The 2009 Report on Manufacturing Stationary Air Purification Equipment, Industrial Dust and Fume Collection Equipment, Electrostatic Precipitation Equipment, Warm Air Furnace Filters, Air Washers, and Other Dust Collection Equipment: World Market Seg
ICON Group International, May 2009, Pages: 358
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment. 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment. 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment. 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment.
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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment” 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment is 333411. It is for this definition of manufacturing stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment that the aggregate latent demand estimates are derived. “Manufacturing stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment” is specifically defined as follows:
333411
This U.S. industry comprises establishments primarily engaged in manufacturing stationary air purification equipment, such as industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment.
3334111
Dust collection & other air purification equip. for indust. gas cleaning systems
33341111
Dust collection and other air purification equipment for industrial gas cleaning systems (for cleaning outgoing air)
3334111110
Dust collection and other air purification equipment for industrial gas cleaning systems (for cleaning outgoing air), except parts
3334111111
Electrostatic precipitators
3334111114
Fabric filters
3334111116
Mechanical collectors
3334111119
Wet scrubbers
333411111C
Catalytic oxidation systems
333411111E
Nitric oxide (NO) control systems
333411111G
Thermal and direct oxidation systems
333411111J
Scrubbers, gas absorber non~FGD (flue gas desulfurization)
333411111M
Wet flue gas desulfurization systems
333411111P
Dry flue gas desulfurization systems
333411111R
Gas adsorbers
333411111U
Other types of industrial air pollution control equipment, nec
3334111165
Parts for industrial air purification equipment
3334113
Dust collection & other air purification equip & parts for cleaning incoming air
33341131
Air filters for air_conditioners and furnaces, etc., of 2400 CFM or less, except parts
3334113103
Air filters for air_conditioners and furnaces, etc., of 2400 CFM or less, except parts
33341132
Other dust collection and air purification equipment, except parts
3334113207
Air washers (purification equipment for cleaning incoming air), except parts
3334113211
Electrostatic precipitation dust collection and air purification equipment, except parts
3334113231
All other dust collection and air purification equipment (including air filters for air_conditioners and furnaces), except parts
33341133
Parts for dust collection and air purification equipment
3334113355
Parts for dust collection and air purification equipment
333411M
Miscellaneous receipts
333411P
Primary products
333411S
Secondary products
333411SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 333411 includes the following:
Air purification equipment, stationary, manufacturing
Air scrubbing systems manufacturing
Air washers (i.e., air scrubbers) manufacturing
Dust and fume collecting equipment manufacturing
Electrostatic precipitation equipment manufacturing
Filters, air-conditioner, manufacturing
Filters, furnace, manufacturing
Furnace filters manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment 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 stationary air purification equipment, industrial dust and fume collection equipment, electrostatic precipitation equipment, warm air furnace filters, air washers, and other dust collection equipment). 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 18
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 20
3 CITY SEGMENTS RANKED BY MARKET SIZE 21
3.1 Top 15 Markets 21
3.2 Markets 16 to 30 22
3.3 Remaining Cities by Market Rank 23
4 CITY SEGMENTS IN ALPHABETICAL ORDER 126
4.1 A: from Aalborg to Az Zawiyah 126
4.2 B: from Bacolod to Bydgoszcz 133
4.3 C: from Caaguazu to Cyangugu 141
4.4 D: from Da Nang to Dzhizak 150
4.5 E: from East London to Esteli 154
4.6 F: from Fagatogo to Funchal 156
4.7 G: from Gabes to Gyumri 159
4.8 H: from Hachinohe to Hyderabad 163
4.9 I: from Iasi to Izmir 167
4.10 J: from Jaboatao to Jyvaskyla 170
4.11 K: from Kabul to Kzyl-Orda 173
4.12 L: from La Ceiba to Lyon 181
4.13 M: from Macae to Mzuzu 187
4.14 N: from Nacala to Nzerekore 197
4.15 O: from Oaklahoma City to Oyem 202
4.16 Ö: from Örebro to Örebro 204
4.17 P: from Pago Pago to Pyuthan 205
4.18 Q: from Qandahar to Quito 212
4.19 R: from Rabat to Rustavi 213
4.20 S: from S. Luis Potosi to Szombathely 216
4.21 T: from Tabligbo to Tyre 228
4.22 U: from Uberaba to Utulei 236
4.23 V: from Vacoas-Phoenix to Vukovar 238
4.24 W: from Wadi Medani to Wuhan 241
4.25 X: from Xalapa to Xian 242
4.26 Y: from Yamagata to Yungkang 243
4.27 Z: from Zadar to Zvishavane 244
5 CITY SEGMENTS RANKED BY COUNTRY 246
5.1 Afghanistan 246
5.2 Albania 246
5.3 Algeria 247
5.4 American Samoa 247
5.5 Andorra 248
5.6 Angola 248
5.7 Antigua and Barbuda 248
5.8 Argentina 249
5.9 Armenia 250
5.10 Aruba 250
5.11 Australia 251
5.12 Austria 251
5.13 Azerbaijan 252
5.14 Bahrain 252
5.15 Bangladesh 253
5.16 Barbados 253
5.17 Belarus 254
5.18 Belgium 254
5.19 Belize 255
5.20 Benin 255
5.21 Bermuda 255
5.22 Bhutan 256
5.23 Bolivia 256
5.24 Bosnia and Herzegovina 257
5.25 Botswana 257
5.26 Brazil 258
5.27 Brunei 263
5.28 Bulgaria 264
5.29 Burkina Faso 264
5.30 Burma 265
5.31 Burundi 265
5.32 Cambodia 265
5.33 Cameroon 266
5.34 Canada 266
5.35 Cape Verde 267
5.36 Central African Republic 267
5.37 Chad 268
5.38 Chile 268
5.39 China 269
5.40 Christmas Island 269
5.41 Colombia 270
5.42 Comoros 270
5.43 Congo (formerly Zaire) 270
5.44 Cook Islands 271
5.45 Costa Rica 271
5.46 Cote dIvoire 272
5.47 Croatia 272
5.48 Cuba 273
5.49 Cyprus 273
5.50 Czech Republic 274
5.51 Denmark 274
5.52 Djibouti 275
5.53 Dominica 275
5.54 Dominican Republic 276
5.55 Ecuador 276
5.56 Egypt 277
5.57 El Salvador 277
5.58 Equatorial Guinea 278
5.59 Estonia 278
5.60 Ethiopia 279
5.61 Fiji 279
5.62 Finland 280
5.63 France 281
5.64 French Guiana 281
5.65 French Polynesia 282
5.66 Gabon 282
5.67 Georgia 283
5.68 Germany 283
5.69 Ghana 284
5.70 Greece 284
5.71 Greenland 285
5.72 Grenada 285
5.73 Guadeloupe 286
5.74 Guam 286
5.75 Guatemala 287
5.76 Guinea 287
5.77 Guinea-Bissau 288
5.78 Guyana 288
5.79 Haiti 288
5.80 Honduras 289
5.81 Hong Kong 289
5.82 Hungary 290
5.83 Iceland 290
5.84 India 291
5.85 Indonesia 292
5.86 Iran 293
5.87 Iraq 293
5.88 Ireland 294
5.89 Israel 294
5.90 Italy 295
5.91 Jamaica 295
5.92 Japan 296
5.93 Jordan 299
5.94 Kazakhstan 299
5.95 Kenya 300
5.96 Kiribati 300
5.97 Kuwait 300
5.98 Kyrgyzstan 301
5.99 Laos 301
5.100 Latvia 302
5.101 Lebanon 302
5.102 Lesotho 302
5.103 Liberia 303
5.104 Libya 303
5.105 Liechtenstein 304
5.106 Lithuania 304
5.107 Luxembourg 304
5.108 Macau 305
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 333
5.169 Spain 334
5.170 Sri Lanka 334
5.171 St. Kitts and Nevis 335
5.172 St. Lucia 335
5.173 St. Vincent and the Grenadines 335
5.174 Sudan 336
5.175 Suriname 336
5.176 Swaziland 337
5.177 Sweden 337
5.178 Switzerland 338
5.179 Syrian Arab Republic 338
5.180 Taiwan 339
5.181 Tajikistan 340
5.182 Tanzania 340
5.183 Thailand 341
5.184 The Bahamas 341
5.185 The British Virgin Islands 341
5.186 The Cayman Islands 342
5.187 The Falkland Islands 342
5.188 The Gambia 342
5.189 The Netherlands 343
5.190 The Netherlands Antilles 343
5.191 The Northern Mariana Island 344
5.192 The U.S. Virgin Islands 344
5.193 The United Arab Emirates 344
5.194 The United Kingdom 345
5.195 The United States 346
5.196 Togo 347
5.197 Tokelau 347
5.198 Tonga 348
5.199 Trinidad and Tobago 348
5.200 Tunisia 348
5.201 Turkey 349
5.202 Turkmenistan 349
5.203 Tuvalu 350
5.204 Uganda 350
5.205 Ukraine 351
5.206 Uruguay 351
5.207 Uzbekistan 352
5.208 Vanuatu 352
5.209 Venezuela 353
5.210 Vietnam 354
5.211 Wallis and Futuna 354
5.212 Western Sahara 354
5.213 Western Samoa 355
5.214 Yemen 355
5.215 Zambia 356
5.216 Zimbabwe 356
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 357
6.1 Disclaimers & Safe Harbor 357
6.2 ICON Group International, Inc. User Agreement Provisions 358
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