The 2007-2012 World Outlook for Manufacturing Electric Housewares and Household Fans
ICON Group International, May 2006, Pages: 191
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 electric housewares and household fans 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 manufacturing electric housewares and household fans 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 electric housewares and household fans on a worldwide basis, we used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created. In this case, we 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 electric housewares and household fans across some 230 countries. The smallest have fewer than 10,000 inhabitants. we 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 electric housewares and household fans. So, latent demand in the long-run has a zero intercept. However, we 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, we will now describe the methodology used to create the latent demand estimates for manufacturing electric housewares and household fans. Since this methodology has been applied 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 electric housewares and household fans.
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, we 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, we 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 electric housewares and household fans” 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 electric housewares and household fans 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 “manufacturing electric housewares and household fans” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing electric housewares and household fans, please see below. The NAICS code for manufacturing electric housewares and household fans is 335211. It is for this definition of manufacturing electric housewares and household fans that the aggregate latent demand estimates are derived. “Manufacturing electric housewares and household fans” is specifically defined as follows:
335211
This U.S. industry comprises establishments primarily engaged in manufacturing small electric appliances and electric housewares for heating, cooking, and other purposes, and electric household-type fans (except attic fans).
3352111
electric fans excluding industrial-type fans
3352113
Small electric household appliances, except fans & baseboard units
3352114
small electric household appliances excluding fans and wall and baseboard heating units for permanent installation
3352115
parts and attachments for small household electric appliances
335211M
Miscellaneous receipts
335211P
Primary products
335211S
Secondary products
Furthermore, the definition of NAICS code 335211 includes the following:
Air purification equipment, portable, manufacturing
Bath fans with integral lighting fixture, residential, manufacturing
Bath fans, residential, manufacturing
Bedcoverings, electric, manufacturing
Blankets, electric, manufacturing
Blenders, household-type electric, manufacturing
Blow dryers, household-type electric, manufacturing
Bottle warmers, household-type electric, manufacturing
Bread machines, household-type electric, manufacturing
Can openers, household-type electric, manufacturing
Casseroles, household-type electric, manufacturing
Ceiling fans with integral lighting fixture, residential, manufacturing
Ceiling fans, residential, manufacturing
Chafing dishes, household-type electric, manufacturing
Coffee makers, household-type electric, manufacturing
Cooking appliances (except convection, microwave ovens), household-type electric
Corn poppers, household-type electric, manufacturing
Crock pots, household-type electric, manufacturing
Curling irons, household-type electric, manufacturing
Deep-fat fryers, household-type electric, manufacturing
Dehumidifiers, portable electric, manufacturing
Desk fans, electric, manufacturing
Dry shavers (i.e., electric razors) manufacturing
Egg cookers, household-type electric, manufacturing
Electric blankets manufacturing
Electric comfort heating equipment, portable, manufacturing
Electric space heaters, portable, manufacturing
Electrically heated bed coverings manufacturing
Fans (except attic), household-type electric, manufacturing
Fans, household-type kitchen, manufacturing
Floor standing fans, household-type electric, manufacturing
Food mixers, household-type electric, manufacturing
Fryers, household-type electric, manufacturing
Griddles and grills, household-type portable electric, manufacturing
Hair clippers for human use, electric, manufacturing
Hair curlers, household-type electric, manufacturing
Hair driers, electric (except equipment designed for beauty parlor use), manufact
Hassock fans, electric, manufacturing
Heaters, portable electric space, manufacturing
Heaters, tape, manufacturing
Heating pads, electric, manufacturing
Heating units for electric appliances manufacturing
Hoods, range, household-type, manufacturing
Hotplates, household-type electric, manufacturing
Humidifiers, portable electric, manufacturing
Ice crushers, household-type electric, manufacturing
Immersion heaters, household-type electric, manufacturing
Irons, household-type electric, manufacturing
Juice extractors, household-type electric, manufacturing
Knives, household-type electric carving, manufacturing
Massage machines, electric (except designed for beauty and barber shop use), manu
Ovens, portable household-type (except microwave and convection ovens), manufactu
Percolators, household-type electric, manufacturing
Popcorn poppers, household-type electric, manufacturing
Portable cooking appliances (except convection, microwave ovens), household-type
Portable electric space heaters manufacturing
Portable hair dryers, electric, manufacturing
Portable humidifiers and dehumidifiers manufacturing
Radiators, portable electric, manufacturing
Range hoods with integral lighting fixtures, household-type, manufacturing
Range hoods, household-type, manufacturing
Razors, electric, manufacturing
Roasters (i.e., cooking appliances), household-type electric, manufacturing
Room heaters, portable electric, manufacturing
Sandwich toasters and grills, household-type electric, manufacturing
Sauna heaters, electric, manufacturing
Scissors, electric, manufacturing
Shoe polishers, household-type electric, manufacturing
Steam cookers, household-type, manufacturing
Teakettles, electric, manufacturing
Toaster ovens, household-type electric, manufacturing
Toasters, household-type electric, manufacturing
Toothbrushes, electric, manufacturing
Trouser pressers, household-type electric, manufacturing
Unit heaters, portable electric, manufacturing
Urns, household-type electric, manufacturing
Vaporizers, household-type electric, manufacturing
Ventilating kitchen fans, household-type electric, manufacturing
Ventilation and exhaust fans (except attic fans), household-type, manufacturing
Waffle irons, household-type electric, manufacturing
Warming trays, electric, manufacturing
Water pulsating devices, household-type electric, manufacturing
Whippers, household-type electric, manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing electric housewares and household fans 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 manufacturing electric housewares and household fans 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 manufacturing electric housewares and household fans 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 manufacturing electric housewares and household fans). 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 worlds 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. we 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.
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 16
1.3.3 Step 3. Filling in Missing Values 16
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 17
1.3.5 Step 5. Fixed-Parameter Linear Estimation 17
1.3.6 Step 6. Aggregation and Benchmarking 18
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 18
2 SUMMARY OF FINDINGS 19
2.1 The Worldwide Market Potential 19
3 AFRICA 21
3.1 Executive Summary 21
3.2 Algeria 22
3.3 Angola 23
3.4 Benin 24
3.5 Botswana 24
3.6 Burkina Faso 25
3.7 Burundi 26
3.8 Cameroon 26
3.9 Cape Verde 27
3.10 Central African Republic 28
3.11 Chad 28
3.12 Comoros 29
3.13 Congo (formerly Zaire) 30
3.14 Cote dIvoire 31
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 35
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 51
3.45 Sudan 52
3.46 Swaziland 53
3.47 Tanzania 53
3.48 The Gambia 54
3.49 Togo 55
3.50 Tunisia 55
3.51 Uganda 56
3.52 Western Sahara 57
3.53 Zambia 57
3.54 Zimbabwe 58
4 ASIA & OCEANA 60
4.1 Executive Summary 60
4.2 American Samoa 61
4.3 Australia 62
4.4 Bangladesh 63
4.5 Bhutan 64
4.6 Brunei 64
4.7 Burma 65
4.8 Cambodia 66
4.9 China 66
4.10 Christmas Island 67
4.11 Cook Islands 68
4.12 Fiji 68
4.13 French Polynesia 69
4.14 Guam 70
4.15 Hong Kong 70
4.16 India 71
4.17 Indonesia 72
4.18 Japan 73
4.19 Kiribati 74
4.20 Laos 74
4.21 Macau 75
4.22 Malaysia 76
4.23 Maldives 77
4.24 Marshall Islands 77
4.25 Micronesia Federation 78
4.26 Mongolia 78
4.27 Nauru 79
4.28 Nepal 80
4.29 New Caledonia 80
4.30 New Zealand 81
4.31 Niue 82
4.32 Norfolk Island 83
4.33 North Korea 83
4.34 Northern Mariana Island 84
4.35 Palau 85
4.36 Papua New Guinea 85
4.37 Philippines 86
4.38 Seychelles 87
4.39 Singapore 88
4.40 Solomon Islands 88
4.41 South Korea 89
4.42 Sri Lanka 90
4.43 Taiwan 90
4.44 Thailand 91
4.45 Tokelau 92
4.46 Tonga 93
4.47 Tuvalu 93
4.48 Vanuatu 94
4.49 Vietnam 94
4.50 Wallis and Futuna 95
4.51 Western Samoa 96
5 EUROPE 97
5.1 Executive Summary 97
5.2 Albania 98
5.3 Andorra 99
5.4 Austria 99
5.5 Belarus 100
5.6 Belgium 101
5.7 Bosnia and Herzegovina 102
5.8 Bulgaria 103
5.9 Croatia 104
5.10 Cyprus 104
5.11 Czech Republic 105
5.12 Denmark 106
5.13 Estonia 107
5.14 Finland 107
5.15 France 108
5.16 Georgia 109
5.17 Germany 110
5.18 Greece 111
5.19 Hungary 111
5.20 Iceland 112
5.21 Ireland 113
5.22 Italy 113
5.23 Kazakhstan 114
5.24 Latvia 115
5.25 Liechtenstein 116
5.26 Lithuania 117
5.27 Luxembourg 117
5.28 Malta 118
5.29 Moldova 119
5.30 Monaco 119
5.31 Netherlands 120
5.32 Norway 121
5.33 Poland 121
5.34 Portugal 122
5.35 Romania 123
5.36 Russia 124
5.37 San Marino 125
5.38 Slovakia 125
5.39 Slovenia 126
5.40 Spain 127
5.41 Sweden 128
5.42 Switzerland 129
5.43 Ukraine 130
5.44 United Kingdom 131
6 LATIN AMERICA 132
6.1 Executive Summary 132
6.2 Argentina 133
6.3 Belize 134
6.4 Bolivia 135
6.5 Brazil 135
6.6 Chile 136
6.7 Colombia 137
6.8 Costa Rica 138
6.9 Ecuador 139
6.10 El Salvador 140
6.11 Falkland Islands 140
6.12 French Guiana 141
6.13 Guatemala 142
6.14 Guyana 142
6.15 Honduras 143
6.16 Mexico 144
6.17 Nicaragua 145
6.18 Panama 145
6.19 Paraguay 146
6.20 Peru 147
6.21 Suriname 148
6.22 Uruguay 148
6.23 Venezuela 149
7 NORTH AMERICA & THE CARIBBEAN 151
7.1 Executive Summary 151
7.2 Antigua and Barbuda 152
7.3 Aruba 153
7.4 Bahamas 154
7.5 Barbados 154
7.6 Bermuda 155
7.7 British Virgin Islands 156
7.8 Canada 156
7.9 Cayman Islands 157
7.10 Cuba 158
7.11 Dominica 159
7.12 Dominican Republic 159
7.13 Greenland 160
7.14 Grenada 161
7.15 Guadeloupe 162
7.16 Haiti 162
7.17 Jamaica 163
7.18 Martinique 164
7.19 Netherlands Antilles 164
7.20 Puerto Rico 165
7.21 St. Kitts and Nevis 166
7.22 St. Lucia 166
7.23 St. Vincent and the Grenadines 167
7.24 Trinidad and Tobago 168
7.25 United States 168
7.26 Virgin Islands, US 169
8 THE MIDDLE EAST 171
8.1 Executive Summary 171
8.2 Afghanistan 172
8.3 Armenia 173
8.4 Azerbaijan 174
8.5 Bahrain 174
8.6 Iran 175
8.7 Iraq 176
8.8 Israel 177
8.9 Jordan 177
8.10 Kuwait 178
8.11 Kyrgyzstan 179
8.12 Lebanon 179
8.13 Oman 180
8.14 Pakistan 181
8.15 Palestine 182
8.16 Qatar 182
8.17 Saudi Arabia 183
8.18 Syrian Arab Republic 184
8.19 Tajikistan 185
8.20 Turkey 185
8.21 Turkmenistan 186
8.22 United Arab Emirates 187
8.23 Uzbekistan 187
8.24 Yemen 188
9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 190
9.1 Disclaimers & Safe Harbor 190
9.2 User Agreement Provisions 191
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