The 2009 Report on Showcase, Partition, Shelving, and Locker Manufacturing: World Market Segmentation by City
ICON Group International, May 2009, Pages: 334
Market Potential Estimation Methodology
Overview
This study covers the world outlook for showcase, partition, shelving, and locker manufacturing 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 showcase, partition, shelving, and locker manufacturing. 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 showcase, partition, shelving, and locker manufacturing 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 showcase, partition, shelving, and locker manufacturing 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 showcase, partition, shelving, and locker manufacturing 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 showcase, partition, shelving, and locker manufacturing 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 showcase, partition, shelving, and locker manufacturing. 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 showcase, partition, shelving, and locker manufacturing. 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 showcase, partition, shelving, and locker manufacturing.
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 “showcase, partition, shelving, and locker manufacturing” 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 showcase, partition, shelving, and locker manufacturing 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 “showcase, partition, shelving, and locker manufacturing” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of showcase, partition, shelving, and locker manufacturing, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for showcase, partition, shelving, and locker manufacturing is 337215. It is for this definition of showcase, partition, shelving, and locker manufacturing that the aggregate latent demand estimates are derived. “Showcase, partition, shelving, and locker manufacturing” is specifically defined as follows:
337215
This U.S. industry comprises establishments primarily engaged in manufacturing wood and nonwood office and store fixtures, shelving, lockers, frames, partitions, and related fabricated products of wood and nonwood materials, including plastics laminated fixture tops. The products are made on a stock basis and may be assembled or unassembled (i.e., knockdown). Establishments exclusively making furniture parts (e.g., frames) are included in this industry.
3372151
Wood partitions (assembled or knock-down) and wood shelving and lockers, except
33721511
Wood lockers, partitions, and shelving (except custom)
3372151111
Wood partitions, prefabricated, assembled and knocked_down (except custom)
3372151121
Wood shelving (except custom)
3372151131
Wood lockers (except custom)
3372154
Wood fixtures for stores, banks, and offices, and other misc. fixtures
33721541
Wood fixtures for stores, banks, and offices, and other miscellaneous fixtures, except custom
3372154111
Wood walls and wall fixtures, manufacturers’ standard, for retail stores
3372154121
Wood center floor tables and gondolas, manufacturers’ standard, for retail stores
3372154131
Other wood fixtures and displays, manufacturers’ standard, for retail stores
3372154141
Other wood show and display cases, including wall types, and tables, nec, except custom
3372154151
Wood cabinets, floor or wall types, for stores, banks, and offices, except custom
3372154161
Wood counters, excluding bank counters, except custom
3372154171
Wood bank fixtures, including bank counters, except custom
3372154181
Other wood fixtures, including backs, telephone booths, cashier stands, miscellaneous display fixtures, etc., except custom
3372155
WOOD BANK, OFFICE, STORE, AND RELATED FIXTURES (EXCEPT CUSTOM)
33721551
Wood bank, office, store, and related fixtures (except custom)
3372155111
Wood retail store walls and wall fixtures (except custom)
3372155121
Wood retail store center floor tables and gondolas (except custom)
3372155131
Other wood retail store fixtures, including display cases (except custom)
3372155141
Other wood bank, office, store, and related table and display fixtures (except custom)
3372155151
Wood bank, office, store, and related cabinets (except custom), including floor and wall cabinets
3372155164
Wood office, store, and related counters (except custom)
3372155183
Other wood bank, office, store, and related wood fixtures (except custom), including cashier stands and wood and plastics laminated wood stock line fixture tops
3372157
Prefabricated partitions, assembled or knock-down, nonwood
33721571
Nonwood partitions, prefabricated, assembled and knock_down
3372157111
Nonwood prefabricated toilet partitions, assembled and knocked_down
3372157121
Nonwood prefabricated movable partitions, assembled and knocked_down, excluding freestanding partitions
3372157131
Other nonwood prefabricated partitions, assembled and knocked_down, excluding accordion and folding door partitions
337215A
Shelving and lockers, nonwood
337215A1
Nonwood commercial shelving
337215A111
Nonwood commercial shelving
337215A2
Nonwood bookstacks and other nonwood shelving
337215A211
Nonwood bookstacks
337215A221
Other nonwood shelving, including computer tape and disk, correspondence, and microfilm shelving
337215A231
Nonwood lockers
337215E
Storage racks and accessories, nonwood
337215E1
Nonwood storage racks and accessories
337215E111
Nonwood drive_in, drive_through, and gravity conveyor storage racks
337215E121
Nonwood cantilever storage racks
337215E131
Nonwood portable stacking racks and frames
337215E141
Nonwood stacker racks
337215E151
Other racks, including conventional pallet racks and accessories, nonwood
337215E161
Nonwood storage racks and accessories for trucks and vans
337215E171
Other nonwood storage racks and accessories, including conventional pallet racks and accessories
337215G
NONWOOD BANK, OFFICE, STORE, AND RELATED FIXTURES
337215G1
Nonwood custom retail store fixtures
337215G111
Nonwood custom retail store fixtures
337215G2
Nonwood manufacturers’ standard retail store fixtures
337215G211
Nonwood manufacturers’ standard retail store fixtures
337215G3
Other nonwood bank, office, store, and related fixtures
337215G311
Other nonwood bank, office, store, and related table and display fixtures
337215G321
Nonwood bank, office, store, and related cabinets, including floor and wall cabinets
337215G333
Other nonwood bank, office, store, and related fixtures, including cashier stands
337215H
Fixtures for stores, banks, and offices, and miscellaneous fixtures, nonwood
337215H1
Custom store fixtures, retail, except retail food stores, nonwood
337215H111
Custom store fixtures for retail stores, nonwood
337215H2
Manufacturers’ standard store fixtures, retail, nonwood
337215H211
Manufacturers’ standard store fixtures, retail, nonwood
337215H3
Other show and display cases, cabinets, and other fixtures, nec, nonwood
337215H311
Other show and display cases (including wall types) and tables, nec, nonwood
337215H321
Cabinets (floor or wall types), nec, for stores, banks, and offices, nonwood
337215H331
Other fixtures (counters, window backs, telephone booths, miscellaneous display fixtures, cashier stands, etc.), nec, nonwood
337215H341
Metal furniture parts, household
337215H351
Metal furniture parts, office
337215J
WOOD FURNITURE FRAMES
337215J1
Wood furniture frames
337215J111
Wood furniture frames for household seating furniture
337215J131
Other wood furniture frames
337215K
Wood furniture frames for household furniture, incl. frames for upholstered furn
337215K1
Wood furniture frames for household furniture, including frames for upholstered furniture
337215K111
Wood furniture frames for household seating
337215K121
Wood furniture frames for other household furniture
337215L
HARDWOOD AND SOFTWOOD FURNITURE DIMENSION FULLY MACHINED READY FOR ASSEMBLY
337215L1
Hardwood and softwood furniture dimension fully machined ready for assembly
337215L121
Hardwood furniture dimension fully machined ready for assembly, for cabinets
337215L131
Hardwood furniture dimension fully machined ready for assembly, not for cabinets
337215L141
Softwood fully machined furniture dimension
337215L151
Finished plastics furniture parts, including plastics furniture frames
337215L161
Other metal furniture parts for household furniture (including metal household furniture frames, metal box spring frames, and metal sleeper mechanisms), excluding metal furniture hardware
337215L171
Other metal furniture parts (including other metal furniture frames), excluding metal furniture hardware
337215M
Miscellaneous receipts
337215P
Primary products
337215S
Secondary products
337215SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 337215 includes the following:
Boxspring frames manufacturing
Cafeteria fixtures manufacturing
Chair glides manufacturing
Chair seats for furniture manufacturing
Counter units (except refrigerated) manufacturing
Countertops (except kitchen and bathroom), wood or plastics laminated on wood, ma
Display cases and fixtures (except refrigerated) manufacturing
Fixtures, store display, manufacturing
Furniture frames and parts, metal, manufacturing
Furniture frames, wood, manufacturing
Furniture parts, finished metal, manufacturing
Furniture parts, finished plastics, manufacturing
Furniture parts, finished wood, manufacturing
Lockers (except refrigerated) manufacturing
Mail carrier cases and tables, wood, manufacturing
Partitions for floor attachment, prefabricated, manufacturing
Partitions, freestanding, prefabricated, manufacturing
Postal service lock boxes manufacturing
Shelving (except wire) manufacturing
Showcases (except refrigerated) manufacturing
Sleeper mechanisms, convertible bed, manufacturing
Stands (except wire), merchandise display, manufacturing
Store display fixtures manufacturing
Telephone booths manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of showcase, partition, shelving, and locker manufacturing 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 showcase, partition, shelving, and locker manufacturing 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 showcase, partition, shelving, and locker manufacturing 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 showcase, partition, shelving, and locker manufacturing). 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 19
1.2.3.3 Step 3. Filling in Missing Values 20
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 20
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 20
1.2.3.6 Step 6. Aggregation and Benchmarking 21
2 USING THE DATA 22
3 CITY SEGMENTS RANKED BY MARKET SIZE 23
3.1 Top 15 Markets 23
3.2 Markets 16 to 30 24
3.3 Remaining Cities by Market Rank 25
4 CITY SEGMENTS IN ALPHABETICAL ORDER 128
4.1 A: from Aalborg to Az Zawiyah 128
4.2 B: from Bacolod to Bydgoszcz 135
4.3 C: from Caaguazu to Cyangugu 143
4.4 D: from Da Nang to Dzhizak 151
4.5 E: from East London to Esteli 155
4.6 F: from Fagatogo to Funchal 157
4.7 G: from Gabes to Gyumri 160
4.8 H: from Hachinohe to Hyderabad 164
4.9 I: from Iasi to Izmir 168
4.10 J: from Jaboatao to Jyvaskyla 171
4.11 K: from Kabul to Kzyl-Orda 173
4.12 L: from La Ceiba to Lyon 181
4.13 M: from Macae to Mzuzu 186
4.14 N: from Nacala to Nzerekore 196
4.15 O: from Oaklahoma City to Oyem 201
4.16 Ö: from Örebro to Örebro 203
4.17 P: from Pago Pago to Pyuthan 204
4.18 Q: from Qandahar to Quito 210
4.19 R: from Rabat to Rustavi 211
4.20 S: from S. Luis Potosi to Szombathely 214
4.21 T: from Tabligbo to Tyre 226
4.22 U: from Uberaba to Utulei 233
4.23 V: from Vacoas-Phoenix to Vukovar 235
4.24 W: from Wadi Medani to Wuhan 238
4.25 X: from Xalapa to Xian 239
4.26 Y: from Yamagata to Yungkang 240
4.27 Z: from Zadar to Zvishavane 241
5 CITY SEGMENTS RANKED BY COUNTRY 242
5.1 Afghanistan 242
5.2 Albania 242
5.3 Algeria 243
5.4 American Samoa 243
5.5 Andorra 243
5.6 Angola 244
5.7 Antigua and Barbuda 244
5.8 Argentina 245
5.9 Armenia 246
5.10 Aruba 246
5.11 Australia 247
5.12 Austria 247
5.13 Azerbaijan 248
5.14 Bahrain 248
5.15 Bangladesh 248
5.16 Barbados 249
5.17 Belarus 249
5.18 Belgium 249
5.19 Belize 250
5.20 Benin 250
5.21 Bermuda 250
5.22 Bhutan 251
5.23 Bolivia 251
5.24 Bosnia and Herzegovina 251
5.25 Botswana 252
5.26 Brazil 253
5.27 Brunei 258
5.28 Bulgaria 258
5.29 Burkina Faso 259
5.30 Burma 259
5.31 Burundi 259
5.32 Cambodia 260
5.33 Cameroon 260
5.34 Canada 260
5.35 Cape Verde 261
5.36 Central African Republic 261
5.37 Chad 261
5.38 Chile 262
5.39 China 262
5.40 Christmas Island 263
5.41 Colombia 263
5.42 Comoros 263
5.43 Congo (formerly Zaire) 264
5.44 Cook Islands 264
5.45 Costa Rica 264
5.46 Cote dIvoire 265
5.47 Croatia 265
5.48 Cuba 265
5.49 Cyprus 266
5.50 Czech Republic 266
5.51 Denmark 267
5.52 Djibouti 267
5.53 Dominica 267
5.54 Dominican Republic 268
5.55 Ecuador 268
5.56 Egypt 269
5.57 El Salvador 269
5.58 Equatorial Guinea 269
5.59 Estonia 270
5.60 Ethiopia 270
5.61 Fiji 270
5.62 Finland 271
5.63 France 271
5.64 French Guiana 272
5.65 French Polynesia 272
5.66 Gabon 272
5.67 Georgia 273
5.68 Germany 273
5.69 Ghana 273
5.70 Greece 274
5.71 Greenland 274
5.72 Grenada 274
5.73 Guadeloupe 275
5.74 Guam 275
5.75 Guatemala 275
5.76 Guinea 276
5.77 Guinea-Bissau 276
5.78 Guyana 276
5.79 Haiti 277
5.80 Honduras 277
5.81 Hong Kong 277
5.82 Hungary 278
5.83 Iceland 278
5.84 India 279
5.85 Indonesia 280
5.86 Iran 281
5.87 Iraq 281
5.88 Ireland 282
5.89 Israel 282
5.90 Italy 283
5.91 Jamaica 283
5.92 Japan 284
5.93 Jordan 286
5.94 Kazakhstan 287
5.95 Kenya 287
5.96 Kiribati 288
5.97 Kuwait 288
5.98 Kyrgyzstan 288
5.99 Laos 288
5.100 Latvia 289
5.101 Lebanon 289
5.102 Lesotho 289
5.103 Liberia 290
5.104 Libya 290
5.105 Liechtenstein 290
5.106 Lithuania 291
5.107 Luxembourg 291
5.108 Macau 291
5.109 Madagascar 292
5.110 Malawi 292
5.111 Malaysia 293
5.112 Maldives 293
5.113 Mali 294
5.114 Malta 294
5.115 Marshall Islands 294
5.116 Martinique 295
5.117 Mauritania 295
5.118 Mauritius 295
5.119 Mexico 296
5.120 Micronesia Federation 297
5.121 Moldova 297
5.122 Monaco 297
5.123 Mongolia 297
5.124 Morocco 298
5.125 Mozambique 298
5.126 Namibia 298
5.127 Nauru 299
5.128 Nepal 299
5.129 New Caledonia 299
5.130 New Zealand 300
5.131 Nicaragua 300
5.132 Niger 301
5.133 Nigeria 301
5.134 Niue 301
5.135 Norfolk Island 302
5.136 North Korea 302
5.137 Norway 302
5.138 Oman 303
5.139 Pakistan 303
5.140 Palau 303
5.141 Palestine 303
5.142 Panama 304
5.143 Papua New Guinea 304
5.144 Paraguay 304
5.145 Peru 305
5.146 Philippines 305
5.147 Poland 306
5.148 Portugal 306
5.149 Puerto Rico 307
5.150 Qatar 307
5.151 Republic of Congo 307
5.152 Reunion 308
5.153 Romania 308
5.154 Russia 309
5.155 Rwanda 309
5.156 San Marino 309
5.157 Sao Tome E Principe 310
5.158 Saudi Arabia 310
5.159 Senegal 310
5.160 Seychelles 311
5.161 Sierra Leone 311
5.162 Singapore 311
5.163 Slovakia 311
5.164 Slovenia 312
5.165 Solomon Islands 312
5.166 Somalia 312
5.167 South Africa 313
5.168 South Korea 313
5.169 Spain 314
5.170 Sri Lanka 314
5.171 St. Kitts and Nevis 315
5.172 St. Lucia 315
5.173 St. Vincent and the Grenadines 315
5.174 Sudan 315
5.175 Suriname 316
5.176 Swaziland 316
5.177 Sweden 316
5.178 Switzerland 317
5.179 Syrian Arab Republic 317
5.180 Taiwan 318
5.181 Tajikistan 319
5.182 Tanzania 319
5.183 Thailand 319
5.184 The Bahamas 320
5.185 The British Virgin Islands 320
5.186 The Cayman Islands 320
5.187 The Falkland Islands 320
5.188 The Gambia 321
5.189 The Netherlands 321
5.190 The Netherlands Antilles 321
5.191 The Northern Mariana Island 322
5.192 The U.S. Virgin Islands 322
5.193 The United Arab Emirates 322
5.194 The United Kingdom 323
5.195 The United States 324
5.196 Togo 325
5.197 Tokelau 325
5.198 Tonga 325
5.199 Trinidad and Tobago 326
5.200 Tunisia 326
5.201 Turkey 327
5.202 Turkmenistan 327
5.203 Tuvalu 327
5.204 Uganda 328
5.205 Ukraine 328
5.206 Uruguay 329
5.207 Uzbekistan 329
5.208 Vanuatu 329
5.209 Venezuela 330
5.210 Vietnam 330
5.211 Wallis and Futuna 331
5.212 Western Sahara 331
5.213 Western Samoa 331
5.214 Yemen 331
5.215 Zambia 332
5.216 Zimbabwe 332
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 333
6.1 Disclaimers & Safe Harbor 333
6.2 ICON Group International, Inc. User Agreement Provisions 334
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