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The 2009-2014 World Outlook for Manufacturing Rubber Products from Natural and Synthetic Rubber Excluding Tires, Hoses and Belting, and Molded, Extruded, and Lathe-Cut Rubber Goods for Mechanical Applications

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WHAT IS LATENT DEMAND AND THE P.I.E.? The concept of latent demand is rather subtle. The term latent typically refers to something that is dormant, not observable, or not yet realized. Demand is the notion of an economic quantity that a target population or market requires under different assumptions of price, quality, and distribution, among other factors. Latent demand, therefore, is commonly defined by economists as the industry earnings of a market when that market becomes accessible and attractive to serve by competing firms. It is a measure, therefore, of potential industry earnings (P.I.E.) or total revenues (not profit) if a market is served in an efficient manner. It is typically expressed as the total revenues potentially extracted by firms. The “market” is defined at a given level in the value chain. There can be latent demand at the retail level, at the wholesale level, the manufacturing level, and the raw materials level (the P.I.E. of higher levels of the value chain being always smaller than the P.I.E. of levels at lower levels of the same value chain, assuming all levels maintain minimum profitability). The latent demand for manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be lower either lower or higher than actual sales if a market is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise from a number of factors, including the lack of international openness, cultural barriers to consumption, regulations, and cartel-like behavior on the part of firms. In general, however, latent demand is typically larger than actual sales in a country market. For reasons discussed later, this report does not consider the notion of “unit quantities”, only total latent revenues (i.e., a calculation of price times quantity is never made, though one is implied). The units used in this report are U.S. dollars not adjusted for inflation (i.e., the figures incorporate inflationary trends) and not adjusted for future dynamics in exchange rates. If inflation rates or exchange rates vary in a substantial way compared to recent experience, actually sales can also exceed latent demand (when expressed in U.S. dollars, not adjusted for inflation). On the other hand, latent demand can be typically higher than actual sales as there are often distribution inefficiencies that reduce actual sales below the level of latent demand. As mentioned in the introduction, this study is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved. If fact, all the current products or services on the market can cease to exist in their present form (i.e., at a brand-, R&D specification, or corporate-image level) and all the players can be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there will still be an international latent demand for manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications at the aggregate level. Product and service offering details, and the actual identity of the players involved, while important for certain issues, are relatively unimportant for estimates of latent demand. THE METHODOLOGY In order to estimate the latent demand for manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications on a worldwide basis, I used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created. In this case, I heavily rely on the use of certain basic economic assumptions. In particular, there is an assumption governing the shape and type of aggregate latent demand functions. Latent demand functions relate the income of a country, city, state, household, or individual to realized consumption. Latent demand (often realized as consumption when an industry is efficient), at any level of the value chain, takes place if an equilibrium is realized. For firms to serve a market, they must perceive a latent demand and be able to serve that demand at a minimal return. The single most important variable determining consumption, assuming latent demand exists, is income (or other financial resources at higher levels of the value chain). Other factors that can pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), and or changes in utility for the product in question. Ignoring, for the moment, exogenous shocks and variations in utility across countries, the aggregate relation between income and consumption has been a central theme in economics. The figure below concisely summarizes one aspect of problem. In the 1930s, John Meynard Keynes conjectured that as incomes rise, the average propensity to consume would fall. The average propensity to consume is the level of consumption divided by the level of income, or the slope of the line from the origin to the consumption function. He estimated this relationship empirically and found it to be true in the short-run (mostly based on cross-sectional data). The higher the income, the lower the average propensity to consume. This type of consumption function is labeled "A" in the figure below (note the rather flat slope of the curve). In the 1940s, another macroeconomist, Simon Kuznets, estimated long-run consumption functions which indicated that the marginal propensity to consume was rather constant (using time series data across countries). This type of consumption function is show as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant. Is it declining or is it constant? A number of other economists, notably Franco Modigliani and Milton Friedman, in the 1950s (and Irving Fisher earlier), explained why the two functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter the time horizon, the more consumption can depend on wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to consume is more constant. Similarly, in the long run, households, industries or countries with no income eventually have no consumption (wealth is depleted). While the debate surrounding beliefs about how income and consumption are related and interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications across some 230 countries. The smallest have fewer than 10,000 inhabitants. I assume that all of these counties fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these countries having wealth, current income dominates the latent demand for manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications. So, latent demand in the long-run has a zero intercept. However, I allow firms to have different propensities to consume (including being on consumption functions with differing slopes, which can account for differences in industrial organization, and end-user preferences). Given this overriding philosophy, I will now describe the methodology used to create the latent demand estimates for manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications. Since ICON Group has asked me to apply this methodology to a large number of categories, the rather academic discussion below is general and can be applied to a wide variety of categories, not just manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications. Step 1. Product Definition and Data Collection Any study of latent demand across countries requires that some standard be established to define “efficiently served”. Having implemented various alternatives and matched these with market outcomes, I have found that the optimal approach is to assume that certain key countries are more likely to be at or near efficiency than others. These countries are given greater weight than others in the estimation of latent demand compared to other countries for which no known data are available. Of the many alternatives, I have found the assumption that the world’s highest aggregate income and highest income-per-capita markets reflect the best standards for “efficiency”. High aggregate income alone is not sufficient (i.e., China has high aggregate income, but low income per capita and can not assumed to be efficient). Aggregate income can be operationalized in a number of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or number of households times average household income per capita). Brunei, Nauru, Kuwait, and Lichtenstein are examples of countries with high income per capita, but not assumed to be efficient, given low aggregate level of income (or gross domestic product); these countries have, however, high incomes per capita but may not benefit from the efficiencies derived from economies of scale associated with large economies. Only countries with high income per capita and large aggregate income are assumed efficient. This greatly restricts the pool of countries to those in the OECD (Organization for Economic Cooperation and Development), like the United States, or the United Kingdom (which were earlier than other large OECD economies to liberalize their markets). The selection of countries is further reduced by the fact that not all countries in the OECD report industry revenues at the category level. Countries that typically have ample data at the aggregate level that meet the efficiency criteria include the United States, the United Kingdom and in some cases France and Germany. Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, and the World Bank). Depending on original data sources used, the definition of “manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications” is established. In the case of this report, the data were reported at the aggregate level, with no further breakdown or definition. In other words, any potential product or service that might be incorporated within manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications falls under this category. Public sources rarely report data at the disaggregated level in order to protect private information from individual firms that might dominate a specific product-market. These sources will therefore aggregate across components of a category and report only the aggregate to the public. While private data are certainly available, this report only relies on public data at the aggregate level without reliance on the summation of various category components. In other words, this report does not aggregate a number of components to arrive at the “whole”. Rather, it starts with the “whole”, and estimates the whole for all countries and the world at large (without needing to know the specific parts that went into the whole in the first place). Given this caveat, this study covers “manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications is 326299. It is for this definition of manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications that the aggregate latent demand estimates are derived. “Manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications” is specifically defined as follows: 326299 This U.S. industry comprises establishments primarily engaged in manufacturing rubber products (except tires; hoses and belting; and molded, extruded, and lathe-cut rubber goods for mechanical applications) from natural and synthetic rubber.  3262991 Rubber sponge, expanded and foam rubber products  3262993 Rubber floor and wall coverings  3262994 Rubber shoe products, elastomer resin  3262995 Rubber druggist and medical sundries (including household gloves)  3262996 Rubber compounds or mixtures for sale or interplant transfer  3262997 Industrial rubber products, nec  3262998 Rubber gloves and clothing  3262999 Rubber goods, nec  326299A RUBBER DRUGGIST AND MEDICAL SUNDRIES, EXCLUDING HOUSEHOLD GLOVES  326299B ALL OTHER MISCELLANEOUS RUBBER GOODS  326299M Miscellaneous receipts  326299P Primary products  326299S Secondary products   Furthermore, the definition of NAICS code 326299 includes the following: Balloons, rubber, manufacturing Bath mats, rubber, manufacturing Birth control devices (i.e., diaphragms, prophylactics) manufacturing Brushes, rubber, manufacturing Combs, rubber, manufacturing Condom manufacturing Curlers, hair, rubber, manufacturing Diaphragms (i.e., birth control device), rubber, manufacturing Dinghies, inflatable rubber, manufacturing Doormats, rubber, manufacturing Erasers, rubber or rubber and abrasive combined, manufacturing Floor mats (e.g., bath, door), rubber, manufacturing Footwear parts (e.g., heels, soles, soling strips), rubber, manufacturing Fuel bladders, rubber, manufacturing Grips and handles, rubber, manufacturing Grommets, rubber, manufacturing Hair care products (e.g., combs, curlers), rubber, manufacturing Hairpins, rubber, manufacturing Hot water bottles, rubber, manufacturing Latex foam rubber manufacturing Life rafts, inflatable rubberized fabric, manufacturing Mattress protectors, rubber, manufacturing Mattresses, air, rubber, manufacturing Nipples and teething rings, rubber, manufacturing Pacifiers, rubber, manufacturing Pipe bits and stems, tobacco, hard rubber, manufacturing Prophylactics manufacturing Rafts, rubber inflatable, manufacturing Reclaiming rubber from waste or scrap Rods, hard rubber, manufacturing Rolls and roll coverings, rubber (e.g., industrial, papermill, painters, steelmi Roofing (i.e., single ply rubber membrane) manufacturing Rubber bands manufacturing Sheeting, rubber, manufacturing Shoe and boot parts (e.g., heels, soles, soling strips), rubber, manufacturing Spatulas, rubber, manufacturing Sponges, rubber, manufacturing Stair treads, rubber, manufacturing Stoppers, rubber, manufacturing Thread, rubber (except fabric covered), manufacturing Tubing, rubber (except extruded, molded, lathe-cut), manufacturing. Step 2. Filtering and Smoothing Based on the aggregate view of manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications as defined above, data were then collected for as many similar countries as possible for that same definition, at the same level of the value chain. This generates a convenience sample of countries from which comparable figures are available. If the series in question do not reflect the same accounting period, then adjustments are made. In order to eliminate short-term effects of business cycles, the series are smoothed using an 2 year moving average weighting scheme (longer weighting schemes do not substantially change the results). If data are available for a country, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a country stricken with foot and mouth disease), these observations were dropped or "filtered" from the analysis. Step 3. Filling in Missing Values In some cases, data are available for countries on a sporadic basis. In other cases, data from a country may be available for only one year. From a Bayesian perspective, these observations should be given greatest weight in estimating missing years. Assuming that other factors are held constant, the missing years are extrapolated using changes and growth in aggregate national income. Based on the overriding philosophy of a long-run consumption function (defined earlier), countries which have missing data for any given year, are estimated based on historical dynamics of aggregate income for that country. Step 4. Varying Parameter, Non-linear Estimation Given the data available from the first three steps, the latent demand in additional countries is estimated using a “varying-parameter cross-sectionally pooled time series model”. Simply stated, the effect of income on latent demand is assumed to be constant across countries unless there is empirical evidence to suggest that this effect varies (i.e., . the slope of the income effect is not necessarily same for all countries). This assumption applies across countries along the aggregate consumption function, but also over time (i.e., not all countries are perceived to have the same income growth prospects over time and this effect can vary from country to country as well). Another way of looking at this is to say that latent demand for manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications is more likely to be similar across countries that have similar characteristics in terms of economic development (i.e., African countries will have similar latent demand structures controlling for the income variation across the pool of African countries). This approach is useful across countries for which some notion of non-linearity exists in the aggregate cross-country consumption function. For some categories, however, the reader must realize that the numbers will reflect a country’s contribution to global latent demand and may never be realized in the form of local sales. For certain country-category combinations this will result in what at first glance will be odd results. For example, the latent demand for the category “space vehicles” will exist for “Togo” even though they have no space program. The assumption is that if the economies in these countries did not exist, the world aggregate for these categories would be lower. The share attributed to these countries is based on a proportion of their income (however small) being used to consume the category in question (i.e., perhaps via resellers). Step 5. Fixed-Parameter Linear Estimation Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the world consists of more than 200 countries, there will always be those countries, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible. For these countries, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a country’s stock of income), but a function of current income (a country’s flow of income). In the long run, if a country has no current income, the latent demand for manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications is assumed to approach zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., countries which earn low levels of income will not use their savings, in the long run, to demand manufacturing rubber products from natural and synthetic rubber excluding tires, hoses and belting, and molded, extruded, and lathe-cut rubber goods for mechanical applications). In a graphical sense, for low income countries, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income countries are assumed to have a latent demand proportional to their income, based on the country closest to it on the aggregate consumption function. Step 6. Aggregation and Benchmarking Based on the models described above, latent demand figures are estimated for all countries of the world, including for the smallest economies. These are then aggregated to get world totals and regional totals. To make the numbers more meaningful, regional and global demand averages are presented. Figures are rounded, so minor inconsistencies may exist across tables. Step 7. Latent Demand Density: Allocating Across Cities With the advent of a “borderless world”, cities become a more important criteria in prioritizing markets, as opposed to regions, continents, or countries. This report also covers the world’s top 2000 cities. The purpose is to understand the density of demand within a country and the extent to which a city might be used as a point of distribution within its region. From an economic perspective, however, a city does not represent a population within rigid geographical boundaries. To an economist or strategic planner, a city represents an area of dominant influence over markets in adjacent areas. This influence varies from one industry to another, but also from one period of time to another. Similar to country-level data, the reader needs to realize that latent demand allocated to a city may or may not represent real sales. For many items, latent demand is clearly observable in sales, as in the case for food or housing items. Consider, again, the category “satellite launch vehicles.” Clearly, there are no launch pads in most cities of the world. However, the core benefit of the vehicles (e.g. telecommunications, etc.) is "consumed" by residents or industries within the worlds cities. Without certain cities, in other words, the world market for satellite launch vehicles would be lower for the world in general. One needs to allocate, therefore, a portion of the worldwide economic demand for launch vehicles to regions, countries and cities. This report takes the broader definition and considers, therefore, a city as a part of the global market. I allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its home country, within its region and across the world total. Not all cities are estimated within each country as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same country, a city’s population is not generally used to allocate latent demand. Rather, the level of economic activity of the city vis-à-vis others.
 
Contents:
1 INTRODUCTION 10 1.1 Overview 10 1.2 What is Latent Demand and the P.I.E.? 10 1.3 The Methodology 11 1.3.1 Step 1. Product Definition and Data Collection 12 1.3.2 Step 2. Filtering and Smoothing 15 1.3.3 Step 3. Filling in Missing Values 16 1.3.4 Step 4. Varying Parameter, Non-linear Estimation 16 1.3.5 Step 5. Fixed-Parameter Linear Estimation 16 1.3.6 Step 6. Aggregation and Benchmarking 17 1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 17 2 SUMMARY OF FINDINGS 18 2.1 The Worldwide Market Potential 18 3 AFRICA & THE MIDDLE EAST 20 3.1 Executive Summary 20 3.2 Afghanistan 22 3.3 Algeria 23 3.4 Angola 24 3.5 Armenia 25 3.6 Azerbaijan 26 3.7 Bahrain 27 3.8 Benin 28 3.9 Botswana 29 3.10 Burkina Faso 30 3.11 Burundi 31 3.12 Cameroon 32 3.13 Cape Verde 33 3.14 Central African Republic 34 3.15 Chad 35 3.16 Comoros 36 3.17 Congo (formerly Zaire) 36 3.18 Cote dIvoire 37 3.19 Djibouti 38 3.20 Egypt 39 3.21 Equatorial Guinea 40 3.22 Ethiopia 40 3.23 Gabon 41 3.24 Ghana 42 3.25 Guinea 43 3.26 Guinea-Bissau 44 3.27 Iran 45 3.28 Iraq 46 3.29 Israel 47 3.30 Jordan 48 3.31 Kenya 49 3.32 Kuwait 50 3.33 Kyrgyzstan 50 3.34 Lebanon 51 3.35 Lesotho 52 3.36 Liberia 52 3.37 Libya 53 3.38 Madagascar 54 3.39 Malawi 55 3.40 Mali 56 3.41 Mauritania 57 3.42 Mauritius 58 3.43 Morocco 59 3.44 Mozambique 60 3.45 Namibia 61 3.46 Niger 62 3.47 Nigeria 63 3.48 Oman 64 3.49 Pakistan 64 3.50 Palestine 65 3.51 Qatar 66 3.52 Republic of Congo 66 3.53 Reunion 67 3.54 Rwanda 68 3.55 Sao Tome E Principe 69 3.56 Saudi Arabia 69 3.57 Senegal 70 3.58 Sierra Leone 71 3.59 Somalia 72 3.60 South Africa 73 3.61 Sudan 74 3.62 Swaziland 75 3.63 Syrian Arab Republic 76 3.64 Tajikistan 77 3.65 Tanzania 78 3.66 The Gambia 79 3.67 The United Arab Emirates 80 3.68 Togo 80 3.69 Tunisia 81 3.70 Turkey 82 3.71 Turkmenistan 83 3.72 Uganda 84 3.73 Uzbekistan 85 3.74 Western Sahara 86 3.75 Yemen 86 3.76 Zambia 87 3.77 Zimbabwe 88 4 ASIA 90 4.1 Executive Summary 90 4.2 Bangladesh 92 4.3 Bhutan 93 4.4 Brunei 94 4.5 Burma 94 4.6 Cambodia 95 4.7 China 96 4.8 Hong Kong 97 4.9 India 97 4.10 Indonesia 98 4.11 Japan 99 4.12 Laos 100 4.13 Macau 101 4.14 Malaysia 102 4.15 Maldives 103 4.16 Mongolia 103 4.17 Nepal 104 4.18 North Korea 105 4.19 Papua New Guinea 106 4.20 Philippines 107 4.21 Seychelles 108 4.22 Singapore 108 4.23 South Korea 109 4.24 Sri Lanka 110 4.25 Taiwan 111 4.26 Thailand 112 4.27 Vietnam 113 5 EUROPE 114 5.1 Executive Summary 114 5.2 Albania 116 5.3 Andorra 117 5.4 Austria 117 5.5 Belarus 118 5.6 Belgium 119 5.7 Bosnia and Herzegovina 120 5.8 Bulgaria 121 5.9 Croatia 122 5.10 Cyprus 123 5.11 Czech Republic 124 5.12 Denmark 125 5.13 Estonia 126 5.14 Finland 127 5.15 France 128 5.16 Georgia 129 5.17 Germany 130 5.18 Greece 131 5.19 Hungary 132 5.20 Iceland 133 5.21 Ireland 134 5.22 Italy 135 5.23 Kazakhstan 136 5.24 Latvia 137 5.25 Liechtenstein 138 5.26 Lithuania 139 5.27 Luxembourg 140 5.28 Malta 141 5.29 Moldova 142 5.30 Monaco 142 5.31 Norway 143 5.32 Poland 144 5.33 Portugal 145 5.34 Romania 146 5.35 Russia 147 5.36 San Marino 148 5.37 Slovakia 148 5.38 Slovenia 149 5.39 Spain 150 5.40 Sweden 151 5.41 Switzerland 152 5.42 The Netherlands 153 5.43 The United Kingdom 154 5.44 Ukraine 155 6 LATIN AMERICA 156 6.1 Executive Summary 156 6.2 Argentina 157 6.3 Belize 158 6.4 Bolivia 159 6.5 Brazil 160 6.6 Chile 161 6.7 Colombia 162 6.8 Costa Rica 163 6.9 Ecuador 164 6.10 El Salvador 165 6.11 French Guiana 166 6.12 Guatemala 167 6.13 Guyana 168 6.14 Honduras 169 6.15 Mexico 170 6.16 Nicaragua 171 6.17 Panama 172 6.18 Paraguay 173 6.19 Peru 174 6.20 Suriname 175 6.21 The Falkland Islands 176 6.22 Uruguay 176 6.23 Venezuela 177 7 NORTH AMERICA & THE CARIBBEAN 179 7.1 Executive Summary 179 7.2 Antigua and Barbuda 181 7.3 Aruba 181 7.4 Barbados 182 7.5 Bermuda 183 7.6 Canada 183 7.7 Cuba 184 7.8 Dominica 185 7.9 Dominican Republic 186 7.10 Greenland 187 7.11 Grenada 188 7.12 Guadeloupe 188 7.13 Haiti 189 7.14 Jamaica 190 7.15 Martinique 191 7.16 Puerto Rico 192 7.17 St. Kitts and Nevis 193 7.18 St. Lucia 193 7.19 St. Vincent and the Grenadines 194 7.20 The Bahamas 195 7.21 The British Virgin Islands 195 7.22 The Cayman Islands 196 7.23 The Netherlands Antilles 197 7.24 The U.S. Virgin Islands 197 7.25 The United States 198 7.26 Trinidad and Tobago 199 8 OCEANA 201 8.1 Executive Summary 201 8.2 American Samoa 203 8.3 Australia 204 8.4 Christmas Island 205 8.5 Cook Islands 205 8.6 Fiji 206 8.7 French Polynesia 207 8.8 Guam 208 8.9 Kiribati 209 8.10 Marshall Islands 209 8.11 Micronesia Federation 210 8.12 Nauru 211 8.13 New Caledonia 211 8.14 New Zealand 212 8.15 Niue 213 8.16 Norfolk Island 214 8.17 Palau 215 8.18 Solomon Islands 215 8.19 The Northern Mariana Island 216 8.20 Tokelau 217 8.21 Tonga 217 8.22 Tuvalu 218 8.23 Vanuatu 219 8.24 Wallis and Futuna 219 8.25 Western Samoa 220 9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 221 9.1 Disclaimers & Safe Harbor 221 9.2 ICON Group International, Inc. User Agreement Provisions 222
 
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