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The 2009 Report on Knee Highs Hosiery: World Market Segmentation by City

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Market Potential Estimation Methodology Overview This study covers the world outlook for knee highs hosiery across more than 2000 cities. For the year reported, estimates are given for the latent demand, or potential industry earnings (P.I.E.), for the city in question (in millions of U.S. dollars), the percent share the city is of the region and of the globe. These comparative benchmarks allow the reader to quickly gauge a city vis-à-vis others. Using econometric models which project fundamental economic dynamics within each country and across countries, latent demand estimates are created. This report does not discuss the specific players in the market serving the latent demand, nor specific details at the product level. The study also does not consider short-term cyclicalities that might affect realized sales. The study, therefore, is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved. This study does not report actual sales data (which are simply unavailable, in a comparable or consistent manner in virtually all of the cities of the world). This study gives, however, my estimates for the worldwide latent demand, or the P.I.E. for knee highs hosiery. It also shows how the P.I.E. is divided across the world’s cities. In order to make these estimates, a multi-stage methodology was employed that is often taught in courses on international strategic planning at graduate schools of business. 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 knee highs hosiery 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 city market. Another reason why sales do not equate to latent demand is exchange rates. In this report, all figures assume the long-run efficiency of currency markets. Figures, therefore, equate values based on purchasing power parities across countries. Short-run distortions in the value of the dollar, therefore, do not figure into the estimates. Purchasing power parity estimates of country income were collected from official sources, and extrapolated using standard econometric models. The report uses the dollar as the currency of comparison, but not as a measure of transaction volume. The units used in this report are: US $ mln. 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 earlier, 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 knee highs hosiery 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 knee highs hosiery on a city-by-city 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 knee highs hosiery 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 knee highs hosiery. 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 knee highs hosiery. 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 knee highs hosiery. 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 or cities are more likely to be at or near efficiency than others. These are given greater weight than others in the estimation of latent demand compared to others 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 “knee highs hosiery” 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 knee highs hosiery 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 cities and the world at large (without needing to know the specific parts that went into the whole in the first place). Given this caveat, in this report we define the retail sales of "knee highs hosiery" as including all commonly understood products falling within this broad category, such as womens sheer, opaque, colored, nude, and other stockings which cover foot to calf, irrespective of product packaging, formulation, size, or form (e.g. the retail sales of products or brands such as Leggs Knee Hi Sheer Toe Nude and Berkshire All Day Sheer Knee High Hosiery). Step 2. Filtering and Smoothing Based on the aggregate view of knee highs hosiery as defined above, data were then collected for as many similar countries and cities as possible for that same definition, at the same level of the value chain. This generates a convenience sample 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 or cities on a sporadic basis. In other cases, data 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), cities 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 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 cities 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 cities along the aggregate consumption function, but also over time (i.e., not all cities are perceived to have the same income growth prospects over time and this effect can vary from city to city as well). Another way of looking at this is to say that latent demand for knee highs hosiery is more likely to be similar across cities that have similar characteristics in terms of economic development (i.e., African cities will have similar latent demand structures controlling for the income variation across the pool of African cities). This approach is useful across cities for which some notion of non-linearity exists in the aggregate consumption function. For some categories, however, the reader must realize that the numbers will reflect a city’s contribution to global latent demand and may never be realized in the form of local sales. For certain 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 cities in “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 cities 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 2000 cities, there will always be those cities, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible. For these cities, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a city’s stock of income), but a function of current income (a city’s flow of income). In the long run, if a city has no current income, the latent demand for knee highs hosiery is assumed to approach zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., cities which earn low levels of income will not use their savings, in the long run, to demand knee highs hosiery). In a graphical sense, for low income cities, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income cities are assumed to have a latent demand proportional to their income, based on the city 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 cities 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.
 
Contents:
1 INTRODUCTION & METHODOLOGY 11 1.1 Overview and Definitions 11 1.2 Market Potential Estimation Methodology 11 1.2.1 Overview 11 1.2.2 What is Latent Demand and the P.I.E.? 12 1.2.3 The Methodology 12 1.2.3.1 Step 1. Product Definition and Data Collection 14 1.2.3.2 Step 2. Filtering and Smoothing 15 1.2.3.3 Step 3. Filling in Missing Values 15 1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 15 1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 16 1.2.3.6 Step 6. Aggregation and Benchmarking 16 2 USING THE DATA 17 3 CITY SEGMENTS RANKED BY MARKET SIZE 18 3.1 Top 15 Markets 18 3.2 Markets 16 to 30 19 3.3 Remaining Cities by Market Rank 20 4 CITY SEGMENTS IN ALPHABETICAL ORDER 123 4.1 A: from Aalborg to Az Zawiyah 123 4.2 B: from Bacolod to Bydgoszcz 130 4.3 C: from Caaguazu to Cyangugu 138 4.4 D: from Da Nang to Dzhizak 146 4.5 E: from East London to Esteli 150 4.6 F: from Fagatogo to Funchal 152 4.7 G: from Gabes to Gyumri 155 4.8 H: from Hachinohe to Hyderabad 159 4.9 I: from Iasi to Izmir 163 4.10 J: from Jaboatao to Jyvaskyla 166 4.11 K: from Kabul to Kzyl-Orda 168 4.12 L: from La Ceiba to Lyon 176 4.13 M: from Macae to Mzuzu 181 4.14 N: from Nacala to Nzerekore 191 4.15 O: from Oaklahoma City to Oyem 196 4.16 Ö: from Örebro to Örebro 198 4.17 P: from Pago Pago to Pyuthan 199 4.18 Q: from Qandahar to Quito 205 4.19 R: from Rabat to Rustavi 206 4.20 S: from S. Luis Potosi to Szombathely 209 4.21 T: from Tabligbo to Tyre 221 4.22 U: from Uberaba to Utulei 228 4.23 V: from Vacoas-Phoenix to Vukovar 230 4.24 W: from Wadi Medani to Wuhan 233 4.25 X: from Xalapa to Xian 234 4.26 Y: from Yamagata to Yungkang 235 4.27 Z: from Zadar to Zvishavane 236 5 CITY SEGMENTS RANKED BY COUNTRY 237 5.1 Afghanistan 237 5.2 Albania 237 5.3 Algeria 238 5.4 American Samoa 238 5.5 Andorra 238 5.6 Angola 239 5.7 Antigua and Barbuda 239 5.8 Argentina 240 5.9 Armenia 241 5.10 Aruba 241 5.11 Australia 242 5.12 Austria 242 5.13 Azerbaijan 243 5.14 Bahrain 243 5.15 Bangladesh 243 5.16 Barbados 244 5.17 Belarus 244 5.18 Belgium 244 5.19 Belize 245 5.20 Benin 245 5.21 Bermuda 245 5.22 Bhutan 246 5.23 Bolivia 246 5.24 Bosnia and Herzegovina 246 5.25 Botswana 247 5.26 Brazil 248 5.27 Brunei 253 5.28 Bulgaria 253 5.29 Burkina Faso 254 5.30 Burma 254 5.31 Burundi 254 5.32 Cambodia 255 5.33 Cameroon 255 5.34 Canada 255 5.35 Cape Verde 256 5.36 Central African Republic 256 5.37 Chad 256 5.38 Chile 257 5.39 China 257 5.40 Christmas Island 258 5.41 Colombia 258 5.42 Comoros 258 5.43 Congo (formerly Zaire) 259 5.44 Cook Islands 259 5.45 Costa Rica 259 5.46 Cote dIvoire 260 5.47 Croatia 260 5.48 Cuba 260 5.49 Cyprus 261 5.50 Czech Republic 261 5.51 Denmark 261 5.52 Djibouti 262 5.53 Dominica 262 5.54 Dominican Republic 262 5.55 Ecuador 263 5.56 Egypt 263 5.57 El Salvador 263 5.58 Equatorial Guinea 264 5.59 Estonia 264 5.60 Ethiopia 264 5.61 Fiji 265 5.62 Finland 265 5.63 France 266 5.64 French Guiana 266 5.65 French Polynesia 267 5.66 Gabon 267 5.67 Georgia 267 5.68 Germany 268 5.69 Ghana 268 5.70 Greece 269 5.71 Greenland 269 5.72 Grenada 269 5.73 Guadeloupe 270 5.74 Guam 270 5.75 Guatemala 270 5.76 Guinea 271 5.77 Guinea-Bissau 271 5.78 Guyana 271 5.79 Haiti 272 5.80 Honduras 272 5.81 Hong Kong 272 5.82 Hungary 273 5.83 Iceland 273 5.84 India 274 5.85 Indonesia 275 5.86 Iran 276 5.87 Iraq 276 5.88 Ireland 277 5.89 Israel 277 5.90 Italy 278 5.91 Jamaica 278 5.92 Japan 279 5.93 Jordan 281 5.94 Kazakhstan 282 5.95 Kenya 282 5.96 Kiribati 283 5.97 Kuwait 283 5.98 Kyrgyzstan 283 5.99 Laos 283 5.100 Latvia 284 5.101 Lebanon 284 5.102 Lesotho 284 5.103 Liberia 285 5.104 Libya 285 5.105 Liechtenstein 285 5.106 Lithuania 286 5.107 Luxembourg 286 5.108 Macau 286 5.109 Madagascar 287 5.110 Malawi 287 5.111 Malaysia 288 5.112 Maldives 288 5.113 Mali 289 5.114 Malta 289 5.115 Marshall Islands 289 5.116 Martinique 290 5.117 Mauritania 290 5.118 Mauritius 290 5.119 Mexico 291 5.120 Micronesia Federation 292 5.121 Moldova 292 5.122 Monaco 292 5.123 Mongolia 292 5.124 Morocco 293 5.125 Mozambique 293 5.126 Namibia 293 5.127 Nauru 294 5.128 Nepal 294 5.129 New Caledonia 294 5.130 New Zealand 295 5.131 Nicaragua 295 5.132 Niger 296 5.133 Nigeria 296 5.134 Niue 296 5.135 Norfolk Island 297 5.136 North Korea 297 5.137 Norway 297 5.138 Oman 298 5.139 Pakistan 298 5.140 Palau 298 5.141 Palestine 298 5.142 Panama 299 5.143 Papua New Guinea 299 5.144 Paraguay 299 5.145 Peru 300 5.146 Philippines 300 5.147 Poland 301 5.148 Portugal 301 5.149 Puerto Rico 302 5.150 Qatar 302 5.151 Republic of Congo 302 5.152 Reunion 303 5.153 Romania 303 5.154 Russia 304 5.155 Rwanda 304 5.156 San Marino 304 5.157 Sao Tome E Principe 305 5.158 Saudi Arabia 305 5.159 Senegal 305 5.160 Seychelles 306 5.161 Sierra Leone 306 5.162 Singapore 306 5.163 Slovakia 306 5.164 Slovenia 307 5.165 Solomon Islands 307 5.166 Somalia 307 5.167 South Africa 308 5.168 South Korea 308 5.169 Spain 309 5.170 Sri Lanka 309 5.171 St. Kitts and Nevis 310 5.172 St. Lucia 310 5.173 St. Vincent and the Grenadines 310 5.174 Sudan 310 5.175 Suriname 311 5.176 Swaziland 311 5.177 Sweden 311 5.178 Switzerland 312 5.179 Syrian Arab Republic 312 5.180 Taiwan 313 5.181 Tajikistan 314 5.182 Tanzania 314 5.183 Thailand 314 5.184 The Bahamas 315 5.185 The British Virgin Islands 315 5.186 The Cayman Islands 315 5.187 The Falkland Islands 315 5.188 The Gambia 316 5.189 The Netherlands 316 5.190 The Netherlands Antilles 316 5.191 The Northern Mariana Island 317 5.192 The U.S. Virgin Islands 317 5.193 The United Arab Emirates 317 5.194 The United Kingdom 318 5.195 The United States 319 5.196 Togo 320 5.197 Tokelau 320 5.198 Tonga 320 5.199 Trinidad and Tobago 321 5.200 Tunisia 321 5.201 Turkey 322 5.202 Turkmenistan 322 5.203 Tuvalu 322 5.204 Uganda 323 5.205 Ukraine 323 5.206 Uruguay 324 5.207 Uzbekistan 324 5.208 Vanuatu 324 5.209 Venezuela 325 5.210 Vietnam 325 5.211 Wallis and Futuna 326 5.212 Western Sahara 326 5.213 Western Samoa 326 5.214 Yemen 326 5.215 Zambia 327 5.216 Zimbabwe 327 6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 328 6.1 Disclaimers & Safe Harbor 328 6.2 ICON Group International, Inc. User Agreement Provisions 329
 
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