The 2009-2014 World Outlook for Coating, Engraving, and Allied Services Applied to Metal Products for Manufacturers Excluding Jewelry and Precious Metals
ICON Group International, September 2008, Pages: 204
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 coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals 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. 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 coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals 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 coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals 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 is 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 coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals 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 coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals. 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 coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals. 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 coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals.
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 “coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals” 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 coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals 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 “coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals is 332812. It is for this definition of coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals that the aggregate latent demand estimates are derived. “Coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals” is specifically defined as follows:
332812
This U.S. industry comprises establishments primarily engaged in one or more of the following: (1) enameling, lacquering, and varnishing metals and metal products; (2) hot dip galvanizing metals and metal products; (3) engraving, chasing, or etching metals and metal products (except jewelry; personal goods carried on or about the person, such as compacts and cigarette cases; precious metal products (except precious plated flatware and other plated ware); and printing plates); (4) powder coating metals and metal products; and (5) providing other metal surfacing services for the trade.
3328120
METAL COATING, ENGRAVING, AND ALLIED SERVICES
33281201
All other metal coating, including curtain coating and wash coating (including organic coatings, enamels, lacquers, alkyds, plastics, etc.)
3328120101
Electronic metal engraving, excluding metal nameplates
3328120106
Photo chemical metal etching, including machining (excluding metal nameplates)
3328120111
Etching and engraving metal nameplates
3328120113
Engraving and etching on nonprecious (except pewter) metal hollowware, flatware, and cutlery
3328120116
Other engraving and etching, except jewelry and silverware
3328120141
All other metal coating, including curtain coating and wash coating (including organic coatings, enamels, lacquers, alkyds, plastics, etc.)
3328120146
Inorganic metal coatings, including porcelain
33281202
Metal galvanizing and other hot dip metal coating
3328120221
Metal galvanizing and other hot dip metal coating
33281203
Metal coil coating (including organic coatings, enamels, lacquers, alkyds, plastics, etc.)
3328120326
Metal coil coating (including organic coatings, enamels, lacquers, alkyds, plastics, etc.)
33281204
Metal liquid spray coating, including electrostatic coating (including organic coatings, enamels, lacquers, alkyds, plastics, etc.)
3328120431
Metal liquid spray coating, including electrostatic coating (including organic coatings, enamels, lacquers, alkyds, plastics, etc.)
33281205
Metal powder coating, including electrostatic and fluidized bed (including organic coatings, enamels, lacquers, alkyds, plastics, etc.)
3328120536
Metal powder coating, including electrostatic and fluidized bed (including organic coatings, enamels, lacquers, alkyds, plastics, etc.)
33281206
Flocking metals and metal products for the trade
3328120631
Flocking metals and metal products for the trade
3328121
Etching, engraving, coating and allied services
33281211
Etching and engraving, incl. etching and engraving nameplates
33281212
Metal coating
332812121
Galvanizing and other hot dip coatings
332812122
Organic coatings, enamels and lacquers, incl. alkyds, plastics, etc.
33281212212
Liquid spray coating, incl electrostatic coating
33281212213
Powder coating, incl. electrostatic and fluidized bed
33281212214
All other organic coatings, incl curtain coating and wash coating
332812123
Inorganic coatings, incl. porcelain coatings
332812M
Miscellaneous receipts
332812P
Primary products
332812S
Secondary products
332812SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 332812 includes the following:
Aluminum coating of metal products for the trade
Bonderizing metal and metal products for the trade
Chasing metals and metal products for the trade
Coating metals and metal products for the trade
Coating of metal and metal products with plastics for the trade
Enameling metals and metal products for the trade
Engraving metals and metal products (except printing plates) for the trade
Etching metals and metal products (except printing plates) for the trade
Flocking metals and metal products for the trade
Galvanizing metals and metal products for the trade
Glazing metals and metal products for the trade
Hot dip galvanizing metals and metal products for the trade
Japanning metals and metal products for the trade
Lacquering metals and metal products for the trade
Painting metals and metal products for the trade
Parkerizing metals and metal products for the trade
Powder coating metals and metal products for the trade
Rustproofing metals and metal products for the trade
Sherardizing of metals and metal products for the trade
Varnishing metals and metal products for the trade.
Step 2. Filtering and Smoothing
Based on the aggregate view of coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals 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 coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals 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 coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals 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 coating, engraving, and allied services applied to metal products for manufacturers excluding jewelry and precious metals). 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. 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.
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 16
1.3.5 Step 5. Fixed-Parameter Linear Estimation 17
1.3.6 Step 6. Aggregation and Benchmarking 17
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 dIvoire 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 Norway 110
5.32 Poland 111
5.33 Portugal 112
5.34 Romania 113
5.35 Russia 114
5.36 San Marino 115
5.37 Slovakia 115
5.38 Slovenia 116
5.39 Spain 117
5.40 Sweden 118
5.41 Switzerland 119
5.42 The Netherlands 120
5.43 The United Kingdom 121
5.44 Ukraine 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 French Guiana 132
6.12 Guatemala 132
6.13 Guyana 133
6.14 Honduras 134
6.15 Mexico 135
6.16 Nicaragua 136
6.17 Panama 137
6.18 Paraguay 138
6.19 Peru 139
6.20 Suriname 140
6.21 The Falkland Islands 140
6.22 Uruguay 141
6.23 Venezuela 142
7 NORTH AMERICA & THE CARIBBEAN 143
7.1 Executive Summary 143
7.2 Antigua and Barbuda 144
7.3 Aruba 145
7.4 Barbados 146
7.5 Bermuda 146
7.6 Canada 147
7.7 Cuba 148
7.8 Dominica 149
7.9 Dominican Republic 149
7.10 Greenland 150
7.11 Grenada 151
7.12 Guadeloupe 152
7.13 Haiti 153
7.14 Jamaica 153
7.15 Martinique 154
7.16 Puerto Rico 155
7.17 St. Kitts and Nevis 156
7.18 St. Lucia 156
7.19 St. Vincent and the Grenadines 157
7.20 The Bahamas 158
7.21 The British Virgin Islands 158
7.22 The Cayman Islands 159
7.23 The Netherlands Antilles 160
7.24 The U.S. Virgin Islands 160
7.25 The United States 161
7.26 Trinidad and Tobago 162
8 OCEANA 164
8.1 Executive Summary 164
8.2 American Samoa 165
8.3 Australia 166
8.4 Christmas Island 167
8.5 Cook Islands 167
8.6 Fiji 168
8.7 French Polynesia 169
8.8 Guam 169
8.9 Kiribati 170
8.10 Marshall Islands 171
8.11 Micronesia Federation 171
8.12 Nauru 172
8.13 New Caledonia 173
8.14 New Zealand 173
8.15 Niue 174
8.16 Norfolk Island 175
8.17 Palau 175
8.18 Solomon Islands 176
8.19 The Northern Mariana Island 177
8.20 Tokelau 177
8.21 Tonga 178
8.22 Tuvalu 179
8.23 Vanuatu 179
8.24 Wallis and Futuna 180
8.25 Western Samoa 181
9 THE MIDDLE EAST 182
9.1 Executive Summary 182
9.2 Afghanistan 183
9.3 Armenia 184
9.4 Azerbaijan 185
9.5 Bahrain 186
9.6 Iran 187
9.7 Iraq 188
9.8 Israel 189
9.9 Jordan 190
9.10 Kuwait 190
9.11 Kyrgyzstan 191
9.12 Lebanon 192
9.13 Oman 192
9.14 Pakistan 193
9.15 Palestine 194
9.16 Qatar 194
9.17 Saudi Arabia 195
9.18 Syrian Arab Republic 196
9.19 Tajikistan 197
9.20 The United Arab Emirates 197
9.21 Turkey 198
9.22 Turkmenistan 199
9.23 Uzbekistan 200
9.24 Yemen 201
10 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 203
10.1 Disclaimers & Safe Harbor 203
10.2 ICON Group International, Inc. User Agreement Provisions 204
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