The 2009 Report on Manufacturing Cut and Sew Gloves and Mittens: World Market Segmentation by City
ICON Group International, May 2009, Pages: 333
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing cut and sew gloves and mittens 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 cut and sew gloves and mittens. 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 cut and sew gloves and mittens 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 cut and sew gloves and mittens 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 cut and sew gloves and mittens 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 cut and sew gloves and mittens 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 cut and sew gloves and mittens. 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 cut and sew gloves and mittens. 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 cut and sew gloves and mittens.
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 cut and sew gloves and mittens” 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 cut and sew gloves and mittens 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 cut and sew gloves and mittens” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing cut and sew gloves and mittens, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing cut and sew gloves and mittens is 315992. It is for this definition of manufacturing cut and sew gloves and mittens that the aggregate latent demand estimates are derived. “Manufacturing cut and sew gloves and mittens” is specifically defined as follows:
315992
This U.S. industry comprises establishments primarily engaged in manufacturing cut and sew gloves (except rubber, metal, and athletic gloves) and mittens from purchased fabric, fur, leather, or from combinations of fabric, fur, or leather. Jobbers, who perform entrepreneurial functions involved in glove and mitten manufacture, including buying raw materials, designing and preparing samples, arranging for gloves and mittens to be made from their materials, and marketing finished gloves and mittens, are included.
3159921
Gloves and mittens made from woven or purchased knit fabric
31599210
Gloves and mittens made from woven or purchased knit fabrics
3159921000
Gloves and mittens made from woven or purchased knit fabrics
3159921002
Work gloves and mittens made from woven fabrics impregnated with rubber~plastic, greater than 50 percent plastics or rubber
3159921004
Work gloves and mittens made from woven fabrics impregnated with rubber~plastic, cotton
3159921006
Work gloves and mittens made from woven fabrics impregnated with rubber~plastic, manmade
3159921008
Work gloves and mittens made from woven fabrics impregnated with rubber~plastic, other
3159921010
Work gloves and mittens made from woven fabrics not impregnated, cotton
3159921012
Work gloves and mittens made from woven fabrics not impregnated, manmade
3159921014
Work gloves and mittens made from woven fabrics not impregnated, wool
3159921016
Work gloves and mittens made from woven fabrics not impregnated, other
3159921018
Work gloves and mittens made from knit fabrics impregnated with rubber~ plastic, greater than 50 percent plastics or rubber
315992101A
Work gloves and mittens made from knit fabrics impregnated with rubber~ plastic, cotton
315992101C
Work gloves and mittens made from knit fabrics impregnated with rubber~ plastic, manmade
315992101E
Work gloves and mittens made from knit fabrics impregnated with rubbe~ plastic, other
315992101G
Work gloves and mittens made from knit fabrics not impregnated, cotton, terry~looped pile
315992101J
Work gloves and mittens made from knit fabrics not impregnated, cotton, jersey, brushed, napped
315992101L
Work gloves and mittens made from knit fabrics not impregnated, cotton, lisle (no nap or brush)
315992101N
Work gloves and mittens made from knit fabrics not impregnated, cotton, other
315992101P
Work gloves and mittens made from knit fabrics not impregnated, manmade
315992101R
Work gloves and mittens made from knit fabrics not impregnated, wool
315992101T
Work gloves and mittens made from knit fabrics not impregnated, other
3159921052
Dress gloves and mittens made from woven fabrics impregnated with rubber~plastic, greater than 50 percent plastics or rubber
3159921054
Dress gloves and mittens made from woven fabrics impregnated with rubber~plastic, cotton
3159921056
Dress gloves and mittens made from woven fabrics impregnated with rubber~plastic, manmade
3159921058
Dress gloves and mittens made from woven fabrics impregnated with rubber~plastic, other
3159921060
Dress gloves and mittens made from woven fabrics not impregnated, cotton
3159921062
Dress gloves and mittens made from woven fabrics not impregnated, manmade
3159921064
Dress gloves and mittens made from woven fabrics not impregnated, wool
3159921066
Dress gloves and mittens made from woven fabrics not impregnated, other
3159921068
Dress gloves and mittens made from knit fabrics impregnated with rubber~ plastic, greater than 50 percent plastics or rubber
315992106A
Dress gloves and mittens made from knit fabrics impregnated with rubber~ plastic, cotton
315992106C
Dress gloves and mittens made from knit fabrics impregnated with rubber~ plastic, manmade
315992106E
Dress gloves and mittens made from knit fabrics impregnated with rubber~ plastic, other
315992106G
Dress gloves and mittens made from knit fabrics not impregnated, cotton, terry~looped pile
315992106J
Dress gloves and mittens made from knit fabrics not impregnated, cotton, jersey, brushed, napped
315992106L
Dress gloves and mittens made from knit fabrics not impregnated, cotton, lisle (no nap or brush)
315992106N
Dress gloves and mittens made from knit fabrics not impregnated, cotton, other
315992106P
Dress gloves and mittens made from knit fabrics not impregnated, manmade
315992106R
Dress gloves and mittens made from knit fabrics not impregnated, wool
315992106T
Dress gloves and mittens made from knit fabrics not impregnated, other
31599211
Gloves and mittens, made from purchased fabrics
3159921100
Gloves and mittens, made from purchased fabrics
3159921132
Gloves, woven fabric impregnated with rubber/plastic, greater than 50 percent plastic or rubber
3159921134
Gloves, woven cotton fabric impregnated with rubber/plastic, 50 percent or less plastic or rubber
3159921136
Gloves, woven manmade fabric impregnated with rubber/plastic, 50 percent or less plastic or rubber
3159921138
Gloves, woven (except cotton or manmade) fabric impregnated with rubber/plastic, 50 percent or less plastic or rubber
315992113A
Gloves, woven cotton fabric
315992113C
Gloves, woven manmade fabric
315992113E
Gloves, woven wool fabric
315992113G
Gloves, woven (except cotton, manmade, or wool) fabric
3159921142
Gloves, knit fabric impregnated with rubber/plastic, greater than 50 percent plastic or rubber
3159921144
Gloves, knit cotton fabric impregnated with rubber/plastic, 50 percent or less plastic or rubber
3159921146
Gloves, knit manmade fabric impregnated with rubber/plastic, 50 percent or less plastic or rubber
3159921148
Gloves, knit (except cotton or manmade) fabric impregnated with rubber/ plastic, 50 percent or less plastic or rubber
315992114A
Gloves, knit cotton terry/looped pile fabric
315992114C
Gloves, knit cotton jersey (including brushed or napped) fabric
315992114E
Gloves, knit cotton lisle (excluding brushed or napped) fabric
315992114G
Gloves, knit cotton (excluding terry/looped pile, jersey, and lisle) fabric
315992114J
Gloves, knit manmade fabric
315992114L
Gloves, knit wool fabric
315992114N
Gloves, knit (except cotton, manmade, or wool) fabric
3159923
Gloves and mittens, leather-and-fabric combinations
31599230
Gloves and mittens made from leather~and~fabrics combinations
3159923000
Gloves and mittens made from leather~and~fabrics combinations
3159923050
Dress gloves and mittens made from leather~and~fabrics combinations
31599231
Gloves and mittens, leather_and_fabric combinations, made from purchased leather and fabrics
3159923100
Gloves and mittens, leather_and_fabric combinations, made from purchased leather and fabrics
3159925
Gloves and mittens, all leather
31599250
Gloves and mittens, all leather
3159925000
Gloves and mittens, all leather
3159925032
Work gloves and mittens, all grain leather
3159925034
Work gloves and mittens, all split leather
3159925036
Work gloves and mittens, all other leather gloves
3159925052
Dress gloves and mittens, all grain leather
3159925054
Dress gloves and mittens, all split leather
3159925056
Dress gloves and mittens, all other leather gloves
31599251
Gloves and mittens, all leather, made from purchased leather
3159925100
Gloves and mittens, all leather, made from purchased leather
3159925122
Gloves and mittens, all grain leather
3159925124
Gloves and mittens, all split leather
3159925126
Gloves and mittens, all other leather
315992M
Miscellaneous receipts
315992P
Primary products
315992S
Secondary products
315992SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 315992 includes the following:
Dress and semidress gloves cut and sewn from purchased fabric (except apparel con
Glove linings (except fur) manufacturing
Gloves and mittens (except athletic), leather, fabric, fur, or combinations, cut
Gloves and mittens, woven or knit, cut and sewn from purchased fabric (except app
Gloves, leather (except athletic, cut and sewn apparel contractors), manufacturin
Knit gloves cut and sewn from purchased fabric (except apparel contractors)
Leather gloves or mittens (except athletic, cut and sewn apparel contractors) man
Mittens cut and sewn from purchased fabric (except apparel contractors)
Mittens, leather (except apparel contractors), manufacturing
Mittens, woven or knit, cut and sewn from purchased fabric (except apparel contra
Work gloves, leather (except apparel contractors), manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing cut and sew gloves and mittens 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 cut and sew gloves and mittens 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 cut and sew gloves and mittens 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 cut and sew gloves and mittens). 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 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 150
4.5 E: from East London to Esteli 154
4.6 F: from Fagatogo to Funchal 156
4.7 G: from Gabes to Gyumri 159
4.8 H: from Hachinohe to Hyderabad 163
4.9 I: from Iasi to Izmir 167
4.10 J: from Jaboatao to Jyvaskyla 170
4.11 K: from Kabul to Kzyl-Orda 172
4.12 L: from La Ceiba to Lyon 180
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 209
4.19 R: from Rabat to Rustavi 210
4.20 S: from S. Luis Potosi to Szombathely 213
4.21 T: from Tabligbo to Tyre 225
4.22 U: from Uberaba to Utulei 232
4.23 V: from Vacoas-Phoenix to Vukovar 234
4.24 W: from Wadi Medani to Wuhan 237
4.25 X: from Xalapa to Xian 238
4.26 Y: from Yamagata to Yungkang 239
4.27 Z: from Zadar to Zvishavane 240
5 CITY SEGMENTS RANKED BY COUNTRY 241
5.1 Afghanistan 241
5.2 Albania 241
5.3 Algeria 242
5.4 American Samoa 242
5.5 Andorra 242
5.6 Angola 243
5.7 Antigua and Barbuda 243
5.8 Argentina 244
5.9 Armenia 245
5.10 Aruba 245
5.11 Australia 246
5.12 Austria 246
5.13 Azerbaijan 247
5.14 Bahrain 247
5.15 Bangladesh 247
5.16 Barbados 248
5.17 Belarus 248
5.18 Belgium 248
5.19 Belize 249
5.20 Benin 249
5.21 Bermuda 249
5.22 Bhutan 250
5.23 Bolivia 250
5.24 Bosnia and Herzegovina 250
5.25 Botswana 251
5.26 Brazil 252
5.27 Brunei 257
5.28 Bulgaria 257
5.29 Burkina Faso 258
5.30 Burma 258
5.31 Burundi 258
5.32 Cambodia 259
5.33 Cameroon 259
5.34 Canada 259
5.35 Cape Verde 260
5.36 Central African Republic 260
5.37 Chad 260
5.38 Chile 261
5.39 China 261
5.40 Christmas Island 262
5.41 Colombia 262
5.42 Comoros 262
5.43 Congo (formerly Zaire) 263
5.44 Cook Islands 263
5.45 Costa Rica 263
5.46 Cote dIvoire 264
5.47 Croatia 264
5.48 Cuba 264
5.49 Cyprus 265
5.50 Czech Republic 265
5.51 Denmark 265
5.52 Djibouti 266
5.53 Dominica 266
5.54 Dominican Republic 266
5.55 Ecuador 267
5.56 Egypt 267
5.57 El Salvador 267
5.58 Equatorial Guinea 268
5.59 Estonia 268
5.60 Ethiopia 268
5.61 Fiji 269
5.62 Finland 269
5.63 France 270
5.64 French Guiana 270
5.65 French Polynesia 271
5.66 Gabon 271
5.67 Georgia 271
5.68 Germany 272
5.69 Ghana 272
5.70 Greece 273
5.71 Greenland 273
5.72 Grenada 273
5.73 Guadeloupe 274
5.74 Guam 274
5.75 Guatemala 274
5.76 Guinea 275
5.77 Guinea-Bissau 275
5.78 Guyana 275
5.79 Haiti 276
5.80 Honduras 276
5.81 Hong Kong 276
5.82 Hungary 277
5.83 Iceland 277
5.84 India 278
5.85 Indonesia 279
5.86 Iran 280
5.87 Iraq 280
5.88 Ireland 281
5.89 Israel 281
5.90 Italy 282
5.91 Jamaica 282
5.92 Japan 283
5.93 Jordan 285
5.94 Kazakhstan 286
5.95 Kenya 286
5.96 Kiribati 287
5.97 Kuwait 287
5.98 Kyrgyzstan 287
5.99 Laos 287
5.100 Latvia 288
5.101 Lebanon 288
5.102 Lesotho 288
5.103 Liberia 289
5.104 Libya 289
5.105 Liechtenstein 289
5.106 Lithuania 290
5.107 Luxembourg 290
5.108 Macau 290
5.109 Madagascar 291
5.110 Malawi 291
5.111 Malaysia 292
5.112 Maldives 292
5.113 Mali 293
5.114 Malta 293
5.115 Marshall Islands 293
5.116 Martinique 294
5.117 Mauritania 294
5.118 Mauritius 294
5.119 Mexico 295
5.120 Micronesia Federation 296
5.121 Moldova 296
5.122 Monaco 296
5.123 Mongolia 296
5.124 Morocco 297
5.125 Mozambique 297
5.126 Namibia 297
5.127 Nauru 298
5.128 Nepal 298
5.129 New Caledonia 298
5.130 New Zealand 299
5.131 Nicaragua 299
5.132 Niger 300
5.133 Nigeria 300
5.134 Niue 300
5.135 Norfolk Island 301
5.136 North Korea 301
5.137 Norway 301
5.138 Oman 302
5.139 Pakistan 302
5.140 Palau 302
5.141 Palestine 302
5.142 Panama 303
5.143 Papua New Guinea 303
5.144 Paraguay 303
5.145 Peru 304
5.146 Philippines 304
5.147 Poland 305
5.148 Portugal 305
5.149 Puerto Rico 306
5.150 Qatar 306
5.151 Republic of Congo 306
5.152 Reunion 307
5.153 Romania 307
5.154 Russia 308
5.155 Rwanda 308
5.156 San Marino 308
5.157 Sao Tome E Principe 309
5.158 Saudi Arabia 309
5.159 Senegal 309
5.160 Seychelles 310
5.161 Sierra Leone 310
5.162 Singapore 310
5.163 Slovakia 310
5.164 Slovenia 311
5.165 Solomon Islands 311
5.166 Somalia 311
5.167 South Africa 312
5.168 South Korea 312
5.169 Spain 313
5.170 Sri Lanka 313
5.171 St. Kitts and Nevis 314
5.172 St. Lucia 314
5.173 St. Vincent and the Grenadines 314
5.174 Sudan 314
5.175 Suriname 315
5.176 Swaziland 315
5.177 Sweden 315
5.178 Switzerland 316
5.179 Syrian Arab Republic 316
5.180 Taiwan 317
5.181 Tajikistan 318
5.182 Tanzania 318
5.183 Thailand 318
5.184 The Bahamas 319
5.185 The British Virgin Islands 319
5.186 The Cayman Islands 319
5.187 The Falkland Islands 319
5.188 The Gambia 320
5.189 The Netherlands 320
5.190 The Netherlands Antilles 320
5.191 The Northern Mariana Island 321
5.192 The U.S. Virgin Islands 321
5.193 The United Arab Emirates 321
5.194 The United Kingdom 322
5.195 The United States 323
5.196 Togo 324
5.197 Tokelau 324
5.198 Tonga 324
5.199 Trinidad and Tobago 325
5.200 Tunisia 325
5.201 Turkey 326
5.202 Turkmenistan 326
5.203 Tuvalu 326
5.204 Uganda 327
5.205 Ukraine 327
5.206 Uruguay 328
5.207 Uzbekistan 328
5.208 Vanuatu 328
5.209 Venezuela 329
5.210 Vietnam 329
5.211 Wallis and Futuna 330
5.212 Western Sahara 330
5.213 Western Samoa 330
5.214 Yemen 330
5.215 Zambia 331
5.216 Zimbabwe 331
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 332
6.1 Disclaimers & Safe Harbor 332
6.2 ICON Group International, Inc. User Agreement Provisions 333
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