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The 2009 Report on Iron and Steel Mills: World Market Segmentation by City

Description:
Market Potential Estimation Methodology Overview This study covers the world outlook for iron and steel mills 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 iron and steel mills. 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 iron and steel mills 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 iron and steel mills 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 iron and steel mills 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 iron and steel mills 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 iron and steel mills. 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 iron and steel mills. 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 iron and steel mills. 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 “iron and steel mills” 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 iron and steel mills 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 “iron and steel mills” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of iron and steel mills, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for iron and steel mills is 331111. It is for this definition of iron and steel mills that the aggregate latent demand estimates are derived. “Iron and steel mills” is specifically defined as follows: 331111 This U.S. industry comprises establishments primarily engaged in one or more of the following: (1) direct reduction of iron ore; (2) manufacturing pig iron in molten or solid form; (3) converting pig iron into steel; (4) making steel; (5) making steel and manufacturing shapes (e.g., bar, plate, rod, sheet, strip, wire); and (6) making steel and forming tube and pipe.  3311111 Coke oven and blast furnace products  33111111 Coke oven and blast furnace products, made in steel mills  3311111101 Coke oven products, coke (excluding screenings and breeze), made in steel mills  3311111103 Coke oven products, screenings and breeze, made in steel mills  3311111105 Coke oven products, crude tar, made in steel mills  3311111107 Coke oven products, crude light oil, made in steel mills  3311111109 Coke oven products, other (including tar derivatives, ammonia, light oil derivations, and coke oven gas), made in steel mills  3311111111 Blast furnace pig iron (excluding ferroalloys), including pig iron with silicon content up to and including 6 percent silicon, made in steel mills  3311111113 Blast furnace slag, excluding ferroalloys, made in steel mills  3311111115 Blast furnace sinter from ore, flue dust, blast furnace gas and other materials (excluding ferroalloys), made in steel mills  3311111117 Other blast furnace products, excluding ferroalloys, made in steel mills  3311112 Iron and steel powders, paste, and flakes  33111121 Iron and steel powders, paste, and flakes  3311112100 Primary iron and steel powders, paste, and flakes  3311113 Steel ingots and semifinished shapes and forms  33111131 Steel ingots and semifinished shapes and forms, made in steel mills  3311113100 Steel ingots and semifinished shapes and forms, made in steel mills  3311113110 Carbon steel ingots  3311113120 Alloy steel ingots  3311113130 Stainless steel ingots  3311113140 Carbon steel blooms, billets, sheet bars, tin mill bars, tube rounds, and skelp  3311113150 Carbon steel slabs  3311113160 Alloy steel blooms, billets, sheet bars, tube rounds, and skelp  3311113170 Alloy steel slabs  3311113180 Stainless steel blooms, billets, slabs, sheet bars, tube rounds, and skelp  3311113190 Carbon steel wire rods  33111131A0 Alloy steel wire rods  33111131B0 Stainless steel wire rods  3311115 Hot rolled steel sheet and strip  33111151 Hot rolled steel sheet and strip (including tin mill products, tinplate, blackplate, terneplate, and tin_free steel), made in steel mills  3311115100 Hot rolled steel sheet and strip (including tin mill products, tinplate, blackplate, terneplate, and tin_free steel), made in steel mills  3311115110 Carbon steel sheet, hot rolled, including hot rolled bands  3311115120 Carbon steel sheet and strip, galvanized, hot dipped  3311115130 Carbon steel sheet and strip, galvanized, electrolytic  3311115140 Carbon steel sheet and strip, electrical  3311115150 Carbon steel sheet and strip, all other metallic coated, including long ternes  3311115160 Carbon steel strip, hot rolled  3311115170 Alloy steel sheet, hot rolled  3311115180 Alloy steel sheet and strip, galvanized hot dipped  3311115190 Alloy steel sheet and strip, all other metallic coated (including electrolytic)  33111151A0 Alloy steel strip, hot rolled  33111151B0 Stainless steel sheet and strip, hot rolled  33111151C0 Carbon steel tin mill products, black plate  33111151D0 Carbon steel tin mill products, electrolytic and hot dipped tin plate  33111151E0 Carbon steel tin mill products, tin free steel  33111151F0 Carbon steel tin mill products, all other tin mill products, including short ternes and foil  3311117 Hot rolled bars, plates, and structural shapes  33111171 Hot rolled steel bars and bar shapes, plates, structural shapes, and piling (including concrete reinforcing and tool steel bars), made in steel mills  3311117100 Hot rolled steel bars and bar shapes, plates, structural shapes, and piling (including concrete reinforcing and tool steel bars), made in steel mills  3311117110 Carbon steel plates, cut lengths  3311117120 Carbon steel plates, in coils  3311117130 Carbon steel structural shapes (heavy), wide flange  3311117140 Carbon steel structural shapes (heavy), standard  3311117150 Alloy steel plates, cut lengths  3311117160 Alloy steel plates, in coils  3311117170 Alloy steel structural shapes (3 in. and under)  3311117180 Stainless steel plates and structurals  3311117190 Carbon steel bars, hot rolled, except concrete reinforcing  33111171A0 Carbon steel bars, light structurals (under 3 in.)  33111171B0 Carbon steel bars, concrete reinforcing  33111171C0 Alloy steel bars, hot rolled, including structural shapes under 3 in.  33111171D0 Stainless steel bars, hot rolled  33111171E0 Carbon steel sheet piling and bearing piles  33111171F0 Alloy tool steel, high speed  33111171G0 Alloy tool steel, other (excluding high speed)  3311119 Steel wire  33111191 Steel wire, including galvanized and other coated wire, made in steel mills producing wire rods or hot rolled bars  3311119100 Steel wire, including galvanized and other coated wire, made in steel mills producing wire rods or hot rolled bars  331111B Steel pipe and tubes  331111B1 Steel pipes and tubes, made in steel mills producing semifinished shapes or plate  331111B100 Steel pipes and tubes, made in steel mills producing semifinished shapes or plate  331111D Cold rolled steel sheets and strip  331111D1 Cold rolled steel sheet and strip, made in steel mills producing hot rolled sheet or strip  331111D100 Cold rolled steel sheet and strip, made in steel mills producing hot rolled sheet or strip  331111F Cold finished steel bars  331111F1 Cold finished steel bars and bar shapes, made in steel mills producing hot rolled bars and bar shapes  331111F100 Cold finished steel bars and bar shapes, made in steel mills producing hot rolled bars and bar shapes  331111H Seamless rolled ring forgings  331111H1 Seamless carbon steel and alloy steel rolled ring forgings (excluding stainless and hi_temperature), made in steel mills  331111H101 Seamless carbon steel and alloy steel rolled ring forgings (excluding stainless and hi_temperature), made in steel mills  331111H2 Seamless stainless steel and hi_temperature (iron, nickel, or cobalt_base alloy) rolled ring forgings, made in steel mills  331111H203 Seamless stainless steel and hi_temperature (iron, nickel, or cobalt_base alloy) rolled ring forgings, made in steel mills  331111J Open die or smith forgings  331111J1 Carbon and alloy steel open die and smith forgings (hammer and press), excluding stainless and hi_temperature, made in steel mills  331111J101 Carbon and alloy steel open die and smith forgings (hammer and press), excluding stainless and hi_temperature, made in steel mills  331111J2 Stainless steel and hi_temperature (iron, nickel, or cobalt_base alloy) open die and smith forgings (hammer and press), made in steel mills  331111J203 Stainless steel and hi_temperature (iron, nickel, or cobalt_base alloy) open die and smith forgings (hammer and press), made in steel mills  331111L Other steel mill products, including steel rails  331111L1 Other steel mill products, including steel rails, except wire products  331111L100 Other steel mill products, including steel rails, except wire products  331111L110 Carbon steel rails, standard tee (over 60 lb per yard)  331111L120 Carbon steel rails, all other, including light (60 lb per yd and under)  331111L130 Carbon steel joint bars  331111L140 Carbon steel tie plates  331111L150 Carbon steel wheels (rolled and forged)  331111L160 Carbon steel axles (rolled and forged)  331111L170 Carbon steel track spikes  331111M Miscellaneous receipts  331111P Primary products  331111S Secondary products  331111SM Secondary products and miscellaneous receipts   Furthermore, the definition of NAICS code 331111 includes the following: Armor plate made in iron and steel mills Axles, rolled or forged, made in iron and steel mills Balls, steel, made in iron and steel mills Bars, iron, made in iron and steel mills Bars, steel, made in iron and steel mills Billets, steel, made in iron and steel mills Blackplate made in iron and steel mills Blast furnaces Blooms, steel, made in iron and steel mills Car wheels, rolled steel, made in iron and steel mills Coke oven products made in iron and steel mills Direct reduction of iron ore Electrometallurgical steel manufacturing Fence posts, iron or steel, made in iron and steel mills Flakes, iron or steel, made in iron and steel mills Flats, iron or steel, made in iron and steel mills Forgings, iron or steel, made in iron and steel mills Frogs, iron or steel, made in iron and steel mills Galvanizing metals and metal formed products made in iron and steel mills Gun forgings made in iron and steel mills Hoops made in iron and steel mills Hoops, galvanized, made in iron and steel mills Hot-rolling iron or steel products in iron and steel mills Ingot made in iron and steel mills Iron ore recovery from open hearth slag Iron sinter made in iron and steel mills Iron, pig, manufacturing Mesh, wire, made in iron and steel mills Mini-mills, steel Nut rods, iron or steel, made in iron and steel mills Paste, iron or steel, made in iron and steel mills Pig iron manufacturing Pilings, iron or steel plain sheet, made in iron and steel mills Pipe, iron or steel, made in iron and steel mills Plate, iron or steel, made in iron and steel mills Powder, iron or steel, made in iron and steel mills Rail joints and fastenings made in iron and steel mills Railroad crossings, iron or steel, made in iron and steel mills Rails rerolled or renewed in iron and steel mills Rails, iron or steel, made in iron and steel mills Rods, iron or steel, made in iron and steel mills Rounds, tube, steel, made in iron and steel mills Sheet pilings, plain, iron or steel, made in iron and steel mills Sheets, steel, made in iron and steel mills Shell slugs, steel, made in iron and steel mills Skelp, iron or steel, made in iron and steel mills Slab, steel, made in iron and steel mills Spike rods made in iron and steel mills Sponge iron Stainless steel made in iron and steel mills Steel balls made in iron and steel mills Steel manufacturing Steel mill products (e.g., bar, plate, rod, sheet, structural shapes) manufacturi Steel mills Steel, from pig iron, manufacturing Strip, galvanized iron or steel, made in iron and steel mills Strip, iron or steel, made in iron and steel mills Structural shapes, iron or steel, made in iron and steel mills Superalloys, iron or steel, manufacturing Template, made in iron and steel mills, manufacturing Terneplate made in iron and steel mills Ternes, iron or steel, long or short, made in iron and steel mills Tie plates, iron or steel, made in iron and steel mills Tin-free steel made in iron and steel mills Tinplate made in iron and steel mills Tool steel made in iron and steel mills Tube rounds, iron or steel, made in iron and steel mills Tube, iron or steel, made in iron and steel mills Tubing, seamless steel, made in iron and steel mills Tubing, wrought iron or steel, made in iron and steel mills Well casings, iron or steel, made in iron and steel mills Wheels, car and locomotive, iron or steel, made in iron and steel mills Wire products, iron or steel, made in iron and steel mills Wrought iron or steel pipe and tubing made in iron and steel mills. Step 2. Filtering and Smoothing Based on the aggregate view of iron and steel mills 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 iron and steel mills 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 iron and steel mills 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 iron and steel mills). 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.
 
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 21 1.2.3.3 Step 3. Filling in Missing Values 22 1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 22 1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 22 1.2.3.6 Step 6. Aggregation and Benchmarking 23 2 USING THE DATA 24 3 CITY SEGMENTS RANKED BY MARKET SIZE 25 3.1 Top 15 Markets 25 3.2 Markets 16 to 30 26 3.3 Remaining Cities by Market Rank 27 4 CITY SEGMENTS IN ALPHABETICAL ORDER 130 4.1 A: from Aalborg to Az Zawiyah 130 4.2 B: from Bacolod to Bydgoszcz 137 4.3 C: from Caaguazu to Cyangugu 145 4.4 D: from Da Nang to Dzhizak 153 4.5 E: from East London to Esteli 157 4.6 F: from Fagatogo to Funchal 159 4.7 G: from Gabes to Gyumri 162 4.8 H: from Hachinohe to Hyderabad 166 4.9 I: from Iasi to Izmir 170 4.10 J: from Jaboatao to Jyvaskyla 173 4.11 K: from Kabul to Kzyl-Orda 175 4.12 L: from La Ceiba to Lyon 183 4.13 M: from Macae to Mzuzu 188 4.14 N: from Nacala to Nzerekore 198 4.15 O: from Oaklahoma City to Oyem 203 4.16 Ö: from Örebro to Örebro 205 4.17 P: from Pago Pago to Pyuthan 206 4.18 Q: from Qandahar to Quito 212 4.19 R: from Rabat to Rustavi 213 4.20 S: from S. Luis Potosi to Szombathely 216 4.21 T: from Tabligbo to Tyre 228 4.22 U: from Uberaba to Utulei 235 4.23 V: from Vacoas-Phoenix to Vukovar 237 4.24 W: from Wadi Medani to Wuhan 240 4.25 X: from Xalapa to Xian 241 4.26 Y: from Yamagata to Yungkang 242 4.27 Z: from Zadar to Zvishavane 243 5 CITY SEGMENTS RANKED BY COUNTRY 244 5.1 Afghanistan 244 5.2 Albania 244 5.3 Algeria 245 5.4 American Samoa 245 5.5 Andorra 245 5.6 Angola 246 5.7 Antigua and Barbuda 246 5.8 Argentina 247 5.9 Armenia 248 5.10 Aruba 248 5.11 Australia 249 5.12 Austria 249 5.13 Azerbaijan 250 5.14 Bahrain 250 5.15 Bangladesh 250 5.16 Barbados 251 5.17 Belarus 251 5.18 Belgium 251 5.19 Belize 252 5.20 Benin 252 5.21 Bermuda 252 5.22 Bhutan 253 5.23 Bolivia 253 5.24 Bosnia and Herzegovina 253 5.25 Botswana 254 5.26 Brazil 255 5.27 Brunei 260 5.28 Bulgaria 260 5.29 Burkina Faso 261 5.30 Burma 261 5.31 Burundi 261 5.32 Cambodia 262 5.33 Cameroon 262 5.34 Canada 262 5.35 Cape Verde 263 5.36 Central African Republic 263 5.37 Chad 263 5.38 Chile 264 5.39 China 264 5.40 Christmas Island 265 5.41 Colombia 265 5.42 Comoros 265 5.43 Congo (formerly Zaire) 266 5.44 Cook Islands 266 5.45 Costa Rica 266 5.46 Cote dIvoire 267 5.47 Croatia 267 5.48 Cuba 267 5.49 Cyprus 268 5.50 Czech Republic 268 5.51 Denmark 268 5.52 Djibouti 269 5.53 Dominica 269 5.54 Dominican Republic 269 5.55 Ecuador 270 5.56 Egypt 270 5.57 El Salvador 270 5.58 Equatorial Guinea 271 5.59 Estonia 271 5.60 Ethiopia 271 5.61 Fiji 272 5.62 Finland 272 5.63 France 273 5.64 French Guiana 273 5.65 French Polynesia 274 5.66 Gabon 274 5.67 Georgia 274 5.68 Germany 275 5.69 Ghana 275 5.70 Greece 276 5.71 Greenland 276 5.72 Grenada 276 5.73 Guadeloupe 277 5.74 Guam 277 5.75 Guatemala 277 5.76 Guinea 278 5.77 Guinea-Bissau 278 5.78 Guyana 278 5.79 Haiti 279 5.80 Honduras 279 5.81 Hong Kong 279 5.82 Hungary 280 5.83 Iceland 280 5.84 India 281 5.85 Indonesia 282 5.86 Iran 283 5.87 Iraq 283 5.88 Ireland 284 5.89 Israel 284 5.90 Italy 285 5.91 Jamaica 285 5.92 Japan 286 5.93 Jordan 288 5.94 Kazakhstan 289 5.95 Kenya 289 5.96 Kiribati 290 5.97 Kuwait 290 5.98 Kyrgyzstan 290 5.99 Laos 290 5.100 Latvia 291 5.101 Lebanon 291 5.102 Lesotho 291 5.103 Liberia 292 5.104 Libya 292 5.105 Liechtenstein 292 5.106 Lithuania 293 5.107 Luxembourg 293 5.108 Macau 293 5.109 Madagascar 294 5.110 Malawi 294 5.111 Malaysia 295 5.112 Maldives 295 5.113 Mali 296 5.114 Malta 296 5.115 Marshall Islands 296 5.116 Martinique 297 5.117 Mauritania 297 5.118 Mauritius 297 5.119 Mexico 298 5.120 Micronesia Federation 299 5.121 Moldova 299 5.122 Monaco 299 5.123 Mongolia 299 5.124 Morocco 300 5.125 Mozambique 300 5.126 Namibia 300 5.127 Nauru 301 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 303 5.135 Norfolk Island 304 5.136 North Korea 304 5.137 Norway 304 5.138 Oman 305 5.139 Pakistan 305 5.140 Palau 305 5.141 Palestine 305 5.142 Panama 306 5.143 Papua New Guinea 306 5.144 Paraguay 306 5.145 Peru 307 5.146 Philippines 307 5.147 Poland 308 5.148 Portugal 308 5.149 Puerto Rico 309 5.150 Qatar 309 5.151 Republic of Congo 309 5.152 Reunion 310 5.153 Romania 310 5.154 Russia 311 5.155 Rwanda 311 5.156 San Marino 311 5.157 Sao Tome E Principe 312 5.158 Saudi Arabia 312 5.159 Senegal 312 5.160 Seychelles 313 5.161 Sierra Leone 313 5.162 Singapore 313 5.163 Slovakia 313 5.164 Slovenia 314 5.165 Solomon Islands 314 5.166 Somalia 314 5.167 South Africa 315 5.168 South Korea 315 5.169 Spain 316 5.170 Sri Lanka 316 5.171 St. Kitts and Nevis 317 5.172 St. Lucia 317 5.173 St. Vincent and the Grenadines 317 5.174 Sudan 317 5.175 Suriname 318 5.176 Swaziland 318 5.177 Sweden 318 5.178 Switzerland 319 5.179 Syrian Arab Republic 319 5.180 Taiwan 320 5.181 Tajikistan 321 5.182 Tanzania 321 5.183 Thailand 321 5.184 The Bahamas 322 5.185 The British Virgin Islands 322 5.186 The Cayman Islands 322 5.187 The Falkland Islands 322 5.188 The Gambia 323 5.189 The Netherlands 323 5.190 The Netherlands Antilles 323 5.191 The Northern Mariana Island 324 5.192 The U.S. Virgin Islands 324 5.193 The United Arab Emirates 324 5.194 The United Kingdom 325 5.195 The United States 326 5.196 Togo 327 5.197 Tokelau 327 5.198 Tonga 327 5.199 Trinidad and Tobago 328 5.200 Tunisia 328 5.201 Turkey 329 5.202 Turkmenistan 329 5.203 Tuvalu 329 5.204 Uganda 330 5.205 Ukraine 330 5.206 Uruguay 331 5.207 Uzbekistan 331 5.208 Vanuatu 331 5.209 Venezuela 332 5.210 Vietnam 332 5.211 Wallis and Futuna 333 5.212 Western Sahara 333 5.213 Western Samoa 333 5.214 Yemen 333 5.215 Zambia 334 5.216 Zimbabwe 334 6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 335 6.1 Disclaimers & Safe Harbor 335 6.2 ICON Group International, Inc. User Agreement Provisions 336
 
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