The 2009-2014 World Outlook for Manufacturing Basic Organic Chemical Products Excluding Aromatic Petrochemicals, Industrial Gases, Synthetic Organic Dyes and Pigments, Gum and Wood Chemicals, Cyclic Crudes and Intermediates, and Ethyl Alcohol
ICON Group International, September 2008, Pages: 226
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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol 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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol 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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol 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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol 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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol. 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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol. 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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol.
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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol” 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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol 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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol is 325199. It is for this definition of manufacturing basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol that the aggregate latent demand estimates are derived. “Manufacturing basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol” is specifically defined as follows:
325199
This U.S. industry comprises establishments primarily engaged in manufacturing basic organic chemical products (except aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol).
3251991
Fatty acids
3251994
Bulk pesticides and other bulk synthetic organic agricultural chemicals
3251997
Industrial organic flavor oil mixtures and blends
325199A
Reagent & high purity grade organic chemicals from purchased technical grades
325199E
Natural organic chemicals, nec
325199G
SYNTHETIC ORGANIC ALCOHOLS, UNMIXED
325199H
Synthetic organic chemicals for use as flavor and perfume materials
325199K
Synthetic organic rubber processing chemicals
325199N
Synthetic organic plasticizers
325199P
Primary products
325199R
Other synthetic organic chemicals, nec
325199T
Miscellaneous end use chemicals and other industrial organic chemicals, nec
325199U
Miscellaneous cyclic and acyclic chemicals, nec
325199V
MISCELLANEOUS CYCLIC AND ACYCLIC CHEMICALS AND CHEMICAL PRODUCTS, EXCLUDING FATTY ACID ESTERS
Furthermore, the definition of NAICS code 325199 includes the following:
Accelerators (i.e., basic synthetic chemical) manufacturing
Acetaldehyde manufacturing
Acetates, not specified elsewhere by process, manufacturing
Acetic acid manufacturing
Acetic anhydride manufacturing
Acetin manufacturing
Acetone, synthetic, manufacturing
Acid esters, not specified elsewhere by process, manufacturing
Acids, organic, not specified elsewhere by process, manufacturing
Acrolein manufacturing
Acrylonitrile manufacturing
Adipic acid esters or amines manufacturing
Adipic acid manufacturing
Adiponitrile manufacturing
Aldehydes manufacturing
Alginates (e.g., calcium, potassium, sodium) manufacturing
Alginic acid manufacturing
Amyl acetate manufacturing
Bleaching agents, organic, manufacturing
Bromochloromethane manufacturing
Butadiene made from alcohol
Butyl acetate manufacturing
Calcium citrate manufacturing
Calcium organic compounds, not specified elsewhere by process, manufacturing
Calcium oxalate manufacturing
Camphor, synthetic, manufacturing
Caprolactam manufacturing
Carbinol manufacturing
Carbon organic compounds, not specified elsewhere by process, manufacturing
Carbon tetrachloride manufacturing
Cellulose acetate (except resins) manufacturing
Cetyl alcohol manufacturing
Chloral manufacturing
Chloroacetic acid manufacturing
Chloroform manufacturing
Chloropicrin manufacturing
Citral manufacturing
Citrates, not specified elsewhere by process, manufacturing
Citric acid manufacturing
Citronellal manufacturing
Coumarin manufacturing
Cream of tartar manufacturing
Decahydronaphthalene manufacturing
Diethylene glycol manufacturing
Dimethyl divinyl acetylene (di-isopropenyl acetylene) manufacturing
Dimethylhydrazine manufacturing
Enzyme proteins (i.e., basic synthetic chemicals) (except pharmaceutical use) man
Essential oils, synthetic, manufacturing
Esters, not specified elsewhere by process, manufacturing
Ethyl acetate, synthetic, manufacturing
Ethyl butyrate manufacturing
Ethyl cellulose (except resins) manufacturing
Ethyl chloride manufacturing
Ethyl ether manufacturing
Ethyl formate manufacturing
Ethyl nitrite manufacturing
Ethyl perhydrophenanthrene manufacturing
Ethylene glycol ether manufacturing
Ethylene glycol manufacturing
Ethylene oxide manufacturing
Fatty acid esters and amines manufacturing
Fatty acids (e.g., margaric, oleic, stearic) manufacturing
Fatty alcohols manufacturing
Flavoring materials (i.e., basic synthetic chemicals such as coumarin) manufactur
Formaldehyde manufacturing
Formalin manufacturing
Formic acid manufacturing
Fuel propellants, solid organic, not specified elsewhere by process, manufacturin
Geraniol manufacturing
Glycerin (i.e., glycerol), synthetic, manufacturing
Halogenated hydrocarbon derivatives (except aromatic) manufacturing
Heterocyclic chemicals, not specified elsewhere by process, manufacturing
Hexadecanol manufacturing
Hexamethylenediamine manufacturing
Hexamethylenetetramine manufacturing
Hexanol manufacturing
Ionone manufacturing
Isopropyl alcohol manufacturing
Ketone compounds, not specified elsewhere by process, manufacturing
Lactic acid manufacturing
Lauric acid esters and amines manufacturing
Linoleic acid esters and amines manufacturing
Malonic dinitrile manufacturing
Margaric acid manufacturing
Metallic soap manufacturing
Methyl alcohol (i.e., methanol), synthetic, manufacturing
Methyl chloride manufacturing
Methyl perhydrofluorine manufacturing
Methyl salicylate manufacturing
Methylamine manufacturing
Methylene chloride manufacturing
Monomethylparaminophenol sulfate manufacturing
Monosodium glutamate manufacturing
Naphthenic acid soaps manufacturing
Natural nonfood coloring, manufacturing
Nitrous ether manufacturing
Oleic acid (i.e., red oil) manufacturing
Oleic acid esters manufacturing
Organo-inorganic compound manufacturing
Oxalates (e.g., ammonium oxalate, ethyl oxalate, sodium oxalate) manufacturing
Oxalic acid manufacturing
Palmitic acid esters and amines manufacturing
Pentaerythritol manufacturing
Perchloroethylene manufacturing
Perfume materials (i.e., basic synthetic chemicals, such as terpineol) manufactur
Peroxides, organic, manufacturing
Phosgene manufacturing
Phosphoric acid esters manufacturing
Phthalate acid manufacturing
Plasticizers (i.e., basic synthetic chemicals) manufacturing
Polyhydric alcohol esters and amines manufacturing
Polyhydric alcohols manufacturing
Potassium bitartrate manufacturing
Potassium organic compounds, not specified elsewhere by process, manufacturing
Propylcarbinol manufacturing
Propylene glycol manufacturing
Red oil (i.e., oleic acid) manufacturing
Saccharin manufacturing
Salicylic acid (except medicinal) manufacturing
Sebacic acid esters manufacturing
Sebacic acid manufacturing
Silicone (except resins) manufacturing
Sodium acetate manufacturing
Sodium alginate manufacturing
Sodium benzoate manufacturing
Sodium glutamate manufacturing
Sodium organic compounds, not specified elsewhere by process, manufacturing
Sodium pentachlorophenate manufacturing
Sodium sulfoxalate formaldehyde manufacturing
Sorbitol manufacturing
Stearic acid esters manufacturing
Stearic acid manufacturing
Stearic acid salts manufacturing
Sugar substitutes (i.e., synthetic sweeteners blended with other ingredients) mad
Synthetic sweeteners (i.e., sweetening agents) manufacturing
Tanning agents, synthetic organic, manufacturing
Tartaric acid manufacturing
Tartrates, not specified elsewhere by process, manufacturing
Tear gas manufacturing
Terpineol manufacturing
Tert-butylated bis (p-phenoxyphenyl) ether fluid manufacturing
Tetrachloroethylene manufacturing
Tetraethyl lead manufacturing
Thioglycolic acid manufacturing
Trichloroethylene manufacturing
Trichlorophenoxyacetic acid manufacturing
Tricresyl phosphate manufacturing
Tridecyl alcohol manufacturing
Triphenyl phosphate manufacturing
Vanillin, synthetic, manufacturing
Vinyl acetate (except resins) manufacturing
Wood alcohol, synthetic, manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol 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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol 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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol 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 basic organic chemical products excluding aromatic petrochemicals, industrial gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic crudes and intermediates, and ethyl alcohol). 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 19
1.3.3 Step 3. Filling in Missing Values 19
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 19
1.3.5 Step 5. Fixed-Parameter Linear Estimation 20
1.3.6 Step 6. Aggregation and Benchmarking 20
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 20
2 SUMMARY OF FINDINGS 22
2.1 The Worldwide Market Potential 22
3 AFRICA & THE MIDDLE EAST 24
3.1 Executive Summary 24
3.2 Afghanistan 26
3.3 Algeria 27
3.4 Angola 28
3.5 Armenia 29
3.6 Azerbaijan 30
3.7 Bahrain 31
3.8 Benin 32
3.9 Botswana 33
3.10 Burkina Faso 34
3.11 Burundi 35
3.12 Cameroon 36
3.13 Cape Verde 37
3.14 Central African Republic 38
3.15 Chad 39
3.16 Comoros 40
3.17 Congo (formerly Zaire) 40
3.18 Cote dIvoire 41
3.19 Djibouti 42
3.20 Egypt 43
3.21 Equatorial Guinea 44
3.22 Ethiopia 44
3.23 Gabon 45
3.24 Ghana 46
3.25 Guinea 47
3.26 Guinea-Bissau 48
3.27 Iran 49
3.28 Iraq 50
3.29 Israel 51
3.30 Jordan 52
3.31 Kenya 53
3.32 Kuwait 54
3.33 Kyrgyzstan 54
3.34 Lebanon 55
3.35 Lesotho 56
3.36 Liberia 56
3.37 Libya 57
3.38 Madagascar 58
3.39 Malawi 59
3.40 Mali 60
3.41 Mauritania 61
3.42 Mauritius 62
3.43 Morocco 63
3.44 Mozambique 64
3.45 Namibia 65
3.46 Niger 66
3.47 Nigeria 67
3.48 Oman 68
3.49 Pakistan 68
3.50 Palestine 69
3.51 Qatar 70
3.52 Republic of Congo 70
3.53 Reunion 71
3.54 Rwanda 72
3.55 Sao Tome E Principe 73
3.56 Saudi Arabia 73
3.57 Senegal 74
3.58 Sierra Leone 75
3.59 Somalia 76
3.60 South Africa 77
3.61 Sudan 78
3.62 Swaziland 79
3.63 Syrian Arab Republic 80
3.64 Tajikistan 81
3.65 Tanzania 82
3.66 The Gambia 83
3.67 The United Arab Emirates 84
3.68 Togo 84
3.69 Tunisia 85
3.70 Turkey 86
3.71 Turkmenistan 87
3.72 Uganda 88
3.73 Uzbekistan 89
3.74 Western Sahara 90
3.75 Yemen 90
3.76 Zambia 91
3.77 Zimbabwe 92
4 ASIA 94
4.1 Executive Summary 94
4.2 Bangladesh 96
4.3 Bhutan 97
4.4 Brunei 98
4.5 Burma 98
4.6 Cambodia 99
4.7 China 100
4.8 Hong Kong 101
4.9 India 101
4.10 Indonesia 102
4.11 Japan 103
4.12 Laos 104
4.13 Macau 105
4.14 Malaysia 106
4.15 Maldives 107
4.16 Mongolia 107
4.17 Nepal 108
4.18 North Korea 109
4.19 Papua New Guinea 110
4.20 Philippines 111
4.21 Seychelles 112
4.22 Singapore 112
4.23 South Korea 113
4.24 Sri Lanka 114
4.25 Taiwan 115
4.26 Thailand 116
4.27 Vietnam 117
5 EUROPE 118
5.1 Executive Summary 118
5.2 Albania 120
5.3 Andorra 121
5.4 Austria 121
5.5 Belarus 122
5.6 Belgium 123
5.7 Bosnia and Herzegovina 124
5.8 Bulgaria 125
5.9 Croatia 126
5.10 Cyprus 127
5.11 Czech Republic 128
5.12 Denmark 129
5.13 Estonia 130
5.14 Finland 131
5.15 France 132
5.16 Georgia 133
5.17 Germany 134
5.18 Greece 135
5.19 Hungary 136
5.20 Iceland 137
5.21 Ireland 138
5.22 Italy 139
5.23 Kazakhstan 140
5.24 Latvia 141
5.25 Liechtenstein 142
5.26 Lithuania 143
5.27 Luxembourg 144
5.28 Malta 145
5.29 Moldova 146
5.30 Monaco 146
5.31 Norway 147
5.32 Poland 148
5.33 Portugal 149
5.34 Romania 150
5.35 Russia 151
5.36 San Marino 152
5.37 Slovakia 152
5.38 Slovenia 153
5.39 Spain 154
5.40 Sweden 155
5.41 Switzerland 156
5.42 The Netherlands 157
5.43 The United Kingdom 158
5.44 Ukraine 159
6 LATIN AMERICA 160
6.1 Executive Summary 160
6.2 Argentina 161
6.3 Belize 162
6.4 Bolivia 163
6.5 Brazil 164
6.6 Chile 165
6.7 Colombia 166
6.8 Costa Rica 167
6.9 Ecuador 168
6.10 El Salvador 169
6.11 French Guiana 170
6.12 Guatemala 171
6.13 Guyana 172
6.14 Honduras 173
6.15 Mexico 174
6.16 Nicaragua 175
6.17 Panama 176
6.18 Paraguay 177
6.19 Peru 178
6.20 Suriname 179
6.21 The Falkland Islands 180
6.22 Uruguay 180
6.23 Venezuela 181
7 NORTH AMERICA & THE CARIBBEAN 183
7.1 Executive Summary 183
7.2 Antigua and Barbuda 185
7.3 Aruba 185
7.4 Barbados 186
7.5 Bermuda 187
7.6 Canada 187
7.7 Cuba 188
7.8 Dominica 189
7.9 Dominican Republic 190
7.10 Greenland 191
7.11 Grenada 192
7.12 Guadeloupe 192
7.13 Haiti 193
7.14 Jamaica 194
7.15 Martinique 195
7.16 Puerto Rico 196
7.17 St. Kitts and Nevis 197
7.18 St. Lucia 197
7.19 St. Vincent and the Grenadines 198
7.20 The Bahamas 199
7.21 The British Virgin Islands 199
7.22 The Cayman Islands 200
7.23 The Netherlands Antilles 201
7.24 The U.S. Virgin Islands 201
7.25 The United States 202
7.26 Trinidad and Tobago 203
8 OCEANA 205
8.1 Executive Summary 205
8.2 American Samoa 207
8.3 Australia 208
8.4 Christmas Island 209
8.5 Cook Islands 209
8.6 Fiji 210
8.7 French Polynesia 211
8.8 Guam 212
8.9 Kiribati 213
8.10 Marshall Islands 213
8.11 Micronesia Federation 214
8.12 Nauru 215
8.13 New Caledonia 215
8.14 New Zealand 216
8.15 Niue 217
8.16 Norfolk Island 218
8.17 Palau 219
8.18 Solomon Islands 219
8.19 The Northern Mariana Island 220
8.20 Tokelau 221
8.21 Tonga 221
8.22 Tuvalu 222
8.23 Vanuatu 223
8.24 Wallis and Futuna 223
8.25 Western Samoa 224
9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 225
9.1 Disclaimers & Safe Harbor 225
9.2 ICON Group International, Inc. User Agreement Provisions 226
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