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The 2006-2011 World Outlook for Tanning, Currying, and Finishing Hides and Skins; Having Others Process Hides and Skins on a Contract Basis; and Dyeing or Dressing FursPublished by: Icon Group International, Inc. Published: Apr. 5, 2005 - 204 Pages Table of Contents1 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 17 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 18 1.3.6 Step 6. Aggregation and Benchmarking 18 1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 19 2 SUMMARY OF FINDINGS 20 2.1 The Worldwide Market Potential 20 3 AFRICA 22 3.1 Executive Summary 22 3.2 Algeria 23 3.3 Angola 24 3.4 Benin 25 3.5 Botswana 26 3.6 Burkina Faso 27 3.7 Burundi 27 3.8 Cameroon 28 3.9 Cape Verde 29 3.10 Central African Republic 29 3.11 Chad 30 3.12 Comoros 31 3.13 Congo (formerly Zaire) 31 3.14 Cote d'Ivoire 32 3.15 Djibouti 33 3.16 Egypt 34 3.17 Equatorial Guinea 35 3.18 Ethiopia 35 3.19 Gabon 36 3.20 Ghana 37 3.21 Guinea 38 3.22 Guinea-Bissau 38 3.23 Kenya 39 3.24 Lesotho 40 3.25 Liberia 40 3.26 Libya 41 3.27 Madagascar 42 3.28 Malawi 42 3.29 Mali 43 3.30 Mauritania 44 3.31 Mauritius 44 3.32 Morocco 45 3.33 Mozambique 46 3.34 Namibia 46 3.35 Niger 47 3.36 Nigeria 48 3.37 Republic of Congo 49 3.38 Reunion 49 3.39 Rwanda 50 3.40 Sao Tome E Principe 51 3.41 Senegal 51 3.42 Sierra Leone 52 3.43 Somalia 53 3.44 South Africa 54 3.45 Sudan 55 3.46 Swaziland 56 3.47 Tanzania 56 3.48 The Gambia 57 3.49 Togo 58 3.50 Tunisia 59 3.51 Uganda 60 3.52 Western Sahara 61 3.53 Zambia 61 3.54 Zimbabwe 62 4 ASIA 64 4.1 Executive Summary 64 4.2 Bangladesh 65 4.3 Bhutan 66 4.4 Brunei 67 4.5 Burma 68 4.6 Cambodia 69 4.7 China 69 4.8 Hong Kong 70 4.9 India 71 4.10 Indonesia 72 4.11 Japan 73 4.12 Laos 74 4.13 Macau 74 4.14 Malaysia 75 4.15 Maldives 76 4.16 Mongolia 77 4.17 Nepal 77 4.18 North Korea 78 4.19 Papua New Guinea 79 4.20 Philippines 79 4.21 Seychelles 80 4.22 Singapore 81 4.23 South Korea 82 4.24 Sri Lanka 83 4.25 Taiwan 84 4.26 Thailand 85 4.27 Vietnam 86 5 EUROPE 87 5.1 Executive Summary 87 5.2 Albania 88 5.3 Andorra 89 5.4 Austria 90 5.5 Belarus 91 5.6 Belgium 92 5.7 Bosnia and Herzegovina 93 5.8 Bulgaria 93 5.9 Croatia 94 5.10 Cyprus 95 5.11 Czech Republic 96 5.12 Denmark 97 5.13 Estonia 98 5.14 Finland 98 5.15 France 99 5.16 Georgia 100 5.17 Germany 101 5.18 Greece 102 5.19 Hungary 103 5.20 Iceland 104 5.21 Ireland 105 5.22 Italy 105 5.23 Kazakhstan 106 5.24 Latvia 107 5.25 Liechtenstein 108 5.26 Lithuania 109 5.27 Luxembourg 109 5.28 Malta 110 5.29 Moldova 111 5.30 Monaco 111 5.31 Netherlands 112 5.32 Norway 113 5.33 Poland 114 5.34 Portugal 115 5.35 Romania 116 5.36 Russia 117 5.37 San Marino 118 5.38 Slovakia 118 5.39 Slovenia 119 5.40 Spain 120 5.41 Sweden 121 5.42 Switzerland 122 5.43 Ukraine 123 5.44 United Kingdom 124 6 LATIN AMERICA 125 6.1 Executive Summary 125 6.2 Argentina 126 6.3 Belize 127 6.4 Bolivia 128 6.5 Brazil 129 6.6 Chile 130 6.7 Colombia 131 6.8 Costa Rica 132 6.9 Ecuador 132 6.10 El Salvador 133 6.11 Falkland Islands 134 6.12 French Guiana 134 6.13 Guatemala 135 6.14 Guyana 136 6.15 Honduras 136 6.16 Mexico 137 6.17 Nicaragua 138 6.18 Panama 139 6.19 Paraguay 140 6.20 Peru 141 6.21 Suriname 142 6.22 Uruguay 142 6.23 Venezuela 143 7 NORTH AMERICA & THE CARIBBEAN 145 7.1 Executive Summary 145 7.2 Antigua and Barbuda 146 7.3 Aruba 147 7.4 Bahamas 148 7.5 Barbados 148 7.6 Bermuda 149 7.7 British Virgin Islands 150 7.8 Canada 150 7.9 Cayman Islands 151 7.10 Cuba 152 7.11 Dominica 153 7.12 Dominican Republic 153 7.13 Greenland 154 7.14 Grenada 155 7.15 Guadeloupe 156 7.16 Haiti 157 7.17 Jamaica 157 7.18 Martinique 158 7.19 Netherlands Antilles 159 7.20 Puerto Rico 159 7.21 St. Kitts and Nevis 160 7.22 St. Lucia 161 7.23 St. Vincent and the Grenadines 161 7.24 Trinidad and Tobago 162 7.25 United States 163 7.26 Virgin Islands, US 164 8 OCEANA 165 8.1 Executive Summary 165 8.2 American Samoa 166 8.3 Australia 167 8.4 Christmas Island 168 8.5 Cook Islands 168 8.6 Fiji 169 8.7 French Polynesia 170 8.8 Guam 170 8.9 Kiribati 171 8.10 Marshall Islands 172 8.11 Micronesia Federation 172 8.12 Nauru 173 8.13 New Caledonia 174 8.14 New Zealand 174 8.15 Niue 175 8.16 Norfolk Island 176 8.17 Northern Mariana Island 176 8.18 Palau 177 8.19 Solomon Islands 178 8.20 Tokelau 178 8.21 Tonga 179 8.22 Tuvalu 180 8.23 Vanuatu 180 8.24 Wallis and Futuna 181 8.25 Western Samoa 182 9 THE MIDDLE EAST 183 9.1 Executive Summary 183 9.2 Afghanistan 184 9.3 Armenia 185 9.4 Azerbaijan 186 9.5 Bahrain 187 9.6 Iran 188 9.7 Iraq 189 9.8 Israel 190 9.9 Jordan 191 9.10 Kuwait 191 9.11 Kyrgyzstan 192 9.12 Lebanon 193 9.13 Oman 193 9.14 Pakistan 194 9.15 Palestine 195 9.16 Qatar 195 9.17 Saudi Arabia 196 9.18 Syrian Arab Republic 197 9.19 Tajikistan 198 9.20 Turkey 198 9.21 Turkmenistan 199 9.22 United Arab Emirates 200 9.23 Uzbekistan 201 9.24 Yemen 202 10 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 203 10.1 Disclaimers & Safe Harbor 203 10.2 ICON Group International, Inc. User Agreement Provisions 204 AbstractWHAT 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 tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs 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 tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs 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 tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs 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 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 tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs 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 tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs. 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 tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs. 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 tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs. 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 “tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs” 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 tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs 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 “tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs is 316110. It is for this definition of tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs that the aggregate latent demand estimates are derived. “Tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs” is specifically defined as follows: 316110 This industry comprises establishments primarily engaged in one or more of the following: (1) tanning, currying, and finishing hides and skins; (2) having others process hides and skins on a contract basis; and (3) dyeing or dressing furs. 3161101 finished and unfinished leather 31611011 full grain and grain split cattle hide and kip side sole, bag, case, and strap leather excluding offal and welting leather 3161101111 full grain and grain split cattle hide and kip side sole leather excluding offal and welting leather 3161101121 full grain and grain split cattle hide and kip side bag, case, and strap leather excluding offal and welting leather 31611012 top grain and machine buff upholstery leather made from full grain and grain split cattle hide and kip side excluding offal and welting leather 3161101231 top grain and machine buff upholstery leather made from full grain and grain split cattle hide and kip side excluding offal and welting leather 31611013 upper and patent leather made from full grain and grain split cattle hide and kip side excluding offal and welting leather 3161101341 upper and patent leather made from full grain and grain split cattle hide and kip side excluding offal and welting leather 31611014 garment leather made from cattle hide and kip side offal, finished cattle hide and kip side leather splits, and other leather grains 3161101451 full grain and grain split cattle hide and kip side garment leather excluding offal and welting leather 3161101461 cattle hide and kip side flat and handbag leather, lining leather, belting and mechanical leather, and other leather grains 3161101471 finished cattle hide and kip side offal and welting leather 3161101481 finished cattle hide and kip side shoulder splits, deep buffs, buffing, fleshers, and other leather splits 31611015 unfinished wet blues and other cattle hide and kip side leathers 3161101591 unfinished cattle hide and kip side leather wet blues 31611015A1 unfinished cattle hide and kip side leathers excluding wet blues 31611016 finished and unfinished calf, sheep, lamb, and other animal leathers excluding cattle hide and kip side 31611016B1 All calf and whole kip leathers, finished and unfinished 31611016C1 finished and unfinished sheep and lamb garment leathers 31611016D1 finished and unfinished sheep and lamb glove, shoe, fleshers, skivers, shearlings, and other leathers excluding garment leathers 31611016E1 finished and unfinished horse, goat, pigskin, colt, mule, ass, and pony leathers 3161104 contract and commission receipts for tanning and finishing leather owned by others 31611041 contract and commission receipts for tanning and finishing leather owned by others 3161104100 contract and commission receipts for tanning and finishing leather owned by others 3161107 bleached and dressed-and-dyed furs 31611071 bleached and dressed-and-dyed furs 3161107121 bleached and dressed-and-dyed furs 3161107131 receipts for dressing-and-dyeing and bleaching furs performed in-plant on materials owned by others 316110M Miscellaneous receipts 316110P Primary products 316110S Secondary products 316110SM Secondary products and miscellaneous receipts Furthermore, the definition of NAICS code 316110 includes the following: Bag leather manufacturing Belting butts, curried or rough, manufacturing Belting leather, manufacturing Bookbinder's leather manufacturing Bridle leather manufacturing Buffings, russet, manufacturing Case leather manufacturing Chamois leather manufacturing Collar leather, manufacturing Coloring leather Convertors, leather Currying furs Currying leather Cutting of leather Dressing (i.e., bleaching, blending, currying, scraping, tanning) furs Dressing hides Dyeing furs Dyeing leather Embossing leather Exotic leathers manufacturing Fancy leathers manufacturing Finishing hides and skins on a contract basis Finishing leather Fleshers, leather (i.e., flesh side of split leather), manufacturing Fur stripping Furs, dressed (e.g., bleached, curried, dyed, scraped, tanned), manufacturing Garment leather manufacturing Glove leather manufacturing Handbag leather manufacturing Harness leather manufacturing Hides and skins, finishing on a contract basis Hides, tanning, currying, dressing, and finishing Japanning of leather Lace leather manufacturing Latigo leather manufacturing Leather coloring, cutting, embossing, and japanning Leather converters Leather tanning, currying, and finishing Lining leather manufacturing Mechanical leather manufacturing Parchment leather manufacturing Patent leather manufacturing Pelts bleaching, currying, dyeing, scraping, and tanning Rawhide manufacturing Roller leather manufacturing Saddlery leather manufacturing Shearling (i.e., prepared sheepskin) manufacturing Skins, tanning, currying and finishing Skirting leather manufacturing Skivers, leather, manufacturing Sole leather manufacturing Specialty leathers manufacturing Splits, leather, manufacturing Strap leather manufacturing Sweatband leather manufacturing Tannery leather manufacturing Tanning and currying furs Upholstery leather manufacturing Upper leather manufacturing Vellum leather manufacturing Welting leather manufacturing Wet blues manufacturing. Step 2. Filtering and Smoothing Based on the aggregate view of tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs 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 tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs 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 tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs 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 tanning, currying, and finishing hides and skins; having others process hides and skins on a contract basis; and dyeing or dressing furs). 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 world's 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. Get Full Details About This Report >> |
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