The 2009 Report on Consumer Non-Riding Lawn, Garden, and Snow Equipment: World Market Segmentation by City
ICON Group International, May 2009, Pages: 332
Market Potential Estimation Methodology
Overview
This study covers the world outlook for consumer non-riding lawn, garden, and snow 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 consumer non-riding lawn, garden, and snow 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 consumer non-riding lawn, garden, and snow 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 consumer non-riding lawn, garden, and snow 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 consumer non-riding lawn, garden, and snow 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 consumer non-riding lawn, garden, and snow 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 consumer non-riding lawn, garden, and snow 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 consumer non-riding lawn, garden, and snow 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 consumer non-riding lawn, garden, and snow 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 “consumer non-riding lawn, garden, and snow 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 consumer non-riding lawn, garden, and snow 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 “consumer non-riding lawn, garden, and snow equipment” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of consumer non-riding lawn, garden, and snow equipment, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for consumer non-riding lawn, garden, and snow equipment is 3331121. It is for this definition of consumer non-riding lawn, garden, and snow equipment that the aggregate latent demand estimates are derived. “Consumer non-riding lawn, garden, and snow equipment” is specifically defined as follows:
3331121
Consumer nonriding lawn, garden, and snow equipment
33311210
Consumer nonriding lawn, garden, and snow equipment
3331121000
Consumer nonriding lawn, garden, and snow equipment
3331121006
Consumer nonriding lawn, garden, and snow equipment, lawnmowers, rotary, self~propelled, gas~powered
3331121021
Consumer nonriding lawn, garden, and snow equipment, lawnmowers, electric, all types, including battery~powered
3331121026
Consumer nonriding lawn, garden, and snow equipment, rotary, garden motor tillers
3331121031
Consumer nonriding lawn, garden, and snow equipment, 2~wheel tractors walking type, except rotary tillers
3331121036
Consumer nonriding lawn, garden, and snow equipment, snow throwers (snow blowers), except attachment~type, single stage
3331121041
Consumer nonriding lawn, garden, and snow equipment, snow throwers (snow blowers), except attachment~type, dual stage
3331121046
Consumer nonriding lawn, garden, and snow equipment, powered lawn edgers~trimmers, fixed blade
3331121051
Consumer nonriding lawn, garden, and snow equipment, powered lawn edgers~trimmers, other than fixed blades
3331121056
Consumer nonriding lawn, garden, and snow equipment, shredders and shredder~grinders
3331121061
Consumer nonriding lawn, garden, and snow equipment, yard vacuums and blowers
3331121066
Consumer nonriding lawn, garden, and snow equipment, other consumer nonriding lawn, garden, and snow equipment
33311211
Consumer nonriding lawn, garden, and snow equipment
3331121100
Consumer nonriding lawn, garden, and snow equipment
3331121104
Consumer nonriding lawn, garden, and snow equipment, lawnmowers, push type, reel (powered and nonpowered) and rotary, gas_powered
3331121106
Consumer nonriding lawn, garden, and snow equipment, lawnmowers, rotary, self_propelled, gas powered
3331121121
Consumer nonriding lawn, garden, and snow equipment, lawnmowers, electric, all types (including battery powered)
3331121126
Consumer nonriding lawn, garden, and snow equipment, rotary garden (motor) tillers
3331121131
Consumer nonriding lawn, garden, and snow equipment, 2_wheel tractors, walking type (except rotary tillers)
3331121136
Consumer nonriding lawn, garden, and snow equipment, snow throwers (snow blowers) (except attachment type), single stage
3331121141
Consumer nonriding lawn, garden, and snow equipment, snow throwers (snow blowers) (except attachment type), dual stage
3331121146
Consumer nonriding lawn, garden, and snow equipment, powered lawn edgers/trimmers, fixed blade
3331121151
Consumer nonriding lawn, garden, and snow equipment, powered lawn edgers/trimmers, other (except fixed blade)
3331121156
Consumer nonriding lawn, garden, and snow equipment, shredders and shredder_grinders
3331121161
Consumer nonriding lawn, garden, and snow equipment, yard vacuums and blowers
3331121166
Other consumer nonriding lawn, garden, and snow equipment
Step 2. Filtering and Smoothing
Based on the aggregate view of consumer non-riding lawn, garden, and snow 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 consumer non-riding lawn, garden, and snow 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 consumer non-riding lawn, garden, and snow 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 consumer non-riding lawn, garden, and snow 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 12
1.2.3.1 Step 1. Product Definition and Data Collection 14
1.2.3.2 Step 2. Filtering and Smoothing 16
1.2.3.3 Step 3. Filling in Missing Values 16
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 17
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 17
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 148
4.5 E: from East London to Esteli 152
4.6 F: from Fagatogo to Funchal 154
4.7 G: from Gabes to Gyumri 157
4.8 H: from Hachinohe to Hyderabad 161
4.9 I: from Iasi to Izmir 165
4.10 J: from Jaboatao to Jyvaskyla 168
4.11 K: from Kabul to Kzyl-Orda 170
4.12 L: from La Ceiba to Lyon 178
4.13 M: from Macae to Mzuzu 183
4.14 N: from Nacala to Nzerekore 193
4.15 O: from Oaklahoma City to Oyem 198
4.16 Ö: from Örebro to Örebro 200
4.17 P: from Pago Pago to Pyuthan 201
4.18 Q: from Qandahar to Quito 207
4.19 R: from Rabat to Rustavi 208
4.20 S: from S. Luis Potosi to Szombathely 211
4.21 T: from Tabligbo to Tyre 223
4.22 U: from Uberaba to Utulei 230
4.23 V: from Vacoas-Phoenix to Vukovar 232
4.24 W: from Wadi Medani to Wuhan 235
4.25 X: from Xalapa to Xian 236
4.26 Y: from Yamagata to Yungkang 237
4.27 Z: from Zadar to Zvishavane 238
5 CITY SEGMENTS RANKED BY COUNTRY 239
5.1 Afghanistan 239
5.2 Albania 239
5.3 Algeria 240
5.4 American Samoa 240
5.5 Andorra 240
5.6 Angola 241
5.7 Antigua and Barbuda 241
5.8 Argentina 242
5.9 Armenia 243
5.10 Aruba 243
5.11 Australia 244
5.12 Austria 244
5.13 Azerbaijan 245
5.14 Bahrain 245
5.15 Bangladesh 245
5.16 Barbados 246
5.17 Belarus 246
5.18 Belgium 246
5.19 Belize 247
5.20 Benin 247
5.21 Bermuda 247
5.22 Bhutan 248
5.23 Bolivia 248
5.24 Bosnia and Herzegovina 248
5.25 Botswana 249
5.26 Brazil 250
5.27 Brunei 255
5.28 Bulgaria 255
5.29 Burkina Faso 256
5.30 Burma 256
5.31 Burundi 256
5.32 Cambodia 257
5.33 Cameroon 257
5.34 Canada 257
5.35 Cape Verde 258
5.36 Central African Republic 258
5.37 Chad 258
5.38 Chile 259
5.39 China 259
5.40 Christmas Island 260
5.41 Colombia 260
5.42 Comoros 260
5.43 Congo (formerly Zaire) 261
5.44 Cook Islands 261
5.45 Costa Rica 261
5.46 Cote dIvoire 262
5.47 Croatia 262
5.48 Cuba 263
5.49 Cyprus 263
5.50 Czech Republic 263
5.51 Denmark 264
5.52 Djibouti 264
5.53 Dominica 264
5.54 Dominican Republic 265
5.55 Ecuador 265
5.56 Egypt 266
5.57 El Salvador 266
5.58 Equatorial Guinea 266
5.59 Estonia 267
5.60 Ethiopia 267
5.61 Fiji 267
5.62 Finland 268
5.63 France 268
5.64 French Guiana 269
5.65 French Polynesia 269
5.66 Gabon 269
5.67 Georgia 270
5.68 Germany 270
5.69 Ghana 270
5.70 Greece 271
5.71 Greenland 271
5.72 Grenada 271
5.73 Guadeloupe 272
5.74 Guam 272
5.75 Guatemala 272
5.76 Guinea 273
5.77 Guinea-Bissau 273
5.78 Guyana 273
5.79 Haiti 274
5.80 Honduras 274
5.81 Hong Kong 274
5.82 Hungary 275
5.83 Iceland 275
5.84 India 276
5.85 Indonesia 277
5.86 Iran 278
5.87 Iraq 278
5.88 Ireland 279
5.89 Israel 279
5.90 Italy 280
5.91 Jamaica 280
5.92 Japan 281
5.93 Jordan 283
5.94 Kazakhstan 284
5.95 Kenya 284
5.96 Kiribati 285
5.97 Kuwait 285
5.98 Kyrgyzstan 285
5.99 Laos 285
5.100 Latvia 286
5.101 Lebanon 286
5.102 Lesotho 286
5.103 Liberia 287
5.104 Libya 287
5.105 Liechtenstein 287
5.106 Lithuania 288
5.107 Luxembourg 288
5.108 Macau 288
5.109 Madagascar 289
5.110 Malawi 289
5.111 Malaysia 290
5.112 Maldives 290
5.113 Mali 291
5.114 Malta 291
5.115 Marshall Islands 291
5.116 Martinique 292
5.117 Mauritania 292
5.118 Mauritius 292
5.119 Mexico 293
5.120 Micronesia Federation 294
5.121 Moldova 294
5.122 Monaco 294
5.123 Mongolia 294
5.124 Morocco 295
5.125 Mozambique 295
5.126 Namibia 295
5.127 Nauru 296
5.128 Nepal 296
5.129 New Caledonia 296
5.130 New Zealand 297
5.131 Nicaragua 297
5.132 Niger 298
5.133 Nigeria 298
5.134 Niue 298
5.135 Norfolk Island 299
5.136 North Korea 299
5.137 Norway 299
5.138 Oman 300
5.139 Pakistan 300
5.140 Palau 300
5.141 Palestine 300
5.142 Panama 301
5.143 Papua New Guinea 301
5.144 Paraguay 301
5.145 Peru 302
5.146 Philippines 302
5.147 Poland 303
5.148 Portugal 303
5.149 Puerto Rico 304
5.150 Qatar 304
5.151 Republic of Congo 304
5.152 Reunion 305
5.153 Romania 305
5.154 Russia 306
5.155 Rwanda 306
5.156 San Marino 307
5.157 Sao Tome E Principe 307
5.158 Saudi Arabia 307
5.159 Senegal 308
5.160 Seychelles 308
5.161 Sierra Leone 308
5.162 Singapore 308
5.163 Slovakia 309
5.164 Slovenia 309
5.165 Solomon Islands 309
5.166 Somalia 310
5.167 South Africa 310
5.168 South Korea 311
5.169 Spain 311
5.170 Sri Lanka 312
5.171 St. Kitts and Nevis 312
5.172 St. Lucia 312
5.173 St. Vincent and the Grenadines 312
5.174 Sudan 313
5.175 Suriname 313
5.176 Swaziland 313
5.177 Sweden 314
5.178 Switzerland 314
5.179 Syrian Arab Republic 315
5.180 Taiwan 316
5.181 Tajikistan 317
5.182 Tanzania 317
5.183 Thailand 317
5.184 The Bahamas 318
5.185 The British Virgin Islands 318
5.186 The Cayman Islands 318
5.187 The Falkland Islands 318
5.188 The Gambia 319
5.189 The Netherlands 319
5.190 The Netherlands Antilles 320
5.191 The Northern Mariana Island 320
5.192 The U.S. Virgin Islands 320
5.193 The United Arab Emirates 321
5.194 The United Kingdom 321
5.195 The United States 322
5.196 Togo 323
5.197 Tokelau 323
5.198 Tonga 323
5.199 Trinidad and Tobago 324
5.200 Tunisia 324
5.201 Turkey 325
5.202 Turkmenistan 325
5.203 Tuvalu 325
5.204 Uganda 326
5.205 Ukraine 326
5.206 Uruguay 327
5.207 Uzbekistan 327
5.208 Vanuatu 327
5.209 Venezuela 328
5.210 Vietnam 328
5.211 Wallis and Futuna 329
5.212 Western Sahara 329
5.213 Western Samoa 329
5.214 Yemen 329
5.215 Zambia 330
5.216 Zimbabwe 330
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 331
6.1 Disclaimers & Safe Harbor 331
6.2 ICON Group International, Inc. User Agreement Provisions 332
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