The 2009-2014 World Outlook for Manufacturing Acyclic, Aliphatic, or Cyclic Aromatic Hydrocarbons Made from Refined Petroleum or Liquid Hydrocarbons Such As Ethylene, Propylene, Butylene, Benzene, Toluene, Styrene, Xylene, Ethyl Benzene, and Cumene
ICON Group International, September 2008, Pages: 219
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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene 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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene 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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene 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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene 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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene. 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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene. 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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene.
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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene” 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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene 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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene is 325110. It is for this definition of manufacturing acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene that the aggregate latent demand estimates are derived. “Manufacturing acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene” is specifically defined as follows:
325110
This industry comprises establishments primarily engaged in (1) manufacturing acyclic (i.e., aliphatic) hydrocarbons such as ethylene, propylene, and butylene made from refined petroleum or liquid hydrocarbon and/or (2) manufacturing cyclic aromatic hydrocarbons such as benzene, toluene, styrene, xylene, ethyl benzene, and cumene made from refined petroleum or liquid hydrocarbons.
3251101
Aromatics (benzene, toluene, xylene, etc), not made in a refinery
32511011
Aromatics (benzene, toluene, xylene, etc.), made in petrochemical plants
3251101111
Aromatics (benzene, toluene, xylene, etc.), for use as a chemical raw material, made in petrochemical plants
3251101121
Aromatics (benzene, toluene, xylene, etc.), for other uses, made in petrochemical plants
3251104
Liquefied refinery gases (aliphatics), not made in a refinery
32511041
Liquefied refinery gases (aliphatics), made in petrochemical plants
3251104111
Liquefied refinery gases (aliphatics), for use as a chemical raw material, made in petrochemical plants
3251104121
Liquefied refinery gases (aliphatics), for other uses, made in petrochemical plants
325110MM
Miscellaneous receipts
325110P
Primary products
325110SM
Secondary products and miscellaneous receipts
325110SS
Secondary products
Furthermore, the definition of NAICS code 325110 includes the following:
Acyclic hydrocarbons (e.g., butene, ethylene, propene) (except acetylene) made fr
Aliphatic (e.g., hydrocarbons) (except acetylene) made from refined petroleum or
Benzene made from refined petroleum or liquid hydrocarbons
Butadiene made from refined petroleum or liquid hydrocarbons
Butane made from refined petroleum or liquid hydrocarbons
Butylene made from refined petroleum or liquid hydrocarbons
Cumene made from refined petroleum or liquid hydrocarbons
Cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons
Dodecene made from refined petroleum or liquid hydrocarbons
Ethane made from refined petroleum or liquid hydrocarbons
Ethylbenzene made from refined petroleum or liquid hydrocarbons
Ethylene made from refined petroleum or liquid hydrocarbons
Heptanes made from refined petroleum or liquid hydrocarbons
Heptenes made from refined petroleum or liquid hydrocarbons
Isobutane made from refined petroleum or liquid hydrocarbons
Isobutene made from refined petroleum or liquid hydrocarbons
Isoprene made from refined petroleum or liquid hydrocarbons
Nonene made from refined petroleum or liquid hydrocarbons
Olefins made from refined petroleum or liquid hydrocarbons
Paraffins made from refined petroleum or liquid hydrocarbons
Pentanes made from refined petroleum or liquid hydrocarbons
Pentenes made from refined petroleum or liquid hydrocarbons
Propylene made from refined petroleum or liquid hydrocarbons
Styrene made from refined petroleum or liquid hydrocarbons
Toluene made from refined petroleum or liquid hydrocarbons
Xylene made from refined petroleum or liquid hydrocarbons.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene 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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene 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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene 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 acyclic, aliphatic, or cyclic aromatic hydrocarbons made from refined petroleum or liquid hydrocarbons such as ethylene, propylene, butylene, benzene, toluene, styrene, xylene, ethyl benzene, and cumene). 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.
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 15
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, EUROPE & THE MIDDLE EAST 20
3.1 Executive Summary 20
3.2 Afghanistan 22
3.3 Albania 23
3.4 Algeria 24
3.5 Andorra 25
3.6 Angola 25
3.7 Armenia 26
3.8 Austria 27
3.9 Azerbaijan 28
3.10 Bahrain 29
3.11 Belarus 30
3.12 Belgium 31
3.13 Benin 32
3.14 Bosnia and Herzegovina 33
3.15 Botswana 34
3.16 Bulgaria 35
3.17 Burkina Faso 36
3.18 Burundi 37
3.19 Cameroon 38
3.20 Cape Verde 39
3.21 Central African Republic 40
3.22 Chad 41
3.23 Comoros 42
3.24 Congo (formerly Zaire) 42
3.25 Cote dIvoire 43
3.26 Croatia 44
3.27 Cyprus 45
3.28 Czech Republic 46
3.29 Denmark 47
3.30 Djibouti 48
3.31 Egypt 49
3.32 Equatorial Guinea 50
3.33 Estonia 50
3.34 Ethiopia 51
3.35 Finland 52
3.36 France 53
3.37 Gabon 54
3.38 Georgia 55
3.39 Germany 56
3.40 Ghana 57
3.41 Greece 58
3.42 Guinea 59
3.43 Guinea-Bissau 59
3.44 Hungary 60
3.45 Iceland 61
3.46 Iran 62
3.47 Iraq 63
3.48 Ireland 64
3.49 Israel 65
3.50 Italy 66
3.51 Jordan 67
3.52 Kazakhstan 68
3.53 Kenya 69
3.54 Kuwait 70
3.55 Kyrgyzstan 70
3.56 Latvia 71
3.57 Lebanon 72
3.58 Lesotho 73
3.59 Liberia 73
3.60 Libya 74
3.61 Liechtenstein 75
3.62 Lithuania 76
3.63 Luxembourg 77
3.64 Madagascar 78
3.65 Malawi 79
3.66 Mali 80
3.67 Malta 81
3.68 Mauritania 82
3.69 Mauritius 83
3.70 Moldova 84
3.71 Monaco 84
3.72 Morocco 85
3.73 Mozambique 86
3.74 Namibia 87
3.75 Niger 88
3.76 Nigeria 89
3.77 Norway 90
3.78 Oman 91
3.79 Pakistan 91
3.80 Palestine 92
3.81 Poland 93
3.82 Portugal 94
3.83 Qatar 95
3.84 Republic of Congo 95
3.85 Reunion 96
3.86 Romania 97
3.87 Russia 98
3.88 Rwanda 99
3.89 San Marino 100
3.90 Sao Tome E Principe 100
3.91 Saudi Arabia 101
3.92 Senegal 102
3.93 Sierra Leone 103
3.94 Slovakia 104
3.95 Slovenia 104
3.96 Somalia 105
3.97 South Africa 106
3.98 Spain 107
3.99 Sudan 108
3.100 Swaziland 109
3.101 Sweden 110
3.102 Switzerland 111
3.103 Syrian Arab Republic 112
3.104 Tajikistan 113
3.105 Tanzania 114
3.106 The Gambia 115
3.107 The Netherlands 116
3.108 The United Arab Emirates 117
3.109 The United Kingdom 117
3.110 Togo 118
3.111 Tunisia 119
3.112 Turkey 120
3.113 Turkmenistan 121
3.114 Uganda 122
3.115 Ukraine 123
3.116 Uzbekistan 124
3.117 Western Sahara 125
3.118 Yemen 125
3.119 Zambia 126
3.120 Zimbabwe 127
4 ASIA 129
4.1 Executive Summary 129
4.2 Bangladesh 131
4.3 Bhutan 132
4.4 Brunei 133
4.5 Burma 133
4.6 Cambodia 134
4.7 China 135
4.8 Hong Kong 136
4.9 India 136
4.10 Indonesia 137
4.11 Japan 138
4.12 Laos 139
4.13 Macau 140
4.14 Malaysia 141
4.15 Maldives 142
4.16 Mongolia 142
4.17 Nepal 143
4.18 North Korea 144
4.19 Papua New Guinea 145
4.20 Philippines 146
4.21 Seychelles 147
4.22 Singapore 147
4.23 South Korea 148
4.24 Sri Lanka 149
4.25 Taiwan 150
4.26 Thailand 151
4.27 Vietnam 152
5 LATIN AMERICA 153
5.1 Executive Summary 153
5.2 Argentina 154
5.3 Belize 155
5.4 Bolivia 156
5.5 Brazil 157
5.6 Chile 158
5.7 Colombia 159
5.8 Costa Rica 160
5.9 Ecuador 161
5.10 El Salvador 162
5.11 French Guiana 163
5.12 Guatemala 164
5.13 Guyana 165
5.14 Honduras 166
5.15 Mexico 167
5.16 Nicaragua 168
5.17 Panama 169
5.18 Paraguay 170
5.19 Peru 171
5.20 Suriname 172
5.21 The Falkland Islands 173
5.22 Uruguay 173
5.23 Venezuela 174
6 NORTH AMERICA & THE CARIBBEAN 176
6.1 Executive Summary 176
6.2 Antigua and Barbuda 178
6.3 Aruba 178
6.4 Barbados 179
6.5 Bermuda 180
6.6 Canada 180
6.7 Cuba 181
6.8 Dominica 182
6.9 Dominican Republic 183
6.10 Greenland 184
6.11 Grenada 185
6.12 Guadeloupe 185
6.13 Haiti 186
6.14 Jamaica 187
6.15 Martinique 188
6.16 Puerto Rico 189
6.17 St. Kitts and Nevis 190
6.18 St. Lucia 190
6.19 St. Vincent and the Grenadines 191
6.20 The Bahamas 192
6.21 The British Virgin Islands 192
6.22 The Cayman Islands 193
6.23 The Netherlands Antilles 194
6.24 The U.S. Virgin Islands 194
6.25 The United States 195
6.26 Trinidad and Tobago 196
7 OCEANA 198
7.1 Executive Summary 198
7.2 American Samoa 200
7.3 Australia 201
7.4 Christmas Island 202
7.5 Cook Islands 202
7.6 Fiji 203
7.7 French Polynesia 204
7.8 Guam 205
7.9 Kiribati 206
7.10 Marshall Islands 206
7.11 Micronesia Federation 207
7.12 Nauru 208
7.13 New Caledonia 208
7.14 New Zealand 209
7.15 Niue 210
7.16 Norfolk Island 211
7.17 Palau 212
7.18 Solomon Islands 212
7.19 The Northern Mariana Island 213
7.20 Tokelau 214
7.21 Tonga 214
7.22 Tuvalu 215
7.23 Vanuatu 216
7.24 Wallis and Futuna 216
7.25 Western Samoa 217
8 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 218
8.1 Disclaimers & Safe Harbor 218
8.2 ICON Group International, Inc. User Agreement Provisions 219
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