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The 2009 Report on Upholstered Wood Household Rocking Chairs and Swivel Rockers Excluding Custom Pieces Sold Directly to the Customer at Retail: World Market Segmentation by City

Description:
Market Potential Estimation Methodology Overview This study covers the world outlook for upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail 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 upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail. 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 upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail 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 upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail 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 upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail 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 upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail 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 upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail. 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 upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail. 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 upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail. 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 “upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail” 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 upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail 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, this study covers “upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail is 33712113. It is for this definition of upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail that the aggregate latent demand estimates are derived. “Upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail” is specifically defined as follows: 33712113 Upholstered wood household rocking chairs (except custom sold directly to the customer at retail), including swivel rockers  3371211311 Upholstered wood household rocking chairs (except custom sold directly to the customer at retail), including swivel rockers  337121132 Chairs, except reclining and rockers  33712113242 Chiefly cotton  33712113243 Chiefly rayon  33712113244 Chiefly olefin  33712113245 Other fibers and blends, including coated fabric and vinyl   Step 2. Filtering and Smoothing Based on the aggregate view of upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail 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 upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail 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 upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail 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 upholstered wood household rocking chairs and swivel rockers excluding custom pieces sold directly to the customer at retail). 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 16 1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 16 1.2.3.6 Step 6. Aggregation and Benchmarking 17 2 USING THE DATA 18 3 CITY SEGMENTS RANKED BY MARKET SIZE 19 3.1 Top 15 Markets 19 3.2 Markets 16 to 30 20 3.3 Remaining Cities by Market Rank 21 4 CITY SEGMENTS IN ALPHABETICAL ORDER 124 4.1 A: from Aalborg to Az Zawiyah 124 4.2 B: from Bacolod to Bydgoszcz 131 4.3 C: from Caaguazu to Cyangugu 139 4.4 D: from Da Nang to Dzhizak 147 4.5 E: from East London to Esteli 151 4.6 F: from Fagatogo to Funchal 153 4.7 G: from Gabes to Gyumri 156 4.8 H: from Hachinohe to Hyderabad 160 4.9 I: from Iasi to Izmir 164 4.10 J: from Jaboatao to Jyvaskyla 167 4.11 K: from Kabul to Kzyl-Orda 170 4.12 L: from La Ceiba to Lyon 178 4.13 M: from Macae to Mzuzu 184 4.14 N: from Nacala to Nzerekore 194 4.15 O: from Oaklahoma City to Oyem 199 4.16 Ö: from Örebro to Örebro 201 4.17 P: from Pago Pago to Pyuthan 202 4.18 Q: from Qandahar to Quito 209 4.19 R: from Rabat to Rustavi 210 4.20 S: from S. Luis Potosi to Szombathely 213 4.21 T: from Tabligbo to Tyre 225 4.22 U: from Uberaba to Utulei 232 4.23 V: from Vacoas-Phoenix to Vukovar 234 4.24 W: from Wadi Medani to Wuhan 237 4.25 X: from Xalapa to Xian 238 4.26 Y: from Yamagata to Yungkang 239 4.27 Z: from Zadar to Zvishavane 240 5 CITY SEGMENTS RANKED BY COUNTRY 241 5.1 Afghanistan 241 5.2 Albania 241 5.3 Algeria 242 5.4 American Samoa 242 5.5 Andorra 242 5.6 Angola 243 5.7 Antigua and Barbuda 243 5.8 Argentina 244 5.9 Armenia 245 5.10 Aruba 245 5.11 Australia 246 5.12 Austria 246 5.13 Azerbaijan 247 5.14 Bahrain 247 5.15 Bangladesh 248 5.16 Barbados 248 5.17 Belarus 249 5.18 Belgium 249 5.19 Belize 250 5.20 Benin 250 5.21 Bermuda 250 5.22 Bhutan 251 5.23 Bolivia 251 5.24 Bosnia and Herzegovina 251 5.25 Botswana 252 5.26 Brazil 253 5.27 Brunei 258 5.28 Bulgaria 258 5.29 Burkina Faso 259 5.30 Burma 259 5.31 Burundi 259 5.32 Cambodia 260 5.33 Cameroon 260 5.34 Canada 261 5.35 Cape Verde 261 5.36 Central African Republic 262 5.37 Chad 262 5.38 Chile 263 5.39 China 263 5.40 Christmas Island 264 5.41 Colombia 264 5.42 Comoros 264 5.43 Congo (formerly Zaire) 265 5.44 Cook Islands 265 5.45 Costa Rica 265 5.46 Cote dIvoire 266 5.47 Croatia 266 5.48 Cuba 267 5.49 Cyprus 267 5.50 Czech Republic 268 5.51 Denmark 268 5.52 Djibouti 269 5.53 Dominica 269 5.54 Dominican Republic 269 5.55 Ecuador 270 5.56 Egypt 270 5.57 El Salvador 271 5.58 Equatorial Guinea 271 5.59 Estonia 271 5.60 Ethiopia 272 5.61 Fiji 272 5.62 Finland 273 5.63 France 273 5.64 French Guiana 274 5.65 French Polynesia 274 5.66 Gabon 274 5.67 Georgia 275 5.68 Germany 275 5.69 Ghana 276 5.70 Greece 276 5.71 Greenland 277 5.72 Grenada 277 5.73 Guadeloupe 278 5.74 Guam 278 5.75 Guatemala 279 5.76 Guinea 279 5.77 Guinea-Bissau 279 5.78 Guyana 280 5.79 Haiti 280 5.80 Honduras 280 5.81 Hong Kong 281 5.82 Hungary 281 5.83 Iceland 281 5.84 India 282 5.85 Indonesia 283 5.86 Iran 284 5.87 Iraq 284 5.88 Ireland 285 5.89 Israel 285 5.90 Italy 286 5.91 Jamaica 286 5.92 Japan 287 5.93 Jordan 290 5.94 Kazakhstan 290 5.95 Kenya 291 5.96 Kiribati 291 5.97 Kuwait 291 5.98 Kyrgyzstan 292 5.99 Laos 292 5.100 Latvia 292 5.101 Lebanon 293 5.102 Lesotho 293 5.103 Liberia 293 5.104 Libya 294 5.105 Liechtenstein 294 5.106 Lithuania 294 5.107 Luxembourg 295 5.108 Macau 295 5.109 Madagascar 295 5.110 Malawi 296 5.111 Malaysia 296 5.112 Maldives 297 5.113 Mali 297 5.114 Malta 297 5.115 Marshall Islands 298 5.116 Martinique 298 5.117 Mauritania 298 5.118 Mauritius 299 5.119 Mexico 300 5.120 Micronesia Federation 301 5.121 Moldova 301 5.122 Monaco 301 5.123 Mongolia 302 5.124 Morocco 302 5.125 Mozambique 303 5.126 Namibia 303 5.127 Nauru 303 5.128 Nepal 304 5.129 New Caledonia 304 5.130 New Zealand 305 5.131 Nicaragua 305 5.132 Niger 306 5.133 Nigeria 306 5.134 Niue 307 5.135 Norfolk Island 307 5.136 North Korea 307 5.137 Norway 308 5.138 Oman 308 5.139 Pakistan 309 5.140 Palau 309 5.141 Palestine 309 5.142 Panama 310 5.143 Papua New Guinea 310 5.144 Paraguay 311 5.145 Peru 311 5.146 Philippines 312 5.147 Poland 312 5.148 Portugal 313 5.149 Puerto Rico 313 5.150 Qatar 314 5.151 Republic of Congo 314 5.152 Reunion 314 5.153 Romania 315 5.154 Russia 315 5.155 Rwanda 316 5.156 San Marino 316 5.157 Sao Tome E Principe 316 5.158 Saudi Arabia 317 5.159 Senegal 317 5.160 Seychelles 318 5.161 Sierra Leone 318 5.162 Singapore 318 5.163 Slovakia 319 5.164 Slovenia 319 5.165 Solomon Islands 319 5.166 Somalia 320 5.167 South Africa 320 5.168 South Korea 321 5.169 Spain 321 5.170 Sri Lanka 322 5.171 St. Kitts and Nevis 322 5.172 St. Lucia 322 5.173 St. Vincent and the Grenadines 323 5.174 Sudan 323 5.175 Suriname 323 5.176 Swaziland 324 5.177 Sweden 324 5.178 Switzerland 325 5.179 Syrian Arab Republic 325 5.180 Taiwan 326 5.181 Tajikistan 327 5.182 Tanzania 327 5.183 Thailand 328 5.184 The Bahamas 328 5.185 The British Virgin Islands 328 5.186 The Cayman Islands 329 5.187 The Falkland Islands 329 5.188 The Gambia 329 5.189 The Netherlands 330 5.190 The Netherlands Antilles 330 5.191 The Northern Mariana Island 330 5.192 The U.S. Virgin Islands 331 5.193 The United Arab Emirates 331 5.194 The United Kingdom 332 5.195 The United States 333 5.196 Togo 334 5.197 Tokelau 334 5.198 Tonga 335 5.199 Trinidad and Tobago 335 5.200 Tunisia 335 5.201 Turkey 336 5.202 Turkmenistan 336 5.203 Tuvalu 336 5.204 Uganda 337 5.205 Ukraine 337 5.206 Uruguay 338 5.207 Uzbekistan 338 5.208 Vanuatu 339 5.209 Venezuela 339 5.210 Vietnam 340 5.211 Wallis and Futuna 340 5.212 Western Sahara 340 5.213 Western Samoa 340 5.214 Yemen 341 5.215 Zambia 341 5.216 Zimbabwe 342 6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 343 6.1 Disclaimers & Safe Harbor 343 6.2 ICON Group International, Inc. User Agreement Provisions 344
 
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