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The 2009 Report on Pick-Up Truck Campers for Sliding on and Off Trucks, Caps, and Box Covers: World Market Segmentation by City

Market Potential Estimation Methodology Overview This study covers the world outlook for pick-up truck campers for sliding on and off trucks, caps, and box covers 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 pick-up truck campers for sliding on and off trucks, caps, and box covers. 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 pick-up truck campers for sliding on and off trucks, caps, and box covers 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 pick-up truck campers for sliding on and off trucks, caps, and box covers 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 pick-up truck campers for sliding on and off trucks, caps, and box covers 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 pick-up truck campers for sliding on and off trucks, caps, and box covers 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 pick-up truck campers for sliding on and off trucks, caps, and box covers. 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 pick-up truck campers for sliding on and off trucks, caps, and box covers. 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 pick-up truck campers for sliding on and off trucks, caps, and box covers. 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 “pick-up truck campers for sliding on and off trucks, caps, and box covers” 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 pick-up truck campers for sliding on and off trucks, caps, and box covers 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 “pick-up truck campers for sliding on and off trucks, caps, and box covers” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of pick-up truck campers for sliding on and off trucks, caps, and box covers, please refer to the Web site at The NAICS code for pick-up truck campers for sliding on and off trucks, caps, and box covers is 33621452. It is for this definition of pick-up truck campers for sliding on and off trucks, caps, and box covers that the aggregate latent demand estimates are derived. “Pick-up truck campers for sliding on and off trucks, caps, and box covers” is specifically defined as follows: 33621452 Truck (pickup) campers (for sliding on and off trucks), caps, and box covers  3362145204 Truck (pickup) campers (for sliding on and off trucks), excluding parts  3362145207 Truck (pickup) caps and box covers, excluding parts   Step 2. Filtering and Smoothing Based on the aggregate view of pick-up truck campers for sliding on and off trucks, caps, and box covers 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 pick-up truck campers for sliding on and off trucks, caps, and box covers 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 pick-up truck campers for sliding on and off trucks, caps, and box covers 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 pick-up truck campers for sliding on and off trucks, caps, and box covers). 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.
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 Step 1. Product Definition and Data Collection 14 Step 2. Filtering and Smoothing 15 Step 3. Filling in Missing Values 15 Step 4. Varying Parameter, Non-linear Estimation 15 Step 5. Fixed-Parameter Linear Estimation 16 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 182 4.14 N: from Nacala to Nzerekore 192 4.15 O: from Oaklahoma City to Oyem 197 4.16 Ö: from Örebro to Örebro 199 4.17 P: from Pago Pago to Pyuthan 200 4.18 Q: from Qandahar to Quito 207 4.19 R: from Rabat to Rustavi 208 4.20 S: from S. Luis Potosi to Szombathely 211 4.21 T: from Tabligbo to Tyre 223 4.22 U: from Uberaba to Utulei 230 4.23 V: from Vacoas-Phoenix to Vukovar 232 4.24 W: from Wadi Medani to Wuhan 235 4.25 X: from Xalapa to Xian 236 4.26 Y: from Yamagata to Yungkang 237 4.27 Z: from Zadar to Zvishavane 238 5 CITY SEGMENTS RANKED BY COUNTRY 239 5.1 Afghanistan 239 5.2 Albania 239 5.3 Algeria 240 5.4 American Samoa 240 5.5 Andorra 240 5.6 Angola 241 5.7 Antigua and Barbuda 241 5.8 Argentina 242 5.9 Armenia 243 5.10 Aruba 243 5.11 Australia 244 5.12 Austria 244 5.13 Azerbaijan 245 5.14 Bahrain 245 5.15 Bangladesh 246 5.16 Barbados 246 5.17 Belarus 247 5.18 Belgium 247 5.19 Belize 248 5.20 Benin 248 5.21 Bermuda 248 5.22 Bhutan 249 5.23 Bolivia 249 5.24 Bosnia and Herzegovina 249 5.25 Botswana 250 5.26 Brazil 251 5.27 Brunei 256 5.28 Bulgaria 256 5.29 Burkina Faso 257 5.30 Burma 257 5.31 Burundi 257 5.32 Cambodia 258 5.33 Cameroon 258 5.34 Canada 259 5.35 Cape Verde 259 5.36 Central African Republic 260 5.37 Chad 260 5.38 Chile 261 5.39 China 261 5.40 Christmas Island 262 5.41 Colombia 262 5.42 Comoros 262 5.43 Congo (formerly Zaire) 263 5.44 Cook Islands 263 5.45 Costa Rica 263 5.46 Cote dIvoire 264 5.47 Croatia 264 5.48 Cuba 265 5.49 Cyprus 265 5.50 Czech Republic 266 5.51 Denmark 266 5.52 Djibouti 267 5.53 Dominica 267 5.54 Dominican Republic 267 5.55 Ecuador 268 5.56 Egypt 268 5.57 El Salvador 269 5.58 Equatorial Guinea 269 5.59 Estonia 269 5.60 Ethiopia 270 5.61 Fiji 270 5.62 Finland 271 5.63 France 271 5.64 French Guiana 272 5.65 French Polynesia 272 5.66 Gabon 272 5.67 Georgia 273 5.68 Germany 273 5.69 Ghana 274 5.70 Greece 274 5.71 Greenland 275 5.72 Grenada 275 5.73 Guadeloupe 276 5.74 Guam 276 5.75 Guatemala 276 5.76 Guinea 277 5.77 Guinea-Bissau 277 5.78 Guyana 277 5.79 Haiti 278 5.80 Honduras 278 5.81 Hong Kong 278 5.82 Hungary 279 5.83 Iceland 279 5.84 India 280 5.85 Indonesia 281 5.86 Iran 282 5.87 Iraq 282 5.88 Ireland 283 5.89 Israel 283 5.90 Italy 284 5.91 Jamaica 284 5.92 Japan 285 5.93 Jordan 288 5.94 Kazakhstan 288 5.95 Kenya 289 5.96 Kiribati 289 5.97 Kuwait 289 5.98 Kyrgyzstan 290 5.99 Laos 290 5.100 Latvia 290 5.101 Lebanon 291 5.102 Lesotho 291 5.103 Liberia 291 5.104 Libya 292 5.105 Liechtenstein 292 5.106 Lithuania 292 5.107 Luxembourg 293 5.108 Macau 293 5.109 Madagascar 293 5.110 Malawi 294 5.111 Malaysia 294 5.112 Maldives 295 5.113 Mali 295 5.114 Malta 295 5.115 Marshall Islands 296 5.116 Martinique 296 5.117 Mauritania 296 5.118 Mauritius 297 5.119 Mexico 298 5.120 Micronesia Federation 299 5.121 Moldova 299 5.122 Monaco 299 5.123 Mongolia 300 5.124 Morocco 300 5.125 Mozambique 301 5.126 Namibia 301 5.127 Nauru 301 5.128 Nepal 302 5.129 New Caledonia 302 5.130 New Zealand 303 5.131 Nicaragua 303 5.132 Niger 304 5.133 Nigeria 304 5.134 Niue 305 5.135 Norfolk Island 305 5.136 North Korea 305 5.137 Norway 306 5.138 Oman 306 5.139 Pakistan 307 5.140 Palau 307 5.141 Palestine 307 5.142 Panama 308 5.143 Papua New Guinea 308 5.144 Paraguay 309 5.145 Peru 309 5.146 Philippines 310 5.147 Poland 310 5.148 Portugal 311 5.149 Puerto Rico 311 5.150 Qatar 312 5.151 Republic of Congo 312 5.152 Reunion 312 5.153 Romania 313 5.154 Russia 313 5.155 Rwanda 314 5.156 San Marino 314 5.157 Sao Tome E Principe 314 5.158 Saudi Arabia 315 5.159 Senegal 315 5.160 Seychelles 316 5.161 Sierra Leone 316 5.162 Singapore 316 5.163 Slovakia 316 5.164 Slovenia 317 5.165 Solomon Islands 317 5.166 Somalia 317 5.167 South Africa 318 5.168 South Korea 318 5.169 Spain 319 5.170 Sri Lanka 319 5.171 St. Kitts and Nevis 320 5.172 St. Lucia 320 5.173 St. Vincent and the Grenadines 320 5.174 Sudan 321 5.175 Suriname 321 5.176 Swaziland 321 5.177 Sweden 322 5.178 Switzerland 322 5.179 Syrian Arab Republic 323 5.180 Taiwan 324 5.181 Tajikistan 325 5.182 Tanzania 325 5.183 Thailand 326 5.184 The Bahamas 326 5.185 The British Virgin Islands 326 5.186 The Cayman Islands 327 5.187 The Falkland Islands 327 5.188 The Gambia 327 5.189 The Netherlands 328 5.190 The Netherlands Antilles 328 5.191 The Northern Mariana Island 328 5.192 The U.S. Virgin Islands 329 5.193 The United Arab Emirates 329 5.194 The United Kingdom 329 5.195 The United States 330 5.196 Togo 331 5.197 Tokelau 331 5.198 Tonga 332 5.199 Trinidad and Tobago 332 5.200 Tunisia 332 5.201 Turkey 333 5.202 Turkmenistan 333 5.203 Tuvalu 333 5.204 Uganda 334 5.205 Ukraine 334 5.206 Uruguay 335 5.207 Uzbekistan 335 5.208 Vanuatu 336 5.209 Venezuela 336 5.210 Vietnam 337 5.211 Wallis and Futuna 337 5.212 Western Sahara 337 5.213 Western Samoa 337 5.214 Yemen 338 5.215 Zambia 338 5.216 Zimbabwe 339 6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 340 6.1 Disclaimers & Safe Harbor 340 6.2 ICON Group International, Inc. User Agreement Provisions 341
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