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The 2009 Report on Manufacturing Electrometallurgical Ferroalloys: World Market Segmentation by City

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Market Potential Estimation Methodology Overview This study covers the world outlook for manufacturing electrometallurgical ferroalloys 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 electrometallurgical ferroalloys. 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 electrometallurgical ferroalloys 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 electrometallurgical ferroalloys 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 electrometallurgical ferroalloys 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 electrometallurgical ferroalloys 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 electrometallurgical ferroalloys. 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 electrometallurgical ferroalloys. 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 electrometallurgical ferroalloys. 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 electrometallurgical ferroalloys” 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 electrometallurgical ferroalloys 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 electrometallurgical ferroalloys” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing electrometallurgical ferroalloys, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing electrometallurgical ferroalloys is 331112. It is for this definition of manufacturing electrometallurgical ferroalloys that the aggregate latent demand estimates are derived. “Manufacturing electrometallurgical ferroalloys” is specifically defined as follows: 331112 This U.S. industry comprises establishments primarily engaged in manufacturing electrometallurgical ferroalloys. Ferroalloys add critical elements, such as silicon and manganese for carbon steel and chromium, vanadium, tungsten, titanium, and molybdenum for low- and high-alloy metals. Ferroalloys include iron-rich alloys and more pure forms of elements added during the steel manufacturing process that alter or improve the characteristics of the metal being made.  3311121 Ferrochromium  33111211 Ferrochromium, including briquettes, ferrochromium silicon, exothermic chromium additives, and other chromium alloys  3311121100 Ferrochromium, including briquettes, ferrochromium silicon, exothermic chromium additives, and other chromium alloys  3311123 Ferrosilicon  33111231 Ferrosilicon, including briquettes, and other silicon alloys  3311123100 Ferrosilicon, including briquettes, and other silicon alloys  3311125 Other ferroalloys and ferrous products  33111251 Ferrous superalloys  3311125101 Ferrous superalloys  33111252 Other ferroalloys, including silvery iron, ferromanganese, manganese metal, silicomanganese and ferrospiegeleisen  3311125203 Other ferroalloys, including silvery iron, ferromanganese, manganese metal, silicomanganese and ferrospiegeleisen  33111253 Other ferrous products made in electric and other furnaces  3311125305 Other ferrous products made in electric and other furnaces  331112M Miscellaneous receipts  331112P Primary products  331112S Secondary products  331112SM Secondary products and miscellaneous receipts   Furthermore, the definition of NAICS code 331112 includes the following: Electrometallurgical ferroalloy manufacturing Ferroalloys manufacturing Ferrochromium manufacturing Ferromanganese manufacturing Ferromolybdenum manufacturing Ferrophosphorus manufacturing Ferrosilicon manufacturing Ferrotitanium manufacturing Ferrotungsten manufacturing Ferrovanadium manufacturing High percentage nonferrous alloying elements (i.e., ferroalloys) manufacturing Manganese metal ferroalloys manufacturing Molybdenum silicon ferroalloys manufacturing Silicomanganese ferroalloys manufacturing Spiegeleisen ferroalloys manufacturing. Step 2. Filtering and Smoothing Based on the aggregate view of manufacturing electrometallurgical ferroalloys 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 electrometallurgical ferroalloys 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 electrometallurgical ferroalloys 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 electrometallurgical ferroalloys). 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.
 
Contents:
1 INTRODUCTION & METHODOLOGY 11 1.1 Overview and Definitions 11 1.2 Market Potential Estimation Methodology 11 1.2.1 Overview 11 1.2.2 What is Latent Demand and the P.I.E.? 12 1.2.3 The Methodology 12 1.2.3.1 Step 1. Product Definition and Data Collection 14 1.2.3.2 Step 2. Filtering and Smoothing 16 1.2.3.3 Step 3. Filling in Missing Values 16 1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 17 1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 17 1.2.3.6 Step 6. Aggregation and Benchmarking 17 2 USING THE DATA 18 3 CITY SEGMENTS RANKED BY MARKET SIZE 19 3.1 Top 15 Markets 19 3.2 Markets 16 to 30 20 3.3 Remaining Cities by Market Rank 21 4 CITY SEGMENTS IN ALPHABETICAL ORDER 124 4.1 A: from Aalborg to Az Zawiyah 124 4.2 B: from Bacolod to Bydgoszcz 131 4.3 C: from Caaguazu to Cyangugu 139 4.4 D: from Da Nang to Dzhizak 147 4.5 E: from East London to Esteli 151 4.6 F: from Fagatogo to Funchal 153 4.7 G: from Gabes to Gyumri 156 4.8 H: from Hachinohe to Hyderabad 160 4.9 I: from Iasi to Izmir 164 4.10 J: from Jaboatao to Jyvaskyla 167 4.11 K: from Kabul to Kzyl-Orda 169 4.12 L: from La Ceiba to Lyon 177 4.13 M: from Macae to Mzuzu 182 4.14 N: from Nacala to Nzerekore 192 4.15 O: from Oaklahoma City to Oyem 197 4.16 Ö: from Örebro to Örebro 199 4.17 P: from Pago Pago to Pyuthan 200 4.18 Q: from Qandahar to Quito 206 4.19 R: from Rabat to Rustavi 207 4.20 S: from S. Luis Potosi to Szombathely 210 4.21 T: from Tabligbo to Tyre 222 4.22 U: from Uberaba to Utulei 229 4.23 V: from Vacoas-Phoenix to Vukovar 231 4.24 W: from Wadi Medani to Wuhan 234 4.25 X: from Xalapa to Xian 235 4.26 Y: from Yamagata to Yungkang 236 4.27 Z: from Zadar to Zvishavane 237 5 CITY SEGMENTS RANKED BY COUNTRY 238 5.1 Afghanistan 238 5.2 Albania 238 5.3 Algeria 239 5.4 American Samoa 239 5.5 Andorra 239 5.6 Angola 240 5.7 Antigua and Barbuda 240 5.8 Argentina 241 5.9 Armenia 242 5.10 Aruba 242 5.11 Australia 243 5.12 Austria 243 5.13 Azerbaijan 244 5.14 Bahrain 244 5.15 Bangladesh 244 5.16 Barbados 245 5.17 Belarus 245 5.18 Belgium 245 5.19 Belize 246 5.20 Benin 246 5.21 Bermuda 246 5.22 Bhutan 247 5.23 Bolivia 247 5.24 Bosnia and Herzegovina 247 5.25 Botswana 248 5.26 Brazil 249 5.27 Brunei 254 5.28 Bulgaria 254 5.29 Burkina Faso 255 5.30 Burma 255 5.31 Burundi 255 5.32 Cambodia 256 5.33 Cameroon 256 5.34 Canada 256 5.35 Cape Verde 257 5.36 Central African Republic 257 5.37 Chad 257 5.38 Chile 258 5.39 China 258 5.40 Christmas Island 259 5.41 Colombia 259 5.42 Comoros 259 5.43 Congo (formerly Zaire) 260 5.44 Cook Islands 260 5.45 Costa Rica 260 5.46 Cote dIvoire 261 5.47 Croatia 261 5.48 Cuba 261 5.49 Cyprus 262 5.50 Czech Republic 262 5.51 Denmark 262 5.52 Djibouti 263 5.53 Dominica 263 5.54 Dominican Republic 263 5.55 Ecuador 264 5.56 Egypt 264 5.57 El Salvador 264 5.58 Equatorial Guinea 265 5.59 Estonia 265 5.60 Ethiopia 265 5.61 Fiji 266 5.62 Finland 266 5.63 France 267 5.64 French Guiana 267 5.65 French Polynesia 268 5.66 Gabon 268 5.67 Georgia 268 5.68 Germany 269 5.69 Ghana 269 5.70 Greece 270 5.71 Greenland 270 5.72 Grenada 270 5.73 Guadeloupe 271 5.74 Guam 271 5.75 Guatemala 271 5.76 Guinea 272 5.77 Guinea-Bissau 272 5.78 Guyana 272 5.79 Haiti 273 5.80 Honduras 273 5.81 Hong Kong 273 5.82 Hungary 274 5.83 Iceland 274 5.84 India 275 5.85 Indonesia 276 5.86 Iran 277 5.87 Iraq 277 5.88 Ireland 278 5.89 Israel 278 5.90 Italy 279 5.91 Jamaica 279 5.92 Japan 280 5.93 Jordan 282 5.94 Kazakhstan 283 5.95 Kenya 283 5.96 Kiribati 284 5.97 Kuwait 284 5.98 Kyrgyzstan 284 5.99 Laos 284 5.100 Latvia 285 5.101 Lebanon 285 5.102 Lesotho 285 5.103 Liberia 286 5.104 Libya 286 5.105 Liechtenstein 286 5.106 Lithuania 287 5.107 Luxembourg 287 5.108 Macau 287 5.109 Madagascar 288 5.110 Malawi 288 5.111 Malaysia 289 5.112 Maldives 289 5.113 Mali 290 5.114 Malta 290 5.115 Marshall Islands 290 5.116 Martinique 291 5.117 Mauritania 291 5.118 Mauritius 291 5.119 Mexico 292 5.120 Micronesia Federation 293 5.121 Moldova 293 5.122 Monaco 293 5.123 Mongolia 293 5.124 Morocco 294 5.125 Mozambique 294 5.126 Namibia 294 5.127 Nauru 295 5.128 Nepal 295 5.129 New Caledonia 295 5.130 New Zealand 296 5.131 Nicaragua 296 5.132 Niger 297 5.133 Nigeria 297 5.134 Niue 297 5.135 Norfolk Island 298 5.136 North Korea 298 5.137 Norway 298 5.138 Oman 299 5.139 Pakistan 299 5.140 Palau 299 5.141 Palestine 299 5.142 Panama 300 5.143 Papua New Guinea 300 5.144 Paraguay 300 5.145 Peru 301 5.146 Philippines 301 5.147 Poland 302 5.148 Portugal 302 5.149 Puerto Rico 303 5.150 Qatar 303 5.151 Republic of Congo 303 5.152 Reunion 304 5.153 Romania 304 5.154 Russia 305 5.155 Rwanda 305 5.156 San Marino 305 5.157 Sao Tome E Principe 306 5.158 Saudi Arabia 306 5.159 Senegal 306 5.160 Seychelles 307 5.161 Sierra Leone 307 5.162 Singapore 307 5.163 Slovakia 307 5.164 Slovenia 308 5.165 Solomon Islands 308 5.166 Somalia 308 5.167 South Africa 309 5.168 South Korea 309 5.169 Spain 310 5.170 Sri Lanka 310 5.171 St. Kitts and Nevis 311 5.172 St. Lucia 311 5.173 St. Vincent and the Grenadines 311 5.174 Sudan 311 5.175 Suriname 312 5.176 Swaziland 312 5.177 Sweden 312 5.178 Switzerland 313 5.179 Syrian Arab Republic 313 5.180 Taiwan 314 5.181 Tajikistan 315 5.182 Tanzania 315 5.183 Thailand 315 5.184 The Bahamas 316 5.185 The British Virgin Islands 316 5.186 The Cayman Islands 316 5.187 The Falkland Islands 316 5.188 The Gambia 317 5.189 The Netherlands 317 5.190 The Netherlands Antilles 317 5.191 The Northern Mariana Island 318 5.192 The U.S. Virgin Islands 318 5.193 The United Arab Emirates 318 5.194 The United Kingdom 319 5.195 The United States 320 5.196 Togo 321 5.197 Tokelau 321 5.198 Tonga 321 5.199 Trinidad and Tobago 322 5.200 Tunisia 322 5.201 Turkey 323 5.202 Turkmenistan 323 5.203 Tuvalu 323 5.204 Uganda 324 5.205 Ukraine 324 5.206 Uruguay 325 5.207 Uzbekistan 325 5.208 Vanuatu 325 5.209 Venezuela 326 5.210 Vietnam 326 5.211 Wallis and Futuna 327 5.212 Western Sahara 327 5.213 Western Samoa 327 5.214 Yemen 327 5.215 Zambia 328 5.216 Zimbabwe 328 6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 329 6.1 Disclaimers & Safe Harbor 329 6.2 ICON Group International, Inc. User Agreement Provisions 330
 
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