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The 2009-2014 World Outlook for Manufacturing and Rebuilding Gasoline Motor Vehicle Engines and Engine Parts Excluding Carburetors, Pistons, Piston Rings, and Valves

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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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves 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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves 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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves 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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves 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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves. 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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves. 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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves. 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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves” 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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves 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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves is 336312. It is for this definition of manufacturing and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves that the aggregate latent demand estimates are derived. “Manufacturing and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves” is specifically defined as follows: 336312 This U.S. industry comprises establishments primarily engaged in manufacturing and/or rebuilding gasoline motor vehicle engines and gasoline motor vehicle engine parts, excluding carburetors, pistons, piston rings, and valves.  3363121 Gasoline engines and engine parts for motor vehicles, new  33631211 Gasoline engines, new (with or without cylinder heads, fuel pumps, water pumps, and other standard accessories), for motor vehicles  3363121101 Gasoline engines, new (with or without cylinder heads, fuel pumps, water pumps, and other standard accessories), for motor vehicles  33631212 Gasoline engine fuel injection systems, new, for motor vehicles  3363121224 Gasoline engine fuel injection systems, new, for motor vehicles  33631213 Gasoline engine fuel and water pump assemblies (excluding kits), new, for motor vehicles  3363121351 Gasoline engine fuel pump assemblies (excluding kits), new, for motor vehicles  3363121354 Gasoline engine water pump assemblies (excluding kits), new, for motor vehicles  33631214 Gasoline engine cooling fans and thermostats, new, for motor vehicles  3363121457 Gasoline engine cooling fans (including hubs and clutches), new, for motor vehicles  3363121467 Gasoline engine thermostats (engine cooling system), new, for motor vehicles  33631215 All other gasoline engines and gasoline engine parts for motor vehicles, new  3363121504 Gasoline engine intake manifolds, new, for motor vehicles  3363121507 Gasoline engine exhaust manifolds, new, for motor vehicles  3363121511 Gasoline engine crankshafts, new, for motor vehicles  3363121514 Gasoline engine camshafts, new, for motor vehicles  3363121517 Gasoline engine rocker arms and parts, new, for motor vehicles  3363121521 Gasoline engine valve guides, seats, and tappets, new, for motor vehicles  3363121527 Gasoline engine flywheels and flexplates, new, for motor vehicles  3363121531 Gasoline engine timing gears, sprockets, and chains, new, for motor vehicles  3363121534 Gasoline engine main engine bearings (halves), new, for motor vehicles  3363121537 Gasoline engine connecting rod, engine bearings (halves), new, for motor vehicles  3363121541 Other gasoline engine bearings (halves) (balance shaft, camshaft, etc.), new, for motor vehicles  3363121544 Gasoline engine oil pumps, new, for motor vehicles  3363121571 Gasoline engine PCV (positive crankcase ventilation) valves, new, for motor vehicles  3363121574 All other parts and accessories for gasoline engines, new, for motor vehicles  3363123 Gasoline engines and engine parts for motor vehicles, rebuilt  33631231 Gasoline engines and engine parts for motor vehicles, rebuilt  3363123101 Motor vehicle fuel pumps, rebuilt  3363123104 Motor vehicle water pumps, rebuilt  3363123107 Car and light truck gasoline engines, rebuilt  3363123111 Heavy truck and bus gasoline engines, rebuilt  3363123121 Other rebuilt engine parts and components  336312M Miscellaneous receipts  336312P Primary products  336312S Secondary products  336312SM Secondary products and miscellaneous receipts   Furthermore, the definition of NAICS code 336312 includes the following: Assembly line rebuilding of automotive and truck gasoline engines Bearings (e.g., camshaft, crankshaft, connecting rod), automotive and truck gasol Connecting rods, automotive and truck gasoline engine, manufacturing Crankshaft assemblies, automotive and truck gasoline engine, manufacturing Cylinder heads, automotive and truck gasoline engine, manufacturing Engine block assemblies, automotive and truck gasoline, manufacturing Engines and parts (except diesel), automotive and truck, manufacturing Flywheels and ring gears, automotive and truck gasoline engine, manufacturing Fuel injection systems and parts, automotive and truck gasoline engine, manufactu Fuel pumps, mechanical, automotive and truck gasoline engine, manufacturing Gasoline engine parts (except carburetors, pistons, piston rings, valves), automo Gasoline engines, automotive and truck, manufacturing Governors for automotive gasoline engines manufacturing Internal combustion engines, automotive and truck gasoline, manufacturing Manifolds (i.e., intake and exhaust), automotive and truck gasoline engine, manuf Positive crankcase ventilation (PCV) valves, engine, manufacturing Pumps (e.g., fuel, oil, water), mechanical, automotive and truck gasoline engine Rebuilding automotive and truck gasoline engines Rocker arms and parts, automotive and truck gasoline engine, manufacturing Timing gears and chains, automotive and truck gasoline engine, manufacturing. Step 2. Filtering and Smoothing Based on the aggregate view of manufacturing and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves 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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves 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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves 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 and rebuilding gasoline motor vehicle engines and engine parts excluding carburetors, pistons, piston rings, and valves). 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 16 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 17 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 19 2.1 The Worldwide Market Potential 19 3 AFRICA 21 3.1 Executive Summary 21 3.2 Algeria 22 3.3 Angola 23 3.4 Benin 24 3.5 Botswana 25 3.6 Burkina Faso 26 3.7 Burundi 26 3.8 Cameroon 27 3.9 Cape Verde 28 3.10 Central African Republic 28 3.11 Chad 29 3.12 Comoros 30 3.13 Congo (formerly Zaire) 30 3.14 Cote dIvoire 31 3.15 Djibouti 32 3.16 Egypt 33 3.17 Equatorial Guinea 34 3.18 Ethiopia 34 3.19 Gabon 35 3.20 Ghana 36 3.21 Guinea 37 3.22 Guinea-Bissau 37 3.23 Kenya 38 3.24 Lesotho 39 3.25 Liberia 39 3.26 Libya 40 3.27 Madagascar 41 3.28 Malawi 41 3.29 Mali 42 3.30 Mauritania 43 3.31 Mauritius 43 3.32 Morocco 44 3.33 Mozambique 45 3.34 Namibia 45 3.35 Niger 46 3.36 Nigeria 47 3.37 Republic of Congo 48 3.38 Reunion 48 3.39 Rwanda 49 3.40 Sao Tome E Principe 50 3.41 Senegal 50 3.42 Sierra Leone 51 3.43 Somalia 52 3.44 South Africa 53 3.45 Sudan 54 3.46 Swaziland 55 3.47 Tanzania 55 3.48 The Gambia 56 3.49 Togo 57 3.50 Tunisia 58 3.51 Uganda 59 3.52 Western Sahara 60 3.53 Zambia 60 3.54 Zimbabwe 61 4 ASIA 63 4.1 Executive Summary 63 4.2 Bangladesh 64 4.3 Bhutan 65 4.4 Brunei 66 4.5 Burma 67 4.6 Cambodia 68 4.7 China 68 4.8 Hong Kong 69 4.9 India 70 4.10 Indonesia 71 4.11 Japan 72 4.12 Laos 73 4.13 Macau 73 4.14 Malaysia 74 4.15 Maldives 75 4.16 Mongolia 76 4.17 Nepal 76 4.18 North Korea 77 4.19 Papua New Guinea 78 4.20 Philippines 78 4.21 Seychelles 79 4.22 Singapore 80 4.23 South Korea 81 4.24 Sri Lanka 82 4.25 Taiwan 83 4.26 Thailand 84 4.27 Vietnam 85 5 EUROPE & THE MIDDLE EAST 86 5.1 Executive Summary 86 5.2 Afghanistan 87 5.3 Albania 88 5.4 Andorra 89 5.5 Armenia 90 5.6 Austria 91 5.7 Azerbaijan 92 5.8 Bahrain 93 5.9 Belarus 93 5.10 Belgium 94 5.11 Bosnia and Herzegovina 95 5.12 Bulgaria 96 5.13 Croatia 97 5.14 Cyprus 98 5.15 Czech Republic 98 5.16 Denmark 99 5.17 Estonia 100 5.18 Finland 101 5.19 France 102 5.20 Georgia 103 5.21 Germany 104 5.22 Greece 105 5.23 Hungary 106 5.24 Iceland 107 5.25 Iran 108 5.26 Iraq 109 5.27 Ireland 110 5.28 Israel 110 5.29 Italy 111 5.30 Jordan 112 5.31 Kazakhstan 113 5.32 Kuwait 114 5.33 Kyrgyzstan 115 5.34 Latvia 116 5.35 Lebanon 116 5.36 Liechtenstein 117 5.37 Lithuania 118 5.38 Luxembourg 118 5.39 Malta 119 5.40 Moldova 120 5.41 Monaco 120 5.42 Norway 121 5.43 Oman 122 5.44 Pakistan 122 5.45 Palestine 123 5.46 Poland 124 5.47 Portugal 125 5.48 Qatar 126 5.49 Romania 126 5.50 Russia 127 5.51 San Marino 128 5.52 Saudi Arabia 129 5.53 Slovakia 130 5.54 Slovenia 130 5.55 Spain 131 5.56 Sweden 132 5.57 Switzerland 133 5.58 Syrian Arab Republic 134 5.59 Tajikistan 135 5.60 The Netherlands 136 5.61 The United Arab Emirates 137 5.62 The United Kingdom 137 5.63 Turkey 138 5.64 Turkmenistan 139 5.65 Ukraine 140 5.66 Uzbekistan 141 5.67 Yemen 142 6 LATIN AMERICA 144 6.1 Executive Summary 144 6.2 Argentina 145 6.3 Belize 146 6.4 Bolivia 147 6.5 Brazil 148 6.6 Chile 149 6.7 Colombia 150 6.8 Costa Rica 151 6.9 Ecuador 151 6.10 El Salvador 152 6.11 French Guiana 153 6.12 Guatemala 153 6.13 Guyana 154 6.14 Honduras 155 6.15 Mexico 156 6.16 Nicaragua 157 6.17 Panama 158 6.18 Paraguay 159 6.19 Peru 160 6.20 Suriname 161 6.21 The Falkland Islands 161 6.22 Uruguay 162 6.23 Venezuela 163 7 NORTH AMERICA & THE CARIBBEAN 164 7.1 Executive Summary 164 7.2 Antigua and Barbuda 165 7.3 Aruba 166 7.4 Barbados 167 7.5 Bermuda 167 7.6 Canada 168 7.7 Cuba 169 7.8 Dominica 170 7.9 Dominican Republic 170 7.10 Greenland 171 7.11 Grenada 172 7.12 Guadeloupe 173 7.13 Haiti 174 7.14 Jamaica 174 7.15 Martinique 175 7.16 Puerto Rico 176 7.17 St. Kitts and Nevis 177 7.18 St. Lucia 177 7.19 St. Vincent and the Grenadines 178 7.20 The Bahamas 179 7.21 The British Virgin Islands 179 7.22 The Cayman Islands 180 7.23 The Netherlands Antilles 181 7.24 The U.S. Virgin Islands 181 7.25 The United States 182 7.26 Trinidad and Tobago 183 8 OCEANA 185 8.1 Executive Summary 185 8.2 American Samoa 186 8.3 Australia 187 8.4 Christmas Island 188 8.5 Cook Islands 188 8.6 Fiji 189 8.7 French Polynesia 190 8.8 Guam 190 8.9 Kiribati 191 8.10 Marshall Islands 192 8.11 Micronesia Federation 192 8.12 Nauru 193 8.13 New Caledonia 194 8.14 New Zealand 194 8.15 Niue 195 8.16 Norfolk Island 196 8.17 Palau 196 8.18 Solomon Islands 197 8.19 The Northern Mariana Island 198 8.20 Tokelau 198 8.21 Tonga 199 8.22 Tuvalu 200 8.23 Vanuatu 200 8.24 Wallis and Futuna 201 8.25 Western Samoa 202 9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 203 9.1 Disclaimers & Safe Harbor 203 9.2 ICON Group International, Inc. User Agreement Provisions 204
 
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