The 2009 Report on Manufacturing Petroleum Products from Refined Petroleum or Coal Products Made in Coke Ovens Not Integrated with Steel Mills Excluding Asphalt Paving, Roofing, and Saturated Materials and Lubricating Oils and Greases: World Market S
ICON Group International, May 2009, Pages: 357
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases across more than 2000 cities. For the year reported, estimates are given for the latent demand, or potential industry earnings (P.I.E.), for the city in question (in millions of U.S. dollars), the percent share the city is of the region and of the globe. These comparative benchmarks allow the reader to quickly gauge a city vis-à-vis others. Using econometric models which project fundamental economic dynamics within each country and across countries, latent demand estimates are created. This report does not discuss the specific players in the market serving the latent demand, nor specific details at the product level. The study also does not consider short-term cyclicalities that might affect realized sales. The study, therefore, is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved.
This study does not report actual sales data (which are simply unavailable, in a comparable or consistent manner in virtually all of the cities of the world). This study gives, however, my estimates for the worldwide latent demand, or the P.I.E. for manufacturing petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases. It also shows how the P.I.E. is divided across the world’s cities. In order to make these estimates, a multi-stage methodology was employed that is often taught in courses on international strategic planning at graduate schools of business.
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 petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases 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 city market.
Another reason why sales do not equate to latent demand is exchange rates. In this report, all figures assume the long-run efficiency of currency markets. Figures, therefore, equate values based on purchasing power parities across countries. Short-run distortions in the value of the dollar, therefore, do not figure into the estimates. Purchasing power parity estimates of country income were collected from official sources, and extrapolated using standard econometric models. The report uses the dollar as the currency of comparison, but not as a measure of transaction volume. The units used in this report are: US $ mln.
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 (i.e., the figures reflect average exchange rates over recent history). 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 earlier, 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 petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases 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 petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases on a city-by-city 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 in 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 petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases 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 petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases. 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 petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases. 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 petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases.
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 or cities are more likely to be at or near efficiency than others. These are given greater weight than others in the estimation of latent demand compared to others 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 petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases” 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 petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases 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 cities 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 petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases is 324199. It is for this definition of manufacturing petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases that the aggregate latent demand estimates are derived. “Manufacturing petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases” is specifically defined as follows:
324199
This U.S. industry comprises establishments primarily engaged in manufacturing petroleum products (except asphalt paving, roofing, and saturated materials and lubricating oils and greases) from refined petroleum and coal products made in coke ovens not integrated with a steel mill.
3241991
Coke oven and blast furnace products, not made in steel mills
32419911
Coke oven and blast furnace products, made in coke oven establishments
3241991111
Coke oven products, coke (excluding screenings and breeze), made in coke oven establishments
3241991121
Coke oven products, screenings and breeze, made in coke oven establishments
3241991131
Coke oven products, crude tar, made in coke oven establishments
3241991141
Coke oven products, crude light oil, made in coke oven establishments
3241991151
Coke oven products, other (including tar derivatives, ammonia, light oil derivations, and coke oven gas), made in coke oven establishments
3241992
All other petroleum and coal products, except coke oven products
32419921
Calcined petroleum coke, made in coke oven establishments
3241992131
Calcined petroleum coke, made in coke oven establishments
32419922
All other petroleum and coal products, made in coke oven establishments
3241992211
Microcrystalline petroleum waxes, made from refined petroleum
3241992221
Crystalline petroleum waxes, made from refined petroleum
3241992241
All other petroleum and coal products, including packaged fuel and fuel briquettes, made in coke oven establishments
324199MM
Miscellaneous receipts
324199P
Primary products
324199SM
Secondary products and miscellaneous receipts
324199SS
Secondary products
Furthermore, the definition of NAICS code 324199 includes the following:
Boulets (i.e., fuel bricks) made from refined petroleum
Briquettes, petroleum, made from refined petroleum
Calcining petroleum coke from refined petroleum
Coke oven products (e.g., coke, gases, tars) made in coke oven establishments
Fuel briquettes or boulets made from refined petroleum
Oil-based additives made from refined petroleum
Petroleum jelly made from refined petroleum
Petroleum waxes made from refined petroleum
Road oils made from refined petroleum
Waxes, petroleum, made from refined petroleum.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases as defined above, data were then collected for as many similar countries and cities as possible for that same definition, at the same level of the value chain. This generates a convenience sample 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 or cities on a sporadic basis. In other cases, data 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), cities 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 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 cities 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 cities along the aggregate consumption function, but also over time (i.e., not all cities are perceived to have the same income growth prospects over time and this effect can vary from city to city as well). Another way of looking at this is to say that latent demand for manufacturing petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases is more likely to be similar across cities that have similar characteristics in terms of economic development (i.e., African cities will have similar latent demand structures controlling for the income variation across the pool of African cities).
This approach is useful across cities for which some notion of non-linearity exists in the aggregate consumption function. For some categories, however, the reader must realize that the numbers will reflect a city’s contribution to global latent demand and may never be realized in the form of local sales. For certain 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 cities in “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 cities 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 2000 cities, there will always be those cities, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible. For these cities, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a city’s stock of income), but a function of current income (a city’s flow of income). In the long run, if a city has no current income, the latent demand for manufacturing petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases is assumed to approach zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., cities which earn low levels of income will not use their savings, in the long run, to demand manufacturing petroleum products from refined petroleum or coal products made in coke ovens not integrated with steel mills excluding asphalt paving, roofing, and saturated materials and lubricating oils and greases). In a graphical sense, for low income cities, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income cities are assumed to have a latent demand proportional to their income, based on the city 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 cities 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.
1 INTRODUCTION & METHODOLOGY 11
1.1 Overview and Definitions 11
1.2 Market Potential Estimation Methodology 11
1.2.1 Overview 11
1.2.2 What is Latent Demand and the P.I.E.? 12
1.2.3 The Methodology 13
1.2.3.1 Step 1. Product Definition and Data Collection 14
1.2.3.2 Step 2. Filtering and Smoothing 17
1.2.3.3 Step 3. Filling in Missing Values 17
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 17
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 18
1.2.3.6 Step 6. Aggregation and Benchmarking 18
2 USING THE DATA 19
3 CITY SEGMENTS RANKED BY MARKET SIZE 20
3.1 Top 15 Markets 20
3.2 Markets 16 to 30 21
3.3 Remaining Cities by Market Rank 22
4 CITY SEGMENTS IN ALPHABETICAL ORDER 125
4.1 A: from Aalborg to Az Zawiyah 125
4.2 B: from Bacolod to Bydgoszcz 132
4.3 C: from Caaguazu to Cyangugu 140
4.4 D: from Da Nang to Dzhizak 149
4.5 E: from East London to Esteli 153
4.6 F: from Fagatogo to Funchal 155
4.7 G: from Gabes to Gyumri 158
4.8 H: from Hachinohe to Hyderabad 162
4.9 I: from Iasi to Izmir 166
4.10 J: from Jaboatao to Jyvaskyla 169
4.11 K: from Kabul to Kzyl-Orda 172
4.12 L: from La Ceiba to Lyon 180
4.13 M: from Macae to Mzuzu 186
4.14 N: from Nacala to Nzerekore 196
4.15 O: from Oaklahoma City to Oyem 201
4.16 Ö: from Örebro to Örebro 203
4.17 P: from Pago Pago to Pyuthan 204
4.18 Q: from Qandahar to Quito 211
4.19 R: from Rabat to Rustavi 212
4.20 S: from S. Luis Potosi to Szombathely 215
4.21 T: from Tabligbo to Tyre 227
4.22 U: from Uberaba to Utulei 235
4.23 V: from Vacoas-Phoenix to Vukovar 237
4.24 W: from Wadi Medani to Wuhan 240
4.25 X: from Xalapa to Xian 241
4.26 Y: from Yamagata to Yungkang 242
4.27 Z: from Zadar to Zvishavane 243
5 CITY SEGMENTS RANKED BY COUNTRY 245
5.1 Afghanistan 245
5.2 Albania 245
5.3 Algeria 246
5.4 American Samoa 246
5.5 Andorra 247
5.6 Angola 247
5.7 Antigua and Barbuda 247
5.8 Argentina 248
5.9 Armenia 249
5.10 Aruba 249
5.11 Australia 250
5.12 Austria 250
5.13 Azerbaijan 251
5.14 Bahrain 251
5.15 Bangladesh 252
5.16 Barbados 252
5.17 Belarus 253
5.18 Belgium 253
5.19 Belize 254
5.20 Benin 254
5.21 Bermuda 254
5.22 Bhutan 255
5.23 Bolivia 255
5.24 Bosnia and Herzegovina 256
5.25 Botswana 256
5.26 Brazil 257
5.27 Brunei 262
5.28 Bulgaria 263
5.29 Burkina Faso 263
5.30 Burma 264
5.31 Burundi 264
5.32 Cambodia 264
5.33 Cameroon 265
5.34 Canada 265
5.35 Cape Verde 266
5.36 Central African Republic 266
5.37 Chad 267
5.38 Chile 267
5.39 China 268
5.40 Christmas Island 268
5.41 Colombia 269
5.42 Comoros 269
5.43 Congo (formerly Zaire) 269
5.44 Cook Islands 270
5.45 Costa Rica 270
5.46 Cote dIvoire 271
5.47 Croatia 271
5.48 Cuba 272
5.49 Cyprus 272
5.50 Czech Republic 273
5.51 Denmark 273
5.52 Djibouti 274
5.53 Dominica 274
5.54 Dominican Republic 275
5.55 Ecuador 275
5.56 Egypt 276
5.57 El Salvador 276
5.58 Equatorial Guinea 277
5.59 Estonia 277
5.60 Ethiopia 278
5.61 Fiji 278
5.62 Finland 279
5.63 France 280
5.64 French Guiana 280
5.65 French Polynesia 281
5.66 Gabon 281
5.67 Georgia 282
5.68 Germany 282
5.69 Ghana 283
5.70 Greece 283
5.71 Greenland 284
5.72 Grenada 284
5.73 Guadeloupe 285
5.74 Guam 285
5.75 Guatemala 286
5.76 Guinea 286
5.77 Guinea-Bissau 287
5.78 Guyana 287
5.79 Haiti 288
5.80 Honduras 288
5.81 Hong Kong 288
5.82 Hungary 289
5.83 Iceland 289
5.84 India 290
5.85 Indonesia 291
5.86 Iran 292
5.87 Iraq 292
5.88 Ireland 293
5.89 Israel 293
5.90 Italy 294
5.91 Jamaica 294
5.92 Japan 295
5.93 Jordan 298
5.94 Kazakhstan 298
5.95 Kenya 299
5.96 Kiribati 299
5.97 Kuwait 299
5.98 Kyrgyzstan 300
5.99 Laos 300
5.100 Latvia 301
5.101 Lebanon 301
5.102 Lesotho 301
5.103 Liberia 302
5.104 Libya 302
5.105 Liechtenstein 303
5.106 Lithuania 303
5.107 Luxembourg 304
5.108 Macau 304
5.109 Madagascar 305
5.110 Malawi 305
5.111 Malaysia 306
5.112 Maldives 306
5.113 Mali 307
5.114 Malta 307
5.115 Marshall Islands 307
5.116 Martinique 308
5.117 Mauritania 308
5.118 Mauritius 309
5.119 Mexico 310
5.120 Micronesia Federation 311
5.121 Moldova 311
5.122 Monaco 311
5.123 Mongolia 312
5.124 Morocco 312
5.125 Mozambique 313
5.126 Namibia 313
5.127 Nauru 313
5.128 Nepal 314
5.129 New Caledonia 314
5.130 New Zealand 315
5.131 Nicaragua 315
5.132 Niger 316
5.133 Nigeria 316
5.134 Niue 317
5.135 Norfolk Island 317
5.136 North Korea 317
5.137 Norway 318
5.138 Oman 318
5.139 Pakistan 319
5.140 Palau 319
5.141 Palestine 319
5.142 Panama 320
5.143 Papua New Guinea 320
5.144 Paraguay 321
5.145 Peru 321
5.146 Philippines 322
5.147 Poland 323
5.148 Portugal 323
5.149 Puerto Rico 324
5.150 Qatar 324
5.151 Republic of Congo 325
5.152 Reunion 325
5.153 Romania 326
5.154 Russia 327
5.155 Rwanda 327
5.156 San Marino 328
5.157 Sao Tome E Principe 328
5.158 Saudi Arabia 329
5.159 Senegal 329
5.160 Seychelles 330
5.161 Sierra Leone 330
5.162 Singapore 330
5.163 Slovakia 331
5.164 Slovenia 331
5.165 Solomon Islands 331
5.166 Somalia 332
5.167 South Africa 332
5.168 South Korea 332
5.169 Spain 333
5.170 Sri Lanka 334
5.171 St. Kitts and Nevis 334
5.172 St. Lucia 334
5.173 St. Vincent and the Grenadines 335
5.174 Sudan 335
5.175 Suriname 335
5.176 Swaziland 336
5.177 Sweden 336
5.178 Switzerland 337
5.179 Syrian Arab Republic 337
5.180 Taiwan 338
5.181 Tajikistan 339
5.182 Tanzania 339
5.183 Thailand 340
5.184 The Bahamas 340
5.185 The British Virgin Islands 340
5.186 The Cayman Islands 341
5.187 The Falkland Islands 341
5.188 The Gambia 341
5.189 The Netherlands 342
5.190 The Netherlands Antilles 342
5.191 The Northern Mariana Island 343
5.192 The U.S. Virgin Islands 343
5.193 The United Arab Emirates 343
5.194 The United Kingdom 344
5.195 The United States 345
5.196 Togo 346
5.197 Tokelau 346
5.198 Tonga 347
5.199 Trinidad and Tobago 347
5.200 Tunisia 347
5.201 Turkey 348
5.202 Turkmenistan 348
5.203 Tuvalu 349
5.204 Uganda 349
5.205 Ukraine 350
5.206 Uruguay 350
5.207 Uzbekistan 351
5.208 Vanuatu 351
5.209 Venezuela 352
5.210 Vietnam 353
5.211 Wallis and Futuna 353
5.212 Western Sahara 353
5.213 Western Samoa 354
5.214 Yemen 354
5.215 Zambia 355
5.216 Zimbabwe 355
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 356
6.1 Disclaimers & Safe Harbor 356
6.2 ICON Group International, Inc. User Agreement Provisions 357
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