The 2009 Report on Non-Chocolate Confectionery Manufacturing: World Market Segmentation by City
ICON Group International, May 2009, Pages: 332
Market Potential Estimation Methodology
Overview
This study covers the world outlook for non-chocolate confectionery manufacturing 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 non-chocolate confectionery manufacturing. 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 non-chocolate confectionery manufacturing 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 non-chocolate confectionery manufacturing 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 non-chocolate confectionery manufacturing 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 non-chocolate confectionery manufacturing 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 non-chocolate confectionery manufacturing. 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 non-chocolate confectionery manufacturing. 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 non-chocolate confectionery manufacturing.
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 “non-chocolate confectionery manufacturing” 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 non-chocolate confectionery manufacturing 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 “non-chocolate confectionery manufacturing” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of non-chocolate confectionery manufacturing, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for non-chocolate confectionery manufacturing is 311340. It is for this definition of non-chocolate confectionery manufacturing that the aggregate latent demand estimates are derived. “Non-chocolate confectionery manufacturing” is specifically defined as follows:
311340
This industry comprises establishments primarily engaged in manufacturing nonchocolate confectioneries. Included in this industry are establishments primary engaged in retailing nonchocolate confectionery products not for immediate consumption made on the premises.
3113401
Nonchocolate-type confectionery products made and packaged for shipment
31134010
Nonchocolate~type confectionery products, including bar goods, granola bars, package goods, specialties, etc.
3113401000
Nonchocolate~type confectionery products, including bar goods, granola bars, package goods, specialties, etc.
3113401001
Hard candy
3113401004
Chewy candy (including granola bars)
3113401007
Soft candy
3113401015
Iced~coated candy
3113401021
Panned candy
3113401026
Licorice and licorice~type candy
31134011
Nonchocolate_type confectionery products made and packaged for shipment, including bar goods, granola bars, package goods, specialties, etc. (not retailed at manufacturing establishment)
3113401100
Nonchocolate_type confectionery products made and packaged for shipment, including bar goods, granola bars, package goods, specialties, etc. (not retailed at manufacturing establishment)
3113401101
Non_chocolate confectionery products, hard candy
3113401104
Non_chocolate confectionery products, chewy candy (including granola bars)
3113401107
Non_chocolate confectionery products, soft candy
3113401115
Non_chocolate confectionery products, iced or coated
3113401121
Non_chocolate confectionery products, panned
3113401126
Non_chocolate confectionery products, licorice and licorice type
3113402
NONCHOCOLATE_TYPE CONFECTIONERY PRODUCTS MADE FROM PURCHASED CHOCOLATE (RETAILED AT MANUFACTURING ESTABLISHMENT)
31134020
Retail nonchocolate~type confectionery products made from purchased chocolate
3113402000
Nonchocolate~type confectionery products manufactured and sold at retail
31134021
Nonchocolate_type confectionery products made from purchased chocolate (retailed at manufacturing establishment)
3113402100
Nonchocolate_type confectionery products made from purchased chocolate (retailed at manufacturing establishment)
3113404
Chewing gum, bubble gum, and chewing gum base
31134041
Chewing gum and bubble gum (nonmedicated), containing sugar
3113404110
Chewing gum and bubble gum (nonmedicated), containing sugar
3113404111
Chewing gum, containing sugar
3113404114
Bubble gum, containing sugar
31134043
Chewing gum and bubble gum (nonmedicated), not containing sugar
3113404320
Chewing gum and bubble gum (nonmedicated), not containing sugar
3113404321
Chewing gum, not containing sugar
3113404324
Bubble gum, not containing sugar
31134045
Chewing gum base
3113404530
Chewing gum base
3113407
Other confectionery-type products, nec, made and packaged for shipment
31134072
Glace, candied, and crystallized fruits, fruit peels, nuts, marshmallow cream, cough drops (except pharmaceutical type), and other confectionery_type products
3113407221
Glace, candied, and crystallized fruits, fruit peels, nuts, and other vegetable substances
3113407231
Marshmallow cream
3113407241
Cough drops, except pharmaceutical type
311340M
Miscellaneous receipts
311340P
Primary products
311340S
Secondary products
311340SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 311340 includes the following:
Breakfast bars, nonchocolate covered, manufacturing
Cake ornaments, confectionery, manufacturing
Candied fruits and fruit peel manufacturing
Candy bars, nonchocolate, manufacturing
Candy stores, nonchocolate, candy made on premises, not for immediate consumption
Chewing gum base manufacturing
Chewing gum manufacturing
Confectionery, nonchocolate, manufacturing
Corn confections manufacturing
Cough drops (except medicated) manufacturing
Crystallized fruits and fruit peel manufacturing
Dates, sugared and stuffed, manufacturing
Fruit peel products (e.g., candied, crystallized, glace, glazed) manufacturing
Fruits (e.g., candied, crystallized, glazed) manufacturing
Fudge, nonchocolate, manufacturing
Granola bars and clusters, nonchocolate, manufacturing
Gum, chewing, manufacturing
Halvah manufacturing
Hard candies manufacturing
Jelly candies manufacturing
Licorice candy manufacturing
Lozenges, nonmedicated, candy, manufacturing
Marshmallow creme manufacturing
Marshmallows manufacturing
Marzipan (i.e., candy) manufacturing
Nuts, covered (except chocolate covered), manufacturing
Popcorn balls manufacturing
Popcorn, candy covered popped, manufacturing
Synthetic chocolate manufacturing
Toffee manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of non-chocolate confectionery manufacturing 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 non-chocolate confectionery manufacturing 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 non-chocolate confectionery manufacturing 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 non-chocolate confectionery manufacturing). 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 18
1.2.3.3 Step 3. Filling in Missing Values 18
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 18
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 19
1.2.3.6 Step 6. Aggregation and Benchmarking 19
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 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 171
4.12 L: from La Ceiba to Lyon 179
4.13 M: from Macae to Mzuzu 184
4.14 N: from Nacala to Nzerekore 194
4.15 O: from Oaklahoma City to Oyem 199
4.16 Ö: from Örebro to Örebro 201
4.17 P: from Pago Pago to Pyuthan 202
4.18 Q: from Qandahar to Quito 208
4.19 R: from Rabat to Rustavi 209
4.20 S: from S. Luis Potosi to Szombathely 212
4.21 T: from Tabligbo to Tyre 224
4.22 U: from Uberaba to Utulei 231
4.23 V: from Vacoas-Phoenix to Vukovar 233
4.24 W: from Wadi Medani to Wuhan 236
4.25 X: from Xalapa to Xian 237
4.26 Y: from Yamagata to Yungkang 238
4.27 Z: from Zadar to Zvishavane 239
5 CITY SEGMENTS RANKED BY COUNTRY 240
5.1 Afghanistan 240
5.2 Albania 240
5.3 Algeria 241
5.4 American Samoa 241
5.5 Andorra 241
5.6 Angola 242
5.7 Antigua and Barbuda 242
5.8 Argentina 243
5.9 Armenia 244
5.10 Aruba 244
5.11 Australia 245
5.12 Austria 245
5.13 Azerbaijan 246
5.14 Bahrain 246
5.15 Bangladesh 246
5.16 Barbados 247
5.17 Belarus 247
5.18 Belgium 247
5.19 Belize 248
5.20 Benin 248
5.21 Bermuda 248
5.22 Bhutan 249
5.23 Bolivia 249
5.24 Bosnia and Herzegovina 249
5.25 Botswana 250
5.26 Brazil 251
5.27 Brunei 256
5.28 Bulgaria 256
5.29 Burkina Faso 257
5.30 Burma 257
5.31 Burundi 257
5.32 Cambodia 258
5.33 Cameroon 258
5.34 Canada 258
5.35 Cape Verde 259
5.36 Central African Republic 259
5.37 Chad 259
5.38 Chile 260
5.39 China 260
5.40 Christmas Island 261
5.41 Colombia 261
5.42 Comoros 261
5.43 Congo (formerly Zaire) 262
5.44 Cook Islands 262
5.45 Costa Rica 262
5.46 Cote dIvoire 263
5.47 Croatia 263
5.48 Cuba 263
5.49 Cyprus 264
5.50 Czech Republic 264
5.51 Denmark 264
5.52 Djibouti 265
5.53 Dominica 265
5.54 Dominican Republic 265
5.55 Ecuador 266
5.56 Egypt 266
5.57 El Salvador 266
5.58 Equatorial Guinea 267
5.59 Estonia 267
5.60 Ethiopia 267
5.61 Fiji 268
5.62 Finland 268
5.63 France 269
5.64 French Guiana 269
5.65 French Polynesia 270
5.66 Gabon 270
5.67 Georgia 270
5.68 Germany 271
5.69 Ghana 271
5.70 Greece 272
5.71 Greenland 272
5.72 Grenada 272
5.73 Guadeloupe 273
5.74 Guam 273
5.75 Guatemala 273
5.76 Guinea 274
5.77 Guinea-Bissau 274
5.78 Guyana 274
5.79 Haiti 275
5.80 Honduras 275
5.81 Hong Kong 275
5.82 Hungary 276
5.83 Iceland 276
5.84 India 277
5.85 Indonesia 278
5.86 Iran 279
5.87 Iraq 279
5.88 Ireland 280
5.89 Israel 280
5.90 Italy 281
5.91 Jamaica 281
5.92 Japan 282
5.93 Jordan 284
5.94 Kazakhstan 285
5.95 Kenya 285
5.96 Kiribati 286
5.97 Kuwait 286
5.98 Kyrgyzstan 286
5.99 Laos 286
5.100 Latvia 287
5.101 Lebanon 287
5.102 Lesotho 287
5.103 Liberia 288
5.104 Libya 288
5.105 Liechtenstein 288
5.106 Lithuania 289
5.107 Luxembourg 289
5.108 Macau 289
5.109 Madagascar 290
5.110 Malawi 290
5.111 Malaysia 291
5.112 Maldives 291
5.113 Mali 292
5.114 Malta 292
5.115 Marshall Islands 292
5.116 Martinique 293
5.117 Mauritania 293
5.118 Mauritius 293
5.119 Mexico 294
5.120 Micronesia Federation 295
5.121 Moldova 295
5.122 Monaco 295
5.123 Mongolia 295
5.124 Morocco 296
5.125 Mozambique 296
5.126 Namibia 296
5.127 Nauru 297
5.128 Nepal 297
5.129 New Caledonia 297
5.130 New Zealand 298
5.131 Nicaragua 298
5.132 Niger 299
5.133 Nigeria 299
5.134 Niue 299
5.135 Norfolk Island 300
5.136 North Korea 300
5.137 Norway 300
5.138 Oman 301
5.139 Pakistan 301
5.140 Palau 301
5.141 Palestine 301
5.142 Panama 302
5.143 Papua New Guinea 302
5.144 Paraguay 302
5.145 Peru 303
5.146 Philippines 303
5.147 Poland 304
5.148 Portugal 304
5.149 Puerto Rico 305
5.150 Qatar 305
5.151 Republic of Congo 305
5.152 Reunion 306
5.153 Romania 306
5.154 Russia 307
5.155 Rwanda 307
5.156 San Marino 307
5.157 Sao Tome E Principe 308
5.158 Saudi Arabia 308
5.159 Senegal 308
5.160 Seychelles 309
5.161 Sierra Leone 309
5.162 Singapore 309
5.163 Slovakia 309
5.164 Slovenia 310
5.165 Solomon Islands 310
5.166 Somalia 310
5.167 South Africa 311
5.168 South Korea 311
5.169 Spain 312
5.170 Sri Lanka 312
5.171 St. Kitts and Nevis 313
5.172 St. Lucia 313
5.173 St. Vincent and the Grenadines 313
5.174 Sudan 313
5.175 Suriname 314
5.176 Swaziland 314
5.177 Sweden 314
5.178 Switzerland 315
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 319
5.191 The Northern Mariana Island 320
5.192 The U.S. Virgin Islands 320
5.193 The United Arab Emirates 320
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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