The 2009 Report on Metal Ore Mining: World Market Segmentation by City
ICON Group International, May 2009, Pages: 334
Market Potential Estimation Methodology
Overview
This study covers the world outlook for metal ore mining 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 metal ore mining. 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 metal ore mining 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 metal ore mining 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 metal ore mining 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 metal ore mining 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 metal ore mining. 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 metal ore mining. 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 metal ore mining.
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 “metal ore mining” 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 metal ore mining 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 “metal ore mining” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of metal ore mining, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for metal ore mining is 2122. It is for this definition of metal ore mining that the aggregate latent demand estimates are derived. “Metal ore mining” is specifically defined as follows:
2122
This industry group comprises establishments primarily engaged in developing mine sites or mining metallic minerals, and establishments primarily engaged in ore dressing and beneficiating (i.e., preparing) operations, such as crushing, grinding, washing, drying, sintering, concentrating, calcining, and leaching. Beneficiating may be performed at mills operated in conjunction with the mines served or at mills, such as custom mills, operated separately.
21221
This industry comprises establishments primarily engaged in (1) developing mine sites, mining, and/or beneficiating (i.e., preparing) iron ores and manganiferous ores valued chiefly for their iron content and/or (2) producing sinter iron ore (except iron ore produced in iron and steel mills) and other iron ore agglomerates.
212210
This industry comprises establishments primarily engaged in (1) developing mine sites, mining, and/or beneficiating (i.e., preparing) iron ores and manganiferous ores valued chiefly for their iron content and/or (2) producing sinter iron ore (except iron ore produced in iron and steel mills) and other iron ore agglomerates.
2122101
CRUDE IRON ORE
21221011
Crude iron ore
2122101111
Direct_shipping crude iron ore
2122101121
Crude iron ore for treatment, concentration, etc.
2122103
IRON ORE CONCENTRATES AND AGGLOMERATES
21221031
Iron ore concentrates and agglomerates
2122103111
Iron ore concentrates (including washed material) for consumption
2122103121
Iron ore concentrates (including washed material) for agglomeration plants not at blast furnaces
2122103131
Iron agglomerates (pellets, sinter, briquets, and other)
2122108
Iron ores
212210P
Primary products
21222
This industry comprises establishments primarily engaged in developing the mine site, mining, and/or beneficiating (i.e., preparing) ores valued chiefly for their gold and or silver content. Establishments primarily engaged in the transformation of the gold and silver into bullion or dore bar in combination with mining activities are included in this industry.
212221
This U.S. industry comprises establishments primarily engaged in developing the mine site, mining, and/or beneficiating (i.e., preparing) ores valued chiefly for their gold content. Establishments primarily engaged in transformation of the gold into bullion or dore bar in combination with mining activities are included in this industry.
2122211
Crude lode gold ores
21222111
Crude lode gold ores
2122211111
Crude lode gold ores mined
2122211121
Crude lode gold ores and residues shipped to smelters
2122211131
Crude lode gold ores and residues shipped to mills
2122211141
Crude gold ore and residues shipped or transferred
2122212
Lode gold concentrates
2122213
Gold mill bullion and placer gold
21222131
Gold concentrates
2122213100
Gold concentrates
2122215
GOLD MILL BULLION AND PLACER GOLD
21222151
Gold mill bullion and placer gold
2122215111
Gold mill bullion, dore, and precipitates
2122215121
Placer gold
212221M
Miscellaneous receipts
212221P
Primary products
212221S
Secondary products
212221SM
Secondary products and miscellaneous receipts
212222
This U.S. industry comprises establishments primarily engaged in developing the mine site, mining, and/or beneficiating (i.e., preparing) ores valued chiefly for their silver content. Establishments primarily engaged in transformation of the silver into bullion or dore bar in combination with mining activities are included in this industry.
2122221
Silver ores
21222211
Crude silver ores
2122221111
Crude silver ores mined
2122221121
Crude silver ores and residues shipped to smelters
2122221131
Crude silver ores and residues shipped to mills
2122221141
Crude silver ore and residues shipped or transferred
2122223
SILVER CONCENTRATES
21222231
Silver concentrates
2122223100
Silver concentrates
2122225
SILVER MILL BULLION AND PLACER SILVER
21222251
Silver mill bullion and placer silver
2122225111
Silver mill bullion, dore, and precipitates
2122225121
Placer silver
212222M
Miscellaneous receipts
212222P
Primary products
212222S
Secondary products
212222SM
Secondary products and miscellaneous receipts
21223
This industry comprises establishments primarily engaged in developing the mine site, mining, and/or beneficiating (i.e., preparing) ores valued chiefly for their copper, nickel, lead, or zinc content. Beneficiating includes the transformation of ores into concentrates.
212231
This U.S. industry comprises establishments primarily engaged in developing the mine site, mining, and/or beneficiating (i.e., preparing) lead ores, zinc ores, or lead-zinc ores.
2122311
Crude lead and zinc ores and residues
21223111
Crude lead and zinc ores
2122311111
Crude lead and zinc ores mined
2122311121
Crude lead and zinc ores and residues shipped to smelters
2122311131
Crude lead and zinc ores and residues shipped to mills
2122311141
Crude lead and zinc ores and residues shipped or transferred
2122312
Lead and zinc concentrates
212231214
Lead concentrates
212231215
Zinc concentrates
2122313
LEAD AND ZINC CONCENTRATES
21223131
Lead and zinc concentrates
2122313111
Lead concentrates
2122313121
Zinc concentrates
212231M
Miscellaneous receipts
212231P
Primary products
212231S
Secondary products
212231SM
Secondary products and miscellaneous receipts
212234
This U.S. industry comprises establishments primarily engaged in: (1) developing the mine site, mining, and/or beneficiating (i.e, preparing) copper and/or nickel ores; and (2) recovering copper concentrates by the precipitation, leaching, or electrowinning of copper ore.
2122341
Crude copper bearing ores
21223411
Crude copper_bearing ores
2122341111
Crude copper ores mined
2122341121
Crude copper ores and residues shipped to smelters
2122341131
Crude copper ores and residues shipped to mills
2122341141
Crude copper ore and residues shipped or transferred
2122343
Copper concentrates
21223431
Copper concentrates
2122343100
Copper concentrates
2122345
Copper precipitates and electrowon copper
21223451
Copper precipitates and electrowon copper recovered from leaching operations
2122345111
Copper precipitates
2122345121
Electrowon copper recovered from leaching operations
212234M
Miscellaneous receipts
212234P
Primary products
212234S
Secondary products
212234SM
Secondary products and miscellaneous receipts
21229
This industry comprises establishments primarily engaged in developing the mine site, mining, and/or beneficiating (i.e., preparing) metal ores (except iron and manganiferous ores valued for their iron content, gold ore, silver ore, copper, nickel, lead, and zinc ore).
212290
Other metal ore mining
2122901
Other metal ore mining
212290M
Miscellaneous receipts
212290P
Primary products
212290S
Secondary products
212290SM
Secondary products and miscellaneous receipts
212291
This U.S. industry comprises establishments primarily engaged in developing the mine site, mining, and/or beneficiating (i.e., preparing) uranium-radium-vanadium ores.
2122911
CRUDE URANIUM_VANADIUM ORES
21229111
Crude uranium_vanadium ores
2122911100
Crude uranium_vanadium ores
2122913
URANIUM_VANADIUM CONCENTRATES
21229131
Uranium_vanadium concentrates
2122913111
Uranium concentrates
2122913121
Vanadium concentrates
212299
This U.S. industry comprises establishments primarily engaged in developing the mine site, mining, and/or beneficiating (i.e., preparing) metal ores (except iron and manganiferous ores valued for their iron content, gold ore, silver ore, copper, nickel, lead, zinc, and uranium-radium-vanadium ore).
2122991
BAUXITE
21229911
Bauxite
2122991100
Bauxite
2122993
FERROALLOY ORES, EXCEPT VANADIUM
21229931
Ferroalloy ores, except vanadium
2122993111
Crude ferroalloy ores (except vanadium and nickel), including manganese and manganiferous ores, chromium, molybdenum, tungsten, etc.
2122993121
Molybdenum concentrates
2122993131
Other ferroalloy concentrates (except molybdenum, vanadium, and nickel), including chromium, manganese, tungsten, etc.
2122995
MISCELLANEOUS METAL ORES AND CONCENTRATES, INCLUDING ANTIMONY, BERYLLIUM, MERCURY, RARE_EARTH METALS, TIN, AND TITANIUM
21229951
Miscellaneous metal ores and concentrates, including antimony, beryllium, mercury, rare_earth metals, tin, and titanium
2122995100
Miscellaneous metal ores and concentrates, including antimony, beryllium, mercury, rare_earth metals, tin, and titanium
Step 2. Filtering and Smoothing
Based on the aggregate view of metal ore mining 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 metal ore mining 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 metal ore mining 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 metal ore mining). 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 20
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 21
2 USING THE DATA 22
3 CITY SEGMENTS RANKED BY MARKET SIZE 23
3.1 Top 15 Markets 23
3.2 Markets 16 to 30 24
3.3 Remaining Cities by Market Rank 25
4 CITY SEGMENTS IN ALPHABETICAL ORDER 128
4.1 A: from Aalborg to Az Zawiyah 128
4.2 B: from Bacolod to Bydgoszcz 135
4.3 C: from Caaguazu to Cyangugu 143
4.4 D: from Da Nang to Dzhizak 151
4.5 E: from East London to Esteli 155
4.6 F: from Fagatogo to Funchal 157
4.7 G: from Gabes to Gyumri 160
4.8 H: from Hachinohe to Hyderabad 164
4.9 I: from Iasi to Izmir 168
4.10 J: from Jaboatao to Jyvaskyla 171
4.11 K: from Kabul to Kzyl-Orda 173
4.12 L: from La Ceiba to Lyon 181
4.13 M: from Macae to Mzuzu 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 210
4.19 R: from Rabat to Rustavi 211
4.20 S: from S. Luis Potosi to Szombathely 214
4.21 T: from Tabligbo to Tyre 226
4.22 U: from Uberaba to Utulei 233
4.23 V: from Vacoas-Phoenix to Vukovar 235
4.24 W: from Wadi Medani to Wuhan 238
4.25 X: from Xalapa to Xian 239
4.26 Y: from Yamagata to Yungkang 240
4.27 Z: from Zadar to Zvishavane 241
5 CITY SEGMENTS RANKED BY COUNTRY 242
5.1 Afghanistan 242
5.2 Albania 242
5.3 Algeria 243
5.4 American Samoa 243
5.5 Andorra 243
5.6 Angola 244
5.7 Antigua and Barbuda 244
5.8 Argentina 245
5.9 Armenia 246
5.10 Aruba 246
5.11 Australia 247
5.12 Austria 247
5.13 Azerbaijan 248
5.14 Bahrain 248
5.15 Bangladesh 248
5.16 Barbados 249
5.17 Belarus 249
5.18 Belgium 249
5.19 Belize 250
5.20 Benin 250
5.21 Bermuda 250
5.22 Bhutan 251
5.23 Bolivia 251
5.24 Bosnia and Herzegovina 251
5.25 Botswana 252
5.26 Brazil 253
5.27 Brunei 258
5.28 Bulgaria 258
5.29 Burkina Faso 259
5.30 Burma 259
5.31 Burundi 259
5.32 Cambodia 260
5.33 Cameroon 260
5.34 Canada 260
5.35 Cape Verde 261
5.36 Central African Republic 261
5.37 Chad 261
5.38 Chile 262
5.39 China 262
5.40 Christmas Island 263
5.41 Colombia 263
5.42 Comoros 263
5.43 Congo (formerly Zaire) 264
5.44 Cook Islands 264
5.45 Costa Rica 264
5.46 Cote dIvoire 265
5.47 Croatia 265
5.48 Cuba 265
5.49 Cyprus 266
5.50 Czech Republic 266
5.51 Denmark 266
5.52 Djibouti 267
5.53 Dominica 267
5.54 Dominican Republic 267
5.55 Ecuador 268
5.56 Egypt 268
5.57 El Salvador 268
5.58 Equatorial Guinea 269
5.59 Estonia 269
5.60 Ethiopia 269
5.61 Fiji 270
5.62 Finland 270
5.63 France 271
5.64 French Guiana 271
5.65 French Polynesia 272
5.66 Gabon 272
5.67 Georgia 272
5.68 Germany 273
5.69 Ghana 273
5.70 Greece 274
5.71 Greenland 274
5.72 Grenada 274
5.73 Guadeloupe 275
5.74 Guam 275
5.75 Guatemala 275
5.76 Guinea 276
5.77 Guinea-Bissau 276
5.78 Guyana 276
5.79 Haiti 277
5.80 Honduras 277
5.81 Hong Kong 277
5.82 Hungary 278
5.83 Iceland 278
5.84 India 279
5.85 Indonesia 280
5.86 Iran 281
5.87 Iraq 281
5.88 Ireland 282
5.89 Israel 282
5.90 Italy 283
5.91 Jamaica 283
5.92 Japan 284
5.93 Jordan 286
5.94 Kazakhstan 287
5.95 Kenya 287
5.96 Kiribati 288
5.97 Kuwait 288
5.98 Kyrgyzstan 288
5.99 Laos 288
5.100 Latvia 289
5.101 Lebanon 289
5.102 Lesotho 289
5.103 Liberia 290
5.104 Libya 290
5.105 Liechtenstein 290
5.106 Lithuania 291
5.107 Luxembourg 291
5.108 Macau 291
5.109 Madagascar 292
5.110 Malawi 292
5.111 Malaysia 293
5.112 Maldives 293
5.113 Mali 294
5.114 Malta 294
5.115 Marshall Islands 294
5.116 Martinique 295
5.117 Mauritania 295
5.118 Mauritius 295
5.119 Mexico 296
5.120 Micronesia Federation 297
5.121 Moldova 297
5.122 Monaco 297
5.123 Mongolia 297
5.124 Morocco 298
5.125 Mozambique 298
5.126 Namibia 298
5.127 Nauru 299
5.128 Nepal 299
5.129 New Caledonia 299
5.130 New Zealand 300
5.131 Nicaragua 300
5.132 Niger 301
5.133 Nigeria 301
5.134 Niue 301
5.135 Norfolk Island 302
5.136 North Korea 302
5.137 Norway 302
5.138 Oman 303
5.139 Pakistan 303
5.140 Palau 303
5.141 Palestine 303
5.142 Panama 304
5.143 Papua New Guinea 304
5.144 Paraguay 304
5.145 Peru 305
5.146 Philippines 305
5.147 Poland 306
5.148 Portugal 306
5.149 Puerto Rico 307
5.150 Qatar 307
5.151 Republic of Congo 307
5.152 Reunion 308
5.153 Romania 308
5.154 Russia 309
5.155 Rwanda 309
5.156 San Marino 309
5.157 Sao Tome E Principe 310
5.158 Saudi Arabia 310
5.159 Senegal 310
5.160 Seychelles 311
5.161 Sierra Leone 311
5.162 Singapore 311
5.163 Slovakia 311
5.164 Slovenia 312
5.165 Solomon Islands 312
5.166 Somalia 312
5.167 South Africa 313
5.168 South Korea 313
5.169 Spain 314
5.170 Sri Lanka 314
5.171 St. Kitts and Nevis 315
5.172 St. Lucia 315
5.173 St. Vincent and the Grenadines 315
5.174 Sudan 315
5.175 Suriname 316
5.176 Swaziland 316
5.177 Sweden 316
5.178 Switzerland 317
5.179 Syrian Arab Republic 317
5.180 Taiwan 318
5.181 Tajikistan 319
5.182 Tanzania 319
5.183 Thailand 319
5.184 The Bahamas 320
5.185 The British Virgin Islands 320
5.186 The Cayman Islands 320
5.187 The Falkland Islands 320
5.188 The Gambia 321
5.189 The Netherlands 321
5.190 The Netherlands Antilles 321
5.191 The Northern Mariana Island 322
5.192 The U.S. Virgin Islands 322
5.193 The United Arab Emirates 322
5.194 The United Kingdom 323
5.195 The United States 324
5.196 Togo 325
5.197 Tokelau 325
5.198 Tonga 325
5.199 Trinidad and Tobago 326
5.200 Tunisia 326
5.201 Turkey 327
5.202 Turkmenistan 327
5.203 Tuvalu 327
5.204 Uganda 328
5.205 Ukraine 328
5.206 Uruguay 329
5.207 Uzbekistan 329
5.208 Vanuatu 329
5.209 Venezuela 330
5.210 Vietnam 330
5.211 Wallis and Futuna 331
5.212 Western Sahara 331
5.213 Western Samoa 331
5.214 Yemen 331
5.215 Zambia 332
5.216 Zimbabwe 332
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 333
6.1 Disclaimers & Safe Harbor 333
6.2 ICON Group International, Inc. User Agreement Provisions 334
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