The 2009 Report on Manufacturing Internal Combustion Engines Excluding Automotive Gasoline and Aircraft Engines: World Market Segmentation by City
ICON Group International, May 2009, Pages: 346
Market Potential Estimation Methodology
Overview
This study covers the world outlook for manufacturing internal combustion engines excluding automotive gasoline and aircraft engines 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 manufacturing internal combustion engines excluding automotive gasoline and aircraft engines. 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 manufacturing internal combustion engines excluding automotive gasoline and aircraft engines 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 manufacturing internal combustion engines excluding automotive gasoline and aircraft engines 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 manufacturing internal combustion engines excluding automotive gasoline and aircraft engines 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 manufacturing internal combustion engines excluding automotive gasoline and aircraft engines 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 manufacturing internal combustion engines excluding automotive gasoline and aircraft engines. 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 manufacturing internal combustion engines excluding automotive gasoline and aircraft engines. 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 manufacturing internal combustion engines excluding automotive gasoline and aircraft engines.
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 “manufacturing internal combustion engines excluding automotive gasoline and aircraft engines” 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 manufacturing internal combustion engines excluding automotive gasoline and aircraft engines 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 “manufacturing internal combustion engines excluding automotive gasoline and aircraft engines” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing internal combustion engines excluding automotive gasoline and aircraft engines, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing internal combustion engines excluding automotive gasoline and aircraft engines is 333618. It is for this definition of manufacturing internal combustion engines excluding automotive gasoline and aircraft engines that the aggregate latent demand estimates are derived. “Manufacturing internal combustion engines excluding automotive gasoline and aircraft engines” is specifically defined as follows:
333618
This U.S. industry comprises establishments primarily engaged in manufacturing internal combustion engines (except automotive gasoline and aircraft).
3336181
Gasoline engines (except aircraft, automobile, highway truck, bus & tank)
33361810
Gasoline and gas~gasoline engines (except aircraft, automobile, highway truck, bus, tank, and outboard marine)
3336181000
Gasoline and gas~gasoline engines (except aircraft, automobile, highway truck, bus, tank, and outboard marine)
3336181011
Gasoline engines under 11 hp
3336181013
Gasoline engines 11 to under 21 hp
3336181015
Gasoline engines 21 to under 61 hp
3336181017
Gasoline engines 61 hp and over
33361811
Gasoline and gas_gasoline engines (except aircraft, automobile, highway truck, bus, tank, and outboard marine)
3336181100
Gasoline and gas_gasoline engines (except aircraft, automobile, highway truck, bus, tank, and outboard marine)
3336181111
Gasoline engines (except automotive), under 11 hp
3336181113
Gasoline engines (except automotive), 11 to under 21 hp
3336181115
Gasoline engines (except automotive), 21 to under 61 hp
3336181117
Gasoline engines (except automotive), 61 hp and over
3336183
Diesel, semidiesel and dual fuel engines (except automotive)
33361830
Diesel, semidiesel, and dual~fuel engines (except automobile, highway truck, bus, and tank)
3336183000
Diesel, semidiesel, and dual~fuel engines (except automobile, highway truck, bus, and tank)
3336183011
Nonautomotive diesel engines under 101 hp
3336183013
Nonautomotive diesel engines 101 to under 151 hp
3336183015
Nonautomotive diesel engines 151 to under 176 hp
3336183017
Nonautomotive diesel engines 176 to under 251 hp
3336183019
Nonautomotive diesel engines 251 to under 301 hp
333618301B
Nonautomotive diesel engines 301 to under 401 hp
333618301D
Nonautomotive diesel engines 401 to under 451 hp
333618301F
Nonautomotive diesel engines 451 to under 601 hp
333618301H
Nonautomotive diesel engines 601 to under 701 hp
333618301J
Nonautomotive diesel engines 701 to under 801 hp
333618301L
Nonautomotive diesel engines 801 to under 1,501 hp
333618301M
Nonautomotive diesel engines 1,501 hp and over
33361831
Diesel, semidiesel, and dual_fuel engines (except automobile, highway truck, bus, and tank)
3336183100
Diesel, semidiesel, and dual_fuel engines (except automobile, highway truck, bus, and tank)
3336183111
Nonautomotive diesel engines, under 101 hp
3336183113
Nonautomotive diesel engines, 101 to under 151 hp
3336183115
Nonautomotive diesel engines, 151 to under 176 hp
3336183117
Nonautomotive diesel engines, 176 to under 251 hp
3336183119
Nonautomotive diesel engines, 251 to under 301 hp
333618311B
Nonautomotive diesel engines, 301 to under 401 hp
333618311D
Nonautomotive diesel engines, 401 to under 451 hp
333618311F
Nonautomotive diesel engines, 451 to under 601 hp
333618311H
Nonautomotive diesel engines, 601 to under 701 hp
333618311J
Nonautomotive diesel engines, 701 to under 801 hp
333618311L
Nonautomotive diesel engines, 801 to under 1,501 hp
333618311M
Nonautomotive diesel engines, 1,501 hp and over
3336185
Diesel, semidiesel, and dual-fuel engines for automobiles, trucks, and buses
33361850
Diesel, semidiesel, and dual~fuel engines for automobiles, highway trucks, and buses
3336185000
Diesel, semidiesel, and dual~fuel engines for automobiles, highway trucks, and buses
3336185011
Automotive diesel engines under 226 hp
3336185013
Automotive diesel engines 226 to under 251 hp
3336185015
Automotive diesel engines 251 hp and over
33361851
Diesel, semidiesel, and dual_fuel engines for automobiles, highway trucks, and buses
3336185100
Diesel, semidiesel, and dual_fuel engines for automobiles, highway trucks, and buses
3336185111
Automotive diesel engines, under 226 hp
3336185113
Automotive diesel engines, 226 to under 251 hp
3336185115
Automotive diesel engines, 251 hp and over
3336187
Outboard motors (including electric)
33361871
Outboard motors
3336187100
Outboard motors
3336189
Piston-type natural gas engines, including LPG engines (excluding gas turbines)
33361890
Piston~type natural gas engines, including LPG engines (excluding gas turbines)
3336189000
Piston~type natural gas engines, including LPG engines (excluding gas turbines)
3336189011
Natural gas and LPG engines under 501 hp
3336189013
Natural gas and LPG engines 501 hp and over
33361891
Piston_type natural gas engines, including LPG (liquified petroleum gas) engines (excluding gas turbines)
3336189100
Piston_type natural gas engines, including LPG (liquified petroleum gas) engines (excluding gas turbines)
3336189111
Natural gas and LPG engines, under 501 hp
3336189113
Natural gas and LPG engines, 501 hp and over
333618A
TANK (EXCEPT GAS TURBINE) AND CONVERTED INTERNAL COMBUSTION ENGINES
333618A1
Tank and converted internal combustion engines
333618A101
Tank engines, except gas turbines
333618A106
Converted internal combustion engines (basic engines, short blocks purchased or intracompany transfer and converted to marine or other uses)
333618B
ALL OTHER MISCELLANEOUS ENGINE EQUIPMENT
333618B1
All other miscellaneous engine equipment
333618B101
Tank engines, except gas turbines
333618B106
Converted internal combustion engines (basic engines, short blocks purchased or intracompany transfer and converted to marine or other uses)
333618B108
Outboard motors (including electric)
333618B110
All other miscellaneous engine equipment
333618F
Parts & accessories (except aircraft and gasoline automotive engines)
333618F1
Electrical machinery, equipment, and supplies, excluding fuel injection system
333618F101
Connecting rods for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F106
Engine crankshafts for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F111
Engine camshafts for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F116
Flywheels for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F121
Main (crankshaft) engine bearings (halves) for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F126
Connecting rod bearings (halves) for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F131
Other engine bearings (halves) (camshaft, balance shaft, etc.) for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F136
Oil pumps, new, for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F141
Fuel pumps, new, for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F146
Water pumps, new, for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F151
Engine blocks for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F156
Cylinder liners (sleeves) for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F161
Cylinder heads for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F166
Intake manifolds and exhaust manifolds for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F171
Valve guides, seats, and tappets for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F176
Rocker arms and parts for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F186
Engine speed governors for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F196
Superchargers, including turbochargers, for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F199
Other parts and accessories for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F2
Fuel injection systems (multipoint) for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618F281
Fuel injection systems (multipoint) for internal combustion engines, except aircraft and gasoline automotive engines and gas turbines
333618M
Miscellaneous receipts
333618P
Primary products
333618S
Secondary products
333618SM
Secondary products and miscellaneous receipts
Furthermore, the definition of NAICS code 333618 includes the following:
Diesel and semidiesel engines manufacturing
Electric outboard motors manufacturing
Engines, diesel and semidiesel, manufacturing
Engines, diesel locomotive, manufacturing
Engines, internal combustion (except aircraft, nondiesel automotive), manufacturi
Engines, natural gas, manufacturing
Gasoline engines (except aircraft, automotive, truck) manufacturing
Governors, diesel engine, manufacturing
Governors, gasoline engine (except automotive), manufacturing
Internal combustion engines (except aircraft, nondiesel automotive, nondiesel tru
Locomotive diesel engines manufacturing
Marine engines manufacturing
Motors, outboard, manufacturing
Natural gas engines manufacturing
Outboard motors manufacturing
Semidiesel engines manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing internal combustion engines excluding automotive gasoline and aircraft engines 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 manufacturing internal combustion engines excluding automotive gasoline and aircraft engines 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 manufacturing internal combustion engines excluding automotive gasoline and aircraft engines 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 manufacturing internal combustion engines excluding automotive gasoline and aircraft engines). 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 20
1.2.3.3 Step 3. Filling in Missing Values 20
1.2.3.4 Step 4. Varying Parameter, Non-linear Estimation 21
1.2.3.5 Step 5. Fixed-Parameter Linear Estimation 21
1.2.3.6 Step 6. Aggregation and Benchmarking 21
2 USING THE DATA 22
3 CITY SEGMENTS RANKED BY MARKET SIZE 23
3.1 Top 15 Markets 23
3.2 Markets 16 to 30 24
3.3 Remaining Cities by Market Rank 25
4 CITY SEGMENTS IN ALPHABETICAL ORDER 128
4.1 A: from Aalborg to Az Zawiyah 128
4.2 B: from Bacolod to Bydgoszcz 135
4.3 C: from Caaguazu to Cyangugu 143
4.4 D: from Da Nang to Dzhizak 151
4.5 E: from East London to Esteli 155
4.6 F: from Fagatogo to Funchal 157
4.7 G: from Gabes to Gyumri 160
4.8 H: from Hachinohe to Hyderabad 164
4.9 I: from Iasi to Izmir 168
4.10 J: from Jaboatao to Jyvaskyla 171
4.11 K: from Kabul to Kzyl-Orda 173
4.12 L: from La Ceiba to Lyon 181
4.13 M: from Macae to Mzuzu 187
4.14 N: from Nacala to Nzerekore 197
4.15 O: from Oaklahoma City to Oyem 202
4.16 Ö: from Örebro to Örebro 204
4.17 P: from Pago Pago to Pyuthan 205
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 251
5.16 Barbados 251
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 264
5.35 Cape Verde 264
5.36 Central African Republic 265
5.37 Chad 265
5.38 Chile 266
5.39 China 266
5.40 Christmas Island 267
5.41 Colombia 267
5.42 Comoros 267
5.43 Congo (formerly Zaire) 268
5.44 Cook Islands 268
5.45 Costa Rica 268
5.46 Cote dIvoire 269
5.47 Croatia 269
5.48 Cuba 270
5.49 Cyprus 270
5.50 Czech Republic 271
5.51 Denmark 271
5.52 Djibouti 272
5.53 Dominica 272
5.54 Dominican Republic 272
5.55 Ecuador 273
5.56 Egypt 273
5.57 El Salvador 274
5.58 Equatorial Guinea 274
5.59 Estonia 274
5.60 Ethiopia 275
5.61 Fiji 275
5.62 Finland 276
5.63 France 276
5.64 French Guiana 277
5.65 French Polynesia 277
5.66 Gabon 277
5.67 Georgia 278
5.68 Germany 278
5.69 Ghana 279
5.70 Greece 279
5.71 Greenland 280
5.72 Grenada 280
5.73 Guadeloupe 281
5.74 Guam 281
5.75 Guatemala 281
5.76 Guinea 282
5.77 Guinea-Bissau 282
5.78 Guyana 282
5.79 Haiti 283
5.80 Honduras 283
5.81 Hong Kong 283
5.82 Hungary 284
5.83 Iceland 284
5.84 India 285
5.85 Indonesia 286
5.86 Iran 287
5.87 Iraq 287
5.88 Ireland 288
5.89 Israel 288
5.90 Italy 289
5.91 Jamaica 289
5.92 Japan 290
5.93 Jordan 293
5.94 Kazakhstan 293
5.95 Kenya 294
5.96 Kiribati 294
5.97 Kuwait 294
5.98 Kyrgyzstan 295
5.99 Laos 295
5.100 Latvia 295
5.101 Lebanon 296
5.102 Lesotho 296
5.103 Liberia 296
5.104 Libya 297
5.105 Liechtenstein 297
5.106 Lithuania 297
5.107 Luxembourg 298
5.108 Macau 298
5.109 Madagascar 298
5.110 Malawi 299
5.111 Malaysia 299
5.112 Maldives 300
5.113 Mali 300
5.114 Malta 300
5.115 Marshall Islands 301
5.116 Martinique 301
5.117 Mauritania 301
5.118 Mauritius 302
5.119 Mexico 303
5.120 Micronesia Federation 304
5.121 Moldova 304
5.122 Monaco 304
5.123 Mongolia 305
5.124 Morocco 305
5.125 Mozambique 306
5.126 Namibia 306
5.127 Nauru 306
5.128 Nepal 307
5.129 New Caledonia 307
5.130 New Zealand 308
5.131 Nicaragua 308
5.132 Niger 309
5.133 Nigeria 309
5.134 Niue 310
5.135 Norfolk Island 310
5.136 North Korea 310
5.137 Norway 311
5.138 Oman 311
5.139 Pakistan 312
5.140 Palau 312
5.141 Palestine 312
5.142 Panama 313
5.143 Papua New Guinea 313
5.144 Paraguay 314
5.145 Peru 314
5.146 Philippines 315
5.147 Poland 315
5.148 Portugal 316
5.149 Puerto Rico 316
5.150 Qatar 317
5.151 Republic of Congo 317
5.152 Reunion 317
5.153 Romania 318
5.154 Russia 318
5.155 Rwanda 319
5.156 San Marino 319
5.157 Sao Tome E Principe 319
5.158 Saudi Arabia 320
5.159 Senegal 320
5.160 Seychelles 321
5.161 Sierra Leone 321
5.162 Singapore 321
5.163 Slovakia 321
5.164 Slovenia 322
5.165 Solomon Islands 322
5.166 Somalia 322
5.167 South Africa 323
5.168 South Korea 323
5.169 Spain 324
5.170 Sri Lanka 324
5.171 St. Kitts and Nevis 325
5.172 St. Lucia 325
5.173 St. Vincent and the Grenadines 325
5.174 Sudan 326
5.175 Suriname 326
5.176 Swaziland 326
5.177 Sweden 327
5.178 Switzerland 327
5.179 Syrian Arab Republic 328
5.180 Taiwan 329
5.181 Tajikistan 330
5.182 Tanzania 330
5.183 Thailand 331
5.184 The Bahamas 331
5.185 The British Virgin Islands 331
5.186 The Cayman Islands 332
5.187 The Falkland Islands 332
5.188 The Gambia 332
5.189 The Netherlands 333
5.190 The Netherlands Antilles 333
5.191 The Northern Mariana Island 333
5.192 The U.S. Virgin Islands 334
5.193 The United Arab Emirates 334
5.194 The United Kingdom 334
5.195 The United States 335
5.196 Togo 336
5.197 Tokelau 336
5.198 Tonga 337
5.199 Trinidad and Tobago 337
5.200 Tunisia 337
5.201 Turkey 338
5.202 Turkmenistan 338
5.203 Tuvalu 338
5.204 Uganda 339
5.205 Ukraine 339
5.206 Uruguay 340
5.207 Uzbekistan 340
5.208 Vanuatu 341
5.209 Venezuela 341
5.210 Vietnam 342
5.211 Wallis and Futuna 342
5.212 Western Sahara 342
5.213 Western Samoa 342
5.214 Yemen 343
5.215 Zambia 343
5.216 Zimbabwe 344
6 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 345
6.1 Disclaimers & Safe Harbor 345
6.2 ICON Group International, Inc. User Agreement Provisions 346
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