21634 ECMt-1 [4.2166] [5.0573] [3.1573] 0.88394 0.79187 0.89967 LYF [27.8327] [18.6931] [24.5095] -0.71228 LYF (-1) [5.7582] 0.78623 0.11487* LRX(-1) [5.8987] [2.7494] -0.058210 LRX(-3) [3.1906] -0.058210 LRX(-4) [3.1906] 0.47946 LRP [4.9469] 0.59839 0.39000 LRP(-2) [4.1966] [3.5298] -0.058210 LRP(-3) [3.1906] 0.40689* LRP(-4) 2.7768 -0.038753* DUM1 [-2.3799] 0.083317 0.095366 DUM2 [6.1431] [6.0106] 2 Adjusted R 0.97487 0.95724 0.96440 SER 0.020473 0.025891 0.024366 2 0.93450 3.4889 0.54884 X sc [0.334] [0.062] [0.459] 2 0.28756 1.0910 0.072632 X ff [0.592] [0.296] [0.788] 2 1.7968 0.65690 0.49929 X n [0.407] [0.720] [0.779] 2 0.28940 1.0173 0.28961 X hs [0.591] [0.313] [0.590]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

219

Table C.9 Dynamic adjustment estimates of demand for Pacific ACPs imports from the EC for 1976 - 2006

Variable GETS FMOLS JML -0.015122* Intercept [2.4530] -1.3536 -1.2459 -1.3771 ECMt-1 [6.8960] [6.6504] [7.3919] 1.0409 1.0274 1.0326 LY [28.1558] [32.3569] [35.8700] -0.76637 -0.59652 -0.75763 LY(-1) [4.3361] [3.8188] [4.5240] 0.58784 0.52328 0.60958 LRM(-1) [3.9400] [3.8811] [4.5090] 0.10860* 0.11815 0.11932 LRM(-4) [2.8171] [3.1686] [3.5018] 0.92651 0.82583 0.89978 LRP [5.1953] [5.6140] [6.4902] 0.035788* 0.039407* 0.037733 DUM1 [2.4784] [2.8485] [3.0193] -0.071016** -0.057535** DUM2 [2.0803] [2.0096] 2 Adjusted R 0.98474 0.98309 0.98594 SER 0.021675 0.022816 0.020806 2 0.18519 0.35702 0.6447E-3 X sc [0.667] [0.550] [0.980] 2 0.048266 0.24688 0.17572 X ff [0.826] [0.619] [0.675] 2 0.59828 1.1892 0.61855 X n [0.741] [0.552] [0.734] 2 0.0062195 0.17286 0.12415 X hs [0.937] [0.678] [0.725]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

220

Table C.10 Dynamic adjustment estimates for exports demand of Pacific ACPs to Australia & New Zealand for 1975 - 2006

Variable GETS FMOLS JML

Intercept -

-0.36128 -0.30152 ECMt-1 [3.7418] - [10.5620] -0.21615* -0.23407 LRX(-3) [2.8456] - [3.1238] 0.93155 0.93601 LYF [10.9076] - [13.4294] 0.80058 0.74283 - LRP(-1) [6.4325] [6.9002] 0.58807 0.50822 - LRP(-2) [4.4702] [4.7468] 0.75063 0.67915 LRP(-3) [5.0844] - [5.5907] -0.063902 -0.056171 DUM1 [2.9919] - [-2.9168] -0.039203** -0.035946* DUM2 [2.0781] - [2.1180] -0.039511** -0.046846 DUM3 [2.0944] - [3.2354] 2 Adjusted R 0.92817 - 0.92715 SER 0.022869 - 0.023031 2 4.0164 0.87603 X sc [0.055] - [0.349] 2 1.6603 1.2800 X ff [0.198] - [0.258] 2 1.4991 0.67831 X n [0.473] - [0.712] 2 0.37858 0.27771 X hs [0.538] - [0.598]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

4. FMOLS did not give significant results.

221

Table C.11 Dynamic adjustment estimates of demand for Pacific ACPs imports from Australia & New Zealand for 1975 - 2006

Variable GETS FMOLS JML 0.018143 0.0070951** Intercept [4.8583] [1.7736] -0.70595 -0.54160 -0.84252 ECMt-1 [6.6470] [4.8890] [9.8944] -0.41554 -0.18836* -0.36328 LRM(-3) [4.2061] [2.8326] [4.4399] 0.77712 0.73453 0.75518 LY [9.6021] [8.7872] [9.3212] 0.38688 0.32323 0.44590 LY(-3) [3.9204] [3.2711] [4.7495] 0.64163 0.63741 0.54312 LRP [7.4089] [8.4496] [7.4442] 0.16763** 0.21764 0.31233 LRP(-1) [1.7542] [3.1767] [4.7072] 0.23075* 0.27865 LRP(-3) [2.0549] [3.2210] -0.026670* -0.029610 DUM1 [2.6640] [3.0900] -0.036436 -0.031371* -0.031522* DUM2 [3.1829] [2.6421] [2.7739] -0.033718* -0.037340 -0.027088 DUM3 [2.9385] [3.4935] [2.7695] 2 Adjusted R 0.96463 0.95535 0.96044 SER 0.012827 0.014725 0.013565 2 0.65210 0.51973 0.0024163 X sc [0.419] [0.471] [0.961] 2 2.0314 4.1254 2.3123 X ff [0.154] [0.052] [0.128] 2 1.3663 0.88787 0.71782 X n [0.505] [0.642] [0.698] 2 0.15314 2.0009 0.64871 X hs [0.696] [0.157] [0.421]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

222

Table C.12 Dynamic adjustment estimates of demand for exports of Fiji to the United Kingdom for 1976 - 2004

Variable GETS FMOLS JML 0.022228* Intercept [2.1932] -0.80498 -0.66807 -0.69477 ECMt-1 [4.1442] [4.7955] [7.0020] 0.87126 0.90735 0.90781 LYF [11.4895] [15.6978] [16.5431] -0.59022 -0.51090 -0.56652 LYF(-2) [3.0179] [3.0244] [4.4408] 0.51037* 0.44143 0.51201 LRX(-2) [2.9482] [2.9925] [4.5995] -0.066151** -0.081142* -0.082008 DUM1 [2.0147] [2.7901] [2.9721] 0.11574* 0.089718 0.094411 DUM2 [2.9155] [3.0011] [4.0821] -0.077172* -0.080862 -0.078512 DUM3 [2.5352] [3.1380] [3.3622] 2 Adjusted R 0.94710 0.94947 0.95046 SER 0.038218 0.037352 0.036472 2 1.1920 1.7962 0.15240 X sc [0.275] [0.180] [0.696] 2 0.069699 0.022721 0.030764 X ff [0.792] [0.880] [0.861] 2 1.3406 3.7345 0.72605 X n [0.512] [0.155] [0.696] 2 0.94690 0.69009 1.1629 X hs [0.331] [0.406] [0.281]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

223

Table C.13 Dynamic adjustment estimates of demand for Fiji’s imports from the United Kingdom for 1976 - 2004

Variable GETS FMOLS JML

Intercept -

-0.45860 -0.34209* - ECMt-1 [3.0642] [3.0154]

1.1721 1.1908 - LRM(-2) [6.5112] [6.4400]

1.6510 1.6945 - LRM(-3) [6.1631] [6.0919]

1.2496 1.2913 - LRM(-4) [5.2355] [5.0116]

.76386 .67214* - LRM(-5) [4.497] [3.1655]

-0.52263 -.49507 - LY(-1) [6.3425] [6.2065]

0.18397 0.15241* - LY(-4) [2.9738] [2.7109]

0.34670 .31058 - LY(-6) [5.6313] [4.9980]

0.2785 0.2318 - LRPE(-2) [5.7634] [5.4070]

0.5310 0.4278 - LRPE(-4) [8.4432] [7.3446]

0.71323 0.7090 - LRPE(-5) [3.9573] [3.9388] 2 Adjusted R 0.88223 - 0.85530 SER 0.046109 - 0.047866 0.91311 0.029268 2 - X sc [0.351] [0.864]

0.29716 3.4451 2 - X ff [0.571] [0.063]

1.822 0.79752 2 - X n [0.510] [0.671]

0.31940 0.090411 2 - X hs [0.573] [0.764]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

4. FMOLS did not give significant results.

224

Table C.14 Dynamic adjustment estimates of demand for exports of Fiji to Germany for 1976 - 2004

Variable GETS FMOLS JML 0.012370* Intercept [2.4409] -0.82143 -0.80357 -0.34280 ECMt-1 [7.2700] [6.7386] [3.7056] 0.97235 0.98190 0.96348 LYF [123.3222] [195.6456] [91.6815] -0.011471* -0.013437* -0.015370* LRX(-2) [2.4260] [2.6154] [2.2229] -0.011471* -0.010256** -0.015370* LRX(-3) [2.4260] [1.8523] [2.2229]

-0.039748 -0.041469 DUM1 [3.0951] [3.3023] 0.040378* 0.022345** DUM2 [2.3357] [1.8868] 2 Adjusted R 0.99954 0.99951 0.99898 SER 0.020540 0.021177 0.030647 2 0.14108 0.59019 0.079933 X sc [0.707] [0.442] [0.777] 2 1.2149 1.0995 2.0596 X ff [0.270] [0.294] [0.151] 2 0.96854 1.9845 1.0264 X n [0.616] [0.371] [0.599] 2 0.87835 0.41718 0.39610 X hs [0.349] [0.518] [0.529]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

225

Table C.15 Dynamic adjustment estimates of demand for imports from Germany to Fiji for 1976 - 2004

Variable GETS FMOLS JML

Intercept -

-0.31354 -0.25334 - ECMt-1 [3.2896] [3.8462]

0.48456 0.48964 - LRM(-2) [3.2190] [4.1359]

-0.18184* -0.18948 - LY(-3) [2.6475] [2.9541]

-0.86583** -0.86235** - LRPE [1.8971] [2.0260]

-0.13656** -0.17395 - DUM1 [1.7740] [2.9416]

0.29050* 0.28427 - DUM2 [2.8099] [3.1970]

-0.14775** -0.15615** - DUM3 [1.9757] [1.7749] 2 Adjusted R 0.65442 - 0.69058 SER 0.088899 - 0.084120 0.28017 0.29116 2 - X sc [0.597] [0.589]

1.4018 1.0302 2 - X ff [0.236] [0.310]

1.3089 0.85263 2 - X n [0.520] [0.653]

0.62487 0.37963 2 - X hs [0.429] [0.538]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

4. FMOLS did not give significant results.

226

Table C.16 Dynamic adjustment estimates of demand for exports of Fiji to Belgium for 1976 – 2004

Variable GETS FMOLS JML

Intercept

-1.2001 -1.2059 -1.1932 ECMt-1 [6.2185] [6.6186] [6.6375] 0.59590 0.60993 0.59301 LYF [11.9423] [16.5794] [15.8819] -0.36963 -0.37000 -0.36868 LYF (-1) [3.0287] [3.2031] [3.2027] 0.53271 0.51594 0.53842 LRX(-1) [3.0968] [3.2704] [3.3752] -0.41115 -0.39741 -0.40764 LRPE [7.2286] [8.5468] [8.7184]

0.16494** 0.14651* 0.16547* LRPE (-1) [2.0932] [2.2620] [2.5053]

-0.066151** -0.31169* -0.29937* DUM1 [2.0147] [2.3504] [2.2691] 2 Adjusted R 0.98258 0.98439 0.98445 SER 0.21637 0.20482 0.20440 2 2.5610 2.2388 2.5007 X sc [0.110] [0.135] [0.114] 2 0.57218 0.36857 0.79114 X ff [0.449] [0.544] [0.374] 2 0.17945 0.022009 0.17295 X n [0.914] [0.989] [0.917] 2 0.033553 0.3012E-4 0.0011603 X hs [0.855] [0.996] [0.973]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

227

Table C.17 Dynamic adjustment estimates of demand for imports from Belgium to Fiji for 1976 - 2004

Variable GETS FMOLS JML 0.079528* 0.059699** Intercept [2.3465] [1.8441]

-0.31983 -0.28574* -.28068* ECMt-1 [2.8362] [2.2094] [2.5610]

0.30796* 0.40798 0.42829 LY [2.4362] [4.1884] [3.8837]

-0.18497** -0.18296* -0.17658** LY(-1) [1.8193] [2.1061] [1.8999]

-0.24112** -0.23094** -0.24458** DUM1 [1.8710] [1.8649] [1.9340]

-0.21210** -0.20912** -0.21047** DUM2 [1.8189] [1.9221] [1.9236] 2 Adjusted R 0.68334 0.66265 0.66682 SER 0.15668 0.15983 0.16184 2 0.96590 1.1810 0.79098 X sc [0.326] [0.277] [0.374]

2 0.0090233 0.021969 0.49959 X ff [0.924] [0.882] [0.480]

2 3.5297 1.4863 1.8394 X n [0.171] [0.476] [0.399]

2 0.28795 0.052760 0.0099480 X hs [0.592] [0.818] [0.921]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

228

Table C.18 Dynamic adjustment estimates of demand for exports of Fiji to the Netherlands for 1976 – 2004

Variable GETS FMOLS JML

Intercept

-1.4024 -1.2059 -1.2196 ECMt-1 [7.3245] [6.6186] [6.4789]

0.67353 0.75512 0.75067 LYF [8.5526] [13.7783] [12.3102]

-0.45809* -0.35970* -0.37420* LYF(-5) [2.9541] [2.6815] [2.4867]

-0.12760* -0.14485* -0.18108 LRX(-1) [2.3872] [2.8422] [3.0517]

0.48255 0.37130* 0.39334* LRX(-5) [3.0249] [2.7166] [2.5424]

-1.2576* -1.6996 -1.4461* LRPE(-1) [2.2151] [3.3243] [2.6050]

-1.5400* -1.5394 -1.3044* LRPE(-2) [2.7704] [-3.0360] [2.3149]

-0.16528** -0.18690* -0.16451** DUM1 [1.8384] [2.3971] [1.8704] -0.22141 DUM2 [2.4821] 2 Adjusted R 0.98722 0.98870 0.98518 SER 0.14416 0.13559 0.15524 2 1.1264 0.45867 2.8109 X sc [0.289] [0.498] [0.094]

2 0.022682 0.31903 0.046987 X ff [0.880] [0.572] [0.828]

2 3.4347 3.6446 5.1580 X n [0.180] [0.162] [0.076]

2 1.3723 1.3267 1.1792 X hs [0.241] [0.249] [0.278]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

229

Table C.19 Dynamic adjustment estimates of demand for imports from the Netherlands to Fiji for 1976 - 2004

Variable GETS FMOLS JML 0.15687 Intercept - [3.7029] -0.76565 -0.91378 - ECMt-1 [3.8362] [5.6324]

0.56243 0.68957 - LY [3.6936] [6.3016]

-2.0661* -2.1926* - LRPE(-1) [2.1656] [2.4406]

-2.0335** -2.5366* - LRPE(-2) [2.0546] [2.7464]

-0.22786* -0.24091* - LRM(-2) [2.1868] [2.3769]

-0.40611 -0.34470 - DUM1 [3.7949] [3.3722]

-.34117* -0.43178 - DUM2 [2.5951] [3.6314] 2 Adjusted R 0.87218 - 0.89990 SER 0.13675 - 0.13015 0.36829 0.039240 2 - X sc [0.544] [0.843]

0.29463 0.24078 2 - X ff [0.587] [0.624]

0.64041 1.9290 2 - X n [0.726] [0.381]

1.4823 0.53740 2 - X hs [0.223] [0.464]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

4. FMOLS did not give significant results.

230

Table C.20 Dynamic adjustment estimates of demand for exports of Fiji to France for 1976 – 2004

Variable GETS FMOLS JML 0.022115* Intercept [2.3871]

-0.37306 -0.39921 -0.35136 ECMt-1 [4.1233] [4.7217] [4.9176]

1.0063 1.0055 1.0044 LYF [91.8537] [118.5540] [121.2760]

-0.69696 -0.69571 -0.67390 LYF (-1) [5.1141] [5.6407] [5.6631]

0.69888 0.69770 0.67772 LRX(-1) [5.1893] [5.6114] [5.6494]

0.019526* 0.018827* 0.019816* LRX(-3) [2.2711] [2.2577] [2.4214]

0.63649 0.62585 0.63479 LRPE(-2) [3.9065] [3.9684] [4.1095] 2 Adjusted R 0.99852 0.99859 0.99865 SER 0.042456 0.041434 0.040518 2 0.21200 0.0035569 0.086593 X sc [0.645] [0.952] [0.769]

2 0.12167 0.32627 0.19931 X ff [0.727] [0.568] [0.655]

2 0.79783 1.1891 0.92199 X n [0.671] [0.552] [0.631]

2 1.1954 1.0051 1.0866 X hs [0.274] [0.316] [0.297]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

231

Table C.21 Dynamic adjustment estimates of demand for imports from France to Fiji for 1976 - 2004

Variable GETS FMOLS JML 0.079927 Intercept [3.5106] -0.82804 -0.72904 -0.79657 ECMt-1 [7.5243] [7.6534] [8.0890] 0.76528 0.69484 0.74923 LY [9.6352] [9.4711] [10.9905] -0.28568* -0.21723* -0.23640 LY(-1) [2.7453] [2.5742] [2.8820] -0.15714** -0.15257** -0.12941** LY(-2) [2.0964] [2.1013] [1.9878]

0.097341** 0.11078** 0.096118** LY(-4) [1.8132] [2.1124] [1.9015] -1.2565** -1.3414* -1.3464* LRPE(-1) [2.0164] [2.2524] [2.3076] 0.20615** DUM1 [1.7304] -0.83375 -0.81937 -0.84185 DUM2 [6.3684] [6.5036] [6.9020] 2 Adjusted R 0.93963 0.94221 0.94408 SER 0.10303 0.10080 0.099158 2 1.1164 0.62157 0.72229 X sc [0.291] [0.430] [0.395] 2 0.037636 0.044773 0.011371 X ff [0.846] [0.832] [0.915] 2 0.15817 0.22239 0.48018 X n [0.924] [0.895] [0.787] 2 0.84348 0.78112 0.58577 X hs [0.358] [0.377] [0.444]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

232

Table C.22 Dynamic adjustment estimates of demand for imports from Denmark to Fiji for 1976 - 2004

Variable GETS FMOLS JML 0.060857** 0.058134** Intercept [1.8691] [1.8726]

-0.76565 -0.65505 -0.47296* ECMt-1 [3.8362] [3.5203] [2.5176]

0.54865 0.59511 0.55694 LY [11.2652] [8.3668] [8.1356]

-0.32158 -0.26029 -0.25886 LY(-1) [3.8488] [3.0944] [3.2740]

-0.19109* -0.15066** LY(-3) [2.5905] [1.9922]

-1.8395* -1.8730* -1.5340* LRPE(-4) [2.7218] [2.5514] [2.1830]

-0.21210** 0.34523 0.33288 LRM(-2) [1.8189] [3.6457] [3.7532]

-0.22393* -0.19334** -0.22255* DUM1 [2.7615] [2.0546] [2.4910] 2 Adjusted R 0.91289 0.88826 0.89990 SER 0.12141 0.13751 0.13015 2 3.3386 0.82803 0.039240 X sc [0.068] [0.363] [0.843]

2 0.0030446 0.2095E-3 0.24078 X ff [0.956] [0.988] [0.624]

2 2.2802 2.1436 1.9290 X n [0.320] [0.342] [0.381]

2 1.2783 0.69633 0.53740 X hs [0.258] [0.404] [0.464]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

233

Table C.23 Dynamic adjustment estimates of demand for imports from Italy to Fiji for 1976 - 2004

Variable GETS FMOLS JML 0.12329 Intercept [6.0231]

-1.0937 -0.73451 -0.70350 ECMt-1 [12.3364] [6.4869] [8.3357]

0.65896 0.76750 0.78970 LY [13.1738] [21.2066] [21.4353]

-2.7730 -2.7140 -2.7544 LRPE(-1) [4.9910] [4.9435] [4.9585]

-3.0582 -2.6648 -2.7588 LRPE(-2) [4.9624] [4.5738] [4.9167]

-2.1745 -1.9438 -2.1933 LRPE(-4) [0.3973] [3.0468] [3.8032]

-0.32559 -0.24251 -0.27600 LRM(-1) [9.6278] [4.3487] [5.6760]

-0.40611 -0.42031 -0.41679 LRM(-2) [3.7949] [9.4546] [10.7411]

-0.30974 -0.12445** DUM1 [3.7184] [-1.8496] 2 Adjusted R 0.98135 0.97982 0.97867 SER 0.083879 0.087259 0.089702 2 0.22509 0.46744 1.2983 X sc [0.635] [0.494] [0.255]

2 0.013824 0.0041532 1.2116 X ff [0.906] [0.949] [0.271]

2 1.1065 0.056238 0.39756 X n [0.575] [0.972] [0.820]

2 0.86376 0.97722 0.35206 X hs [0.353] [0.323] [0.553]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

234

Table C.24 Dynamic adjustment estimates of demand for exports of Fiji to Australia for 1976 – 2004

Variable GETS FMOLS JML

Intercept

-0.60817 -0.54635 ECMt-1 [4.3873] [4.2539] 0.99569 0.99712 0.99902 LYF [68.5190] [83.9735] [59.4142] -0.033729* -0.036206 -0.42144* LYF (-3) [2.6596] [2.9637] [2.1336] 0.31596** LRPE(-1) [1.8410]

-0.24014 -0.20916 LRPE(-2) [3.0344] [3.0132] 0.18713* 0.21800 LRPE(-4) [2.3371] [2.9883] 0.39986** LRX(-1) [2.0558] -0.044375* LRX(-3) [2.7590] 0.035873 0.026658 DUM1 [3.1629] [2.9735] 2 Adjusted R 0.99625 0.99642 0.99386 SER 0.023011 0.022484 0.029365 2 3.7223 3.6034 0.22847 X sc [0.054] [0.058] [0.633] 2 .0067724 .0010054 1.1965 X ff [0.934] [0.975] [0.274] 2 1.9039 0.78420 .037924 X n [0.386] [0.676] [0.981] 2 1.6671 1.2943 .036544 X hs [0.197] [0.255] [0.848]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

235

Table C.25 Dynamic adjustment estimates of demand for imports from Australia to Fiji for 1975 - 2006

Variable GETS FMOLS JML 0.071329 0.038620* Intercept [4.6088] [2.2650] -0.49637 -0.45954 -0.47503 ECMt-1 [5.1097] [5.4006] [5.4459] 0.26479** 0.28231* 0.30861* LRM(-1) [1.8277] [2.2252] [2.3956] 0.21560** 0.23195* 0.21542** LRM(-2) [1.9435] [2.2107] [2.0916] -0.41072* -0.35733 -0.40212 LRM(-4) [2.9937] [3.0800] [3.5237]

-0.19981 -0.18978 -0.19508 LY(-4) [4.4650] [4.5431] [4.6937] -0.55216* -0.50300* -0.55410* LRPE(-1) [2.5018] [2.5166] [2.7103] -1.2272 -1.2145 -1.2014 LRPE(-3) [6.2877] [6.5222] [6.5318] -0.10736** -0.075988** -0.092812* DUM1 [2.0797] [2.0532] [2.5136] -0.24156 -0.25258 -0.24870 DUM2 [4.3266] [4.8109] [4.7478] 2 Adjusted R 0.77870 0.79474 0.79694 SER 0.049622 0.047789 0.047532 2 4.2343 4.1613 3.8209 X sc [0.051] [0.059] [0.099] 2 0.0047499 0.36083 .12831 X ff [0.945] [0.548] [0.720] 2 0.017148 0.12148 .19344 X n [0.991] [0.941] [0.908] 2 0.27361 0.32359 .51522 X hs [0.601] [0.569] [0.473]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

236

Table C.26 Dynamic adjustment estimates of demand for exports of Fiji to New Zealand for 1976 – 2004

Variable GETS FMOLS JML 0.018378 Intercept [3.4233] -0.12198* -0.13696* -0.12216* ECMt-1 [2.4959] [2.7050] [2.2727] 0.27858 0.33085 0.32424 LRX(-1) [3.3948] [4.1098] [3.7444] 0.96634 0.96467 0.96593 LYF [49.3653] [57.0384] [54.5917] -0.27713 -0.32896 -0.32515 LYF (-1) [3.5364] [4.2577] [3.9146]

-0.034503* -0.037325* -0.037233* LYF (-3) [2.7274] [2.8282] [2.6865] 0.68634 0.62679 0.62376 LRPE [11.0721] [11.1930] [10.1851] 0.17335* 0.18906* 0.17513* LRPE(-2) [2.4824] [2.6147] [2.2932] 0.059544 0.052284* 0.049213* DUM1 [3.2552] [2.8165] [2.5522] 0.065353 0.060368 0.061724 DUM2 [4.0352] [3.6991] [3.5709] 2 Adjusted R 0.99642 0.99605 0.99566 SER 0.020725 0.021774 0.022807 2 3.2521 1.7881 0.61154 X sc [0.093] [0.181] [0.434] 2 0.21984 1.1208 1.7267 X ff [0.639] [0.290] [0.189] 2 0.46363 1.4219 1.4601 X n [0.793] [0.491] [0.482] 2 1.2417 1.5490 1.1575 X hs [0.265] [0.213] [0.282]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

237

Table C.27 Dynamic adjustment estimates of demand for imports from New Zealand to Fiji for 1975 - 2006

Variable GETS FMOLS JML 0.042164* Intercept [2.7958] -0.45529 -0.51644 -0.42925 ECMt-1 [3.9402] [4.7778] [3.0108] 0.41971* 0.47176 0.47797* LRM (-1) [2.2870] [3.3000] [2.4293] 0.39402* 0.48290 0.36729** LRM(-2) [2.7083] [3.4174] [2.0888] -0.10500* -0.11090* -0.098333** LY(3) [2.4219] [2.5863] [1.8413]

-1.0038 -0.98709 -0.82383* LRPE(-1) [3.3088] [3.5027] [2.2871] -0.97853 -1.0097 0.83046* LRPE(-4) [3.7263] [3.9343] [2.6569] 0.11140* 0.077677* 0.071002** DUM1 [2.3142] [2.1193] [1.7478] -0.22975 -0.21311 -0.21741* DUM2 [3.7636] [-3.6232] [2.9040] 2 Adjusted R 0.67216 0.67568 0.55529 SER 0.054843 0.054548 0.068047 2 3.4424 0.25403 1.1617 X sc [0.085] [0.614] [0.281] 2 0.037336 0.066240 0.018377 X ff [0.847] [0.797] [0.892] 2 0.10426 0.23985 0.77837 X n [0.949] [0.887] [0.678] 2 0.025004 0.083816 0.063765 X hs [0.874] [0.772] [0.801]

Notes: 1. The T-Ratio is given in the parenthesis.

2. The variables are found to be 1 % significant if not indicated otherwise.

3. * indicates 5% significance and ** 10% significance.

238 C.5 Johansen cointegration test (JCT) results:

Table C.28 JCT for demand for Pacific ACPs’ exports to the EC

# of VAR = 2 restricted intercepts and no trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 26.9449 22.0400 r 1 6.4270 15.8700 r 2 4.5810 9.1600

F P V lnX = 0.57728 + 0.67612Y – 0.63592 G D t W t F t P H F t X

Table C.29 JCT for demand for Pacific ACPs’ imports from the EC

# of VAR = 3 restricted intercepts and no trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 35.9542 22.0400 r 1 11.6987 15.8700 r 2 3.6727 9.1600

F P V lnM = -1.6357 + 1.1607Y + 0.50369 G D t W t Dt P H F t X

Table C.30 JCT for demand for PICs’ exports to Australia & New Zealand

# of VAR = 5 restricted intercepts and no trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 30.0182 22.0400 r 1 24.6407 15.8700 r 2 8.4342 9.1600

F P V lnX = -3.2159 + 0.93211Y – 0.8995 G Dt W t F t P H F t X

239 Table C.31 JCT for demand for PICs’ imports from Australia & New Zealand

# of VAR = 5 restricted intercepts and no trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 47.5614 22.0400 r 1 15.3631 15.8700 r 2 10.3744 9.1600

F P V lnM = -1.28169 + 1.0037Y + 0.26735 G D t W t Dt P H F t X

Table C.32 JCT for demand for Fiji’s exports to United Kingdom

# of VAR = 4 restricted intercepts and no trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 24.7135 22.0400 r 1 10.4254 15.8700 r 2 3.3233 9.1600

F P V lnX = -3.4693 + 1.0228Y – 0.82641 G D t W t F t E P H t F t X

Table C.33 JCT for demand for Fiji’s imports from the United Kingdom

# of VAR = 1 no intercepts or trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 17.3521 17.6800 r 1 1.6498 11.0300 r 2 .58337 4.1600

F P V lnM = 0.92458Y + 0.58278 G Dt W t Dt E P H t F t X

Table C.34 JCT for demand for Fiji’s exports to Germany

# of VAR = 2 restricted intercepts and no trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 32.4926 22.0400 r 1 10.8937 15.8700 r 2 5.0609 9.1600

F P V lnX = -1.9426 + 0.84472Y – 0.39782 G D t W t F t E P H t F t X

240 Table C.35 JCT for demand for Fiji’s imports from Germany

# of VAR = 6 no intercepts or trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 74.8718 17.6800 r 1 8.5541 11.0300 r 2 2.0212 4.1600

F P V lnM = 0.93071Y + 0.76291 G Dt W t Dt E P H t F t X

Table C.36 JCT for demand for Fiji’s exports to Belgium

# of VAR = 5 restricted intercepts and no trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 22.6264 22.0400 r 1 9.6051 15.8700 r 2 2.8191 9.1600

F P V lnX = -3.3082 + 0.66853Y – 0.36069 G D t W t F t E P H t F t X

Table C.37 JCT for demand for Fiji’s imports from Belgium

# of VAR = 6 no intercepts or trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 18.7724 17.6800 r 1 9.3257 11.0300 r 2 2.8875 4.1600

F P V lnM = 0.91238Y + 1.0039 G Dt W t Dt E P H t F t X

Table C.38 JCT for demand for Fiji’s exports to Netherlands

# of VAR = 1 restricted intercepts and no trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 35.3921 22.0400 r 1 23.1129 15.8700 r 2 4.1220 9.1600

F P V lnX = -7.1340 + 0.96181Y – 0.34070 G D t W t F t E P H t F t X

241 Table C.39 JCT for demand for Fiji’s imports from the Netherlands

# of VAR = 1 no intercepts or trends

# of cointegrating vectors LR statistic 90% Critical Value r = 0 17.2035 15.5700 r 1 2.6533 9.2800 r 2 .023850 3.0400

F P V lnM = 0.92323Y + 0.35527 G Dt W t Dt E P H t F t X

Table C.40 JCT for demand for Fiji’s exports to France

# of VAR = 6 restricted intercepts and no trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 22.5465 17.6800 r 1 15.0453 11.0300 r 2 5.2969 4.1600

F P V lnX = -1.8859 + 0.99223Y – 0.83408 G D t W t F t E P H t F t X

Table C.41 JCT for demand for Fiji’s imports from France

# of VAR = 6 no intercepts or trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 31.3034 17.6800 r 1 8.5502 11.0300 r 2 1.3400 4.1600

F P V lnM = 0.90825Y + 1.1333 G Dt W t Dt E P H t F t X

Table C.42 JCT for demand for Fiji’s imports from Denmark

# of VAR = 1 restricted intercepts and no trends

# of cointegrating vectors LR statistic 90% Critical Value r = 0 16.4980 15.5700 r 1 3.0278 9.2800 r 2 0.13307 3.0400

F P V lnM = 1.8272 + 0.59805Y + 0.84305 G D t W t Dt E P H t F t X

242 Table C.43 JCT for demand for Fiji’s imports from Italy

# of VAR = 3 no intercepts or trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 24.4898 17.6800 r 1 4.9532 11.0300 r 2 .46007 4.1600

F P V lnM = 0.90825Y + 1.1333 G Dt W t Dt E P H t F t X

Table C.44 JCT for demand for Fiji’s exports to Australia

# of VAR = 1 restricted intercepts and no trends

# of cointegrating vectors LR statistic 90% Critical Value r = 0 20.0195 19.8600 r 1 6.2850 13.8100 r 2 4.4371 7.5300

F P V lnX = -3.4684 + 1.0774Y – 0.094097 G D t W t F t E P H t F t X

Table C.45 JCT for demand for Fiji’s imports from Australia

# of VAR = 5 restricted intercepts and no trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 30.7753 22.0400 r 1 7.1780 15.8700 r 2 2.2137 9.1600

F P V lnM = 8.4524 + 0.78822Y + 0.54531 G D t W t Dt E P H t F t X

Table C.46 JCT for demand for Fiji’s exports to New Zealand

# of VAR = 4 restricted intercepts and no trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 22.0597 22.0400 r 1 15.9402 15.8700 r 2 6.1239 9.1600

F P V lnX = 1.5453 + 0.74979Y – 0.53535 G Dt W t F t E P H t F t X

243

Table C.47 JCT for demand for Fiji’s imports from New Zealand

# of VAR = 6 restricted intercepts and no trends

# of cointegrating vectors LR statistic 95% Critical Value r = 0 26.5077 22.0400 r 1 13.0364 15.8700 r 2 10.8887 9.1600

F P V lnM = 4.8717 + 0.74508Y + 0.37908 G D t W t Dt E P H t F t X

C.6 Stability test results – plots of cumulative sum of recursive residuals and cumulative sum of squared residuals

The cumulative sum of recursive residuals and cumulative sum of squared residuals are two statistical methods to test for structural stability of the restricted autoregressive models. In the graphs, two straight lines are presented – the 5% critical bounds where the null hypothesis, assuming stable parameters for each of the variables, is rejected if any of the lines is significantly crossed. The null hypothesis is not rejected if the plot stays in general within the 5% boundaries. The cumulative sum of recursive residuals test shows whether the coefficients of the regression are changing significantly, whereas the cumulative sum of squared residuals test reveals suddenly changing regression coefficients.

In the following the plots of cumulative sum of recursive residuals and cumulative sum of squared residuals of the import and export estimations are given.

244

Figure C.1 Stability test for exports demand of PACPs to EC

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1977 1982 1987 1992 1997 2002 2006

The straight lines represent critical bounds at 5% significance level

Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

1.0

0.5

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-0.5 1977 1982 1987 1992 1997 2002 2006

The straight lines represent critical bounds at 5% significance level

Figure C.2 Stability test for PACPs’ imports demand of EC

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1982 1987 1992 1997 2002 2006

The straight lines represent critical bounds at 5% significance level

245

Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

1.0

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1982 1987 1992 1997 2002 2006

The straight lines represent critical bounds at 5% significance level

Figure C.3 Stability test for exports demand of PICs to Australia & New Zealand

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1980 1985 1990 1995 2000 2005

The straight lines represent critical bounds at 5% significance level

Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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The straight lines represent critical bounds at 5% significance level

246

Figure C.4 Stability test for PACPs’ imports demand of Australia & New Zealand

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1977 1982 1987 1992 1997 2002

The straight lines represent critical bounds at 5% significance level

Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

1.0

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The straight lines represent critical bounds at 5% significance level

Figure C.5 Stability test for exports demand of Fiji to the United Kingdom

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5

1976 1981 1986 1991 1996 2001 2004

The straight lines represent critical bounds at 5% significance level

247

Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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The straight lines represent critical bounds at 5% significance level

Figure C.6 Stability test for Fiji’s imports demand of the United Kingdom

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1976 1981 1986 1991 1996 2001 2004 The straight lines represent critical bounds at 5% significance level

The straight lines represent critical bounds at 5% significance level

Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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248 Figure C.7 Stability test for exports demand of Fiji to Germany

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1976 1981 1986 1991 1996 2001 2004

The straight lines represent critical bounds at 5% significance level

Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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The straight lines represent critical bounds at 5% significance level

Figure C.8 Stability test for Fiji’s imports demand of Germany

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1976 1981 1986 1991 1996 2001 2004

The straight lines represent critical bounds at 5% significance level

249 Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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Figure C.9 Stability test for exports demand of Fiji to Belgium

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1980 1985 1990 1995 2000

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Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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250 Figure C.10 Stability test for Fiji’s imports demand of Belgium

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1976 1981 1986 1991 1996 2001 2004

The straight lines represent critical bounds at 5% significance level

Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

1.0

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The straight lines represent critical bounds at 5% significance level

Figure C.11 Stability test for exports demand of Fiji to the Netherlands

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1976 1981 1986 1991 1996 2001 2004

The straight lines represent critical bounds at 5% significance level

251 Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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Figure C.12 Stability test for Fiji’s imports demand of the Netherlands

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1976 1981 1986 1991 1996 2001 2004

The straight lines represent critical bounds at 5% significance level

Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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252 Figure C.13 Stability test for exports demand of Fiji to France

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1976 1981 1986 1991 1996 2001

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Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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Figure C.14 Stability test for Fiji’s imports demand of France

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1976 1981 1986 1991 1996 2001 2004

The straight lines represent critical bounds at 5% significance level

253 Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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Figure C.15 Stability test for Fiji’s imports demand of Denamrk

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1976 1981 1986 1991 1996 2001 2004

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Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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254 Figure C.16 Stability test for Fiji’s imports demand of Italy

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1976 1981 1986 1991 1996 2001 2004

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Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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Figure C.17 Stability test for exports demand of Fiji to Australia

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1976 1981 1986 1991 1996 2001 2006

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255 Plot of Cumulative Sum of Squares of Recursive Residuals

1.5

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Figure C.18 Stability test for Fiji’s imports demand of Australia

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1977 1982 1987 1992 1997 2002

The straight lines represent critical bounds at 5% significance level

Plot of Cumulative Sum of Squares of Recursive Residuals

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256 Figure C.19 Stability test for exports demand of Fiji to New Zealand

Plot of Cumulative Sum of Recursive Residuals

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Plot of Cumulative Sum of Squares of Recursive Residuals

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Figure C.20 Stability test for Fiji’s imports demand of New Zealand

Plot of Cumulative Sum of Recursive Residuals

15 10 5 0 -5 -10 -15 1977 1982 1987 1992 1997 2002

The straight lines represent critical bounds at 5% significance level

257 Plot of Cumulative Sum of Squares of Recursive Residuals

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258 Appendix D: Definition and Details

D.1 Mode 4

GATS itself was a response to the fast growth of the service sector over the last centuries. In 2005, services accounted for more than 60% of global output and 30% of the total employments. The trade share of services in total trade was at 20%.144

GATS covers four different “modes of supply” whereas Mode 4 covers the temporary labor “movement of natural persons”, seen to be beneficial for developing countries.

In Mode 4, important issues such as “temporary” are not defined but the common language is that business visitors are usually granted 3 months and others (intra- corporate transfers) up to 5 year visas. The actual length of stay is negotiated in special country agreements and usually depends on the type of work in terms of highly skilled, less skilled or unskilled labor.

In the trade negotiation process of the IPA/EPA between the Pacific Island Countries and the European Community, MODE 4 was topic for the possible inclusion of services. The PICs requested that MODE 4 would be included and rejected the whole service offer of the EC because MODE 4 was not included due to the fact that the EC does not have the mandate over MODE 4.

For PACER and the country specific PACER Plus negotiations MODE 4 will play an important role and the chances for the inclusion in the service part of PACER Plus seems more than likely due to the fact that Australia has a strong demand for unskilled labor which the PICs could satisfy.

Further research in this area is necessary to fully identify the benefits and possibilities of MODE 4.

144 WTO – GATS; Development Research Centre on Migration, Globalization & Poverty.

259 D.2 Article 5 Agreement on Agriculture The article regulates measures to provide for mechanism of safeguard action. It also sets rules for emergency situations in which importing members may legally violate the agreement to meet the issues of emergency such as sudden surge in the quantity of product available or the artificial fall in prices, owing to imports and thus, to protect the domestic agriculture sector. The common law of transparency is to be used if protecting measurements are reinstalled.

D.3 GATT 1994 Article VII: Valuation for Customs Purposes The article states that the actual value of the commodity shall be used for any customs procedure using the domestic (importing) country’s legislation. It determines and regulates the procedure of the valuation (e.g. the value is to be determined under fully competitive market situation, excluding any tax, non- discrimination of origin, IMF’s or IMF accepted exchange rates are to be used if the value of the commodity needs to be converted into another currency, complete transparency for e.g. importers and exporters).

D.4 GATT 1994 Article XXIV: Territorial Application - Frontier Traffic - Customs Unions and Free-trade Areas Determines and differentiates customs union, or the interim agreement leading to the formation of a customs union, and free-trade area, or the interim agreement leading to the formation of a free-trade area, including regulations on such as customs procedures, and states that neither one shall lead to on the whole higher or more restrictive trade.

260

D.5 GATT 1994 Article XIX: Emergency Action on Imports of Particular Products The article sets regulations and restrictions for the reinstatement for possible customs, tariffs or other procedures and obligations in the case of unforeseeable developments such as a severe increase (e.g. due to special preferences) of imports or a general situation that means a threat to or might cause a severe injury of the domestic producers, to protect the domestic industry. The article further determines the limitations and asks for full transparency.

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