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The 2009 Report on Production and Processing of Alumina and Aluminum: World Market Segmentation by City

Description:
Market Potential Estimation Methodology Overview This study covers the world outlook for production and processing of alumina and aluminum 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 production and processing of alumina and aluminum. 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 production and processing of alumina and aluminum 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 production and processing of alumina and aluminum 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 production and processing of alumina and aluminum 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 production and processing of alumina and aluminum 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 production and processing of alumina and aluminum. 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 production and processing of alumina and aluminum. 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 production and processing of alumina and aluminum. 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 “production and processing of alumina and aluminum” 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 production and processing of alumina and aluminum 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 “production and processing of alumina and aluminum” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of production and processing of alumina and aluminum, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for production and processing of alumina and aluminum is 3313. It is for this definition of production and processing of alumina and aluminum that the aggregate latent demand estimates are derived. “Production and processing of alumina and aluminum” is specifically defined as follows: 3313 Alumina and Aluminum Production and Processing  33131 This industry comprises establishments primarily engaged in one or more of the following: (1) refining alumina; (2) making (i.e., the primary production) aluminum from alumina; (3) recovering aluminum from scrap or dross; (4) alloying purchased aluminum; and (5) manufacturing aluminum primary forms (e.g., bar, foil, pipe, plate, rod, sheet, tube, wire).  331311 This U.S. industry comprises establishments primarily engaged in refining alumina (i.e., aluminum oxide) generally from bauxite.  3313110 ALUMINUM OXIDE, EXCEPT NATURAL ALUMINA  33131101 Aluminum oxide, except natural alumina  3313110100 Aluminum oxide, except natural alumina  3313111 Aluminum oxide, except natural alumina  331311M Miscellaneous receipts  331311P Primary products  331311S Secondary products  331311SM Secondary products and miscellaneous receipts  331312 This U.S. industry comprises establishments primarily engaged in (1) making aluminum from alumina and/or (2) making aluminum from alumina and rolling, drawing, extruding, or casting the aluminum they make into primary forms (e.g., bar, billet, ingot, plate, rod, sheet, strip). Establishments in this industry may make primary aluminum or aluminum-based alloys from alumina.  3313121 PRIMARY ALUMINUM INGOT, PRODUCED IN PRIMARY ALUMINUM REDUCTION PLANTS, INCLUDING PIGS, SOWS, AND MOLTEN METAL, EXCLUDING BILLET  33131211 Primary aluminum ingot, produced in primary aluminum reduction plants, including pigs, sows, and molten metal, excluding billet  3313121100 Primary refining of aluminum  3313122 ALUMINUM INGOT, INCLUDING BILLET, MADE IN PRIMARY ALUMINUM REDUCTION PLANTS  33131221 Aluminum ingot, including billet, made in primary aluminum reduction plants  3313122100 Aluminum ingot, including billet, made in primary aluminum reduction plants  3313123 PRIMARY ALUMINUM EXTRUSION INGOT (BILLET), PRODUCED IN PRIMARY ALUMINUM REDUCTION PLANTS  33131231 Primary aluminum extrusion ingot (billet), produced in primary aluminum reduction plants  3313123100 Primary aluminum extrusion ingot (billet), produced in primary aluminum reduction plants  3313127 Primary aluminum, except extrusion billet  3313128 Aluminum extrusion billet  331312M Miscellaneous receipts  331312P Primary products  331312S Secondary products  331312SM Secondary products and miscellaneous receipts  331314 This U.S. industry comprises establishments primarily engaged in (1) recovering aluminum and aluminum alloys from scrap and/or dross (i.e., secondary smelting) and making billet or ingot (except by rolling) and/or (2) manufacturing alloys, powder, paste, or flake from purchased aluminum.  3313141 Aluminum ingot, including pigs, sows, and molten metal, excluding billet, produced  33131411 Aluminum and aluminum~base alloys powders, paste, and flakes  3313141100 Aluminum ingot, including pigs, sows, and molten metal, excluding billet, produced by secondary smelters  3313142 ALUMINUM INGOT, INCLUDING BILLET, MADE BY SECONDARY SMELTERS  33131421 Aluminum ingot, including billet, made by secondary smelters  3313142100 Aluminum ingot, including billet, made by secondary smelters  3313143 Aluminum extrusion ingot (billet), produced by secondary smelters  33131431 Aluminum extrusion ingot (billet), produced by secondary smelters  3313143100 Aluminum extrusion ingot (billet), produced by secondary smelters  3313145 Aluminum and aluminum-base alloys powders, paste, and flakes  33131451 Aluminum and aluminum_base alloy powders, paste, and flakes from purchased aluminum  3313145100 Aluminum and aluminum_base alloy powders, paste, and flakes from purchased aluminum  3313145115 Aluminum and aluminum base alloys, powder and paste, atomized  3313145119 Aluminum and aluminum base alloys, other flake, powder, and paste  331314M Miscellaneous receipts  331314P Primary products  331314S Secondary products  331314SM Secondary products and miscellaneous receipts  331315 This U.S. industry comprises establishments primarily engaged in (1) flat rolling or continuous casting sheet, plate, foil and welded tube from purchased aluminum; and/or (2) recovering aluminum from scrap and flat rolling or continuous casting sheet, plate, foil, and welded tube in integrated mills.  3313151 Aluminum plate  33131511 Aluminum plate (thickness of 0.25 in. or more), including continuous cast  3313151101 Heat_treatable aluminum plate (thickness of 0.25 in. or more), including continuous cast  3313151106 Nonheat_treatable aluminum plate (thickness of 0.25 in. or more), including continuous cast  331315113 Heat-treatable  331315115 Nonheat-treatable  3313152 Aluminum sheet  331315223 Flat, heat-treatable  331315224 Flat, nonheat-treatable  331315227 Coiled, heat-treatable  331315231 Coiled, nonheat-treatable, bare  33131523114 Coiled, nonheat-treatable, bare, beverage can stock  33131523115 Coiled, nonheat-treatable, bare, all others  331315233 Coiled, nonheat-treatable, precoated  3313153 Aluminum foil  33131531 Flat aluminum sheet and strip, including continuous cast  3313153101 Flat, heat_treatable aluminum sheet and strip, including continuous cast  3313153106 Flat, nonheat_treatable, bare and precoated aluminum sheet and strip, including continuous cast  33131532 Coiled aluminum sheet and strip, including continuous cast  3313153211 Coiled, heat_treatable aluminum sheet and strip, including continuous cast  3313153216 Coiled, nonheat_treatable, bare aluminum sheet and strip, including continuous cast  3313153221 Coiled, nonheat_treatable, precoated, including only permanent finishes such as enameling and vinyl coatings aluminum sheet and strip, including continuous cast  331315351 Plain aluminum foil (under . 006 in.)  3313154 Aluminum welded tube  331315455 Plain aluminum welded tube  3313155 PLAIN ALUMINUM FOIL (LESS THAN .006 IN. THICK)  33131551 Plain aluminum foil (less than .006 in. thick)  3313155100 Plain aluminum foil (less than .006 in. thick)  3313157 ALUMINUM WELDED TUBE  33131571 Aluminum welded tube  3313157100 Aluminum welded tube  331315M Miscellaneous receipts  331315P Primary products  331315S Secondary products  331315SM Secondary products and miscellaneous receipts  331316 This U.S. industry comprises establishments primarily engaged in (1) extruding aluminum bar, pipe, and tube blooms or extruding or drawing tube from purchased aluminum; and/or (2) recovering aluminum from scrap and extruding bar, pipe, and tube blooms or drawing tube in integrated mills.  3313161 Extruded aluminum rod, bar, and other extruded shapes except tube  33131611 Extruded aluminum rod and bar, alloys other than 2000 and 7000 series  3313161101 Extruded aluminum rod and bar, alloys other than 2000 and 7000 series  331316115 Extruded rod and bar with alloys other than 2000 and 7000 series  33131611511 Rod  33131611512 Bar  331316118 Extruded rod and bar with alloys in 2000 and 7000 series  33131611811 Rod  33131611812 Bar  33131612 Extruded aluminum rod and bar, alloys in 2000 and 7000 series  3313161206 Extruded aluminum rod and bar, alloys in 2000 and 7000 series  331316125 Other extruded shapes except tube, with alloys other than 2000 and 7000 series  33131612501 Other extruded shapes, circle size 1 to, not including 2  33131612502 Other extruded shapes, circle size 2 to, not including3  33131612503 Other extruded shapes, circle size 3 to, not including 4  33131612504 Other extruded shapes, circle size 4 to, not including 5  33131612505 Other extruded shapes, circle size 5 to, not including 6  33131612506 Other extruded shapes, circle size 6 to, not including 10  33131612512 Other extruded shapes, circle size 10 and over  331316128 Other extruded shapes except tube, with alloys in 2000 and 7000 series  33131612801 Other extruded shapes, circle sizes 1 to, not including 5  33131612803 Other extruded shapes, circle sizes 5 and over  33131613 Other extruded aluminum shapes (except tube), alloys other than 2000 and 7000 series  3313161311 Other extruded aluminum shapes (except tube), alloys other than 2000 and 7000 series  33131614 Other extruded aluminum shapes (except tube), alloys in 2000 and 7000 series  3313161416 Other extruded aluminum shapes (except tube), alloys in 2000 and 7000 series  3313163 Aluminum extruded and drawn pipe and tube  33131631 Extruded and drawn aluminum tube  3313163101 Extruded and drawn aluminum tube, alloys other than 2000 and 7000 series  3313163106 Extruded and drawn aluminum tube, alloys in 2000 and 7000 series  331316311 Hard alloy pipe and tube, 2000 and 7000 series  33131631101 Seamless pipe and tube, hard alloy  33131631102 Hard alloy pipe and tube other than seamless  331316313 Soft alloy pipe and tube, alloys other than 2000 and 7000 series  33131631301 Seamless pipe and tube, soft alloy  33131631302 Soft alloy pipe and tube other than seamless  331316M Miscellaneous receipts  331316P Primary products  331316S Secondary products  331316SM Secondary products and miscellaneous receipts  331319 This U.S. Industry comprises establishments primarily engaged in (1) rolling, drawing, or extruding shapes (except flat rolled sheet, plate, foil, and welded tube; extruded rod, bar, pipe, and tube blooms; and drawn or extruded tube) from purchased aluminum and/or (2) recovering aluminum from scrap and rolling, drawing or extruding shapes (except flat rolled sheet, plate, foil, and welded tube; extruded rod, bar, pipe, and tube blooms; and drawn or extruded tube) in integrated mills.  3313191 Aluminum wire & cable (exc covered or insul), inc acscr, made in aluminum rollin  33131911 Aluminum and aluminum~base alloy wire and cable (except covered or insulated) including ACSR, produced in aluminum rolling mills  3313191100 Aluminum and aluminum~base alloy wire and cable (except covered or insulated), including ACSR, produced in aluminum rolling mills  3313192 ALUMINUM AND ALUMINUM_BASE ALLOY WIRE AND CABLE (EXCEPT COVERED OR INSULATED), INCLUDING ACSR, MADE IN ALUMINUM ROLLING MILLS AND PLANTS THAT DRAW WIRE FROM PURCHASED OR RECOVERED ALUMINUM  33131921 Aluminum and aluminum_base alloy wire and cable (except covered or insulated), including ACSR, made in aluminum rolling mills and plants that draw wire from purchased or recovered aluminum  3313192100 Aluminum and aluminum_base alloy wire and cable (except covered or insulated), including ACSR, made in aluminum rolling mills and plants that draw wire from purchased or recovered aluminum  3313193 Rolled aluminum rod, bar, including continuous cast  33131931 Rolled aluminum rod and bar (including continuous cast), made in aluminum rolling mills and plants that draw wire from purchased or recovered aluminum  3313193100 Rolled aluminum rod and bar (including continuous cast), made in aluminum rolling mills and plants that draw wire from purchased or recovered aluminum  3313197 Aluminum wire & cable (exc covered or insul), inc acscr, made in nonferrous wire  33131971 Aluminum and aluminum~base alloy wire and cable (except covered or insulated), including ACSR, made in nonferrous plants that draw wire  3313197100 Aluminum and aluminum~base alloy wire and cable (except covered or insulated), including ACSR, made in nonferrous plants that draw wire  3313199 ALUMINUM INGOT, EXCLUDING BILLET, PRODUCED IN ALUMINUM ROLLING MILLS  33131991 Aluminum ingot, excluding billet, produced in aluminum rolling mills  3313199100 Aluminum ingot, excluding billet, produced in aluminum rolling mills  331319A ALUMINUM WIRE CLOTH AND WOVEN WIRE PRODUCTS  331319A1 Aluminum wire cloth and woven wire products  331319A100 Aluminum wire cloth and woven wire products, made in nonferrous plants that draw wire  331319C Aluminum extrusion ingot (billet), produced in aluminum rolling mills  331319C1 Aluminum extrusion ingot (billet), produced in aluminum rolling mills  331319C100 Aluminum extrusion ingot (billet), produced in aluminum rolling mills  331319D ALUMINUM INGOT, INCLUDING BILLET, MADE IN ALUMINUM ROLLING MILLS AND PLANTS THAT DRAW WIRE FROM PURCHASED OR RECOVERED ALUMINUM  331319D1 Aluminum ingot, including billet, made in aluminum rolling mills and plants that draw wire from purchased or recovered aluminum  331319D100 Aluminum ingot, including billet, made in aluminum rolling mills and plants that draw wire from purchased or recovered aluminum  331319E ALUMINUM INSULATED WIRE AND CABLE, MADE IN ALUMINUM ROLLING AND DRAWING PLANTS  331319E1 Aluminum insulated wire and cable, including apparatus and magnet wire, made in aluminum rolling and drawing plants  331319E101 Aluminum apparatus wire and cord and flexible cord sets (except wiring harnesses and fiber optic), made in aluminum rolling and drawing plants  331319E102 Aluminum magnet wire (except fiber optic), made in aluminum rolling and drawing plants  331319E103 Aluminum insulated wire and cable for electrical transmission, made in aluminum rolling and drawing plants  331319M Miscellaneous receipts  331319P Primary products  331319S Secondary products  331319SM Secondary products and miscellaneous receipts   Step 2. Filtering and Smoothing Based on the aggregate view of production and processing of alumina and aluminum 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 production and processing of alumina and aluminum 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 production and processing of alumina and aluminum 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 production and processing of alumina and aluminum). 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 an
 
Contents:
1 INTRODUCTION & METHODOLOGY 11 1.1 Overview and Definitions 11 1.2 Market Potential Estimation Methodology 11 1.2.1 Overview 11 1.2.2 What is Latent Demand and the P.I.E.? 12 1.2.3 The Methodology 12 1.2.3.1 Step 1. Product Definition and Data Collection 14 1.2.3.2 Step 2. Filtering and Smoothing 22 1.2.3.3 Step 3. Filling in Missing Values 22 1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 23 1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 23 1.2.3.6 Step 6. Aggregation and Benchmarking 24 2 USING THE DATA 25 3 CITY SEGMENTS RANKED BY MARKET SIZE 26 3.1 Top 15 Markets 26 3.2 Markets 16 to 30 27 3.3 Remaining Cities by Market Rank 28 4 CITY SEGMENTS IN ALPHABETICAL ORDER 131 4.1 A: from Aalborg to Az Zawiyah 131 4.2 B: from Bacolod to Bydgoszcz 138 4.3 C: from Caaguazu to Cyangugu 146 4.4 D: from Da Nang to Dzhizak 154 4.5 E: from East London to Esteli 158 4.6 F: from Fagatogo to Funchal 160 4.7 G: from Gabes to Gyumri 163 4.8 H: from Hachinohe to Hyderabad 167 4.9 I: from Iasi to Izmir 171 4.10 J: from Jaboatao to Jyvaskyla 174 4.11 K: from Kabul to Kzyl-Orda 176 4.12 L: from La Ceiba to Lyon 184 4.13 M: from Macae to Mzuzu 189 4.14 N: from Nacala to Nzerekore 199 4.15 O: from Oaklahoma City to Oyem 204 4.16 Ö: from Örebro to Örebro 206 4.17 P: from Pago Pago to Pyuthan 207 4.18 Q: from Qandahar to Quito 213 4.19 R: from Rabat to Rustavi 214 4.20 S: from S. Luis Potosi to Szombathely 217 4.21 T: from Tabligbo to Tyre 229 4.22 U: from Uberaba to Utulei 236 4.23 V: from Vacoas-Phoenix to Vukovar 238 4.24 W: from Wadi Medani to Wuhan 241 4.25 X: from Xalapa to Xian 242 4.26 Y: from Yamagata to Yungkang 243 4.27 Z: from Zadar to Zvishavane 244 5 CITY SEGMENTS RANKED BY COUNTRY 245 5.1 Afghanistan 245 5.2 Albania 245 5.3 Algeria 246 5.4 American Samoa 246 5.5 Andorra 246 5.6 Angola 247 5.7 Antigua and Barbuda 247 5.8 Argentina 248 5.9 Armenia 249 5.10 Aruba 249 5.11 Australia 250 5.12 Austria 250 5.13 Azerbaijan 251 5.14 Bahrain 251 5.15 Bangladesh 251 5.16 Barbados 252 5.17 Belarus 252 5.18 Belgium 252 5.19 Belize 253 5.20 Benin 253 5.21 Bermuda 253 5.22 Bhutan 254 5.23 Bolivia 254 5.24 Bosnia and Herzegovina 254 5.25 Botswana 255 5.26 Brazil 256 5.27 Brunei 261 5.28 Bulgaria 261 5.29 Burkina Faso 262 5.30 Burma 262 5.31 Burundi 262 5.32 Cambodia 263 5.33 Cameroon 263 5.34 Canada 263 5.35 Cape Verde 264 5.36 Central African Republic 264 5.37 Chad 264 5.38 Chile 265 5.39 China 265 5.40 Christmas Island 266 5.41 Colombia 266 5.42 Comoros 266 5.43 Congo (formerly Zaire) 267 5.44 Cook Islands 267 5.45 Costa Rica 267 5.46 Cote dIvoire 268 5.47 Croatia 268 5.48 Cuba 268 5.49 Cyprus 269 5.50 Czech Republic 269 5.51 Denmark 269 5.52 Djibouti 270 5.53 Dominica 270 5.54 Dominican Republic 270 5.55 Ecuador 271 5.56 Egypt 271 5.57 El Salvador 271 5.58 Equatorial Guinea 272 5.59 Estonia 272 5.60 Ethiopia 272 5.61 Fiji 273 5.62 Finland 273 5.63 France 274 5.64 French Guiana 274 5.65 French Polynesia 275 5.66 Gabon 275 5.67 Georgia 275 5.68 Germany 276 5.69 Ghana 276 5.70 Greece 277 5.71 Greenland 277 5.72 Grenada 277 5.73 Guadeloupe 278 5.74 Guam 278 5.75 Guatemala 278 5.76 Guinea 279 5.77 Guinea-Bissau 279 5.78 Guyana 279 5.79 Haiti 280 5.80 Honduras 280 5.81 Hong Kong 280 5.82 Hungary 281 5.83 Iceland 281 5.84 India 282 5.85 Indonesia 283 5.86 Iran 284 5.87 Iraq 284 5.88 Ireland 285 5.89 Israel 285 5.90 Italy 286 5.91 Jamaica 286 5.92 Japan 287 5.93 Jordan 289 5.94 Kazakhstan 290 5.95 Kenya 290 5.96 Kiribati 291 5.97 Kuwait 291 5.98 Kyrgyzstan 291 5.99 Laos 291 5.100 Latvia 292 5.101 Lebanon 292 5.102 Lesotho 292 5.103 Liberia 293 5.104 Libya 293 5.105 Liechtenstein 293 5.106 Lithuania 294 5.107 Luxembourg 294 5.108 Macau 294 5.109 Madagascar 295 5.110 Malawi 295 5.111 Malaysia 296 5.112 Maldives 296 5.113 Mali 297 5.114 Malta 297 5.115 Marshall Islands 297 5.116 Martinique 298 5.117 Mauritania 298 5.118 Mauritius 298 5.119 Mexico 299 5.120 Micronesia Federation 300 5.121 Moldova 300 5.122 Monaco 300 5.123 Mongolia 300 5.124 Morocco 301 5.125 Mozambique 301 5.126 Namibia 301 5.127 Nauru 302 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 304 5.135 Norfolk Island 305 5.136 North Korea 305 5.137 Norway 305 5.138 Oman 306 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 307 5.145 Peru 308 5.146 Philippines 308 5.147 Poland 309 5.148 Portugal 309 5.149 Puerto Rico 310 5.150 Qatar 310 5.151 Republic of Congo 310 5.152 Reunion 311 5.153 Romania 311 5.154 Russia 312 5.155 Rwanda 312 5.156 San Marino 312 5.157 Sao Tome E Principe 313 5.158 Saudi Arabia 313 5.159 Senegal 313 5.160 Seychelles 314 5.161 Sierra Leone 314 5.162 Singapore 314 5.163 Slovakia 314 5.164 Slovenia 315 5.165 Solomon Islands 315 5.166 Somalia 315 5.167 South Africa 316 5.168 South Korea 316 5.169 Spain 317 5.170 Sri Lanka 317 5.171 St. Kitts and Nevis 318 5.172 St. Lucia 318 5.173 St. Vincent and the Grenadines 318 5.174 Sudan 318 5.175 Suriname 319 5.176 Swaziland 319 5.177 Sweden 319 5.178 Switzerland 320 5.179 Syrian Arab Republic 320 5.180 Taiwan 321 5.181 Tajikistan 322 5.182 Tanzania 322 5.183 Thailand 322 5.184 The Bahamas 323 5.185 The British Virgin Islands 323 5.186 The Cayman Islands 323 5.187 The Falkland Islands 323 5.188 The Gambia 324 5.189 The Netherlands 324 5.190 The Netherlands Antilles 324 5.191 The Northern Mariana Island 325 5.192 The U.S. Virgin Islands 325 5.193 The United Arab Emirates 325 5.194 The United Kingdom 326 5.195 The United States 327 5.196 Togo 328 5.197 Tokelau 328 5.198 Tonga 328 5.199 Trinidad and Tobago 329 5.200 Tunisia 329 5.201 Turkey 330 5.202 Turkmenistan 330 5.203 Tuvalu 330 5.204 Uganda 331 5.205 Ukraine 331 5.206 Uruguay 332 5.207 Uzbekistan 332 5.208 Vanuatu 332 5.209 Venezuela 333 5.210 Vietnam 333 5.211 Wallis and Futuna 334 5.212 Western Sahara 334 5.213 Western Samoa 334 5.214 Yemen 334 5.215 Zambia 335 5.216 Zimbabwe 335 6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 336 6.1 Disclaimers & Safe Harbor 336 6.2 ICON Group International, Inc. User Agreement Provisions 337
 
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