The 2009 Report on Industrial and Non-Industrial Personal Safety Equipment and Clothing: World Market Segmentation by City
ICON Group International, May 2009, Pages: 340
Market Potential Estimation Methodology
Overview
This study covers the world outlook for industrial and non-industrial personal safety equipment and clothing 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 industrial and non-industrial personal safety equipment and clothing. 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 industrial and non-industrial personal safety equipment and clothing 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 industrial and non-industrial personal safety equipment and clothing 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 industrial and non-industrial personal safety equipment and clothing 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 industrial and non-industrial personal safety equipment and clothing 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 industrial and non-industrial personal safety equipment and clothing. 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 industrial and non-industrial personal safety equipment and clothing. 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 industrial and non-industrial personal safety equipment and clothing.
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 “industrial and non-industrial personal safety equipment and clothing” 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 industrial and non-industrial personal safety equipment and clothing 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 “industrial and non-industrial personal safety equipment and clothing” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of industrial and non-industrial personal safety equipment and clothing, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for industrial and non-industrial personal safety equipment and clothing is 3391136. It is for this definition of industrial and non-industrial personal safety equipment and clothing that the aggregate latent demand estimates are derived. “Industrial and non-industrial personal safety equipment and clothing” is specifically defined as follows:
3391136
PERSONAL INDUSTRIAL AND NONINDUSTRIAL SAFETY EQUIPMENT AND CLOTHING
33911361
Personal industrial and nonindustrial safety equipment and clothing
3391136101
Respiratory protection equipment, including abrasive masks, canister masks, and gas masks
3391136106
Industrial helmets (hardhats)
3391136111
Eye and face protection equipment, including face shields, masks, and welding helmets (excluding eye protectors and industrial goggles)
3391136114
Industrial rubber gloves
3391136116
Other protective clothing (except footwear and gloves), including rubber and rubberized protective clothing
3391136121
First aid, snake bite, and burn kits, including household and industrial kits
3391136131
Other personal safety equipment, including life preservers (buoys, jackets, and vests) (except cork life preservers), and auto racing and motorcycle helmets
Step 2. Filtering and Smoothing
Based on the aggregate view of industrial and non-industrial personal safety equipment and clothing 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 industrial and non-industrial personal safety equipment and clothing 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 industrial and non-industrial personal safety equipment and clothing 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 industrial and non-industrial personal safety equipment and clothing). 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 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 169
4.12 L: from La Ceiba to Lyon 177
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 206
4.19 R: from Rabat to Rustavi 207
4.20 S: from S. Luis Potosi to Szombathely 210
4.21 T: from Tabligbo to Tyre 222
4.22 U: from Uberaba to Utulei 229
4.23 V: from Vacoas-Phoenix to Vukovar 231
4.24 W: from Wadi Medani to Wuhan 234
4.25 X: from Xalapa to Xian 235
4.26 Y: from Yamagata to Yungkang 236
4.27 Z: from Zadar to Zvishavane 237
5 CITY SEGMENTS RANKED BY COUNTRY 238
5.1 Afghanistan 238
5.2 Albania 238
5.3 Algeria 239
5.4 American Samoa 239
5.5 Andorra 239
5.6 Angola 240
5.7 Antigua and Barbuda 240
5.8 Argentina 241
5.9 Armenia 242
5.10 Aruba 242
5.11 Australia 243
5.12 Austria 243
5.13 Azerbaijan 244
5.14 Bahrain 244
5.15 Bangladesh 245
5.16 Barbados 245
5.17 Belarus 246
5.18 Belgium 246
5.19 Belize 247
5.20 Benin 247
5.21 Bermuda 247
5.22 Bhutan 248
5.23 Bolivia 248
5.24 Bosnia and Herzegovina 248
5.25 Botswana 249
5.26 Brazil 250
5.27 Brunei 255
5.28 Bulgaria 255
5.29 Burkina Faso 256
5.30 Burma 256
5.31 Burundi 256
5.32 Cambodia 257
5.33 Cameroon 257
5.34 Canada 258
5.35 Cape Verde 258
5.36 Central African Republic 259
5.37 Chad 259
5.38 Chile 260
5.39 China 260
5.40 Christmas Island 261
5.41 Colombia 261
5.42 Comoros 261
5.43 Congo (formerly Zaire) 262
5.44 Cook Islands 262
5.45 Costa Rica 262
5.46 Cote dIvoire 263
5.47 Croatia 263
5.48 Cuba 264
5.49 Cyprus 264
5.50 Czech Republic 265
5.51 Denmark 265
5.52 Djibouti 266
5.53 Dominica 266
5.54 Dominican Republic 266
5.55 Ecuador 267
5.56 Egypt 267
5.57 El Salvador 268
5.58 Equatorial Guinea 268
5.59 Estonia 268
5.60 Ethiopia 269
5.61 Fiji 269
5.62 Finland 270
5.63 France 270
5.64 French Guiana 271
5.65 French Polynesia 271
5.66 Gabon 271
5.67 Georgia 272
5.68 Germany 272
5.69 Ghana 273
5.70 Greece 273
5.71 Greenland 274
5.72 Grenada 274
5.73 Guadeloupe 275
5.74 Guam 275
5.75 Guatemala 275
5.76 Guinea 276
5.77 Guinea-Bissau 276
5.78 Guyana 276
5.79 Haiti 277
5.80 Honduras 277
5.81 Hong Kong 277
5.82 Hungary 278
5.83 Iceland 278
5.84 India 279
5.85 Indonesia 280
5.86 Iran 281
5.87 Iraq 281
5.88 Ireland 282
5.89 Israel 282
5.90 Italy 283
5.91 Jamaica 283
5.92 Japan 284
5.93 Jordan 287
5.94 Kazakhstan 287
5.95 Kenya 288
5.96 Kiribati 288
5.97 Kuwait 288
5.98 Kyrgyzstan 289
5.99 Laos 289
5.100 Latvia 289
5.101 Lebanon 290
5.102 Lesotho 290
5.103 Liberia 290
5.104 Libya 291
5.105 Liechtenstein 291
5.106 Lithuania 291
5.107 Luxembourg 292
5.108 Macau 292
5.109 Madagascar 292
5.110 Malawi 293
5.111 Malaysia 293
5.112 Maldives 294
5.113 Mali 294
5.114 Malta 294
5.115 Marshall Islands 295
5.116 Martinique 295
5.117 Mauritania 295
5.118 Mauritius 296
5.119 Mexico 297
5.120 Micronesia Federation 298
5.121 Moldova 298
5.122 Monaco 298
5.123 Mongolia 299
5.124 Morocco 299
5.125 Mozambique 300
5.126 Namibia 300
5.127 Nauru 300
5.128 Nepal 301
5.129 New Caledonia 301
5.130 New Zealand 302
5.131 Nicaragua 302
5.132 Niger 303
5.133 Nigeria 303
5.134 Niue 304
5.135 Norfolk Island 304
5.136 North Korea 304
5.137 Norway 305
5.138 Oman 305
5.139 Pakistan 306
5.140 Palau 306
5.141 Palestine 306
5.142 Panama 307
5.143 Papua New Guinea 307
5.144 Paraguay 308
5.145 Peru 308
5.146 Philippines 309
5.147 Poland 309
5.148 Portugal 310
5.149 Puerto Rico 310
5.150 Qatar 311
5.151 Republic of Congo 311
5.152 Reunion 311
5.153 Romania 312
5.154 Russia 312
5.155 Rwanda 313
5.156 San Marino 313
5.157 Sao Tome E Principe 313
5.158 Saudi Arabia 314
5.159 Senegal 314
5.160 Seychelles 315
5.161 Sierra Leone 315
5.162 Singapore 315
5.163 Slovakia 315
5.164 Slovenia 316
5.165 Solomon Islands 316
5.166 Somalia 316
5.167 South Africa 317
5.168 South Korea 317
5.169 Spain 318
5.170 Sri Lanka 318
5.171 St. Kitts and Nevis 319
5.172 St. Lucia 319
5.173 St. Vincent and the Grenadines 319
5.174 Sudan 320
5.175 Suriname 320
5.176 Swaziland 320
5.177 Sweden 321
5.178 Switzerland 321
5.179 Syrian Arab Republic 322
5.180 Taiwan 323
5.181 Tajikistan 324
5.182 Tanzania 324
5.183 Thailand 325
5.184 The Bahamas 325
5.185 The British Virgin Islands 325
5.186 The Cayman Islands 326
5.187 The Falkland Islands 326
5.188 The Gambia 326
5.189 The Netherlands 327
5.190 The Netherlands Antilles 327
5.191 The Northern Mariana Island 327
5.192 The U.S. Virgin Islands 328
5.193 The United Arab Emirates 328
5.194 The United Kingdom 328
5.195 The United States 329
5.196 Togo 330
5.197 Tokelau 330
5.198 Tonga 331
5.199 Trinidad and Tobago 331
5.200 Tunisia 331
5.201 Turkey 332
5.202 Turkmenistan 332
5.203 Tuvalu 332
5.204 Uganda 333
5.205 Ukraine 333
5.206 Uruguay 334
5.207 Uzbekistan 334
5.208 Vanuatu 335
5.209 Venezuela 335
5.210 Vietnam 336
5.211 Wallis and Futuna 336
5.212 Western Sahara 336
5.213 Western Samoa 336
5.214 Yemen 337
5.215 Zambia 337
5.216 Zimbabwe 338
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 339
6.1 Disclaimers & Safe Harbor 339
6.2 ICON Group International, Inc. User Agreement Provisions 340
Customers who bought this item also bought
All rights reserved. © Copyright 2013 Research and Markets WWW5
Terms and Conditions Privacy Policy Publishers Employment Opportunities Site Map Link to us Webmaster Affiliate Network
