The 2009 Report on Manufacturing Mayonnaise, Salad Dressing, Vinegar, Mustard, Horseradish, Soy Sauce, Tartar Sauce, Worcestershire Sauce, and Other Prepared Sauces Excluding Tomato-Based Sauces and Gravy: World Market Segmentation by City
ICON Group International, May 2009, Pages: 355
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy. 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy. 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy. 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy.
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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy” 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy is 311941. It is for this definition of manufacturing mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy that the aggregate latent demand estimates are derived. “Manufacturing mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy” is specifically defined as follows:
311941
This U.S. industry comprises establishments primarily engaged in manufacturing mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tarter sauce, Worcestershire sauce, and other prepared sauces (except tomato-based and gravy).
3119411
Vinegar and cider
31194111
Vinegar and cider
3119411111
Cider
3119411121
Vinegar, fermented (basis equivalent to 40 grain)
3119411131
Vinegar, distilled (basis equivalent to 100 grain)
3119414
Prepared sauces (except tomato)
31194141
Prepared mustard
3119414111
Prepared mustard
31194142
Other prepared sauces, except tomato (worcestershire, soy, horseradish, meat, vegetable, seafood, etc.)
3119414221
Other prepared sauces, except tomato (worcestershire, soy, horseradish, meat, vegetable, seafood, etc.)
3119417
Mayonnaise, salad dressings and sandwich spreads
31194171
Spoon_type salad dressing
3119417111
Spoon_type salad dressing
31194172
Spoon_type mayonnaise
3119417221
Spoon_type mayonnaise
31194173
Other spoon_type dressing, including sandwich spreads, refrigerated dressings, and all other semisolid_type dressing
3119417331
Other spoon_type dressing, including sandwich spreads, refrigerated dressings, and all other semisolid_type dressing
31194174
Pourable salad dressing (including reduced calorie, cheese, vinegar and oil, etc.)
3119417441
Pourable salad dressing (including reduced calorie, cheese, vinegar and oil, etc.)
311941M
Miscellaneous receipts
311941P
Primary products
311941S
Secondary products
311941SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 311941 includes the following:
Cheese based salad dressing manufacturing
Cider vinegar manufacturing
Cider, nonalcoholic, manufacturing
Dips (except cheese and sour cream based) manufacturing
Horseradish, prepared sauce, manufacturing
Mayonnaise manufacturing
Mustard, prepared, manufacturing
Prepared sauces (except gravy, tomato based) manufacturing
Salad dressings manufacturing
Sandwich spreads, salad dressing based, manufacturing
Sauces (except tomato based) manufacturing
Sauces for meat (except tomato based) manufacturing
Sauces for seafood (except tomato based) manufacturing
Sauces for vegetable (except tomato based) manufacturing
Soy sauce manufacturing
Tartar sauce manufacturing
Vinegar manufacturing
Worcestershire sauce manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy 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 mayonnaise, salad dressing, vinegar, mustard, horseradish, soy sauce, tartar sauce, Worcestershire sauce, and other prepared sauces excluding tomato-based sauces and gravy). 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) 270
5.44 Cook Islands 270
5.45 Costa Rica 271
5.46 Cote dIvoire 271
5.47 Croatia 272
5.48 Cuba 272
5.49 Cyprus 273
5.50 Czech Republic 273
5.51 Denmark 274
5.52 Djibouti 274
5.53 Dominica 275
5.54 Dominican Republic 275
5.55 Ecuador 276
5.56 Egypt 276
5.57 El Salvador 277
5.58 Equatorial Guinea 277
5.59 Estonia 277
5.60 Ethiopia 278
5.61 Fiji 278
5.62 Finland 279
5.63 France 279
5.64 French Guiana 280
5.65 French Polynesia 280
5.66 Gabon 280
5.67 Georgia 281
5.68 Germany 281
5.69 Ghana 282
5.70 Greece 282
5.71 Greenland 283
5.72 Grenada 283
5.73 Guadeloupe 284
5.74 Guam 284
5.75 Guatemala 285
5.76 Guinea 285
5.77 Guinea-Bissau 285
5.78 Guyana 286
5.79 Haiti 286
5.80 Honduras 287
5.81 Hong Kong 287
5.82 Hungary 288
5.83 Iceland 288
5.84 India 289
5.85 Indonesia 290
5.86 Iran 291
5.87 Iraq 291
5.88 Ireland 292
5.89 Israel 292
5.90 Italy 293
5.91 Jamaica 293
5.92 Japan 294
5.93 Jordan 297
5.94 Kazakhstan 297
5.95 Kenya 298
5.96 Kiribati 298
5.97 Kuwait 298
5.98 Kyrgyzstan 299
5.99 Laos 299
5.100 Latvia 299
5.101 Lebanon 300
5.102 Lesotho 300
5.103 Liberia 300
5.104 Libya 301
5.105 Liechtenstein 301
5.106 Lithuania 302
5.107 Luxembourg 302
5.108 Macau 302
5.109 Madagascar 303
5.110 Malawi 303
5.111 Malaysia 304
5.112 Maldives 304
5.113 Mali 305
5.114 Malta 305
5.115 Marshall Islands 305
5.116 Martinique 306
5.117 Mauritania 306
5.118 Mauritius 307
5.119 Mexico 308
5.120 Micronesia Federation 309
5.121 Moldova 309
5.122 Monaco 309
5.123 Mongolia 310
5.124 Morocco 310
5.125 Mozambique 311
5.126 Namibia 311
5.127 Nauru 311
5.128 Nepal 312
5.129 New Caledonia 312
5.130 New Zealand 313
5.131 Nicaragua 313
5.132 Niger 314
5.133 Nigeria 314
5.134 Niue 315
5.135 Norfolk Island 315
5.136 North Korea 315
5.137 Norway 316
5.138 Oman 316
5.139 Pakistan 317
5.140 Palau 317
5.141 Palestine 317
5.142 Panama 318
5.143 Papua New Guinea 318
5.144 Paraguay 319
5.145 Peru 319
5.146 Philippines 320
5.147 Poland 321
5.148 Portugal 321
5.149 Puerto Rico 322
5.150 Qatar 322
5.151 Republic of Congo 323
5.152 Reunion 323
5.153 Romania 324
5.154 Russia 324
5.155 Rwanda 325
5.156 San Marino 325
5.157 Sao Tome E Principe 325
5.158 Saudi Arabia 326
5.159 Senegal 326
5.160 Seychelles 327
5.161 Sierra Leone 327
5.162 Singapore 327
5.163 Slovakia 328
5.164 Slovenia 328
5.165 Solomon Islands 328
5.166 Somalia 329
5.167 South Africa 329
5.168 South Korea 330
5.169 Spain 331
5.170 Sri Lanka 331
5.171 St. Kitts and Nevis 332
5.172 St. Lucia 332
5.173 St. Vincent and the Grenadines 332
5.174 Sudan 333
5.175 Suriname 333
5.176 Swaziland 334
5.177 Sweden 334
5.178 Switzerland 335
5.179 Syrian Arab Republic 335
5.180 Taiwan 336
5.181 Tajikistan 337
5.182 Tanzania 337
5.183 Thailand 338
5.184 The Bahamas 338
5.185 The British Virgin Islands 338
5.186 The Cayman Islands 339
5.187 The Falkland Islands 339
5.188 The Gambia 339
5.189 The Netherlands 340
5.190 The Netherlands Antilles 340
5.191 The Northern Mariana Island 340
5.192 The U.S. Virgin Islands 341
5.193 The United Arab Emirates 341
5.194 The United Kingdom 342
5.195 The United States 343
5.196 Togo 344
5.197 Tokelau 344
5.198 Tonga 345
5.199 Trinidad and Tobago 345
5.200 Tunisia 345
5.201 Turkey 346
5.202 Turkmenistan 346
5.203 Tuvalu 347
5.204 Uganda 347
5.205 Ukraine 348
5.206 Uruguay 348
5.207 Uzbekistan 349
5.208 Vanuatu 349
5.209 Venezuela 350
5.210 Vietnam 351
5.211 Wallis and Futuna 351
5.212 Western Sahara 351
5.213 Western Samoa 352
5.214 Yemen 352
5.215 Zambia 352
5.216 Zimbabwe 353
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 354
6.1 Disclaimers & Safe Harbor 354
6.2 ICON Group International, Inc. User Agreement Provisions 355
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