The 2007-2012 World Outlook for Manufacturing Leather Goods Excluding Footwear, Luggage, Handbags, Purses, and Personal Leather Goods
ICON Group International, May 2006, Pages: 201
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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods 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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods 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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods 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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods 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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods. 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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods. 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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods.
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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods” 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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods 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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing leather goods excluding footwear, luggage, handbags, purses, and personal leather goods, please see below. The NAICS code for manufacturing leather goods excluding footwear, luggage, handbags, purses, and personal leather goods is 316999. It is for this definition of manufacturing leather goods excluding footwear, luggage, handbags, purses, and personal leather goods that the aggregate latent demand estimates are derived. “Manufacturing leather goods excluding footwear, luggage, handbags, purses, and personal leather goods” is specifically defined as follows:
316999
This U.S. industry comprises establishments primarily engaged in manufacturing leather goods (except footwear, luggage, handbags, purses, and personal leather goods).
3169991
BOOT AND SHOE CUT STOCK AND FINDINGS
31699911
Boot and shoe cut stock and findings
3169991111
Boot and shoe cut stock and findings outer soles and innersoles of leather
3169991121
Other boot and shoe leather cut stock (heels, counters, box toes, taps, etc.)
3169991131
Wood heel blocks made for sale as such
3169991141
Other (shanks, welting, etc.)
3169992
All other leather goods, n.e.c.
3169994
ALL OTHER MISCELLANEOUS LEATHER GOODS
31699941
Leather saddlery, harness and accouterment, dog collars, leashes, and other household pet accessories made of leather
3169994121
Leather saddlery, harness, and accouterments
3169994141
Dog collars, leashes, and other household pet accessories made of leather
31699942
Other leather goods (leather novelties, belting, desk sets, holsters, etc.)
3169994211
Leather novelties
3169994231
Industrial leather belting and other industrial leather products made wholly or mostly of leather
3169994251
Other leather goods (leather novelties, belting ,desk sets, holsters, etc.)
316999M
Miscellaneous receipts
316999P
Primary products
316999S
Secondary products
316999SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 316999 includes the following:
Aprons for textile machinery, leather, manufacturing
Aprons, leather (e.g., blacksmiths, welders), manufacturing
Belt laces, leather, manufacturing
Belting for machinery, leather, manufacturing
Belts, leather safety, manufacturing
Blacksmiths aprons, leather, manufacturing
Boot and shoe cut stock and findings, leather, manufacturing
Bows, shoe, leather, manufacturing
Box toes (i.e., shoe cut stock), leather, manufacturing
Boxes, leather, manufacturing
Caps, heel and toe, leather, manufacturing
Clasps, shoe (leather), manufacturing
Collars and collar pads (i.e., harness) manufacturing
Collars, dog, manufacturing
Corners, luggage, leather, manufacturing
Counters (i.e., shoe cut stock), leather, manufacturing
Crops, riding, manufacturing
Cut stock for boots and shoes manufacturing
Desk sets, leather, manufacturing
Dog furnishings (e.g., collars, harnesses, leashes, muzzles), manufacturing
Feed bags for horses manufacturing
Findings, boot and shoe, manufacturing
Handles (e.g., luggage, whip), leather, manufacturing
Harnesses and harness parts, leather, manufacturing
Harnesses, dog, manufacturing
Heel caps, leather or metal, manufacturing
Heel lifts, leather, manufacturing
Heels, boot and shoe, leather, manufacturing
Holsters, leather, manufacturing
Horse boots and muzzles manufacturing
Inner soles, leather, manufacturing
Jackets, welders, leather, manufacturing
Laces (e.g., shoe), leather, manufacturing
Lashes (i.e., whips) manufacturing
Leashes, dog, manufacturing
Leather belting manufacturing
Leather cut stock for shoe and boot manufacturing
Leather welting manufacturing
Leggings, welders, leather, manufacturing
Lifts, heel, leather, manufacturing
Linings, boot and shoe, leather, manufacturing
Mill strapping for textile mills, leather, manufacturing
Novelties, leather (e.g., cigarette lighter covers, key fobs), manufacturing
Pegs, leather shoe, manufacturing
Quarters (i.e., shoe cut stock), leather, manufacturing
Rands (i.e., shoe cut stock), leather, manufacturing
Razor strops manufacturing
Riding crops manufacturing
Saddles and parts, leather, manufacturing
Safety belts, leather, manufacturing
Seatbelts, leather, manufacturing
Shanks, shoe, leather, manufacturing
Shoe soles, leather, manufacturing
Sleeves, welders, leather, manufacturing
Soles, boot and shoe, leather, manufacturing
Spats, leather, manufacturing
Stays, shoe, leather, manufacturing
Straps (except watch), leather, manufacturing
Taps, shoe, leather, manufacturing
Textile leathers (e.g., apron picker leather, mill strapping) manufacturing
Tips, shoe, leather, manufacturing
Toe caps, leather, manufacturing
Tongues, boot and shoe, leather, manufacturing
Top lifts, boot and shoe, leather, manufacturing
Transmission belting, leather, manufacturing
Trimmings, shoe, leather, manufacturing
Uppers (i.e., shoe cut stock), leather, manufacturing
Vamps, leather, manufacturing
Welders aprons, leather, manufacturing
Welders jackets, leggings, and sleeves, leather, manufacturing
Whips, horse, manufacturing
Whipstocks manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing leather goods excluding footwear, luggage, handbags, purses, and personal leather goods 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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods 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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods 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 leather goods excluding footwear, luggage, handbags, purses, and personal leather goods). 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 17
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 & THE MIDDLE EAST 63
4.1 Executive Summary 63
4.2 Afghanistan 64
4.3 Armenia 65
4.4 Azerbaijan 66
4.5 Bahrain 67
4.6 Bangladesh 68
4.7 Bhutan 69
4.8 Brunei 69
4.9 Burma 70
4.10 Cambodia 71
4.11 China 71
4.12 Hong Kong 72
4.13 India 73
4.14 Indonesia 74
4.15 Iran 75
4.16 Iraq 76
4.17 Israel 77
4.18 Japan 78
4.19 Jordan 79
4.20 Kuwait 79
4.21 Kyrgyzstan 80
4.22 Laos 81
4.23 Lebanon 81
4.24 Macau 82
4.25 Malaysia 83
4.26 Maldives 84
4.27 Mongolia 84
4.28 Nepal 85
4.29 North Korea 86
4.30 Oman 87
4.31 Pakistan 87
4.32 Palestine 88
4.33 Papua New Guinea 89
4.34 Philippines 89
4.35 Qatar 90
4.36 Saudi Arabia 91
4.37 Seychelles 92
4.38 Singapore 92
4.39 South Korea 93
4.40 Sri Lanka 94
4.41 Syrian Arab Republic 95
4.42 Taiwan 96
4.43 Tajikistan 97
4.44 Thailand 97
4.45 Turkey 98
4.46 Turkmenistan 99
4.47 United Arab Emirates 100
4.48 Uzbekistan 101
4.49 Vietnam 102
4.50 Yemen 102
5 EUROPE 104
5.1 Executive Summary 104
5.2 Albania 105
5.3 Andorra 106
5.4 Austria 107
5.5 Belarus 108
5.6 Belgium 109
5.7 Bosnia and Herzegovina 110
5.8 Bulgaria 110
5.9 Croatia 111
5.10 Cyprus 112
5.11 Czech Republic 113
5.12 Denmark 114
5.13 Estonia 115
5.14 Finland 115
5.15 France 116
5.16 Georgia 117
5.17 Germany 118
5.18 Greece 119
5.19 Hungary 120
5.20 Iceland 121
5.21 Ireland 122
5.22 Italy 122
5.23 Kazakhstan 123
5.24 Latvia 124
5.25 Liechtenstein 125
5.26 Lithuania 126
5.27 Luxembourg 126
5.28 Malta 127
5.29 Moldova 128
5.30 Monaco 128
5.31 Netherlands 129
5.32 Norway 130
5.33 Poland 131
5.34 Portugal 132
5.35 Romania 133
5.36 Russia 134
5.37 San Marino 135
5.38 Slovakia 135
5.39 Slovenia 136
5.40 Spain 137
5.41 Sweden 138
5.42 Switzerland 139
5.43 Ukraine 140
5.44 United Kingdom 141
6 LATIN AMERICA 142
6.1 Executive Summary 142
6.2 Argentina 143
6.3 Belize 144
6.4 Bolivia 145
6.5 Brazil 146
6.6 Chile 147
6.7 Colombia 148
6.8 Costa Rica 149
6.9 Ecuador 149
6.10 El Salvador 150
6.11 Falkland Islands 151
6.12 French Guiana 151
6.13 Guatemala 152
6.14 Guyana 153
6.15 Honduras 153
6.16 Mexico 154
6.17 Nicaragua 155
6.18 Panama 156
6.19 Paraguay 157
6.20 Peru 158
6.21 Suriname 159
6.22 Uruguay 159
6.23 Venezuela 160
7 NORTH AMERICA & THE CARIBBEAN 162
7.1 Executive Summary 162
7.2 Antigua and Barbuda 163
7.3 Aruba 164
7.4 Bahamas 165
7.5 Barbados 165
7.6 Bermuda 166
7.7 British Virgin Islands 167
7.8 Canada 167
7.9 Cayman Islands 168
7.10 Cuba 169
7.11 Dominica 170
7.12 Dominican Republic 170
7.13 Greenland 171
7.14 Grenada 172
7.15 Guadeloupe 173
7.16 Haiti 174
7.17 Jamaica 174
7.18 Martinique 175
7.19 Netherlands Antilles 176
7.20 Puerto Rico 176
7.21 St. Kitts and Nevis 177
7.22 St. Lucia 178
7.23 St. Vincent and the Grenadines 178
7.24 Trinidad and Tobago 179
7.25 United States 180
7.26 Virgin Islands, US 181
8 OCEANA 182
8.1 Executive Summary 182
8.2 American Samoa 183
8.3 Australia 184
8.4 Christmas Island 185
8.5 Cook Islands 185
8.6 Fiji 186
8.7 French Polynesia 187
8.8 Guam 187
8.9 Kiribati 188
8.10 Marshall Islands 189
8.11 Micronesia Federation 189
8.12 Nauru 190
8.13 New Caledonia 191
8.14 New Zealand 191
8.15 Niue 192
8.16 Norfolk Island 193
8.17 Northern Mariana Island 193
8.18 Palau 194
8.19 Solomon Islands 195
8.20 Tokelau 195
8.21 Tonga 196
8.22 Tuvalu 197
8.23 Vanuatu 197
8.24 Wallis and Futuna 198
8.25 Western Samoa 199
9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 200
9.1 Disclaimers & Safe Harbor 200
9.2 User Agreement Provisions 201
Customers who bought this item also bought
All rights reserved. © Copyright 2013 Research and Markets WWW6
Terms and Conditions Privacy Policy Publishers Employment Opportunities Site Map Link to us Webmaster Affiliate Network
