The 2007-2012 World Outlook for Manufacturing Millwork Excluding Wood Windows, Wood Doors, and Cut Stock
ICON Group International, May 2006, Pages: 199
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 millwork excluding wood windows, wood doors, and cut stock 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 millwork excluding wood windows, wood doors, and cut stock 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 millwork excluding wood windows, wood doors, and cut stock 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 millwork excluding wood windows, wood doors, and cut stock 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 millwork excluding wood windows, wood doors, and cut stock. 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 millwork excluding wood windows, wood doors, and cut stock. 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 millwork excluding wood windows, wood doors, and cut stock.
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 millwork excluding wood windows, wood doors, and cut stock” 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 millwork excluding wood windows, wood doors, and cut stock 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 millwork excluding wood windows, wood doors, and cut stock” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing millwork excluding wood windows, wood doors, and cut stock, please see below. The NAICS code for manufacturing millwork excluding wood windows, wood doors, and cut stock is 321918. It is for this definition of manufacturing millwork excluding wood windows, wood doors, and cut stock that the aggregate latent demand estimates are derived. “Manufacturing millwork excluding wood windows, wood doors, and cut stock” is specifically defined as follows:
321918
This U.S. industry comprises establishments primarily engaged in manufacturing millwork (except wood windows, wood doors, and cut stock).
3219181
wood moldings excluding prefinished moldings made from purchased moldings
32191811
wood moldings excluding prefinished moldings made from purchased moldings
3219181111
pine wood moldings excluding prefinished moldings made from purchased moldings
3219181121
Other softwood moldings, except prefinished moldings made from purchased moldings, including moldings covered with metal, plastics, etc.
3219181131
hardwood moldings excluding prefinished moldings made from purchased moldings
3219183
prefinished wood moldings made from purchased moldings
32191831
prefinished wood moldings made from purchased moldings
3219183100
prefinished wood moldings made from purchased moldings
3219183111
Prefinished softwood moldings made from purchased moldings, including softwood covered with metal, plastics, etc.
3219183121
Prefinished hardwood moldings made from purchased moldings, including lauan and hardwood covered with metal, plastics, etc.
3219185
Other wood millwork products, inc stairwork, exterior millwork, and softwood fl
32191851
Other wood millwork products, including stairwork, exterior millwork, and softwood flooring
3219185111
softwood treads, risers, balusters, brackets, crooks, newels, rails, and other stairwork
3219185121
hardwood treads, risers, balusters, brackets, crooks, newels, rails, and other stairwork
3219185131
exterior wood millwork, porch columns, porch rails, newels, trellises, and entrances
3219185141
non-standard or specialty softwood moldings, carvings, and ornaments
3219185151
non-standard or specialty hardwood moldings, carvings, and ornaments
3219185161
Softwood flooring
3219185181
Other wood millwork products, including shutters, interior millwork, and softwood flooring
3219185191
Other wood millwork products, n.e.c., including shutters and interior millwork
3219187
hardwood flooring
32191871
oak flooring
3219187111
Oak flooring (3/4", 1/2", 3/8" T&G and EM strip; 5/16" square edge strip)
3219187121
oak parquetry
3219187131
Other oak flooring
32191872
hardwood flooring excluding oak flooring
3219187222
Other hardwood flooring
3219187241
maple flooring
3219187251
glued laminated hardwood truck trailer flooring and railroad car decking
3219187291
Other hardwood flooring
321918M
Miscellaneous receipts
321918P
Primary products
321918S
Secondary products
321918SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 321918 includes the following:
Baseboards, floor, wood, manufacturing
Brackets, wood, manufacturing
Clear and finger joint wood moldings manufacturing
Columns, porch, wood, manufacturing
Cornices, wood, manufacturing
Decorative wood moldings (e.g., base, chair rail, crown, shoe) manufacturing
Door shutters, wood, manufacturing
Door trim, wood molding, manufacturing
Exterior wood shutters manufacturing
Floor baseboards, wood, manufacturing
Flooring, wood, manufacturing
Moldings, clear and finger joint wood, manufacturing
Moldings, wood and covered wood, manufacturing
Newel posts, wood, manufacturing
Ornamental woodwork (e.g., cornices, mantels) manufacturing
Panel work, wood millwork, manufacturing
Parquet flooring, hardwood, manufacturing
Parquetry, hardwood, manufacturing
Planing mills, millwork
Porch work (e.g., columns, newels, rails, trellises), wood, manufacturing
Railings, wood stair, manufacturing
Shutters, door and window, wood and covered wood, manufacturing
Shutters, wood, manufacturing
Stair railings, wood, manufacturing
Stairwork (e.g., newel posts, railings, staircases, stairs), wood, manufacturing
Trellises, wood, manufacturing
Trim, wood and covered wood, manufacturing
Venetian blind slats, wood, manufacturing
Wainscots, wood, manufacturing
Weather strip, wood, manufacturing
Window trim, wood and covered wood moldings, manufacturing
Wood flooring manufacturing
Wood moldings (e.g., pre-finished, unfinished), clear and finger joint, manufactu
Wood shutters manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing millwork excluding wood windows, wood doors, and cut stock 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 millwork excluding wood windows, wood doors, and cut stock 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 millwork excluding wood windows, wood doors, and cut stock 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 millwork excluding wood windows, wood doors, and cut stock). 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 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 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 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 52
3.45 Sudan 53
3.46 Swaziland 54
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 59
3.53 Zambia 60
3.54 Zimbabwe 61
4 ASIA 62
4.1 Executive Summary 62
4.2 Bangladesh 63
4.3 Bhutan 64
4.4 Brunei 65
4.5 Burma 65
4.6 Cambodia 66
4.7 China 67
4.8 Hong Kong 68
4.9 India 68
4.10 Indonesia 69
4.11 Japan 70
4.12 Laos 71
4.13 Macau 72
4.14 Malaysia 73
4.15 Maldives 74
4.16 Mongolia 74
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 & THE MIDDLE EAST 85
5.1 Executive Summary 85
5.2 Afghanistan 86
5.3 Albania 87
5.4 Andorra 88
5.5 Armenia 88
5.6 Austria 89
5.7 Azerbaijan 90
5.8 Bahrain 91
5.9 Belarus 92
5.10 Belgium 93
5.11 Bosnia and Herzegovina 94
5.12 Bulgaria 94
5.13 Croatia 95
5.14 Cyprus 96
5.15 Czech Republic 96
5.16 Denmark 97
5.17 Estonia 98
5.18 Finland 99
5.19 France 100
5.20 Georgia 101
5.21 Germany 101
5.22 Greece 102
5.23 Hungary 103
5.24 Iceland 104
5.25 Iran 105
5.26 Iraq 106
5.27 Ireland 107
5.28 Israel 107
5.29 Italy 108
5.30 Jordan 109
5.31 Kazakhstan 110
5.32 Kuwait 111
5.33 Kyrgyzstan 112
5.34 Latvia 113
5.35 Lebanon 113
5.36 Liechtenstein 114
5.37 Lithuania 115
5.38 Luxembourg 115
5.39 Malta 116
5.40 Moldova 117
5.41 Monaco 117
5.42 Netherlands 118
5.43 Norway 119
5.44 Oman 120
5.45 Pakistan 120
5.46 Palestine 121
5.47 Poland 122
5.48 Portugal 123
5.49 Qatar 124
5.50 Romania 124
5.51 Russia 125
5.52 San Marino 126
5.53 Saudi Arabia 127
5.54 Slovakia 128
5.55 Slovenia 128
5.56 Spain 129
5.57 Sweden 130
5.58 Switzerland 131
5.59 Syrian Arab Republic 132
5.60 Tajikistan 133
5.61 Turkey 134
5.62 Turkmenistan 135
5.63 Ukraine 135
5.64 United Arab Emirates 136
5.65 United Kingdom 137
5.66 Uzbekistan 138
5.67 Yemen 139
6 LATIN AMERICA 140
6.1 Executive Summary 140
6.2 Argentina 141
6.3 Belize 142
6.4 Bolivia 143
6.5 Brazil 144
6.6 Chile 145
6.7 Colombia 146
6.8 Costa Rica 147
6.9 Ecuador 147
6.10 El Salvador 148
6.11 Falkland Islands 149
6.12 French Guiana 149
6.13 Guatemala 150
6.14 Guyana 151
6.15 Honduras 151
6.16 Mexico 152
6.17 Nicaragua 153
6.18 Panama 154
6.19 Paraguay 155
6.20 Peru 156
6.21 Suriname 157
6.22 Uruguay 157
6.23 Venezuela 158
7 NORTH AMERICA & THE CARIBBEAN 160
7.1 Executive Summary 160
7.2 Antigua and Barbuda 161
7.3 Aruba 162
7.4 Bahamas 163
7.5 Barbados 163
7.6 Bermuda 164
7.7 British Virgin Islands 165
7.8 Canada 165
7.9 Cayman Islands 166
7.10 Cuba 167
7.11 Dominica 168
7.12 Dominican Republic 168
7.13 Greenland 169
7.14 Grenada 170
7.15 Guadeloupe 171
7.16 Haiti 172
7.17 Jamaica 172
7.18 Martinique 173
7.19 Netherlands Antilles 174
7.20 Puerto Rico 174
7.21 St. Kitts and Nevis 175
7.22 St. Lucia 176
7.23 St. Vincent and the Grenadines 176
7.24 Trinidad and Tobago 177
7.25 United States 178
7.26 Virgin Islands, US 179
8 OCEANA 180
8.1 Executive Summary 180
8.2 American Samoa 181
8.3 Australia 182
8.4 Christmas Island 183
8.5 Cook Islands 183
8.6 Fiji 184
8.7 French Polynesia 185
8.8 Guam 185
8.9 Kiribati 186
8.10 Marshall Islands 187
8.11 Micronesia Federation 187
8.12 Nauru 188
8.13 New Caledonia 189
8.14 New Zealand 189
8.15 Niue 190
8.16 Norfolk Island 191
8.17 Northern Mariana Island 191
8.18 Palau 192
8.19 Solomon Islands 193
8.20 Tokelau 193
8.21 Tonga 194
8.22 Tuvalu 195
8.23 Vanuatu 195
8.24 Wallis and Futuna 196
8.25 Western Samoa 197
9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 198
9.1 Disclaimers & Safe Harbor 198
9.2 User Agreement Provisions 199
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