The 2007-2012 World Outlook for Primary Smelting and Refining of Non-Ferrous Metal Excluding Aluminum and Copper
ICON Group International, May 2006, Pages: 200
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 primary smelting and refining of non-ferrous metal excluding aluminum and copper 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 primary smelting and refining of non-ferrous metal excluding aluminum and copper 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 primary smelting and refining of non-ferrous metal excluding aluminum and copper 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 primary smelting and refining of non-ferrous metal excluding aluminum and copper 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 primary smelting and refining of non-ferrous metal excluding aluminum and copper. 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 primary smelting and refining of non-ferrous metal excluding aluminum and copper. 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 primary smelting and refining of non-ferrous metal excluding aluminum and copper.
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 “primary smelting and refining of non-ferrous metal excluding aluminum and copper” 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 primary smelting and refining of non-ferrous metal excluding aluminum and copper 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 “primary smelting and refining of non-ferrous metal excluding aluminum and copper” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of primary smelting and refining of non-ferrous metal excluding aluminum and copper, please see below. The NAICS code for primary smelting and refining of non-ferrous metal excluding aluminum and copper is 331419. It is for this definition of primary smelting and refining of non-ferrous metal excluding aluminum and copper that the aggregate latent demand estimates are derived. “Primary smelting and refining of non-ferrous metal excluding aluminum and copper” is specifically defined as follows:
331419
This U.S. industry comprises establishments primarily engaged in (1) making (i.e., the primary production) nonferrous metals by smelting ore and/or (2) the primary refining of nonferrous metals by electrolytic methods or other processes.
3314191
PRIMARY ZINC RESIDUES AND OTHER ZINC SMELTER PRODUCTS NOT OF COMMERCIAL GRADE, PRODUCED FOR FURTHER REFINING, INCLUDING BASE BULLION, MATTE, SPEISS, ETC.
33141911
Primary zinc residues and other zinc smelter products not of commercial grade, produced for further refining, including base bullion, matte, speiss, etc.
3314191100
Primary zinc residues and other zinc smelter products not of commercial grade, produced for further refining, including base bullion, matte, speiss, etc
3314192
Primary lead
3314193
Primary zinc
33141931
Refined primary unalloyed zinc slab and zinc_base alloy, including unalloyed dust
3314193101
Refined primary unalloyed zinc, including all ASTM specification zinc
3314193111
Refined primary zinc_base alloys
3314195
Precious metals
3314197
Other nonferrous metals, n.e.c.
33141971
Primary gold and gold alloys
3314197101
Primary gold and gold alloys
33141972
Primary silver and silver alloys
3314197206
Primary silver and silver alloys
33141973
Primary platinum and platinum alloys, including platinum_group metals
3314197311
Primary platinum and platinum alloys, including platinum_group metals
3314199
OTHER PRIMARY NONFERROUS METALS, NEC
33141991
Primary lead smelter products not of commercial grade, produced for further refining
3314199101
Primary lead and lead~base alloys
3314199103
Primary magnesium and magnesium~base alloys
3314199106
Primary nickel, nickel~base alloys, and tin
3314199121
Primary unalloyed silicon
3314199126
Other primary unrefined nonferrous metals, including metal bearing furnace residues and other metal products
3314199131
Other primary refined nonferrous metals and their alloys, including cadmium, antimony, cobalt, molybdenum, titanium sponge, etc.
331419A
ALL OTHER MISCELLANEOUS PRIMARY NONFERROUS METALS
331419A1
Primary lead smelter products not of commercial grade, produced for further refining
331419A101
Primary lead and lead_base alloys
331419A103
Primary magnesium and magnesium_base alloys
331419A121
Primary unalloyed silicon
331419A127
Other primary unrefined nonferrous metals, including metal bearing furnace residues, zinc residues, zinc smelter products (base bullion, matte, speiss, etc.) and other metal products
331419A131
Other primary refined nonferrous metals and alloys (including nickel, tin, cadmium, antimony, cobalt, molybdenum, titanium sponge, etc.)
331419M
Miscellaneous receipts
331419P
Primary products
331419S
Secondary products
331419SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 331419 includes the following:
Antimony refining, primary
Beryllium refining, primary
Bismuth refining, primary
Cadmium refining, primary
Chromium refining, primary
Cobalt refining, primary
Germanium refining, primary
Gold bullion or dore bar produced at primary metal refineries
Gold refining, primary
Ingot, primary, nonferrous metals (except aluminum, copper), manufacturing
Iridium refining, primary
Lead smelting and refining, primary
Magnesium refining, primary
Nickel refining, primary
Niobium refining, primary
Nonferrous metal (except aluminum, copper) shapes made in primary nonferrous meta
Nonferrous metals (except aluminum, copper) made in primary nonferrous metal smel
Nonferrous metals (except aluminum, copper) smelting and refining, primary
Platinum refining, primary
Precious metals refining, primary
Primary refining of nonferrous metals (except aluminum, copper)
Primary smelting of nonferrous metals (except aluminum, copper)
Refining nonferrous metals and alloys (except aluminum, copper), primary
Rhenium refining, primary
Selenium refining, primary
Silver bullion or dore bar produced at primary metal refineries
Silver refining, primary
Slab, nonferrous metals (except aluminum, copper), primary
Smelting of nonferrous metals (except aluminum, copper), primary
Tantalum refining, primary
Tellurium refining, primary
Tin base alloys made in primary tin smelting and refining mills
Tin refining, primary
Titanium refining, primary
Uranium refining, primary
Zinc refining, primary
Zirconium refining, primary.
Step 2. Filtering and Smoothing
Based on the aggregate view of primary smelting and refining of non-ferrous metal excluding aluminum and copper 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 primary smelting and refining of non-ferrous metal excluding aluminum and copper 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 primary smelting and refining of non-ferrous metal excluding aluminum and copper 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 primary smelting and refining of non-ferrous metal excluding aluminum and copper). 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 25
3.6 Burkina Faso 26
3.7 Burundi 26
3.8 Cameroon 27
3.9 Cape Verde 28
3.10 Central African Republic 28
3.11 Chad 29
3.12 Comoros 30
3.13 Congo (formerly Zaire) 30
3.14 Cote dIvoire 31
3.15 Djibouti 32
3.16 Egypt 33
3.17 Equatorial Guinea 34
3.18 Ethiopia 34
3.19 Gabon 35
3.20 Ghana 36
3.21 Guinea 37
3.22 Guinea-Bissau 37
3.23 Kenya 38
3.24 Lesotho 39
3.25 Liberia 39
3.26 Libya 40
3.27 Madagascar 41
3.28 Malawi 41
3.29 Mali 42
3.30 Mauritania 43
3.31 Mauritius 43
3.32 Morocco 44
3.33 Mozambique 45
3.34 Namibia 45
3.35 Niger 46
3.36 Nigeria 47
3.37 Republic of Congo 48
3.38 Reunion 48
3.39 Rwanda 49
3.40 Sao Tome E Principe 50
3.41 Senegal 50
3.42 Sierra Leone 51
3.43 Somalia 52
3.44 South Africa 53
3.45 Sudan 54
3.46 Swaziland 55
3.47 Tanzania 55
3.48 The Gambia 56
3.49 Togo 57
3.50 Tunisia 58
3.51 Uganda 59
3.52 Western Sahara 60
3.53 Zambia 60
3.54 Zimbabwe 61
4 ASIA & OCEANA 63
4.1 Executive Summary 63
4.2 American Samoa 64
4.3 Australia 65
4.4 Bangladesh 66
4.5 Bhutan 67
4.6 Brunei 67
4.7 Burma 68
4.8 Cambodia 69
4.9 China 69
4.10 Christmas Island 70
4.11 Cook Islands 71
4.12 Fiji 71
4.13 French Polynesia 72
4.14 Guam 73
4.15 Hong Kong 73
4.16 India 74
4.17 Indonesia 75
4.18 Japan 76
4.19 Kiribati 77
4.20 Laos 77
4.21 Macau 78
4.22 Malaysia 79
4.23 Maldives 80
4.24 Marshall Islands 80
4.25 Micronesia Federation 81
4.26 Mongolia 82
4.27 Nauru 82
4.28 Nepal 83
4.29 New Caledonia 84
4.30 New Zealand 84
4.31 Niue 85
4.32 Norfolk Island 86
4.33 North Korea 86
4.34 Northern Mariana Island 87
4.35 Palau 88
4.36 Papua New Guinea 88
4.37 Philippines 89
4.38 Seychelles 90
4.39 Singapore 91
4.40 Solomon Islands 92
4.41 South Korea 92
4.42 Sri Lanka 93
4.43 Taiwan 94
4.44 Thailand 95
4.45 Tokelau 96
4.46 Tonga 97
4.47 Tuvalu 97
4.48 Vanuatu 98
4.49 Vietnam 99
4.50 Wallis and Futuna 99
4.51 Western Samoa 100
5 EUROPE 101
5.1 Executive Summary 101
5.2 Albania 102
5.3 Andorra 103
5.4 Austria 104
5.5 Belarus 105
5.6 Belgium 106
5.7 Bosnia and Herzegovina 107
5.8 Bulgaria 107
5.9 Croatia 108
5.10 Cyprus 109
5.11 Czech Republic 110
5.12 Denmark 111
5.13 Estonia 112
5.14 Finland 112
5.15 France 113
5.16 Georgia 114
5.17 Germany 115
5.18 Greece 116
5.19 Hungary 117
5.20 Iceland 118
5.21 Ireland 119
5.22 Italy 119
5.23 Kazakhstan 120
5.24 Latvia 121
5.25 Liechtenstein 122
5.26 Lithuania 123
5.27 Luxembourg 123
5.28 Malta 124
5.29 Moldova 125
5.30 Monaco 125
5.31 Netherlands 126
5.32 Norway 127
5.33 Poland 128
5.34 Portugal 129
5.35 Romania 130
5.36 Russia 131
5.37 San Marino 132
5.38 Slovakia 132
5.39 Slovenia 133
5.40 Spain 134
5.41 Sweden 135
5.42 Switzerland 136
5.43 Ukraine 137
5.44 United Kingdom 138
6 LATIN AMERICA 139
6.1 Executive Summary 139
6.2 Argentina 140
6.3 Belize 141
6.4 Bolivia 142
6.5 Brazil 143
6.6 Chile 144
6.7 Colombia 145
6.8 Costa Rica 146
6.9 Ecuador 146
6.10 El Salvador 147
6.11 Falkland Islands 148
6.12 French Guiana 148
6.13 Guatemala 149
6.14 Guyana 150
6.15 Honduras 150
6.16 Mexico 151
6.17 Nicaragua 152
6.18 Panama 153
6.19 Paraguay 154
6.20 Peru 155
6.21 Suriname 156
6.22 Uruguay 156
6.23 Venezuela 157
7 NORTH AMERICA & THE CARIBBEAN 159
7.1 Executive Summary 159
7.2 Antigua and Barbuda 160
7.3 Aruba 161
7.4 Bahamas 162
7.5 Barbados 162
7.6 Bermuda 163
7.7 British Virgin Islands 164
7.8 Canada 164
7.9 Cayman Islands 165
7.10 Cuba 166
7.11 Dominica 167
7.12 Dominican Republic 167
7.13 Greenland 168
7.14 Grenada 169
7.15 Guadeloupe 170
7.16 Haiti 171
7.17 Jamaica 171
7.18 Martinique 172
7.19 Netherlands Antilles 173
7.20 Puerto Rico 173
7.21 St. Kitts and Nevis 174
7.22 St. Lucia 175
7.23 St. Vincent and the Grenadines 175
7.24 Trinidad and Tobago 176
7.25 United States 177
7.26 Virgin Islands, US 178
8 THE MIDDLE EAST 179
8.1 Executive Summary 179
8.2 Afghanistan 180
8.3 Armenia 181
8.4 Azerbaijan 182
8.5 Bahrain 183
8.6 Iran 184
8.7 Iraq 185
8.8 Israel 186
8.9 Jordan 187
8.10 Kuwait 187
8.11 Kyrgyzstan 188
8.12 Lebanon 189
8.13 Oman 189
8.14 Pakistan 190
8.15 Palestine 191
8.16 Qatar 191
8.17 Saudi Arabia 192
8.18 Syrian Arab Republic 193
8.19 Tajikistan 194
8.20 Turkey 194
8.21 Turkmenistan 195
8.22 United Arab Emirates 196
8.23 Uzbekistan 197
8.24 Yemen 198
9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 199
9.1 Disclaimers & Safe Harbor 199
9.2 User Agreement Provisions 200
Customers who bought this item also bought
All rights reserved. © Copyright 2013 Research and Markets WWW5
Terms and Conditions Privacy Policy Publishers Employment Opportunities Site Map Link to us Webmaster Affiliate Network
