The 2009 Report on Non-Metallic Cloth-Resin and Waterproof-Bond Shapes, That Have Been Coated or Impregnated with any Natural or Artificial Abrasive Material: World Market Segmentation by City
ICON Group International, May 2009, Pages: 346
Market Potential Estimation Methodology
Overview
This study covers the world outlook for non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material 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 non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material. 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 non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material 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 non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material 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 non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material 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 non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material 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 non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material. 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 non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material. 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 non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material.
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 “non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material” 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 non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material 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 “non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material is 3279107221. It is for this definition of non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material that the aggregate latent demand estimates are derived. “Non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material” is specifically defined as follows:
3279107221
Other nonmetallic shapes, coated or impregnated with any natural or artificial abrasive material, cloth_resin and waterproof bond
Step 2. Filtering and Smoothing
Based on the aggregate view of non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material 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 non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material 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 non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material 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 non-metallic cloth-resin and waterproof-bond shapes, that have been coated or impregnated with any natural or artificial abrasive material). 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
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 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 169
4.12 L: from La Ceiba to Lyon 177
4.13 M: from Macae to Mzuzu 183
4.14 N: from Nacala to Nzerekore 193
4.15 O: from Oaklahoma City to Oyem 198
4.16 Ö: from Örebro to Örebro 200
4.17 P: from Pago Pago to Pyuthan 201
4.18 Q: from Qandahar to Quito 208
4.19 R: from Rabat to Rustavi 209
4.20 S: from S. Luis Potosi to Szombathely 212
4.21 T: from Tabligbo to Tyre 224
4.22 U: from Uberaba to Utulei 231
4.23 V: from Vacoas-Phoenix to Vukovar 233
4.24 W: from Wadi Medani to Wuhan 236
4.25 X: from Xalapa to Xian 237
4.26 Y: from Yamagata to Yungkang 238
4.27 Z: from Zadar to Zvishavane 239
5 CITY SEGMENTS RANKED BY COUNTRY 240
5.1 Afghanistan 240
5.2 Albania 240
5.3 Algeria 241
5.4 American Samoa 241
5.5 Andorra 241
5.6 Angola 242
5.7 Antigua and Barbuda 242
5.8 Argentina 243
5.9 Armenia 244
5.10 Aruba 244
5.11 Australia 245
5.12 Austria 245
5.13 Azerbaijan 246
5.14 Bahrain 246
5.15 Bangladesh 247
5.16 Barbados 247
5.17 Belarus 248
5.18 Belgium 248
5.19 Belize 249
5.20 Benin 249
5.21 Bermuda 249
5.22 Bhutan 250
5.23 Bolivia 250
5.24 Bosnia and Herzegovina 250
5.25 Botswana 251
5.26 Brazil 252
5.27 Brunei 257
5.28 Bulgaria 257
5.29 Burkina Faso 258
5.30 Burma 258
5.31 Burundi 258
5.32 Cambodia 259
5.33 Cameroon 259
5.34 Canada 260
5.35 Cape Verde 260
5.36 Central African Republic 261
5.37 Chad 261
5.38 Chile 262
5.39 China 262
5.40 Christmas Island 263
5.41 Colombia 263
5.42 Comoros 264
5.43 Congo (formerly Zaire) 264
5.44 Cook Islands 264
5.45 Costa Rica 265
5.46 Cote dIvoire 265
5.47 Croatia 266
5.48 Cuba 266
5.49 Cyprus 267
5.50 Czech Republic 267
5.51 Denmark 268
5.52 Djibouti 268
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 281
5.81 Hong Kong 281
5.82 Hungary 282
5.83 Iceland 282
5.84 India 283
5.85 Indonesia 284
5.86 Iran 285
5.87 Iraq 285
5.88 Ireland 286
5.89 Israel 286
5.90 Italy 287
5.91 Jamaica 287
5.92 Japan 288
5.93 Jordan 291
5.94 Kazakhstan 291
5.95 Kenya 292
5.96 Kiribati 292
5.97 Kuwait 292
5.98 Kyrgyzstan 293
5.99 Laos 293
5.100 Latvia 293
5.101 Lebanon 294
5.102 Lesotho 294
5.103 Liberia 294
5.104 Libya 295
5.105 Liechtenstein 295
5.106 Lithuania 296
5.107 Luxembourg 296
5.108 Macau 296
5.109 Madagascar 297
5.110 Malawi 297
5.111 Malaysia 298
5.112 Maldives 298
5.113 Mali 299
5.114 Malta 299
5.115 Marshall Islands 299
5.116 Martinique 300
5.117 Mauritania 300
5.118 Mauritius 301
5.119 Mexico 302
5.120 Micronesia Federation 303
5.121 Moldova 303
5.122 Monaco 303
5.123 Mongolia 304
5.124 Morocco 304
5.125 Mozambique 305
5.126 Namibia 305
5.127 Nauru 305
5.128 Nepal 306
5.129 New Caledonia 306
5.130 New Zealand 307
5.131 Nicaragua 307
5.132 Niger 308
5.133 Nigeria 308
5.134 Niue 309
5.135 Norfolk Island 309
5.136 North Korea 309
5.137 Norway 310
5.138 Oman 310
5.139 Pakistan 311
5.140 Palau 311
5.141 Palestine 311
5.142 Panama 312
5.143 Papua New Guinea 312
5.144 Paraguay 313
5.145 Peru 313
5.146 Philippines 314
5.147 Poland 314
5.148 Portugal 315
5.149 Puerto Rico 315
5.150 Qatar 316
5.151 Republic of Congo 316
5.152 Reunion 316
5.153 Romania 317
5.154 Russia 317
5.155 Rwanda 318
5.156 San Marino 318
5.157 Sao Tome E Principe 318
5.158 Saudi Arabia 319
5.159 Senegal 319
5.160 Seychelles 320
5.161 Sierra Leone 320
5.162 Singapore 320
5.163 Slovakia 321
5.164 Slovenia 321
5.165 Solomon Islands 321
5.166 Somalia 322
5.167 South Africa 322
5.168 South Korea 323
5.169 Spain 323
5.170 Sri Lanka 324
5.171 St. Kitts and Nevis 324
5.172 St. Lucia 324
5.173 St. Vincent and the Grenadines 325
5.174 Sudan 325
5.175 Suriname 325
5.176 Swaziland 326
5.177 Sweden 326
5.178 Switzerland 327
5.179 Syrian Arab Republic 327
5.180 Taiwan 328
5.181 Tajikistan 329
5.182 Tanzania 329
5.183 Thailand 330
5.184 The Bahamas 330
5.185 The British Virgin Islands 330
5.186 The Cayman Islands 331
5.187 The Falkland Islands 331
5.188 The Gambia 331
5.189 The Netherlands 332
5.190 The Netherlands Antilles 332
5.191 The Northern Mariana Island 332
5.192 The U.S. Virgin Islands 333
5.193 The United Arab Emirates 333
5.194 The United Kingdom 334
5.195 The United States 335
5.196 Togo 336
5.197 Tokelau 336
5.198 Tonga 337
5.199 Trinidad and Tobago 337
5.200 Tunisia 337
5.201 Turkey 338
5.202 Turkmenistan 338
5.203 Tuvalu 338
5.204 Uganda 339
5.205 Ukraine 339
5.206 Uruguay 340
5.207 Uzbekistan 340
5.208 Vanuatu 341
5.209 Venezuela 341
5.210 Vietnam 342
5.211 Wallis and Futuna 342
5.212 Western Sahara 342
5.213 Western Samoa 343
5.214 Yemen 343
5.215 Zambia 343
5.216 Zimbabwe 344
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 345
6.1 Disclaimers & Safe Harbor 345
6.2 ICON Group International, Inc. User Agreement Provisions 346
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