The 2009 Report on Manufacturing Metal Bolts, Nuts, Screws, Rivets, and Other Industrial Fasteners: World Market Segmentation by City
ICON Group International, May 2009, Pages: 344
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing metal bolts, nuts, screws, rivets, and other industrial fasteners 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 metal bolts, nuts, screws, rivets, and other industrial fasteners. 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 metal bolts, nuts, screws, rivets, and other industrial fasteners 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 metal bolts, nuts, screws, rivets, and other industrial fasteners 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 metal bolts, nuts, screws, rivets, and other industrial fasteners 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 metal bolts, nuts, screws, rivets, and other industrial fasteners 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 metal bolts, nuts, screws, rivets, and other industrial fasteners. 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 metal bolts, nuts, screws, rivets, and other industrial fasteners. 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 metal bolts, nuts, screws, rivets, and other industrial fasteners.
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 metal bolts, nuts, screws, rivets, and other industrial fasteners” 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 metal bolts, nuts, screws, rivets, and other industrial fasteners 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 metal bolts, nuts, screws, rivets, and other industrial fasteners” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing metal bolts, nuts, screws, rivets, and other industrial fasteners, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing metal bolts, nuts, screws, rivets, and other industrial fasteners is 332722. It is for this definition of manufacturing metal bolts, nuts, screws, rivets, and other industrial fasteners that the aggregate latent demand estimates are derived. “Manufacturing metal bolts, nuts, screws, rivets, and other industrial fasteners” is specifically defined as follows:
332722
This U.S. industry comprises establishments primarily engaged in manufacturing metal bolts, nuts, screws, rivets, and washers, and other industrial fasteners using machines, such as headers, threaders, and nut forming machines.
3327221
AIRCRAFT FASTENERS, EXCEPT PLASTICS (INCLUDING AEROSPACE) (MEET SPECIFICATIONS FOR FLYING VEHICLES)
33272211
Aircraft fasteners, except plastics (including aerospace) (meet specifications for flying vehicles)
3327221101
Aircraft bolts, except plastics (including aerospace), less than 161 KSI tensile (meets specifications for flying vehicles)
3327221106
Aircraft bolts, except plastics (including aerospace), 161 KSI tensile or more (meets specifications for flying vehicles)
3327221115
Aircraft screws and studs, except plastics (including aerospace) (meets specifications for flying vehicles)
3327221145
Aircraft locknuts, except plastics (including aerospace), including flanged locknuts (meets specifications for flying vehicles)
3327221159
Other internally threaded aircraft fasteners, except plastics (including aerospace), including flanged nuts (all types except flanged locknuts), hex square nuts (all types) and sheet metal fasteners
3327221172
Aircraft washers, except plastics (including aerospace), all types
3327221178
Aircraft rivets, except plastics (including aerospace), all types
3327221184
Aircraft pints, except plastics (including aerospace), all types
3327223
EXTERNALLY THREADED METAL FASTENERS (EXCEPT AIRCRAFT TYPES)
33272231
Externally threaded metal fasteners (except aircraft types)
3327223111
Mine roof bolts
3327223122
Hex bolts, including heavy, tap_and_joint (excluding high_strength structural and aircraft types)
3327223133
Other metal bolts, including square, round, plow, high_strength structural, and bent bolts (except aircraft types)
3327223144
Cap, set, machine, lag, flange, and self_locking screws (except aircraft types)
3327223155
Tapping screws (including fillister, flat, hex, oval, pan, and truss) and wood screws (including flat, oval, and round) (except aircraft types)
3327223199
Other externally threaded metal fasteners, including studs (except aircraft types)
3327224
Externally threaded metal fasteners, except aircraft
332722411
Mine roof bolts
332722412
Hex bolts, including heavy, tap-and-joint, excluding high strength structural
332722419
other metal bolts, incl. square, round, plow, high-strength structural & bent
332722439
Cap, set, machine, lag, flange, and self-locking screws
332722445
tapping screws incl flat, hex, oval & pan; wood screws incl flat, oval & round
332722489
Other externally threaded metal fasteners, including studs
3327225
Internally threaded fasteners, exc aircraft
33272251
Internally threaded metal fasteners (except aircraft types)
3327225104
Hex nuts, including flanges, double chamfered, washer face, flat, jam, slotted, thick, castle, heavy, machine, and locking (except aircraft types)
3327225129
Square nuts (including flat, washer, crowned, heavy, track, sleeve, and machine), sheet metal nuts, weld nuts, wing nuts, nut retainers, etc. (except aircraft types)
3327225189
Other internally threaded metal fasteners, including flanged nuts and locknuts (except aircraft types)
3327226
Nonthreaded fasteners, exc. aircraft
3327227
Aircraft/aerospace fasteners
33272271
Nonthreaded metal fasteners (except aircraft types)
3327227109
Solid rivets (except aircraft types)
3327227115
Tubular, split (including rivet caps) and blind rivets (except aircraft types)
3327227135
Washers (except aircraft types)
3327227179
Other nonthreaded metal fasteners, including pins (except aircraft types)
3327228
other formed fasteners incl auto, hshd, aircraft, & ordnance (not plastic)
3327229
PRODUCTS, OTHER THAN FASTENERS, MADE BY COLD~ HEADING (OR WARM~ OR HOT~HEADING) PROCESSES
33272291
Products, other than fasteners, made by cold~heading (or warm~ or hot~ heading) processes
3327229105
Aircraft parts, other than fasteners, made by cold~heading (or warm~ or hot~ heading) processes
3327229115
Automotive parts, other than fasteners, made by cold~heading (or warm~ or hot~heading) processes
3327229135
Parts for household appliances (including radio and television), other than fasteners, made by cold~heading (or warm~ or hot~heading) processes
3327229199
Other products (except fasteners) made by cold~heading (or warm~ or hot~ heading) processes
332722A
PRODUCTS (EXCEPT FASTENERS), MADE BY COLD_HEADING (OR WARM_ OR HOT_HEADING) PROCESSES
332722A1
Products (except fasteners), made by cold_heading (or warm_ or hot_heading) processes
332722A105
Aircraft parts (except fasteners), made by cold_heading (or warm_ or hot_ heading) processes
332722A115
Automotive parts (except fasteners), made by cold_heading (or warm_ or hot_ heading) processes
332722A135
Parts for household appliances (except fasteners), including radio and television, made by cold_heading (or warm_ or hot_heading) processes
332722A198
Turnbuckles and hose clamps, made by cold_heading (or warm_ or hot_ heading) processes
332722A199
Other products (except fasteners), made by cold_heading (or warm_ or hot_ heading) processes
332722M
Miscellaneous receipts
332722P
Primary products
332722S
Secondary products
332722SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 332722 includes the following:
Bolts, metal, manufacturing
Cotter pins, metal, manufacturing
Dowel pins, metal, manufacturing
Hook and eye latches, metal, manufacturing
Hooks (i.e., general purpose fasteners), metal, manufacturing
Hooks, metal screw, manufacturing
Hose clamps, metal, manufacturing
Lock washers, metal, manufacturing
Machine keys, metal, manufacturing
Nuts, metal, manufacturing
Rivets, metal, manufacturing
Screw eyes, metal, manufacturing
Screws, metal, manufacturing
Spring pins, metal, manufacturing
Spring washers, metal, manufacturing
Toggle bolts, metal, manufacturing
Turnbuckles, metal, manufacturing
Washers, metal, manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing metal bolts, nuts, screws, rivets, and other industrial fasteners 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 metal bolts, nuts, screws, rivets, and other industrial fasteners 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 metal bolts, nuts, screws, rivets, and other industrial fasteners 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 metal bolts, nuts, screws, rivets, and other industrial fasteners). In a graphical sense, for low income cities, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income cities are assumed to have a latent demand proportional to their income, based on the city closest to it on the aggregate consumption function.
Step 6. Aggregation and Benchmarking
Based on the models described above, latent demand figures are estimated for all cities of the world, including for the smallest economies. These are then aggregated to get world totals and regional totals. To make the numbers more meaningful, regional and global demand averages are presented. Figures are rounded, so minor inconsistencies may exist across tables.
1 INTRODUCTION & METHODOLOGY 11
1.1 Overview and Definitions 11
1.2 Market Potential Estimation Methodology 11
1.2.1 Overview 11
1.2.2 What is Latent Demand and the P.I.E.? 12
1.2.3 The Methodology 12
1.2.3.1 Step 1. Product Definition and Data Collection 14
1.2.3.2 Step 2. Filtering and Smoothing 18
1.2.3.3 Step 3. Filling in Missing Values 18
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 19
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 19
1.2.3.6 Step 6. Aggregation and Benchmarking 19
2 USING THE DATA 20
3 CITY SEGMENTS RANKED BY MARKET SIZE 21
3.1 Top 15 Markets 21
3.2 Markets 16 to 30 22
3.3 Remaining Cities by Market Rank 23
4 CITY SEGMENTS IN ALPHABETICAL ORDER 126
4.1 A: from Aalborg to Az Zawiyah 126
4.2 B: from Bacolod to Bydgoszcz 133
4.3 C: from Caaguazu to Cyangugu 141
4.4 D: from Da Nang to Dzhizak 149
4.5 E: from East London to Esteli 153
4.6 F: from Fagatogo to Funchal 155
4.7 G: from Gabes to Gyumri 158
4.8 H: from Hachinohe to Hyderabad 162
4.9 I: from Iasi to Izmir 166
4.10 J: from Jaboatao to Jyvaskyla 169
4.11 K: from Kabul to Kzyl-Orda 171
4.12 L: from La Ceiba to Lyon 179
4.13 M: from Macae to Mzuzu 185
4.14 N: from Nacala to Nzerekore 195
4.15 O: from Oaklahoma City to Oyem 200
4.16 Ö: from Örebro to Örebro 202
4.17 P: from Pago Pago to Pyuthan 203
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 249
5.16 Barbados 249
5.17 Belarus 250
5.18 Belgium 250
5.19 Belize 251
5.20 Benin 251
5.21 Bermuda 251
5.22 Bhutan 252
5.23 Bolivia 252
5.24 Bosnia and Herzegovina 252
5.25 Botswana 253
5.26 Brazil 254
5.27 Brunei 259
5.28 Bulgaria 259
5.29 Burkina Faso 260
5.30 Burma 260
5.31 Burundi 260
5.32 Cambodia 261
5.33 Cameroon 261
5.34 Canada 262
5.35 Cape Verde 262
5.36 Central African Republic 263
5.37 Chad 263
5.38 Chile 264
5.39 China 264
5.40 Christmas Island 265
5.41 Colombia 265
5.42 Comoros 265
5.43 Congo (formerly Zaire) 266
5.44 Cook Islands 266
5.45 Costa Rica 266
5.46 Cote dIvoire 267
5.47 Croatia 267
5.48 Cuba 268
5.49 Cyprus 268
5.50 Czech Republic 269
5.51 Denmark 269
5.52 Djibouti 270
5.53 Dominica 270
5.54 Dominican Republic 270
5.55 Ecuador 271
5.56 Egypt 271
5.57 El Salvador 272
5.58 Equatorial Guinea 272
5.59 Estonia 272
5.60 Ethiopia 273
5.61 Fiji 273
5.62 Finland 274
5.63 France 274
5.64 French Guiana 275
5.65 French Polynesia 275
5.66 Gabon 275
5.67 Georgia 276
5.68 Germany 276
5.69 Ghana 277
5.70 Greece 277
5.71 Greenland 278
5.72 Grenada 278
5.73 Guadeloupe 279
5.74 Guam 279
5.75 Guatemala 279
5.76 Guinea 280
5.77 Guinea-Bissau 280
5.78 Guyana 280
5.79 Haiti 281
5.80 Honduras 281
5.81 Hong Kong 281
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 299
5.116 Martinique 299
5.117 Mauritania 299
5.118 Mauritius 300
5.119 Mexico 301
5.120 Micronesia Federation 302
5.121 Moldova 302
5.122 Monaco 302
5.123 Mongolia 303
5.124 Morocco 303
5.125 Mozambique 304
5.126 Namibia 304
5.127 Nauru 304
5.128 Nepal 305
5.129 New Caledonia 305
5.130 New Zealand 306
5.131 Nicaragua 306
5.132 Niger 307
5.133 Nigeria 307
5.134 Niue 308
5.135 Norfolk Island 308
5.136 North Korea 308
5.137 Norway 309
5.138 Oman 309
5.139 Pakistan 310
5.140 Palau 310
5.141 Palestine 310
5.142 Panama 311
5.143 Papua New Guinea 311
5.144 Paraguay 312
5.145 Peru 312
5.146 Philippines 313
5.147 Poland 313
5.148 Portugal 314
5.149 Puerto Rico 314
5.150 Qatar 315
5.151 Republic of Congo 315
5.152 Reunion 315
5.153 Romania 316
5.154 Russia 316
5.155 Rwanda 317
5.156 San Marino 317
5.157 Sao Tome E Principe 317
5.158 Saudi Arabia 318
5.159 Senegal 318
5.160 Seychelles 319
5.161 Sierra Leone 319
5.162 Singapore 319
5.163 Slovakia 319
5.164 Slovenia 320
5.165 Solomon Islands 320
5.166 Somalia 320
5.167 South Africa 321
5.168 South Korea 321
5.169 Spain 322
5.170 Sri Lanka 322
5.171 St. Kitts and Nevis 323
5.172 St. Lucia 323
5.173 St. Vincent and the Grenadines 323
5.174 Sudan 324
5.175 Suriname 324
5.176 Swaziland 324
5.177 Sweden 325
5.178 Switzerland 325
5.179 Syrian Arab Republic 326
5.180 Taiwan 327
5.181 Tajikistan 328
5.182 Tanzania 328
5.183 Thailand 329
5.184 The Bahamas 329
5.185 The British Virgin Islands 329
5.186 The Cayman Islands 330
5.187 The Falkland Islands 330
5.188 The Gambia 330
5.189 The Netherlands 331
5.190 The Netherlands Antilles 331
5.191 The Northern Mariana Island 331
5.192 The U.S. Virgin Islands 332
5.193 The United Arab Emirates 332
5.194 The United Kingdom 332
5.195 The United States 333
5.196 Togo 334
5.197 Tokelau 334
5.198 Tonga 335
5.199 Trinidad and Tobago 335
5.200 Tunisia 335
5.201 Turkey 336
5.202 Turkmenistan 336
5.203 Tuvalu 336
5.204 Uganda 337
5.205 Ukraine 337
5.206 Uruguay 338
5.207 Uzbekistan 338
5.208 Vanuatu 339
5.209 Venezuela 339
5.210 Vietnam 340
5.211 Wallis and Futuna 340
5.212 Western Sahara 340
5.213 Western Samoa 340
5.214 Yemen 341
5.215 Zambia 341
5.216 Zimbabwe 342
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 343
6.1 Disclaimers & Safe Harbor 343
6.2 ICON Group International, Inc. User Agreement Provisions 344
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