The 2009 Report on Manufacturing Sawmill and Woodworking Machinery, Circular and Band Sawing Equipment, Planing Machinery, and Sanding Machinery Excluding Handheld Machinery: World Market Segmentation by City
ICON Group International, May 2009, Pages: 357
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery. 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery. 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery. 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery.
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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery” 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery is 333210. It is for this definition of manufacturing sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery that the aggregate latent demand estimates are derived. “Manufacturing sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery” is specifically defined as follows:
333210
This industry comprises establishments primarily engaged in manufacturing sawmill and woodworking machinery (except handheld), such as circular and band sawing equipment, planing machinery, and sanding machinery.
3332101
Woodworking machinery including parts, excluding home workshop types
333210112
Sawmill equipment
333210118
Veneer, plywood, particleboard, and hardboard-making equipment
333210121
Assembling, gluing, laminating, and finishing machines
333210162
Sawing machines, except sawmill equipment
333210173
Straight-line machinery, including jointers, moulders, planers, sanders, surfacers, etc.
333210175
Boring machinery, carving machinery, dovetailers, mortisers, routers, shapers, and tenoners
333210197
Other woodworking machinery, including lathes, clamping machinery, presses, roll coaters, etc.
333210199
Parts, attachments, and accessories, excluding saw blades and cutting tools
3332102
Woodworking machinery designed primarily for home workshops, incl. parts
333210221
Saws, including circular
333210298
Other woodworking machinery for home workshops, incl. lathes, drilling machines, etc.
333210299
Parts, attachments, and accessories for home workshop machinery
3332103
WOODWORKING MACHINERY, INCLUDING PARTS, ATTACHMENTS, AND ACCESSORIES
33321031
Woodworking sawmill equipment
3332103101
Woodworking sawmill circular saws (head rigs)
3332103106
Woodworking sawmill band saws (head rigs)
3332103111
Woodworking sawmill equipment, removing bark from logs
3332103116
Woodworking sawmill equipment, chipping or splitting wood
3332103121
Woodworking sawmill equipment, specialized sawmill materials handling equipment
3332103126
Other woodworking sawmill equipment
33321032
Woodworking machines and equipment, including moulders
3332103231
Woodworking sawing machines (except sawmill equipment)
3332103236
Woodworking planing machinery (including single and double planers, facers, jointers, and abrasive planers)
3332103241
Woodworking sanding machines
3332103246
Woodworking boring machines
3332103251
Woodworking mortising and tenoning machines
3332103256
Woodworking lathes or turning machines
3332103261
Woodworking routers
3332103266
Woodworking shapers and profilers
3332103271
Woodworking assembling, gluing, laminating, and finishing machines
3332103276
Multifunction woodworking machines
3332103279
Woodworking bending machines
3332103283
Woodworking milling or molding (by cutting) machines
3332103287
Woodworking dryers machinery
3332103291
Other woodworking machines and equipment
3332103292
Other woodworking machines and equipment
33321033
Parts, attachments, and accessories for woodworking machinery (sold separately), excluding saw blades and cutting tools
3332103396
Parts, attachments, and accessories for woodworking machinery (sold separately), excluding saw blades and cutting tools
3332105
WOODWORKING MACHINERY FOR HOME WORKSHOPS, GARAGES, AND SERVICE SHOPS (EXCLUDING CHAINSAWS AND OTHER POWER_DRIVEN HANDTOOLS)
33321051
Parts, attachments, and accessories for woodworking machinery for home workshops, garages, and service shops (sold separately)
3332105101
Parts, attachments, and accessories for woodworking machinery for home workshops, garages, and service shops (sold separately)
33321052
Woodworking saws, machines, and equipment for home workshops, garages, and service shops
3332105211
Woodworking saws (including circular and band saws, excluding chain saws) for home workshops, garages, and service shops
3332105221
Woodworking planing, milling, or molding (by cutting) machines for home workshops, etc.
3332105231
Woodworking grinding, sanding or polishing machines for home workshops, etc
3332105241
Woodworking drilling or morticing machines for home workshops, etc.
3332105251
Other woodworking machines and home workshop, etc (except power~driven handtools)
33321053
Woodworking saws, machines, and equipment for home workshops, garages and service shops
3332105311
Woodworking saws, including circular and band saws (excluding chain saws) for home workshops, garages, and service shops
3332105351
Other woodworking machinery for home workshops, garages, and service shops (except power_driven handtools)
333210M
Miscellaneous receipts
333210P
Primary products
333210S
Secondary products
333210SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 333210 includes the following:
Bandsaws, woodworking-type, manufacturing
Circular saws, woodworking-type, stationary, manufacturing
Dovetailing machines, woodworking-type, manufacturing
Drill presses, woodworking-type, manufacturing
Jigsaws, woodworking-type, stationary, manufacturing
Jointers, woodworking-type, manufacturing
Lathes, woodworking-type, manufacturing
Mortisers, woodworking-type, manufacturing
Planers woodworking-type, stationary, manufacturing
Presses for making composite woods (e.g., hardboard, medium density fiberboard (M
Sanding machines, woodworking-type, stationary, manufacturing
Sawmill equipment manufacturing
Saws, bench and table, power-driven, woodworking-type, manufacturing
Scarfing machines, woodworking-type, manufacturing
Shapers, woodworking-type, manufacturing
Veneer and plywood forming machinery manufacturing
Wood verneer laminating and gluing machines manufacturing
Woodworking machines (except handheld) manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery 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 sawmill and woodworking machinery, circular and band sawing equipment, planing machinery, and sanding machinery excluding handheld machinery). In a graphical sense, for low income cities, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income cities are assumed to have a latent demand proportional to their income, based on the city closest to it on the aggregate consumption function.
Step 6. Aggregation and Benchmarking
Based on the models described above, latent demand figures are estimated for all cities of the world, including for the smallest economies. These are then aggregated to get world totals and regional totals. To make the numbers more meaningful, regional and global demand averages are presented. Figures are rounded, so minor inconsistencies may exist across tables.
1 INTRODUCTION & METHODOLOGY 11
1.1 Overview and Definitions 11
1.2 Market Potential Estimation Methodology 11
1.2.1 Overview 11
1.2.2 What is Latent Demand and the P.I.E.? 12
1.2.3 The Methodology 13
1.2.3.1 Step 1. Product Definition and Data Collection 14
1.2.3.2 Step 2. Filtering and Smoothing 18
1.2.3.3 Step 3. Filling in Missing Values 19
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 19
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 20
1.2.3.6 Step 6. Aggregation and Benchmarking 20
2 USING THE DATA 21
3 CITY SEGMENTS RANKED BY MARKET SIZE 22
3.1 Top 15 Markets 22
3.2 Markets 16 to 30 23
3.3 Remaining Cities by Market Rank 24
4 CITY SEGMENTS IN ALPHABETICAL ORDER 127
4.1 A: from Aalborg to Az Zawiyah 127
4.2 B: from Bacolod to Bydgoszcz 134
4.3 C: from Caaguazu to Cyangugu 142
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 174
4.12 L: from La Ceiba to Lyon 182
4.13 M: from Macae to Mzuzu 188
4.14 N: from Nacala to Nzerekore 198
4.15 O: from Oaklahoma City to Oyem 203
4.16 Ö: from Örebro to Örebro 205
4.17 P: from Pago Pago to Pyuthan 206
4.18 Q: from Qandahar to Quito 213
4.19 R: from Rabat to Rustavi 214
4.20 S: from S. Luis Potosi to Szombathely 217
4.21 T: from Tabligbo to Tyre 229
4.22 U: from Uberaba to Utulei 237
4.23 V: from Vacoas-Phoenix to Vukovar 239
4.24 W: from Wadi Medani to Wuhan 242
4.25 X: from Xalapa to Xian 243
4.26 Y: from Yamagata to Yungkang 244
4.27 Z: from Zadar to Zvishavane 245
5 CITY SEGMENTS RANKED BY COUNTRY 247
5.1 Afghanistan 247
5.2 Albania 247
5.3 Algeria 248
5.4 American Samoa 248
5.5 Andorra 249
5.6 Angola 249
5.7 Antigua and Barbuda 249
5.8 Argentina 250
5.9 Armenia 251
5.10 Aruba 251
5.11 Australia 252
5.12 Austria 252
5.13 Azerbaijan 253
5.14 Bahrain 253
5.15 Bangladesh 254
5.16 Barbados 254
5.17 Belarus 255
5.18 Belgium 255
5.19 Belize 256
5.20 Benin 256
5.21 Bermuda 256
5.22 Bhutan 257
5.23 Bolivia 257
5.24 Bosnia and Herzegovina 258
5.25 Botswana 258
5.26 Brazil 259
5.27 Brunei 264
5.28 Bulgaria 265
5.29 Burkina Faso 265
5.30 Burma 266
5.31 Burundi 266
5.32 Cambodia 266
5.33 Cameroon 267
5.34 Canada 267
5.35 Cape Verde 268
5.36 Central African Republic 268
5.37 Chad 269
5.38 Chile 269
5.39 China 270
5.40 Christmas Island 270
5.41 Colombia 271
5.42 Comoros 271
5.43 Congo (formerly Zaire) 272
5.44 Cook Islands 272
5.45 Costa Rica 273
5.46 Cote dIvoire 273
5.47 Croatia 274
5.48 Cuba 274
5.49 Cyprus 275
5.50 Czech Republic 275
5.51 Denmark 276
5.52 Djibouti 276
5.53 Dominica 277
5.54 Dominican Republic 277
5.55 Ecuador 278
5.56 Egypt 278
5.57 El Salvador 279
5.58 Equatorial Guinea 279
5.59 Estonia 279
5.60 Ethiopia 280
5.61 Fiji 280
5.62 Finland 281
5.63 France 281
5.64 French Guiana 282
5.65 French Polynesia 282
5.66 Gabon 282
5.67 Georgia 283
5.68 Germany 283
5.69 Ghana 284
5.70 Greece 284
5.71 Greenland 285
5.72 Grenada 285
5.73 Guadeloupe 286
5.74 Guam 286
5.75 Guatemala 287
5.76 Guinea 287
5.77 Guinea-Bissau 287
5.78 Guyana 288
5.79 Haiti 288
5.80 Honduras 289
5.81 Hong Kong 289
5.82 Hungary 290
5.83 Iceland 290
5.84 India 291
5.85 Indonesia 292
5.86 Iran 293
5.87 Iraq 293
5.88 Ireland 294
5.89 Israel 294
5.90 Italy 295
5.91 Jamaica 295
5.92 Japan 296
5.93 Jordan 299
5.94 Kazakhstan 299
5.95 Kenya 300
5.96 Kiribati 300
5.97 Kuwait 300
5.98 Kyrgyzstan 301
5.99 Laos 301
5.100 Latvia 301
5.101 Lebanon 302
5.102 Lesotho 302
5.103 Liberia 302
5.104 Libya 303
5.105 Liechtenstein 303
5.106 Lithuania 304
5.107 Luxembourg 304
5.108 Macau 304
5.109 Madagascar 305
5.110 Malawi 305
5.111 Malaysia 306
5.112 Maldives 306
5.113 Mali 307
5.114 Malta 307
5.115 Marshall Islands 307
5.116 Martinique 308
5.117 Mauritania 308
5.118 Mauritius 309
5.119 Mexico 310
5.120 Micronesia Federation 311
5.121 Moldova 311
5.122 Monaco 311
5.123 Mongolia 312
5.124 Morocco 312
5.125 Mozambique 313
5.126 Namibia 313
5.127 Nauru 313
5.128 Nepal 314
5.129 New Caledonia 314
5.130 New Zealand 315
5.131 Nicaragua 315
5.132 Niger 316
5.133 Nigeria 316
5.134 Niue 317
5.135 Norfolk Island 317
5.136 North Korea 317
5.137 Norway 318
5.138 Oman 318
5.139 Pakistan 319
5.140 Palau 319
5.141 Palestine 319
5.142 Panama 320
5.143 Papua New Guinea 320
5.144 Paraguay 321
5.145 Peru 321
5.146 Philippines 322
5.147 Poland 323
5.148 Portugal 323
5.149 Puerto Rico 324
5.150 Qatar 324
5.151 Republic of Congo 325
5.152 Reunion 325
5.153 Romania 326
5.154 Russia 326
5.155 Rwanda 327
5.156 San Marino 327
5.157 Sao Tome E Principe 327
5.158 Saudi Arabia 328
5.159 Senegal 328
5.160 Seychelles 329
5.161 Sierra Leone 329
5.162 Singapore 329
5.163 Slovakia 330
5.164 Slovenia 330
5.165 Solomon Islands 330
5.166 Somalia 331
5.167 South Africa 331
5.168 South Korea 332
5.169 Spain 333
5.170 Sri Lanka 333
5.171 St. Kitts and Nevis 334
5.172 St. Lucia 334
5.173 St. Vincent and the Grenadines 334
5.174 Sudan 335
5.175 Suriname 335
5.176 Swaziland 336
5.177 Sweden 336
5.178 Switzerland 337
5.179 Syrian Arab Republic 337
5.180 Taiwan 338
5.181 Tajikistan 339
5.182 Tanzania 339
5.183 Thailand 340
5.184 The Bahamas 340
5.185 The British Virgin Islands 340
5.186 The Cayman Islands 341
5.187 The Falkland Islands 341
5.188 The Gambia 341
5.189 The Netherlands 342
5.190 The Netherlands Antilles 342
5.191 The Northern Mariana Island 342
5.192 The U.S. Virgin Islands 343
5.193 The United Arab Emirates 343
5.194 The United Kingdom 344
5.195 The United States 345
5.196 Togo 346
5.197 Tokelau 346
5.198 Tonga 347
5.199 Trinidad and Tobago 347
5.200 Tunisia 347
5.201 Turkey 348
5.202 Turkmenistan 348
5.203 Tuvalu 349
5.204 Uganda 349
5.205 Ukraine 350
5.206 Uruguay 350
5.207 Uzbekistan 351
5.208 Vanuatu 351
5.209 Venezuela 352
5.210 Vietnam 353
5.211 Wallis and Futuna 353
5.212 Western Sahara 353
5.213 Western Samoa 354
5.214 Yemen 354
5.215 Zambia 354
5.216 Zimbabwe 355
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 356
6.1 Disclaimers & Safe Harbor 356
6.2 ICON Group International, Inc. User Agreement Provisions 357
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