The 2009 Report on Manufacturing Canned, Pickled, and Brined Fruits and Vegetables: World Market Segmentation by City
ICON Group International, May 2009, Pages: 343
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing canned, pickled, and brined fruits and vegetables 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 canned, pickled, and brined fruits and vegetables. 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 canned, pickled, and brined fruits and vegetables 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 canned, pickled, and brined fruits and vegetables 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 canned, pickled, and brined fruits and vegetables 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 canned, pickled, and brined fruits and vegetables 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 canned, pickled, and brined fruits and vegetables. 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 canned, pickled, and brined fruits and vegetables. 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 canned, pickled, and brined fruits and vegetables.
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 canned, pickled, and brined fruits and vegetables” 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 canned, pickled, and brined fruits and vegetables 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 canned, pickled, and brined fruits and vegetables” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing canned, pickled, and brined fruits and vegetables, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing canned, pickled, and brined fruits and vegetables is 311421. It is for this definition of manufacturing canned, pickled, and brined fruits and vegetables that the aggregate latent demand estimates are derived. “Manufacturing canned, pickled, and brined fruits and vegetables” is specifically defined as follows:
311421
This U.S. industry comprises establishments primarily engaged in manufacturing canned, pickled, and brined fruits and vegetables. Examples of products made in these establishments are canned juices; canned jams and jellies; canned tomato-based sauces, such as catsup, salsa, chili, spaghetti, barbeque, and tomato paste; pickles, relishes, and sauerkraut.
3114211
Canned fruits, except baby foods
31142111
Canned fruits, except baby foods
3114211111
Canned apples
3114211121
Canned applesauce
3114211131
Canned apricots
3114211141
Canned cherries, red pitted
3114211151
Canned cherries, sweet
3114211161
Canned cranberries and cranberry sauce
3114211171
Canned fruit cocktail
3114211181
Canned fruits for salads (including mixed fruits other than fruit cocktail)
3114211191
Canned olives, ripe and green ripe (including stuffed) (drained net weight)
31142111A1
Canned peaches, including spiced
31142111B1
Canned pears, including spiced
31142111C1
Canned pineapple (all styles)
31142111D1
Other canned fruits
31142111E1
Canned apple pie mixes
31142111F1
Canned cherry pie mixes
31142111G1
Canned peach pie mixes
31142111H1
Other canned fruit pie mixes
3114214
Canned vegetables, except hominy and mushrooms
31142141
Canned vegetables, except hominy and mushrooms
3114214111
Canned green lima beans
3114214121
Canned green and wax beans (including blue lake)
3114214131
Canned carrots
3114214141
Canned vegetable combinations (mixed vegetables, succotash, carrots and peas, vegetable salad, etc.)
3114214151
Canned green peas
3114214161
Other canned peas (black_eyed, crowder, purple hull, field, etc.)
3114214171
Canned pumpkin and squash, including pie mix
3114214181
Canned spinach
3114214191
Canned sweet potatoes, including pie mix
31142141A1
Canned white potatoes
31142141B1
Canned sauerkraut
31142141C1
Canned asparagus
31142141D1
Canned beets
31142141E1
Canned sweet corn, whole kernel
31142141F1
Canned sweet corn, cream style
31142141G1
Canned tomatoes (including stewed)
31142141H1
Other canned vegetables
3114217
CANNED HOMINY AND MUSHROOMS
31142171
Canned hominy and mushrooms
3114217111
Canned hominy
3114217121
Canned mushrooms
311421A
CANNED VEGETABLE JUICES
311421A1
Canned vegetable juices
311421A111
Canned tomato juice (including combinations containing 70 percent or more tomato juice)
311421A121
Other canned vegetable juices
311421D
Catsup and other canned tomato sauces, pastes, etc.
311421D1
Canned spaghetti, pizza, and marinara sauces, with or without other added ingredients, except salsa, including those with less than 20 percent meat
311421D111
Canned spaghetti, pizza, and marinara sauces, with or without other added ingredients, except salsa, including those with less than 20 percent meat
311421D2
Canned tomato, catsup, chili, and barbecue sauces, tomato paste, and tomato pulp and puree
311421D221
Canned tomato sauce, except pulp, puree, and paste, 7.1 oz to 10 oz (8 oz tall, etc.)
311421D231
Canned tomato sauce, except pulp, puree, and paste, other sizes
311421D241
Canned catsup, 14 oz to 32 oz
311421D251
Canned catsup, all other sizes (including individual serving sizes)
311421D261
Canned chili sauce
311421D271
Canned barbecue sauce
311421D281
Canned tomato paste
311421D291
Canned tomato pulp and puree
311421D3
Canned salsa
311421D3A1
Canned salsa, 16 oz
311421D3B1
Canned salsa, 7 oz to 12 oz
311421D3C1
Canned salsa, other sizes
311421G
Canned jams, jellies and preserves
311421G1
Canned jams, jellies, and preserves
311421G111
Canned strawberry jams and preserves, pure
311421G121
Canned raspberry jams and preserves, pure
311421G131
Other canned jams and preserves, pure
311421G141
Canned grape jelly, pure
311421G151
Other canned jellies, pure
311421G161
Fruit spread
311421G171
Canned imitation jellies, jams, and preserves
311421G181
Canned marmalades
311421G191
Canned fruit butter
311421G1A1
Canned maraschino cherries (excluding glace and candied)
311421J
Canned fruit juices, nectars and concentrates
311421J1
Canned orange juice, single strength
311421J111
Canned orange juice, single strength
311421J2
Canned fruit juices except orange
311421J221
Canned apple juice, single_strength
311421J231
Canned grapefruit juice, single strength
311421J241
Canned prune juice, single strength
311421J251
Other canned whole fruit juices and mixtures of whole fruit juices
311421J261
Canned nectars, single strength
311421J271
Fruit juices, concentrated, hot pack
311421M
Miscellaneous receipts
311421M1
Fresh fruit juices and nectars
311421M111
Fresh orange juices and nectars, single strength
311421M121
Other fresh juices and nectars, single strength
311421M131
Concentrated fruit juice (except for fountain use)
311421N
Fresh fruit juices and nectars, single strength
311421P
Primary products
311421P1
Pickles and other pickled products
311421P111
Finished dill cucumber pickles
311421P121
Finished sour cucumber pickles
311421P131
Finished sweet cucumber pickles
311421P141
Refrigerated finished cucumber pickles, including overnight, half_sour, artificially acidified, etc.
311421P151
Other finished pickles and pickled products (mushrooms, peppers, onions, etc.)
311421P161
Finished horseradish (excluding sauce)
311421P171
Finished relishes
311421P181
Finished sauerkraut
311421P191
Other finished pickled products
311421P1A1
Unfinished pickles (salt stock)
311421P1B1
Unfinished brined cherries
311421P1C1
Other bulk unfinished pickled products, such as mushrooms, sauerkraut, etc.
311421Q
Pickles and other pickled products
311421S
Secondary products
311421SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 311421 includes the following:
Artichokes, canned, manufacturing
Barbecue sauce manufacturing
Berries, canned, manufacturing
Beverages, fruit and vegetable juice, manufacturing
Brining of fruits and vegetables
Canning fruits and vegetables
Canning jams and jellies
Catsup manufacturing
Chili sauce manufacturing
Fruit brining
Fruit butters manufacturing
Fruit juice canning
Fruit juices, fresh, manufacturing
Fruit pickling
Fruit pie fillings, canning
Fruits pickling
Fruits, canned, manufacturing
Hominy, canned, manufacturing
Horseradish (except sauce) canning
Jellies and jams manufacturing
Juices, fruit or vegetable, canned manufacturing
Juices, fruit or vegetable, fresh, manufacturing
Ketchup manufacturing
Marmalade manufacturing
Mushrooms canning
Olives brined
Onions pickled
Pastes, fruit and vegetable, canning
Pickles manufacturing
Pickling fruits and vegetables
Preserves (e.g., imitation) canning
Relishes canning
Salsa canning
Sauces, tomato-based, canning
Sauerkraut manufacturing
Spaghetti sauce canning
Vegetable brining
Vegetable canning
Vegetable juices canning
Vegetable juices, fresh, manufacturing
Vegetables pickling.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing canned, pickled, and brined fruits and vegetables 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 canned, pickled, and brined fruits and vegetables 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 canned, pickled, and brined fruits and vegetables 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 canned, pickled, and brined fruits and vegetables). 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 20
1.2.3.3 Step 3. Filling in Missing Values 21
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 21
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 21
1.2.3.6 Step 6. Aggregation and Benchmarking 22
2 USING THE DATA 23
3 CITY SEGMENTS RANKED BY MARKET SIZE 24
3.1 Top 15 Markets 24
3.2 Markets 16 to 30 25
3.3 Remaining Cities by Market Rank 26
4 CITY SEGMENTS IN ALPHABETICAL ORDER 129
4.1 A: from Aalborg to Az Zawiyah 129
4.2 B: from Bacolod to Bydgoszcz 136
4.3 C: from Caaguazu to Cyangugu 144
4.4 D: from Da Nang to Dzhizak 152
4.5 E: from East London to Esteli 156
4.6 F: from Fagatogo to Funchal 158
4.7 G: from Gabes to Gyumri 161
4.8 H: from Hachinohe to Hyderabad 165
4.9 I: from Iasi to Izmir 169
4.10 J: from Jaboatao to Jyvaskyla 172
4.11 K: from Kabul to Kzyl-Orda 174
4.12 L: from La Ceiba to Lyon 182
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 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 234
4.23 V: from Vacoas-Phoenix to Vukovar 236
4.24 W: from Wadi Medani to Wuhan 239
4.25 X: from Xalapa to Xian 240
4.26 Y: from Yamagata to Yungkang 241
4.27 Z: from Zadar to Zvishavane 242
5 CITY SEGMENTS RANKED BY COUNTRY 243
5.1 Afghanistan 243
5.2 Albania 243
5.3 Algeria 244
5.4 American Samoa 244
5.5 Andorra 244
5.6 Angola 245
5.7 Antigua and Barbuda 245
5.8 Argentina 246
5.9 Armenia 247
5.10 Aruba 247
5.11 Australia 248
5.12 Austria 248
5.13 Azerbaijan 249
5.14 Bahrain 249
5.15 Bangladesh 250
5.16 Barbados 250
5.17 Belarus 251
5.18 Belgium 251
5.19 Belize 252
5.20 Benin 252
5.21 Bermuda 252
5.22 Bhutan 253
5.23 Bolivia 253
5.24 Bosnia and Herzegovina 253
5.25 Botswana 254
5.26 Brazil 255
5.27 Brunei 260
5.28 Bulgaria 260
5.29 Burkina Faso 261
5.30 Burma 261
5.31 Burundi 261
5.32 Cambodia 262
5.33 Cameroon 262
5.34 Canada 263
5.35 Cape Verde 263
5.36 Central African Republic 264
5.37 Chad 264
5.38 Chile 265
5.39 China 265
5.40 Christmas Island 266
5.41 Colombia 266
5.42 Comoros 266
5.43 Congo (formerly Zaire) 267
5.44 Cook Islands 267
5.45 Costa Rica 267
5.46 Cote dIvoire 268
5.47 Croatia 268
5.48 Cuba 269
5.49 Cyprus 269
5.50 Czech Republic 270
5.51 Denmark 270
5.52 Djibouti 271
5.53 Dominica 271
5.54 Dominican Republic 271
5.55 Ecuador 272
5.56 Egypt 272
5.57 El Salvador 273
5.58 Equatorial Guinea 273
5.59 Estonia 273
5.60 Ethiopia 274
5.61 Fiji 274
5.62 Finland 274
5.63 France 275
5.64 French Guiana 275
5.65 French Polynesia 276
5.66 Gabon 276
5.67 Georgia 276
5.68 Germany 277
5.69 Ghana 277
5.70 Greece 278
5.71 Greenland 278
5.72 Grenada 279
5.73 Guadeloupe 279
5.74 Guam 279
5.75 Guatemala 280
5.76 Guinea 280
5.77 Guinea-Bissau 280
5.78 Guyana 281
5.79 Haiti 281
5.80 Honduras 281
5.81 Hong Kong 282
5.82 Hungary 282
5.83 Iceland 282
5.84 India 283
5.85 Indonesia 284
5.86 Iran 285
5.87 Iraq 285
5.88 Ireland 286
5.89 Israel 286
5.90 Italy 287
5.91 Jamaica 287
5.92 Japan 288
5.93 Jordan 291
5.94 Kazakhstan 291
5.95 Kenya 292
5.96 Kiribati 292
5.97 Kuwait 292
5.98 Kyrgyzstan 293
5.99 Laos 293
5.100 Latvia 293
5.101 Lebanon 294
5.102 Lesotho 294
5.103 Liberia 294
5.104 Libya 295
5.105 Liechtenstein 295
5.106 Lithuania 295
5.107 Luxembourg 296
5.108 Macau 296
5.109 Madagascar 296
5.110 Malawi 297
5.111 Malaysia 297
5.112 Maldives 298
5.113 Mali 298
5.114 Malta 298
5.115 Marshall Islands 298
5.116 Martinique 299
5.117 Mauritania 299
5.118 Mauritius 299
5.119 Mexico 300
5.120 Micronesia Federation 301
5.121 Moldova 301
5.122 Monaco 301
5.123 Mongolia 302
5.124 Morocco 302
5.125 Mozambique 303
5.126 Namibia 303
5.127 Nauru 303
5.128 Nepal 304
5.129 New Caledonia 304
5.130 New Zealand 305
5.131 Nicaragua 305
5.132 Niger 306
5.133 Nigeria 306
5.134 Niue 307
5.135 Norfolk Island 307
5.136 North Korea 307
5.137 Norway 308
5.138 Oman 308
5.139 Pakistan 309
5.140 Palau 309
5.141 Palestine 309
5.142 Panama 310
5.143 Papua New Guinea 310
5.144 Paraguay 311
5.145 Peru 311
5.146 Philippines 312
5.147 Poland 312
5.148 Portugal 313
5.149 Puerto Rico 313
5.150 Qatar 314
5.151 Republic of Congo 314
5.152 Reunion 314
5.153 Romania 315
5.154 Russia 315
5.155 Rwanda 316
5.156 San Marino 316
5.157 Sao Tome E Principe 316
5.158 Saudi Arabia 317
5.159 Senegal 317
5.160 Seychelles 318
5.161 Sierra Leone 318
5.162 Singapore 318
5.163 Slovakia 318
5.164 Slovenia 319
5.165 Solomon Islands 319
5.166 Somalia 319
5.167 South Africa 320
5.168 South Korea 320
5.169 Spain 321
5.170 Sri Lanka 321
5.171 St. Kitts and Nevis 322
5.172 St. Lucia 322
5.173 St. Vincent and the Grenadines 322
5.174 Sudan 323
5.175 Suriname 323
5.176 Swaziland 323
5.177 Sweden 324
5.178 Switzerland 324
5.179 Syrian Arab Republic 325
5.180 Taiwan 326
5.181 Tajikistan 327
5.182 Tanzania 327
5.183 Thailand 328
5.184 The Bahamas 328
5.185 The British Virgin Islands 328
5.186 The Cayman Islands 329
5.187 The Falkland Islands 329
5.188 The Gambia 329
5.189 The Netherlands 330
5.190 The Netherlands Antilles 330
5.191 The Northern Mariana Island 330
5.192 The U.S. Virgin Islands 331
5.193 The United Arab Emirates 331
5.194 The United Kingdom 331
5.195 The United States 332
5.196 Togo 333
5.197 Tokelau 333
5.198 Tonga 334
5.199 Trinidad and Tobago 334
5.200 Tunisia 334
5.201 Turkey 335
5.202 Turkmenistan 335
5.203 Tuvalu 335
5.204 Uganda 336
5.205 Ukraine 336
5.206 Uruguay 337
5.207 Uzbekistan 337
5.208 Vanuatu 338
5.209 Venezuela 338
5.210 Vietnam 339
5.211 Wallis and Futuna 339
5.212 Western Sahara 339
5.213 Western Samoa 339
5.214 Yemen 340
5.215 Zambia 340
5.216 Zimbabwe 341
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 342
6.1 Disclaimers & Safe Harbor 342
6.2 ICON Group International, Inc. User Agreement Provisions 343
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