The 2006-2011 World Outlook for Integrated Microcircuits, Semiconductor Networks,

The 2006-2011 World Outlook for Integrated Microcircuits, Semiconductor Networks, Microprocessors, and MOS Memories

Product Type: Market Research Report
Published by: Icon Group International, Inc.
Published: April 2005
Product Code: R307-21245

Description

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 integrated microcircuits, semiconductor networks, microprocessors, and MOS memories 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 country market.

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 in the introduction, 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 integrated microcircuits, semiconductor networks, microprocessors, and MOS memories 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 integrated microcircuits, semiconductor networks, microprocessors, and MOS memories on a worldwide 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 integrated microcircuits, semiconductor networks, microprocessors, and MOS memories 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 integrated microcircuits, semiconductor networks, microprocessors, and MOS memories. 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 integrated microcircuits, semiconductor networks, microprocessors, and MOS memories. 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 integrated microcircuits, semiconductor networks, microprocessors, and MOS memories.

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 are more likely to be at or near efficiency than others. These countries are given greater weight than others in the estimation of latent demand compared to other countries 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 “integrated microcircuits, semiconductor networks, microprocessors, and MOS memories” 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 integrated microcircuits, semiconductor networks, microprocessors, and MOS memories 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 countries 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 “integrated microcircuits, semiconductor networks, microprocessors, and MOS memories” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). The NAICS code for integrated microcircuits, semiconductor networks, microprocessors, and MOS memories is 33441311. It is for this definition of integrated microcircuits, semiconductor networks, microprocessors, and MOS memories that the aggregate latent demand estimates are derived. “Integrated microcircuits, semiconductor networks, microprocessors, and MOS memories” is specifically defined as follows:

33441311
integrated microcircuits, semiconductor networks, microprocessors, and MOS memories

3344131100
integrated microcircuits, semiconductor networks, microprocessors, and MOS memories

3344131103
Metal oxide semiconductor (MOS) field effect transistors, nondigital silicon monolithic integrated circuits, other, analog

3344131106
Metal oxide semiconductor (MOS) field effect transistors, nondigital silicon monolithic integrated circuits, other, including mixed signal (analog/digital) , logic

3344131109
Metal oxide semiconductor (MOS) field effect transistors, nondigital silicon monolithic integrated circuits, other, including mixed signal (analog/digital) , other

3344131112
Other integrated microcircuit packages

3344131115
Monolithic integrated circuits, digital, silicon, bipolar transistors, static read_ write random access memory (SRAM)

3344131121
Monolithic integrated circuits, digital, silicon, bipolar transistors, other memory

3344131124
Monolithic integrated circuits, digital, silicon, other bipolar transistors, transistor_transistor logic (TTL)

3344131132
Monolithic integrated circuits, digital, silicon, other bipolar transistors, other logic (including ECL)

3344131133
Metal oxide semiconductor (MOS) field effect transistors, other memory (microcontrollers, ASICS, PLAS, etc.)

3344131136
Monolithic integrated circuits, digital, nonsilicon, memory

3344131139
Metal oxide semiconductor (MOS) field effect transistors, other digital, silicon, complementary BIMOS (BICMOS) memory

3344131142
Metal oxide semiconductor (MOS) field effect transistors, other digital, silicon, other complementary BiMOS (BiCMOS) memory, including logic

3344131145
Metal oxide semiconductor (MOS) field effect transistors, other digital, silicon, other ICS

3344131148
Monolithic integrated circuits, digital, nonsilicon, logic and other

3344131151
Hybrid integrated circuits, other

3344131154
Metal oxide semiconductor (MOS) field effect transistors, microprocessors having an internal data bus of 8 bits or less

3344131157
Metal oxide semiconductor (MOS) field effect transistors, microprocessors having an internal data bus of 16 bits

3344131160
Metal oxide semiconductor (MOS) field effect transistors, microprocessors having an internal data bus of 32 bits or more

3344131162
Metal oxide semiconductor (MOS) field effect transistors, nondigital silicon monolithic integrated circuits, radio frequency

3344131168
Metal oxide semiconductor (MOS) field effect transistors, volatile memory dynamic read_write random access (DRAMS), not over 3MB

3344131170
Metal oxide semiconductor (MOS) field effect transistors, volatile memory dynamic read_write random access (DRAMS), over 3MB but not over 15 MB

3344131172
Metal oxide semiconductor (MOS) field effect transistors, volatile memory dynamic read_write random access (DRAMS), over 15 MB

3344131179
Metal oxide semiconductor (MOS) fuekd effect transistors, nonvolatile erasable (except electrically) programmable read_only memory (EPROMs)

3344131180
Metal oxide semiconductor (MOS) fuekd effect transistors, other nonvolatile memory

3344131182
Metal oxide semiconductor (MOS) fuekd effect transistors, volatile memory static read_write random access (SRAMS), not over 40K

3344131184
Metal oxide semiconductor (MOS) fuekd effect transistors, volatile memory static read_write random access (SRAMS), over 40K but not over 80K

3344131186
Metal oxide semiconductor (MOS) fuekd effect transistors, volatile memory static read_write random access (SRAMS), over 80K

3344131192
Metal oxide semiconductor (MOS) fuekd effect transistors, nonvolatile electrically erasable programmable read_only memory (EEPROMs), not over 80K

3344131194
Metal oxide semiconductor (MOS) fuekd effect transistors, nonvolatile electrically erasable programmable read_only memory (EEPROMs), over 80K

Step 2. Filtering and Smoothing

Based on the aggregate view of integrated microcircuits, semiconductor networks, microprocessors, and MOS memories as defined above, data were then collected for as many similar countries as possible for that same definition, at the same level of the value chain. This generates a convenience sample of countries 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 on a sporadic basis. In other cases, data from a country 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), countries 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 in additional countries 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 countries 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 countries along the aggregate consumption function, but also over time (i.e., not all countries are perceived to have the same income growth prospects over time and this effect can vary from country to country as well). Another way of looking at this is to say that latent demand for integrated microcircuits, semiconductor networks, microprocessors, and MOS memories is more likely to be similar across countries that have similar characteristics in terms of economic development (i.e., African countries will have similar latent demand structures controlling for the income variation across the pool of African countries).

This approach is useful across countries for which some notion of non-linearity exists in the aggregate cross-country consumption function. For some categories, however, the reader must realize that the numbers will reflect a country’s contribution to global latent demand and may never be realized in the form of local sales. For certain country-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 “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 countries 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 200 countries, there will always be those countries, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible. For these countries, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a country’s stock of income), but a function of current income (a country’s flow of income). In the long run, if a country has no current income, the latent demand for integrated microcircuits, semiconductor networks, microprocessors, and MOS memories is assumed to approach zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., countries which earn low levels of income will not use their savings, in the long run, to demand integrated microcircuits, semiconductor networks, microprocessors, and MOS memories). In a graphical sense, for low income countries, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income countries are assumed to have a latent demand proportional to their income, based on the country 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 countries 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.

Step 7. Latent Demand Density: Allocating Across Cities

With the advent of a “borderless world”, cities become a more important criteria in prioritizing markets, as opposed to regions, continents, or countries. This report also covers the world’s top 2000 cities. The purpose is to understand the density of demand within a country and the extent to which a city might be used as a point of distribution within its region. From an economic perspective, however, a city does not represent a population within rigid geographical boundaries. To an economist or strategic planner, a city represents an area of dominant influence over markets in adjacent areas. This influence varies from one industry to another, but also from one period of time to another.

Similar to country-level data, the reader needs to realize that latent demand allocated to a city may or may not represent real sales. For many items, latent demand is clearly observable in sales, as in the case for food or housing items. Consider, again, the category “satellite launch vehicles.” Clearly, there are no launch pads in most cities of the world. However, the core benefit of the vehicles (e.g. telecommunications, etc.) is "consumed" by residents or industries within the world's cities. Without certain cities, in other words, the world market for satellite launch vehicles would be lower for the world in general. One needs to allocate, therefore, a portion of the worldwide economic demand for launch vehicles to regions, countries and cities. This report takes the broader definition and considers, therefore, a city as a part of the global market. I allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its home country, within its region and across the world total. Not all cities are estimated within each country as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same country, a city’s population is not generally used to allocate latent demand. Rather, the level of economic activity of the city vis-à-vis others.

Table of Contents

1 INTRODUCTION 10

1.1 Overview 10

1.2 What is Latent Demand and the P.I.E.? 10

1.3 The Methodology 11

1.3.1 Step 1. Product Definition and Data Collection 12

1.3.2 Step 2. Filtering and Smoothing 15

1.3.3 Step 3. Filling in Missing Values 15

1.3.4 Step 4. Varying Parameter, Non-linear Estimation 16

1.3.5 Step 5. Fixed-Parameter Linear Estimation 16

1.3.6 Step 6. Aggregation and Benchmarking 16

1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 17

2 SUMMARY OF FINDINGS 18

2.1 The Worldwide Market Potential 18

3 AFRICA 20

3.1 Executive Summary 20

3.2 Algeria 21

3.3 Angola 22

3.4 Benin 23

3.5 Botswana 24

3.6 Burkina Faso 25

3.7 Burundi 25

3.8 Cameroon 26

3.9 Cape Verde 27

3.10 Central African Republic 27

3.11 Chad 28

3.12 Comoros 29

3.13 Congo (formerly Zaire) 29

3.14 Cote d'Ivoire 30

3.15 Djibouti 31

3.16 Egypt 32

3.17 Equatorial Guinea 33

3.18 Ethiopia 33

3.19 Gabon 34

3.20 Ghana 35

3.21 Guinea 36

3.22 Guinea-Bissau 36

3.23 Kenya 37

3.24 Lesotho 38

3.25 Liberia 38

3.26 Libya 39

3.27 Madagascar 40

3.28 Malawi 40

3.29 Mali 41

3.30 Mauritania 42

3.31 Mauritius 42

3.32 Morocco 43

3.33 Mozambique 44

3.34 Namibia 44

3.35 Niger 45

3.36 Nigeria 46

3.37 Republic of Congo 47

3.38 Reunion 47

3.39 Rwanda 48

3.40 Sao Tome E Principe 49

3.41 Senegal 49

3.42 Sierra Leone 50

3.43 Somalia 51

3.44 South Africa 52

3.45 Sudan 53

3.46 Swaziland 54

3.47 Tanzania 54

3.48 The Gambia 55

3.49 Togo 56

3.50 Tunisia 57

3.51 Uganda 58

3.52 Western Sahara 59

3.53 Zambia 59

3.54 Zimbabwe 60

4 ASIA 62

4.1 Executive Summary 62

4.2 Bangladesh 63

4.3 Bhutan 64

4.4 Brunei 65

4.5 Burma 66

4.6 Cambodia 67

4.7 China 67

4.8 Hong Kong 68

4.9 India 69

4.10 Indonesia 70

4.11 Japan 71

4.12 Laos 72

4.13 Macau 72

4.14 Malaysia 73

4.15 Maldives 74

4.16 Mongolia 75

4.17 Nepal 75

4.18 North Korea 76

4.19 Papua New Guinea 77

4.20 Philippines 77

4.21 Seychelles 78

4.22 Singapore 79

4.23 South Korea 80

4.24 Sri Lanka 81

4.25 Taiwan 82

4.26 Thailand 83

4.27 Vietnam 84

5 EUROPE 85

5.1 Executive Summary 85

5.2 Albania 86

5.3 Andorra 87

5.4 Austria 88

5.5 Belarus 89

5.6 Belgium 90

5.7 Bosnia and Herzegovina 91

5.8 Bulgaria 91

5.9 Croatia 92

5.10 Cyprus 93

5.11 Czech Republic 94

5.12 Denmark 95

5.13 Estonia 96

5.14 Finland 96

5.15 France 97

5.16 Georgia 98

5.17 Germany 99

5.18 Greece 100

5.19 Hungary 101

5.20 Iceland 102

5.21 Ireland 103

5.22 Italy 103

5.23 Kazakhstan 104

5.24 Latvia 105

5.25 Liechtenstein 106

5.26 Lithuania 107

5.27 Luxembourg 107

5.28 Malta 108

5.29 Moldova 109

5.30 Monaco 109

5.31 Netherlands 110

5.32 Norway 111

5.33 Poland 112

5.34 Portugal 113

5.35 Romania 114

5.36 Russia 115

5.37 San Marino 116

5.38 Slovakia 116

5.39 Slovenia 117

5.40 Spain 118

5.41 Sweden 119

5.42 Switzerland 120

5.43 Ukraine 121

5.44 United Kingdom 122

6 LATIN AMERICA 123

6.1 Executive Summary 123

6.2 Argentina 124

6.3 Belize 125

6.4 Bolivia 126

6.5 Brazil 127

6.6 Chile 128

6.7 Colombia 129

6.8 Costa Rica 130

6.9 Ecuador 130

6.10 El Salvador 131

6.11 Falkland Islands 132

6.12 French Guiana 132

6.13 Guatemala 133

6.14 Guyana 134

6.15 Honduras 134

6.16 Mexico 135

6.17 Nicaragua 136

6.18 Panama 137

6.19 Paraguay 138

6.20 Peru 139

6.21 Suriname 140

6.22 Uruguay 140

6.23 Venezuela 141

7 NORTH AMERICA & THE CARIBBEAN 143

7.1 Executive Summary 143

7.2 Antigua and Barbuda 144

7.3 Aruba 145

7.4 Bahamas 146

7.5 Barbados 146

7.6 Bermuda 147

7.7 British Virgin Islands 148

7.8 Canada 148

7.9 Cayman Islands 149

7.10 Cuba 150

7.11 Dominica 151

7.12 Dominican Republic 151

7.13 Greenland 152

7.14 Grenada 153

7.15 Guadeloupe 154

7.16 Haiti 155

7.17 Jamaica 155

7.18 Martinique 156

7.19 Netherlands Antilles 157

7.20 Puerto Rico 157

7.21 St. Kitts and Nevis 158

7.22 St. Lucia 159

7.23 St. Vincent and the Grenadines 159

7.24 Trinidad and Tobago 160

7.25 United States 161

7.26 Virgin Islands, US 162

8 OCEANA 163

8.1 Executive Summary 163

8.2 American Samoa 164

8.3 Australia 165

8.4 Christmas Island 166

8.5 Cook Islands 166

8.6 Fiji 167

8.7 French Polynesia 168

8.8 Guam 168

8.9 Kiribati 169

8.10 Marshall Islands 170

8.11 Micronesia Federation 170

8.12 Nauru 171

8.13 New Caledonia 172

8.14 New Zealand 172

8.15 Niue 173

8.16 Norfolk Island 174

8.17 Northern Mariana Island 174

8.18 Palau 175

8.19 Solomon Islands 176

8.20 Tokelau 176

8.21 Tonga 177

8.22 Tuvalu 178

8.23 Vanuatu 178

8.24 Wallis and Futuna 179

8.25 Western Samoa 180

9 THE MIDDLE EAST 181

9.1 Executive Summary 181

9.2 Afghanistan 182

9.3 Armenia 183

9.4 Azerbaijan 184

9.5 Bahrain 185

9.6 Iran 186

9.7 Iraq 187

9.8 Israel 188

9.9 Jordan 189

9.10 Kuwait 189

9.11 Kyrgyzstan 190

9.12 Lebanon 191

9.13 Oman 191

9.14 Pakistan 192

9.15 Palestine 193

9.16 Qatar 193

9.17 Saudi Arabia 194

9.18 Syrian Arab Republic 195

9.19 Tajikistan 196

9.20 Turkey 196

9.21 Turkmenistan 197

9.22 United Arab Emirates 198

9.23 Uzbekistan 199

9.24 Yemen 200

10 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 201

10.1 Disclaimers & Safe Harbor 201

10.2 ICON Group International, Inc. User Agreement Provisions 202

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