The 2009-2014 World Outlook for Manufacturing Laboratory Apparatus, Laboratory and Hospital Furniture, Hospital Beds, Operating Room Tables, and Laboratory Balances and Scales Excluding Dental Furniture
ICON Group International, September 2008, Pages: 218
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 laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture 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 manufacturing laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture 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 laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture 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 manufacturing laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture 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 manufacturing laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture. 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 manufacturing laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture. 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 manufacturing laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture.
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 “manufacturing laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture” 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 laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture 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 laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture is 339111. It is for this definition of manufacturing laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture that the aggregate latent demand estimates are derived. “Manufacturing laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture” is specifically defined as follows:
339111
This U.S. industry comprises establishments primarily engaged in manufacturing laboratory apparatus and laboratory and hospital furniture (except dental). Examples of products made by these establishments are hospital beds, operating room tables, laboratory balances and scales, furnaces, ovens, centrifuges, cabinets, cases, benches, tables, and stools.
3391110
LABORATORY APPARATUS AND FURNITURE
33911101
Laboratory apparatus and furniture
3391110110
Laboratory and scientific apparatus
3391110111
Laboratory balances and scales with sensitivity of 5 centigrams or better
3391110113
Laboratory balances and scales with sensitivity of less than 5 centigrams units . S MA334B
3391110115
Laboratory furnaces and ovens
3391110117
Laboratory centrifuges, table~top type
3391110119
Laboratory centrifuges, floor type
3391110121
Laboratory distillation apparatus and rectifying apparatus
3391110123
Laboratory vacuum pumps (including value of drivers, and including replacement parts)
3391110125
Laboratory sterilizers and autoclaves
3391110127
Laboratory filtering or purifying apparatus
3391110129
Laboratory burners and hot plates
3391110131
Laboratory heat exchangers
3391110133
Laboratory granulators, mills, and other particle size reduction apparatus
3391110135
Laboratory drying and evaporation apparatus
3391110137
Laboratory blenders, mixers, shakers, dispensers, fraction collectors, and other liquid sample preparation apparatus
3391110139
Laboratory incubators
3391110141
Environmental test chambers
3391110143
Laboratory freezers
3391110145
Microtomes
3391110151
All other laboratory apparatus, n.e.c. (excluding analytical instruments)
3391110155
Components, parts and accessories for laboratory equipment (sold separately)
33911102
Laboratory furniture and parts sold separately
3391110230
Laboratory furniture and parts sold separately
3391110231
Laboratory furniture, including cabinets, cases, benches, tables, stools, reagent shelves, etc. (excluding drafting tables)
3391110233
Parts and components for laboratory furniture (sold separately)
3391111
Laboratory apparatus
3391112
Laboratory furniture
3391114
HOSPITAL BEDS
33911141
Hospital beds
3391114100
Hospital beds
3391116
HOSPITAL FURNITURE
33911161
Hospital furniture
3391116102
Hospital operating room furniture, including cabinets, cases, and tables
3391116105
Hospital patient room furniture, including cabinets, desks, dressers, and over_bed tables (excluding beds and chairs)
3391116109
Other hospital furniture, including back rests, bassinets, cases, chart racks, and other tables (excluding hospital beds, operating room furniture, and patient room furniture)
3391118
LABORATORY APPARATUS AND FURNITURE
33911181
Laboratory furniture and parts sold separately
3391118120
Laboratory furniture and parts sold separately
33911182
Laboratory and scientific apparatus
3391118240
Laboratory and scientific apparatus
339111M
Miscellaneous receipts
339111P
Primary products
339111S
Secondary products
339111SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 339111 includes the following:
Autoclaves, laboratory-type (except dental), manufacturing
Balances and scales, laboratory-type, manufacturing
Beds, hospital, manufacturing
Benches, laboratory-type, manufacturing
Blood testing apparatus, laboratory-type, manufacturing
Bunsen burners manufacturing
Dryers, laboratory-type, manufacturing
Furnaces, laboratory-type (except dental), manufacturing
Furniture, hospital (e.g., hospital beds, operating room furniture), manufacturin
Furniture, laboratory-type (e.g., benches, cabinets, stools, tables), manufacturi
Hospital beds manufacturing
Hospital furniture (e.g., hospital beds, operating room furniture) manufacturing
Incubators, laboratory-type, manufacturing
Laboratory-type centrifuges manufacturing
Laboratory-type distilling apparatus manufacturing
Laboratory-type equipment (e.g., balances, centrifuges, furnaces) manufacturing
Laboratory-type evaporation apparatus manufacturing
Laboratory-type freezers manufacturing
Laboratory-type furniture (e.g., benches, cabinets, stools, tables) (except denta
Laboratory-type sample preparation apparatus manufacturing
Operating room tables manufacturing
Ovens, laboratory-type, manufacturing
Scales and balances, laboratory-type, manufacturing
Scales, laboratory, manufacturing
Sterilizers, laboratory-type (except dental), manufacturing
Vacuum pumps, laboratory-type, manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture 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 laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture 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 laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture 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 laboratory apparatus, laboratory and hospital furniture, hospital beds, operating room tables, and laboratory balances and scales excluding dental furniture). 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 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 & THE MIDDLE EAST 21
3.1 Executive Summary 21
3.2 Afghanistan 23
3.3 Algeria 24
3.4 Angola 25
3.5 Armenia 26
3.6 Azerbaijan 27
3.7 Bahrain 28
3.8 Benin 28
3.9 Botswana 29
3.10 Burkina Faso 30
3.11 Burundi 31
3.12 Cameroon 32
3.13 Cape Verde 33
3.14 Central African Republic 34
3.15 Chad 35
3.16 Comoros 36
3.17 Congo (formerly Zaire) 36
3.18 Cote dIvoire 37
3.19 Djibouti 38
3.20 Egypt 39
3.21 Equatorial Guinea 40
3.22 Ethiopia 40
3.23 Gabon 41
3.24 Ghana 42
3.25 Guinea 43
3.26 Guinea-Bissau 43
3.27 Iran 44
3.28 Iraq 45
3.29 Israel 46
3.30 Jordan 47
3.31 Kenya 48
3.32 Kuwait 49
3.33 Kyrgyzstan 49
3.34 Lebanon 50
3.35 Lesotho 51
3.36 Liberia 51
3.37 Libya 52
3.38 Madagascar 53
3.39 Malawi 54
3.40 Mali 54
3.41 Mauritania 55
3.42 Mauritius 56
3.43 Morocco 57
3.44 Mozambique 58
3.45 Namibia 59
3.46 Niger 60
3.47 Nigeria 60
3.48 Oman 61
3.49 Pakistan 62
3.50 Palestine 63
3.51 Qatar 63
3.52 Republic of Congo 64
3.53 Reunion 65
3.54 Rwanda 66
3.55 Sao Tome E Principe 67
3.56 Saudi Arabia 67
3.57 Senegal 68
3.58 Sierra Leone 69
3.59 Somalia 70
3.60 South Africa 71
3.61 Sudan 72
3.62 Swaziland 73
3.63 Syrian Arab Republic 74
3.64 Tajikistan 75
3.65 Tanzania 76
3.66 The Gambia 77
3.67 The United Arab Emirates 78
3.68 Togo 78
3.69 Tunisia 79
3.70 Turkey 80
3.71 Turkmenistan 81
3.72 Uganda 82
3.73 Uzbekistan 83
3.74 Western Sahara 84
3.75 Yemen 84
3.76 Zambia 85
3.77 Zimbabwe 86
4 ASIA 88
4.1 Executive Summary 88
4.2 Bangladesh 90
4.3 Bhutan 91
4.4 Brunei 92
4.5 Burma 92
4.6 Cambodia 93
4.7 China 94
4.8 Hong Kong 95
4.9 India 95
4.10 Indonesia 96
4.11 Japan 97
4.12 Laos 98
4.13 Macau 99
4.14 Malaysia 100
4.15 Maldives 101
4.16 Mongolia 101
4.17 Nepal 102
4.18 North Korea 103
4.19 Papua New Guinea 104
4.20 Philippines 105
4.21 Seychelles 106
4.22 Singapore 106
4.23 South Korea 107
4.24 Sri Lanka 108
4.25 Taiwan 109
4.26 Thailand 110
4.27 Vietnam 111
5 EUROPE 112
5.1 Executive Summary 112
5.2 Albania 114
5.3 Andorra 115
5.4 Austria 115
5.5 Belarus 116
5.6 Belgium 117
5.7 Bosnia and Herzegovina 118
5.8 Bulgaria 119
5.9 Croatia 120
5.10 Cyprus 121
5.11 Czech Republic 122
5.12 Denmark 123
5.13 Estonia 124
5.14 Finland 125
5.15 France 126
5.16 Georgia 127
5.17 Germany 128
5.18 Greece 129
5.19 Hungary 130
5.20 Iceland 131
5.21 Ireland 132
5.22 Italy 132
5.23 Kazakhstan 133
5.24 Latvia 134
5.25 Liechtenstein 135
5.26 Lithuania 136
5.27 Luxembourg 137
5.28 Malta 138
5.29 Moldova 138
5.30 Monaco 139
5.31 Norway 140
5.32 Poland 141
5.33 Portugal 142
5.34 Romania 143
5.35 Russia 144
5.36 San Marino 145
5.37 Slovakia 145
5.38 Slovenia 146
5.39 Spain 147
5.40 Sweden 148
5.41 Switzerland 149
5.42 The Netherlands 150
5.43 The United Kingdom 151
5.44 Ukraine 152
6 LATIN AMERICA 153
6.1 Executive Summary 153
6.2 Argentina 154
6.3 Belize 155
6.4 Bolivia 156
6.5 Brazil 157
6.6 Chile 158
6.7 Colombia 159
6.8 Costa Rica 160
6.9 Ecuador 161
6.10 El Salvador 162
6.11 French Guiana 163
6.12 Guatemala 164
6.13 Guyana 165
6.14 Honduras 165
6.15 Mexico 166
6.16 Nicaragua 167
6.17 Panama 168
6.18 Paraguay 169
6.19 Peru 170
6.20 Suriname 171
6.21 The Falkland Islands 172
6.22 Uruguay 173
6.23 Venezuela 174
7 NORTH AMERICA & THE CARIBBEAN 175
7.1 Executive Summary 175
7.2 Antigua and Barbuda 177
7.3 Aruba 177
7.4 Barbados 178
7.5 Bermuda 179
7.6 Canada 179
7.7 Cuba 180
7.8 Dominica 181
7.9 Dominican Republic 182
7.10 Greenland 183
7.11 Grenada 184
7.12 Guadeloupe 184
7.13 Haiti 185
7.14 Jamaica 186
7.15 Martinique 187
7.16 Puerto Rico 188
7.17 St. Kitts and Nevis 189
7.18 St. Lucia 189
7.19 St. Vincent and the Grenadines 190
7.20 The Bahamas 191
7.21 The British Virgin Islands 191
7.22 The Cayman Islands 192
7.23 The Netherlands Antilles 193
7.24 The U.S. Virgin Islands 193
7.25 The United States 194
7.26 Trinidad and Tobago 195
8 OCEANA 197
8.1 Executive Summary 197
8.2 American Samoa 199
8.3 Australia 200
8.4 Christmas Island 201
8.5 Cook Islands 201
8.6 Fiji 202
8.7 French Polynesia 203
8.8 Guam 204
8.9 Kiribati 204
8.10 Marshall Islands 205
8.11 Micronesia Federation 206
8.12 Nauru 206
8.13 New Caledonia 207
8.14 New Zealand 208
8.15 Niue 209
8.16 Norfolk Island 209
8.17 Palau 210
8.18 Solomon Islands 211
8.19 The Northern Mariana Island 211
8.20 Tokelau 212
8.21 Tonga 213
8.22 Tuvalu 213
8.23 Vanuatu 214
8.24 Wallis and Futuna 215
8.25 Western Samoa 215
9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 217
9.1 Disclaimers & Safe Harbor 217
9.2 ICON Group International, Inc. User Agreement Provisions 218
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
