The 2009 Report on Liquefied Refinery Gases and Aliphatics Not Made in a Refinery: World Market Segmentation by City
ICON Group International, May 2009, Pages: 335
Market Potential Estimation Methodology
Overview
This study covers the world outlook for liquefied refinery gases and aliphatics not made in a refinery 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 liquefied refinery gases and aliphatics not made in a refinery. 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 liquefied refinery gases and aliphatics not made in a refinery 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 liquefied refinery gases and aliphatics not made in a refinery 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 liquefied refinery gases and aliphatics not made in a refinery 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 liquefied refinery gases and aliphatics not made in a refinery 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 liquefied refinery gases and aliphatics not made in a refinery. 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 liquefied refinery gases and aliphatics not made in a refinery. 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 liquefied refinery gases and aliphatics not made in a refinery.
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 “liquefied refinery gases and aliphatics not made in a refinery” 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 liquefied refinery gases and aliphatics not made in a refinery 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 “liquefied refinery gases and aliphatics not made in a refinery” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of liquefied refinery gases and aliphatics not made in a refinery, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for liquefied refinery gases and aliphatics not made in a refinery is 3251104. It is for this definition of liquefied refinery gases and aliphatics not made in a refinery that the aggregate latent demand estimates are derived. “Liquefied refinery gases and aliphatics not made in a refinery” is specifically defined as follows:
3251104
Liquefied refinery gases (aliphatics), not made in a refinery
32511041
Liquefied refinery gases (aliphatics), made in petrochemical plants
3251104111
Liquefied refinery gases (aliphatics), for use as a chemical raw material, made in petrochemical plants
3251104121
Liquefied refinery gases (aliphatics), for other uses, made in petrochemical plants
Step 2. Filtering and Smoothing
Based on the aggregate view of liquefied refinery gases and aliphatics not made in a refinery 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 liquefied refinery gases and aliphatics not made in a refinery 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 liquefied refinery gases and aliphatics not made in a refinery 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 liquefied refinery gases and aliphatics not made in a refinery). 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 15
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 16
1.2.3.6 Step 6. Aggregation and Benchmarking 16
2 USING THE DATA 17
3 CITY SEGMENTS RANKED BY MARKET SIZE 18
3.1 Top 15 Markets 18
3.2 Markets 16 to 30 19
3.3 Remaining Cities by Market Rank 20
4 CITY SEGMENTS IN ALPHABETICAL ORDER 123
4.1 A: from Aalborg to Az Zawiyah 123
4.2 B: from Bacolod to Bydgoszcz 130
4.3 C: from Caaguazu to Cyangugu 138
4.4 D: from Da Nang to Dzhizak 146
4.5 E: from East London to Esteli 150
4.6 F: from Fagatogo to Funchal 152
4.7 G: from Gabes to Gyumri 155
4.8 H: from Hachinohe to Hyderabad 159
4.9 I: from Iasi to Izmir 163
4.10 J: from Jaboatao to Jyvaskyla 166
4.11 K: from Kabul to Kzyl-Orda 168
4.12 L: from La Ceiba to Lyon 176
4.13 M: from Macae to Mzuzu 181
4.14 N: from Nacala to Nzerekore 191
4.15 O: from Oaklahoma City to Oyem 196
4.16 Ö: from Örebro to Örebro 198
4.17 P: from Pago Pago to Pyuthan 199
4.18 Q: from Qandahar to Quito 205
4.19 R: from Rabat to Rustavi 206
4.20 S: from S. Luis Potosi to Szombathely 209
4.21 T: from Tabligbo to Tyre 221
4.22 U: from Uberaba to Utulei 228
4.23 V: from Vacoas-Phoenix to Vukovar 230
4.24 W: from Wadi Medani to Wuhan 233
4.25 X: from Xalapa to Xian 234
4.26 Y: from Yamagata to Yungkang 235
4.27 Z: from Zadar to Zvishavane 236
5 CITY SEGMENTS RANKED BY COUNTRY 237
5.1 Afghanistan 237
5.2 Albania 237
5.3 Algeria 238
5.4 American Samoa 238
5.5 Andorra 238
5.6 Angola 239
5.7 Antigua and Barbuda 239
5.8 Argentina 240
5.9 Armenia 241
5.10 Aruba 241
5.11 Australia 242
5.12 Austria 242
5.13 Azerbaijan 243
5.14 Bahrain 243
5.15 Bangladesh 244
5.16 Barbados 244
5.17 Belarus 244
5.18 Belgium 245
5.19 Belize 245
5.20 Benin 245
5.21 Bermuda 246
5.22 Bhutan 246
5.23 Bolivia 246
5.24 Bosnia and Herzegovina 247
5.25 Botswana 247
5.26 Brazil 248
5.27 Brunei 253
5.28 Bulgaria 253
5.29 Burkina Faso 254
5.30 Burma 254
5.31 Burundi 254
5.32 Cambodia 255
5.33 Cameroon 255
5.34 Canada 256
5.35 Cape Verde 256
5.36 Central African Republic 257
5.37 Chad 257
5.38 Chile 258
5.39 China 258
5.40 Christmas Island 259
5.41 Colombia 259
5.42 Comoros 259
5.43 Congo (formerly Zaire) 260
5.44 Cook Islands 260
5.45 Costa Rica 260
5.46 Cote dIvoire 261
5.47 Croatia 261
5.48 Cuba 262
5.49 Cyprus 262
5.50 Czech Republic 262
5.51 Denmark 263
5.52 Djibouti 263
5.53 Dominica 263
5.54 Dominican Republic 264
5.55 Ecuador 264
5.56 Egypt 265
5.57 El Salvador 265
5.58 Equatorial Guinea 265
5.59 Estonia 266
5.60 Ethiopia 266
5.61 Fiji 266
5.62 Finland 267
5.63 France 267
5.64 French Guiana 268
5.65 French Polynesia 268
5.66 Gabon 268
5.67 Georgia 269
5.68 Germany 269
5.69 Ghana 270
5.70 Greece 270
5.71 Greenland 271
5.72 Grenada 271
5.73 Guadeloupe 272
5.74 Guam 272
5.75 Guatemala 272
5.76 Guinea 273
5.77 Guinea-Bissau 273
5.78 Guyana 273
5.79 Haiti 274
5.80 Honduras 274
5.81 Hong Kong 274
5.82 Hungary 275
5.83 Iceland 275
5.84 India 276
5.85 Indonesia 277
5.86 Iran 278
5.87 Iraq 278
5.88 Ireland 279
5.89 Israel 279
5.90 Italy 280
5.91 Jamaica 280
5.92 Japan 281
5.93 Jordan 283
5.94 Kazakhstan 284
5.95 Kenya 284
5.96 Kiribati 285
5.97 Kuwait 285
5.98 Kyrgyzstan 285
5.99 Laos 286
5.100 Latvia 286
5.101 Lebanon 286
5.102 Lesotho 287
5.103 Liberia 287
5.104 Libya 287
5.105 Liechtenstein 288
5.106 Lithuania 288
5.107 Luxembourg 288
5.108 Macau 289
5.109 Madagascar 289
5.110 Malawi 289
5.111 Malaysia 290
5.112 Maldives 290
5.113 Mali 291
5.114 Malta 291
5.115 Marshall Islands 291
5.116 Martinique 292
5.117 Mauritania 292
5.118 Mauritius 292
5.119 Mexico 293
5.120 Micronesia Federation 294
5.121 Moldova 294
5.122 Monaco 294
5.123 Mongolia 295
5.124 Morocco 295
5.125 Mozambique 296
5.126 Namibia 296
5.127 Nauru 296
5.128 Nepal 297
5.129 New Caledonia 297
5.130 New Zealand 298
5.131 Nicaragua 298
5.132 Niger 299
5.133 Nigeria 299
5.134 Niue 299
5.135 Norfolk Island 300
5.136 North Korea 300
5.137 Norway 300
5.138 Oman 301
5.139 Pakistan 301
5.140 Palau 301
5.141 Palestine 302
5.142 Panama 302
5.143 Papua New Guinea 302
5.144 Paraguay 303
5.145 Peru 303
5.146 Philippines 304
5.147 Poland 304
5.148 Portugal 305
5.149 Puerto Rico 305
5.150 Qatar 306
5.151 Republic of Congo 306
5.152 Reunion 306
5.153 Romania 307
5.154 Russia 307
5.155 Rwanda 308
5.156 San Marino 308
5.157 Sao Tome E Principe 308
5.158 Saudi Arabia 309
5.159 Senegal 309
5.160 Seychelles 309
5.161 Sierra Leone 310
5.162 Singapore 310
5.163 Slovakia 310
5.164 Slovenia 311
5.165 Solomon Islands 311
5.166 Somalia 311
5.167 South Africa 312
5.168 South Korea 312
5.169 Spain 313
5.170 Sri Lanka 313
5.171 St. Kitts and Nevis 314
5.172 St. Lucia 314
5.173 St. Vincent and the Grenadines 314
5.174 Sudan 314
5.175 Suriname 315
5.176 Swaziland 315
5.177 Sweden 316
5.178 Switzerland 316
5.179 Syrian Arab Republic 317
5.180 Taiwan 318
5.181 Tajikistan 319
5.182 Tanzania 319
5.183 Thailand 320
5.184 The Bahamas 320
5.185 The British Virgin Islands 320
5.186 The Cayman Islands 321
5.187 The Falkland Islands 321
5.188 The Gambia 321
5.189 The Netherlands 322
5.190 The Netherlands Antilles 322
5.191 The Northern Mariana Island 322
5.192 The U.S. Virgin Islands 323
5.193 The United Arab Emirates 323
5.194 The United Kingdom 323
5.195 The United States 324
5.196 Togo 325
5.197 Tokelau 325
5.198 Tonga 325
5.199 Trinidad and Tobago 326
5.200 Tunisia 326
5.201 Turkey 327
5.202 Turkmenistan 327
5.203 Tuvalu 327
5.204 Uganda 328
5.205 Ukraine 328
5.206 Uruguay 329
5.207 Uzbekistan 329
5.208 Vanuatu 330
5.209 Venezuela 330
5.210 Vietnam 331
5.211 Wallis and Futuna 331
5.212 Western Sahara 331
5.213 Western Samoa 331
5.214 Yemen 332
5.215 Zambia 332
5.216 Zimbabwe 333
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 334
6.1 Disclaimers & Safe Harbor 334
6.2 ICON Group International, Inc. User Agreement Provisions 335
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